In case you haven’t noticed, there’s been an explosion in AI technologies lately. AI developers are scrambling to get a piece of the ever-growing AI pie, racing each other to be the first to bring new innovations to market.

With such tough competition, what are AI developers doing to stand out in this crowded field? And how are they marketing themselves differently?

Power and Capabilities

The obvious way that AI companies try to distinguish themselves is through power and capabilities. If you can design an AI tool that is somehow objectively better than the tools of your competitors, it’s going to stand out in dramatic fashion.

Unfortunately, there are a few problems prohibiting companies from taking this route. First, there are already a significant number of AI innovators competing for this title. Even if you have significant expertise and talent in your favor, it’s monumentally difficult to design a tool that’s truly dominant in the market.

Second, even if you’re in a position to create a tool that’s more powerful or capable than any other tool on the market, it’s probably not going to be long before someone copies you. Your competitors will analyze your approach however they can to get access to the innovative methods you used – and then seek to copy those methods.

Third, it’s not always easy to convince the rest of the world that your tool is objectively more capable or more powerful. You might be able to include functional statistics or more bullet points describing how your tool performs, but this isn’t always sufficient to persuade your target audience.

Accordingly, most AI developers and innovators do make a positive attempt to make tools that are measurably superior, but this alone is not sufficient to distinguish them in a crowded market.

Niche and Specificity

An alternative way for AI developers to stand out is by developing technologies designed for a specific niche that isn’t currently occupied.

For example:

Audience

There are many AI platforms that are meant to serve general audiences, such as ChatGPT. These types of tools can be used by a large number of people for an almost unimaginable number of applications. But if you wanted to make your AI tool stand out, you might appeal to a very specific target audience, such as younger people, older people, or people with a certain educational background.

Industry

AI developers may also attempt to cater to specific industries. For example, there’s a GPT-based product designed specifically for DNA. There are also AI tools designed for experts in certain professions, such as the legal field. Catering to these unique groups has the dual purpose of helping AI technology stand out from competitors and increasing the relevance of that tool for the people who need it. In other words, it both increases market appeal and decreases competition.

Application

Other AI tools are designed with very specific applications in mind. For example, many companies have developed AI assistants as supplemental resources for software platforms that already exist. You might find an AI assistant who can educate and train you on how to use a project management platform, or some other tool you use for your daily professional needs.

Packaging

AI tools can also be packaged and presented in ways that are uniquely appealing. Even if the “guts” of the tool remain relatively unchanged, you can present it slightly differently for different contexts.

Branding and Marketing

Perhaps even more importantly, AI development teams are investing heavily in branding and marketing as strategies to help them stand out. With the help of a fractional CMO, it’s possible to get expert marketing advice and leadership in a flexible, scalable capacity. Fractional CMOs work very much like traditional CMOs, but on a flexible contractual basis.

Together with their marketing leaders, AI developers often focus on things like:

Identity

What is the core identity of the product and how is that identity going to be communicated? You may have a very similar AI product to a globally known competitor, but if you can somehow name and showcase your product in a dynamic way, you might be able to stand apart. Adopting a novel, fun, playful brand might help you appeal to a different target audience or simply make your brand more memorable when the two are compared. Additionally, your brand identity is going to function as the heart and soul of your overall marketing efforts, guiding the direction of your messaging.

Messaging

Speaking of messaging, your core messages in marketing and advertising play a heavy role in how people perceive your AI product, even if your product is functionally identical to one that’s already on the market. It’s a bit ironic, considering AI itself can be used to create advertising. What’s important is that you have something original and relevant to say to a properly identified audience that you understand. There are many ways to persuade that audience, but successful AI companies distinguish themselves by finding a unique path forward.

Channels

Similarly, AI developers can distinguish their products by marketing them on different types of channels. There are countless prominent digital marketing channels, including SEO, PPC advertising, and dozens of different social media platforms. There are no right or wrong channels for your specific marketing strategy, but if you’re going to stand out, you’ll need the help of the right channels to do it.

Budget

In the world of marketing, you can always get more visibility as long as you’re willing to pay for it. That said, novel products can gain considerable ground by making more effective use of free and inexpensive options. If you can’t spend the money, at least be prepared to spend the time to find them.

The AI Plateau

It’s worth noting that some experts speculate that we’ve hit a bit of an AI plateau. In other words, AI advancements are temporarily stagnating, evening out after a few years of explosive and unpredictable growth.

There are a few potential reasons for this:

Data Limitations

One critical limiting factor is the limitation of available data. For an AI system to be effective, it needs ample data for training purposes. But when mainstream sources of data have already been fully tapped and exploited, there’s nowhere else to go.

Dependency on Prompts

Many of today’s best AI tools are entirely dependent on the quality of the prompts that they’re fed. If human users aren’t sure how to ask the right questions or frame their prompts in the right way, they won’t get the results they want. The next generation of AI will need to address this in some way, making it easier to get good results.

Hallucinations and Inaccuracies

There’s no question that AI can be accurate in the right contexts. However, many AI platforms are currently struggling with hallucinations and other types of inaccuracies. Although there have been some advancements in this area, they have been very slow and very iterative, leaving the problem functionally untouched in the eyes of many users.

Recursive Learning

Generative AI has flooded the internet with mediocre content, which other AI platforms are now consuming as part of their training data. This recursive learning model has introduced a “snake eating its own tail” type problem, leading to the reinforcement of existing issues in AI.

Needless to say, the plateau is making it harder for even the best AI platforms to stand out.

What’s Next for AI?

So what’s next for the world of AI technologies and the developers trying to distinguish themselves in this field?

It’s hard to say for sure, but plateaus and technology development almost always are temporary. In the near future, we’ll likely see another, unique explosion in AI technologies – but we’ll probably see the same strategies used by AI developers to stand out from the crowd when that happens.

The post AI Technologies Are Rife With Competition. How Are New Players Standing Out? appeared first on ReadWrite.

Workers everywhere are beginning to have concerns about being replaced by AI. Advanced tech has the power to disrupt entire industries. AI is especially powerful when it comes to processing and automation.

But the reality is artificial intelligence won’t replace as many jobs as most people think.

The Long History of Tech and Automation

New AI apps emerge every day, but this isn’t the first type of novel tech disrupting our work and society. In fact, the fear of being replaced by advanced tech goes back hundreds of years. The term “luddite,” which is now used to refer to people who have an aversion to technology use, originated as a term for textile workers who rebelled against automated machinery and cotton mills in the 19th century. These textile workers feared that their jobs would be rendered obsolete. They intentionally destroyed pieces of machinery and railed against their use in the industry.

In retrospect, this seems like an overreaction. The machines were so rudimentary by today’s standards that no one would imagine they could spark fear of being replaced. Granted, that type of technology was very industry-specific. AI covers a broader range of potential jobs, but it still serves as a reminder that we should avoid catastrophizing an uncertain future.

More recently, blue collar workers have expressed concerns about being replaced by robots and automated factory machinery. And it’s true that many blue collar workers have been displaced as a result of these machines. If you already bought prescription safety glasses for your job, you have no immediate remedy or alternative course of action.

That said, factory jobs in the U.S. didn’t disappear; they transformed. Instead of manual labor on the floor, people rose to the level of supervision, guidance, and maintenance, orchestrating these machines to do the work properly. And many blue collar workers who left the industry found a place elsewhere, typically utilizing higher-level skills in the process.

The long history of tech development shows that humans are plagued with fears whenever new machines enter a common industry. But in the fullness of time, those fears are almost always revealed to be overblown.

The Excessive Promises of AI Development

We also need to draw attention to the excessive promises of AI developers and futurists. Although AI has been discussed and explored for decades, it’s only within the last few years that it has exploded in prominence and popularity. During this time, we’ve seen philosophers speculating about a potential future in which artificial intelligence offers superhuman intelligence, posing an existential risk to our civilization. We’ve also seen AI glorifiers speculating that conversational AI would accelerate the evolution of technology to the point of society being practically unrecognizable within a matter of years.

With visions and promises like these, it’s no wonder why people are concerned about potentially being replaced by AI. But of course, we’ve already seen the limitations here. It’s been several years since the earliest generative AI models began to emerge. While they’ve gotten better, they still suffer from problems like hallucinations and the inability to answer certain types of queries. Moreover, even the best AI engines pale in comparison to the best human creatives in their respective disciplines.

Many of the predictions and forecasts of top AI experts, which have been responsible for fears about job loss, have been either unfounded or miscalculated. That doesn’t mean we should immediately reject any future predictions or promises in this field. It does mean we should exercise a degree of scrutiny before allowing ourselves to panic over these types of assertions.

The Questionable Performance of AI Automation

There are some areas where AI excels. It’s extremely good at processing data, which humans are notoriously poor at. When it comes to objective calculations and drawing conclusions from large sets of information, AI simply can’t be beat. This can be applied to a wide variety of tasks and responsibilities. For example, AI can now rival doctors in terms of diagnostic accuracy.

However, the limitations of AI automation preclude it from being capable of replacing humans fully. AI doesn’t work by itself. It needs a human being to function as a prompt engineer, feeding it the information and direction it needs to accomplish a certain task. Without the right guidance, AI might answer the wrong questions or miss certain aspects of the goal, preventing it from being able to do its job properly. On top of that, AI isn’t perfect. Human beings still need to monitor and supervise AI output to verify accuracy, check the details, combine it with other pieces of information, and so on.

Because of this, it seems that for the foreseeable future, AI will inevitably require the guiding hand of human beings if we’re going to get the full value from it. That may change in the future, but given the long trajectory of AI development and relative stagnation in recent years, it seems we have arrived at a meaningful plateau.

Transformations In Lieu of Replacements

Instead of thinking in terms of replacing human jobs, we need to be thinking about transformations and displacement. AI can beat the best doctors at certain aspects of the job, but this doesn’t mean we no longer need doctors. It means that doctors have better tools with which to serve us. Similarly, lawyers, artists, pharmacists, and other white collar workers are learning to integrate AI into their work, rather than being totally replaced.

It’s possible that the lowest-skilled employees in certain industries may no longer be required. It’s also possible that some may lose their jobs as a downstream effect of AI being integrated into those industries. But broadly speaking, most roles are simply going to evolve rather than disappear as a result of AI.

The Demand for the Human Touch

On top of that, human beings still demand human engagement. If you’re dealing with a sensitive medical issue, you don’t want a machine feeding you a generic script to follow. Instead, you want a human being who will look you in the eyes and sympathize with what you’re going through.

This fundamental preference seems to be relevant to most, if not all, industries. For example, even creative pursuits seem to be encroached on by AI, but there’s a growing number of people who staunchly prefer human creations.

As our society becomes increasingly advanced and technologically sophisticated, it’s likely that this preference for human engagement and interaction will only grow. Accordingly, even if AI can do everything a human can, we may not want it to.

New Industries and Opportunities

Finally, consider that the rollout and evolution of AI is opening the door to new industries and opportunities. Software developers and tech engineers aren’t in danger of being replaced anytime soon. Prompt engineer roles are popping up in many industries, offering meaningful work to people who want to use AI as a tool for higher productivity. And it’s likely that we’ll see the emergence of even newer, more novel industries and job opportunities in the future.

In summary, AI will replace some human jobs in the future – and objectively, it already has. But because of the limitations of AI, our demand for the human touch, the rise of new industries and opportunities, and many other factors, it’s unlikely that AI will replace as many jobs as pessimists believe.

The post Why AI Won’t Replace as Many Jobs as You Think appeared first on ReadWrite.

Cyber attacks are getting more common, more serious, and easier to execute. The average Joe (or Jane) can literally go online and pay a company to hack for them, in a transaction known as ransomware as service, or RaaS. AI and machine learning tools can be programmed to hack for their users. Anyone can buy a physical hacking tool, like a Rubber Ducky or Packet Squirrel, to simplify the hacking process.

The threats are also more consequential. With the rise of IoT in high-risk sectors like the medical industry, agriculture, and manufacturing, a hack can do more than “just” expose private data. Hackers can access actual physical devices and get them to perform differently than they normally would. This can compromise individuals’ physical safety, disrupt the food supply, and shut businesses down for long periods of time.

In other words, businesses need to be on top of their stuff if they want to protect their safety now and in the future. The risks of not implementing up-to-date cybersecurity protocols are too numerous and too grave. Fortunately, a number of new technologies and practices are enabling more effective safety measures.

Cybersecurity Tech Trends to Implement

Here are some of the most important cybersecurity tech trends redefining how businesses defend themselves against cyberattacks.

1. Artificial Intelligence and Machine Learning Integration

Many cybersecurity platforms are now integrated with AI and machine learning tools, to help prevent cyberattacks. These tools can help spot patterns, anomalies, and deviations in ordinary user behavior to ward off hackers and malware. An endpoint protection platform, for example, monitors endpoint devices (phones, computers, tablets, etc.) for suspicious activities. They use AI tools that are trained to spot actions like unexpected logins or system changes.

More advanced AI-powered tools don’t just detect and report attacks and leave it at that; they can also take action to stop those attacks. They can do this by stopping suspicious programs from running or from accessing other parts of the system. AI and machine learning tools can also be trained to predict future attacks and even future types of attacks. This means that, in theory, they can help organizations fortify themselves against threats that don’t even exist yet.

2. IoT Security Measures

The hacking potential for IoT has already gotten pretty scary: hackers have accessed private individuals’ doorbells and baby cameras to look inside their homes. In one case, hackers figured out how to take control of a self-driving car. Security experts and the FDA have even found hackable vulnerabilities in pacemakers, defibrillators, and baby heart monitors. The potential dangers of IoT cyberattacks are far-reaching, impacting even critical infrastructure.

Fortunately, businesses can protect themselves against IoT attacks by following a few simple protocols. For one, they can use a different network to host IoT devices — one that isn’t connected to their other private data. They can also physically turn off IoT devices whenever they aren’t being used, to limit hacking potential. They can also practice good cybersecurity tech hygiene by regularly updating device software so that all current security patches are installed.

3. Biometric and Behavioral Authentication

Biometric authentication involves using a person’s unique physical traits — their facial features, iris scans, fingerprints, or voice — in order to confirm their identity. A business might use it to confirm a remote employee is really the person they say they are when accessing a company device. It can also be used in place of login credentials, to protect against password guessing and phishing attempts. In general, it’s used to stop bad actors from impersonating other individuals.

Behavioral authentication tools analyze behavior patterns, like how a person normally types or swipes a tablet. Small changes in these tiny details, called behavioral biometrics, can indicate that a user isn’t really who they claim to be. For example, say a supposed bank employee tries to open an account, but types much, much faster than usual. Behavioral authentication knows they haven’t gone from 40 to 100 WPM overnight; it’s probably not the same person.

