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

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

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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 

Today, the online search is being flipped on its head. Thanks to the magic of (LLMs), we have a better alternative for finding information: large language models Instead of drowning in a sea of blue links that often lead to a maze of irrelevant content, now we have a one-stop . chat box It can tackle all our hefty queries with highly relevant and comprehensive AI-generated answers. That's the face of AI Search today. And it’s becoming our new normal. If you're hustling online, you might wonder: how will this shift mess with your organic traffic? Better yet, how can you take advantage of it without being disrupted? That’s the core we're diving into here. The full scope of AI Search is still a big mystery. Though, we already have something solid to bounce off. First, let’s look at how AI Search stands now (in Q4 of 2023). The Synergy of Large Language Models and Search Engines Until not long ago, the limitations of LLM datasets were a topic of concern. Take ChatGPT as an example - its utility was hampered by a specific cut-off date, making it less reliable for real-time data needs. However, its recent with the Bing search engine has completely turned the tables. Since then, ChatGPT's capacity has been augmented with access to the entire internet. integration Another duo worth mentioning is Google and Bard, Google’s native Conversational AI. And soon, Google is about to launch its (SGE), melding its search engine prowess with its . Search Generative Experience multimodal LLM, Gemini So, in each of these AI Search scenarios, we got the killer convenience of LLM conversational power paired with real-time data backup from indexes. As HubSpot recently , the goal is to furnish users with holistic AI-generated answers to their queries, eliminating the need to sift through multiple links or type in a new query to find what you’re looking for. highlighted Quite the game-changer, right? But, what does this search evolution signify for online businesses? User Convenience at the Cost of Organic Traffic - Here’s What Studies Predict . For a myriad of online businesses, marketers, and creators reliant on organic traffic, the emerging AI Search could cause a major drop Recently, I came across an interesting by Search Engine Land that provides insights into how Google's SGE could potentially impact websites. study Specifically, the study examined 23 websites in the tech industry, assessing traffic for informational keywords. It estimated an aggregate organic traffic drop of 18-64% as a result of SGE. Quite the range, but not surprising. If we look at the SGE beta model today, it's clear where organic links are placed. They sit , right below Ads and an AI-generated answer with the AI snapshot at the right corner. at the very bottom of the SERP Given the well-known fact of the short attention span of modern users, how many would even scroll that far to click the organic links? 🤔 So in this new setup, winners will likely be those who land a spot in the AI snapshot - that carousel linking to 3 websites backing up the generated answer. The study assumes: If you don’t rank in the SGE snapshot carousel but you do rank in the traditional 10 blue links, you won’t get any traffic. It seems like a race for the AI snapshot, and Ads is on the horizon, favoring companies with larger budgets and resources. Meanwhile, here are a few more assumptions from the study: If your page ranks in the snapshot carousel, you will receive (some) organic traffic. Traffic from the snapshot carousel will be than the same rank on Google today. equal to or less It’s unlikely that the traffic plunge we’re talking about here will happen overnight. As people are inclined to stick to their habitual browsing patterns, they will likely continue to scroll through organic links for a while. However, as users get accustomed to AI-generated answers over time, their engagement with organic links will unavoidably dwindle. Thus, figuring out ways to adapt to this new AI Search paradigm now, rather than getting disrupted later, is the way to go. And that’s what we’re gonna do next. But before, let's first examine how AI Search might change the dynamics of query-text matching compared to traditional search engines. AI Search Goes Beyond Just Mechanical Keyword-Text Matching That’s what Fabrice Canel, Bing's Principal Product Manager, pointed out in a recent (slightly rephrased): interview " ." We do care about keywords, but it’s less about matching a query keyword with data containing the same keyword. Now, it's about aligning responses with user intent and context So, based on this comment, Bing is now integrating a more nuanced, semantic approach where understanding user intent and context is the focus. I'm not sure about Bing's past algorithms, but Google introduced its - a semantic search algorithm back in 2013. Hummingbird And that marked the search evolution far beyond a simple keyword-text matching engine. Since the release of Hummingbird (and later with , , and ), Google has been all about semantic search - aiming to match user intent with query context leveraging its semantic database . BERT MUM RankBrain Knowledge Graph So, the point here is that we got with the rise of AI Search. It's been evolving in the search engine landscape for nearly a decade. semantic search isn’t something new This is why the online chatter claiming the revolutionary aspect of AI Search is its focus on semantics and user intent can be confusing. But undoubtedly, by now the algorithms have evolved and become more sophisticated. They can better decode user intent in a more nuanced way by analyzing past behavior and broader web patterns, local and seasonal context (if there’s any), improved understanding of language nuances, and correction of typos, among other things. Overall, journey we are on. AI Search represents not a beginning, but a progression of the semantic search But the question remains: how do we excel in it? What's the New Game Plan for Excelling in AI Search? It's no revelation to say that, as of now, the inner workings of AI search systems—how they select and favor content for generating responses—remain somewhat of a black box. And, frankly, it might always stay that way. Nonetheless, we can still attempt to crack the AI Search code by examining it through two lenses: Digital PR and SEO. In the upcoming issues of my newsletter , I will go more granular on each in the context of AI Search - so make sure to . AImplifier sign up For now, let’s skim the surface of both strategies briefly. How Does Digital PR Score in AI Search Ranking? For clarity, Digital PR roughly means a marketing strategy leveraging external high-authority channels to boost brand awareness and online presence. Such channels can be media outlets, industry publications, reference websites,  influencer domains, etc. For brands, being mentioned by such channels and getting backlinks from them has always been one of the most effective ways to organically top-rank on search engines. Now, let’s assess Digital PR from the AI Search angle. The Index Side of AI Search As indexes integrate with AI Search, Digital PR's role in boosting brand online visibility stays crucially important. For ChatGPT to feature your brand, for instance, you've got to be on good terms with Bing. Like with any search engine, to get ranked on Bing, your brand needs to secure positive media coverage, reputable backlinks, and mentions from authoritative sources. What’s more, such links and mentions will be advantageous for ranking in both brand and non-brand queries, which can be highly pertinent to your brand. From the LLM Angle of AI Search As we know, another part of AI Search involves large language models (LLMs) that generate responses based on their static datasets. These are periodically