SAN DIEGO, Oct. 27, 2023 -- alwaysAI, a computer vision leader, is thrilled to announce the release of enhanced MLOps features to its comprehensive

alwaysAI Strengthens Its Computer Vision Platform with New Cutting-Edge MLOps Features

SAN DIEGO, Oct. 26, 2023 (GLOBE NEWSWIRE) -- alwaysAI, a computer vision leader, is thrilled to announce the release of enhanced MLOps features to its comprehensive computer vision platform. These integrated capabilities solidify alwaysAI as a premier ...

alwaysAI Strengthens its Computer Vision Platform With New Cutting-Edge MLOps Features

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Artificial Intelligence of Things Market Size to Surpass USD 253.86 Billion by 2030, exhibiting a CAGR of 32.4%

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The starting point of building a successful computer vision application is the model. Computer vision model training can be time-consuming and challenging if one doesn’t have a background in data science. Nonetheless, it is a requirement for customized applications. Several developers who build computer vision applications have experience with Python, but do not have experience with data science. This poses a challenge to most developers who want to customize a model. There are tools that you can use to train your own computer vision model, such as TensorFlow, PyTorch and Keras. alwaysAI also offers a Model Training Toolkit which leverages transfer learning in order to modify an existing model instead of training a model from scratch. This article will go over some basic concepts of model training and then give an example of how to train a custom model. Leveraging transfer learning is the key to efficiently customizing a computer vision model. Transfer learning is essentially taking the knowledge gained from training on a dataset, such as the COCO dataset, and improving upon it or modifying it to your specific task/dataset. This saves the time of learning fundamentals like edge detection and feature primitives. The COCO dataset is a popular open source dataset that we use as the basis of our transfer learning. It is computer vision specific and due to its wide variety classes and large size is the dataset commonly used for open-source computer vision models. With alwaysAI you can get all of the power of the COCO dataset tuned to your specific task or application. alwaysAI provides access to pre-trained computer vision models as well as the ability to leverage transfer learning to train your own models. Using a pre-trained model can certainly expedite computer vision app development. However, pre-trained models can also impose limitations on what applications you can build. In order to build a unique, custom application, you are going to need to train your own model. Using transfer-learning enabled model re-training not only requires less training data but can also significantly cut down the time it takes to train a new model. There are three major components involved in training a model using the alwaysAI Model Training Toolkit: Data Collection: Our Image Capture application helps you with your data collection, so that you can collect videos and images directly from your edge device. See this repo for more on this. GitHub Data Annotation: We have CVAT, a data annotation tool, built right into our Model Training Toolkit so that you can easily annotate your data. Transfer Learning: As mentioned earlier, train a custom MobileNet SSD model using transfer learning to take the knowledge of the COCO dataset and improve upon it or modify it to your unique dataset. After training a model, you can upload it and use it to build your own custom computer vision application with our easy to use Python API. With alwaysAI, you can customize a computer vision model with no coding, and little experience. Model training is no longer relegated to the domain of data scientists. You can try our Model Training Toolkit for free and gain access to our support team on Discord, our free , and our weekly . Webinars Hacky Hours Here is a quick example of how to train a new model, which detects specific hand gestures using our Model Training Toolkit. This example also highlights the benefit of using two models in conjunction in an application. By using the alwaysAI hand model in our application to first detect hands, we can then feed ‘hand detections’ as an input into our gesture model. Since our hand model was trained on a wide variety of data, it will detect hands of various sizes, with different backgrounds, in images with different lighting and from different distances. We then use the alwaysAI Python library to cut out the portion of the original image that contains the hand, and feed this new image into the gesture model. The result of this is that the input into the gesture model will always be the area that exactly surrounds the hand meaning we don’t have to vary the training data in terms of background, or sizes or distances of the hand! Therefore, we need a lot less training data, and less time training. As mentioned above, the first step in training your own model is data collection. Since we’re training specific gestures, we can just take videos of a single gesture moving around in the video to get different lighting and some variation in sizes. By having each video contain just one gesture, we’ll make the annotation step, which we’ll cover next, much easier. The second step to training your own model is data annotation. alwaysAI makes this easy by integrating the Computer Vision Annotation Tool directly into the alwaysAI software. Using the tracking feature in CVAT along with the ‘fast-forward’ annotation, we can have CVAT interpolate some of the bounding boxes so we can easily annotate an entire video containing a single gesture in 20 or so minutes. Once we have the data collection and annotation complete, we can train our model! With alwaysAI, you can train using the command line interface, or a Jupyter Notebook. The latter option is helpful if you want to see a plot of your training and testing validation. Finally, once our model is trained, we can prototype our application. Just using the gesture model alone only works when the hand is very close to the screen. To make the model work when the hand is farther away, we could collect images in which the hand is farther away from the camera, and then annotate this data, include it in training, and the model will be more robust. However, another option, which is a bit quicker, is to use our gesture model with input that is generated from first using our hand model. By using the hand model first, we can detect hands that are farther away and then use this portion of the image as input for the gesture model. Now we can detect gestures from far away! Another issue we discover when we test out our gesture model in the prototype is that we don’t pick up gestures when the hand is rotated (e.g. sideways). We could do another iteration of data collection, annotation, training in this case as well, using training data where hands displaying different gestures are in different positions. While this is doable, it might be better to test our hypothesis that this is the issue first. We can use alwaysAI’s API to rotate the image of the hand that is generated by the hand model’s bounding box and then feed that into the gesture model. Now we have a working model that detects unique gestures! This tutorial was covered in one of our previous Hacky Hours focusing on Data Collection and Annotation. You can watch the recording here: If you would like to learn more about the alwaysAI Model Training Toolkit in action and expand on this application, please join our next free Hacky Hour. It takes place on Thursday, November 5 and will be more focused on transfer learning. This virtual workshop will cover building out our prototype application to interact with external devices, and turn gestures into actionable responses in our computer vision application. You can sign up for our free Hacky Hours here.

How to Train Computer Vision Models Efficiently

Artificial intelligence (AI) is the field of making computers able to act intelligently, to make decisions in real environments that will have favorable outcomes. This is obviously a broad, and somewhat vague, definition, and there are many fields within this umbrella term. One example of such a field is that of computer vision, in which computers can process images as a human would, and make inferences about what is in an image so that computer programs can then use that information to make decisions that have favorable outcomes. It is becoming more common to see artificial intelligence applications such as computer vision integrated into new business models and products. Computer vision has many real-world applications, analyzing traffic patterns, detecting changes in posture, counting the number of persons in an area, etc. Learning how to build any computer vision application requires a steep learning curve, and deploying it to the edge adds an extra layer of complication. While deploying and running applications in the cloud has become more popular in recent years, this isn’t always the best solution, especially for applications that require heavy processing or that use sensitive data, both of which are often the case with computer vision applications. In such circumstances, the best option is often to deploy your application to the edge. Deploying computer vision applications on the edge has numerous benefits, which we will go through in this article. We will also describe the challenges of running computer vision on the edge and how to overcome them. What is ‘the edge’? In general, edge devices are small, lightweight, devices on which a computer vision application can be deployed and run. Many edge devices today even have a Graphical Processing Unit (GPU), or Visual Processing Unit (VPU), which enable usage of a greater range of models and application complexity. In the context of this article, an edge device is a device such as a Raspberry Pi, NVIDIA Jetson devices like the Jetson Nano or NVIDIA Jetson Xavier NX, or various Internet of Things (Iot) devices, which are edge devices that have some capability to sense or assess, and possibly interact with, the environment in which they are used. Benefits of Running Computer Vision on the Edge The three main benefits of running a computer vision application on the edge are time, security, and cost. 1. Reduced Latency While an internet connection can be used to deploy the application, once the application is on the edge device, there is no need for it to have cloud connectivity to function. This means that any inferencing the application does is on the edge device itself, not in the cloud, and this drastically reduces the time it takes for those inferences to be turned into actions by the application. For certain use cases, such as self-driving vehicles or security cameras, this is imperative. Aside from the risk that data may be lost being sent to and from the cloud, the additional time required for using a cloud approach can mean not responding to a task in time, which can be catastrophic for tasks like autonomous driving. There are also specialized peripheral devices, in particular cameras, that don’t have internet connectivity themselves and are used by edge devices to improve performance or expand application functionality. Such devices take the concept of processing on the edge even further. Instead of a USB or ribbon camera transmitting the image data to an edge device for processing, these devices incorporate a processor into the camera itself, further reducing processing latency. 2. Improved Security Secondly, because the data doesn’t need to travel to the cloud with edge deployment, all data can stay in a closed circuit on the device itself. Edge computing distributes the risk of exposure across multiple devices, and can perform all processing disconnected form a central server - a more secure and private architecture. For example, edge devices can be installed inside of a person’s home, process real-time data and act on that data without relying on a shared cloud service that could compromise the privacy of their day-to-day activity. Because computer vision data may involve people or other sensitive data, this is much more secure. 3. Reduced cost Finally, without the need for cloud processing, deploying a CV application on the edge is much less expensive, as cloud companies charge for inferencing per endpoint per minute. This is a great, flexible option for those who want to pay on an ‘as needed’ basis, but becomes burdensome for organizations that demand large amounts of real-time processing - such as a smart city, hospital or warehouse with many cameras and sensors running 24 hours a day. Challenges of Running Computer Vision on the Edge Building a computer vision application entails its own challenges, and is often an iterative process, and therefore any pipeline you use to build computer vision applications must be flexible enough to handle changes such as the model or libraries used, or device that the application is deployed on. Let’s walk through the challenges of deploying computer vision applications on the edge with a story. First, you need to get a computer vision model, either by 1) scouring the web for an open-source model that fits your needs, or 2) by building an image dataset and t The second option requires collecting your own dataset, which is labor-intensive, as well as an understanding of several tools for image annotation and the training itself. raining your own model. Next, you need to build your application. Let’s say you have a working model and you are prototyping your computer vision application. One popular computer vision library is OpenCV. Developers can easily use the C++ or Python Application Programming Interfaces (APIs) of OpenCV. For example, developers can access the Python OpenCV API simply by using . You spend some time learning this API and have a working prototype that integrates well with the output of your model. pip install opencv-python Now that you’re happy with the app, you’d like to deploy it to an edge device, like a Raspberry Pi. The first thing you’ll find is that the OpenCV build you were using isn’t compatible with the Raspberry Pi processor. Other dependencies may also have the same problem. This leads you to compile OpenCV on the Raspberry Pi. Next, you need to figure out how to see the results! On your laptop, this was easy with cv2.imshow(), but if you’re working on a headless device (no graphical interface), you’ll need to find another way. Now that your app is up and running, you see that the amazingly fast performance you saw on your laptop is long gone. Of course, nobody expects a $35 single-board computer to outperform your Macbook, but you want better performance. You’ve read that you can use Intel’s Neural Compute Stick 2 to get better performance out of a Pi, but now you need to build OpenVINO as well. Since OpenVINO’s interface is different from OpenCV, you’ll need to decide whether to rewrite your app to take advantage of the NCS2, or rebuild OpenCV so it can run the OpenVINO inference engine. When it’s all said and done, you may find that your model requires more tweaking, possibly the output changes, and you may be looping through the steps again! How to Overcome These Challenges Overcoming these challenges requires IoT experience in addition to computer vision knowledge. However, a developer platform such as can also help solve these problems. At alwaysAI, we have the singular mission of making the process of building and deploying computer vision apps to edge devices as easy as possible. That includes training your model, building your app, and deploying your app to edge devices such as the Raspberry Pi, Jetson Nano, and many others. alwaysAI Once you have installed alwaysAI, you only need to know a few basic commands, our command line interface (CLI) to collect data, annotate it, train a model, and deploy and start your applications. Obtaining a Computer Vision Model alwaysAI has a catalog of that are guaranteed to work with any application built with our pipeline. You can also train your own object detection model using the alwaysAI , which is built using TensorFlow as the backend framework and does transfer learning on mobilnet_ssd, which was trained on the COCO dataset. publicly available models model training toolkit The model training toolkit enables users to start working with TensorFlow without learning a new API or installing any extra modules by hand. We also include the Computer Vision Annotation Tool (CVAT) as part of the toolkit, so users can annotate new datasets without needing to download and install CVAT from scratch. Developing your Application We have our own Python API, called edgeiq, which wraps some of OpenCV’s core functionality and which can be used to interact with. This means you only need to know one API to get up and running with computer vision applications. Since we ourselves love to use OpenCV, we’ve built it in as a core piece of our edge runtime environment, which means you can add `import cv2` to every alwaysAI application and use OpenCV directly in your app, regardless of your choice of deployment device. Deploying your Application We leverage containerization via Docker images and Python virtual environments, and this makes deploying your application simple no matter what your device is. This also makes it easy to add any necessary requirements your application may need, using either the Dockerfile or requirements.txt files. Running computer vision on the edge saves both time and cost, and has the additional benefit of being more secure. While developing and deploying any computer vision application comes with its own set of challenges, this can be especially true when deploying on the edge. Our main goal at alwaysAI is to overcome these challenges and make developing and deploying computer vision applications on the edge as easy as possible for users. You can ! try our platform yourself for free

