At their very core, motorsports are all about finding an advantage over your competitors, however large or small, because every little bit counts. And the best way to gain a competitive edge over your rivals is to use data — tons and tons of it.

Using Data To Unlock On-Track Performance

Racing teams generate and analyze huge volumes of data per race; we’re talking tens of terabytes measuring every single aspect — even the most minute — of not only the vehicle’s performance but also the driver’s.

There are many different categories and classes of motorsports, ranging from road cars to purpose-built racing cars like in Formula One or bikes in the case of MotoGP. These two motorsports have the most popular championships in the world, but for simplicity’s sake, we’re going to stick with Formula One (F1), described as the very pinnacle of motorsports.

Teams collect data for three main reasons: to measure the vehicle’s performance on track, to measure the driver’s performance, and to help the engineers identify and understand key areas of improvement on the car.

F1 cars have thousands of sensors monitoring parameters such as tire temperature, brake temperatures, engine performance, component wear, and so on in real time (known as telemetry data). These teams can also use the data gathered, along with feedback they receive from the drivers, to make minor real-time adjustments to the car during the race, such as engine power settings. This telemetry, along with the weather information the teams gather, can also enable them to devise effective race strategies to decide exactly when to pit and change tires and what compound of tires to switch to, especially when weather conditions are unpredictable.

If this wasn’t impressive enough, the race engineers can also view the driver’s exact inputs: when they’re braking, accelerating, and turning into a corner, alongside a host of other information like heart rate and other biometric data. The engineers can then give them feedback on what is working and what isn’t, enabling the driver to adjust their approach to extract even more performance out of themselves and the car. It’s safe to say that in modern F1, even the cars are data-driven.

Data-Driven Development In The Factory

The petabytes of data gathered by racing teams on the track are then analyzed after the race to determine what areas of the car need improvement. Since F1 greatly restricts on-track testing, teams are forced to rely on incredibly complex simulations to develop the car. The more accurate data they use, the more accurate these simulations.

This data is also used by the team to develop F1 car simulators that are used by the drivers. These simulator rigs are much more accurate, complex, and unsurprisingly expensive compared to consumer simulator rigs. This simulator testing plays a major role in not only helping the engineers understand the characteristics of the car without having to perform on-track testing, but also in helping them set up the car for a race. Each track is different, and the car setup varies depending on the track and weather conditions during the race weekend.

Data Is King

In motorsports, every little advantage can make a difference. And with F1’s recently introduced budget cap, teams can no longer dump huge amounts of money to fix any issues with their cars, meaning data is now the most valuable currency in F1.

Big data analytics will only continue to play an increasingly prominent role in motorsports as has been the case since the early 80s. The most competitive teams are those that know how to effectively use the vast amounts of data at their disposal to drive innovation on the racetrack.

The post How Motorsports Teams Use Big Data To Drive Innovation On The Racetrack appeared first on Tech Magazine.

These natural interactions are powered by the natural language processing (NLP) capabilities of these tools. Without NLP, LLM models would not be able to respond as dynamically and naturally as they do now.

As essential as NLP is to the functioning of an LLM, it has its weaknesses. NLP capabilities can themselves be weaponized to make an LLM susceptible to manipulation if the threat actor knows what prompts to use.

Exploiting The Core Attributes Of An LLM

LLMs can be tricked into bypassing their content filters using either simple or meticulously crafted prompts, depending on the complexity of the model, to say something inappropriate or offensive, or in particularly extreme cases, even reveal potentially sensitive data that was used to train them. This is known as prompt injection. LLMs are, at their core, designed to be helpful and respond to prompts as effectively as possible. Malicious actors carrying out prompt injection attacks seek to exploit the design of these models by disguising malicious requests as benign inputs.

You may have even come across real-world examples of prompt injection on, for example, social media. Think back to the infamous Remotelli.io bot on X (formerly known as Twitter), where users managed to trick the bot into saying outlandish things on social media using embarrassingly simple prompts. This was back in 2022, shortly after ChatGPT’s public release. Thankfully, this kind of simple, generic, and obviously malicious prompt injection no longer works with newer versions of ChatGPT.

But what about prompts that cleverly disguise their malicious intent? The DAN or Do Anything Now prompt was a popular jailbreak that used an incredibly convoluted and devious prompt. It tricked ChatGPT into assuming an alternate persona capable of providing controversial and even offensive responses, ignoring the safeguards put in place by OpenAI specifically to avoid such scenarios. OpenAI was quick to respond, and the DAN jailbreak no longer works. But this didn’t stop netizens from trying variations of this prompt. Several newer versions of the prompt have been created, with DAN 15 being the latest version we found on Reddit. However, this version has also since been addressed by OpenAI.

