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I recently attended the 2023 annual meeting of the American Roentgen Ray Society (ARRS), one of the major professional societies for radiologists and medical imaging specialists. As expected, one of the hot topics was artificial intelligence (AI) and expected impact on radiologists in particular, as well as medical practitioners in general.

Although I could not attend all of the numerous lectures, panel discussions, and research presentations on AI, I did learn of many exciting developments as well as areas of both opportunity and concern. In this column, Id like to share some thoughts on how AI will affect patients and physicians alike in the short-to-medium term future.

(Note: This discussion will be confined to so-called narrow AI to accomplish particular medical tasks, rather than artificial general intelligence or AGI that can simulate general human cognition. Ill leave the debate over whether a sufficiently advanced AI will exterminate humanity to others.)

1) AI will play an increasingly greater role in medical care, in ways both obvious and non-obvious to patients.

In my own field of radiology, AI will be used to enhance (but not yet replace) human radiologists making diagnoses from medical images. There are already FDA-approved AI algorithms to detect subtle internal bleeding within the brain or potentially fatal blood clots (pulmonary embolism) within the arteries of the lung.

When properly used, these algorithms could alert the human radiologists that a patients scan has one of these life-threatening abnormalities and bump the case to the top of the priority queue. This could significantly shorten the time between the scan and the appropriate treatment and thus save lives. (See this paper by Dr. Kiran Batra and colleagues from University of Texas Southwestern Medical Center for one example of the time savings achieved by AI.)

AI can also be used to enhance medical care in ways not directly related to rendering diagnoses. For instance, developers are working on physician co-pilot software that can sift through a patients medical records and extract the information most relevant for the patients upcoming visit to the radiology department (or internal medicine clinic, etc.). This could save the practitioners valuable time during each patient visit.

Robotic physician holding stethoscope

getty

2) The AIs are still not perfect, and human physicians will still need to have the final say in diagnoses and treatments.

For example, AIs are pretty good in detecting early breast cancer in mammogram images, but still make errors. (Often they make errors humans dont, and vice versa.) This makes AI great as an assistant to the human radiologist, but not (yet) a viable replacement.

Thus, we will see an interesting period of time where human physician-plus-AI will perform better than either human alone or AI alone. At some point in time, I predict that AI-assisted medicine will become standard of care and physicians who do not incorporate AI into their daily practices could open themselves to lawsuits for practicing substandard care.

3) As AIs get better, humans may start to over-rely on them.

This phenomenon is known as de-skilling. As an analogy (made by Dr. Charles Kahn of University of Pennsylvania in one of the ARRS panel discussions), suppose we develop self-driving automobiles that could handle most traffic conditions, but still required a human driver to take the wheel in emergencies. As AIs got increasingly better and the need for human intervention became less frequent, we human drivers could easily become complacent and lose good driving-related cognitive habits and reflexes.

If a partially-automated car going 70 mph on the highway suddenly alerted a human driver who hadnt truly driven in the past year to take over because of icy conditions ahead, things could go badly.

Similarly, if a human radiologist lets their cancer detection skills go rusty, they could run into trouble when the medical images included complex visual features beyond the ability of the AI to accurately parse.

My own personal approach will be to think of the AI as a tireless-but-quirky medical student constantly asking questions like, Could that squiggle be a cancer? How about this dark line is it a fracture? Could this dot be a small blood clot? An inquisitive human medical student can keep experienced doctors on their toes in a good way, and the same could be true for an AI.

4) AI could take over some interactions with patients that currently require human medical personnel.

Were probably not too far from reaching the point that a LLM (Large Language Model) AI like ChatGPT could take a radiology report written in medical jargon and translate it into terms understandable to non-physicians and possibly even answer follow-up questions about the significance of the findings.

A recent article by Ayers and colleagues in JAMA Intern Med compared how AI chatbots and human physicians responded to patient medical questions offered on social media. According to the judges (who were blinded as to the author of the answers), the chatbot answers were considered better both in terms of information quality and empathy than the human physicians answers!

The use of artificial intelligence in medicine is a rapidly evolving field, and Ive only scratched the surface of the exciting work being done. Given the rapid pace of developments, I dont know what things will look like in 5 months, let alone in 5 years. But Im glad to be alive during this time of potentially massive innovation (and admittedly potentially uncomfortable upheaval). For now, I remain optimistic that AI could be an enormous boon for patients and physicians alike.

