intelligence – AI in Media and Society

We should not talk about sentience

July 25, 2022 Mindy McAdams

I guess it should be no surprise that people want to talk about sentient machines when the term artificial intelligence has become more common than bread and butter. I was hoping this July 2022 article in The Wall Street Journal would go further than it does to assert that there are no grounds at all for talking about sentience in today’s AI, but I was disappointed. The two authors at least did not try to “both sides” the spurious claims.

First, they state that there are a lot of exaggerated claims from companies selling so-called AI products and “solutions.” Second, they touch on the danger this holds for policy decisions — when our elected officials, lawyers, judges, etc., don’t have a clear idea of how AI systems work, they are bound to make poor laws and poor rulings. AI ethicists warn that the hype is “distorting policy makers’ views of the power and fallibility” of AI systems. The reporters quote Oren Etzioni, CEO of the nonprofit Allen Institute for Artificial Intelligence, as saying policy makers are “well-intentioned and ask good questions, but they’re not super well-informed.”

“The belief that AI is becoming — or could ever become — conscious remains on the fringes in the broader scientific community, researchers say.”
—Hao and Kruppa, in “Tech Giants Pour Billions into AI, but Hype Doesn’t Always Match Reality”

The WSJ article also covers the claims of a (now former) Google engineer who claimed the LaMDA chatbot is sentient. On July 22, The Verge was among several news organizations reporting that the engineer has been fired. That article links to a YouTube video that explains “how LaMDA works and how it could produce the responses that convinced [the engineer] without actually being sentient.”

I was dismayed that the media gave so much attention to the engineer’s claims — which he never should have made in the first place, being an engineer. If you take some time to learn about how chatbots are created (or voice assistants — my undergrad college students are decidedly unimpressed with Siri and Alexa), you’ll understand that they cannot possibly have sentience. These conversational systems are prediction machines — they predict “the likelihood of a token (character, word or string) given either its preceding context or … its surrounding context” (source: Bender et al., 2021). The results can be astoundingly good, or hilariously awful. Either way, the process that generates the responses is the result of computational predictions and not the product of a sentient being.

The same is true of the output from DALL-E (and the newer DALL-E 2), which creates an image based on a text description. The less you know about today’s algorithms and powerful AI hardware, the more likely you are to wonder whether there’s a humanlike intelligence behind the system that can produce these graphics. The output is extraordinary and literally was not even possible just a few years ago. What makes it possible today are the combination of massively parallel computational structures and the algorithms designed by humans to enable really, really good guesses (predictions) at what images would best match the descriptive text.

When I say we shouldn’t talk about sentience, I am being a bit coy. I do think we should be talking about what constitutes intelligence in humans and animals, how we know an entity is conscious, and what it means to think, to feel, to perceive the world. I don’t think we should be looking for sentience in our computers — not today and not for a long, long time to come. It distracts us from what today’s AI systems are actually doing, which is making guesses that then affect real sentient humans’ real lives.

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Explaining common misconceptions about AI

August 27, 2021August 27, 2021 Mindy McAdams

Sometimes people make a statement that an artificial intelligence system is a computer system that learns, or that learns on its own.

That is inaccurate. Machine learning is a subset of artificial intelligence, not the whole field. Machine learning systems are computer systems that learn from data. Other AI systems do not. Various systems are wholly programmed by humans to follow explicit rules and do not generate any code or instructions on their own.

The error probably arises from the fact that many of the exciting advances in AI since 2012 have involved some form of machine learning.

The recent successes of machine learning have much to do with neural networks, each of which is a system of algorithms that (in some respects) mimics the way neurons work in the brains of humans and other animals — but only in some respects. In other words, a neural network shares some features with human brains, but is not extremely similar to a human brain in all its complexity.

Advances in neural networks have been made possible not only by new algorithms (written by humans) but also by new computer hardware that did not exist in the earlier decades of AI development. The main advance concerns graphical processing units, commonly called GPUs. If you’ve noticed how computer games have evolved from simple flat pixel blocks (e.g. Pac-Man) to vast 3D worlds through which the player can fly or run at high speed, turning in different directions to view vast new landscapes, you can extrapolate how the advanced hardware has increased the speed of processing of graphical information by many orders of magnitude.

Without today’s GPUs, you can’t create a neural network that runs multiple algorithms in parallel fast enough to achieve the amazing things that AI systems have achieved. To be clear, the GPUs are just engines, powering the code that creates a neural network.

More about the role of GPUs in today’s AI: Computational Power and the Social Impact of Artificial Intelligence (2018), by Tim Hwang.