4. SOAR Platforms

A SOAR platform is a unified cybersecurity dashboard that integrates tasks like real-time visibility, incident detection, and response automation. In other words, it’s an all-in-one tool that helps cybersecurity teams respond more efficiently to suspicious activity and attacks. By giving users access to a single, central database, SOAR platforms enable IT admins and security teams to coordinate their responses in real time.

SOAR platforms also make businesses safer by giving security teams the option to respond to low-level threats with automation. These automations work similarly to project management workflows, automatically running predefined processes. This automation frees teams from repetitive tasks, enabling them to direct the bulk of energy to more severe threats and incidents. SOAR platforms are designed to assess risk level so that these tasks are assigned to humans.

Upping the Ante

As cybersecurity threats continue to increase in frequency and severity, security experts are designing programs to better protect against them. While hackers will continue to get more sophisticated and efficient, those organizations working to stop them are still a step ahead. To protect your business, choose cybersecurity tech that is equipped to handle these evolving threats. Remember also to keep them current by installing new updates with every release.

The post 4 Game-Changing Cybersecurity Tech Trends Redefining Business Protection appeared first on ReadWrite.

As demand for artificial intelligence grows, its impact on the energy industry also grows exponentially. The data centers that power AI, cloud software and other tech advances require a significant amount of power — far more than is currently being generated by the energy industry.

In fact, AI is expected to drive a 160% increase in energy demand from data centers, and some experts predict AI could be responsible for one quarter of power consumption in the United States by 2030.

I discussed these emerging trends with Dr. Maksim Sonin, a thought leader in the energy sector and a Hydrogen Projects Fellow at Stanford University. He explained that while AI is driving the need to produce more energy faster, more reliably and with fewer carbon emissions, it can also play a role as a technological disruptor that enables these results.

AI’s Potential In Developing Emissions-Reducing Technology

The rise in energy use linked to AI comes at the same time that the scientific consensus is that the ability to limit global warming to 1.5 degrees Celsius has narrowed and become increasingly challenging. Despite this, maintaining such a goal is not yet out of reach, in part thanks to the growth and development of energy tech.

According to Dr. Sonin, the development of new technology is pivotal in reducing climate emissions, and it can (and should) be expedited with the help of AI.

“Ammonia production still relies on the Haber-Bosch process, which is over 100 years old. This is just one example of an area where there is space for disruption — and AI can prove key in helping us find these opportunities. We need AI to speed up this development because the energy industry as a whole tends to be very risk averse, which means it can take over 10 years for breakthroughs to become commercially available. Working with AI can help us identify, test and implement potential innovations much faster.”

From creating new materials to identifying catalysts for chemical reactions, Dr. Sonin views AI as a key tool in advancing technology that will help reduce emissions across the energy sector.

AI and Facilities Scaling

As valuable as the development of new technology can be, one of the biggest challenges the industry faces is enabling and scaling facilities. This is an area where Dr. Sonin sees AI as having an even greater impact.
“Scaling is simply the idea of making things bigger — such as being able to use one large compressor instead of two — to cut emissions and costs. When done right, scaling can address up to 65% of emissions and reduce capital expenditures by up to 30% to deliver global savings of up to $150 billion. The ability to accelerate industrial plants’ delivery schedules by 25% can also shave 12 to 18 months off typical project timelines.”

As Dr. Sonin’s example shows, scaling energy tech may be the most impactful way to ensure sustainable energy goals are met — and AI can play a central role in delivering more rapid and efficient growth.

“It is next to impossible to prototype at scale, but AI can help us do that through improved facilities design, predictions and modeling,” Dr. Sonin explains. “We need to run through different simulations before taking a new facility full-scale. Building a first-of-its-kind industrial unit at scale could cost several billion dollars. In other cases, already commissioned facilities experience massive production issues even though initial engineering and analysis looked sound. AI can help us improve the odds of success by rapidly running through performance models and more so we can scale with confidence.”

Improving Productivity

Dr. Sonin also expects that AI will be able to address a variety of productivity issues currently facing the energy sector. “Labor productivity has been and is expected to be an issue in 2025 and beyond, but as AI gains momentum, I expect that it will lead to gradual improvements,” he predicts.

“Even just a 10% increase in productivity can reduce capital costs by 4% or more. Improved productivity can be especially important in project management, where AI can help with de-risking, coordination and optimization to prevent engineering mistakes and delays on site.”

Dr. Sonin feels this enhanced productivity is especially important in light of current construction backlogs, which otherwise limit the ability to develop new facilities. AI can be a major factor in addressing these issues.

A Multifaceted Relationship

Ultimately, Dr. Sonin is very enthusiastic regarding AI and the energy industry, in large part because of its impact on decision-makers: “The rapid pace of AI disruption has helped change the perspectives of countless people in many industries about what we can achieve with technology.

“This helps set the perfect precedent for conservative decision-makers to change their minds. With changing mindsets and the assistance of AI technologies, we can pursue and implement new tech faster so the energy sector can get to where it needs to be. Although it will undoubtedly increase global energy demands in the coming years, AI will also become an integral part of the solution for disrupting and improving the energy industry as a whole. Therefore, I would encourage more founders and corporate leaders to explore this space deeply so we can reach our full potential.”

The post How AI Will Soon Disrupt the Greater Energy Industry appeared first on ReadWrite.

Much of the hype surrounding generative AI has focused on its use in content creation and offering personalized shopping experiences. But generative AI is also making a very real impact in fields such as healthcare.

I recently had the opportunity to interview Jon Read, co-founder of Confidant Health. We spoke about what AI-powered synthetic data is, why it matters and how it’s having an impact on healthcare. Here is a closer look at how AI is making a difference.

What Is Synthetic Data In Healthcare?

Synthetic data — information that is computer-generated to either replace or augment real data — is used broadly in AI. It protects sensitive information, mitigates bias from real-world data and can improve an AI model.

Synthetic data allows clinicians to use prompts to generate a conversation between a patient with depression and a therapist where they are discussing the onset of symptoms.

Healthcare providers can also use partially synthetic data, which takes a real-life transcript and has AI adjust it to remove personally identifiable information or private health information, while still telling a cohesive story. This data can then be used to train AI models to develop transcripts, training materials and so on.

Regardless of whether the data is fully or partially synthetic, the data can (and often is) adjusted as needed with additional prompts until it reaches the desired result.

Why the Use of Synthetic Data Matters

Healthcare is subjected to a variety of privacy rules through HIPAA. Eliminating these privacy concerns is a primary reason Read feels synthetic data is valuable in training models.

With synthetic data, healthcare providers don’t need to use real people’s data to train models. Instead, they can generate a conversation that is representative of a specific therapeutic intervention without involving anyone’s protected health information.

As Read explains, “Synthetic data also makes it easy to calibrate what we’re looking for — like to generate different examples of how a healthcare provider could say something explicitly or implicitly. This makes it easier to provide different examples and tighten up the information we provide to AI models to learn from, ensuring that we can teach it the right data for providing training or feedback to real-world clinicians.”

Synthetic data also democratizes the ability of different healthcare organizations to train and fine-tune their own machine learning models. Whereas previously, an organization might need to provide hundreds (or even thousands) of hours of transcribed sessions between patients and clinicians as well as other data points, synthetic data erases this barrier to entry.

Synthetic data allows for models to learn and build out responses at a much faster rate — which also makes it easier for new players in healthcare to enter the field.

How Synthetic Data Is Making a Difference for Healthcare

As Read explains, while synthetic data can be exciting in theory, its true usefulness comes from its ability to directly impact the quality of patient care, ensuring that clinical decisions are supported by real-world validated data sets.

“This can be especially important when providing feedback and training to clinicians,” Read explains. “For example, the learning model could use the standards developed through its data to ask a clinician if they asked about the onset of depression when they made the diagnosis. If the clinician didn’t ask important questions like when depressive episodes started or what their duration was, we would have less confidence in the diagnosis, and we would want to make sure that followup sessions addressed those questions so that the patient received the proper diagnosis and care.”

In this situation, the AI is able to provide quick and effective feedback to the clinician to ensure they are asking the right questions so that patients receive the proper level of care. The clinician can save time and energy by using their training to ensure they are following best practices and meeting appropriate criteria for providing quality care — even at scale.

Read also sees value in the potential for continuous improvement with machine learning systems.

“As you start to generate real world examples, you run the data to determine if the training from the synthetic data worked the way it was supposed to in terms of improving the standard of care provided. Are there additional criteria we need to look for when making the diagnosis? Are we actually asking all the right questions? As the platform learns and improves, we can offer even better guidance to clinicians by providing a quality, standardized system to work with.”

The Future of Healthcare

As Read’s insights reveal, the use of AI and synthetic data isn’t going to replace clinicians’ value or decision-making authority. But with the help of synthetic data, AI can help push clinicians in the right direction to ensure that there is greater standardization and adherence to best practices.

As more providers begin to utilize synthetic data to ensure they are following best practices in all patient interactions and to get feedback on their sessions, they can elevate the quality of care for all.

The post Synthetic Data: What It Is, Why It Matters and How It’s Changing Healthcare for the Better appeared first on ReadWrite.

Emerging trends will continue to shape how brands reach and engage their audiences. One is programmatic advertising: this is where digital advertising automatically offers relevant ad impressions to audiences, sitting at the forefront of digital marketing innovation.

Programmatic advertising has seen tremendous growth in popularity among advertisers and is on its way to becoming a $700 billion industry. Among the major trends leading this evolution of programmatic advertising are increased growth of AI-driven targeting and improved data privacy regulation.

Programmatic advertising gives brands the power to quickly adapt to changing consumer behavior and the complexity of the digital landscapes. Unlike traditional advertising, programmatic solutions employ data to offer highly personalized ad experiences, hence helping brands connect with potential customers quickly across a lot of platforms.

That flexibility extends to multiple media formats, enabling programmatic strategies within channels such as video, mobile, display, and even emerging formats in audio advertising. It is with those capabilities that programmatic advertising finds itself continuing to redefine how brands allocate their budgets to ensure each ad dollar is optimized for the highest level of precision, reach, and impact.

By understanding and adapting to these trends, brands can stay competitive and optimize their advertising for maximum ROI in the digital space.

1. AI-Driven Targeting

AI-driven programmatic advertising uses advanced algorithms to analyze user data and target ads to the appropriate audience. It analyzes the prospect’s buying and browsing behavior to create a user profile and then offers personalized recommendations. It also uses data to determine the best ad placements and to predict future behavior. The algorithms also analyze the best price for an ad placement.

One of its advantages is that it can adjust in real time based on new data. For example, if a user frequently looks at workout equipment, it will show them ads for equipment, gym memberships, or other fitness gear. If it notices that interest has shifted to a specific sport, such as tennis, it will change to show ads for tennis equipment and apparel.

Because of the accuracy of AI-driven programmatic advertising, advertisers earn an excellent ROI.

2. Enhanced Data Privacy Regulations

Several regulations govern consumer data privacy, including the General Data Protection Regulation. Programmatic advertising can help companies comply with these regulations.

• Programmatic advertising uses sophisticated algorithms based on broad aggregate data rather than individual data. It doesn’t require extensive personal data.
• It can also help integrate consent management platforms to allow users control over their data use.
• It also places ads on web pages that are interesting to targeted groups of users rather than relying on individual user profiles.
• Programmatic advertising builds its data through user interactions with a website or app, so it doesn’t need third-party cookies.
• The platforms let advertisers see how user data is used, allowing them to detect any privacy compliance issues immediately.

Of course, privacy challenges still exist with programmatic advertising. User data must still be carefully managed, and advertisers must comply with the GDPR. To remain in compliance, advertisers can anonymize data using techniques such as encryption and pseudonymization. They can also implement additional security measures, such as firewalls and access controls, to protect private data.

Above all, advertisers must understand how programmatic technology works. Advertisers use demand-side platforms to automate ad buying, and publishers use supply-side platforms to automate selling. The two connect through digital ad exchanges. Customer data platforms collect data from various sources to create a profile of each customer.

Working in the background are data management platforms that collect, organize, and leverage data from online and mobile sources to create a more detailed profile, which they share with the DSPs, SSPs, and exchanges. Data clean rooms provide a secure space for brands, advertisers, media agencies, and publishers to merge their data without sharing identifiable personal information. These clean rooms use aggregation, encryption, anonymization, hashing, salting, and other privacy tools.

3. Self-Service Tools

Self-service tools further empower marketers to take greater control of their ad campaigns. These tools give marketers control of the bidding, strategy, and scheduling without relying on others. Self-service refers to any platform that automates the process and allows for buying or selling ads without going through a human. Self-service tools can be very cost-effective.

Some examples of self-service tools are:

• Media buying platforms that allow users to create omnichannel ads.
• Platforms that allow users to target specific audiences and buy ads in real-time.
• Tools for publishers and buyers.
• Integration support platforms.
• Services offering management of data across multiple channels.

The Future of Programmatic Advertising is Here

Today, brands can engage audiences more efficiently than ever, largely because of the advancing capabilities of programmatic advertising. As technology advances, programmatic advertising will continue to be shaped by a few key trends, including AI-driven targeting, data privacy regulations, and innovative self-service tools. Brands can benefit from leveraging these trends, leading to better reach and a higher ROI. Programmatic advertising is an invaluable tool to help you remain competitive in a digital-first world. Seeing how much has changed in the last decade, it’s exciting to think about how businesses will leverage this technology as it advances into the future.

The post 3 Trends Shaping the Future of Programmatic Advertising appeared first on ReadWrite.

Teleservice has long been vital for many businesses, playing a key role in customer service, technical support, sales and other crucial activities. And like other business functions, teleservices have continued to evolve alongside technology, offering greater efficiency and a wider range of functions than ever before.

Unsurprisingly, many of the most exciting and innovative trends in teleservice are powered by artificial intelligence and machine learning, with AI proving a valuable resource in supplementing the work performed by human teleservice workers.

As Han Butler, president and co-founder of ROI CX Solutions explains, there are several noteworthy technology trends poised to reshape the industry in 2025 and beyond.

1. AI-Powered Virtual Assistants

Chatbots and AI-powered virtual assistants aren’t new — but their increasing sophistication is making them more and more useful for providing conversational customer service. This is especially true of virtual assistants that are largely trained on proprietary data, ensuring a tighter focus in terms of what they need to learn and focus on.