The Challenges of Running Computer Vision on the Edge

‘Computer Vision’ (CV) refers to processing visual data as a human would with their eyes, so that we can make conclusions about what is in an image. Once we know what is in an image, we can make our application respond, much like a human would when processing visual data. This is what enables technology like self-driving cars. Developing a CV application involves building machine learning models and integrating these models with external libraries as well as various hardware, including operating systems, accelerators, and cameras. Getting all of these components to work in harmony is exactly what alwaysAI does, and we do it with abstraction and containerization. alwaysAI employs abstraction with two key components: our command line interface (CLI) and our application programming interface (API), edgeIQ. A CLI is a tool that can be used to interact with software. If you open up cmd.exe or PowerShell on Windows, or Terminal on Mac, you’ll see a prompt that accepts text input and enables you to interact with your operating system: navigate directories, move files, copy input, open a text editor… This is the same way the alwaysAI CLI works: the alwaysAI software recognizes these commands and performs the desired functions: build the application, download computer vision models, configure your project, even accessing the annotation and training software and packaging your application as it’s own Docker image, is all done with just a handful of base commands. This means you only need to know a dozen commands to be able to annotate data, train a model, build your application, and deploy it to a device. An API also simplifies the development process, by serving as a kind of negotiator between a complicated backend and an end user. It defines what kinds of requests can be made by the user and how data is returned. This simplifies usage for the user, by hiding some of the complexities involved in these calls, and helps ensure the only appropriate requests can be made. You’ll see examples of this simplification throughout the rest of this article! Finally, alwaysAI leverages containerization to make deploying computer vision applications as painless as possible. Containerization refers to the process of bundling up software and dependencies in one self-contained unit, which can then be executed in different environments. alwaysAI uses different to enable deployment on different devices, including Raspberry Pi and Jetson Nano. Another form of containerization you may already be familiar with is a virtual environment. This is popular with Python applications, but they only containerize Python dependencies. alwaysAI utilizes both of these tools to help containerize computer vision applications. By using a Python virtual environment inside a Docker container, all application and system dependencies can be bundled together and the application will work as a self-contained unit. Docker images Development Process Computer Vision Models Whether a computer vision application works in a real-world environment depends first and foremost on the model it uses and this depends the quality of the data that the computer vision model is trained on; garbage in garbage out! If your goal is to get a prototype up and running quickly, it may be sufficient to choose from one of the high-quality, pre-trained models on the alwaysAI . Typically, different models may use different serialization formats or require different pre-processing, which means even integrating a pre-built model into an application can be challenging, especially if you have to change models or use more than one model in an application. All the models in the catalog have been fit into the alwaysAI workflow, so you don’t have to worry about compatibility, you can easily swap one out for another or use different computer vision models together.  However, we know that more specialized projects require custom models, so we have also built an object detection model training tool into our platform. This means you don’t have to worry about installing and configuring complex training packages yourself! You can even collect and annotate your own datasets using alwaysAI, so you can build your own CV applications from data all the way to deployment. If you want to read more about CV models, you can check out this model catalog article. Integration with Computer Vision Libraries Once you have your computer vision model, you still need to get visual data into your application and feed it into your model. Getting video streams set up on your laptop or another device can be another pain point, and researching which libraries to use in your application, becoming familiar with different packages and modules can require a steep learning curve and large time investment as well. Here at alwaysAI, we recognize the immense value that developers receive from using existing computer vision libraries, like OpenCV. OpenCV enables developers to access over 2500 algorithms to support the development of computer vision applications, however installing this package from scratch can be daunting. alwaysAI complements OpenCV, wrapping some of the most common functionality like reading and writing data, and manipulating input frames, directly into the alwaysAI application programming interface (API). And you can simply import cv2 into any alwaysAI app to access any of OpenCV’s other functionality, no installation required. You can your other favorite Python libraries into your applications with alwaysAI as well. install and import Deployment Computer Vision on the Edge What is the edge? In general, edge devices are small, lightweight devices on which a computer vision application can be deployed and run. Many edge devices today even have a Graphical Processing Unit (GPU), or Visual Processing Unit (VPU), which enable usage of a greater range of models and application complexity. In this context, an edge device is a device such as a Raspberry Pi, NVIDIA Jetson devices like the Jetson Nano or Xavier NX, or various Internet of Things (Iot) devices, which are edge devices that have some capability to sense or assess, and possibly interact with, the environment in which they are used. Why deploy to the edge? While an internet connection can be used to deploy the application, once the application is on the edge device, there is no need for it to have cloud connectivity to function. This means that any inferencing the application does is on the edge device itself, not in the cloud, drastically reducing the time it takes for those inferences to be turned into actions by the application. For certain use cases, such as self-driving vehicles or security cameras, this is imperative. Aside from the risk that data may be lost being sent to and from the cloud, the additional time required for using a cloud approach can mean not responding to a task in time, which can be catastrophic for tasks like autonomous driving. Additionally, because the data doesn’t need to travel to the cloud with edge deployment, all data can stay in a closed circuit on the device itself, which is much more secure, and, without the need for cloud processing, much less expensive. Build Your App For the Edge Building computer vision applications and running them on edge devices can be a challenge as well if not using an edge-based development tool like alwaysAI. Our goal is to make the process of building and deploying computer vision apps to edge devices as easy as possible. alwaysAI apps are built in Python and can run natively on Mac and Windows, as well as in our containerized edge runtime environment, which is optimized for different architectures commonly used in edge devices, such as 32 and 64 bit ARM architectures. While alwaysAI enables developers to write their code on an edge device, such as a Raspberry Pi or Jetson Nano, the real benefit of using alwaysAI comes with building your application on a regular development machine, and then running that application on an edge device. Edge devices can quickly overheat without a fan and heatsink, and even with these additions, they likely can’t handle running an IDE or support having a web browser with lots of open tabs, as is usually the case when writing application code. And as we mentioned before, installing a library like OpenCV on a Raspberry Pi requires building it from source; this is because many libraries don’t have compiled packages for ARM architectures. With alwaysAI, you get the benefit of developing on your regular machine, and deploying your app to the edge. alwaysAI , based in San Diego, California, is a deep learning computer vision platform. alwaysAI provides developers with a simple and flexible way to deliver computer vision to a wide variety of edge devices. With alwaysAI, you can build computer vision applications that use Object Detection, Object Tracking, Image Classification, Semantic Segmentation & Pose Estimation right on your laptop or single board computer! Try it for yourself for free !