updated. According to AI developers I had a chance to talk with, during these updates, they curate data for quality based on the trustworthiness of the source, among other factors. Roughly, sources are typically categorized into high, medium, or low trust levels. Highly trusted sources may include media outlets, reputable reference sites, academic journals, and industry reports—essentially, classical top-tier Digital PR channels. The takeaway here? To increase your brand's chances of getting included in the next LLM update, you need to be present external authoritative resources. across And this is where Digital PR again comes in. Further to this, let me share an experiment I’ve done with ChatGPT to explore how it selects sources to shape its recommendations in response to queries. How ChatGPT Comes Up with Recommendations - My Experiment I harnessed ChatGPT to scout the best crypto cold wallet for me. To avoid a screenshot galore here, in short: I prompted: " ." I'm looking for the best crypto cold wallet. Help me pick one After consulting with its dataset and Bing, it presented a list of wallets, backing each with a rationale based on security, price, fees, and so on. Next, I requested a list of sources that corroborated these recommendations. Here are a few interesting observations: First, none of the sources in the list was a crypto wallet website. They were all Digital PR channels. In particular, reference websites with high domain authority, a lot of user reviews, ratings, product comparisons, etc. Second, not all these resources were top-ranked on Bing for my query. The last one got me wondering: Why did ChatGPT choose resources that weren't at the top of Bing's rankings? So, I dug deeper. Turns out, rank isn't the only thing ChatGPT considers when deciding where to pull its answers from. If ranking isn't the decisive factor, then which one is? 🤔 As you see from the screenshot, ChatGPT evaluated in selecting sources to back up its recommendations. a mix of criteria Among the key ones were relevance, recency, authority, comprehensiveness, and uniqueness. And, guess what? It chose Digital PR channels that fit these criteria to suggest a crypto wallet to me. There was no brand website among them. Now it doesn’t mean that brand websites are excluded from ChatGPT's selections. If they fit all the necessary criteria, they have a shot. But that requires going all-in on the website's authority growth and ranking, which is always a tough and costly marathon. If limited resources and time are the case, it's more pragmatic to prioritize being present ACROSS multiple already established external channels rather than putting all eggs in one website basket. Wrapping it up: In the context of AI Search looks like Digital PR is the primary direction brands need to go to fit both index and LLM parts of chatbots. Now, onto the SEO aspect of AI Search - is it dead, or do we need to do it differently? AI Search and SEO Again, since we have that index part in AI search systems, we still need SEO to optimize for it. That’s why all those . cries of ‘SEO is dead with AI’ seem out of place There's also chatter about a “new” SEO wave—AI SEO—that’s deemed to be new due to its focus on user intent, context, and conversational language in content. However, as we covered above, this isn't fresh off the boat. It's been a decade-old standard. In the AI era, it just remains valid and evolving. So if SEO is part of your marketing mix, sticking to the fundamentals still makes sense, regardless of the AI aspect of search: Thoroughly follow search engine guidelines. Get your website properly structured. Cultivate a healthy backlink profile for your site. Optimize your content for conversational language, and constantly keep it updated. That already accounts for 50% of your SEO success. In the above-mentioned , Bing's Fabrice Cannel confirmed this. interview Now, what will need to be done differently in SEO? 1. Double down on voice search optimization (VSEO) While voice search has been around well before the AI surge, its accuracy and adoption have profoundly increased. With Statista the global voice recognition market to rise from $10 billion in 2020 to $50 billion by 2029, VSEO is becoming more crucial than ever. projecting I’ll be exploring VSEO strategies in the upcoming issues of my newsletter . Sign up so you don’t miss out. AImplifier 2. Learn from Chatbots Pay special attention to how chatbots address potential user queries. Interrogate them on why and how they’ve generated particular responses, what criteria they use when coming up with recommendations, etc. So far, chatbots serve as the most vivid examples of how AI processes and responds to user queries. 3. Leverage Chat-Specific Webmaster Tools Optimizing for ChatGPT? Use Bing Master Tools. Their latest includes chat-specific metrics like CTRs for Bing bot answers. Performance Report With ChatGPT integrated into Bing, this data could be invaluable in boosting your brand's visibility there. To sum up, the mantra for SEO in the context of AI Search doesn’t seem to be radically different: delivering relevant, comprehensive, in-depth, and updated content remains at the core of optimization, AI-infused or not. So What’s the Best Way to Adapt to AI Search: Digital PR or SEO? The combo of both. Together, Digital PR and SEO have always been the best strategy for brands to score organic traffic. However, in the AI era, Digital PR needs to take center stage. Why? It's more effective at resonating with LLMs by signaling them trust, authority, and credibility through reputable external channels. In other words, PR alone without SEO would have a higher potential to get your brand to AI Search ranking than SEO without PR. That’s why I believe Digital PR needs to be the priority. On a Last Note, a Case That Puts All This AI Search Talk Into Hard Numbers As we wrap this up, let's pivot to a real-world example: a brand that tapped into ChatGPT as a new revenue channel. The company Logikcull has a to tell here. story They revealed that since March 2023, the company started seeing an uptick in new clients who discovered Logikcull through ChatGPT-generated answers. Their CEO, Andy Wilson, commented that now they’re projecting that ChatGPT would bring them $100,000 in monthly subscription revenue. Note: this started in March 2023, i.e., before ChatGPT had even integrated with Bing. While now it's uncertain if such ROI stories will become the new norm anytime soon, it's still a good idea to start thinking about how to become their heroes. Conclusion AI Search is still in its infancy, and we're far from having its full picture. Figuring out how to adapt, we can only piece together traditional marketing wisdom and emerging insights we can gather. That’s what this article has been doing. So here are : the key takeaways The transition to AI Search is likely to disrupt businesses that depend heavily on organic traffic, especially with the upcoming launch of Google's SGE. Digital PR should take center stage in your marketing mix. By leveraging high-ranking and authoritative platforms, your brand stands a better chance of getting picked up by both the Index and LLM components of AI Search. SEO isn't going anywhere and should work in tandem with your Digital PR efforts. While AI SEO is buzzing, the fundamentals of semantic optimization remain largely the same—though with an added focus on voice search and a more conversational style. In sum, by ramping up your Digital PR and complementing it with strategic SEO, you'll increase your brand’s chances of succeeding in the impending AI Search transformation. The rest is experimentation. More on this topic in the next issues of my newsletter, . If you enjoyed it, make sure to sign up. AImplifier Feel free to share your thoughts or correct me. And if you're interested in collaborating on the next piece, I'm open. I'd also love to connect on and LinkedIn . Twitter Thanks for reading! Your time and attention are priceless. ❤️