Another reason why AI has leapt onto the public stage recently is Big Data. Headlines alerted us to the existence and importance of Big Data a few years ago, and it’s tied to AI because how else could we process that ginormous quantity of data? If all we were doing with Big Data was adding sums, well, that’s no big deal. What businesses and governments and the military really want from Big Data, though, is insights. Predictions. They want to analyze very, very large datasets and discover information there that helps them control populations, make greater profits, manage assets, etc.

Big Data became available to businesses, governments, the military, etc., because so much that used to be stored on paper is now digital. As the general population embraced digital devices for everyday use (fitness, driving cars, entertainment, social media), we contributed even more data than we ever had before.

Very large language models (an aspect of AI that contributes to Google Translate, automatic subtitles on YouTube videos, and more) are made possible by very, very large collections of text that are necessary to train those models. Something I read recently that made an impression on me: For languages that do not have such extensive text corpuses, it can be difficult or even impossible to train an effective model. The availability of a sufficiently enormous amount of data is a prerequisite for creating much of the AI we hear and read about today.

If you ever wonder where all the data comes from — don’t forget that a lot of it comes from you and me, as we use our digital devices.

Perhaps the biggest misconception about AI is that machines will soon become as intelligent as humans, or even more intelligent than all of us. As a common feature in science fiction books and movies, the idea of a super-intelligent computer or robot holds a rock-solid place in our minds — but not in the real world. Not a single one of the AI systems that have achieved impressive results is actually intelligent in the way humans (even baby humans!) are intelligent.

The difference is that we learn from experience, and we are driven by curiosity and the satisfaction we get from experiencing new things — from not being bored. Every AI system is programmed to perform particular tasks on the data that is fed to it. No AI system can go and find new kinds of data. No AI system even has a desire to do so. If a system is given a new kind of data — say, we feed all of Wikipedia’s text to a face-recognition AI system — it has no capability to produce meaningful outputs from that new kind of input.

GPT-3 and automated text generation

August 18, 2020August 24, 2020 Mindy McAdams

GPT-3 has to be the most-hyped AI technology of the past year. Headlines said its predecessor, GPT-2, was “too dangerous” to be released publicly. Then it was released. The world did not end.

Less than a year later, the more advanced (next generation) GPT-3 was released by OpenAI. Why are people so excited about GPT-3? See for yourself in the video below.

GPT-3 is a natural language generation (NLG) system. Given instructions about what you want, it writes original text that — in most (but not all) cases — sounds like a human wrote it. The technology could be used to rapidly write 10,000 fake user comments into a discussion forum, for example. Or 10,000 fake restaurant reviews.

Don’t worry about the first examples in the video showing GPT-3 writing computer code, if that’s not something you’re well acquainted with — it quickly moves on to show the system extracting text from long documents and writing summaries on the fly. The presenter does a good job of demonstrating the breadth and variety of tasks GPT-3 can be used for. You might be flat-out amazed.

Bear in mind that the examples shown in the video are different, separate applications of GPT-3. You don’t just install GPT-3 and it does all of those things.

Developers can apply to gain access to the GPT-3 API. This enables them to create applications that use GPT-3 but not to see or modify the actual code that makes GPT-3 work. You can view more examples of GPT-3 applications at that same link.

Another nice thing about the video above is the explanation of generative pre-training. Instead of training the GPT-3 model (or models) only with labeled data (supervised learning), the OpenAI researchers used “a semi-supervised approach for language understanding tasks using a combination of unsupervised pre-training and supervised fine-tuning.” The pre-training for GPT-2 included a dataset of more than 7,000 unpublished books “from a variety of genres including Adventure, Fantasy, and Romance.” Because entire books were used — instead of sentences separated from their context — the model was able to learn long-range structure.

GPT-3 used even more long-form texts for pre-training (described in a technical paper):

*Above: Screenshot from “Language Models Are Few-Shot Learners,” Brown et al., July 2020*

Once again we can see that tremendous advances in AI capability are made possible precisely because today’s computer hardware has the ability to run through enormous quantities of data very quickly. It’s not only that we now have billions of pages of text in digital form. It’s not just that we can store that Himalayan mountain range of data. It’s very much because processors are able to run multiple calculations simultaneously at lightning speed.

An important point about GPT-3 that’s not covered in the video: None of these applications, or GPT-3 itself, understands the meaning of the text that is being generated.

It’s going to be very easy for people to jump to conclusions about the “intelligence” of a computer system when it’s able to generate responses and explanations that are so human-like. There is no comprehension here. There is no knowledge of the world — there is only knowledge about language itself.

To learn more about how GPT-3 does what it does: GPT-3 Explained in Under 3 Minutes.