Computer Vision Applications: The Development and Deployment Processes 

In the past decade, the tech world has witnessed its fair share of evangelists—those who champion revolutionary technologies, promising to change the world. First, it was the blockchain and Web3 movement, touting the dawn of a decentralized future. Now, it’s artificial intelligence (AI), a field evolving at breakneck speed, with its advocates either touting it as the end-all solution to every problem or sounding alarm bells over its potential for destruction. But there’s a critical distinction between how these evangelists communicate. AI evangelists, especially in the last few years, have leaned towards extremes—either as doomers predicting catastrophic outcomes or hyper-optimists seeing AI as humanity’s salvation. This narrative intensity is alienating for many people, making AI seem more like a future science fiction scenario rather than an immediate and impactful tool. In contrast, blockchain/Web3 evangelists, while occasionally prone to over-promising, largely emphasized ideals of freedom, privacy, and financial decentralization—a more hopeful, if sometimes vague, vision of the future. The irony in all this is that while blockchain's potential has been slow to materialize, AI has already had a massive and measurable impact on industries, economies, and even personal lives. Yet, the communication strategies of AI proponents often obscure this reality, making it easy to dismiss the technology as either too dangerous or too futuristic. The Blockchain Hype vs. AI’s Reality Blockchain evangelism often felt like a dream half-built. Early proponents spoke of a decentralized internet, one free from the control of corporations and centralized entities. They promised a world where individuals could truly own their data and financial systems would be transparent and accessible to all. It was, in theory, a utopia of independence and power redistribution. The problem? That utopia has largely remained theoretical. Years after its initial hype, blockchain’s biggest contributions, like cryptocurrency and non-fungible tokens (NFTs), have struggled to go mainstream or prove their broader utility. In contrast, AI’s journey has been the opposite. From image generation and natural language processing to predictive algorithms and automation, AI has embedded itself into the fabric of daily life without many even realizing it. Whether it's recommendation engines on Netflix, fraud detection systems in banks, or diagnostic tools in healthcare, AI is already transforming industries. While blockchain waited for use cases to catch up with its philosophy, AI’s use cases exploded in every direction, leaving the philosophical debates trailing behind. The Doomerism Trap So why does AI feel so polarizing? A significant part of this is due to the nature of AI evangelism itself. With figures like Elon Musk warning of AI's potential to "destroy civilization" and organizations pushing for regulations against the "existential risks" of AI, the narrative around artificial intelligence often veers into doomsday territory. While concerns about AI safety are valid, this hyper-focus on AI as an uncontrollable force often turns people off. It paints a picture of technology as something to fear rather than embrace. At the other extreme, hyper-optimists claim AI will solve all problems, from climate change to world hunger, with little attention to its current limitations or the complex challenges that remain. This "silver bullet" mentality makes AI seem distant and detached from the real, incremental progress that is already happening. Blockchain never had this problem. Evangelists framed it as empowering and liberating, rarely speaking in terms of existential threats. Even if the technology's promises were overstated, the message was at least aspirational, drawing people in rather than scaring them away. The Web3 community built a narrative around personal control, freedom, and a better digital society—ideas that resonated with many. Missing the Immediate Impact What AI evangelists often miss in their doomsday warnings or utopian visions is the tangible, present-day value AI is already providing. Unlike blockchain, where practical applications still feel niche or speculative, AI's influence is clear, whether in helping businesses optimize supply chains, allowing doctors to make faster and more accurate diagnoses, or enabling more personalized education through adaptive learning tools. AI is already making people’s lives easier, saving companies millions, and revolutionizing sectors like finance, healthcare, and entertainment. This distinction is crucial. Blockchain has always been about potential—a potential future decentralized world. But AI, whether you’re a doomer or a hyper-optimist, is affecting industries and lives right now. In a way, blockchain was lucky—its biggest challenge was over-promising a utopian vision that didn’t materialize quickly. AI faces the opposite challenge: it has delivered beyond expectations but is constantly framed as either an existential risk or a panacea, making it harder for people to see and appreciate its current utility. The Path Forward To win over a skeptical public, AI evangelists need to step back from the extremes. AI is neither the apocalypse nor a one-size-fits-all solution to humanity’s problems. It’s a tool—a powerful one—that’s already improving lives and transforming industries in meaningful ways. Instead of framing it as an existential crisis or a futuristic miracle, AI evangelists need to focus on the real, everyday impact AI is having right now. By learning from the missteps of blockchain evangelism—promising too much too soon—and avoiding the current trap of apocalyptic doom, AI evangelists can present a more balanced, grounded perspective. If AI evangelists focus on how the technology is already here and making a difference, they stand a better chance of keeping the public engaged and excited about its potential without scaring them away. In the end, AI has proven itself far more effective and transformative in the short term than blockchain ever did. The next challenge is getting people to see that without all the noise. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com In the past decade, the tech world has witnessed its fair share of evangelists—those who champion revolutionary technologies, promising to change the world. First, it was the blockchain and Web3 movement, touting the dawn of a decentralized future. Now, it’s artificial intelligence (AI), a field evolving at breakneck speed, with its advocates either touting it as the end-all solution to every problem or sounding alarm bells over its potential for destruction. But there’s a critical distinction between how these evangelists communicate. AI evangelists, especially in the last few years, have leaned towards extremes—either as doomers predicting catastrophic outcomes or hyper-optimists seeing AI as humanity’s salvation. This narrative intensity is alienating for many people, making AI seem more like a future science fiction scenario rather than an immediate and impactful tool. In contrast, blockchain/Web3 evangelists, while occasionally prone to over-promising, largely emphasized ideals of freedom, privacy, and financial decentralization—a more hopeful, if sometimes vague, vision of the future. The irony in all this is that while blockchain's potential has been slow to materialize, AI has already had a massive and measurable impact on industries, economies, and even personal lives. Yet, the communication strategies of AI proponents often obscure this reality, making it easy to dismiss the technology as either too dangerous or too futuristic. The Blockchain Hype vs. AI’s Reality Blockchain evangelism often felt like a dream half-built. Early proponents spoke of a decentralized internet, one free from the control of corporations and centralized entities. They promised a world where individuals could truly own their data and financial systems would be transparent and accessible to all. It was, in theory, a utopia of independence and power redistribution. The problem? That utopia has largely remained theoretical . Years after its initial hype, blockchain’s biggest contributions, like cryptocurrency and non-fungible tokens (NFTs), have struggled to go mainstream or prove their broader utility. largely remained theoretical In contrast, AI’s journey has been the opposite. From image generation and natural language processing to predictive algorithms and automation, AI has embedded itself into the fabric of daily life without many even realizing it. Whether it's recommendation engines on Netflix, fraud detection systems in banks, or diagnostic tools in healthcare, AI is already transforming industries. While blockchain waited for use cases to catch up with its philosophy, AI’s use cases exploded in every direction , leaving the philosophical debates trailing behind. exploded in every direction The Doomerism Trap So why does AI feel so polarizing? A significant part of this is due to the nature of AI evangelism itself. With figures like Elon Musk warning of AI's potential to "destroy civilization" and organizations pushing for regulations against the "existential risks" of AI, the narrative around artificial intelligence often veers into doomsday territory . While concerns about AI safety are valid, this hyper-focus on AI as an uncontrollable force often turns people off. It paints a picture of technology as something to fear rather than embrace. artificial intelligence often veers into doomsday territory At the other extreme, hyper-optimists claim AI will solve all problems, from climate change to world hunger, with little attention to its current limitations or the complex challenges that remain. This "silver bullet" mentality makes AI seem distant and detached from the real, incremental progress that is already happening . real, incremental progress that is already happening Blockchain never had this problem. Evangelists framed it as empowering and liberating, rarely speaking in terms of existential threats. Even if the technology's promises were overstated, the message was at least aspirational, drawing people in rather than scaring them away. The Web3 community built a narrative around personal control, freedom, and a better digital society—ideas that resonated with many . resonated with many Missing the Immediate Impact What AI evangelists often miss in their doomsday warnings or utopian visions is the tangible, present-day value AI is already providing. Unlike blockchain, where practical applications still feel niche or speculative, AI's influence is clear, whether in helping businesses optimize supply chains, allowing doctors to make faster and more accurate diagnoses, or enabling more personalized education through adaptive learning tools. AI is already making people’s lives easier, saving companies millions, and revolutionizing sectors like finance, healthcare, and entertainment. doomsday warnings or utopian visions This distinction is crucial. Blockchain has always been about potential—a potential future decentralized world. But AI, whether you’re a doomer or a hyper-optimist, is affecting industries and lives right now. In a way, blockchain was lucky—its biggest challenge was over-promising a utopian vision that didn’t materialize quickly. AI faces the opposite challenge: it has delivered beyond expectations but is constantly framed as either an existential risk or a panacea, making it harder for people to see and appreciate its current utility. The Path Forward To win over a skeptical public, AI evangelists need to step back from the extremes. AI is neither the apocalypse nor a one-size-fits-all solution to humanity’s problems. It’s a tool—a powerful one—that’s already improving lives and transforming industries in meaningful ways. Instead of framing it as an existential crisis or a futuristic miracle, AI evangelists need to focus on the real, everyday impact AI is having right now. By learning from the missteps of blockchain evangelism—promising too much too soon—and avoiding the current trap of apocalyptic doom, AI evangelists can present a more balanced, grounded perspective. If AI evangelists focus on how the technology is already here and making a difference, they stand a better chance of keeping the public engaged and excited about its potential without scaring them away. In the end, AI has proven itself far more effective and transformative in the short term than blockchain ever did. The next challenge is getting people to see that without all the noise. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com https://docligot.com

Comparing AI vs. Blockchain Hype

In October 2022 I published the most embarrassing article of my career, but 2 years later I am starting to think I might have been right. Strolling through the colossal vendor hall at BlackHat in 2022, every stall made some bold claim about AI. “AI-powered analytics”, “Combining AI with real-time detection” or some other slogan. Whenever you talk to the sales team to ask exactly what their AI did, you either get a succession of AI jargon or a claim about secret research teams in underground vaults in Antarctica. Following on from that, I confidently published an article calling out AI as nothing more than marketing BS… “BlackHat 2022, more A than I”. Fast forward a couple of months and ChatGPT was released. All of a sudden, AI became real and suddenly my brilliant article was a joke, and blatantly wrong. But, after strolling through the BlackHat 2024 vendor hall this year, maybe I wasn’t so far off. It is undeniable that AI is indeed real, and that it has huge potential to change the landscape. And in many ways it already has. Something I didn’t believe 2 years ago. But, as you walk through the expansive halls of Mandalay Bay for BlackHat 2024, you still see the same slogans and I still have yet to find a meaningful implementation of AI in security that I can point to that remotely matches the bold claims. “One thing I notice [in BlackHat] was that people were tired of AI”
Ashish Rajan AI CyberSecurity Podcast Why has AI failed to be effective in security? To preempt your objections, I am aware security vendors are indeed using AI within their products. But so far there are no game-changing results that we can point to, and a lot of this comes down to how they are implementing AI. Following the release of ChatGPT and the subsequent AI models, a lot of vendors rushed to add these new tools to their platforms. The go-to implementation was ‘AI-powered remediation guidelines’ and ‘additional context around alerts’. But here is the reality behind this: that implementation helps mostly the vendor not the user.  This simply means they don’t need to spend as much time on their documentation. Not only that, contextual information is always at least 2 years out of date for most commercial AI models. The reality is that a lot of this implementation was simply a way to make good on vendors' promises behind their ‘AI’ powered products. Another problem that limits AI's ability within security is being able to train AI models on specific security data. Models like ChatGPT are great for showcasing what an AI model can do, but are far from reliable for specific use cases. As Dean De Beer said on the AI + A16z Podcast “ChatGPT is a great hammer, [but] I would certainly not recommend it when you start to productize and perform tasks at scale”. Training a security focused model right now needs extraordinary computational power and even if - and it's a big if - a small company could buy enough GPUs to perform this, the cost would be astronomical! “We have not hit the moment where AI is more than a product side feature”
Caleb Sima AI CyberSecurity Podcast This means only a few big players have the ability to implement AI features that go beyond sending generic data to generic AI trained models. Today AI mostly remains a side-feature in the majority of security tools, almost a gimmick marketing teams can blast into the world. How can you do AI right? For all my skepticism, I will admit there are a few interesting implementations of AI. Purple AI  by SentinelOne, marketed as an AI security Analyst, certainly is impressive to see in action (at least in a demo). So how can you use AI, especially if you don’t have the budget of a Formula 1 team? Empower humans, don’t try and replace them
The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans.


Focus on what AI is good at right now, classification.
The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities.