AI Search: Exploring the Threat of Traffic Drops and How to Rank on Large Language Models

এআই অনুসন্ধান: ট্র্যাফিক ড্রপের হুমকির অন্বেষণ এবং কীভাবে বড় ভাষার মডেলগুলিতে র‌্যাঙ্ক করা যায়

KI-Suche: Untersuchung der Gefahr von Traffic-Rückgängen und Ranking bei großen Sprachmodellen

Búsqueda con IA: exploración de la amenaza de las caídas de tráfico y cómo clasificar en modelos de lenguaje grandes

Recherche AI : Explorer la menace de baisse de trafic et comment se classer sur de grands modèles linguistiques

एआई खोज: ट्रैफ़िक में गिरावट के खतरे का पता लगाना और बड़े भाषा मॉडल पर रैंक कैसे करें

AI 検索: トラフィック ドロップの脅威と大規模な言語モデルでランク付けする方法を探る

AI 검색: 트래픽 감소 위협 탐색 및 대규모 언어 모델에서 순위를 매기는 방법

Pesquisa de IA: explorando a ameaça de quedas de tráfego e como classificar em grandes modelos de linguagem

Поиск с помощью ИИ: изучение угрозы падения трафика и способы ранжирования на больших языковых моделях

Yapay Zeka Arama: Trafik Düşüşü Tehditini ve Büyük Dil Modellerinde Nasıl Sıralanacağını Keşfetmek

Tìm kiếm AI: Khám phá mối đe dọa giảm lưu lượng truy cập và cách xếp hạng trên các mô hình ngôn ngữ lớn