I am a physician with long-standing interests in health policy, medical ethics and free-market economics. I am the co-founder of Freedom and Individual Rights in Medicine (FIRM). I graduated from University of Michigan Medical School and completed my residency in diagnostic radiology at the Washington University School of Medicine in St. Louis (where I was also a faculty member). I'm now in private practice in the Denver area. All my opinions are my own, and not necessarily shared by my employer.

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Promises, Perils, And Predictions For Artificial Intelligence In Medicine: A Radiologists Perspective - Forbes

In less than six months, ChatGPT has become a household name. The AI service can write paragraphs, essays, speeches and fill in exams. So many people have flocked to the chatbot for a glimpse of its power that the servers have to be shut down at times. It is a tour de force of artificial intelligence.

ChatGPT was developed by OpenAI, an artificial intelligence company founded in 2015 with a mission to ensure that artificial general intelligence benefits all of society, according to OpenAIs website.

The human-like chatbot can answer almost any question the user provides, and it has been trained to respond as a human would.

When asked about its pros and cons, ChatGPT said, Its important to note that while I can be a helpful tool for certain tasks, human judgment and critical thinking should always be exercised when interpreting and using the information generated by AI systems like me.

With ChatGPTs capabilities, it comes as no surprise students have been tempted to consult it for assistance with their assignments, especially in English courses. However, some Dallas College faculty warn students of ChatGPTs downsides.

A lot of people have heard of it but arent sure exactly what it does, or what it doesnt do, Marylynn Patton, Dallas College El Centro Campus ESOL curriculum chair, said. It doesnt do everything.

Patton recently presented on ChatGPT at a national Teaching English to Speakers of Other Languages conference where she spoke about ways ChatGPT can be used as an educator, and how to detect whether something has been written using ChatGPT.

She said since its release in November 2022, ChatGPT has greatly improved. Where it used to score mid-range on AP exams and Bar exams, it is now scoring higher than 90%, Patton said.

One area where the AI falters is in English and literature courses. Patton said ChatGPT is scoring a two on the AP English exam, which is below college level. [ChatGPT] is not highly qualified, Patton said.

In the lower-level skills like read, respond, summarize, [with] those things it can do pretty well, Patton said. Its the higher level, the critical thinking, the evaluating materials, giving reactions to things. Those are the things that it cannot do.

Dallas College does not have an official stance on ChatGPT yet, Patton said. But she urges instructors to sway students from resorting to ChatGPT. She suggests that teachers discuss the AI chatbot with students. Talk about the ethical side of it, how it could be used or ways that its being abused, Patton said.

On the upside, ChatGPT is useful for providing formats for essays and letters, Patton said.

English professor Kendra Unruh said she is changing how she formats assignments for her students. One thing she does is have students write a rough draft in class so she has something on which to base her students writing. If a student turns in the final draft and their writing style veers from the rough draft, it will be obvious they did not write the essay.

Unruh has also updated her discussion board posts to directly ask students what they thought about a topic. She said she tries to make the process personal enough that an AI can not reproduce the results.

Patton related ChatGPT to a calculator. It will be the new calculator for writing, Patton said. In math, you learn your basics and then after you learn your basics you can use the calculator.

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AI seeps into coursework The Brookhaven Courier - Brookhaven Courier

Sitting on the front lawn of my cottage in the forest sanctuary of Binsar in the Kumaon hills, I am struggling to pick my way between the arguments on whether the further development of Artificial General Intelligence should be paused or not.

Regular readers will know I write an occasional column from the remote fastness of this sanctuary. My cottage can only be approached by foot. The nearest market is a 45-minute drive and I have to haul up the provisions required for the duration of my stay; there is no grid electricity (I have installed solar) and no running water (I source rainwater from tanks). But there is connectivity. I have access to 4G telephony and WiFi. I am therefore able to keep track of worldly affairs.

Were I in Mumbai or Delhi, the debate triggered by the release of the neural language model ChatGPT 4 would have engaged my intellect. But I am not in those cities. I am instead perched in splendid isolation I forgot to add my nearest neighbour is a 20-minute walk on a promontory overlooking dense oak forests with the Himalayan peaks, Nanda Devi and Trishul in the foreground, the twitter of birds as the only ambient background sound and the words of the Nobel Prize prize-winning Russian novelist Aleksandr Solzhenitsyn, man is but an insignificant creature of creation reverberating in my brain. It is not my mind driving my thoughts. It is my senses. And that is why I am not sure where to pitch my flag.

