Richard Dawkins is a public intellectual of some renown, although not without his controversies. So it is noteworthy when he writes an article claiming that the chatbot Claude is likely conscious. I found the article fascinating, not because I agree with his core claim or feel that he has contributed anything significant to the conversation, but because it seems to represent a scholar and deep thinker writing about a topic in which he lacks specific expertise. I also see no evidence in the article that he engaged meaningfully, or at least adequately, with a topic expert. As a result he makes some thoughtful and instructive errors.

He begins with a discussion of the Turing test, which has long been discussed as an early thought experiment about how we might determine if an AI is actually conscious. Dawkins essentially accepts the Turing test and write:

“It was one thing to grant consciousness to a hypothetical machine that — just imagine! — could one day succeed at the Imitation Game. But now that LLMs can actually pass the Turing Test? “Well, er, perhaps, um… Look here, I didn’t really mean it when, back then, I accepted Turing’s operational definition of a conscious being…””

He feels saying that LLMs have passed the Turing test but still not accepting them as conscious is moving the goalpost. However, the Turing test was never generally accepted by AI experts or philosophers as a true test of consciousness. Rather, it was understood that such a test really is only a measure of a machine’s ability to imitate human speech. I wrote about it in 2008, writing: “Ever since Alan Turing proposed his test it has provoked two still relevant questions: what does it mean to be intelligent, and what is the Turing test actually testing.” I went on to write:

“But I can imagine a day in the not-too-distant future when such AI can pass a Turing test. The algorithms will have to become much more complex, allow for varying answers to the same question, and make what seem to be abstract connections which take the conversation is new and unanticipated directions. You can liken computer AI simulating conversation to computer graphics (CG) simulating people. At first they appeared cartoonish, but in the last 20 years we have seen steady progress. Movement is now more natural, textures more subtle and complex. One of the last layers of realism to be added was imperfection. CG characters still seem CG when they are perfect, and so adding imperfections adds to the sense of reality. Similarly, an AI conversation might want to sprinkle some random quirkiness into the responses.

The questions is – will sophisticated-enough algorithms running on powerful-enough computers ever be conscious? What Loebner is saying, and I agree, is that the answer is no. Something more is needed.”

Basically, the limitation of the Turing test is that it is looking only at output, and therefore there is no way to distinguish the output of true consciousness from a really good simulation. This is not a new idea, and no one is moving the goalpost. We need to know something about how a computer is working to conclude whether or not it is conscious. What LLM experts will tell you is that these chatbots are just really good autocompletes – they are mimicking language, and since language is how we communicate thoughts, this creates the powerful illusion that they are mimicking thought, but they aren’t. They do not think, they do not truly understand.

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This interesting case was reported in the literature in 2007. For some reason it was then widely published in the mainstream media in 2015. Now it is making the rounds again on social media to support a false narrative about brain function. The story is of a 20 year old German woman who suffered a traumatic brain injury in a car accident. Over the next several months she started to slowly lose her vision – which is an important detail, it was not a sudden loss as a result of the physical trauma. After evaluation she was diagnosed with psychogenic blindness, meaning that it was not due to any physical damage to her visual system but was rather due to psychological stress. This patient also has what is now called dissociative disorder, or multiple personality, with 10 distinct personalities.

What makes the case even more interesting is that, with therapy, some of her personalities regained vision while others did not. Eventually eight of her ten personalities regained vision. This presented a rare, perhaps unique, opportunity to study the underlying neuroanatomical correlates of psychogenic blindness – what is happening in the brain when someone loses the ability for conscious sight despite their visual system working?

Psychogenic or functional neurological disorders are a complex and poorly understood phenomenon in which emotional stress and trauma presents as physical neurological symptoms. Common presentations include paralysis, language difficulty, sensory loss, and blindness. The diagnosis is mostly one of exclusion, which means sufficient examination and study is done to rule out any demonstrable damage, lesion, or other physical cause. This does not mean the patient is faking (technically called malingering) – that is a distinct condition that can usually be distinguished from a functional disorder. Usually patients with a functional disorder are very distressed by their symptoms and want further examination to find out what is wrong. In addition to simply ruling out physical causes, the diagnosis of a functional disorder can be supported by some positive evidence from the neurological exam. With psychogenic blindness, for example, patients will have normal pupillary responses (assuming no separate baseline deficit), and will have a normal reaction to optokinetic testing.  This involves moving vertical black and white stripes horizontally across their vision. This will cause an involuntary response of tracking the stripes with eye movements. If this happens then we know that visual information is getting in and making its way to the visual cortex.