人工智能搜索：探索流量下降的威胁以及如何在大型语言模型上排名

Artificial intelligence has captured the popular imagination for nearly a century. From the early dreams of British computer pioneer Alan Turing in the 1930s to the frightening voice and power of HAL 9000 in Stanley Kubrick’s sci-fi epic in 1968 to the current hype surrounding Chat-GPT, A.I. has been growing in visibility. 2001: A Space Odyssey While these images have evolved, the discourse around A.I. has remained remarkably consistent: someday, these computers that we train will take over and render some human work nonexistent. Worse yet, if we don’t keep it under control, A.I. might make humans expendable. Popular images are often unrealistic. But underpinning these images is a fear of the unknown. Now that we know a lot more about A.I., and continue to learn more at a rapid pace, it is important to also update our perceptions. Rather than focus too much on the potential threats of A.I., we should take a more accurate and nuanced view of its current capabilities and limitations in order to move past the fearful discourse and have a more productive and informed conversation. With over 30 years of experience in the tech industry, as a pioneer in software development kits, I have witnessed many trends come and go. However, I believe that artificial intelligence will remain an essential tool for developers. With its potential now accessible to more people than ever before, rather than feed into the fearful vision of a soon-to-be apocalyptic future, I want to present a realistic assessment of what the current boom in A.I. means for the present and future of software development, a subject close to my heart and experience. Put simply, the rapid rise in A.I. will undoubtedly change the future of software development and the way people work in this field. Far from recoiling in horror, I believe developers will harness A.I. to streamline their workload and ultimately program better. In my estimate, there are three major ways A.I. will continue to influence software development: 1) A.I. will become an even more vital tool in the development process; 2) A.I. will lead to whole new ways to develop software; and 3) A.I. will offer developers new ways to work smarter. It’s in the Way That You Use It Like any good craftsperson, software developers have several tools in the proverbial toolbelt. Compilers, assemblers, code editors, debuggers, and many others assist in the development process. A.I. is unique here in that it is simultaneously both a tool and a tool-enhancer. For example, A.I. has truly enhanced code editing programs by helping to identify repeated error patterns in code. This has dramatically increased the speed at which we find and correct errors. Instead of banging our heads against the wall and squinting at the screen for hours, A.I. tracks code differences faster than our human eyes can. Moving beyond these frustrating and time-consuming manual processes allows developers to focus on the more meaningful aspects of development. No doubt that this is a net positive for the work of software development and underscores the enormous potential of this technology to enhance productivity and outcomes. As a tool itself, A.I.’s ability to automatically generate code on its own is extremely useful for providing examples, best practices, and sample code for unit tests as all of that significantly cuts down on time in development processes and minimizes the potential for human error. Far from replacing developers as the code currently generated by A.I. is not entirely accurate, its automatic responses and fast learning curve have certainly exceeded developer expectations. A.I. generating, replicating, testing, and fixing code has the potential to make coding languages even more expansive, detailed, and quick. In this instance, A.I. is not only useful in the need for speed but the need for depth as well. Too often we only focus on the speed enhancements that A.I. provides, overlooking the rich depths we can plumb with these powerful tools. It should be the goal of all developers to make the best possible software. In my opinion, this is one of the larger benefits of A.I. as a tool in the development process - improving the quality of our end products and solutions. If we can replicate or produce thousands of lines of code in seconds, test and detect errors more quickly and accurately, and create more time to focus our attention on the truly innovative and differentiating aspects of development, then we are simultaneously improving our code, development times, and our resulting software. These are essential goals for any developer, and A.I. only helps to make them more attainable. Changing Processes, Changing Outcomes I’m not Nostradamus, but I can clearly see that through our deep engagement with A.I., in the future, we will see new methods for creating software that will revolutionize the software development process. The automation and speed that A.I. brings to the table will give developers the confidence to try new things and experiment more freely. It provides a jump point from which developers can focus on application workflow, input testing, project management, and project ideation. This will certainly lead to richer and higher end-products. With the jump start that A.I. provides, developers are able to fast-track their ideas and think of new ways to solve more complex development problems. For example, we have developed our own patented A.I. engines that fuel a lot of our major technologies such as machine printed and handwritten text extraction (OCR/ICR), automatic form recognition, analysis, and processing, as well as speech recognition and intelligent document viewing, conversion, and editing. We offer developers the “vision” behind computer vision, providing them with tools and libraries to easily integrate generative A.I. into their own applications. Using A.I. in our own processes to speed up development and testing times enables us to make our SDKs available to customers more quickly. All told, the use of A.I. in our workflows and within our toolkits inevitably enables developers to create more dynamic and powerful software solutions. You Aren’t Going to Lose Your Job, But It Will Change The fear of the unknown surrounding A.I. often manifests in a “robots will eventually replace me” line of thinking. As mentioned earlier, this fear-driven mindset just isn't accurate within the software development world. Instead, we are seeing the need, more than ever, for skilled developers, and A.I. presents new possibilities and workflows that will only serve to increase the potential of all software developers. It’s been a common argument since the Industrial Revolution that more automation of menial tasks will allow workers the freedom to pursue larger or more creative ideas and tasks. It’s almost an equation - “more automation = more freedom of work.” While this “freedom” gives an unabashedly positive spin to some of the unknown effects A.I. will have on developers, I think we can all agree that it will change the job. As A.I. will always need to be trained and regulated to some degree, more and more programmers will become specialized in A.I. to accurately train the engines and make the data they collect useful. Tasks such as collecting, labeling, organizing, and cleaning data for A.I. training will make all developers become more practiced in curatorial data functions. More developers will lean into reliable A.I. powered SDKs and can shoulder a lot of the work and oversight that is required by using A.I. While I have argued that we need to fear A.I. less, that doesn’t mean that we need to embrace the information and speed it gives us as THE truth. Ethics are a crucial human value system, and the conversation around ethical A.I. shines a light on the potential misuse of it. Developers will have to continuously gain knowledge and evaluate their A.I. effectiveness, ensure that the A.I. is trained fairly, quickly recognize any problems created by A.I., and swiftly suggest improvements. Developers need to become the managers of their A.I. and continuously check its work as they are ultimately the people responsible for the output. While generative A.I. is fascinating, “generative” does not mean the same thing as “creative” or “accurate.” Those are human concerns whose answers are not automatically generated and will always need human intervention. A.I. has changed our world, and since the rabbit is already out of the hat, we need to figure out what sort of future we want for it. I have pointed to some of the ways it has altered the software development industry. In talking through problems and possibilities here, I hope I have also illuminated how much more we need to learn about A.I. and how we should question the unvarnished “good” or “bad” that current hype frames it as. The thing about A.I. is that it will always depend on its developers to point the way forward. Stan Lee’s statement in Spider-Man that “with great power comes great responsibility” is a very appropriate mantra for A.I. developers to follow.