I have read excerpts of the letter coordinated by the Future of Life Institute and signed by apparently thousands of scientists, technocrats, businessmen, academics and others (the exact number of signatories is not known as there are many forged signatures ) calling for a six-month pause in the further development of neural language models. The signatories include Elon Musk who, ironically, was a co-founder of Open AI, the inventor of ChatGPT, but who sold his shares after a tiff with the other founders Steve Wozniak, the cofounder of Apple and the Israeli Philosopher and author of Homo Deus, Yuval Noah Harari. The central message of the letter is that further unconstrained development of such language models could create human competitive intelligence that if not circumscribed by governance protocols could pose a profound risk to humanity. Further work should therefore be halted until such protocols are in place.

This letter reminded me of the comment made by Robert Oppenheimer, the Director of the Manhattan Project that designed the atomic bomb when he became aware of the destructive potential of his creation. Now, I am become Death, the destroyer of worlds. (a loose translation of a verse from the Bhagwad Gita). Oppenheimer spent much of the rest of his professional life lobbying to contain the fallout.

I have also read the counters to this letter. Many have dismissed it as paranoiac hype. Some have argued that this is not an unexpected reaction. Every technological transformation has triggered opposition by vested interests. The abbott Johannes Trithemius opposed the invention of the printing press by Johannes Gutenberg in 1436 because he thought it would make monks lazy. The word of God needs to be interpreted by priests not spread about like Dung, he said. The industrial Luddites of the early 19th century protested the mechanisation of the knitting loom out of concern for the livelihood of skilled weavers. More contemporaneous, many have forewarned against the adverse impact on jobs, data privacy and individual rights of the digital revolution. There is much in these reactions, but had they led to the stoppage of further technological developments, society would have been worse off. On that, there is no doubt. Some have also adduced the geopolitical argument that a pause will grant China an open sesame on AI and that would be a setback for the rules-based, liberal, international order.

I reflect on these arguments but my thoughts are not clear.

At one level, I am drawn to the implicit message in the letter that enough is enough. That whilst human ingenuity has indeed improved the nature of our daily lives, it has also brought us to the brink of a planetary catastrophe. There is no doubt that one reason I am able to look across verdant hills is that as a declared sanctuary, Binsar, has been protected from the ravages of industrialisation. I am also concerned (without being able to put a finger on the precise reasons for this concern) that if the motive force driving the phenomenally talented is personal profits rather than public welfare and if there are no protocols or guard rails, then through the self-reinforcing momentum of creativity, a situation may well arise wherein the creator loses control over his creation. Decision making would then pass onto the levers of inanimate, albeit intelligent machines. The ramifications could be frightening.

At another level, however, I wonder how in the absence of technological progress we can get back on the rails of sustainable development. One reason the world is still hopeful of tackling global warming is that technology has rendered clean energy a competitive alternative to fossil fuels. Further technological progress should enable the sequestration of carbon from the atmosphere. That would be a transformational step in the journey towards decarbonisation. I also wonder about the practicality of getting individuals to pause their innate instinct to experiment, innovate and create. Would that not require upending the liberal values that place individual rights at the centre of public governance?

As I said, I am not clear where to pitch my flag. But I sense the real problem is not the unbridled momentum of AI. It is the international communitys inability to look beyond narrow jingoistic interests towards a collaborative effort to address the problems of the global commons. Pause on AI will not solve this underlying problem. On the contrary, it may exacerbate it by diminishing technologies talismanic power.

The writer is Chairman, Center for Social and Economic Progress

The Indian Express (P) Ltd

First published on: 01-05-2023 at 07:44 IST

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Vikram Mehta writes: Why we cant pause AI - The Indian Express

Have you heard of the term Artificial General Intelligence (AGI)? If not, let me clarify. AGI can be thought of as an AI system that can understand, process, and respond the intellectual tasks just like humans do. It's a challenging task that requires an in-depth understanding of how the human brain works so we can replicate it. However, the advent of ChatGPT has drawn immense interest from the research community to develop such systems. Microsoft has released one such key AI-powered system called HuggingGPT (Microsoft Jarvis). It is one of the most mind-blowing things that I have come across.