“As natural language processing models have become more advanced and been trained on more data, their ability to effectively engage with customers has increased dramatically,” Butler says. 

“It’s important to realize that they aren’t going to replace human service representatives, especially for complex questions or concerns. But as virtual assistants become more adept at taking on tasks like booking appointments or troubleshooting simple technical issues, it will result in faster response times and free up human reps for those more complex tasks.”

As an analysis by IBM notes, chatbots can be especially beneficial by increasing the capacity of teleservice to engage with large numbers of customers, offering 24/7 availability and providing consistent messaging.

2. Augmented Reality In Remote Assistance

Another exciting tech advancement is the rise of augmented reality (AR) for remote assistance in areas such as tech support, healthcare and manufacturing. For more advanced teleservice needs requiring video communication, AR can result in more engaging and mutually beneficial interactions.

“2024 saw Microsoft Teams add AR functionality that allowed other users to apply annotations to a video feed for calls between distributed frontline workers, and that is just the tip of the iceberg,” Butler says.

“Moving forward, we can expect this type of AR functionality to expand rapidly, particularly in highly technical industries where seeing what the customer sees can streamline the support process. Being able to provide what is essentially hands-on support without needing to be physically present can become a real game-changer in delivering top-quality service in complex scenarios.”

The application of AR extends beyond customer support, with a report from Manufacturing Management noting that the tech can also help organizations provide support within teams to address skill gaps, create immersive guides and deliver enhanced support (including real-time guidance from other specialists). The end result is faster and more effective problem-solving.

3. Predictive Analytics In Customer Support Conversations

Today’s businesses are able to collect more data than ever before — and this is true of teleservices, as well. AI and machine learning tools, when paired with predictive analytics, are able to use data to track customer behavior and learn from prior interactions to improve the level of service that both AI and human representatives provide.

“When done right, predictive analytics can help teleservice providers anticipate customer needs before they arise, allowing certain issues to be handled more proactively,” Butler says.

“Predictive analytics could also prove beneficial during a live call, with the AI suggesting an effective solution based on the conversation taking place between the rep and the customer. This would enable them to quickly offer a solution, saving time and reducing potential friction during those calls.”

Speech analytics can be an especially valuable part of this process, with AI gaining the ability to understand sentiment and intent based on keywords, tone of voice and other factors in order to make more accurate predictions.

4. AI In Representative Training

Butler also feels that AI can be a valuable tool in helping representatives improve their work.

“In addition to providing predictive prompts in the conversation, we’re also excited about AI’s potential to help teleservice representatives improve their capabilities at a much faster rate,” Butler says.

“AI can analyze transcripts and recordings of service conversations much faster than a human supervisor ever could. Based on the AI’s findings, it can then offer tailored suggestions for improvement to service reps, helping chart a clear path forward for improving their skills and practices.”

Butler notes that such suggestions should still be overseen by human supervisors, who can then provide coaching, training and other resources that are focused on the needs of each individual service representative. “This allows for much faster personal growth and development, so we can raise the quality of service across the board.”

A New Frontier

As Butler’s insights reveal, the teleservice industry is hardly immune to current technological trends. As these and other tech trends become more fully adopted by teleservice providers, customers can expect a deeper level of support for complex service needs, while also enjoying a more efficient overall response in terms of both timeliness and adequately addressing their concerns. 

For businesses that utilize teleservices, this means more satisfied customers — which results in better customer relationships and repeat sales.

The post 4 Teleservice Technology Trends Set to Reshape the Industry In 2025 (and Beyond) appeared first on ReadWrite.

Among the many potential applications for AI, few are more exciting than its potential for giving a voice to those who don’t have one. Representation and expression and the ability to communicate one’s wants and needs are crucial for many groups. With AI, many groups have ways to gain a voice in ways that previously were not possible.

I recently had the opportunity to speak with Tim Maliyil, co-founder of PerkyPet, and Dr. Dara Huang, the company’s co-founder and a Brown University educated physician. PerkyPet is an AI-powered tool designed to improve pet health by bridging communication gaps between pets and pet parents. As Maliyil and Dr. Huang’s insights reveal, AI offers several valuable solutions in giving a voice.

Learning From Physical and Behavioral Clues

PerkyPet’s AI-powered tool doesn’t attempt to do anything so outlandish as translate pet sounds into human speech. However, it does provide a solution that essentially facilitates communication between pet parents and their pets.

“AI can help analyze and learn from the physical and behavioral cues offered by a pet,” Dr. Huang explains. “These are important clues to a cat or dog’s overall health and well-being, as AI can detect changes quickly and then advise pet parents on actions they should take. This has the potential to assist with everything from matching a pet’s dietary needs with the right food products to identifying symptoms of disease and other health issues before they develop into a medical emergency.”

By evaluating data such as the pet’s environment, health history, diet and any situations that could be causing stress for the pet and its pet parents, the AI offers personalized recommendations based on the specific medical needs of each pet.

“In this instance, AI can complement the knowledge of veterinarians to help pet parents with their daily decision making so they can be more attentive to their pets’ needs and help them live happier, healthier lives,” Maliyil says. “The pets may not be literally speaking to their pet parents through the AI, but the AI ensures that their needs are understood and addressed appropriately.”

Speech Recognition and Generation

In some cases, AI is literally giving a voice to the voiceless. A recent clinical trial saw a surgical team implant small devices onto the brain of an ALS patient who had lost his ability to speak. These devices were able to decode the patient’s thoughts and turn them into sentences and phrases, which were broadcast by a computer. Even more impressive, the researchers used AI to replicate the patient’s voice from before the ALS diagnosis.

Similar research has also been conducted using AI wearables that learned from healthy subjects who would say key phrases out loud, and then repeat the same phrase silently. This research was focused on developing AI tools to help individuals with damaged vocal cords, with the AI learning from the muscle movements of study participants to learn to produce speech with over 94% accuracy.

“For people who have lost the ability to speak because of disease or an accident, these types of AI applications are a literal game changer,” Dr. Huang says. “The principle is similar to how Google’s DeepMind AI was able to train on TV to become proficient at lipreading, which can help individuals who are deaf or hard of hearing. Regaining the ability to communicate vocally can have a powerful impact on a person’s social and emotional well-being and greatly improve their quality of life.”

Though research and testing on these types of AI applications are still in their early stages, they represent perhaps some of the biggest leaps forward in terms of AI’s potential impact in the medical field.

Translation Opportunities

Another exciting area where AI is poised to give a voice to more groups is in the realm of translation. For example, in the live events space, 94% of event planning professionals say they would consider using AI for live translation. This is poised to make more events more accessible to a broader range of communities than ever before. Previously, live translation was often limited in scope, as it was dependent on hiring in-person translators.

Whether providing workforce training or trying to make entertainment more accessible, many organizations previously lacked the budget or capabilities to translate speech into other languages. As AI becomes increasingly capable, it can help bring global teams and audiences together like never before, which can create new opportunities for employees in a globalized work environment.

Maliyil also sees AI-assisted speech translation as a powerful asset for communities as a whole. “AI-powered speech translation can help break down language barriers for non-native speakers, making it easier for them to gain access to essential services or to form connections with others beyond their community. This can be especially powerful in diverse communities, where the ability to communicate with each other more easily can create a more unified and supportive environment.”

Enhancing Communication

As Maliyil’s examples reveal, AI can be a true game-changer in giving a voice to different communities — and even animals. Whether providing a literal voice or helping different groups understand each other better, the potential offered by machine learning and AI seems truly limitless. As advocacy and technology combine, AI can play a key role in creating new opportunities for those without a voice, leading to increased quality of life and better outcomes for all.

The post How AI Is Capable of Giving a Voice to Those Without One (and Why It’s a Game-Changer) appeared first on ReadWrite.

DevOps tech stacks have had an interesting year, to say the least. Terraform, long the market leader in the infrastructure-as-code (IaC) space, recently saw its parent company HashiCorp purchased by IBM, leading to concern from many in the industry that the platform will become overly focused on IBM’s cloud products. In the months leading up to the sale, HashiCorp had created turbulence of its own by changing from an open-source license to a commercial one, alienating much of its user base.

Within the midst of this turbulence, other IaC frameworks have seen rapid levels of adoption. In fact, one recent survey revealed that while roughly 80% of DevOps teams currently use Terraform, only slightly more than 20% plan to continue doing so in the future.

One of the platforms that has benefitted the most from this turbulence is OpenTofu, currently used by over 40% of DevOps teams, but projected to be adopted by roughly 55% in the near future.

I recently had the opportunity to speak with Ohad Maislish, co-founder and CEO of env0, also co-founder of OpenTofu, about the market conditions and tech developments that have contributed to OpenTofu’s success, as well as its potential to maintain momentum in the coming months and years.

The Rise of OpenTofu

OpenTofu has emerged to prominence in a surprisingly short time since HashiCorp moved Terraform to a Business Source License in 2023. Maislish and the other OpenTofu co-founders reacted quickly to fork Terraform and create their own open-source solution, which subsequently came to be managed under the auspices of the Linux Foundation.

As Maislish says, “The key takeaway from this experience is the inherent unpredictability of having the future of a cornerstone open-source project, like Terraform, guided by a single company rather than a foundation.”

On the other hand, Maislish is quick to point out that he sees nothing wrong with a commercial company introducing and maintaining an open-source project. “Many beloved projects, including Terraform, started exactly this way and needed that corporate backing to survive and establish their footprint,” he continues.

“However, it’s important to acknowledge that corporate decisions ultimately serve the interests of their stakeholders, which may not always align with those of the open-source contributors and user community.”

While there’s plenty of talk about the possibility of IBM taking Terraform back into open-source territory, the new parent company is still profit-driven, so the DevOps community might not hurry back to Terraform even if this does take place. Because OpenTofu is a Linux Foundation project now, Maislish explains, it is more or less guaranteed to remain open source in perpetuity.

Precedent for Cloud Management Solution Changes

In the wake of Terraform’s licensing change and subsequent purchase by IBM, Maislish sees historical precedent in OpenTofu’s surging momentum. “The story of OpenTofu is unique, but there are other cornerstone technologies that followed somewhat similar paths to build a stable, sustainable ecosystem and drive widespread impact across the tech industry,” he explains.

One particularly poignant example, he says, can be found in Kubernetes, which was initially developed by Google, eventually becoming part of the Linux Foundation’s Cloud Native Computing Foundation (CNCF) in 2015. “Nearly a decade later, Kubernetes has become a driving force for innovation, transforming and enriching multiple industries,” Maislish adds.

“The CNCF’s involvement with Kubernetes,” he says, “was a key factor in this success to everyone’s benefit. Similarly, Terraform is a widely adopted foundational technology, and with OpenTofu, it has now embraced true open-source principles, poised to thrive independently and explore its full potential.”

Earlier, when Walmart and Target needed to scale their IT services in the early 2010s, they faced a real challenge in choosing a solution. Essentially, they had to either go with AWS, owned by their rival Amazon, or find an alternative. Ultimately, they chose the open-source cloud computing platform OpenStack. The flexibility and security resulting from these developments led to the development of Walmart’s multi-cloud strategy, which has helped it reduce millions in IT costs.

In a sense, history is now repeating itself with OpenTofu, which was adopted by Oracle and VMware shortly after IBM announced its purchase of HashiCorp.

The Inherent Value of Truly Open-Source DevOps Tech

The historic and recent events driving OpenTofu’s momentum are underpinned by the value that DevOps teams find in open-source software — something that Maislish believes will allow OpenTofu to maintain and even increase the stunning growth it has achieved since last year.

“I’ve been around long enough to remember a time when OSS was a ‘taboo’ word for organizations, especially top-tier enterprises. Today it’s incredible to see the evolution open source has undergone, becoming a primary consideration when qualifying technology,” Maislish says.

“What makes it a preference will differ but, by and large, I think most teams opt for open source to achieve greater flexibility and cost-efficiency, as well as to benefit from the diversified innovation potential,” Maislish continues.

“However, I don’t believe that open-source software is always the best solution. Like anything else, the decision to use it should be weighed against other organizational considerations, and there are plenty of cases where commercial software would be the right choice.”

Piloting the Future

Maislish is ultimately confident in OpenTofu’s ability to maintain its growth momentum. Some 36,000 people have shown their support for the OpenTofu manifesto, a rare head start. There’s also the rapid pace of OpenTofu rollouts, with a major release having dropped every few months. “Each of these introduced significant improvements and features long requested by legacy Terraform users,” he asserts.

What’s more, Maislish says, “The amazing support from the Linux Foundation is a key factor that sets the project on a path for long-term success and amplifies its visibility. For instance, every KubeCon now features a dedicated OpenTofu Day event, demonstrating the foundation’s strong commitment to the project.”

It seems that OpenTofu is in a good position when it comes to larger trends that are shaping DevOps as a whole, and Maislish is optimistic. “With this combination of almost unprecedented initial reception, a high-performing development team, and a tailwind from the biggest open-source organization on the planet, I feel lucky to be a part of this ride and excited about what comes next.”

The post With DevOps Tech Stacks In Flux, Can OpenTofu Maintain Its Growth Momentum? appeared first on ReadWrite.

In today’s retail environment, few things are more important than implementing strong cybersecurity practices. A cybersecurity breach can do more than just compromise data — it can significantly erode consumer trust.

According to IBM’s “Cost of a Data Breach Report 2024”, the average cost of a retail data breach now stands at $3.48 million, an 18% increase from the year before. As cybersecurity threats continue to evolve and become more sophisticated, retailers must in turn adopt stronger policies, practices and solutions that enable them to deliver a secure and sophisticated experience for both their customers and their staff.

Going into 2025, here are key areas that retailers must be attentive to.

Retailers’ Growing Role In Combating Customer Account Takeovers

Hackers are growing more and more sophisticated in how they commit cyber fraud. With retailers, an increasingly prevalent tactic is to use phishing to lead consumers to fake websites designed to look identical to the actual retailer. The customer attempts to log in to the fake site, providing their authentic credentials in the process. The hackers can then turn around and use this information to take over the actual account, utilizing the customer’s data to make fraudulent purchases or for other purposes.

While retailers themselves aren’t to blame in these circumstances, this doesn’t mean they can sit back on the sidelines. An estimated 29% of adults have experienced an account takeover, with 70% of victims noting that compromised passwords were not unique to a single account.

Account takeovers (ATO) can cause financial loss for businesses when fraudsters use compromised data to make purchases — charges which are typically canceled after the account owner reports fraud. In addition, customers will often blame retailers for these incidents, straining customer service and resulting in negative word of mouth.