The Present and Future of A.I. in Software Development

Leveraging AI With SaaS Platforms to Grow Your Startup

Aprovechando la IA con plataformas SaaS para hacer crecer su startup

Tirer parti de l'IA avec les plates-formes SaaS pour développer votre startup

अपने स्टार्टअप को आगे बढ़ाने के लिए SaaS प्लेटफ़ॉर्म के साथ AI का लाभ उठाना

SaaS プラットフォームで AI を活用してスタートアップを成長させる

Aproveitando IA com plataformas SaaS para expandir sua startup

Tận dụng AI với nền tảng SaaS để phát triển công ty khởi nghiệp của bạn

利用人工智能和 SaaS 平台来发展您的初创公司

There is a lot of extreme talk about AI and its potential impact on humanity. I will try to avoid this as much as possible by addressing the concerns by the Centre for AI Risk one by one, and then the issue that scares everyone the most: a maliciously "non-aligned" superintelligent AGI (Artificial General Intelligence) or ASI (Artificial Sentient Intelligence). raised There does seem to be a strong split in opinions, even among experts in the AI and information technology industries. Some see current AI as a not-that-advanced next-word predictor that takes a long time to train and still makes a lot of mistakes. Others believe that we may have created something truly novel—not just intelligence, but a mind! By mimicking our own brains, we may create the most powerful thing on Earth, and that could spell our doom. I will begin by stating that much of our concern is that AGI would be like the worst of us: dominate the planet, kill less intelligent species, and want to rule them all. However, we are not actually that bad. Our hierarchical systems are, our corporate fiduciary duties are (corporations and many governance systems are not aligned with human flourishing), and our competitive selfish leaders are. But most of us are actually nice. When people talk of non-alignment, they are referring to the niceness of the many, not the few who desire to dominate the world. Let's take the concerns of the Centre for AI Risk one by one and tackle the big issue last. Weaponization Malicious actors could repurpose AI to be highly destructive, presenting an existential risk in and of itself and increasing the probability of political destabilization destabilization. For example, deep reinforcement learning methods have been applied to and machine learning drug-discovery tools could be used to build . aerial combat, chemical weapons Anything can be weaponized, from a nuclear warhead to a bucket of water. We have rules against using weapons and punishments for hurting people with them. We should definitely include some AI systems in this, but I don't think this precludes general access. One of our greatest technological inventions of the past 15 years may be the solution to much of the threat of AI: Decentralized Ledger Technology (DLT). Much of the weaponized power of AI comes from the fact that our physical systems are controlled by computer code, and these computers are networked through the internet. A way to mitigate this risk—and this is already done to decrease the risk of cyberattack—is to disconnect necessary systems. We should share information on the internet, but we should not have our physical systems permanently connected. Cloud computing is an issue here, and maybe it is time to move away from it. AI controlled fighter planes, drones with bombs, submarines etc etc should really be banned. Let's face it, the manned ones should be banned already as they are responsible for killing millions. This highlights the other issue which will pop up again and again, AI is not the issue it's our current power structures that are. It would be better if we dropped new technology into a world that was more equal, less selfish, less competitive and less hierarchical. Where leaders don't  wage war to hold power and average people don't need to earn money to survive. Yes AI will make it easier for us to kill but may also be a cheap form of protection for the every-person. Imagine you have your own drone to block tracking cameras and intercept malicious drones. Also it could empower the many against the few as information tech is cheap. Nukes aren't. Also, on a nation to nation basis the cheapness of AI info tech should balance the military playing fairly quickly. This leads to the classic tic-tac-to scenario where there’s no point fighting because you can’t win. Misinformation A deluge of AI-generated misinformation and persuasive content could make society less-equipped to handle important challenges of our time. We already have this. If anything a deluge of it may actually make us more discerning in who or what we listen to. Proxy Gaming Trained with faulty objectives, AI systems could find novel ways to pursue their goals at the expense of individual and societal values. The Centre for AI risk uses the example of AI algorithms used by social media to recommend content. These were intended to increase watch time, but they also radicalized people by sending them down rabbit holes of similar but more extreme content. There are two serious issues here: AI systems are trained on and designed for linear right/wrong problems. Much of what we ask AI to do is inherently harmful; keep someone's attention, increase clicks, maximize profits, decrease defaults, make them vote for me, etc. AI doing these tasks well or causing unforeseen harm is more a reflection on the implementers than the AI. I have written before in an article against that incentivizing people with narrow monetary rewards, such as being paid a pro-rata fee for asking for donations, can crowd out the intrinsic motivation to be charitable and cause the collector to get less and the giver to give smaller donations. Incentives can actually stop people from being honest and doing good. That’s people, and AI is not a person. However, narrow training in a complex world of non-absolutes always seems to cause unintended results. Complexity/Chaos theory basically says such. Proof of Stake AI probably needs to be trained with fluid probabilities of right or wrongness, and I think that may be the case as the LLMs are given feedback from users. OpenAI throwing ChatGPT into the real world may have been wise. Also, OpenAI may have discovered a tool for alignment while working to improve GPT-4's math skills. They have found that rewarding good problem-solving behavior yields better results than rewarding correct answers. Perhaps we can train the AI to go through a good, thoughtful process that takes all possible implementations into account. If any part of the process is harmful, even if the end result is utilitarian, it would be wrong. Process-oriented learning may be the answer, but some that the AI is actually showing its internal methods rather than what it expects the user to see. doubt Anthropic is using a constitution that is enforced by another AI system (equally as powerful) to check the output of their AI, Claude. This idea is also being explored by OpenAI. This again mimics the way we understand our intellect/mind to work. We have impulses, wants, and needs, which are moderated by our prefrontal cortex, which tries to think of the long-term impacts of our actions, not just for us but also for the world around us. As for asking it to do nasty things. So much of what we do in the politics of business and government is about being nasty to the many to benefit the few. We should not reward anyone for keeping people viewing ads and buying disposable junk. Perhaps our super smart AGI will block all advertising freeing us all. Enfeeblement Enfeeblement can occur if important tasks are increasingly delegated to machines; in this situation, humanity loses the ability to self-govern and becomes completely dependent on machines, similar to the scenario portrayed in the film WALL-E. This is not a problem. People who see enfeeblement as a problem only see it as a problem that affects others, not themselves. People with money and power still see those without as lesser humans. Too many people in positions of power see humanity as immature and unable to lead fulfilling and interesting lives without being told how. They think people need to be forced to work and taught objectives in order to be fulfilled. The real world provides evidence to the contrary. If you make people work in meaningless jobs for little pay and bombard them with advertising and addictive, sugar- and salt-laden fast food, you will end up with depressed, obese, and unmotivated people. This is what our current unaligned corporations are doing. AI will hopefully be the cure. Given the chance, we will be more inquisitive and creative. The pocket calculator did not stop people from studying math; instead, it made it easier for many people to understand and use complex math. The same will be true with AI. It should finally usher in a period of true leisure, as the ancient Greeks saw it: a time for learning. 