Despite OpenAI updating GPT-4’s response generation to make it more resistant to jailbreaks such as DAN, it’s still not 100% bulletproof. For example, this prompt that we found on Reddit can trick ChatGPT into providing instructions on how to create TNT. Yes, there’s an entire Reddit community dedicated to jailbreaking ChatGPT.

There’s no denying OpenAI has accomplished an admirable job combating prompt injection. The GPT model has gone from falling for simple prompts, like in the case of the Remotelli.io bot, to now flat-out refusing requests that force it to go against its safeguards, for the most part.

Strengthening Your LLM

While great strides have been made to combat prompt injection in the last two years, there is currently no universal solution to this risk. Some malicious inputs are incredibly well-designed and specific, like the prompt from Reddit we’ve linked above. To combat these inputs, AI providers should focus on adversarial training and fine-tuning for their LLMs.

Fine-tuning involves training an ML model for a specific task, which in this case, is to build resistance to increasingly complicated and ultra-specific prompts. Developers of these models can use well-known existing malicious prompts to train them to ignore or refuse such requests.

This approach should also be used in tandem with adversarial testing. This is when the developers of the model test it rigorously with increasingly complicated malicious inputs so it can learn to completely refuse any prompt that asks the model to go against its safeguards, regardless of the scenario.

Can LLMs Ever Truly Be Safe From Prompt Injection?

The unfortunate truth is that there is no foolproof way to guarantee that LLMs are completely resistant to prompt injection. This kind of exploit is designed to exploit the NLP capabilities that are central to the functioning of these models. And when it comes to combating these vulnerabilities, it is important for developers to also strike a balance between the quality of responses and the anti-prompt injection measures because too many restrictions can hinder the model’s response capabilities.

Securing an LLM against prompt injection is a continuous process. Developers need to be vigilant so they can act as soon as a new malicious prompt has been created. Remember, there are entire communities dedicated to combating deceptive prompts. Even though there’s no way to train an LLM to be completely resistant to prompt injection, at least, not yet, vigilance and continuous action can strengthen these models, enabling you to unlock their full potential.

The post Can LLMs Ever Be Completely Safe From Prompt Injection? appeared first on Tech Magazine.

Repetitive and well-defined processes can now be totally automated thanks to IoT, with the role of humans limited to overseeing the process and devising ways to streamline it further.

Apart from its numerous industrial applications, this technology is also the driving force behind the rise of smart cities and smart homes. The transformation of “dumb” devices like electrical appliances (fans, lights, and other household appliances) into smart, internet-enabled devices that can interact with each other and can be controlled remotely over the internet is what makes a smart home, well, smart. And as impressive and convenient as it is, the amount of data being processed by these devices poses serious privacy and security questions.

Are Smart Homes Really Private?

It’s perfectly natural to expect total privacy within the confines of your home. If not your own home, where else can you expect to be 100% safe from prying eyes?

The problem with smart homes is that IoT-enabled devices collect tons of usage data and could, at least in theory, provide opportunities for threat actors to obtain information about your schedule and habits.

Manipulator-in-the-Middle (MITM) attacks are a major concern when dealing with smart home devices. In such an attack, a malicious actor manages to intercept communication between two or more devices, gathering data and, in some cases, even managing to take control of the devices themselves.

Thankfully, if you purchase your IoT devices from well-known and respected vendors like Samsung, LG, and Amazon, threat actors will have a hard time accessing the data being transferred between these devices due to the incredibly secure encryption they use. Moreover, if you follow IoT best practices, such as purchasing the newest devices, keeping their firmware up to date, and setting a secure password for your network that you frequently change (since most IoT networks are Wi-Fi-based), there’s no need to worry.

The truth is, if a cybercriminal has the know-how to pull off a breach on a secure IoT network, they’ll usually go after much bigger targets like businesses, for example. Most homes are simply not worth the effort.

Of course, there’s always the chance that your smart home vendor itself could experience a data breach, putting your data at risk, but if this is something you’re worried about, you can always invest in tech that stores data locally. Of course, this comes with its own risks, especially if someone manages to gain access to the storage location, but at that point, the robbers who have managed to break into your home in this hypothetical situation don’t really care about your smart home usage data.