Make sure you use the right model
The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Will the ‘A’ ever be ‘I’ in security? Intelligence is a bold - and fairly ambiguous - word. Each day AI is finding itself more embedded into our everyday lives and security tooling has huge potential for AI. I have little doubt that effective innovative implementations of AI will absolutely be available in security tooling. But we aren’t there yet. Security vendors have been stretching their AI abilities for years, and when ChatGPT threw them a lifeline they tried to bend the tools to their slogans. The reality is that effective implementation will be different to what we had first imagined. AI will be effective when the slogans match the tool, and not the other way around. In October 2022 I published the most embarrassing article of my career, but 2 years later I am starting to think I might have been right. In October 2022 I published the most embarrassing article of my career, but 2 years later I am starting to think I might have been right. Strolling through the colossal vendor hall at BlackHat in 2022, every stall made some bold claim about AI. “ AI-powered analytics”, “Combining AI with real-time detection” or some other slogan. Whenever you talk to the sales team to ask exactly what their AI did, you either get a succession of AI jargon or a claim about secret research teams in underground vaults in Antarctica. AI-powered analytics”, “Combining AI with real-time detection” Following on from that, I confidently published an article calling out AI as nothing more than marketing BS… “BlackHat 2022, more A than I”. Fast forward a couple of months and ChatGPT was released. All of a sudden, AI became real and suddenly my brilliant article was a joke, and blatantly wrong. But, after strolling through the BlackHat 2024 vendor hall this year, maybe I wasn’t so far off. It is undeniable that AI is indeed real, and that it has huge potential to change the landscape. And in many ways it already has. Something I didn’t believe 2 years ago. But, as you walk through the expansive halls of Mandalay Bay for BlackHat 2024, you still see the same slogans and I still have yet to find a meaningful implementation of AI in security that I can point to that remotely matches the bold claims. “One thing I notice [in BlackHat] was that people were tired of AI” Ashish Rajan AI CyberSecurity Podcast “One thing I notice [in BlackHat] was that people were tired of AI” “One thing I notice [in BlackHat] was that people were tired of AI” Ashish Rajan AI CyberSecurity Podcast Ashish Rajan AI CyberSecurity Podcast AI CyberSecurity Podcast AI CyberSecurity Podcast Why has AI failed to be effective in security? Why has AI failed to be effective in security? To preempt your objections, I am aware security vendors are indeed using AI within their products. But so far there are no game-changing results that we can point to, and a lot of this comes down to how they are implementing AI. Following the release of ChatGPT and the subsequent AI models, a lot of vendors rushed to add these new tools to their platforms. The go-to implementation was ‘AI-powered remediation guidelines’ and ‘additional context around alerts’. But here is the reality behind this: that implementation helps mostly the vendor not the user.  This simply means they don’t need to spend as much time on their documentation. Not only that, contextual information is always at least 2 years out of date for most commercial AI models. The reality is that a lot of this implementation was simply a way to make good on vendors' promises behind their ‘AI’ powered products. Another problem that limits AI's ability within security is being able to train AI models on specific security data. Models like ChatGPT are great for showcasing what an AI model can do, but are far from reliable for specific use cases. As Dean De Beer said on the AI + A16z Podcast “ChatGPT is a great hammer, [but] I would certainly not recommend it when you start to productize and perform tasks at scale” . Training a security focused model right now needs extraordinary computational power and even if - and it's a big if - a small company could buy enough GPUs to perform this, the cost would be astronomical! AI + A16z Podcast AI + A16z Podcast “ChatGPT is a great hammer, [but] I would certainly not recommend it when you start to productize and perform tasks at scale” “We have not hit the moment where AI is more than a product side feature” Caleb Sima AI CyberSecurity Podcast “We have not hit the moment where AI is more than a product side feature” “We have not hit the moment where AI is more than a product side feature” Caleb Sima AI CyberSecurity Podcast AI CyberSecurity Podcast AI CyberSecurity Podcast AI CyberSecurity Podcast This means only a few big players have the ability to implement AI features that go beyond sending generic data to generic AI trained models. Today AI mostly remains a side-feature in the majority of security tools, almost a gimmick marketing teams can blast into the world. How can you do AI right? For all my skepticism, I will admit there are a few interesting implementations of AI. Purple AI by SentinelOne, marketed as an AI security Analyst, certainly is impressive to see in action (at least in a demo). Purple AI Purple AI Purple AI So how can you use AI, especially if you don’t have the budget of a Formula 1 team? Empower humans, don’t try and replace them
The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans. Focus on what AI is good at right now, classification.
The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities. Make sure you use the right model
The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Empower humans, don’t try and replace them The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans. Empower humans, don’t try and replace them The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans. Focus on what AI is good at right now, classification. The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities. Focus on what AI is good at right now, classification. The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities. Make sure you use the right model The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Make sure you use the right model The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Will the ‘A’ ever be ‘I’ in security? Intelligence is a bold - and fairly ambiguous - word. Each day AI is finding itself more embedded into our everyday lives and security tooling has huge potential for AI. I have little doubt that effective innovative implementations of AI will absolutely be available in security tooling. But we aren’t there yet. Security vendors have been stretching their AI abilities for years, and when ChatGPT threw them a lifeline they tried to bend the tools to their slogans. The reality is that effective implementation will be different to what we had first imagined. AI will be effective when the slogans match the tool, and not the other way around.

AI is Failing in Cyber

KI misluk in kuber

Süni intellekt kiberdə uğursuzluğa düçar olur

La IA está fallando en el ámbito cibernético

AI epäonnistuu kyberissä

AIはサイバー空間で失敗している

AI កំពុង​បរាជ័យ​ក្នុង​អ៊ីនធឺណិត

AI žlunga kibernetinėje erdvėje

A IA está falhando no ciberespaço

AI iri Kukundikana muCyber

AI กำลังล้มเหลวในโลกไซเบอร์

AI سائبر میں ناکام ہو رہا ہے۔

人工智能在网络领域失败

Alright, folks, let’s talk about the paradox of AI. I mean, yeah, sure—AI can’t exactly replace us. We’ve all read those headlines: “AI Takes Jobs,” “Chatbots to Replace Customer Service Reps,” and the occasional “AI Now Writes Better Love Letters Than You Ever Did” (not that it’s hard, buddy, you just haven’t been loved enough). But here's a thought: What if AI isn’t just taking jobs? What if it’s taking away our ability to think? Hold on, hold on—don’t throw your smart gadgets out the window just yet. Let’s dig into this, in my style, starting with. AI is Our New Best Friend or Brain Drain? Remember when we used to Google stuff? It’s like, how many calories are in an apple? Then, you’d scroll through forums and pages to find one reliable source. Sometimes it even led us through rabbit holes. Well, not that’s not so long ago. Now? Nah. Just ask ChatGPT and boom! Instant answer. Need a 5-day meal plan that caters to your gluten intolerance? A friendly AI will whip one up before you even finish your morning coffee. It’s magic, right? Like a genie that doesn’t grant three wishes but answers infinite random questions. But let’s get real: Are we getting lazier—or dare I say—dumber? AI use cases are exponentially growing every day, even as I am scripting this blog I bet some dude from his dorm is coming up with weird ideas to put AI to use in our lives. We’ve got AI tools solving our math problems, drafting our emails, and even telling us the most efficient route to Starbucks. But what happens to our brain muscles when they’re no longer lifting the weight of everyday tasks? Here is a study from The University of Sydney that sheds light on what exactly happens in the brain while performing complex tasks. When we stop challenging ourselves, our cognitive skills can atrophy, kind of like a gym membership we’ve neglected since New Year’s. It is AI vs Dopamine, Which Should be Ideally AI and Dopamine Think about it. You’re swiping through TikTok or Instagram, and those cute AI-generated memes or voiceover dogs pop up—hilarious, right? And I am not even getting into the influence of generative AI on the political advertising in 2024. Instant dopamine hit. AI-powered recommendations know exactly what makes us tick. And it’s not just TikTok. Netflix’s AI knows what you wanna watch even before you do. Now, don't get me wrong, I’m as guilty as the next person of binging an entire season of Game of Thrones (old seasons) in one weekend. But it’s a little unnerving, isn't it? The way AI reads us like an open book, feeding our brain with easy pleasures, which—according to neuroscientists—gives us quick dopamine hits, but nothing in the way of genuine satisfaction. There’s this 2022 study from the Cureus Journal of Medical Science that found an increase in screen time, correlates with Declining Physical, Psychological Health, and Sleep Patterns. It also affects the parts of the brain associated with critical thinking and emotional regulation. It’s like feeding your brain a diet of marshmallows instead of a balanced meal. Sure, it feels good, but after a while, you’re missing out on the brainy nutrients—complex problem-solving, analytical thinking, and self-reflection. Story Time Oh, and I’ve got stories, plenty of them. Ever heard about people using ChatGPT to write their wedding vows? Yeah, because nothing says commitment like a chatbot-generated “I promise to always do the dishes.” The best part? Most people won’t even notice. And maybe that’s cute in a sitcom way, but isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? When AI does the heavy lifting, our brains chill on a virtual hammock, sipping Piña Coladas while forgetting how to get back to work. So, the Cost of Convenience is leading to Brainpower on the Decline? Now, let’s talk about the real paradox here: AI makes life easier, no doubt. It’s helping us discover new drugs, predict climate change impacts, and even save the bees (and if we don’t save the bees, we're in big trouble, right?). But the question is, are we paying a hidden price for all this convenience? Take language learning, for example. Why bother memorizing vocabulary when your phone can translate for you? There’s evidence—like a study from Frontiers in Psychology—that suggests when people rely too much on digital tools for memory tasks, their brain's hippocampus (that part responsible for memory) becomes less active. In other words, it's the mental equivalent of skipping leg day. Okay not everyone is Joey when it comes to learning new languages. Or how about basic math skills? No need to know your multiplication tables when your smartphone has a built-in calculator. But get this—Christina Miles, a researcher from Old Dominion University found that students who regularly use calculators during problem-solving exercises tend to have a weaker grasp of numerical concepts compared to those who work things out manually. It’s like, we’ve got all the answers at our fingertips, but are we forgetting how to ask questions? It is just a TOOL, let’s say it Together Again Alright, let’s not get too dystopian here. I mean, AI isn’t some evil overlord plotting to turn us into mindless zombies (though The Terminator might have you think otherwise). It’s just a tool—like a hammer. It can build or it can destroy, depending on how you use it. The problem is, we’re starting to use it like a Swiss Army knife that does everything. And that means we’re relying on it for stuff we used to handle ourselves. The real question is, where do we draw the line? When do we step back and say, “Hey, maybe I should figure this out myself instead of asking the virtual assistant”? It’s like cooking—yeah, a meal kit subscription is great, but you might miss out on the joy (or pain) of learning to sauté without setting off the smoke alarm. So, Is AI Making Us Dumber? Well, let’s break it down. AI makes life easier. No doubt about it. It’s helping scientists, doctors, and even pizza delivery drivers work more efficiently. But when it comes to the rest of us—folks who aren’t busy sequencing genomes or exploring Mars—there’s a risk of cognitive atrophy. We’ve got AI doing the hard thinking while our brains sit back and enjoy the ride. And that, my friends, is the paradox. As the good ol' philosopher Descartes might say (with a bit of a twist): “I think, therefore I am… Oh, wait, I let my chatbot think for me. Am I still?” My Point is… Look, I’m not saying we should all throw our gadgets into a bonfire and go back to reading maps and growing our own food. AI is here to stay, and it’s doing a lot of good. But maybe, just maybe, we should challenge ourselves a little more. Solve that Sudoku without the app. Write a thank-you note without asking ChatGPT for a template. Teach your kid to tie their shoes without relying on YouTube. Let’s keep those brain muscles from turning to mush. After all, it’s not about ditching the tech—it’s about not letting it ditch us. Alright, folks, let’s talk about the paradox of AI. I mean, yeah, sure—AI can’t exactly replace us. We’ve all read those headlines: “AI Takes Jobs,” “Chatbots to Replace Customer Service Reps,” and the occasional “AI Now Writes Better Love Letters Than You Ever Did” (not that it’s hard, buddy, you just haven’t been loved enough). But here's a thought: What if AI isn’t just taking jobs? What if it’s taking away our ability to think? Hold on, hold on—don’t throw your smart gadgets out the window just yet. Let’s dig into this, in my style, starting with. AI is Our New Best Friend or Brain Drain? Remember when we used to Google stuff? It’s like, how many calories are in an apple? Then, you’d scroll through forums and pages to find one reliable source. Sometimes it even led us through rabbit holes. Well, not that’s not so long ago. Now? Nah. Just ask ChatGPT and boom! Instant answer. Need a 5-day meal plan that caters to your gluten intolerance? A friendly AI will whip one up before you even finish your morning coffee. It’s magic, right? Like a genie that doesn’t grant three wishes but answers infinite random questions. But let’s get real: Are we getting lazier—or dare I say—dumber? AI use cases are exponentially growing every day, even as I am scripting this blog I bet some dude from his dorm is coming up with weird ideas to put AI to use in our lives. AI use cases are exponentially growing We’ve got AI tools solving our math problems, drafting our emails, and even telling us the most efficient route to Starbucks. But what happens to our brain muscles when they’re no longer lifting the weight of everyday tasks? Here is a study from The University of Sydney that sheds light on what exactly happens in the brain while performing complex tasks. When we stop challenging ourselves, our cognitive skills can atrophy, kind of like a gym membership we’ve neglected since New Year’s. study from The University of Sydney It is AI vs Dopamine, Which Should be Ideally AI and Dopamine Think about it. You’re swiping through TikTok or Instagram, and those cute AI-generated memes or voiceover dogs pop up—hilarious, right? And I am not even getting into the influence of generative AI on the political advertising in 2024 . influence of generative AI on the political advertising in 2024 Instant dopamine hit. AI-powered recommendations know exactly what makes us tick. And it’s not just TikTok. Netflix’s AI knows what you wanna watch even before you do. Now, don't get me wrong, I’m as guilty as the next person of binging an entire season of Game of Thrones (old seasons) in one weekend. But it’s a little unnerving, isn't it? The way AI reads us like an open book, feeding our brain with easy pleasures, which—according to neuroscientists—gives us quick dopamine hits, but nothing in the way of genuine satisfaction. There’s this 2022 study from the Cureus Journal of Medical Science that found an increase in screen time, correlates with Declining Physical, Psychological Health, and Sleep Patterns . It also affects the parts of the brain associated with critical thinking and emotional regulation. It’s like feeding your brain a diet of marshmallows instead of a balanced meal. Sure, it feels good, but after a while, you’re missing out on the brainy nutrients—complex problem-solving, analytical thinking, and self-reflection. Declining Physical, Psychological Health, and Sleep Patterns Story Time Oh, and I’ve got stories, plenty of them. Ever heard about people using ChatGPT to write their wedding vows ? Yeah, because nothing says commitment like a chatbot-generated “I promise to always do the dishes.” The best part? Most people won’t even notice. And maybe that’s cute in a sitcom way, but isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? people using ChatGPT to write their wedding vows isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? When AI does the heavy lifting, our brains chill on a virtual hammock, sipping Piña Coladas while forgetting how to get back to work. So, the Cost of Convenience is leading to Brainpower on the Decline? Now, let’s talk about the real paradox here: AI makes life easier, no doubt. It’s helping us discover new drugs, predict climate change impacts, and even save the bees (and if we don’t save the bees, we're in big trouble, right?). But the question is, are we paying a hidden price for all this convenience? save the bees are we paying a hidden price for all this convenience? are we paying a hidden price for all this convenience? Take language learning, for example. Why bother memorizing vocabulary when your phone can translate for you? There’s evidence—like a study from Frontiers in Psychology —that suggests when people rely too much on digital tools for memory tasks, their brain's hippocampus (that part responsible for memory) becomes less active. In other words, it's the mental equivalent of skipping leg day. Okay not everyone is Joey when it comes to learning new languages. study from Frontiers in Psychology Or how about basic math skills? No need to know your multiplication tables when your smartphone has a built-in calculator. But get this—Christina Miles, a researcher from Old Dominion University found that students who regularly use calculators during problem-solving exercises tend to have a weaker grasp of numerical concepts compared to those who work things out manually. It’s like, we’ve got all the answers at our fingertips, but are we forgetting how to ask questions? It is just a TOOL, let’s say it Together Again Alright, let’s not get too dystopian here. I mean, AI isn’t some evil overlord plotting to turn us into mindless zombies (though The Terminator might have you think otherwise). It’s just a tool—like a hammer. It can build or it can destroy, depending on how you use it. It’s just a tool—like a hammer. It can build or it can destroy, depending on how you use it. The problem is, we’re starting to use it like a Swiss Army knife that does everything. And that means we’re relying on it for stuff we used to handle ourselves. The real question is, where do we draw the line? When do we step back and say, “Hey, maybe I should figure this out myself instead of asking the virtual assistant”? It’s like cooking—yeah, a meal kit subscription is great, but you might miss out on the joy (or pain) of learning to sauté without setting off the smoke alarm. So, Is AI Making Us Dumber? Well, let’s break it down. AI makes life easier. No doubt about it. It’s helping scientists, doctors, and even pizza delivery drivers work more efficiently. But when it comes to the rest of us—folks who aren’t busy sequencing genomes or exploring Mars—there’s a risk of cognitive atrophy. We’ve got AI doing the hard thinking while our brains sit back and enjoy the ride. And that, my friends, is the paradox. As the good ol' philosopher Descartes might say (with a bit of a twist): “I think, therefore I am… Oh, wait, I let my chatbot think for me. Am I still?” My Point is… Look, I’m not saying we should all throw our gadgets into a bonfire and go back to reading maps and growing our own food. AI is here to stay, and it’s doing a lot of good. But maybe, just maybe, we should challenge ourselves a little more. Solve that Sudoku without the app. Write a thank-you note without asking ChatGPT for a template. Teach your kid to tie their shoes without relying on YouTube. Let’s keep those brain muscles from turning to mush. After all, it’s not about ditching the tech—it’s about not letting it ditch us.