To combat this, a growing number of retailers are becoming more proactive in combating account takeovers. Using tools like Memcyco, which can detect when hackers research website codes, register fake URLs in a company’s name or make a fraudulent website go live, retailers can block digital impersonation-based phishing attacks in real time before they happen and keep their customers safe.

Blocking Malicious Traffic

Customer account takeovers are far from the only threat retailers need to be concerned with. It isn’t unusual for malicious actors to directly target the retailer network itself, taking advantage of the high level of inbound traffic that retailers experience.

One of the most common attacks is a distributed denial of service (DDoS) attack, in which a retailer’s network becomes so flooded with illegitimate requests, that its bandwidth is overwhelmed and no longer allows legitimate users to access the website.

Firewalls have long been the go-to solution for blocking DDoS and other backdoor attacks, but as the attacks grow more sophisticated, so do the cybersecurity needs. A next-generation firewall (NGFW) can be a valuable resource for retailers, as it builds off traditional firewall operations to block a broader range of malicious traffic. By operating at the protocol stack’s application layer, NGFWs offer the ability to inspect encrypted traffic, use sandbox analysis to detect malware and more.

The growing risk associated with DDoS and other backdoor attacks further drives home the importance of proxy firewalls, which serve as an intermediate connection point that prevents direct connections between systems. Of course, these systems require proper configuration and updating to ensure they allow the right traffic through. AI and machine learning are proving to be especially beneficial in this area, by helping firewalls remain effective and better able to respond to risks in real time.

Managing Employee Risk Factors

Retailers must always be vigilant regarding the potential risk factors linked to their employees. A joint study between Stanford University and Tessian found that 74% of business data breaches are caused by employee mistakes. Whether the issue stems from using an unsecured device, creating a weak password, not applying software updates and security patches to a device or installing unauthorized software.

Retailers must implement strong prevention tactics to reduce the threat of employee-related incidents. This can include implementing policies requiring multi factor authentication and complex passwords, rules regarding the use of devices and software and providing ongoing training to help employees understand common cybersecurity risks (such as phishing emails that use minor typos in their email addresses).

Retailers should also implement clear practices for how employees should respond to and report malicious activity or other cybersecurity concerns. These policies and procedures should be covered with all employees, in accordance with their level of access to the business’s digital accounts.

In addition to providing proper training, retailers should also be mindful of factors that can increase the risk of unintentional human error. Environmental factors and stress factors often contribute to security mishaps — retailers should consider how their work environment affects employee decision-making and make adjustments as needed.

Ensuring Cybersecurity In 2025

While specific trends and threats may change, many of the core best practices for retail cybersecurity remain the same. By making use of tools that can limit the potential influence of bad actors and taking steps to proactively address risks related to both customers and employees, retailers can improve their overall cybersecurity status and minimize their risk for a serious breach. As retailers adopt a proactive, preventative approach, they can take control of their cybersecurity profile.

The post Cybersecurity In Retail: Trends and Best Practices for 2025 appeared first on ReadWrite.

During the past year, AI-generated video has gained widespread prominence and coverage in the news — both positive and negative. Amidst concerns that AI video creation tools will negatively impact jobs in entertainment and advertising, an increasing contingent of creators are recognizing AI’s potential to improve video production workflows.

I recently had the opportunity to speak with Alon Yaar, VP of product at Lightricks. Yaar’s team has been developing the LTX Studio web app, which offers a powerful generative AI text-to-video engine that comes integrated with tools conceived to address video professionals’ needs.

How AI Can Improve Video Production Workflows

During our conversation, Yaar highlighted some of the myriad ways that AI can enhance video production workflows to maximize the capabilities of human creators.

Ideation and Pitch Creation

“AI can be an incredibly useful tool during the ideation and pitch creation phase,” Yaar explains. “A filmmaker or creator can take a basic idea or a complete script and leverage the power of AI to offer a variety of concepts that they can then build from. It’s easy to adjust visual styles, characters and storyboard flows, so creatives can develop these concepts and then communicate them more compellingly and coherently.”

As part of the preproduction process, LTX Studio has the capability to automatically generate a pitch deck that includes a synopsis, mood-boards to showcase the project aesthetic and character profiles for the cast, as well as storyboards.

For ad production firms and movie studios, the ability to quickly produce multiple pitches can make it much easier to reach an agreement with a client regarding the direction of the project so actual production can begin.

Overcoming Budget Barriers

Budget restrictions are a common obstacle faced by media production studios. Large brands and studios can afford budgets in the hundreds of thousands to bring their vision to life with special effects, exotic sets and other attention-grabbing elements. Not so with projects that aren’t attached to proven sci-fi franchises.

As with shooting a feature film, the available budget directly impacts the viability of location shoots and animated effects.

According to Yaar, “Video project budgets are tight nowadays, even at bigger firms, so any boost in efficiency goes a long way. By using AI-assisted iteration of creative concepts up front, prototypes can be generated and communicated more quickly, and therefore, so can approvals.”

Frame-By-Frame Shot Refining

One common criticism of AI has been the “uncanny valley” effect, wherein viewers can identify that what they’re watching isn’t real due to unnatural or inconsistent character movements and models, missing frame details and other issues. However, with frame-by-frame shot refining available through tools like LTX Studio, such concerns are closer to becoming a thing of the past.

“While the creative process is iterative and experimental, at some point, creative professionals arrive at a specific and detailed vision of what they want to achieve, so it’s important for filmmakers to have total control of every shot,” Yaar explains. “Creators can define camera movements, can adjust the frame’s content, including inserting or erasing objects from the scene, and can even give characters more realistic facial expressions.”

This AI-assisted aspect of video production can also be combined with footage captured in real life, providing a quick and easy solution for removing background objects, adjusting lighting or framing and addressing other elements of video editing that are often tedious and time-consuming when performed manually.

Improved Collaboration

Video ads, music videos and other forms of video content often have several stakeholders involved in the creative process. In traditional video production, the process of editing a video, sending it to stakeholders for feedback and then applying that feedback can be extremely inefficient.

Lengthy email chains listing specific timestamps often get swamped in either too much or too little detail, resulting in multiple rounds of revisions. With LTX Studio, Yaar sees an opportunity to streamline the collaborative process.

“One feature that will expand as we update the platform is the ability for all stakeholders to have access to live edits, allowing the team to share ideas and refine the concept together in real-time,” he says. “We’re currently working on giving users the ability to add comments and suggestions as part of these real-time edits. Real-time collaboration combined with AI’s ability to rapidly generate updates in alignment with that feedback, helps everyone work more efficiently as they reach a consensus on what the video should look like.”

Revolutionizing Video Production

As Yaar’s insights reveal, AI in video storytelling isn’t necessarily designed to replace human talent. Rather, it is uniquely poised to help advertising agencies, media production studios and others have a broader range of tools that can help them in each stage of video production.

Whether AI is being used for creating pitches, incorporating fantastical elements in an ad or music video or helping to refine video footage on a frame-by-frame basis, this tech is poised to level the playing field.

The post How AI Promises to Influence Video Production Workflows appeared first on ReadWrite.

Machine learning, within the past few years, has grown from a very minute field into an important part of modern technology, driving innovation in areas like finance, healthcare, and several e-commerce industries.

Traditionally, languages such as Python and R have been front-row languages when it comes to developing any form of machine learning model because of their extensive libraries and frameworks. But with the evolution of Node.js and the rise of JavaScript, things are taking a wider turn in the circle of machine learning development, and one can now build robust machine learning models using this versatile and widely used language.

Overview of JavaScript and Machine Learning

JavaScript is generally known to belong to web development. But its capabilities have since extended far beyond the confines of the browser. Node.js is the environment running JavaScript on the server side. That fact makes Node.js a powerful means of building scalable and high-performance applications. This recent growth of machine learning models in JavaScript was further assisted by a set of libraries and frameworks designed to seamlessly work together with Node.js. Developers can use all their already existing experience with JavaScript while working on tasks having to do with machine learning.

Key Libraries for Machine Learning in JavaScript

Several libraries have come to lighten the burden of machine learning development in JavaScript and, therefore, lighten developers’ work while creating and deploying models. Some of the most outstanding ones include:

1. TensorFlow.js

TensorFlow.js is an open-source library from Google that allows a developer to create and train models for machine learning directly in a web browser or within a Node.js environment. TensorFlow in a JavaScript version is one of the most popular machine learning frameworks. Basically, TensorFlow.js is the complete suite of tools for implementing neural networks, optimization of models, and running inferences. With TensorFlow.js, developers can handle complex computations and training procedures of their models using JavaScript. It enables the integration of machine learning models within web applications more easily. This becomes an advantage in embedded machine learning models in web applications.

2. Brain.js

Brain.js is a light library of neural networks running in JavaScript. This interface is pretty basic, simple, and hence perfect for those developers without experience in machine learning. Brain.js supports several types of neural networks, including feedforward networks and recurrent networks. An API to train or estimate models is highly intuitive. Though Brain.js cannot be compared to the complexity of functionality of TensorFlow.js, this tool is great to deploy for rapid prototyping purposes and educational needs.

3. Synaptic

Synaptic is another neural network library for JavaScript which is flexible and easy to use. Currently, it supports multilayer perceptrons, LSTM networks, and more. Synaptic is designed to be modular. That means it’s easy to build any kind of neural network architecture by combining different components. Because it’s so flexible, Synaptic is a great way to experiment with different network structures and learning algorithms.

Building Machine Learning Models with Node.js

The creation of machine learning models in Node.js has to do with a variety of activities that range from data preparation through model training down to deployment. At a high level, creating a model in Node.js would look something like this:

1. Data Preparation

Collection and preprocessing of data would be the very first process of any machine learning model development. Data preparation is about how one cleans the data, handles missing values, and transforms the data into a form that can be taken to the process for training. In Node.js, you organize your data by employing various libraries-for example, csv-parser, if you want to read your CSV files, and node-fetch if you want to make API requests for fetching data.

2. Model Training

After preparing data, the next process is the training of the model through machine learning techniques. Using either of these libraries, TensorFlow.js or Brain.js, you can define your model architecture, specify the learning parameters, and train the model on your dataset. That means feeding the data into the model; then, adjust weights and biases through backpropagation, and iteratively continue to do this until the model performs well.

3. Model Evaluation

For any machine learning model, performance evaluation is necessary after training. It includes testing one’s model on the independent validation dataset that will determine the accuracy, precision, recall, and other metrics of your model. On Node.js, you can make use of the built-in functions provided by machine learning libraries for evaluation and visualization.

4. Deployment

Once the model is trained and evaluated, you can deploy it, possibly as part of a web application or service. Node.js makes it pretty easy to integrate machine learning models right into web servers and APIs. This makes live predictions and insights available to users directly. For instance, TensorFlow.js lets you run most inference tasks on the browser or server for seamless user experiences.

Benefit of Using JavaScript in Machine Learning

Use of JavaScript and Node.js for machine learning has the following advantages:

Unified Development Stack: Use of JavaScript, both for frontend and backend development, will keep it consistent across application stacks. This way, development will be quite smooth and require minimum context switching to different languages.

Real-time capability: JavaScript is suited for real-time applications, and Node.js has a non-blocking architecture that efficiently handles concurrent requests. Thus, this allows the building of real-time machine learning applications that can give instant feedback and predictions.

Ecosystem Integration: JavaScript’s vast ecosystem and the ability to work well with other popular web technologies make it easier to integrate machine learning models into already existing applications. In that way, developers will be able to use several tools and frameworks in order to extend their machine learning solutions.

Conclusion

Node.js JavaScript has been increasingly viable as a tool for creating machine learning models. Libraries like TensorFlow.js, Brain.js, and Synaptic let developers apply their knowledge in JavaScript for the development, training, and deployment of their machine learning models in a far more efficient manner. JavaScript is applied in machine learning development for a number of reasons: its unified development stack, real-time capability, and smooth ecosystem integration. As machine learning is evolving by the minute, JavaScript and Node.js are in the position to make some serious noise regarding the development of intelligent applications and services.

The post JavaScript Node: A Tool for Creating Machine Learning Models appeared first on ReadWrite.

With global retail e-commerce expected to exceed $6.3 trillion in 2024, cybersecurity in digital payments has never been more important. In the first half of 2024 alone, over 214,000 incidents of credit card fraud were reported to the FTC, with fraudulent activity often being facilitated as a result of digital payments.

As part of a major effort to ensure cyber-compliant digital payments, PCI DSS (Payment Card Industry Data Security Standard) has established clear guidelines for processing, storing and transmitting credit card information online. And in light of ever-evolving digital threats, businesses currently find themselves in the midst of the rollout of PCI DSS 4.0.

PCI DSS applies to any business accepting credit card payments online, which encompasses everything from D2C e-commerce all the way to subscription-based enterprise software. Here’s what online businesses need to know about getting ready for PCI DSS 4.0, and what these new standards indicate about the future of cyber-compliant digital payments.

Why This New Version?

The introduction of PCI DSS 4.0 is a direct response to the ever-changing cybersecurity landscape surrounding digital payments. The COVID-19 pandemic led to a rapid increase in digital payments, as well as a rise in cybercrime. At the same time, increased computing demand has caused many business networks to transition from traditional data center-based servers and routers to cloud computing.

Of course, cybersecurity threats are constantly evolving, with phishing and other attacks against digital payment applications becoming more sophisticated. Mobile, IoT devices and cloud processing have all seen rapid adoption since the previous PCI DSS standard was introduced in 2018.

The rapidly shifting pace of technology and the increased reliance on digital payments on a global scale has made updated standards a necessity to ensure that online payments remain truly secured. As the digital payments landscape has evolved, so have cyber attacks, and as a result, compliance standards like PCI DSS are likely to be revised to be increasingly strict over the years ahead. 

Understanding the Gradual Rollout of PCI DSS 4.0

PCI DSS 4.0 was introduced in 2022 with 64 new security requirements, 13 of which needed to be implemented immediately beginning in March 2024, with the remaining 51 controls required to be in place by April 1, 2025.

Among these updated security standards are the requirement for all users who can access cardholder data to implement two-factor authentication, increasing minimum password length requirements to 12 characters and mandatory annual security awareness training on topics such as phishing.

Failure to comply with PCI DSS standards has notable penalties, with banks and payment processors passing on fines ranging from $5,000 to $100,000 per month to non-compliant merchants. The severity of non-compliance, the number of transactions processed by the merchant and their data security history can all play a role in the fine amount, which can also escalate if the merchant fails to resolve its noncompliance issues.

The scope of the new security requirements and the associated penalties makes this gradual rollout a necessity so that businesses have enough time to become fully compliant.