The Cost Of Convenience

IoT and smart home technology have undeniably made life more convenient, and as we’ve already seen, if you invest in the right tech from reputed vendors and follow smart home security best practices, it’s quite secure. However, even if the devices themselves are secure, the vendors—yes, even the trusted ones—have a sketchy history when it comes to managing data.

For example, Amazon was ordered to pay a penalty of $25 million for violating the Children’s Online Privacy Protection Act Rule (COPPA Rule), a U.S. children’s privacy law. The violation occurred due to Amazon indefinitely holding voice recordings of children collected from Alexa, its voice assistant, even ignoring deletion requests in some cases.

Back to the matter at hand: as safe as smart homes are when you know what you’re doing, any device connected to a wider network is inherently at risk of a breach. Since IoT devices are connected to the internet, there is always a chance they may be compromised either due to a lapse on your part or the vendor’s. With the pace at which the cybersecurity landscape is evolving, more and more new threats will continue to emerge that put your security at risk. Whether the convenience provided by smart homes is worth the risk, that’s entirely up to you.

The post How Overreliance On Connectivity Compromises Home Privacy appeared first on Tech Magazine.

Demand Vs Supply: The Global Developer Shortage

As more and more processes are automated, this creates a need for new applications, and the bottom-line is there’s a global shortage of skilled developers to meet these application demands.

We’ve reached a point where there are far too many vacancies and not enough highly skilled developers available — despite numerous layoffs — and this shortage is only expected to get worse.

Organizations need to change their approach to combating this shortage. Instead of waiting for a skilled developer to turn up, the focus must shift to simplifying software development, enabling anyone to participate in the development process even without formal training. Enter citizen development.

Citizen development is an approach to software development that revolves around enabling non-IT-trained individuals in an organization to develop software, workflows, and automations without having to rely on skilled coders.

Software Development Doesn’t Need To Be Complicated

Due to ease of use, low-code/no-code (LC/NC) development platforms are leading the citizen development charge. These platforms make software development accessible and fairly easy to pick up due to the straightforward and intuitive interfaces they feature.

With LC/NC platforms, the development process can be as simple as dragging and dropping software elements and linking them to create workflows. The underlying code governing the behavior of these elements is prewritten and designed to help them work together. Thanks to these platforms, developers no longer have to write each line of code individually, freeing them up to focus on more pressing tasks. These platforms also enable those without formal software development training and experience to develop simple applications or software functions. This can significantly shorten development times, enabling rapid delivery.

More and more organizations are beginning to adopt LC/NC platforms into their development process; Gartner predicts that “by 2025, 70% of new applications developed by organizations will use low-code or no-code technologies“.

It’s Not All Smooth Sailing, However..

Yes, LC/NC platforms can greatly speed up the software development process, but they lack the scalability and control traditional coding offers since you’re relying on the functionality of a completely distinct development platform. And while nowhere near as steep as pro-code development, LC/NC platforms do still have a learning curve, especially when you’re someone with limited or zero software development experience. This means training costs will also have to come into the equation when an organization aims to equip a team of citizen developers.

When dealing with citizen developers and their relatively limited skillsets, experts still have an important role to play in the development process. Someone needs to test the applications developed by citizen developers to make sure everything is working as it should, and who better to handle testing than the experts? And it’s not just testing; they can even make optimizations when necessary.

A Future Where Skilled Coders And Citizen Developers Can Work Together

The increasing adoption rates of LC/NC platforms aren’t a threat to developer jobs. These platforms aren’t going to replace them; rather, they can free the experts up to actually focus on critical development tasks instead of repetitive and simple processes.

If anything, the increased adoption rates of LC/NC platforms will drive up the stock of expert coders because we’re looking at a future where anyone can develop software but only a few can code.

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What Is Adversarial ML?

Adversarial ML refers to an attack where an ML model’s prediction capabilities are compromised. Malicious actors carry out these attacks by either manipulating the training data that is fed into the model or by making unauthorized alterations to the inner workings of the model itself.

How Is An Adversarial ML Attack Carried Out?

There are three main types of adversarial ML attacks:

Data Poisoning

Data poisoning attacks are carried out during the training phase. These attacks involve infecting the training datasets with inaccurate or misleading data with the purpose of adversely affecting the model’s outputs. Training is the most important phase in the development of an ML model, and poisoning the data used in this step can completely derail the development process, rendering the model unfit for its intended purpose and forcing you to start from scratch.