The Paradox of AI: If It Can't Replace us, Is It Making Us Dumber? 

مفارقة الذكاء الاصطناعي: إذا لم يتمكن من استبدالنا، فهل يجعلنا أكثر غباءً؟

AI এর প্যারাডক্স: এটি যদি আমাদের প্রতিস্থাপন করতে না পারে, তবে এটি কি আমাদেরকে নির্বোধ করে তুলছে? 

La paradoja de la IA: si no puede reemplazarnos, ¿nos está haciendo más tontos?

Paradoks AI: Si li pa ka ranplase nou, èske li fè nou bebè? 

AI-ի պարադոքսը. եթե այն չի կարող մեզ փոխարինել, արդյոք դա մեզ հիմա՞ր է դարձնում: 

AI のパラドックス: AI が人間に取って代わることができなければ、人間はより愚かになるのか?

ხელოვნური ინტელექტის პარადოქსი: თუ ის ვერ შეგვანაცვლებს, ის გვაქცევს სულელებს? 

Paradoxe ya AI: Soki Ekoki Ko remplacer biso te, Est-ce que Ezali Kosala Biso Dumber? 

Paradoksi i AI: Nëse nuk mund të na zëvendësojë, a na bën më të trashë? 

Kitendawili cha AI: Ikiwa Haiwezi Kuchukua Nafasi Yetu, Je, Inatufanya Wajinga? 

AI இன் முரண்பாடு: அது நம்மை மாற்ற முடியாவிட்டால், அது நம்மை ஊமையாக்குகிறதா? 

Indida ye-AI: Uma Ingakwazi Ukusithatha Indawo, Ingabe Isenza Sibe Iziduli? 