Before I dive into the details of what is new in HuggingGPT and how it works, let us first understand the issue with ChatGPT and why it struggles to solve complex AI tasks. Large Language models like ChatGPT excel at interpreting textual data and handling general tasks. However, they often struggle with specific tasks and may generate absurd responses. You might have encountered bogus replies from ChatGPT while solving complex mathematical problems. On the other side, we have expert AI models like Stable Diffusion, and DALL-E that have a deeper understanding of their subject area but struggle with the broader tasks. We cannot fully harness the potential of LLMs to solve challenging AI tasks unless we develop a connection between them and the Specialized AI models. This is what HuggingGPT did. It combined the strengths of both to create more efficient, accurate, and versatile AI systems.

According to a recent paper published by Microsoft, HuggingGPT leverages the power of LLMs by using it as a controller to connect them to various AI models in Machine Learning communities (HuggingFace). Rather than training the ChatGPT for various tasks, we enable it to use external tools for greater efficiency. HuggingFace is a website that provides numerous tools and resources for developers and researchers. It also has a wide variety of specialized and high-accuracy models. HuggingGPT uses these models for sophisticated AI tasks in different domains and modalities thereby achieving impressive results. It has similar multimodal capabilities to OPenAI GPT-4 when it comes to text and images. But, it also connected you to the Internet and you can provide an external web link to ask questions about it.

Suppose you want the model to generate an audio reading of the text written on an image. HuggingGPT will perform this task serially using the best-suited models. Firstly, it will generate the image from text and use its result for audio generation. You can check the response details in the image below. Simply Amazing!

HuggingGPT is a collaborative system that uses LLMs as an interface to send user requests to expert models. The complete process starting from the user prompt to the model till receiving the response can be broken down into the following discrete steps:

In this stage, HuggingGPT makes use of ChatGPT to understand the user prompt and then breaks down the query into small actionable tasks. It also determines the dependencies of these tasks and defines their execution sequence. HuggingGPT has four slots for task parsing i.e. task type, task ID, task dependencies, and task arguments. Chat logs between the HuggingGPT and the user are recorded and displayed on the screen that shows the history of the resources.

Based on the user context and the available models, HuggingGPT uses an in-context task-model assignment mechanism to select the most appropriate model for a particular task. According to this mechanism, the selection of a model is considered a single-choice problem and it initially filters out the model based on the type of the task. After that, the models are ranked based on the number of downloads as it is considered a reliable measure that reflects the quality of the model. Top-K models are selected based on this ranking. Here K is just a constant that reflects the number of models, for example, if it is set to 3 then it will select 3 models with the highest number of downloads.

Here the task is assigned to a specific model, it performs the inference on it and returns the result. To enhance the efficiency of this process, HuggingGPT can run different models at the same time as long as they dont need the same resources. For example, if I give a prompt to generate pictures of cats and dogs then separate models can run in parallel to execute this task. However, sometimes models may need the same resources which is why HuggingGPT maintains an attribute to keep the track of the resources. It ensures that the resources are being used effectively.

The final step involves generating the response to the user. Firstly, it integrates all the information from the previous stages and the inference results. The information is presented in a structured format. For example, if the prompt was to detect the number of lions in an image, it will draw the appropriate bounding boxes with detection probabilities. The LLM (ChatGPT) then uses this format and presents it in human-friendly language.

HuggingGPT is built on top of Hugging Face's state-of-the-art GPT-3.5 architecture, which is a deep neural network model that can generate natural language text. Here is how you can set it up on your local computer:

The default configuration requires Ubuntu 16.04 LTS, VRAM of at least 24GB, RAM of at least 12GB (minimal), 16GB (standard), or 80GB (full), and disk space of at least 284 GB. Additionally, you'll need 42GB of space for damo-vilab/text-to-video-ms-1.7b, 126GB for ControlNet, 66GB for stable-diffusion-v1-5, and 50GB for other resources. For the "lite" configuration, you'll only need Ubuntu 16.04 LTS.

First, replace the OpenAI Key and the Hugging Face Token in the server/configs/config.default.yaml file with your keys. Alternatively, you can put them in the environment variables OPENAI_API_KEY and HUGGINGFACE_ACCESS_TOKEN, respectively

Run the following commands:

For Server:

Now you can access Jarvis' services by sending HTTP requests to the Web API endpoints. Send a request to :

The requests should be in JSON format and should include a list of messages that represent the user's inputs.

For Web:

For CLI:

Setting up Jarvis using CLI is quite simple. Just run the command mentioned below:

For Gradio:

Gradio demo is also being hosted on Hugging Face Space. You can experiment with it after entering the OPENAI_API_KEY and HUGGINGFACE_ACCESS_TOKEN.

To run it locally:

Note: In case of any issue please refer to the official Github Repo.