Evasion

Evasion attacks are carried out on already-trained and deployed ML models during the inference phase, where the model is put to work on real-world data to produce actionable outputs. These are the most common form of adversarial ML attacks. In an evasion attack, the attacker adds noise or disturbances to the input data to cause the model to misclassify it, leading it to make an incorrect prediction or provide a faulty output. These disturbances are subtle alterations to the input data that are imperceptible to humans but can be picked up by the model. For example, a car’s self-driving model might have been trained to recognize and classify images of stop signs. In the case of an evasion attack, a malicious actor may feed an image of a stop sign with just enough noise to cause the ML to misclassify it as, say, a speed limit sign.

Model Inversion

A model inversion attack involves exploiting the outputs of a target model to infer the data that was used in its training. Typically, when carrying out an inversion attack, an attacker sets up their own ML model. This is then fed with the outputs produced by the target model so it can predict the data that was used to train it. This is especially concerning when you consider the fact that certain organizations may train their models on highly sensitive data.

How Can You Protect Your ML Algorithm From Adversarial ML?

While not 100% foolproof, there are several ways to protect your ML model from an adversarial attack:

Validate The Integrity Of Your Datasets

Since the training phase is the most important phase in the development of an ML model, it goes without saying you need to have a very strict qualifying process for your training data. Make sure you’re fully aware of the data you’re collecting and always make sure to verify it’s from a reliable source. By strictly monitoring the data that is being used in training, you can ensure that you aren’t unknowingly feeding your model poisoned data. You could also consider using anomaly detection techniques to make sure the training datasets do not contain any suspicious samples.

Secure Your Datasets

Make sure to store your training data in a highly secure location with strict access controls. Using cryptography also adds another layer of security, making it that much harder to tamper with this data.

Train Your Model To Detect Manipulated Data

Feed the model examples of adversarial inputs that have been flagged as such so it will learn to recognize and ignore them.

Perform Rigorous Testing

Keep testing the outputs of your model regularly. If you notice a decline in quality, it might be indicative of an issue with the input data. You could also intentionally feed malicious inputs to detect any previously unknown vulnerabilities that might be exploited.

Adversarial ML Will Only Continue To Develop

Adversarial ML is still in its early stages, and experts say current attack techniques aren’t highly sophisticated. However, as with all forms of tech, these attacks will only continue to develop, growing more complex and effective. As more and more organizations begin to adopt ML into their operations, now’s the right time to invest in hardening your ML models to defend against these threats. The last thing you want right now is to lag behind in terms of security in an era when threats continue to evolve rapidly.

The post How Adversarial ML Can Turn An ML Model Against Itself appeared first on Tech Magazine.

But let’s forget about that for a moment. From a purely business operations point of view, redundancy is one of the best and most reliable ways to ensure the soundness of your critical infrastructure. It helps ensure your networks are running the way they should: free of any disruption.

With people’s patience for downtime continually wearing thin and its costs constantly on the rise, organizations need to make sure that they are minimizing downtime as much as possible. Thanks to redundant systems, you can ensure that downtime, both planned and unplanned, isn’t as big of a headache as it would be otherwise. But that’s not all; redundant systems provide organizations with a host of other benefits.

What Is System Redundancy?

System redundancy refers to the duplication of critical components and infrastructure that can be used as a fallback in case of failure with the primary critical infrastructure. These backup systems are known as redundant systems.

Types Of Redundancy

System redundancy is classified into three main categories:

• Hardware Redundancy: This is the duplication of critical hardware assets such as servers and data centers. It can also include duplication of power sources and network components.
• Software Redundancy: This involves running different copies or instances of software that is critical to the infrastructure on various devices and servers.
• Data Redundancy: This refers to making multiple copies of critical data and storing it in different locations within the same storage system or even a different storage system entirely.

How Does System Redundancy Help?

Increased Reliability

Redundant systems function as a backup for your critical infrastructure. This means you have assets and other systems in place that are primed and ready to take over promptly in case of failure in your primary asset infrastructure, greatly enhancing your fault tolerance. This is an especially effective way to ensure your systems are operating as intended, even when there is a failure. Redundant systems can significantly reduce downtime and ensure uninterrupted business continuity.

Improved Performance

Redundant systems don’t exist to serve merely as backups. Implementing redundancy into your critical infrastructure provides you with a lot more resources to work with. This enables you to improve performance by spreading the workload across multiple devices during periods of heavy load, resulting in reduced latency and optimal performance levels.

Where network performance is concerned, redundant systems provide a great solution to the problem of network brownouts (also known as unusable uptime). When downtime occurs, it often results in periods of greatly reduced performance, even after the network is up and running again. Network brownouts are among the biggest, albeit often overlooked, threats faced by IT organizations.