In recent years, there’s been a lot of hype about artificial intelligence (AI) revolutionizing industries and making certain jobs obsolete. One of the most frequent predictions is that AI will take over call centers, automating customer service entirely and leaving human agents behind. But this narrative is far too simplistic. In fact, next time you hear someone say AI will replace call center workers, it might be time to run—because the reality is much more complicated, and in many cases, AI is far from ready to take over. Yes, AI is making strides in areas like chatbots, virtual assistants, and automated customer support systems. However, this technology still has major limitations, especially when it comes to the human aspects of customer service. And recent examples have shown that even the most advanced AI systems still require human oversight. The Human Touch Still Matters Customer service is fundamentally about human interaction. People need to feel heard, understood, and supported—especially when dealing with frustrating or sensitive issues. AI may be able to process large amounts of data, but it lacks the empathy and emotional intelligence that humans bring to customer service. For many customers, the ability to communicate with a real person who understands their situation is non-negotiable. Recent examples highlight just how crucial human intervention still is in AI-driven systems. For instance, Amazon Fresh promoted its AI-driven shopping experience as cutting-edge, but it turned out that much of the work was actually being done by humans. Specifically, an Indian business process outsourcing (BPO) team was watching customers via CCTV and stepping in when the AI couldn’t handle certain tasks. Far from replacing human workers, AI actually required their presence behind the scenes. AI Struggles with Complexity While AI systems can handle routine inquiries and straightforward tasks, they often fall short when problems become complex or unexpected. Human agents, on the other hand, excel in creative problem-solving and thinking outside the box, something that AI simply isn’t capable of doing. One recent blunder involved a GM chatbot that was fooled by a customer into offering a Chevrolet Tahoe for $1. The AI-powered chatbot was tricked into providing an outrageous discount—a glaring reminder that AI systems can be easily manipulated and are often incapable of detecting subtle deception. In scenarios like these, human agents are essential for catching errors and ensuring that transactions run smoothly. AI, as this incident proves, is still vulnerable to making costly mistakes. Cultural and Contextual Understanding Is Critical Customer service also requires an understanding of cultural nuances and local contexts, which AI struggles to interpret. Human agents, with their real-world experience, are far better equipped to handle culturally sensitive interactions. This is particularly important for global customer support, where understanding local customs and context is key to effective communication. We’ve also seen how AI falls short in language understanding. Del Taco, for example, introduced an AI-powered drive-through system that was supposed to streamline ordering. But it turned out that the AI wasn’t handling everything—human agents from a call center in the Philippines were managing the orders behind the scenes. Once again, the “AI solution” was actually relying heavily on human support. The Limits of AI in Language and Context AI may have made strides in natural language processing, but it’s still far from perfect. Accents, slang, and dialects often trip up AI systems, whereas human agents can adapt and respond flexibly. This limitation is particularly noticeable in regions with diverse languages and speaking styles, where human understanding and adaptability become essential. The Future Is Hybrid, Not Automated Instead of viewing AI as a replacement for human workers, we should be thinking about how AI can augment their capabilities. The most effective customer service systems will likely be hybrid models, where AI handles routine tasks, and humans step in for more complex or sensitive issues. This allows human agents to focus on higher-value interactions while letting AI handle the low-hanging fruit. Far from eliminating jobs, this hybrid model actually creates new opportunities for human workers. Customers Still Prefer Humans Despite all the advancements in AI, customer preferences tell a different story. Most people still want to speak to a real person when handling complex issues, like billing disputes or technical problems. AI simply can’t provide the emotional reassurance that a human agent can offer, and as long as that preference exists, call center workers will continue to be in demand. Ethical and Security Concerns Another key concern is trust. Many customers are reluctant to share sensitive information with an AI system, especially when it involves financial data or personal details. Humans, by contrast, can be held accountable for their actions, which provides an additional layer of security and comfort for customers. AI systems, on the other hand, can be prone to errors or manipulation, as demonstrated by the GM chatbot that was tricked into offering a car for a dollar. Call Center Jobs Are Changing, Not Disappearing The rise of AI doesn’t mean the end of call center jobs—it means those jobs are evolving. Rather than eliminating human agents, AI will likely change their roles, moving them toward more supervisory or quality assurance positions. Human agents will still be essential in overseeing AI systems, stepping in when needed, and providing the kind of nuanced, culturally aware, and emotionally intelligent service that AI can’t match. So, the next time someone tells you AI will replace call center workers, consider the examples of Amazon Fresh, Del Taco, and the GM chatbot incident. These cases make it clear that human workers are still the backbone of customer service, even when AI is involved. In the end, the future of customer service isn’t about replacing humans with machines—it’s about blending AI with human intelligence to provide the best possible experience for customers. AI may be good at handling basic queries, but when it comes to complex problems, cultural understanding, and emotional support, human agents are irreplaceable. Customer service is, and always will be, a people business. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com In recent years, there’s been a lot of hype about artificial intelligence (AI) revolutionizing industries and making certain jobs obsolete. One of the most frequent predictions is that AI will take over call centers, automating customer service entirely and leaving human agents behind. But this narrative is far too simplistic. In fact, next time you hear someone say AI will replace call center workers, it might be time to run—because the reality is much more complicated, and in many cases, AI is far from ready to take over. Yes, AI is making strides in areas like chatbots, virtual assistants, and automated customer support systems. However, this technology still has major limitations, especially when it comes to the human aspects of customer service. And recent examples have shown that even the most advanced AI systems still require human oversight. The Human Touch Still Matters The Human Touch Still Matters Customer service is fundamentally about human interaction. People need to feel heard, understood, and supported—especially when dealing with frustrating or sensitive issues. AI may be able to process large amounts of data, but it lacks the empathy and emotional intelligence that humans bring to customer service. For many customers, the ability to communicate with a real person who understands their situation is non-negotiable. Recent examples highlight just how crucial human intervention still is in AI-driven systems. For instance, Amazon Fresh promoted its AI-driven shopping experience as cutting-edge, but it turned out that much of the work was actually being done by humans . Specifically, an Indian business process outsourcing (BPO) team was watching customers via CCTV and stepping in when the AI couldn’t handle certain tasks. Far from replacing human workers, AI actually required their presence behind the scenes. much of the work was actually being done by humans AI Struggles with Complexity AI Struggles with Complexity While AI systems can handle routine inquiries and straightforward tasks, they often fall short when problems become complex or unexpected. Human agents, on the other hand, excel in creative problem-solving and thinking outside the box, something that AI simply isn’t capable of doing. One recent blunder involved a GM chatbot that was fooled by a customer into offering a Chevrolet Tahoe for $1. The AI-powered chatbot was tricked into providing an outrageous discount—a glaring reminder that AI systems can be easily manipulated and are often incapable of detecting subtle deception. In scenarios like these, human agents are essential for catching errors and ensuring that transactions run smoothly. AI, as this incident proves, is still vulnerable to making costly mistakes. was fooled by a customer Cultural and Contextual Understanding Is Critical Cultural and Contextual Understanding Is Critical Customer service also requires an understanding of cultural nuances and local contexts, which AI struggles to interpret. Human agents, with their real-world experience, are far better equipped to handle culturally sensitive interactions. This is particularly important for global customer support, where understanding local customs and context is key to effective communication. We’ve also seen how AI falls short in language understanding. Del Taco, for example, introduced an AI-powered drive-through system that was supposed to streamline ordering. But it turned out that the AI wasn’t handling everything—human agents from a call center in the Philippines were managing the orders behind the scenes. Once again, the “AI solution” was actually relying heavily on human support. AI-powered drive-through system The Limits of AI in Language and Context The Limits of AI in Language and Context AI may have made strides in natural language processing, but it’s still far from perfect. Accents, slang, and dialects often trip up AI systems, whereas human agents can adapt and respond flexibly. This limitation is particularly noticeable in regions with diverse languages and speaking styles, where human understanding and adaptability become essential. The Future Is Hybrid, Not Automated The Future Is Hybrid, Not Automated Instead of viewing AI as a replacement for human workers, we should be thinking about how AI can augment their capabilities. The most effective customer service systems will likely be hybrid models, where AI handles routine tasks, and humans step in for more complex or sensitive issues. This allows human agents to focus on higher-value interactions while letting AI handle the low-hanging fruit. Far from eliminating jobs, this hybrid model actually creates new opportunities for human workers. Customers Still Prefer Humans Customers Still Prefer Humans Despite all the advancements in AI, customer preferences tell a different story. Most people still want to speak to a real person when handling complex issues, like billing disputes or technical problems. AI simply can’t provide the emotional reassurance that a human agent can offer, and as long as that preference exists, call center workers will continue to be in demand. Ethical and Security Concerns Ethical and Security Concerns Another key concern is trust. Many customers are reluctant to share sensitive information with an AI system, especially when it involves financial data or personal details. Humans, by contrast, can be held accountable for their actions, which provides an additional layer of security and comfort for customers. AI systems, on the other hand, can be prone to errors or manipulation, as demonstrated by the GM chatbot that was tricked into offering a car for a dollar. Call Center Jobs Are Changing, Not Disappearing Call Center Jobs Are Changing, Not Disappearing The rise of AI doesn’t mean the end of call center jobs—it means those jobs are evolving. Rather than eliminating human agents, AI will likely change their roles, moving them toward more supervisory or quality assurance positions. Human agents will still be essential in overseeing AI systems, stepping in when needed, and providing the kind of nuanced, culturally aware, and emotionally intelligent service that AI can’t match. So, the next time someone tells you AI will replace call center workers, consider the examples of Amazon Fresh, Del Taco, and the GM chatbot incident. These cases make it clear that human workers are still the backbone of customer service, even when AI is involved. In the end, the future of customer service isn’t about replacing humans with machines—it’s about blending AI with human intelligence to provide the best possible experience for customers. AI may be good at handling basic queries, but when it comes to complex problems, cultural understanding, and emotional support, human agents are irreplaceable. Customer service is, and always will be, a people business. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com

