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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

A former key researcher at OpenAI believes there is a decent chance that artificial intelligence will take control of humanity and destroy it.

"I think maybe there's something like a 10-20% chance of AI takeover, [with] many [or] most humans dead, " Paul Christiano, who ran the language model alignment team at OpenAI, said on the Bankless podcast. "I take it quite seriously."

Christiano, who now heads the Alignment Research Center, a non-profit aimed at aligning AIs and machine learning systems with human interests, said that hes particularly worried about what happens when AIs reach the logical and creative capacity of a human being. "Overall, maybe we're talking about a 50/50 chance of catastrophe shortly after we have systems at the human level," he said.

Christiano is in good company. Recently scores of scientists around the world signed an online letter urging that OpenAI and other companies racing to build faster, smarter AIs, hit the pause button on development. Big wigs from Bill Gates to Elon Musk have expressed concern that, left unchecked, AI represents an obvious, existential danger to people.

Why would AI become evil? Fundamentally, for the same reason that a person does: training and life experience.

Like a baby, AI is trained by receiving mountains of data without really knowing what to do with it. It learns by trying to achieve certain goals with random actions and zeroes in on correct results, as defined by training.

So far, by immersing itself in data accrued on the internet, machine learning has enabled AIs to make huge leaps in stringing together well-structured, coherent responses to human queries. At the same time, the underlying computer processing that powers machine learning is getting faster, better, and more specialized. Some scientists believe that within a decade, that processing power, combined with artificial intelligence, will allow these machines to become sentient, like humans, and have a sense of self.

Thats when things get hairy. And its why many researchers argue that we need to figure out how to impose guardrails now, rather than later. As long as AI behavior is monitored, it can be controlled.

But if the coin lands on the other side, even OpenAIs co-founder says that things could get very, very bad.

This topic has been on the table for years. One of the most famous debates on the subject took place 11 years ago between AI researcher Eliezer Yudkowsky and the economist Robin Hanson. The two discussed the possibility of reaching foomwhich apparently stands for Fast Onset of Overwhelming Masterythe point at which AI becomes exponentially smarter than humans and capable of self improvement. (The derivation of the term foom is debatable.)

Eliezer and his acolytes believe its inevitable AIs will go 'foom' without warning, meaning, one day you build an AGI [artificial general intelligence] and hours or days later the thing has recursively self-improved into godlike intelligence and then eats the world. Is this realistic?" Perry Metzger, a computer scientist active in the AI community, tweeted recently.

Metzger argued that even when computer systems reach a level of human intelligence, theres still plenty of time to head off any bad outcomes. Is 'foom' logically possible? Maybe. Im not convinced," he said. "Is it real world possible? Im pretty sure no. Is long term deeply superhuman AI going to be a thing? Yes, but not a foom

Another prominent figure, Yann Le Cun, also raised his voice, claiming it is "utterly impossible," for humanity to experience an AI takeover. Lets hope so.

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Former OpenAI Researcher: Theres a 50% Chance AI Ends in 'Catastrophe' - Decrypt

Slashdot reader wiredmikey writes: Mira Murati, CTO of ChatGPT creator OpenAI, says artificial general intelligence (AGI) systems should be "absolutely" be regulated. In a recent interview, Murati said the company is constantly talking with governments and regulators and other organizations to agree on some level of standards. "We've done some work on that in the past couple of years with large language model developers in aligning on some basic safety standards for deployment of these models," Murati said. "But I think a lot more needs to happen. Government regulators should certainly be very involved." Murati specifically discussed OpenAI's approach to AGI with "human-level capability."OpenAI's specific vision around it is to build it safely and figure out how to build it in a way that's aligned with human intentions, so that the AI systems are doing the things that we want them to do, and that it maximally benefits as many people out there as possible, ideally everyone.

Q: Is there a path between products like GPT-4 and AGI?

A: We're far from the point of having a safe, reliable, aligned AGI system. Our path to getting there has a couple of important vectors. From a research standpoint, we're trying to build systems that have a robust understanding of the world similarly to how we do as humans. Systems like GPT-3 initially were trained only on text data, but our world is not only made of text, so we have images as well and then we started introducing other modalities.

The other angle has been scaling these systems to increase their generality. With GPT-4, we're dealing with a much more capable system, specifically from the angle of reasoning about things. This capability is key. If the model is smart enough to understand an ambiguous direction or a high-level direction, then you can figure out how to make it follow this direction. But if it doesn't even understand that high-level goal or high-level direction, it's much harder to align it. It's not enough to build this technology in a vacuum in a lab. We really need this contact with reality, with the real world, to see where are the weaknesses, where are the breakage points, and try to do so in a way that's controlled and low risk and get as much feedback as possible.

Q: What safety measures do you take?

A: We think about interventions at each stage. We redact certain data from the initial training on the model. With DALL-E, we wanted to reduce harmful bias issues we were seeing... In the model training, with ChatGPT in particular, we did reinforcement learning with human feedback to help the model get more aligned with human preferences. Basically what we're trying to do is amplify what's considered good behavior and then de-amplify what's considered bad behavior. One final quote from the interview: "Designing safety mechanisms in complex systems is hard... The safety mechanisms and coordination mechanisms in these AI systems and any complex technological system [are] difficult and require a lot of thought, exploration and coordination among players."