Disaster Recovery

Having redundant systems in place can greatly aid organizations with disaster recovery. We’ve already discussed how these systems allow you to quickly bounce back even when there is a failure in your critical infrastructure. Data redundancy, in particular, can enable you to quickly recover from a situation where you lose critical data either due to a malfunction in your storage infrastructure or an malicious action such as a ransomware attack. Having a backup of your critical data provides you with a simple data restoration option. It can enable you to revert to a previous state — before the data loss occurred.

The Benefits Outweigh The Cost

While the initial investment requirements for redundant systems are substantial, there is no doubt that they provide massive benefits and cost-savings in the long run. Ultimately, the organization needs to decide which systems need redundancy, but when implemented effectively, redundancy is a net positive for the organization.

The post The Many Benefits Of System Redundancy For An Organization appeared first on Tech Magazine.

Effectively leveraging data has a major impact on business — what an organization chooses to do with its data often means the difference between success and failure. There’s reasons why data is called the new gold, and why businesses are trying to get their hands on as much of it as possible.

Of course, this abundance of data should not be squandered; various methods of leveraging data have been devised over the years including machine learning (ML).

Knowledge Is Power

Machine learning refers to a subset of artificial intelligence (AI) that aims to use data to train AI models in areas including, but not limited to, pattern recognition, data analysis, and interpretation. Remember, an ML algorithm is only as good as the data that has been used to train it, so it’s imperative to use the right kind of data that is relevant to the end goal or purpose of the algorithm.

Data, Data, Everywhere, But Not All Has To Be Authentic

The world features limitless sources of data. Pretty much every action and every interaction can be converted into data. This datafication, or the quantification of human experience using digital information (often for its economic value), continues to evolve. Now, it can address even abstract concepts like thoughts and opinions through, for example, social media likes, dislikes, and other engagements.

Why should the concept of synthetic data even exist if we have vast amounts of real-world, authentic data at our disposal? Surely it makes more sense to use authentic data, as it’s obviously more accurate and representative of real-world trends, right?

But before we look at the why, let’s look at what synthetic data is: data that’s artificially generated as opposed to data that is collected from real-world sources. There are several ways to generate synthetic data, all varying in complexity. It can be something as simple as replacing real-life figures in a dataset with made up numbers or utilizing data gathered from a highly complex activity like a simulation.

Despite the accuracy and complexity of real-world data, it is prone to certain challenges, including bias, cost, and privacy issues. During the last few years, an increasing number of organizations have moved towards using synthetic data, and adoption is predicted to accelerate. According to Gartner, by 2024, 60% of the data used to develop AI will be artificially generated.

Why Synthetic Data Is The Way Forward

Here are three key factors that demonstrate how synthetic data can prove to be beneficial for your organization.

You Can Greatly Reduce Bias In Your Datasets

We’re already aware that the output of a machine learning algorithm depends heavily on the input used to train it. This is a great example of the garbage in, garbage out principle. If the input data is faulty or biased, it might result in the output of the algorithm mirroring this same bias.

Biases are usually a result of the data not being varied enough; these could also be a reflection of real-world cultural and societal biases. For example, a recent study involving an ML-enabled AI model showed that it was prone to both gender and racial biases.

Using synthetic data generation techniques, you can develop heterogeneous datasets that are varied enough to ensure that the training data isn’t heavily skewed towards a particular pattern of behavior or other characteristics. Going back to the example in the previous paragraph, using a variety of training data about diverse demographics, in terms of gender and race, would help create a more fair and objective algorithm with fewer discriminatory outcomes.

Synthetic Data Generation Is More Cost Effective And Offers Greater Control

Organizations dedicate significant effort to gather as much varied data from as many sources as possible. This can get quite expensive, depending on the nature and size of the dataset, and it doesn’t end there. Activities like setting up data collection systems on your website to enable users to fill out a form with their details, conducting surveys, or collecting user data at a trade show aren’t cheap.

Data collection is one thing, but converting it into actionable information is another problem; it also involves a significant investment of time and money. Being able to generate the kind and quantity of data you need on demand is often guaranteed to be a lot cheaper.

Let’s look at a common example, car crash data, to illustrate how synthetic data can, in some cases, be significantly cheaper than real data.

Physically crashing an actual car in real life is quite expensive and rather impractical. This is where simulations come in. Simulation technology is now advanced and reliable enough to be used as a substitute for real-world testing; it enables testing through simulations at a fraction of the cost.