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Remember The Last Starfighter from 1984? In that movie a trailer-park kid with limited prospects spends his time on an arcade-style video game, Starfighter. He plays the game so much that he beats the final level, and it turns out he is the first person to ever do so. He is heavily criticized for spending so much time playing a game, which is seen as a sign of boredom and lack of ambition – a waste of time. The twist (42 year old spoiler incoming) is that the game was actually a test (the Excalibur test – a deliberate reference to King Arthur) to find a skilled pilot for an actual real-life starfighter. He goes on to save the galaxy from invasion.

The interesting premise of the movie is that playing a video game is not only a test of real-life skill, but can be used to train such skill. In 1984 this was  kind of a new idea, and appealing to a generation of kids newly hooked on video games. Video games have been significantly mainstreamed over the last half century, but there is still a bit of a cultural stigma attached to them – they are seen as the realm of dorks and geeks, with inevitable jokes about how avid video gamers with “never get laid” (or something to that effect). Since the beginning of their popularity parents have worried, with such worry being fed by a sensationalist media, that video games were going to “rot” their kids’ brains, turn them into losers who can never get a skilled job, and might even cause violent behavior. Every mass shooting someone brings up violent video games.

But the evidence simply does not support these concerns. One big problem with the research is that it shows correlation only, not causation. Sure, people who play aggressive video games tend to be more aggressive, but that doesn’t mean the game is the cause. Further, there are many confounding factors, and more recent research shows that violence in the game is not the key feature. It has more to do with the level of difficulty and the resulting frustration that seems to raise aggression, not violence in the game. More competitive and difficult games tend to be more stimulating, regardless of the level of violence. The bottom line – after decades of research, systematic reviews conclude: “There is insufficient scientific evidence to support a causal link between violent video games and violent behavior.”

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Many people might find this to be an easy question and simple concept – what is your favorite color? In fact it was used as the quintessential easy question by the bridge guardian in Monty Python and the Holy Grail. But it is a good rule of thumb that everything is much more complicated than you think or than it may at first appear, and this is no exception. We recently had a casual discussion about this topic on the SGU, and it left me unsatisfied, so I thought I would do a deeper dive. Perhaps there is a neuroscientific answer to this question.

The panel differed in their reactions to the question of favorite color (we were just giving our subjective feelings, not discussing research or evidence). Cara felt that “favorite color” is largely arbitrary. Kids are asked to pick a favorite color, which they do (under pressure) and then often just stick with that answer as they get older. She also felt the question was meaningless without context – are you referring to clothes, cars, house color, or something else? Jay was at the other end of the spectrum – he has a strong affiliation for the color orange which gives him a pleasant feeling. The rest were somewhere in between these two extremes.

I knew there had to be a science of “favorite color”, which I thought might be interesting. Indeed there is – and it is interesting.

First, what is the distribution of favorite color, across the world and demographically? Blue is, far and away, the most favorite color, in most countries across the world, so it seems to be very cross-cultural. It is also the favorite across age groups and gender. The second-most favorite color is either green, red, or purple. Brown is almost universally the least favorite color. Gender has an effect on favorite color, with more women favoring pink, and reds in general (but still preferring blue overall). Republicans still prefer blue over red, but more Republicans prefer red than Democrats. There are country-specific differences as well. Red is a higher preference in China than many other countries, for example.

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I have a love-hate relationship with TikTok, as I do social media in general. It is a great communication tool and allows scientists and science communicators to get their content out to a larger audience cheaply and easily. If you know how to use the internet and social media as a resource, you can find a video about almost any topic. I particularly love the “how to” videos. And yet these applications are also used (mostly used) to spread nonsense and misinformation, or at least inaccurate, misleading, or overly generalized information. The low bar of entry cuts both ways.