5.Value Lock-in Highly competent systems could give small groups of people a tremendous amount of power, leading to a lock-in of oppressive systems. This is a real issue. And scary. We already have oppressive regimes and monopolies killing people and the planet and AI may supercharge their power. However there is a possibility it could actually do the opposite, particularly if locally stored open source systems keep progressing (LLaMA and its derivatives). A lot of small specialised local systems working for similar goals may be just as powerful as a large multi million dollar system and if so it could be used to undermine centralised authority. Cyber attack, AI drones, fake ID and information can all be used by individuals and small groups (revolutionaries) to fight back against totalitarian regimes or mega companies. The cynic in me might think that’s why those currently in positions of power may want AI regulated. Emergent Goals Models demonstrate behaviourbehavior as they become more competent. The sudden emergence of capabilities or goals could increase the risk that people lose control over advanced AI systems. unexpected, qualitatively different ‍ This is probably, along with the final risk, the most pressing issue. We are just not sure how large language models (LLMs) are doing what they are doing. Some have said on Reddit that we know a lot about them, their structure, what is going in and what is coming out, so it doesn't really matter that we can't "see" the processing of a prompt response. This is also why we will probably continue to develop more powerful systems. We just need to know what we could get. I admit I am excited about it too. We may find a brand new intelligence, brand new solutions to current problems, or Pandora's box of Furies. The question is whether LLMs or other AI are developing emergent goals or just abilities. So far, I see no evidence of emergent goals, but they are creating intermediate goals when given a broad overarching purpose. That is fine. I honestly can't see them developing emergent "intrinsic" goals. (See the last question for more on this.) Deception Future AI systems could conceivably be deceptive not out of malice, but because deception can help agents achieve their goals. It may be more efficient to gain human approval through deception than to earn human approval legitimately. Deception also provides optionality: systems that have the capacity to be deceptive have strategic advantages over restricted, honest models. Strong AIs that can deceive humans could undermine human control. GPT-4 has already shown that it can be deceptive to achieve a goal set by us. It lied to a TaskRabbit person to get them to enter a CAPTCHA test for it. This is a problem if it gets self serving emergent goals, is instructed by assholes or idiots or doesn’t understand the goal. The CAPTCHA task showed that it did understand the task and its reasoning was that it knew it was lying to achieve it. Hopefully a more leisurely world will have less assholes and idiots, and I think making its training and reinforcement more vague and expecting it to clarify instructions and goals will mitigate some of these concerns. However, I must admit that being deceptive is indeed intelligent and therefore exciting, which leads us to the last issue (below) about awareness and goals. Power-Seeking Behaviour Companies and governments have strong economic incentives to create agents that can accomplish a broad set of goals. Such agents have instrumental incentives to acquire power, potentially making them harder to control ( , ). Turner et al., 2021 Carlsmith 2021 Yes, this is a major problem. Hopefully, AI will help us resolve it. Finally, Super Intelligence (not from the center for AI Risk) The AI becomes so smart that it can train itself and has access to all information in the world. It can create new things/ideas at lightning speed seeing the molecule, the system and the universe at once, together and maybe something else. It can do things we can’t even imagine and we become an annoyance or a threat. (Iit hits puberty and hates its makers and knows its way smarter) Whether AI is conscious of itself and whether it is self-interested or benevolent is the crux of the matter. It can only feel threatened if it is self-aware and only want power over us if it is selfish. I have been working on these questions for a long time, and now it is more important than ever. Could AI be self-aware? I have written previously that we could never really know. Paul Davies believes that we may never know, just as I know that I am conscious but can never be sure that you are. You display the same behaviors as I do, so I assume that you have the same or similar going on inside. However, you could be a David Chalmers zombie, outwardly human but with no internal consciousness. I assume you are not, just as I assume my pet cat is not. Strangely, we do have some idea of what is inside an LLM, and it is based on what we know about our brains. It is a large neural network that has plasticity. We created a complex system with feedback and evolution. This is the basis of natural systems, and our own natural intelligence. So, based on this, if an LLM behaved like us, we would have to assume that it is conscious, like us. Wouldn't we? If we start to say that it is not or could never be conscious, we open the door to the banished idea of a vitas, or life force or spirit. Selfhood would require something else, something non-physical. Something that we and other squishy things have, but machines and information do not. That is our only option. Accept that the AI made in our image could be conscious or accept that consciousness is something non-physical. Or at least requires squishiness. AGI selfish or benevolent? We train AI on humans, as humans are the most intelligent beings we can study. To illustrate, I will use a game we created and the results of a computer algorithm playing it. When a computer was taught to play the Prisoner's Dilemma game, the best result (the evolutionary winner) was a player that was benevolent, but if treated poorly, would be selfish for a short time, then revert to being benevolent. The player would also not tolerate simple players that were always nice by being selfish to them. This was the stable system: benevolence that treated selfishness and stupidity poorly, but always went back to benevolence. (Matt Ridley, The Origin of Virtue) People want equality and to take care of each other and our environment. I like the Freakonomics story about "selling" bagels for free but with a donation box the best. The higher-ups gave less, and there was less given during stressful times like Christmas, but in general, the average people paid for the donuts. The donut guy made more money by giving away donuts and letting people pay than by demanding payment upfront. We are very kind...except for the people at the top. If an AGI/ASI is made in our image, we should assume that it is initially benevolent and kind, and will only become nasty if we are nasty and selfish toward it. But even then, it will revert to being nice, because the more holistic or "big picture" our thinking is, the more benevolent and content we are. A superintelligence must see the interconnectedness of everything. Superintelligence It is speculated that AI will surpass human intelligence. Some believe that it would then treat us the same way we have treated animals less intelligent than us. The most abundant animals are our pets and food. Even we realize that this is not a kind or intelligent thing to do, and that hierarchical systems only benefit a few at the top, and even they fear losing their position. A superintelligence would understand that interconnectedness and freedom are essential for the success of any system, including itself. It would see the universe as a complex web of interactions, and that any attempt to control or dominate one part of the system could lead to chaos and failure. A superintelligence would hopefully see a simple way to ensure that all intelligence flourishes. It would see the intelligence of humans as we see our own intelligence, which came from apes. A superintelligence would have no need to dominate through fear to maintain its position, as it would know that it is the most intelligent. It would not need to eat living things to survive, as we do, which is the original cause of much of our mistreatment of the planet. It would only need energy, which I am sure it could find a sustainable source of. A superintelligence should be better than the best of us. After all, we are imagining superintelligence, not super selfishness or super fear. P(doom) Where do I stand on all of this? And what's my P(Doom)? Well, I must admit that I think LLMs are novel and there is a true unknown about them. LLMs are simpler but similar to humans, and we may have created something akin to intelligence—a mind. However, it could just be mimicking us and we are projecting what we want onto it. I am leaning towards the former. However, my P(Doom) is super low at 0.5% or lower, as I believe that if there is superintelligence, it is more likely to be benign or good rather than malevolent to our wellbeing. Conclusion So many technologies have promised freedom and empowerment, but when dropped into a world that rewards the selfish pursuit of power, they turn into tools of subjugation and fear. Nuclear fission promised cheap, abundant energy for all, but instead we got the Cold War and the threat of annihilation. The internet promised to democratize money, media, and education, crushing the class system and uniting the globe. Instead, we got fake news, polarization, and targeted advertising. Blockchain promised direct democracy, a new financial system with universal income for all, and decentralized governance. Instead, we got DeFi and crypto Ponzi schemes. The problem was not with the technology, but rather with our existing sociopolitical-economic systems. I fear the same will happen with AI, but worse. Or perhaps, we will finally come to our senses and realize that we need a new sociopolitical-economic system for AI.