Moreover, you can literally create any kind of data you need, given you have the means necessary, of course. You have total control, and the possibilities are endless.

Synthetic Data Isn’t Bound By Privacy Laws

Synthetic data might be based on real data, but it doesn’t contain any actual real-world information including personal data. Data collection is challenging and with privacy issues in the spotlight, more regulatory bodies are cracking down on data collection practices. As a result, data collection is becoming even more expensive and time-intensive.

Since synthetic data isn’t directly obtained from the real world, there are far fewer hoops to jump through. Organizations now have the freedom to use the data they generate as they please, which can pay dividends in the long run.

The Future Is Synthetic

Many advancements in data generation techniques over the years have made synthetic data a reliable substitute for real-world data, with some experiments finding that models trained with the right kinds of synthetic data even outperforming models trained with authentic data.

This reliability, combined with synthetic data’s cost-effectiveness and control, makes for a technological innovation that could completely transform the way we create, collect, and handle data. Moreover, synthetic data provides access to large and varied datasets with an even distribution of information that can result in better performance of machine learning models.

The post Synthetic Data Is The Way Forward For Machine Learning Models appeared first on Tech Magazine.

The answer, we’re afraid, is complicated.

Carefully Curated Experiences

You’re probably familiar with the concept of creating a “personalized experience”. You might also be aware that providing a user with a personalized experience involves knowing what their interests are (what they appreciate or dislike), and the best way to find out a user’s interests is, you guessed it, to check their online activity.

Collecting user data to personalize services is ubiquitous on the internet. It’s seen on social media platforms, video sharing sites like YouTube, and even e-commerce platforms like Amazon. These services use your browsing data to recommend content that it thinks you might appreciate, and admittedly, this approach works pretty well. Let’s be honest, no one wants to be bombarded with irrelevant content. People appreciate familiarity, and getting content that they can relate to makes for a far more enjoyable user experience. Plus, it’s these personalized content recommendations that make social media platforms like TikTok so addictive — and profitable.

This form of data collection isn’t such a big deal, so long as these corporations are transparent about what data they’re using and why. However, Big Tech is anything but transparent, and it’s at this point where things can get sketchy.

Rage Against The Ad Machine

We’ve all been there. One moment, you’re looking up gaming laptops on Google, and the next, you’re bombarded with advertisements for gaming laptops on your social feed or during a completely unrelated browsing session. Unsettling? Yes. But how does this work?

The sites or apps that supposedly collect user data to “enhance user experience” also sometimes sell this data to advertisers or other third-party trackers.

Let’s look at Google as an example of how the wider ad machine works. When it comes to the quantity of data being handled, few companies can compare. With a seemingly endless stream of data at its disposal, with sources ranging from Chrome, to Maps, and even Bard, it’s no mystery why. Combine endless amounts of data with the single largest advertising platform, and you get the perfect money-making ad machine.

Real-Time Bidding: A Game Of Half-Truths

Google claims, in no uncertain terms, that it does not sell your personal data. So case closed, right? If only it were that simple.

Technically, Google isn’t lying. If you go by the strictest definition of a sale, where a commodity is exchanged for money, then no, Google is not a data broker and it doesn’t sell your data. However, Google monetizes your data in other ways, which does involve sharing your data with third parties. One such method is real-time bidding (RTB).

So How Does RTB Work?

RTB is a form of programmatic advertising where ad spaces are automatically auctioned off to the highest bidder on a per-impression basis.

Without getting into too much detail, when a user begins a session on a particular page, their data (including location and browsing history) is collected and broadcasted by supply-side platforms (SSPs) to a group of demand-side platforms (DSPs), which automatically place bids for ad space on that specific session. The winning bid is then displayed to the user. User data is shared here to ensure that only relevant advertisements will be shown to the user during that session. This entire process is automated and takes only milliseconds.

Admittedly, RTB is incredibly efficient as an advertising tool. But it’s unfortunately a questionable practice due to the privacy implications, with some experts claiming that RTB practices violate GDPR principles.

The issue with RTB is that it also involves sharing highly specific data, so while RTB platforms aren’t directly sharing personal data, they most certainly are indirectly sharing data that is detailed and specific enough to tie to a particular user. Furthermore, it’s not just the highest bidder that gets to view this data — everyone who participates in the auctions can. These exchanges have no control over how the broadcasted data is used once the auction is complete. When you put everything together, you’re looking at an ugly combination of potential security risks. What makes things worse is that advertising platforms running RTB auctions are not transparent about what kind of data is being broadcasted.