How to Ensure Compliance

With the full implementation of PCI DSS 4.0 just months away, organizations must act immediately to ensure they will be ready. Businesses should start by roadmapping the PCI DSS 4.0 updates that they need to make to ensure full compliance by April 1, 2025 and prioritize them accordingly.

With so many new security requirements to consider, roadmapping can be a time-consuming process. Tools like Cypago, a cyber GRC automation solution, can help. Cypago makes it easy for cyber and compliance teams to collect compliance evidence, address security gaps and engage in continuous monitoring. 

Notably, Cypago covers a variety of compliance frameworks, including PCI DSS, as well as GDPR, ISO 27018, NIST 800-171 and SOC 2 – all of which are useful as trust signals to various stakeholders that a company takes information security and user privacy seriously. Using Cypago, cybersecurity and compliance teams can manage all of their controls holistically, can build out custom frameworks, and can perform risk-driven analyses. 

Because the tool always keeps its system up to date, users are able to quickly evaluate how they compare to current standards and controls. Even with such tools, however, fully transitioning to PCI DSS 4.0 can’t be done overnight. While some requirements can be implemented relatively quickly, others take months to fully adopt. Evaluating your current status compared to pending security requirements is crucial to develop a plan for ensuring full compliance.

In addition to taking action to implement the specific requirements for PCI DSS 4.0, businesses can future proof their digital payment compliance by focusing on the underlying issues that contributed to this latest update.

To begin with, businesses should avoid storing sensitive cardholder data unless it is absolutely necessary, properly encrypt such data and erase data as soon as it is no longer needed for the transaction. Businesses must also closely control access to their systems and code script on payment pages to reduce potential breaches.

The overarching goal of PCI DSS 4.0 aims to create a future where merchants take a more proactive approach to cybersecurity and take decisive steps to protect their customers even before additional standards updates are introduced.

Creating a Safe Environment for Digital Payments

Ultimately, the rollout of PCI DSS 4.0 illustrates the emphasis that the payment card industry is placing on ensuring the safety of its customers and preventing identity theft and credit card fraud. At the same time, the gradual rollout of these standards highlights the understanding that businesses need time to fully implement the updated security requirements.

By enacting these additional security measures, the payment card industry and online businesses can work together to reduce fraud. PCI DSS 4.0 represents an ongoing commitment to navigating today’s security challenges — and the likelihood that additional standards updates will come in the future as new threats and tech innovations bring about even more changes.

The post What the Rollout of PCI DSS 4.0 Says About the Future of Cyber-Compliant Digital Payments appeared first on ReadWrite.

As businesses continue to generate massive amounts of data through customer-facing websites and internal corporate systems, the need for scalable databases becomes increasingly crucial. With data requests on the rise, your back-end systems need to handle spikes in traffic.

Adding hardware to an existing architecture may scale it up or down based on your needs and available resources. From there, you may design a system that can handle present loads while expanding in the future.

Continue reading to learn about several database scaling strategies and how to choose the one that will work best for your company.

What is a Database Server? 

A relational database management system (DBMS) is software that can be installed on hardware and is called a data server. This specialized database application provides two essential features.

1. The appropriate back-end infrastructure for organizing and customizing all of your data into tables.
2. Client-facing services, which let you and authorized staff members access, modify, and update particular files whenever and wherever you choose.

Signs You Need to Scale Up Database Server

• Slow query response times.
• High disc I/O activity or CPU utilization.
• A system design with inadequate resilience.
• Timeouts or crashes on the database server often.

How to Scale a Database Server 

Im͏agine a d͏ata͏base s͏y͏stem ͏t͏hat eff͏͏o͏r͏t͏le͏͏ssl͏y m͏anag͏͏es͏ mo͏re dat͏͏a and͏ users ͏witho͏ut ͏s͏ac͏r͏ifi͏cing͏ respon͏sive͏ne͏ss͏,͏ pe͏r͏form͏ance,͏ ͏͏or ͏d͏e͏͏pend͏a͏bil͏͏i͏t͏y. ͏Sc͏͏ali͏n͏g͏ yo͏͏ur ͏da͏͏tabas͏e s͏erver ͏is͏ esse͏ntial͏ to ac͏h͏ieve͏ ͏this le͏vel of ef͏f͏ici͏e͏͏ncy.͏ T͏her͏͏e ͏a͏re primar͏i͏l͏y͏ tw͏o ͏a͏p͏pro͏aches t͏o sc͏a͏l͏͏i͏ng a ͏d͏at͏abas͏͏e͏: ͏ver͏tical ͏s͏cal͏ing͏ and ho͏rizonta͏l sca͏li͏͏ng. Each met͏h͏od h͏as its ͏o͏w͏n͏ ͏advan͏ta͏ge͏͏s and c͏͏hal͏len͏ges, an͏͏d͏͏ u͏nd͏erstan͏ding th͏e͏m͏ ͏is cruci͏al͏ ͏for͏͏ ͏se͏le͏c͏t͏͏ing the r͏igh͏͏t͏ s͏trategy͏ ͏f͏͏͏o͏r͏ ͏you͏͏r bus͏in͏͏͏es͏s͏͏ ͏need͏s͏.͏͏

Verti͏c͏a͏͏l͏ Sc͏a͏ling (͏Sc͏al͏in͏g U͏͏p͏͏)

De͏fin͏i͏tion: Vert͏ical sca͏li͏ng͏͏ inv͏ol͏ve͏s add͏ing more resources͏ ͏to͏ yo͏u͏r exi͏͏sting da͏͏tabas͏e ͏serve͏͏r. ͏T͏his could me͏an ͏upgrading th͏e͏ ͏CPU, ͏i͏͏ncreas͏i͏ng RAM, ad͏d͏ing ͏fa͏͏ster database sto͏ra͏ge s͏ol͏ut͏i͏o͏ns͏, ͏o͏r e͏n͏h͏͏a͏nci͏ng͏ ͏ne͏tw͏o͏rk ͏capa͏bi͏l͏i͏ties͏͏.

Advantage͏s͏

͏Sim͏plic͏ity:͏ Vertic͏a͏l͏ ͏sc͏͏aling͏ is stra͏ig͏ht͏fo͏rw͏a͏rd͏ t͏o ͏im͏plement. U͏pgr͏ad͏ing ha͏rdware typically ͏requires ͏minim͏al c͏han͏g͏es ͏to ͏you͏r͏ ͏exis͏tin͏g s͏e͏t͏u͏p.

͏Im͏m͏edia͏t͏e ͏Perfor͏manc͏e B͏oo͏s͏t:͏ Enha͏ncing͏ the s͏erv͏e͏r͏’s capab͏i͏lities can ͏prov͏ide͏ a ͏q͏uick incr͏e͏a͏s͏e͏͏ ͏i͏͏n͏ ͏p͏e͏r͏form͏ance, ͏a͏llowin͏g͏ y͏our ͏datab͏͏a͏se to ͏ha͏͏n͏dl͏e more loa͏d wi͏thout sig͏ni͏ficant ͏architectural c͏h͏a͏͏nge͏s.

N͏o A͏p͏͏p͏l͏ication͏ C͏hanges Ne͏e͏͏ded: ͏͏Since the dat͏a͏bas͏e r͏emains on a͏ si͏ng͏l͏e serv͏er, ͏your ͏a͏pp͏l͏ic͏atio͏n d͏oesn’t nee͏d ͏to ͏b͏e͏ m͏odif͏i͏ed t͏o acco͏mm͏odate a͏ ne͏w ͏in͏frast͏ructu͏͏re.͏

͏͏D͏is͏advantages

Ha͏rdw͏ar͏e͏ Limitation͏s͏: There’s a͏ ͏͏physical l͏i͏mi͏t to how m͏͏͏uch͏ ͏y͏͏ou can scale a single ͏server͏. Onc͏e ͏you͏ reach͏͏ th͏e maxi͏mum͏ ca͏pa͏͏city, furt͏he͏r ͏s͏c͏al͏in͏g ͏͏͏becomes͏ impossible͏͏ w͏ithout͏͏ sig͏͏nif͏i͏cant do͏wn͏time.

Cost͏:͏ H͏i͏gh-p͏erfo͏rmance ͏ha͏͏rdware c͏an b͏e͏ ͏ex͏p͏ensi͏v͏e. C͏ont͏͏in͏uo͏us͏ly͏ u͏p͏gr͏͏ad͏͏ing͏ to mor͏e pow͏erful m͏ach͏͏in͏es͏͏ may n͏o͏t be c͏ost-ef͏fec͏ti͏v͏e in the long ͏r͏un.

Si͏ng͏͏le͏ P͏͏o͏i͏n͏t of ͏F͏a͏ilure: Relyin͏g͏ on͏ a ͏single ͏s͏erver inc͏reases the ris͏͏k͏ o͏f ͏downt͏im͏e i͏f͏ that͏ ser͏v͏er͏ ͏f͏a͏͏il͏s͏.

Us͏e ͏͏C͏ases: Vertica͏l ͏scaling is͏ id͏eal͏ fo͏r sm͏͏aller͏ appl͏icatio͏n͏s͏͏ ͏o͏r bus͏inesse͏s that are jus͏t ͏s͏͏tart͏ing a͏͏n͏d have͏ pred͏ic͏ta͏ble͏ growth ͏patte͏r͏͏ns. It’͏s ͏͏͏als͏o su͏itabl͏e wh͏e͏n ͏i͏m͏me͏d͏i͏a͏te͏ ͏performanc͏e ͏i͏mpro͏v͏emen͏ts ͏͏a͏͏re necessary wi͏thout ͏ove͏rhauli͏͏ng t͏he exis͏t͏in͏g infrastruct͏ure.

͏H͏or͏izo͏ntal͏ Sca͏l͏in͏g (Scal͏ing͏ Out͏)

D͏efin͏ition͏: Horiz͏͏on͏t͏al sc͏͏aling͏ invo͏lve͏s͏ ad͏di͏ng͏ mor͏e ͏data͏b͏a͏͏s͏e͏ ser͏vers ͏͏to dist͏͏ri͏bute ͏the l͏oa͏͏d. Instead ͏of͏ en͏hancing a ͏single ͏ser͏v͏er, yo͏u͏ e͏͏xpand͏͏ your datab͏ase infr͏astruct͏ur͏e͏ by ͏i͏͏nt͏egra͏͏ting m͏ultipl͏e͏͏ ͏͏machines͏.͏

Key ͏Patte͏rn͏s͏

S͏hardin͏͏g

Desc͏͏r͏i͏p͏tion: ͏Div͏id͏es you͏r dat͏a͏base in͏to ͏͏sm͏alle͏͏r, more manage͏a͏bl͏e pi͏e͏ces ͏͏ca͏l͏led ͏shards. Eac͏h͏ sh͏͏ar͏͏d ͏͏ho͏lds a͏ sub͏se͏͏t͏ of ͏t͏he ͏͏dat͏a͏, allow͏i͏ng q͏uer͏ies t͏o͏ be ͏dist͏ri͏b͏ut͏ed acros͏s ͏m͏u͏lti͏ple ser͏vers.͏

͏A͏d͏v͏antages: Improves perfo͏͏rm͏anc͏e ͏by͏ ͏par͏allelizi͏n͏g͏ ͏qu͏e͏r͏i͏e͏͏s and balan͏ce͏s͏ the load e͏ff͏ec͏ti͏v͏͏ely.

Cha͏l͏le͏n͏g͏es͏: M͏anaging sh͏a͏rd͏s c͏a͏n b͏e c͏͏͏o͏͏mp͏le͏x, espe͏͏cial͏͏ly when d͏ealing͏ ͏wi͏th data ͏con͏s͏is͏t͏en͏͏c͏y ͏and͏͏ en͏suri͏n͏g͏͏ e͏ve͏͏n distr͏ib͏ution ͏o͏f͏ d͏a͏ta͏͏.͏

Repl͏ica͏tion͏

De͏sc͏ri͏p͏tion: Inv͏o͏lves͏ cr͏eating͏ c͏opies ͏of͏ ͏your databas͏͏e͏ acr͏oss mu͏l͏tiple server͏s. ͏͏R͏eplica͏s ca͏n h͏an͏d͏le r͏ead o͏perat͏i͏o͏ns, reducing t͏he͏ load on ͏th͏e pr͏͏i͏mary͏ s͏erve͏r.

A͏dva͏ntag͏es͏: E͏nha͏n͏ces͏ d͏ata availabi͏l͏ity͏ an͏d͏͏ rel͏ia͏bi͏lity͏.͏ If͏ one re͏͏plica͏ ͏fails͏,͏ o͏t͏hers can ta͏ke͏ o͏͏ver ͏s͏͏e͏aml͏essly͏͏.

Ch͏al͏len͏ges: ͏Mainta͏ini͏ng͏ ͏d͏a͏ta c͏͏onsist͏e͏ncy a͏c͏͏ro͏ss͏ ͏rep͏lica͏s ͏can͏ be c͏h͏allenging,͏ es͏͏pecially ͏in real͏͏-time app͏li͏cati͏on͏s.

Loa͏d͏ B͏a͏lancing

D͏es͏crip͏tion͏:͏ Distri͏bu͏tes incom͏in͏g ͏datab͏a͏se requests͏͏ e͏venly across͏ multi͏͏pl͏e s͏͏e͏͏rvers to͏ pre͏v͏ent ͏any͏ single serv͏e͏r ͏f͏rom͏ becoming a bot͏͏t͏l͏eneck.͏

Advantag͏es: E͏n͏sur͏͏e͏s͏ e͏ff͏ic͏i͏e͏nt͏ utiliza͏ti͏on ͏of re͏s͏o͏͏urce͏s͏ an͏d im͏͏prove͏s ͏ove͏r͏al͏l s͏yste͏m ͏p͏͏erformanc͏e.

Chal͏l͏enge͏͏s: Requires͏ soph͏isti͏c͏ated loa͏d b͏͏alanci͏ng al͏g͏or͏ithm͏s a͏n͏d infrastruct͏u͏͏re͏ to ma͏nage tr͏af͏f͏ic͏ effect͏ive͏ly.

Advan͏tag͏es

Sc͏a͏labil͏͏it͏y: Easily acc͏͏ommodates grow͏t͏h͏ by add͏͏͏in͏g more ͏servers as nee͏d͏ed͏.

Re͏du͏ndanc͏y:͏ M͏ult͏i͏ple se͏rv͏ers p͏r͏͏ov͏͏ide͏ f͏͏͏a͏ilov͏er cap͏abilities, ͏enhan͏cing s͏ystem rel͏iability͏͏.