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OpenAI CTO Says AI Systems Should 'Absolutely' Be Regulated - Slashdot

By Desa Philadelphia

Is AI Creative?That was the central question of discussion at a forum held at the USC School of Cinematic Arts (SCA) on Wednesday April 26, that brought together specialists in engineering, computer science, and filmmaking to talk about the capabilities, and limitations, of platforms like ChatGPT, Midjourney and DALL.E.The event, AI, Creativity & The Future of Film, was conceived by SCA alumnus Jon Dudkowski, a director and editor whose credits include Star Trek: Discovery, and Karim Jerbi, a Visiting Scholar at the Brain Imaging Group at USCs Ming Hsieh Institute, which is in the Department of Electrical Engineering at the USC Viterbi School of Engineering. Sponsored by Adobe, and presented as a joint effort between SCA and the USC Viterbi School of Engineering, the evening was an exercise in level-setting, to dispel myths about what AI is currently capable of creating. The answer? Nobodys job is in dangeryet.

The night began with a presentation by Yves Bergquist, Director of the AI & Blockchain in Media Projectat USCs Entertainment Technology Center at the School of Cinematic Arts, on the science behind the most popular emerging platforms. He explained the generative models at the heart of the technologiesfrom the Transformers like ChatGPT, which is able to sequence data to produce text for essays, prose, poetry, scripts etc.; through Diffusion models, like DALL.E, which add and then remove noise from existing images to create new ones; to efforts at integrating existing models. He then offered this definitive assessment of ChatGPT, the essay-writing bot that has been at the center of plagiarism concerns across the university: It is very good at writing bad and boring text. It is not going to be able to write a story. It is not going to be able to write a script. It does not understand the world at a level of symbolism, at a level of depth that we understand.

Jerbi took the standing-room-only audience through demonstrations of the kinds of experiments being done by researchers in Neuro-AI, a new field of inquiry that compares the brain activities of humans and machines that are performing the same tasks. The goal is to compare the biological networks of the brain with the artificial ones. We are seeing tremendous progress in AI but still far from human level intelligence, said Jerbi. Some things a toddler can do that the most advanced AI cant do. Jerbi however offered this discomforting fact. The next generation of AI, dubbed Artificial General Intelligence, is focused on closing that gap. The key word is General meaning the ability to apply instructions innovatively. Todays AI might use a hammer to just hit the nail it is instructed to pound; but generalized intelligence might then apply the hammer in breaking up rocks, without being told.Dudkowski then moderated a panel discussion in which Bergquist and Jerbi were joined by filmmakers Chad Nelson, whose film Critterz features characters created using DALL.E; Mary Sweeney, who produced and/or edited several David Lynch projects, and SCA alumna Athena Wickham, the executive producer of Westword and The Peripheral. They were also joined by William Swartout, Chief Technology Officer of the USC Institute for Creative Technologies.

No one on the panel yielded to any suggestion that AI, in and of itself, can be creative. Instead the consensus was that as a tool, it could facilitate faster iterations of works like script drafts, storyboards and production design. What excites me for myself is being able to use it like a tool to accelerate the process and to see what you have and dont have more quickly and inexpensively, said Wickham. What scares me is people getting lazy with it. I do worry that Im going to start getting a lot of scripts and pitches that feel like someone hasnt taken the time to edit it and put their own spin on it and thats going to piss me off.

Nelson concurred: I personally havent seen an AI image where I think thats all that needs to happen, its done. It doesnt know good from bad. Someone still has to say thats good.Sweeney worried that AI platforms will encourage more of the kind of device addiction that has been linked to depression in young people. But she described her approach as cautiously curious and compared new approaches to the shift from analog to digital film editing. Im always interested in new tools.

Essentially reading the room, Swartout acknowledged the attention AI platforms have been receiving in the press lately, and succinctly summarized the state of AI creativity at this moment: In the popular mind we are going to think we are much further than we are.

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USC Cinematic Arts - University of Southern California

If artificial intelligence (AI) and machine learning are to become so advanced as to compete with highly trained professionals, such as accountants, lawyers, and doctors, writes Baruch President S. David Wu in his April blog post, it may be time for us to think how this ups the game for humans. His advice for students: Embrace the kind of education that will best prepare you for a future where AI, machine learning, and other forms of white collar automation are reality.

In late March, I chatted with a few Baruch faculty members after an annual cross-college research symposium. Over the course of our conversation, the topic turned to artificial intelligence (AI)specifically ChatGPTand whether it is any different from other high-tech breakthroughs we have experienced in our lifetime.

Two opposing views quickly emerged: first, AI is similar to other technological toolssuch as smartphones and the internetthat altered how we do things and we will learn to adapt to it too; and second, AI is entirely different from anything we have seen before and will change every facet of our lives, replace a massive number of jobs, and create an element of uncertainty that could make the doomsday scenario of robots taking over the world a real possibility. Like other predictions of this kind, I believe it is important to give them due respect and consideration, because when we understand the risks of a technology, we can take steps to mitigate those risks.