Next Time You Hear Someone Say AI Will Replace Call Center Agents, Run

Fears about automation taking over jobs are nothing new. As artificial intelligence (AI) has grown, though, another question has arisen. What if robots won’t take your job, but they will interview you for it? It may seem strange, but chatbot-conducted interviews are already a reality. What began as businesses using AI to sort through resumes or walk applicants through onboarding has led to fully automated assessments. As the practice becomes more common, it could have interesting implications for the future of hiring. AI Interviews Today As of mid-2023, just 10% of companies performed AI interviews, but another 34% said they planned on implementing this technology by the end of 2024. Interestingly, 15% of those businesses said AI would handle the entire hiring process. In addition to screening applicants, the chatbot would make the final hiring decision with no human input. Most use cases today aren’t quite so automated. However, interviews themselves are increasingly chatbot-centric, even if humans retain the power to decide who to hire. How lifelike these chatbots are can vary between organizations. One candidate said she was given a series of questions on the screen with 60 seconds to respond, but there was no interaction beyond that. Another said they received a phone call where the chatbot introduced itself as a human would, and the applicant didn’t even realize it was a robot until later in the call. The technology behind these robot interviewers is the same underlying concept behind customer service chatbots. A natural language processing (NLP) model asks questions based on a script and interprets users’ responses to assess the situation. Instead of learning a customer’s needs, though, the bot builds a profile of the candidate to compare it to the ideal worker for a given role. Machine learning models can then predict whether an interviewee will be a good fit. Does AI Do a Good Job at Interviewing People? It’s easy to see why businesses would want to conduct interviews with AI. If these interviews are successful enough, companies could save costs by eventually cutting out human recruiters altogether. But even if businesses never get to that point, recruiters still say the technology saves staff managers time by ensuring only the most qualified candidates make it to the later stages of the application process. AI can also pinpoint strengths and weaknesses that humans may miss after fatigue sets in from going through multiple applications. How effective the actual interview is remains up for debate. On the one hand, today’s NLP models are more than capable of assessing answers to pre-written questions and can complete more screenings in less time. On the other, these meetings may give less insight into the worker’s personality because they don’t always feel natural. As advanced as AI chatbots are, they still often have an “uncanny valley” effect, not feeling quite human. This could cause uneasiness in job applicants that hinders them from performing their best, which could cause businesses to look over some promising personnel. How Do AI Interviewers Impact Workers? Unfortunately, such a feeling of unease is precisely what candidates report experiencing in AI interviews. Some applicants say they feel self-conscious being alone in a video call, which distracts them from the questions they’re supposed to answer. They say the lack of a human connection can heighten their anxiety, in turn affecting their performance. Experiences like these make many people worry that they may not get jobs they’re qualified for. According to one survey, 44% of American adults think AI would do worse than humans at seeing the potential in workers who don’t perfectly fit the description. Ideally, an interview should be an opportunity to showcase such qualifications, but that advantage disappears if chatbots cannot pick up on it. Will AI Become the Norm in Job Interviews? Given these ups and downs, it’s unclear if AI chatbots will become the new normal in job interviews. Robot interviewers will likely become increasingly common over the next few years. However, businesses may scale back on these investments depending on how this initial rollout goes. Most job applicants oppose the use of AI in hiring. Consequently, some companies may reverse this trend to improve their public image. At the same time, others may increase their use of interview chatbots if they save enough time and money in doing so. There are regulatory obstacles to consider, too. The Equal Employment Opportunity Commission (EEOC) is now prioritizing action against AI bias, as AI can heighten human prejudices that exist in its training data. In light of this trend, organizations may need to take a more careful, human-centric approach to interview tech to prevent AI bias from affecting their compliance. How to Prepare for an AI Interview Regardless of how chatbot interviews grow or shrink in the long term, job-seekers have a good chance of encountering them today. Consequently, it helps to learn how to approach AI-driven assessments. The first step is to recognize that businesses may use AI to conduct the interview. Overcoming the initial shock of a robot on the other end can help manage the related anxiety. Applicants should focus on their qualifications and how they would answer common questions, not so much on the human connection side. AI can be a helpful tool for candidates, too. Mock interview programs analyze users’ speech patterns and other communication cues to give feedback on how to improve. Taking advantage of this software could give job-seekers some needed confidence and preparation before speaking to a chatbot when a career is on the line. In general, applicants should pretend they’re speaking to a human so they remain calm and natural. However, it may also help to recognize automation’s quirks. Short answers are better than long ones because there may be a built-in time limit for responses. Candidates can also look for repeated keywords in the job description and incorporate these into their answers to catch the chatbot’s attention. Job Interviews Face an Uncertain Future in the Age of AI The extent to which AI will play a role in hiring and onboarding is unclear. Still, a rising number of companies will incorporate the technology into interviews in some capacity. Job-seekers need to be aware of this trend to ensure they’re ready for an increasingly automated future. Fears about automation taking over jobs are nothing new. As artificial intelligence (AI) has grown, though, another question has arisen. What if robots won’t take your job, but they will interview you for it? Fears about automation taking over jobs are nothing new. As artificial intelligence (AI) has grown, though, another question has arisen. What if robots won’t take your job, but they will interview you for it? It may seem strange, but chatbot-conducted interviews are already a reality. What began as businesses using AI to sort through resumes or walk applicants through onboarding has led to fully automated assessments. As the practice becomes more common, it could have interesting implications for the future of hiring. AI Interviews Today As of mid-2023, just 10% of companies performed AI interviews , but another 34% said they planned on implementing this technology by the end of 2024. Interestingly, 15% of those businesses said AI would handle the entire hiring process. In addition to screening applicants, the chatbot would make the final hiring decision with no human input. 10% of companies performed AI interviews 10% of companies performed AI interviews Most use cases today aren’t quite so automated. However, interviews themselves are increasingly chatbot-centric, even if humans retain the power to decide who to hire. How lifelike these chatbots are can vary between organizations. One candidate said she was given a series of questions on the screen with 60 seconds to respond , but there was no interaction beyond that. Another said they received a phone call where the chatbot introduced itself as a human would, and the applicant didn’t even realize it was a robot until later in the call. with 60 seconds to respond with 60 seconds to respond The technology behind these robot interviewers is the same underlying concept behind customer service chatbots. A natural language processing (NLP) model asks questions based on a script and interprets users’ responses to assess the situation. Instead of learning a customer’s needs, though, the bot builds a profile of the candidate to compare it to the ideal worker for a given role. Machine learning models can then predict whether an interviewee will be a good fit. Does AI Do a Good Job at Interviewing People? It’s easy to see why businesses would want to conduct interviews with AI. If these interviews are successful enough, companies could save costs by eventually cutting out human recruiters altogether. But even if businesses never get to that point, recruiters still say the technology saves staff managers time by ensuring only the most qualified candidates make it to the later stages of the application process. AI can also pinpoint strengths and weaknesses that humans may miss after fatigue sets in from going through multiple applications. saves staff managers time saves staff managers time How effective the actual interview is remains up for debate. On the one hand, today’s NLP models are more than capable of assessing answers to pre-written questions and can complete more screenings in less time. On the other, these meetings may give less insight into the worker’s personality because they don’t always feel natural. As advanced as AI chatbots are, they still often have an “uncanny valley” effect, not feeling quite human. This could cause uneasiness in job applicants that hinders them from performing their best, which could cause businesses to look over some promising personnel. How Do AI Interviewers Impact Workers? Unfortunately, such a feeling of unease is precisely what candidates report experiencing in AI interviews. Some applicants say they feel self-conscious being alone in a video call, which distracts them from the questions they’re supposed to answer. They say the lack of a human connection can heighten their anxiety , in turn affecting their performance. can heighten their anxiety can heighten their anxiety Experiences like these make many people worry that they may not get jobs they’re qualified for. According to one survey, 44% of American adults think AI would do worse than humans at seeing the potential in workers who don’t perfectly fit the description. Ideally, an interview should be an opportunity to showcase such qualifications, but that advantage disappears if chatbots cannot pick up on it. 44% of American adults 44% of American adults Will AI Become the Norm in Job Interviews? Given these ups and downs, it’s unclear if AI chatbots will become the new normal in job interviews. Robot interviewers will likely become increasingly common over the next few years. However, businesses may scale back on these investments depending on how this initial rollout goes. Most job applicants oppose the use of AI in hiring. Consequently, some companies may reverse this trend to improve their public image. At the same time, others may increase their use of interview chatbots if they save enough time and money in doing so. There are regulatory obstacles to consider, too. The Equal Employment Opportunity Commission (EEOC) is now prioritizing action against AI bias , as AI can heighten human prejudices that exist in its training data. In light of this trend, organizations may need to take a more careful, human-centric approach to interview tech to prevent AI bias from affecting their compliance. now prioritizing action against AI bias now prioritizing action against AI bias How to Prepare for an AI Interview Regardless of how chatbot interviews grow or shrink in the long term, job-seekers have a good chance of encountering them today. Consequently, it helps to learn how to approach AI-driven assessments. The first step is to recognize that businesses may use AI to conduct the interview. Overcoming the initial shock of a robot on the other end can help manage the related anxiety. Applicants should focus on their qualifications and how they would answer common questions, not so much on the human connection side. AI can be a helpful tool for candidates, too. Mock interview programs analyze users’ speech patterns and other communication cues to give feedback on how to improve. Taking advantage of this software could give job-seekers some needed confidence and preparation before speaking to a chatbot when a career is on the line. analyze users’ speech patterns analyze users’ speech patterns In general, applicants should pretend they’re speaking to a human so they remain calm and natural. However, it may also help to recognize automation’s quirks. Short answers are better than long ones because there may be a built-in time limit for responses. Candidates can also look for repeated keywords in the job description and incorporate these into their answers to catch the chatbot’s attention. Job Interviews Face an Uncertain Future in the Age of AI The extent to which AI will play a role in hiring and onboarding is unclear. Still, a rising number of companies will incorporate the technology into interviews in some capacity. Job-seekers need to be aware of this trend to ensure they’re ready for an increasingly automated future.

Companies Are Now Using Chatbots as Job Interviewers

Las empresas ahora utilizan chatbots como entrevistadores de trabajo

Ընկերություններն այժմ օգտագործում են Chatbots-ը որպես աշխատանքի հարցազրուցավար

企業は面接官としてチャットボットを活用し始めている

Компаниялар азыр чатботторду жумуш интервьючу катары колдонушат

شرکتونه اوس د کار مرکه کونکو په توګه چیټ بوټونه کاروي

As empresas agora estão usando chatbots como entrevistadores de emprego

Компаније сада користе четботе као анкетаре за посао

ሕጂ ትካላት ቻትቦት ከም ናይ ስራሕ ቃለ መሕትት ይጥቀማ ኣለዋ።

Şirketler Artık İş Görüşmecileri Olarak Chatbot'ları Kullanıyor

Компанії тепер використовують чат-ботів як інтерв’юерів

Iinkampani ngoku zisebenzisa iiChatbots njengabaNxibelelanisi beMisebenzi

公司现在使用聊天机器人作为面试官

So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Doc: I still call myself a data analyst since that was my gateway into tech. But I’ve worn many hats over the years: researcher, software developer, entrepreneur and technologist. My body of work spans human rights, public health, food security, political risk and fighting disinformation and infodemics through the use of computational social science, social listening, remote sensing, artificial intelligence and data engineering. My current focus is on AI literacy and AI safety, especially around Generative AI. I’m also passionate about reaching audiences not typically targeted by tech events and content. So yes, that’s me with the nuns in that photo. The Daughters of St. Paul. And they are passionate about AI. Interesting! What was your latest Hackernoon Top story about? Doc: I wrote about the current fixation on Retrieval Augmented Generation (RAG) - which is basically a hacky way of making Large Language Models (LLMs) smarter. RAG still is an amazing pattern to use, but I noticed the dev-types are abusing RAG without realizing it. Seems RAG is a hammer, and everything is a nail these days. Ironically I think these RAG abusers are the same devs who probably also stigmatize Prompt Engineering - not realizing RAG is not a substitute for proper prompting, but rather prompting is the heart and soul of it. That will be another piece I will write about. Do you usually write on similar topics? If not, what do you usually write about? Doc: I have recently been writing about various topics around Generative AI - but trying to look at the technology from a more pragmatic lens. I also write about the social impact of this tech, good and bad, and try to demystify the lore around it. People who follow my work cite this - the ability to break down complex topics into digestable bits - as the single biggest reason they like hearing me talk. Great! What is your usual writing routine like (if you have one?) Doc: I’m a knowledge hoarder! I’ve set up massive aggregators of content of all sorts about topics - Generative AI being the current obsession - and from this treasure trove of info I let my ideas percolate. I don’t have pressures to write for or against any vendor or technology, and this gives me the flexibility of being more authentic about my message. I also write articles that I would love to read myself - since in the current hype-filled environment around AI, there’s literally a lot of garbage out there (not counting AI-generated crap). I also use AI to help me brainstorm ideas and pitches - it would be ironic if I didn’t. I also nurture threads for multiple articles - since new information always presents itself. People who read me will notice that while I keep to a few key messages over the years, the messaging continues to evolve. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Doc: Allocating time is the biggest hurdle. I consider communication (speeches, articles, podcasts) to be one of my strong suits, but it takes full-on dedication to put yourself out there. Criticisms are also expected - in fact, I live on feedback, good or bad. But I have to admit, after long stretches, this stuff knocks me out, too. Need to recharge to re-ignite the ideas. What is the next thing you hope to achieve in your career? Doc: As of this writing, I’ve been in tech for nearly three decades. Although I could probably still punt an app or two, I humbly admit that younger folks will have the advantage in terms of picking up new tech. So, I feel the best use of my productive years is coalescing all my past experience with tech, combined with some honest views about what’s happening now, into stories that people can relate with and also use for their own journey. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Doc: I love meeting up with like-minded (and occasionally not-like-minded) people over coffee or drinks and swapping ideas. Occasionally it gets me a podcast guest, sometimes a business collaboration. But I live for the stories - there are many coffee chats that have become an article at some point. Do you have a non-tech-related hobby? If yes, what is it? Doc: This question almost feels like an attack, haha. But more than tech, books were my first love. Whenever I can, I love browsing used book stores (there are less and less of them these days), and occasionally find a gem. In a world where the internet and AI can spoil you with fast information, finding time to settle on a good book is an unparalleled luxury, kinda like enjoying a good bottle of wine (which ties to the guilty pleasure above). What can the Hacker Noon community expect to read from you next? Doc: Hype cycles all end the same way - a crash. Then, hopefully, enlightenment. It happened to all major tech trends in the past, and it will happen to Gen AI. As it happens, I will be there to talk about it. What’s your opinion on HackerNoon as a platform for writers? Doc: It’s certainly underrated. But the quality of the information on HN has stayed pretty good over the years, which is a massive achievement in itself. I hope to write and read more on HN. Thanks for taking time to join our “Meet the writer” series. It was a pleasure. Do you have any closing words? Doc: Pro-tip on writing: cliches are the easiest fodder for good writing. No one will get tired of reading pieces that surface the cliches and dismantle them. So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Doc: I still call myself a data analyst since that was my gateway into tech. But I’ve worn many hats over the years: researcher, software developer, entrepreneur and technologist. My body of work spans human rights, public health, food security, political risk and fighting disinformation and infodemics through the use of computational social science, social listening, remote sensing, artificial intelligence and data engineering. My current focus is on AI literacy and AI safety, especially around Generative AI. I’m also passionate about reaching audiences not typically targeted by tech events and content. So yes, that’s me with the nuns in that photo. The Daughters of St. Paul. And they are passionate about AI. Interesting! What was your latest Hackernoon Top story about? Doc: I wrote about the current fixation on Retrieval Augmented Generation (RAG) - which is basically a hacky way of making Large Language Models (LLMs) smarter. RAG still is an amazing pattern to use, but I noticed the dev-types are abusing RAG without realizing it. Seems RAG is a hammer, and everything is a nail these days. Ironically I think these RAG abusers are the same devs who probably also stigmatize Prompt Engineering - not realizing RAG is not a substitute for proper prompting, but rather prompting is the heart and soul of it. That will be another piece I will write about. Do you usually write on similar topics? If not, what do you usually write about? Doc: I have recently been writing about various topics around Generative AI - but trying to look at the technology from a more pragmatic lens. I also write about the social impact of this tech, good and bad, and try to demystify the lore around it. People who follow my work cite this - the ability to break down complex topics into digestable bits - as the single biggest reason they like hearing me talk. Great! What is your usual writing routine like (if you have one?) Doc: I’m a knowledge hoarder! I’ve set up massive aggregators of content of all sorts about topics - Generative AI being the current obsession - and from this treasure trove of info I let my ideas percolate. I don’t have pressures to write for or against any vendor or technology, and this gives me the flexibility of being more authentic about my message. I also write articles that I would love to read myself - since in the current hype-filled environment around AI, there’s literally a lot of garbage out there (not counting AI-generated crap). I also use AI to help me brainstorm ideas and pitches - it would be ironic if I didn’t. I also nurture threads for multiple articles - since new information always presents itself. People who read me will notice that while I keep to a few key messages over the years, the messaging continues to evolve. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Doc: Allocating time is the biggest hurdle. I consider communication (speeches, articles, podcasts) to be one of my strong suits, but it takes full-on dedication to put yourself out there. Criticisms are also expected - in fact, I live on feedback, good or bad. But I have to admit, after long stretches, this stuff knocks me out, too. Need to recharge to re-ignite the ideas. What is the next thing you hope to achieve in your career? Doc: As of this writing, I’ve been in tech for nearly three decades. Although I could probably still punt an app or two, I humbly admit that younger folks will have the advantage in terms of picking up new tech. So, I feel the best use of my productive years is coalescing all my past experience with tech, combined with some honest views about what’s happening now, into stories that people can relate with and also use for their own journey. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Doc: I love meeting up with like-minded (and occasionally not-like-minded) people over coffee or drinks and swapping ideas. Occasionally it gets me a podcast guest, sometimes a business collaboration. But I live for the stories - there are many coffee chats that have become an article at some point. Do you have a non-tech-related hobby? If yes, what is it? Doc: This question almost feels like an attack, haha. But more than tech, books were my first love. Whenever I can, I love browsing used book stores (there are less and less of them these days), and occasionally find a gem. In a world where the internet and AI can spoil you with fast information, finding time to settle on a good book is an unparalleled luxury, kinda like enjoying a good bottle of wine (which ties to the guilty pleasure above). What can the Hacker Noon community expect to read from you next? Doc: Hype cycles all end the same way - a crash. Then, hopefully, enlightenment. It happened to all major tech trends in the past, and it will happen to Gen AI. As it happens, I will be there to talk about it. What’s your opinion on HackerNoon as a platform for writers? Doc: It’s certainly underrated. But the quality of the information on HN has stayed pretty good over the years, which is a massive achievement in itself. I hope to write and read more on HN. Thanks for taking time to join our “ Meet the writer ” series. It was a pleasure. Do you have any closing words? Meet the writer Doc: Pro-tip on writing: cliches are the easiest fodder for good writing. No one will get tired of reading pieces that surface the cliches and dismantle them.