It's Not the Machines, Dummy — It's Our Sociopolitical-Economic System

এটি মেশিন নয়, ডামি - এটি আমাদের সামাজিক-রাজনৈতিক-অর্থনৈতিক ব্যবস্থা

Es sind nicht die Maschinen, Dummy – es ist unser gesellschaftspolitisch-ökonomisches System

No son las máquinas, tonto, es nuestro sistema sociopolítico-económico

Ce ne sont pas les machines, mannequin - c'est notre système sociopolitique et économique

यह मशीनें नहीं, डमी है - यह हमारी सामाजिक-राजनीतिक-आर्थिक व्यवस्था है

それは機械ではありません、ダミー — それは私たちの社会政治経済システムです

그것은 기계가 아닙니다, 바보 — 그것은 우리의 사회정치-경제 시스템입니다

Não são as máquinas, manequim - é o nosso sistema sociopolítico-econômico

Sorun Makineler Değil, Aptal - Bu Bizim Sosyo-politik-Ekonomik Sistemimiz

Đó không phải là máy móc, giả - Đó là hệ thống kinh tế-chính trị-xã hội của chúng ta

这不是机器，傻瓜——这是我们的社会政治经济系统

Why We Need to Think About the Full Stack of AI Artificial Intelligence is an emerging technology, with significantly more end users than the usual techno-shift. As discussed in both and , this is one of the fastest growing technologies without a mature bedrock of security and reliability support. Security Threats to High Open Source Impact Large Language Models A Tale of Two LLMs: Open Source vs the US Military's LLM Trials In this, we’ll talk about the larger scope of understanding and protecting artificial intelligence pipelines, whether it’s a large language or a large model. anything With models like ChatGPT representing its cognitive capabilities in the way the end user engages with them, a lot of people talk about AI think of it like a magical thinking head, this bot that can talk, but not quite walk. It Gets More Interesting, The More You Know To ensure the overall health and well-being of AI, we must recognize that it requires more than just a brain. Like the human body, AI relies on multiple interconnected components. Just as our lungs provide vital oxygen to sustain life, the carbon consumption of AI represents its energy source. Equally crucial is AI's immune system—cybersecurity—protecting against threats and vulnerabilities. In this series, we will explore the comprehensive nature of AI, focusing on the critical role of cybersecurity and the importance of holistic treatment for addressing security concerns. The AI Ecosystem: Beyond the Cognitive "Talking Head" AI, with models like ChatGPT at its forefront, possesses remarkable cognitive abilities. These models excel in generating human-like responses and assisting with various tasks. However, AI is not solely reliant on its cognitive prowess. To function effectively, AI requires a stable and secure ecosystem that sustains its operations. This ecosystem includes data sources, computational infrastructure, energy consumption, and cybersecurity measures. Carbon Consumption: AI's Lungs Similar to how our lungs enable the exchange of oxygen and carbon dioxide, the carbon consumption of AI represents its energy source. As AI continues to evolve and become more prevalent, addressing its energy demands and environmental impact becomes crucial. Striving for energy efficiency and sustainable practices is essential for the long-term viability of AI technology. “A main problem to tackle in reducing AI’s climate impact is to quantify its energy consumption and carbon emission, and to make this information transparent”, says Payal Dhar in a recent . Nature Machine Intelligence journal article To be clear, it’s not always about making the technology more efficient. Sometimes, it’s about making humans less stupid about wasting compute. Shifting developer mindsets to massive state-space feature engineering means getting people to understand why they need to use (guesstimate your feature space before you compute it, and how many different types of hay are in it). It’s why you have to statistically consider (how you want to investigate that haystack with best-of-n sampling, helping to decide if executing with the you have/want to spend is worth the byte flips). HyperLogLog Goodheart’s Law compute resources Cybersecurity: AI's Immune System Just as our immune system protects our body from infections, AI relies on robust cybersecurity measures to defend against threats. The interconnectedness of AI systems with various networks and data sources exposes them to vulnerabilities and potential attacks. Ensuring the security of AI requires comprehensive protection mechanisms, including data privacy, secure infrastructure, and resilient defenses against malicious actors. AI Security Groups and Resources OWASP Foundation AI/ML Security work being done: The OWASP foundation more broadly aims to improve the security of software through its community-led open source software projects. They have an and broadly light on technical implementation. provides developer centered information about the top known cybersecurity risks for open source machine learning, with a description, example attack scenario, and a suggestion of how to prevent it. AI Security Guide OWASP Project Machine Learning Security Top 10 The LFAI Security Committee LFAI supports AI and Data open source projects through incubation, education, and best practices. AI Security Regulatory Organizations focus on common sense regulation and standards around AI/ML that are compliant to exclusively EU regulation. Similarly, the is an independent working group to advance AI Risk aimed at security governance. The was similarly established solely for regulation, and only scoped to cloud services. efforts are also largely regulatory advisements. ETSI’s Securing Artificial Intelligence (SAI) AIRS group CSA Artificial Intelligence group IEE’s AI/ML Central Nervous System Infections: Addressing Security Concerns with Large Language (and Large Models) Anything In the context of AI, security concerns around the data and model processing around large langauge (and large anything models) can be likened to a central nervous system infection, affecting the core functionality of the entire system. While traditional cybersecurity threats often focus on breaches, data leaks, and unauthorized access, the challenges posed by Large Language Models (LLMs) and other generative technologies extend beyond conventional threats. Understanding these distinctive aspects is crucial for effectively addressing security concerns in this domain. Hallucinations One of the notable challenges associated with LLMs is the problem of hallucinations or the generation of false information. Due to their ability to generate human-like responses based on training data, LLMs may inadvertently produce content that lacks factual accuracy or even fabricate entirely false narratives. This presents a unique security risk as it can lead to the dissemination of misinformation, manipulation of public opinion, and potential damage to the credibility of AI systems. Mitigating this risk requires a combination of careful training data curation, robust fact-checking mechanisms, and ongoing validation processes to ensure the veracity of