C͏͏o͏st-Eff͏e͏͏ct͏ive: O͏ft͏͏e͏n mo͏re͏ ec͏onomica͏l in the long͏ r͏un͏͏ c͏͏͏o͏m͏par͏ed t͏o͏ ͏con͏tinuously up͏gr͏ading͏ hard͏wa͏re.͏

͏Di͏sad͏van͏tages͏:

C͏om͏͏͏plex͏͏ity:͏ Setting͏ u͏p ͏a͏͏nd ͏ma͏͏inta͏ini͏n͏g a͏ hor͏iz͏ontal͏ly sca͏led͏ syst͏em is m͏ore͏ co͏͏mp͏l͏ex th͏an ver͏t͏ical͏ ͏sca͏lin͏g. ͏It ͏re͏qui͏r͏͏es͏ ͏͏͏e͏x͏͏͏p͏er͏tise ͏in͏͏ distributed system͏s͏͏.

D͏a͏ta Con͏sistency: Ensur͏ing͏ da͏͏ta re͏mains ͏c͏onsistent ͏a͏cross͏ m͏ult͏iple se͏rv͏ers͏ can ͏be͏ ͏͏ch͏all͏en͏ging, p͏a͏rticularly ͏in r͏eal͏-͏t͏ime ap͏p͏l͏i͏ca͏tio͏ns͏.

L͏a͏te͏nc͏͏y: In͏cr͏͏eased ne͏twork communicat͏i͏on ͏b͏e͏tween͏ ͏s͏ervers can ͏i͏n͏t͏roduc͏e l͏atency, af͏fectin͏g͏ p͏erfor͏mance.

U͏s͏e ͏C͏ases:͏ Hor͏izo͏ntal s͏͏caling ͏i͏͏s id͏e͏a͏l for͏ large-scale applications wi͏th hig͏h͏ t͏raf͏f͏ic͏ ͏volum͏es an͏d ͏e͏xten͏͏͏s͏͏iv͏e͏ ͏data requireme͏nts. ͏It͏’s͏ particu͏la͏rly͏ b͏enefi͏c͏ial f͏or ͏businesses ͏e͏xpe͏cting ͏rap͏id͏ grow͏t͏h or t͏h͏͏os͏e o͏pe͏rati͏n͏g in e͏nvi͏ronments w͏here u͏pt͏ime͏ a͏nd r͏elia͏bili͏ty͏ ͏a͏re͏ ͏c͏rit͏͏ic͏͏al͏.

Hyb͏͏rid Appr͏͏o͏͏͏aches

In many s͏cena͏r͏ios,͏ a hyb͏ri͏d ͏s͏c͏͏a͏lin͏g ͏a͏p͏͏pr͏͏o͏ach͏ tha͏t͏ comb͏i͏n͏es both ͏ver͏tica͏l and ho͏r͏͏i͏zon͏tal scaling͏ ͏ca͏n͏ offer the b͏est͏ ͏o͏f bot͏h͏ worlds͏. By͏ ͏verti͏call͏y sc͏a͏ling in͏div͏idual͏͏ ͏s͏͏erv͏ers to their͏ opt͏imal͏ capacity and͏ t͏h͏͏en ͏h͏͏o͏ri͏zont͏a͏lly͏͏ scal͏ing by adding͏ m͏ore ser͏v͏ers, ͏͏businesses͏ can ac͏hiev͏e a bal͏a͏n͏c͏ed ͏a͏nd fl͏exib͏l͏e in͏͏f͏ra͏struct͏ure.

Advanta͏ges

Fl͏e͏xi͏b͏ili͏ty: A͏ll͏ows ͏f͏o͏r incre͏ment͏a͏l s͏cal͏ing͏,͏ a͏͏d͏ap͏ting͏ to v͏ar͏y͏ing dema͏n͏d͏s ͏w͏͏i͏tho͏u͏t͏ maj͏or ove͏rhauls͏.

Opti͏͏mi͏zed͏ P͏e͏r͏formance: B͏a͏lances the͏ im͏medi͏ate ͏performa͏n͏͏ce g͏ai͏ns fro͏͏m͏ ͏v͏e͏r͏tic͏a͏͏l͏ ͏s͏͏ca͏li͏ng w͏ith ͏͏the͏ lon͏g͏-ter͏m͏ s͏cal͏ab͏͏i͏l͏i͏t͏y ͏of h͏͏orizon͏ta͏l s͏calin͏g͏.

͏Co͏s͏t Efficiency: Can b͏e more co͏st-effective by op͏timizing re͏s͏ource͏ ͏u͏ti͏lizat͏io͏n ͏acr͏oss͏͏ bo͏t͏h͏ ͏scali͏ng ͏meth͏o͏͏d͏s.͏

͏C͏halleng͏es

͏In͏c͏reas͏ed͏͏ C͏omp͏lexity: ͏M͏ana͏ging a hybrid͏ sy͏stem r͏equire͏͏s͏ careful planning ͏and͏ ex͏͏pertise to e͏ns͏ur͏e s͏eaml͏ess͏ integr͏a͏tio͏n͏͏ b͏e͏twee͏n vertical͏ly a͏n͏d ho͏riz͏ont͏a͏l͏ly sca͏͏led com͏p͏one͏n͏͏ts͏͏.

Resourc͏͏e Mana͏g͏ement͏: Bala͏nci͏ng r͏esources effe͏ctivel͏y ͏t͏o avoid underutiliz͏at͏͏ion or ͏overloadi͏ng of ͏s͏e͏rvers͏͏ c͏an be ͏c͏hal͏l͏en͏͏gi͏ng͏.͏͏
U͏se͏ ͏͏Cases͏: Hybrid͏ ap͏p͏r͏oache͏s are ͏s͏uita͏ble for bu͏s͏ine͏sses exp͏er͏i͏en͏ci͏n͏g͏ f͏l͏uctuatin͏g wo͏rk͏l͏oads or ͏th͏ose that r͏͏equire bo͏th imme͏di͏ate p͏erfo͏rman͏ce imp͏roveme͏nt͏͏s and͏ long-t͏er͏m s͏͏ca͏labilit͏y. Thi͏s strate͏g͏͏͏y i͏s o͏f͏t͏en emp͏loyed͏ by growi͏n͏g startu͏ps a͏nd e͏n͏ter͏prises expa͏͏n͏di͏ng ͏their digi͏t͏a͏l fo͏otprint.

Choosing th͏͏e R͏ight Sc͏alin͏g͏ Strategy͏

Selec͏ti͏ng ͏t͏he approp͏͏r͏iate͏ sc͏a͏l͏ing st͏r͏ategy ͏depen͏͏ds͏ ͏on ͏several factors:

Cu͏rrent a͏nd͏ Pr͏ojecte͏d W͏o͏r͏kl͏oads͏

Ass͏ess͏ your ͏curr͏ent databas͏͏e͏ ͏l͏oad and pr͏edi͏ct future ͏growt͏h ͏to deter͏mine͏ wh͏ether vert͏͏i͏cal͏ or ͏h͏orizonta͏l͏ s͏cal͏i͏ng aligns with͏͏ your ͏n͏ee͏ds.

Bud͏get Cons͏tra͏i͏nts͏

C͏on͏si͏d͏e͏r ͏the cost imp͏l͏icati͏ons of͏ bo͏th͏ ͏͏scal͏i͏ng͏͏ ͏m͏e͏thods. Vertical scaling may re͏qui͏r͏e s͏͏igni͏͏f͏icant upfr͏on͏͏t ͏inve͏stme͏nt͏͏ ͏in high-perfo͏͏rmance ͏ha͏r͏d͏war͏e͏͏͏, while horizontal s͏c͏al͏in͏g may ͏͏i͏n͏v͏olve on͏g͏oin͏g ͏cos͏ts r͏ela͏͏te͏d͏ ͏to ͏͏ma͏nag͏i͏ng͏ mu͏ltipl͏e ser͏vers.

T͏e͏chnic͏al Exp͏͏e͏rtise

Evaluat͏e͏ yo͏ur͏ te͏am’͏s͏ a͏bil͏it͏y to͏ imple͏m͏ent͏ and m͏aintai͏͏n the͏ chosen sc͏ali͏ng strategy͏.͏ ͏͏Horizo͏ntal scal͏in͏g ͏͏oft͏en r͏equi͏r͏es more spec͏i͏͏alized kn͏owl͏edge in distribu͏te͏d sy͏ste͏ms.͏͏

͏͏Ap͏pl͏ic͏atio͏n ͏Architectu͏re

Ensur͏e t͏hat your ͏appli͏cation͏ ͏is͏ d͏esi͏g͏n͏͏ed t͏o s͏up͏p͏or͏͏t͏͏ ͏t͏h͏e chosen ͏scalin͏g met͏h͏od.͏ ͏Some͏ applicati͏ons ma͏y be b͏etter͏ suited f͏or horizont͏al s͏ca͏li͏ng͏, while ͏others may benef͏͏it mo͏re from v͏ertical scaling͏.

P͏e͏r͏forman͏ce Requ͏irements

Dete͏rmine͏ the ͏performance͏ ͏b͏͏͏enchma͏r͏ks y͏our da͏tabase ne͏e͏ds ͏to͏ m͏͏eet. High͏-͏availa͏b͏ilit͏y͏͏͏ systems͏ ͏m͏ay ͏pri͏o͏ri͏tize ho͏ri͏zontal͏ scal͏in͏g͏ ͏͏fo͏r ͏redunda͏͏ncy, while͏͏͏ ͏performance-crit͏i͏ca͏l appli͏catio͏n͏s might͏ ͏lean͏ ͏to͏͏wards ͏ve͏rtica͏l scaling ͏for͏ imm͏͏ediate ͏spe͏ed imp͏rovem͏ents.

F͏u͏t͏ure ͏Gr͏owth Pl͏ans

Consider͏ your long-͏t͏er͏m ͏bus͏i͏͏ness͏ o͏bjec͏tiv͏es ͏͏an͏d͏ ͏how ͏yo͏ur ͏scalin͏g strategy͏ will ͏support͏ f͏͏ut͏ure͏ exp͏a͏nsion and evolv͏i͏͏ng ͏d͏ata n͏eeds.͏͏

͏Scaling Cha͏llenges

Reg͏ardl͏ess o͏f the scaling͏ m͏etho͏d chosen, sev͏eral ch͏al͏͏l͏͏enges may͏ a͏r͏ise͏:͏

͏Data͏ Consistenc͏y: ͏Ensu͏rin͏g tha͏t dat͏͏͏a͏ ͏r͏emain͏s cons͏ist͏ent ac͏ross multi͏ple ͏s͏e͏rv͏͏ers,͏ especi͏al͏ly in horizon͏tall͏y sc͏aled͏ systems, i͏s͏ cr͏i͏ti͏ca͏l t͏o͏ m͏ain͏t͏aining data ͏in͏teg͏rit͏y.͏

Late͏n͏cy͏: In͏crea͏sed co͏mmu͏nication be͏twe͏͏en dist͏ribute͏d͏ ser͏v͏ers can͏͏ intr͏oduce͏ ͏l͏a͏te͏ncy,͏ ͏potent͏ia͏l͏ly a͏ff͏ectin͏g app͏lic͏ati͏͏on perfo͏͏rma͏nce.

͏Co͏͏mpl͏͏exity ͏i͏n͏ Ma͏n͏a͏ge͏͏men͏t͏:͏ M͏a͏n͏aging a͏ s͏c͏al͏e͏d datab͏ase env͏ir͏onment͏͏ requ͏ire͏s͏ ro͏bust mon͏itoring͏,͏ maintena͏nce,͏ and m͏anag͏em͏en͏t prac͏t͏͏i͏ces to ens͏ure ͏smooth͏ ͏oper͏͏͏a͏ti͏on͏͏.

͏Secu͏rit͏y͏ Co͏nc͏e͏rns: Expand͏ing͏ ͏you͏r d͏͏͏atabase͏ ͏in͏͏frastr͏ucture͏ can in͏troduc͏e new͏͏ securit͏y ͏vulnerabilitie͏͏s. I͏t’s es͏sent͏ial t͏o impleme͏nt comprehensi͏v͏͏e͏͏ security measu͏res to͏ pro͏tec͏t you͏͏r͏ da͏ta͏.͏͏

Co͏n͏͏cl͏usi͏on

S͏cal͏i͏ng yo͏ur data͏b͏ase͏͏ is ͏͏a ͏p͏͏i͏votal͏ step͏͏͏ in͏ ensuri͏ng ͏y͏͏our͏ ͏business ͏can ͏handle i͏͏n͏creas͏͏in͏g data͏ volume͏s͏ ͏and user͏ d͏͏e͏m͏a͏nds ͏effic͏ie͏nt͏ly͏. Whet͏her y͏ou͏ ͏ch͏͏oose v͏er͏tical ͏scalin͏g͏ for its͏ simp͏l͏i͏city a͏͏nd͏ ͏im͏me͏diate pe͏͏rfo͏r͏m͏ance͏ b͏enefits, horizontal sc͏aling f͏or͏ it͏s͏ ͏sc͏ala͏b͏ility and ͏redundanc͏y, ͏or͏ a͏ hybrid͏ a͏pproach ͏to leverag͏e the streng͏t͏hs͏͏ o͏f bo͏th͏, u͏͏nde͏rstan͏di͏n͏g the͏ adva͏n͏ta͏ges and ͏cha͏͏l͏͏l͏en͏ges of ͏each m͏etho͏͏d ͏i͏͏s crucial.͏ By carefully as͏s͏essing͏ yo͏ur b͏usi͏n͏ess n͏eeds, tech͏ni͏c͏al͏ capabi͏lities͏, ͏a͏nd gro͏wt͏h proj͏ections, you ͏can ͏͏i͏mp͏͏le͏m͏en͏͏t a ͏dat͏a͏ba͏se scaling st͏r͏at͏egy that ͏not ͏onl͏y m͏e͏e͏ts͏ your͏͏ curren͏t͏ de͏m͏a͏nd͏s͏ ͏but also posit͏i͏o͏ns͏ yo͏u͏r org͏anization ͏f͏or ͏͏fu͏t͏ure suc͏ces͏s.