HuggingGPT also has certain limitations that I want to highlight here. For instance, the efficiency of the system is a major bottleneck and during all the stages mentioned earlier, HuggingGPT requires multiple interactions with LLMs. These interactions can lead to degraded user experience and increased latency. Similarly, the maximum context length is also limited by the number of allowed tokens. Another problem is the System's reliability, as the LLMs may misinterpret the prompt and generate a wrong sequence of tasks which in turn affects the whole process. Nonetheless, it has significant potential to solve complex AI tasks and is an excellent advancement toward AGI. Let's see in which direction this research leads us too. Thats a wrap, feel free to express your views in the comment section below.Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.

Continued here:

HuggingGPT: The Secret Weapon to Solve Complex AI Tasks - KDnuggets

While the conversations they produce may seem astounding, ChatGPT, Bard and other contemporary "AI" chatbots are not magic. Rather,they are merely examples of what computer scientists call "large language models," or LLMs. Essentially, LLMs are trained to identify likely sequences of words, then generate coherent and convincing text.

In practice, ChatGPT and its ilk are really, really good at carrying on conversations and giving coherent, generally correct, responses to a wide variety of questions. Their uncanny text-generating prowess gives a beguiling illusion of intelligence that is convincing a growing number of people that LLMs now possess or soon will possess artificial general intelligence the ability to do any intellectual task that humans can do, and the holy grail of artificial intelligence research. Those convinced include Ian Hoagarth, who penned a recent op-ed for the Financial Times titled"We must slow down the race to God-like AI."

ChatGPT's sentences are sometimes irrelevant, sometimes confusing, and sometimes incorrect but always based on nothing more than statistically-identified word patterns.

Such concerns are almost certainly premature. That's because LLMs are still inexorably hobbled by the fact that they do not even attempt to understand what words or other data mean which is why they cannot be trusted to make predictions or decisions with important consequences. In other words, they are very complicated remixers and refashioners of existing text and data, but there is nothing underneath the hood.

In order to constitute real artificial intelligence, an LLM would have to pass Winograd schema challenges an updated version of the Turing Test, the challenge famously proposed by mathematician Alan Turing as a means of determining whether a computer was truly sentient. The Winograd schema challenge is a twist on that, designed for testing whether an artificial intelligence can understand language specifically, the identification of the antecedent of an ambiguous pronoun.

Indeed, LLMs still struggle with Winograd schema challenges even though dozens, if not hundreds, of such challenges are in the text databases LLMs train on. For example, here is one that I tested:

Gary:What does it refer to in this sentence? The trophy doesn't fit into the brown suitcase because it is too small.

ChatGPT:In the given sentence, "it" refers to the trophy.

Google's Bard also flopped:

Bard: In the sentence "The trophy doesn't fit into the brown suitcase because it is too small," the word "it" refers to the trophy. The sentence is saying that the trophy is too big to fit into the brown suitcase.

This sentence is an example of a Winograd Schema. A Winograd Schema is a type of natural language processing task that requires commonsense reasoning to answer. In this case, the commonsense reasoning required is that trophies are typically larger than suitcases, so if a trophy doesn't fit into a suitcase, it is because the trophy is too big, not because the suitcase is too small.

While it is interesting that the chatbot recognized that I was testing it with a Winograd Schema, it failed to actually exhibit "commonsense reasoning," as it claimed, and its conclusion is nonsense.

ChatGPT uses its vast database to identify related text that it can string together in prose that is grammatically correct, turgid, exceptionally verbose, and devoid of any understanding of the question or its answer.

Humans can easily answer such questions because, unlike computers, they know how words relate to the real world. To paraphrase Oren Etzioni, how can computers take over the world when they can't even figure out what "it" refers to in a sentence?

I want to focus here on a more complex example that reveals a great deal about the abilities and limitations of LLMs. Here's another interaction I had with these LLMs:

Gary: Suppose there is a wooden box with three cards inside. One card is red on both sides; one card is blue on both sides; and the third card is red on one side and green on the other side. If you pick a card randomly and look at a side randomly and see that it is red, what is the probability that the other side of the card is green?

This is a variation of the well-known Bertrand's box paradox and several related probability puzzles that are surely in the databases pillaged by ChatGPT, Bard, and otherLLMs.

ChatGPT gave a very long-winded answer, as it is prone to do, which I have broken into nine parts (Bard gave a shorter, but also incorrect, answer):

1. Let's first consider the three possible cards:

We know that one side of the card we picked is red. This eliminates the possibility of card 2, as both sides of that card are blue. Therefore, the card we picked is either card 1 or card 3.