As a result I spend part of my time as a communicator with my finger in the dike of social media pseudoscience and science denial. For example, this individual feels his insights into the workings of the human brain need to be shared with the world. His musings are based entirely on a false premise, his apparent misunderstanding of what neuroscientists understand about brain function. He begins with the nicely vague statement, “scientists have discovered”, followed by a completely incorrect statement – that thoughts come to our brain from outside the brain.

Before I get into this old “brain as receiver” claim, I want to point out that this format is extremely common on TikTok in particular and social media in general. This is more worrying than any individual claim – the culture is to present some random nonsense in the format of “isn’t this crazy”, or with with a cynical tone implying something nefarious is going on. Such authors may or may not believe what they say, they may just be trying to amplify their engagement with a total disregard toward whether what they are saying is true or not. They may even be a full Poe – knowing that what they say is nonsense. Either way, they feel it is appropriate to spend the time to record and upload a video without spending the few minutes that would be needed to check to see if what they are saying is even true. The very platform they are using to spread their nonsense often has all the information they need to answer their alleged questions. The culture is profoundly incurious, intellectually vacuous, lacking all scholarship or quality control, and seems to value only engagement. Thrown into the mix are true believers, grifters, and those who display classic symptoms of some form of thought disorder. This is “infotainment” taken to its ultimate expression.

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It’s not easy being a futurist (which I guess I technically am, having written a book about the future of technology). It never was, judging by the predictions of past futurists, but it seems to be getting harder as the future is moving more and more quickly. Even if we don’t get to something like “The Singularity”, the pace of change in many areas of technology is speeding up. Actually it’s possible this may, paradoxically, be good for futurists. We get to see fairly quickly how wrong our predictions were, and so have a chance at making adjustments and learning from our mistakes.

We are now near the beginning of many transformative technologies – genetic engineering, artificial intelligence, nanotechnology, additive manufacturing, robotics, and brain-machine interface. Extrapolating these technologies into the future is challenging. How will they interact with each other? How will they be used and accepted? What limitations will we run into? And (the hardest question) what new technologies not on that list will disrupt the future of technology?

While we are dealing with these big question, let’s focus on one specific technology – controllable robotic prosthetics. I have been writing about this for years, and this is an area that is advancing more quickly than I had anticipated. The reason for this is, briefly, AI. Recent advances in AI are allowing for far better brain-machine interface control than previously achievable. Recent advances in AI allow for technology that is really good at picking out patterns from tons of noisy data. This includes picking out patterns in EEG signals from a noisy human brain.

This matters when the goal is having a robotic prosthetic limb controlled by the user through some sort of BMI (from nerves, muscles, or directly from the brain). There are always two components to this control – the software driving the robotic limb has to learn what the user wants, and the user has to learn how to control the limb. Traditionally this takes weeks to months of training, in order to achieve a moderate but usable degree of control. By adding AI to the computer-learning end of the equation, this training time is reduced to days, with far better results. This is what has accelerated progress by a couple of decades beyond where I thought it would be.

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There are many ways in which our brains can be hacked. It is a complex overlapping set of algorithms evolved to help us interact with our environment to enhance survival and reproduction. However, while we evolved in the natural world, we now live in a world of technology, which gives us the ability to control our environment. We no longer have to simply adapt to the environment, we can adapt the environment to us. This partly means that we can alter the environment to “hack” our adaptive algorithms. Now we have artificial intelligence (AI) that has become a very powerful tool to hack those brain pathways.

In the last decade chatbots have blown past the Turing Test – which is a type of test in which a blinded evaluator has to tell the difference between a live person and an AI through conversation alone. We appear to still be on the steep part of the curve in terms of improvements in these large language model and other forms of AI. What these applications have gotten very good at is mimicking human speech – including pauses, inflections, sighing, “ums”, and all the other imperfections that make speech sound genuinely human.

As an aside, these advances have rendered many sci-fi vision of the future quaint and obsolete. In Star Trek, for example, even a couple hundred years in the future computers still sounded stilted and artificial. We could, however, retcon this choice to argue that the stilted computer voices of the sci-fi future were deliberate, and not a limitation of the technology. Why would they do this? Well…

Current AI is already so good at mimicking human speech, including the underlying human emotion, that people are forming emotional attachments to them, or being emotionally manipulated by them. People are, literally, falling in love with their chatbots. You might argue that they just “think” they are falling in love, or they are pretending to fall in love, but I see no reason not to take them at their word. I’m also not sure there is a meaningful difference between thinking one has fallen in love and actually falling in love – the same brain circuits, neurotransmitters, and feelings are involved.