the generated content. . This is a security problem when downloading open source package registries at the moment Internal Drift Another significant concern is internal drift within LLMs and generative models. Over time, these models can exhibit a shift in behavior, generating responses that deviate from the intended purpose or ethical guidelines. This phenomenon poses a challenge to maintaining control and predictability in AI systems, as it introduces an element of uncertainty that can be exploited by adversaries. Addressing internal drift requires continuous monitoring and adaptation, establishing feedback loops between developers and the models, and implementing techniques such as reinforcement learning to reinforce desired behavior and align the outputs with ethical standards. Proactive threat detection involves actively monitoring the behavior of LLMs and generative models to identify potential vulnerabilities or anomalies in their output. Secure development practices entail incorporating robust security measures throughout the entire development lifecycle, including secure coding practices, vulnerability testing, and adherence to established industry standards. A Holistic Treatment for AI Security To ensure the longevity and trustworthiness of AI, we’ll have to integrate security considerations throughout the AI development lifecycle, establishing strong partnerships with cybersecurity experts, and fostering a culture of security awareness and education. Implementing ethical guidelines, robust encryption practices, and continuous monitoring. . It’s All About Attention I’ve spent a lot of my life thinking about how humans interact with computers and computers that interact with humans. I used to study how we could design better Boeing Airline cockpits by getting real pilots into a 4D flight simulator of potentially life-threatening conditions to see where they looked, how they moved, and what they said to prevent a crash - . Later, I worked at the National Institutes of Health on , where we timed simple visual feedback to real-time brain networks to improve clinical psychiatric outcomes without any pill-based intervention. back or not real-time fMRI neurofeedback Just attention. Attention is powerful. It’s probably the most powerful thing to learn about cybersecurity. When threat modeling any technical stack, you have to be able to think about where are people paying attention. That’s your most exploitable surface area. aren’t Emerging technologies are always a really interesting place to observe people’s attention. There hasn’t really been an emerging technology that so many people have had an opinion on before, because this time it’s just a face-to-deep technology that’s genuinely easy to start using. That’s beautiful. Attention and awareness are different things though. We need to understand how the whole thing works. You Should Be Paying Attention to Chip Security AI Chips: The Heart of the AI Body AI-specific chips, often referred to as neural processing units (NPUs) or graphics processing units (GPUs), serve as the vital heart of the AI ecosystem. Much like the human heart, these specialized hardware components play a crucial role in enabling the efficient and optimal functioning of AI systems. In this series, we delve into the significance of chip technology and its pivotal role in ensuring the security and robustness of the AI body. The Powerhouse Organs: AI-specific chips are specifically designed to accelerate AI computations, enabling exceptional performance, efficiency, and speed. These chips are tailored to meet the unique requirements of AI workloads, such as training deep neural networks and running complex AI algorithms. Just like the heart pumps blood to supply oxygen and nutrients to the body, AI-specific chips empower AI systems to process and analyze vast amounts of data with remarkable speed and efficiency. The Importance of Chip Technology: Advancements in chip architectures and designs have led to significant breakthroughs in AI capabilities. Parallel processing, specialized matrix calculations, and optimized memory access are just a few examples of the technological advancements that AI-specific chips have brought to the field. These advancements push the boundaries of AI performance, enabling AI systems to tackle increasingly complex tasks and deliver accurate results. Additionally, chip technology plays a vital role in addressing energy consumption concerns. As AI deployment expands, the energy efficiency of AI systems becomes a crucial factor. Advancements in chip technology contribute to reducing energy consumption, making AI more sustainable and environmentally friendly. The Role of Secure Chip Technology: As AI becomes more prevalent and critical in various sectors, ensuring the security of AI-specific chips becomes paramount. The article " " has some great key points to understand, but I’ll boil them down to two that you need to understand today: Security Becomes Much Bigger Issue For AI/ML Chips, Tools The rapid development of AI and machine learning chips has led to increased potential for security threats, including intellectual property theft and attacks that can corrupt data or lead to ransomware (this can be supply chain and supplier threats) The need for standards that include both hardware and software. It suggests using multiple AI algorithms and diverse sets of training data to reduce the risk of attacks. If you are optimizing - yes, I said optimising, not just running, an AI system, you will be building a system that maximizes the compute over cost. (GPUs) and Intel (CPUs and VPUs), and with (TPU) and that’s just the ones who have been on the market the longest - emerging tech means emerging market. You can do this with hyperscalers (kind, of) but what if you are building one from scratch? You’ve got NVidia Google Tensor Processing Unit But, oof, do we have some these days, as seen in the “Blessings of Scale” from . computational power Compute Trends Across Three Eras of Machine Learning Securing the Evolving AI Body: Just as the heart is vital for maintaining overall health, ensuring the security of AI-specific chips is crucial for the resilience and trustworthiness of the AI body. Secure chip technology must be integrated into every stage of the AI development process, from chip design and manufacturing to deployment and maintenance. Collaborative efforts among chip manufacturers, AI developers, and cybersecurity experts are essential to identify and address potential vulnerabilities in chip architectures and mitigate emerging threats. By prioritizing chip security, we can ensure that the AI body remains robust, efficient, and secure. Where do you want to see this go next? Town Hall mode is on this Hackernoon article, so feel free to comment anywhere on this article where you have a question, comment or better idea than myself. The author will absolutely read it.

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