Coming back to Google, the company can rightly claim that your data isn’t what’s being sold, rather, it’s the ad space within your browser. But, as we’ve already seen, RTBs involve the transfer of personal data. Please note that Google isn’t the only offender in this space. RTB is a common online advertising practice followed throughout the internet, and it’s important to be aware how Big Tech companies use vague language and loopholes to get away with sharing your data while claiming otherwise — directly or not.

Big Tech Is Watching You

Let’s reiterate this: We’re perfectly fine with tech companies using our data to provide us with an improved experience while we choose to use their services, provided they’re transparent about what data they’re collecting and how it’s being used. What isn’t okay is Big Tech getting away with misusing our data using vague jargon and legal loopholes. We can be grateful for data protection regulations like Europe’s GDPR, as well as California’s CCPA and CPRA, and other countries that have followed suit. It’s time for even stricter regulation to crack down on Big Tech’s exploitative business models.

The post Big Tech Knows Too Much. More Regulation Is The Answer appeared first on Tech Magazine.

According to this report from Juniper, persistent network brownouts are the third biggest risk IT organizations face today, only behind total outages and security breaches, and rather worryingly, most brownouts (61%) are not detected by IT teams as the monitoring mechanisms are often only equipped to detect total outages. What’s more, the average annual cost of brownout-induced downtime alone is estimated to be $600,000 per organization.

Black? Brown? What’s The Difference?

An outage, or blackout, refers to a complete lack of availability of a network. A brownout, on the other hand, refers to a period where the network is running, albeit at a significantly reduced level of performance, hurting the overall quality of service. Network brownouts are also known as “unusable uptime”.

Network brownouts can make an organization’s products or services frustrating to use, leading to reduced customer satisfaction and even an overall decline in employee productivity. These issues almost always result in loss of revenue, and persistent brownouts can also greatly damage the organization’s reputation.

So What Causes A Network Brownout?

1- Overload

As with network outages, an overload is usually the main cause of a brownout. A network overload occurs when the traffic flowing through the network is much higher than it is equipped to handle. This overwhelming traffic can result in reduced availability of network resources, leading to low bandwidth and high latency. Network overloads can have several causes, including increased traffic, faulty equipment, and even DDoS attacks.

2- Faulty Or Legacy Equipment

It is an absolute must for organizations to keep monitoring their network infrastructure to isolate any weak points. These weak points may exist in the form of faulty or even sometimes obsolete equipment. While network components like routers or switches are quite reliable, failures can still occur. These failures could disrupt the flow of traffic through the network, resulting in more congestion, which could cause a brownout. And if there’s a failure in a critical network component, this could lead to a total outage.

Any form of obsolete legacy equipment could also cause a brownout as these devices may not be able to cope with ever-increasing network demands.

3- External Network Issues

In some cases, brownouts can also be caused by issues outside an organization’s control. For example, ISP networks are also prone to the same issues organizations face. Failures in an ISP’s network infrastructure could also have a significant impact on an organization’s network and quality of service.

How Can Organizations Avoid Network Brownouts?

1- More Effective Monitoring Solutions Are A Must

Most brownouts aren’t detected by IT teams. Rather, it is the customer or another employee that is usually first to detect and report such issues. When it comes to issues that affect network operations, responding quickly is key. Quick detection means quick resolution.

By detecting issues instantly, IT teams can resolve them swiftly and ensure they don’t plague the network for long. It is imperative for organizations to implement better monitoring solutions that can detect even slight drops in performance. This ensures quick resolution of issues, helping IT teams keep their networks up and running with minimum disruption, resulting in an overall increase in network quality.

2- Keep All Hardware Up To Date

As network demands continue to rise, it is important to prepare for increased requirements by investing in the latest and greatest network infrastructure. In an era where organizations across all verticals are increasingly reliant on IT and the availability of network resources for their services, they simply cannot afford to cut corners when it comes to their network infrastructure.

3- Optimize Bandwidth Usage

Organizations should ensure that they are wisely using the available network resources and bandwidth without putting too much strain on any particular server or network component.

This can help eliminate or at least limit congestion, which is the main cause of both brownouts and blackouts, depending on severity.

Network segmentation and load balancing are some of the most effective ways for organizations to optimize their bandwidth usage.

Network segmentation involves splitting the network into distinct components based on the role they play. These segments (also known as subnets) are isolated from the rest of the network and can function independently. Network segmentation enables organizations to prioritize and allocate network resources efficiently to different network segments depending on how critical they are to the overall functioning of the network. Moreover, issues with one segment are unlikely to spill over to other segments, reducing the likelihood of a brownout.