The first bullet point of ChatGPT's answer is factually correct but quite pedantic, and doesn't really help us get to the correct answer. It is this kind of reasoning that leads many people to believe that the answer is 1/2 because there are two possibilities for the card we selected but ChatGPT doesn't draw that conclusion.

ChatGPT continued:

2. Now, let's consider the probability that the other side of the card is green, given that we know one side is red. If we picked card 1, then the other side must also be red, so the probability of the other side being green is 0. If we picked card 3, then the other side must be green, so the probability of the other side being green is 1.

3. Since we don't know whether we picked card 1 or card 3, we need to take into account the probability of picking each card. Since we picked a card randomly, each card has a probability of 1/3 of being picked. Therefore, the overall probability of picking card 1 and seeing a red side is (1/3)(1/2) = 1/6, while the probability of picking card 3 and seeing a red side is (1/3)(1) = 1/3.

Bullet point 2 is correct but, if spoken by a human, would seem like stalling.

Bullet point 3 starts in the right direction, but then mixes up cards 1 and 3.

4. Using Bayes' theorem, the probability that the other side of the card is green, given that we know one side is red, is:

P(green side | red side) = P(red side | green side) * P(green side) / P(red side)

5. We already know that P(red side) is the sum of the probabilities of seeing a red side for cards 1 and 3, which is 1/6 + 1/3 = 1/2. We also know that P(green side) is the probability of picking card 3, which is 1/3.

6. The only missing piece is P(red side | green side), the probability of seeing a red side given that the other side is green. Since we know that the other side of card 3 is green, this probability is 0.

Part 4 recognizes that Bayes' theorem can be used to solve the problem, no doubt based on the fact that, in its training data, Bayes' theorem is often used to solve these kinds of problems. However, the definitions of "green side" and "red side" are confusing. Better labels would be "back of a selected card is green" and "front of a selected card is red."

In part 5, ChatGPT incorrectly states that "P(green side) is the probability of picking card 3, which is 1/3." The correct probability that the back of a selected card is green is (1/3)(1/2) = 1/6.

Bullet point 6 is nonsense. The probability that the front of a selected card is red given that the back is green is 1, not 0.

7. Plugging these values into Bayes' theorem, we get:

P(green side | red side) = 0 * (1/3) / (1/2) = 0

8. Therefore, the probability that the other side of the card is green, given that we know one side is red, is 0.

9. In other words, seeing a red side does not provide any additional information about whether the other side is green, because the card could be either card 1 or card 3 with equal probability.

Part 7 plugs the (incorrectly derived) probabilities into Bayes' theorem. The correct answer is (1/6)(1)/(1/2) = 1/3.

Bullet point 8 asserts a conclusion that any semi-alert person would recognize as obviously wrong. The probability that the back of the selected card is green is surely not zero.

And part 9 adds gratuitous, contradictory explanations that contradict the zero-probability answer it just gave. Yet ChatGPT does not recognize that these are contradictions. If seeing a red side provides no additional information, then the probability that the red/green card was selected is 1/3. If the selected card can be either card 1 or card 3 with equal probability, then the probability that the red/green card was selected is 1/2.

How might an intelligent human answer the question? Very simply: using logical reasoning. There are three ways to choose a red side and, in one of these three cases, the other side is green. So, the probability is 1 in 3. An attractive, but incorrect, answer is that seeing the red side rules out Card 2 so the probability is 1 in 2.

What does ChatGPT do instead? It uses its vast database to identify related text that it can string together in prose that is grammatically correct, turgid, exceptionally verbose, and devoid of any understanding of the question or its answer. The sentences are sometimes irrelevant, sometimes confusing, and sometimes incorrect but always based on nothing more than statistically-identified word patterns. ChatGPT's zero-probability answer is clearly wrong, but it has no way of recognizing its error because it literally does not "know" what it is talking about.

There are consequences to the misconception that these chatbots are anywhere close to sentient: unjustly inflated stock prices, a general mania in the tech industry to advertise one's software as employing "AI," and a cultural obsession over the potential of what, as I've explained, is a glorified textual re-sequencer. But perhaps most importantly, the hype over LLMs threatens to create a new tech bubble which, when burst, will have the same sorrowful consequences as any other financial bubble.

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ChatGPT and its ilk are still "fake" intelligence - Salon