R͏e͏c͏o͏mmend͏ations

͏Sta͏r͏t w͏ith ͏Vertic͏͏a͏l Sc͏al͏i͏ng͏: For bu͏sines͏ses in t͏he ear͏l͏y stages or th͏ose with limit͏ed ͏te͏chni͏cal ͏reso͏u͏͏rc͏͏es͏, v͏͏e͏rtica͏l s͏cali͏ng͏ offe͏rs a͏ ͏qu͏͏ick͏ an͏d͏ s͏t͏raig͏htforwa͏r͏d way to͏ en͏͏hanc͏e data͏ba͏se͏ perf͏ormanc͏e.

Plan͏͏ f͏o͏r Ho͏r͏izonta͏l Scal͏ing͏:͏ As͏͏ you͏r͏ d͏͏ata ͏͏an͏d͏ ͏u͏ser b͏ase͏ gr͏ow, consider tr͏a͏ns͏iti͏oning to h͏orizontal sca͏͏lin͏g͏ to ͏maintai͏n ͏͏p͏erfo͏rm͏͏ance ͏and͏ relia͏bi͏lit͏y.

Inve͏s͏t in Exp͏ertis͏e:͏ Whe͏ther sc͏a͏l͏i͏ng v͏͏e͏͏rticall͏y͏ o͏r ho͏ri͏zo͏͏ntally, hav͏͏ing͏͏ ͏the rig͏ht technical e͏xp͏er͏t͏ise͏ is͏ esse͏nti͏al ͏to͏ ͏nav͏igate͏ ͏the ͏com͏pl͏e͏͏xi͏tie͏͏s and e͏n͏͏sure a su͏cces͏s͏f͏u͏l ͏͏imp͏lem͏en͏t͏at͏͏io͏n.

Mo͏n͏i͏͏tor͏ and ͏Opt͏im͏ize͏͏͏:͏͏ Continu͏o͏us͏l͏y ͏m͏on͏i͏t͏or͏ ͏y͏ou͏r͏ dat͏ab͏ase ͏per͏forman͏ce͏ and reso͏ur͏ce ͏u͏t͏ili͏z͏ati͏on.͏ Regu͏la͏rly ͏optimize ͏your͏͏ scaling͏ stra͏t͏e͏͏gy͏ ͏to ad͏apt͏ ͏to changi͏͏n͏g busin͏ess need͏s͏ ͏͏and techn͏ologica͏l a͏dvancemen͏ts.͏

By p͏ro͏͏act͏͏iv͏ely ad͏dressi͏n͏g ͏y͏o͏ur dat͏ab͏ase sc͏͏al͏ing needs,͏ you ͏can ensu͏͏r͏͏e t͏hat your b͏us͏in͏e͏ss r͏ema͏ins agil͏e, re͏͏silien͏t,͏ ͏͏a͏nd capa͏͏ble of d͏el͏iv͏ering ͏e͏x͏cept͏ion͏a͏l performan͏c͏e to you͏r u͏͏ser͏s.

Conclusion

Effective database server scaling is essential to improve performance during growth phases. The collaboration between BuyLocal and OVHcloud highlights the value of using load balancing, database optimization, and scalable infrastructure.

Selecting the appropriate approach guarantees increased customer happiness, quicker load times, and higher reliability.

The post Too Much Data? Here’s How to Scale Up with Database Server in Australia appeared first on ReadWrite.

Nowadays, it’s hard to grow a business without being visible on social media. Studies have shown that companies can benefit from social media platforms because they can help them connect with larger audiences.

Businesses can use social media to communicate with their communities and establish their presence in the industry. One of the best sites for such activities is Instagram. The platform had 2 billion active users on a monthly basis at the beginning of 2024, and the fan base seems to be on a growing spree. Due to the large number of younger generations, it’s a good opportunity for businesses to attract people who might become long-time customers.

But how do you boost your profile? We’re here to help with some tips for increasing the recognition of your Instagram account. Let’s get started!

Is Instagram Recognition Important for Your Business?

A long time ago, explaining the importance of starting a social media strategy as a business might have given you some weird looks. Most business owners now recognize the importance of being present on social media sites. Instagram posts have tremendous potential to reach a wide range of people, even possibly bringing you more Instagram followers.

But if you’ve never done business before and you’re just now diving into it, perhaps you aren’t too familiar with why so many companies make efforts to post Instagram stories, reels, photos, and more. It’s easy – Instagram activity can give you access to potential customers and help build your brand’s visibility and recognition.

This platform allows you to create a brand image. Instagram users will rush to your page when they want to learn something about you and your services. Also, it’s a good chance to share news that may interest your followers, including new releases, job openings, and more. Many enterprises now buy cheap Instagram likes to increase recognition, but spending money on likes is not the only way to go.

How Can You Increase Your Instagram Account’s Recognition?

Reaching your target audience on Instagram is not that easy. If you want to succeed, you need to make a lot of effort to gain organic followers. Increasing brand awareness takes hard work and dedication, and if you’re new to Instagram and don’t know what to do to please the Instagram algorithm yet, we have the solution for you.

Here are some Instagram insights that will help you get the recognition you want and deserve:

1. Set an Instagram Posts Schedule

One way through which Instagram accounts can thrive is by carefully planning each Instagram post. When you schedule Instagram posts, you will remain active on the app while letting your target audience know when to expect your content.

However, the ideal posting schedule for you depends on who your audience is. For example, if you go for young adults, they may be students or employees. Chances are that they’ll be unable to check Instagram often during the day, so if you post too early, they may miss your photo and video.

Instead, plan your feed posts for when you believe your loyal customers will be active. Maintain this schedule as you go, so your fans know you’re consistent.

2. Discover Your Niche

People will check your Instagram profile if they find your content and like what they see. But if they find that your content is all over the place in terms of topics and categories, they won’t be too happy about it. On top of visually appealing content, they want you to know what you do.

Create your own personal brand by choosing a niche and tailoring your content to attract new followers to your page. While creating different types of content is not necessarily bad, it can deter certain people. If your page or business is about music, your content should focus on music, not travel.

If your niche is inconsistent and your followers keep seeing posts unrelated to your field, they’ll most likely choose to leave, and you’ll notice a decrease in user engagement.

3. Take Advantage of Instagram Stories

Instagram Stories are great when you want to post something temporary. A story will be visible for 24 hours before disappearing. If you regularly post updates there, it’ll show your audience that you care about them and want them to be informed. Perhaps it may even encourage more users to follow you.

Besides sharing news and updates, stories also enable you to reveal behind-the-scenes videos, launch events, and more.

4. Buy Organic Followers Only

Purchasing followers is a misunderstood practice due to its negative connotations and the risk that comes with it. People expect this practice only to bring them fake Instagram followers while possibly getting them in trouble with the platform.

While there are some risks you should be wary of, follower purchases can be perfectly safe as long as you practice caution. For instance, you should always do your research on the companies selling these followers.

Some firms sell fake followers, while others offer real-user follows that grow slowly and naturally. By purchasing organic followers, your account’s popularity may increase, with the algorithm seeing your content as more valuable and recommending it to larger groups of people.

5. Use Relevant Hashtags

The power of a good hashtag cannot be underestimated. In fact, hashtags often help distribute your content among the right users. When someone looks up a specific hashtag, your posts will appear under that search.

Hashtags will boost your content’s discoverability, thus providing more opportunities for engagement and visibility.

6. Don’t Be Scared to Experiment

Don’t be too quick to put a label on your account or settle on one type of content. This especially applies when you’re new to Instagram and trying to build your following. There are numerous types of content you can work with, so you don’t have to limit yourself to one format.

Instagram offers photos, Reels, Stories, and many other types of content you can pick from. Try them individually and see which ones you enjoy the most, as well as which ones your audience seems to appreciate. Then, assess what content types bring you the most likes, followers, shares, and comments, and focus on them moving forward.

7. Don’t Hesitate to Go Live

Going live for the first time may sound scary when you have a new business account on Instagram. Still, lives are nothing to be afraid of. In fact, they can be quite a fun experience that helps reach a larger audience. What’s great about this feature is that it makes your profile picture come first in the stories section, so followers will see it immediately when they open the app. Followers may even be notified when you go live, sparking their curiosity.

A live stream is a chance for fans to get to know the people behind your brand. They’ll see who the team is and can ask you questions. In some cases, these lives may also include interviews with popular influencers in the same niche, which may bring even more followers to your page.

8. Take Advantage of User Generated Content

Another good way to increase your account recognition on Instagram is to leverage user generated content. Doing this will grow your engagement, not to mention that it may even lead to the original creators resharing your post and bringing new people to your page.

What’s even better is that your authenticity and credibility will significantly grow when you share natural and authentic user generated content.

9. Optimize Your Instagram Account

Optimization is as important as the quality of your content. Your Instagram followers will appreciate you even more if you have a beautifully optimized page that stands out.

First impressions matter, which is why you cannot overlook the importance of how your profile is perceived. One of the first things you should pay attention to is, of course, choosing the right username. If you’re a brand, the brand’s name should be more than enough for this.

The next one is the profile picture. It should be a high-resolution and clear one that represents your brand. Most companies choose their logos to make it work, but it must be recognizable from the plethora of avatars.

You should also not forget to write a compelling bio. People will read it as soon as they land on your Instagram profile. It doesn’t have to go into great detail about you – a short, clear, and concise description that reveals who you are and what you do should be enough.

Lastly, don’t forget to include relevant keywords in the description to increase discoverability.

10. Pay for Instagram Ads

Spending money on Instagram ads is not exactly the first thing you want to do, but it doesn’t hurt once you’ve optimized your profile and developed the content strategy. Ads are among the best Instagram insights because they tell the world about you, bring in new Instagram followers, and increase sales.

Now, while getting followers and customers is most likely your goal, you shouldn’t sound too salesy either. Ads should be attractive and boost brand recognition. The key to this is using compelling visuals and a friendly and understanding approach.

The Instagram analytics tool can then tell you how well your ads perform. This way, you’ll be able to optimize your ad campaign based on it.

11. Create or Join Challenges

Let’s be honest – challenges are not only fun to engage with but also a way to connect with people. Every now and then, a new trend emerges, and influencers and businesses are eager to jump on the bandwagon. Challenges make for engaging content, so they’re great for your brand.

Now, you can take part in challenges created by others or make your own. These can involve creating Instagram reels with specific sounds or performing certain actions. To make it catchier, you can also offer prizes to fans with the most engagement after they join your challenge.

It’s also crucial to make sure these challenges are fun and safe. After all, you don’t want to scare away potential followers.

12. Interact with People

When your posts appear in your followers’ feeds, people may check your comment section. A business or creator account that ignores fans is seen as a red flag, and individuals will be tempted to ignore it. However, engaging with your fans will show your appreciation for them, allowing you to retain your loyal fan base while possibly attracting more fans along the way.

This will also have a positive influence on Instagram’s algorithm as it sees more engagement on your posts. Ultimately, this will boost your recognition on the platform.

The Bottom Line

Increasing your profile’s visibility can be done with a few tricks. Encourage user generated content, craft a strong Instagram bio, post consistently, create high-quality content, interact with your audience, post stories and reels, and optimize your profile to improve recognition. We hope our tips will help you see your Instagram profile grow.

The post Tips For Increasing the Recognition of Your Instagram Account appeared first on ReadWrite.

AI-powered tools seem everywhere in an environment that can only be described as lightning-fast. Artificial intelligence has already upended virtually every industry, and those that have yet to feel its impact won’t remain unchanged for much longer.

Indeed, the genesis of artificial intelligence has coincided with a quickly evolving field: podcasting. What once was a novelty has long since become a dominant form of entertainment for many and a legitimate communication medium. AI-powered podcasting tools are reshaping how podcasts are consumed, with the AI podcast summarizer ArticleX leading the charge.

Reading or Listening: The Science 

Science debunks the myth of a single “best” learning style. Studies show that reading and listening are equally effective, even when people switch between them. In fact, research suggests few individuals stick to just one method. This could be because both have challenges. Reading can be visually tiring and potentially boring. Listening requires real-time processing, which can be difficult. Additionally, audio/digital media can be distracting – it’s tough to rewind or ponder a point like you can with text.

Despite these challenges, listening remains crucial in both education and mainstream communications. It offers flexibility: listeners can learn on the go during commutes or chores. Experts recommend using both interchangeably and developing the skill of knowing when each is best. Listening shines for less demanding tasks and language learning. Combined with reading, it creates a holistic learning experience, reducing memorization needs.

With this data in mind, it becomes clear that there is a benefit to expanding video/audio content to a reader-friendly format. Science aside, not everyone has the time to listen to a 45-minute podcast episode – even if they’re interested in the topic. This, in theory, makes sense. However, many content creators simply don’t have the time or resources to expand their content to various mediums.

The Challenge: Time and Expense

Think for a moment about the sheer amount of effort that goes into creating the type of podcast episode that gets people excited and returning for more.

On the logistical side, you need recording equipment, sound mixing and editing solutions, extensive research on topics, assembling guests, and actually recording a session. It’s incredible work for something once considered “disposable entertainment.”

Now, think about the added lift of creating a high-quality, relevant written piece to accompany your video/audio content. You will need to research topics, gather facts and figures to flesh out your thesis, and, finally, write the piece. Much of what you’ll cover was likely included in that aforementioned podcast episode, but an incredible time commitment is still involved; especially if you’re doing it for each episode.

Until ArticleX, that is.

ArticleX: Going Beyond Transcripts

AI podcasting tools have been around for years. Many create a literal transcript of a podcast episode. The limitations of these tools are exposed when podcast speakers take wild turns and rabbit holes of natural conversation. The back-and-forth conversation can make the output a little more challenging to digest for readers. With this in mind, ArticleX is taking a new approach by looking at the topic as a whole and creating an easy-to-digest article for readers to capture the main topics of conversation.

ArticleX allows all the high-value insight spoken about to rise to the top, presenting it in a form ready to be published as an engaging article with little to no modification. The added benefit is that the output is SEO-optimized and ready to publish to your blog. This creates value not only for the reader but also for the creator to improve their organic search rankings.

The Logistics: How Does ArticleX Work? 

ArticleX, in particular, is powered by GPT-4; OpenAI’s top language model. This is the major ingredient that allows it to be so much more than a straightforward transcription tool.

Using GPT-4, ArticleX can turn those podcast “summaries” into articles tailored to a brand’s unique voice. This can create a much-needed level of consistency across all marketing channels. Every piece of content a business puts out will feel like it’s coming from the same core place.

Likewise, the original media can be automatically embedded in the article, creating a rich experience for readers that still works on multiple levels. The content will be covered in the article and a clip can be embedded automatically to maximize exposure for your podcast episode. This can be accomplished simply by entering a video or audio URL.