Meet the Writer: HackerNoon's Contributor Dominic Ligot, AI Technologist

AI companions are more than just a passing trend; they could become a $150 billion industry by 2030, according to a recent article by Ark Invest. With the potential to revolutionize how we interact with technology and each other, AI companionship represents the next frontier in digital entertainment. Companion apps like Replika, Gatebox, HeraHaven, and SoulMachines attract millions of users every month, according to Similarweb data. But if you’re considering building an AI companion, where do you start? Specifically, which large language model (LLM) should you choose to power your app? Let me walk you through the best open-source LLMs currently available and why they might be the perfect fit for your project. Choosing the Right LLM for AI Companionship When thinking about AI companionship, one of the first decisions you’ll need to make is what kind of open-source LLM to use. There are several types of LLMs, but they generally fall into three categories: base models, instruction-tuned models, and chat-tuned models. Why Chat-Tuned Models Are Essential Base models are the raw, unrefined versions of LLMs that have been trained on large datasets but lack the fine-tuning required for specific tasks. Instruction-tuned models have been refined to follow human instructions more accurately. However, when it comes to creating AI companions, chat-tuned models are the way to go. These models are specifically optimized for conversational interactions, making them better suited for the nuanced, ongoing dialogues that characterize AI companionship. Model Size: How Big Is Big Enough? Another key consideration is the size of the model. For most conversational chatbots, an 8B parameter model should suffice. These models offer a good balance between performance and resource efficiency, making them ideal for most applications. However, if you’re looking to go above and beyond—perhaps aiming to create a more sophisticated AI—you might want to explore models with up to 70B parameters. The 5 Best Open-Source LLMs for AI Companionship Here’s a list of some of the best open-source LLMs that are particularly well-suited for creating AI companions: 1. Hermes-3 Llama-3.1-8B Hermes-3 Llama-3.1-8B is a powerful model that’s been finely tuned for chat applications. It offers a good balance between capability and efficiency, making it a top choice for AI companionship. 2. Yi-1.5-9B-Chat Yi-1.5-9B-Chat is another excellent option, boasting a slightly larger parameter count, which allows for more complex and nuanced conversations. If your AI companion needs to handle a wide range of topics with ease, this model is worth considering. 3. InternLM2 5-7B Chat InternLM2 5-7B Chat is a lighter model that still packs a punch. Its smaller size makes it ideal for applications where resources are limited but conversational depth is still a priority. 4. Humanish-Roleplay-Llama-3.1-8B Humanish-Roleplay-Llama-3.1-8B is designed for roleplay scenarios, making it perfect for users who want an AI companion that can take on different personas. Whether it’s a friendly chat or a more complex interaction, this model excels at maintaining character. 5. OpenChat-3.5-1210 OpenChat-3.5-1210 is one of the larger models on this list and is optimized for deep, engaging conversations. If your AI companion needs to offer an immersive experience, this model is a strong contender. If you’re interested in seeing how these models stack up against others, you can visit the Open LLM Leaderboard for the most recent comparisons. Taking Your AI Companion to the Next Level with Fine Tuning While these models are impressive right out of the box, there’s always room for improvement. With enough prompting, you should be able to get decent results for most applications. However, if you’re looking for a more advanced approach, consider fine-tuning one of these models to better suit your specific needs. Fine-tuning allows you to tweak the model’s behavior, making it more aligned with your desired user experience. In conclusion, the potential for AI companions is vast, and the right LLM can make all the difference in creating a compelling, engaging experience. Whether you’re building the next million user app or a more niche application, these open-source models provide a strong foundation for success. So, dive in, experiment, and see what you can create with the incredible power of modern LLMs. AI companions are more than just a passing trend; they could become a $150 billion industry by 2030, according to a recent article by Ark Invest . With the potential to revolutionize how we interact with technology and each other, AI companionship represents the next frontier in digital entertainment. according to a recent article by Ark Invest Companion apps like Replika, Gatebox, HeraHaven, and SoulMachines attract millions of users every month, according to Similarweb data. But if you’re considering building an AI companion, where do you start? Specifically, which large language model (LLM) should you choose to power your app? Let me walk you through the best open-source LLMs currently available and why they might be the perfect fit for your project. Choosing the Right LLM for AI Companionship When thinking about AI companionship, one of the first decisions you’ll need to make is what kind of open-source LLM to use. There are several types of LLMs, but they generally fall into three categories: base models, instruction-tuned models, and chat-tuned models. Why Chat-Tuned Models Are Essential Base models are the raw, unrefined versions of LLMs that have been trained on large datasets but lack the fine-tuning required for specific tasks. Instruction-tuned models have been refined to follow human instructions more accurately. However, when it comes to creating AI companions, chat-tuned models are the way to go. These models are specifically optimized for conversational interactions, making them better suited for the nuanced, ongoing dialogues that characterize AI companionship. Model Size: How Big Is Big Enough? Another key consideration is the size of the model. For most conversational chatbots, an 8B parameter model should suffice. These models offer a good balance between performance and resource efficiency, making them ideal for most applications. However, if you’re looking to go above and beyond—perhaps aiming to create a more sophisticated AI—you might want to explore models with up to 70B parameters. The 5 Best Open-Source LLMs for AI Companionship Here’s a list of some of the best open-source LLMs that are particularly well-suited for creating AI companions: 1. Hermes-3 Llama-3.1-8B 1. Hermes-3 Llama-3.1-8B Hermes-3 Llama-3.1-8B is a powerful model that’s been finely tuned for chat applications. It offers a good balance between capability and efficiency, making it a top choice for AI companionship. 2. Yi-1.5-9B-Chat 2. Yi-1.5-9B-Chat Yi-1.5-9B-Chat is another excellent option, boasting a slightly larger parameter count, which allows for more complex and nuanced conversations. If your AI companion needs to handle a wide range of topics with ease, this model is worth considering. 3. InternLM2 5-7B Chat 3. InternLM2 5-7B Chat InternLM2 5-7B Chat is a lighter model that still packs a punch. Its smaller size makes it ideal for applications where resources are limited but conversational depth is still a priority. 4. Humanish-Roleplay-Llama-3.1-8B 4. Humanish-Roleplay-Llama-3.1-8B Humanish-Roleplay-Llama-3.1-8B is designed for roleplay scenarios, making it perfect for users who want an AI companion that can take on different personas. Whether it’s a friendly chat or a more complex interaction, this model excels at maintaining character. 5. OpenChat-3.5-1210 5. OpenChat-3.5-1210 OpenChat-3.5-1210 is one of the larger models on this list and is optimized for deep, engaging conversations. If your AI companion needs to offer an immersive experience, this model is a strong contender. If you’re interested in seeing how these models stack up against others, you can visit the Open LLM Leaderboard for the most recent comparisons. Open LLM Leaderboard Taking Your AI Companion to the Next Level with Fine Tuning While these models are impressive right out of the box, there’s always room for improvement. With enough prompting, you should be able to get decent results for most applications. However, if you’re looking for a more advanced approach, consider fine-tuning one of these models to better suit your specific needs . Fine-tuning allows you to tweak the model’s behavior, making it more aligned with your desired user experience. consider fine-tuning one of these models to better suit your specific needs In conclusion, the potential for AI companions is vast, and the right LLM can make all the difference in creating a compelling, engaging experience. Whether you’re building the next million user app or a more niche application, these open-source models provide a strong foundation for success. So, dive in, experiment, and see what you can create with the incredible power of modern LLMs.

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