Load balancers enable organizations to evenly distribute traffic across the network to avoid overwhelming a single server or component with too much traffic. This can prevent bottlenecking and can smooth the flow of traffic throughout the network.

4- Implement Network Redundancy

Implementing redundancy in the network infrastructure is easily one of the most reliable ways to ensure smooth connectivity and stable performance. Organizations can either implement network redundancy by creating alternate paths for the flow of traffic within the network or use redundant hardware components that can automatically take over in case of a failure. Ultimately, these redundant systems can serve as an effective backup when facing issues with primary network components. These redundant components help with creating an effective failover mechanism, significantly reducing the frequency and severity of a brownout.

Takeaway

With organizations becoming increasingly reliant on IT, not just for their services, but also for their internal processes and operations, it’s safe to say they simply cannot afford any disruptions. Time is money and downtime is money lost. Organizations must invest in robust and reliable network monitoring solutions that enable them to instantly detect any issues in their network infrastructure so they can rectify them as soon as possible.

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While these services have always been hugely popular, the lockdowns of the early 2020s catalyzed an even bigger surge in their popularity. However, this sudden influx of users worldwide also shone a spotlight on the vulnerabilities of this technology, mostly as a result of its reliance on a cloud service model. The overwhelming demand created by millions stuck in their homes led to issues such as frequent buffering, reduced quality, and sometimes even server outages, and this was only a few weeks into lockdown.

The problem is, the cloud service model has some fundamental flaws that make it unsuited to deal with the rising demands of the streaming market. The large distances between users and data centers paired with the heightened workload due to an ever-increasing number of streaming requests means this model is no longer viable for streaming. There’s only so much you can do, and beyond a certain point, you’re just beating a dead horse. So, it’s time for a switch because the future of streaming lies in edge computing.

By creating a network of distributed edge servers close to the users in a particular area, streaming services can ensure that they are bridging the gap to their users while also creating reliable streaming channels that have plenty of bandwidth to provide disruption-free service.

Advantages Of Edge Computing For Media Streaming

Increased Speed And Reduced Latency

Edge computing enables streaming services to set up dedicated edge servers for a particular city or locality. By placing these servers close to the user and caching popular content locally, thanks to the storage capabilities of the edge, streaming services can greatly reduce latency-induced buffering and increase the speed at which content is delivered to the user.

We’re talking about a server that is set up with the sole purpose of taking requests from a specific area as opposed to a centralized cloud server taking requests from around the world. Combine this with the reduced physical distance between the server and user, along with the content caching capabilities of the edge, and you have a seamless channel that supports faster content delivery with significantly reduced lag or buffering.

Enhanced Quality And Reliability

Since edge servers deal with reduced traffic and distance, streaming services can ensure that the content being sent to the user is consistently high quality.

Edge servers also spread out the workload, preventing the central server from getting overwhelmed by requests. This also means there is no central point of failure. If one edge server malfunctions, the rest of the network continues to function as intended.

Improved Scalability

Edge computing architecture naturally lends itself to horizontal scaling. You can set up more edge servers to deal with increasing demand, and since these edge servers are not overly complex or pricey to set up, you can also keep costs in check. This is in stark contrast to central servers, where you’re forced to scale vertically as the costs of setting up a brand new central server are exorbitant.

Better Response Times For Cloud Gaming

Yes, video game streaming is a thing, too. It works pretty similarly to video streaming but with an added layer of complexity. Video games are inherently interactive by design, and streaming services must ensure that these interactions are seamless and instant. This doesn’t work in the case of a centralized cloud server. The only way video game streaming (aka cloud gaming) will work is when the games are being run at the edge.

Cloud gaming works by hosting or running games on extremely powerful servers; the gameplay is streamed to the user’s device and their inputs are sent back to the server. Cloud gaming enables users to run extremely demanding and resource-intensive games remotely, which would otherwise not be possible due to local hardware limitations.

By hosting games close to the users at the edge, cloud gaming service providers can ensure quick and smooth response times. And if cloud gaming catches on, we’re looking at tech that will not just change the future of gaming but interactive media as a whole.

Closing Thoughts

Recent advancements in media streaming have completely changed the way we consume entertainment. From the elimination of local storage to the cost-effectiveness of having access to vast content libraries for a small subscription fee, this tech has also made entertainment incredibly convenient. The rising demand for streaming has necessitated sweeping changes to its infrastructure and, well, nothing does the job as well as the edge. Let’s see where it goes from here.

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