Exploring AI

The rapid development of artificial intelligence has led people to wonder whether AI might one day take power into its own hands. There are plenty of people reassuring us that everything is in human hands and that, ultimately, humans are responsible for everything. But are we? AI may not seize power in the sense of enslaving humanity, but we are already outsourcing power and responsibility to it effectively today. We Worship the Machine Clumsy and poorly designed systems are part of our everyday lives. We already have to spend time clicking buttons in the correct order or remembering to do something in a certain system. And if we fail in these rituals of machine worship, we must sacrifice more working time at the machine’s altar, repeating magic words like “oh, that went wrong,” “hold on, what happened here” and “how did we get there again”. The more time we spend trying to please the machine, the more it heats up its processor – the human is enslaved. Humans must press buttons in the correct order, lest the machine gets angry and punishes them. And how often have you found yourself in a situation where nothing could be done because the machine wouldn’t allow it? These situations have surely happened to many of us. A public transit ticket couldn’t be bought because the app froze, or you missed out on loyalty points at the grocery store because the system didn’t recognize your card. Luckily, a human is ultimately responsible – the same human who can only shrug, because the real power lies with the machine. AI is Already Guiding Us Who gets to decide what truth we believe in? To a large extent, that decision-making power has already been outsourced to artificial intelligences. We often solve our problems by Googling them, but Google doesn't give us answers based on their usefulness or truthfulness – the answers are ranked by AI. Where is the human who takes responsibility when Google's AI feeds us false information or hides things from us? Nowhere – the power lies with AI. AI Easily Learns Which Strings to Pull Large international online stores like Amazon and Temu boldly use AI to steer users toward certain products. Sometimes the cheapest options are hard to find because the smart search has figured out you’re willing to pay more. The responsibility, of course, lies with the person – well, you bought it, didn’t you? We Are Eagerly Handing Over More Power to AI Probably nothing in this text is surprising to anyone; what’s most surprising is the contradiction in our values. The same people who fear AI dominance are often the ones outsourcing more power to AI to make their work easier. One of the funniest examples from the academic world is Turnitin and the automatic checking of essays using AI. We humans are happily giving AI the keys to power Let’s go ahead and let AI decide whose thesis gets approved or rejected, and who gets what grade for an essay. The final responsibility lies with the teacher – who may be incapable of evaluating the reliability of the AI. What could possibly go wrong with this setup?

Artificial intelligence is often criticized with claims that it can only repeat its training data, and therefore always produces plagiarized and average output. Is there any truth to these claims? Claim 1: AI retrieves its answers from a database I’ve encountered this claim often. The idea is that AI retrieves answers from its database, and thus it plagiarizes or fails to find the correct answer. Large language models and image-generating AI models do not, by default, have access to any kind of database. Instead, these models have learned to generate responses independently. The image or poem produced by AI, for example, does not exist as-is in any database. Large language models don’t use databases, but they can be connected to one Today, large language models can indeed be connected to a database. Currently, the most common method for doing this is the so-called RAG model (Retrieval Augmented Generation). In this setup, the AI can retrieve information from a database to support its answer. However, the AI still writes the response itself. Claim 2: AI only produces average answers This claim is more complex, as there are many types of generative AI models. Images are often produced using diffusion models, which begin with a random mess of pixels and gradually transform that noise into a better image. The AI aims to reach some sort of average optimal output, so its tendency is toward the mean. Diffusion models run iteratively – each iteration creates a better image, one that’s also closer to the average. Somewhere between the initial noise and the average optimal lies an iteration where the AI produces good images that haven’t yet converged into uniform, average-looking ones. These images are by no means simply average, even if they inevitably share something with the optimal average. With an update, Adobe Firefly began producing better, though very similar, images What about large language models? They also aim to produce the best possible answer, which often results in an average-like response depending on the prompt. However, large language models have a feature that allows you to adjust the temperature, which influences how average or creative the responses are. At the extremes, adjusting the temperature can make the model generate either extremely bland text or pure nonsensical gibberish. Emergent intelligence The intelligence of large language models is emergent. They can generalize from what they’ve learned to completely new tasks. This simply means that AI models can generate responses to questions they’ve never encountered in their training data. These responses are not merely average repetitions of what’s already been learned, as the AI cannot just mimic its data like a parrot would. Adobe Firefly’s training data guides it so heavily that it cannot generate a wine glass filled to the brim Image-generating AI models do not show the same level of emergent intelligence, as their training data influences their output more heavily than with text models. It can often be nearly impossible to get certain kinds of images from them. Average or not? The claim that AI only produces average responses oversimplifies things. Training data influences AI more or less depending on the model, but that doesn’t mean AI is only capable of producing dull, obvious answers. AI also doesn’t just repeat what it has learned, since it’s trained to provide responses to problems it has never encountered before.

Lately, an interesting argument has come to my attention: ChatGPT only looks into the past, whereas humans can look into the future. This idea stems from the fact that AI is trained on past data, and for instance, ChatGPT's knowledge of the world is limited to the last date of its training material. However, this does not directly mean that AI is only looking at the past. Machine Learning Always Faces the Unknown The fundamental principle of machine learning has always been to train AI on past data and test it on new, unseen data. This ensures that AI functions as expected even when encountering entirely new information. Machine learning aims to work with new data Before large language models, language technology-based machine learning models often struggled when faced with completely new types of data. For example, AI trained on product reviews did not perform well in identifying positive and negative expressions in literary texts. However, these limitations have been overcome with large language models, as they can generalize their learning to perform multiple different tasks. Do Humans Really Look into the Future Any Better? When we humans encounter something new, we often rely on past knowledge to act. Our own "training data" also ends at the present moment. If we see an unfamiliar furry creature on a leash walking towards us, we logically assume it is a dog. This assumption is based on previous knowledge. If it turns out to be a completely new and unknown animal species, we are surprised by the encounter. Similarly, AI relies on existing knowledge when encountering new things. The difference is that, at present, we do not have AI tools capable of dynamically learning from their experiences and updating themselves. AI will always assume that the furry creature is a dog until its training data includes information that a new pet-friendly species has been discovered. A human, however, would learn this instantly. Foreseeing the Future is Reasoning Just as humans predict the future using reasoning and scenario planning, AI can also predict the future by drawing logical conclusions. Large language models are already capable of reasoning and performing tasks that require thought. AI can therefore look into the future if properly guided with prompts to make predictions. Many AI tools, such as ChatGPT and Perplexity, can also fetch additional information from the web, allowing them to base their reasoning on up-to-date data.