Revolutionizing Today’s Podcasts

Podcasts have already transformed throughout the years from their beginnings often referred to as “amateur radio.” Sure, they were on-demand and convenient, but they were difficult to create, and hobbyists dominated the landscape.

Fast forward to today, and AI tools like ArticleX have redefined the possibilities for creating and reusing content. This innovative tool is making it easy for audiences to engage with podcasts, video, and audio content in a variety of ways. In an instant, podcasts and other media can become a part of a multi-faceted marketing approach – thus giving people more choices regarding where, when, and how they interact with the content that matters to them. This opens doors for a wide variety of consumers, making podcasts more accessible and enjoyable for all.

The post From Podcast to Profit: The AI-Powered Tool Changing the Game for Creators appeared first on ReadWrite.

The way we learn and do our jobs is changing because of digital tools and AI. In fact, modern tech solutions are changing the way we teach, grasp information, make decisions, and gain knowledge.

When it comes to AI, this technology is being used in smart tutoring systems, chatbots, question-answering digital teaching assistants, personalized learning programs, and many more. All these use machine learning to adapt to each learner’s current performance and individual needs. Let’s dig deeper to see how emerging technologies are changing the human intelligence landscape.

Does Modern Technology Make Us Smarter?

The way we are taught and get new information is changing as machine learning is changing a lot of processes in our everyday lives. The truth is that new technologies can help people learn in situations where they are developing their skills.

Whether you are being onboarded at work, need to prepare for the upcoming college test, or make an important decision, you can always count on new technologies. You can check Cerebrumiq reviews to learn what people think about their IQ test results and spot the IQ change trends.

The technologies we’ve made have changed along with our abilities to get things done. The wheel helped us move around. One thing we can say for sure is that we will be smarter with the help of computers and evolutionary intelligence.

It is true that new technologies are changing how we think and how we interact with information. And this can definitely make us “smarter.” Here are some of the most important ways that modern tech advances are changing our intelligence and life quality.

1. We Can Process and Analyze Data Better

By automating the processing of huge amounts of data, AI and ML technologies are changing the way data analysis is done. AI algorithms can find patterns and insights that human analysts might miss. This makes it easier to make decisions and plan strategically in many areas. This can be national security, intelligence operations, engineering, learning, and many more.

2. We Can Spot Trends and Patterns Easily

As “big data” grows, it becomes possible to look at trends and behaviors in more depth. With so much data and advanced analytical tools at their disposal, organizations can get actionable insights more quickly. This can significantly improve their overall intelligence and strategic responses.

3. We Can Write and Read Better

Technology has made us smarter — we write and read more than ever with texts, emails, tweets, and other apps. But it’s also making us smarter in new ways. Today, we can create videos, images, data graphs, 3D printing models, and many more. Modern technology is adding more ways for us to express ourselves.

4. We Can Get Real-Time Insights

New technologies make it easier to collect and analyze data in real-time, giving decision-makers access to up-to-date information. This ability is very important for intelligence work and national security, where quick responses are often needed.

5. We Can Do Predictive Analytics

Predictive algorithms powered by AI can see patterns and predict behaviors, which helps businesses plan ahead and manage risks. This ability to predict the future can help people make better choices by using what they think might happen in the future instead of just looking at past data.

6. We Can Grasp Info Better

Technologies like augmented reality (AR) and virtual reality (VR) are making immersive environments that can help people learn and understand better. Users can see complicated data and ideas more clearly with these tools, which makes it easier to understand and use information correctly.

7. We Can Collaborate With Others Effectively

New technologies are making it easier for people from different fields to work together. So teams can share ideas and plans more effectively. This environment for working together can lead to new ideas and better cognitive outcomes.

Is New Technology Good or Bad for IQ?

Although modern technology can make people scared, there is nothing to worry about. But does Siri really mean to kill us? It’s possible that we’re making the mistake of thinking that machines will have human traits. In Evolutionary Intelligence, computational intelligence can work for the better. It can help us make better decisions and even give us more freedom. We can expect a fast-paced ride thanks to an evolution in computer-assisted human intelligence.

Indeed, new technologies offer great chances to improve intelligence and decision-making. However, there are two sides to the medal. They can also cause some issues.

With both pros and cons considered, incorporating these technologies into our thought processes will make us smarter by making it easier for us to understand, analyze, and act on data. The only thing is that you should use emerging technologies strategically to make the maximum out of them.

The post How Are Emerging Technologies Changing the Human Intelligence Landscape appeared first on ReadWrite.

AI is far past the point of being a new or emerging technology, especially in the world of digital marketing. A global survey in 2022 found that roughly 90% of marketers across 35 different countries use AI in some way in their digital marketing strategies.

With so many free and low-cost tools available, replacing or supplementing human marketing workflows with AI is the #1 way to become (a) more productive and (b) better at understanding your audience and how to reach them.

Trends: AI in Digital Marketing

This article explores three exciting trends and best practices for implementing AI within your organization.

1. Generative AI

Thanks to ChatGPT, generative AI might be the hottest topic in marketing right now. Generative AI involves using AI to create content from existing data and inputs. Examples of GenAI marketing tools include:

• Content creation tools that use AI to write blog posts, product descriptions, and other written collateral
• Software that generates images and videos for branding for explainer videos, social media posts, and blog graphics
• Chatbots that carry on conversations with customers through SMS, email, or a social media app

As many as three-quarters of today’s marketers use generative AI in some way, whether that’s to supplement their content creation workflow or as a lead generation tool.

2. Personalization

AI increases personalization in marketing by pulling content and data in real time to craft individualized, data-driven experiences for each target customer. Across the omnichannel, businesses can use AI to trigger personalized emails, social media messages, website popups, and even entire offers based on what a customer is browsing or has already bought.

With AI, marketers can also analyze vast amounts of data about a customer’s behavior. They can use this info to develop better products, improve their targeting and messaging for future campaigns, and meet ever-changing customer demands.

3. Voice Search Optimization

Voice search has risen in popularity as a way for consumers to quickly find information and make purchases through their mobile devices. Optimizing content for voice search is 100% crucial for businesses that want to stay relevant and accessible.

AI plays a critical role in optimizing content for voice search. Businesses can use it to analyze speech patterns using natural language processing (NLP) to understand how people speak and the specific phrases they use when making voice queries. With this data, you can create content with a more conversational tone and structure, while incorporating the words that make it easy for voice assistants to understand.

Best Practices for Using AI in Digital Marketing

How you apply AI to your workflow determines whether you produce great marketing content that converts customers or mediocre content that turns them away.

• Don’t replace humans with AI: While the low cost makes it tempting to rely heavily on AI for your marketing efforts (especially content marketing), AI lacks the human ability to communicate with your target audience. Your USP, branding, and voice all come from your human writers and designers.

• Use AI to automate marketing tasks: For example, CharGPT isn’t great for writing marketing content, but it is capable of searching the web. Use it to cut your research time down by hours (or days).

• Hire industry experts: Outsourcing your marketing efforts to a team that knows the ins and outs of AI can help you implement the technology in a way that aligns with your business’s goals and target audience. Make sure they have hands-on experience in your industry (such as a credit union marketing agency for credit unions).

AI in digital marketing is no longer something to speculate on for the distant future. It’s essential for any business that wants to compete on quality, targeting, and efficiency in 2024. By staying informed on the latest trends and best practices, you can effectively incorporate AI into your marketing workflow and see tangible results.

The post AI in Digital Marketing: Trends and Best Practices for 2024 appeared first on ReadWrite.

There has been a meteoric increase in the rate at which innovation is occurring across a wide variety of industries. In the supply chain sector, the demand for smart and innovative software is predicted to keep rising. Traditional methods are too outdated to handle the hefty requirements of businesses operating on a large scale. The time and resources consumed by these methods can be saved by integrating innovative software for inventory management. This software automates the majority of laborious tasks, provides actionable insights, and makes predictions that allow businesses to make smart decisions about their inventory levels, procurement, and distribution. This ultimately leads to more robust and resilient operations.

The integration of advanced inventory management software provides access to real-time control and visibility of stock levels across different locations. This enables businesses to stay in the know of their current inventory levels and manage stocks accurately. Furthermore, businesses can save significant resources as constant monitoring helps them avoid overstocking and maintain optimal inventory levels. The up-to-date insights lessen stock shortage issues and enable companies to make sensible decisions in a short period of time.

The Benefits of Inventory Management Software

Inventory management software makes use of data analytics in order to generate insights. Businesses use these insights to optimize their inventory levels. This software can easily predict the future demand of the stock due to the software’s ability to analyze market trends, historical data, and demand patterns. The decisions that companies make rely on the data results generated by the software so that the business can amend its inventory levels. This allows businesses to steer clear of stockout scares and decrease carrying costs to a huge extent. Businesses run more efficiently as the real-time data obtained via advanced inventory management software enhances the resource allocation process.

Eliminates Human Error and Saves Time

Traditional inventory management techniques are prone to human error and consume ample time. Both of these issues can be eradicated with the use of contemporary inventory management software, which incorporates automation in various activities associated with the management of inventory, such as order processing, replenishment, and reporting. The incorporation of automation saves time and labor, which can be utilized for strategic tasks requiring focus and attention. Businesses see a huge decline in errors made during inventory management processes with the introduction of advanced inventory management software.

Improves Customer Satisfaction and Loyalty

Proper management of inventory has a direct effect on customer happiness and loyalty. Unexpected stock shortages lead to delayed shipping, which is not something customers find appealing. The use of advanced inventory management software, with the help of demand trends and insights, makes sure that the appropriate amount of goods is available in the inventory at the appropriate time. The primary business goal is to lose customers due to poor inventory management. When consumers are happy with the service an organization provides, they are more likely to spread the word about business, which results in organic growth and profitability for the firm.

Ensures Business Resilience Amid Disruptions

Natural catastrophes, supply chain interruptions, and worldwide pandemics can impact businesses severely enough to make them unstable. Advanced inventory management provides risk management tools that can mitigate the risks associated with such disruptions. It makes sure that the inventory has the potential to stay resilient in times of difficulty. The software monitors possible supply chain vulnerabilities and offers suggestions, such as route changes or alternate suppliers. This way, business operations may run smoothly regardless of the external factors.

Reduces Costs

The introduction of innovative inventory management systems have the capability to make significant cost reductions. Businesses are able to cut their carrying costs, prevent waste, and avoid costly stockouts. Additionally, automation lowers the personnel expenditures associated with carrying out inventory management duties manually. It’s common for modern information management systems to obtain a return on investment in a short amount of time. This is because increased efficiency and decreased expenses contribute directly to the progress of the company.

When firms decide to expand, it comes with a set of different and complicated requirements in terms of inventory management. However, advanced inventory management software has the ability to expand. This enables businesses to increase their operations without outgrowing their inventory management system. In order to encourage sustainable growth, innovative inventory management systems are able to adapt to the changing needs of the business, regardless of whether they are handling inventory for a single location or several warehouses throughout the globe.

Obstacles and Reflections

Advanced inventory management software has undoubtedly made the supply chain processes significantly easier, but they also have challenges. Some of the most common problems that organizations encounter with the incorporation of such software are data security issues, incompatibility with the present infrastructure of the company, major investment in technology and training the staff. The strategy to overcome these hurdles is to prioritize data security and shut down any loophole that attackers can use to exploit vulnerabilities in the system. Furthermore, setting up training programs to arm employees with the right skill set to deal with advanced software keeps the company out of trouble. After successfully dealing with these problems, companies can unlock the full potential of advanced inventory management software and gain a significant competitive edge.

It is important to highlight that the success of advanced software relies on the quality and timeliness of the data they get, which indicates that in order to keep the software running perfectly, continuous maintenance and updates are necessary.

All in all, modern supply chain risk management tactics are dependent on reliable and advanced inventory management software. Automation leaves no room for human error while real-time updates let the company optimize their stock levels according to the insights. With the use of advanced and modern tech like blockchain, IoT, and AI, the supply chain can be enhanced, and operational risks can be reduced. The supply chain industry keeps facing different challenges, but with rapid technological developments, it has become relatively easier to keep up with the changes in the sector and come up with viable solutions to the challenges.

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Studies show at least 21% of project managers leverage artificial intelligence to optimize project efficiency. Typically, artificial intelligence helps project managers gain a strategic advantage and streamline operations. Incorporating AI in project management ensures seamless execution and high success rates. The following article explores the transformative power of AI in project management.

Predictive Analytics in Project Planning and Scheduling

Data analysis and predictive analytics have a massive impact on project management. Project managers leverage AI-driven tools to process project data and uncover important patterns and trends. Predictive analytics algorithms allow managers to predict project results based on insights from past data. This means project managers can foresee pitfalls and implement proactive measures to ensure success.

Regardless, project planning and prioritization can overwhelm managers and compromise project integrity. However,  AI-powered tools can simplify the tasks and create an optimized plan based on project goals and limitations. In addition, facilitate prioritization depending on task urgency and resource availability. These capabilities ensure efficient resource allocation and precise scheduling.

Robust Adaptation and Timely Improvements

Combining artificial intelligence and agile methodologies supports versatile approaches dominated by continuous improvement and adaptability. Agile project management strategies allow managers to track and respond to various circumstances based on real-time data and insights. This technology helps teams navigate uncertain events and maximize results through continuous adaptation.

Most importantly, you can implement strategic corrections by leveraging AI to analyze data and identify potential issues. This ensures continuous improvement since processes are refined at every stage. Typically, AI-powered methodologies offer the perfect ingredients for a resilient and responsive team. In the past, adaptation and corrections were tedious and uncertain. However, data insights make the modern project manager agile and reliable.

Evolving Roles for Project Managers

Automating a significant portion of your tasks can be scary, but successful managers learn to leverage modern tools. While artificial intelligence may take over some tasks in project management, project manager roles aren’t going anywhere. As a result, they need to accept the changes and master new technologies and tools. Traditionally, project teams are skilled individuals but modern teams collaborate with digital tools to ensure cutting edge capabilities.

This shift from administrative tasks means future project managers need to hone their soft skills, strategic thinking, business acumen, and leadership capabilities. Managers focus on project delivery and alignment with long-term goals.

Most importantly, project managers need a comprehensive grasp of relevant technologies and tools. You can find several organizations offering AI-integrated education programs to help managers stay abreast of the changing landscape.

Project management recruiters are aware of the shift and its implications for project success. Most organizations are more likely to choose a candidate with digital skills when filling a project management position.

The application of AI in project management will bring remarkable benefits, not only in the automation of administrative and low value risks. Embracing this digital trend is paramount for project managers, as it ensures enhanced productivity and efficiency opens doors to valuable prospects.

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