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Definitely the most fascinating and perhaps controversial topic in neuroscience, and one of the most intense debates in all of science, is the ultimate nature of consciousness. What is consciousness, specifically, and what brain functions are responsible for it? Does consciousness require biology, and if not what is the path to artificial consciousness? This is a debate that possibly cannot be fully resolved through empirical science alone (for reasons I have stated and will repeat here shortly). We also need philosophy, and an intense collaboration between philosophy and neuroscience, informing each other and building on each other.

A new paper hopes to push this discussion further – On biological and artificial consciousness: A case for biological computationalism. Before we delve into the paper, let’s set the stage a little bit. By consciousness we mean not only the state of being wakeful and conscious, but the subjective experience of our own existence and at least a portion of our cognitive state and function. We think, we feel things, we make decisions, and we experience our sensory inputs. This itself provokes many deep questions, the first of which is – why? Why do we experience our own existence? Philosopher David Chalmers asked an extremely provocative question – could a creature have evolved that is capable of all of the cognitive functions humans have but not experience their own existence (a creature he termed a philosophical zombie, or p-zombie)?

Part of the problem of this question is that – how could we know if an entity was experiencing its own existence? If a p-zombie could exist, then any artificial intelligence (AI), even one capable of duplicating human-level intelligence, could be a p-zombie. If so, what is different between the AI and biological consciousness? At this point we can only ask these questions, some of them may need to wait until we actually develop human-level AI.

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We have all likely had the experience that when we learn a task it becomes easier to learn a distinct but related task. Learning to cook one dish makes it easier to learn other dishes. Learning how to repair a radio helps you learn to repair other electronics. Even more abstractly – when you learn anything it can improve your ability to learn in general. This is partly because primate brains are very flexible – we can repurpose knowledge and skills to other areas. This is related to the fact that we are good at finding patterns and connections among disparate items. Language is also a good example of this – puns or witty linguistic humor is often based on making a connection between words in different contexts (I tried to tell a joke about chemistry, but there was no reaction).

Neuroscientists are always trying to understand what we call the “neuroanatomical correlates” of cognitive function – what part of the brain is responsible for specific tasks and abilities? There is no specific one-to-one correlation. I think the best current summary of how the brain is organized is that it is made of networks of modules. Modules are nodes in the brain that do specific processing, but they participate in multiple different networks or circuits, and may even have different functions in different networks. Networks can also be more or less widely distributed, with the higher cognitive functions tending to be more complex than specific simple tasks.

What, then, is happening in the brain when we exhibit this cognitive flexibility, repurposing elements of one learned task to help learn a new task? To address this question Princeton researchers looked at rhesus macaques. Specifically they wanted to know if primates engage in what is called “compositionality” – breaking down a task into specific components that can then be combined to perform the task. Those components can then be combined in new arrangements to compose a new task, like building with legos.

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I am currently in Dubai at the Future Forum conference, and later today I am on a panel about the future of the mind with two other neuroscientists. I expect the conversation to be dynamic, but here is the core of what I want to say.

As I have been covering here over the years in bits and pieces, there seems to be several technologies converging on at least one critical component of research into consciousness and sentience. The first is the ability to image the functioning of the brain, in addition to the anatomy, in real time. We have functional MRI scanning, PET, and EEG mapping which enable us to see cerebral blood flow, metabolism and electrical activity. This allows researchers to ask questions such as: what parts of the brain light up when a subject is experiencing something or performing a specific task. The data is relatively low resolution (compared to the neuronal level of activity) and noisy, but we can pull meaningful patterns from this data to build our models of how the brain works.

The second technology which is having a significant impact on neuroscience research is computer technology, including but not limited to AI. All the technologies I listed above are dependent on computing, and as the software improves, so does the resulting imaging. AI is now also helping us make sense of the noisy data. But the computing technology flows in the other direction as well – we can use our knowledge of the brain to help us design computer circuits, whether in neural networks or even just virtually in software. This creates a feedback loop whereby we use computers to understand the brain, and the resulting neuroscience to build better computers.

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