On college campuses, another obvious concern is the potential for widespread cheating using the software, a problem that has received a great deal of national attention. Because numerous articles have already addressedquite eloquentlyhow to ensure academic integrity in the face of ChatGPT and other tools, I wont spend time discussing that topic here. Id rather focus on a broader issue: If AI and machine learning are to become so advanced as to compete with highly trained professionals, such as accountants, lawyers, and doctors, it may be time for us to think how this ups the game for humans. As for our students, you need to embrace the kind of education that will best prepare you for a future where AI, machine learning, and other forms of white collar automation are reality.

For millennia, humans existed as members of agrarian societiesuntil the last three centuries, when expeditious advancements in technology resulted in dramatic shifts in the way we think and live. The steam engine, initially invented in 1712 and then improved upon over several decades, helped usher in the Industrial Revolution. The internal combustion engine, first invented around 1860, was revolutionary for transportation and travel. Access to electricity, signified by the invention of the modern light bulb in 1880, then set the stage for life today.

Between the 1880s and 1920s, the combination of the telephone, wireless telegraph, and radio made it possible to broadly share news and information, connecting the world like never before. The popularization of television in the 1950s quickly transformed mass communication and merged it with entertainment, creating an even-larger shared cultural experience. More recently, the computer and internet further expanded communication and commerce and helped create a truly global economy and society.

History shows us that society has been continuously and profoundly impacted by technological advances. While affected unequally by the associated social changes and transitions, humans have always found ways to adapt to new realities. With each generational innovation, we adjusted our responsibilitiesas well as our expectations and aspirationsby adopting new skills that worked with the technology, which moved mankind to a higher rung on the evolutionary ladder. Through this timeline, we can see that the human race has not only survived and prevailed, butby most objective measuresthrived. I think of AI as the next step of that evolutionary process.

When I started my career as a professor more than three decades ago, I taught a series of graduate courses in artificial intelligencepartly because I was fascinated by the technology and its potential. While I am astonished by the rapid developments made in AI since then, most of the basic principles that created programs such as ChatGPT were known years ago.

The program is a large language model that can be trained to mimic the human brain in its capacity to collect data, detect patterns, learn, and evolve. ChatGPT was trained on massive amounts of data, millions of documents, and trillions of wordsleveraging rapid advancements in computing power that allow mere concepts to become reality. What amazes people is ChatGPTs ability to generate massive strings of computer code or even creative text formatssuch as emails, letters, poems, scripts, and musical piecesand to do so at a rate of 200 words per second.

Even more impressive is the rate of advancement. OpenAI, the company that created the product, said its latest version, GPT-4, recently passed a simulated law school bar exam with a score around the top 10 percent of test takers. OpenAI also reported GPT-4performed strongly on the LSAT, GRE, SAT, and many AP exams. By contrast, less than a year ago, the prior version scored in the bottom 10 percent on those same exams.

With the development and rapid progress of AI, the fear of human replacement is very realand not completely unfounded.

ChatGPT and similar programs have already been used for a variety of tasks in businessesat accounting firms, it has automated data entry and saved companies millions; at law firms to research legal precedents; and at small businesses to generate marketing materials. As this technology continues to evolve, it is likely we will see even more innovative applications in the not-so-distant future that go beyond replacing routine duties.

In fact, the next major milestone for AI and machine learning is artificial general intelligence (AGI). According to Stanford Encyclopedia of Philosophy, AGI is a computer system that would have the ability to understand or learn any intellectual task that a human being can.Some experts believe AGI is achievable in the near future, while others believe that it is still decades away. As the field of AGI is rapidly evolving, it is possible we will see significant progress in the years to come. Further, it is conceivable that a large amount of work currently done by human beings will be replaced by such mechanismsat least ones that have well-defined tasks.

An October 2020 report by the World Economic Forum projected that, while machines with AI will assume about 85 million jobs by 2025, nearly 100 million jobs will be created simultaneously thanks to the same technology. There is no doubt then that the best way for all professionalsespecially you, our studentsto prepare for and adapt to these forthcoming opportunities is to understand how your chosen path is likely to evolve and to develop the parts of yourselves that cannot be replaced by technology. This means cultivating creativity, empathy, and other qualities that make us uniquely human. It means finding new ways to connect and build meaningful relationships. It also means learning to think critically and to question assumptions, rather than simply following established conventions. While AI systems can appear to be creative, that is achieved largely by permutation (i.e., trying millions of combinations by brute force and picking the best one) and not by imagination. By thinking outside the box, human beings are able to come up with thoughtful and far-reaching solutions.

The education that prepares you for the future is one that requires you to move beyond traditional classrooms and learn how to be human again. It is more important than ever that you not only hone your technical skills but develop excellent social and emotional intelligence. I made the point earlier that exposure to art, music, and other cultures expands our experiential base and our ability to relate to others, to learn how to work collaboratively, communicate effectively, and build relationships based on trust, respect, and mutual understanding. These are the foundations of ethical decision making and problem solving.

The education that prepares students for the future is one that develops them into changemakers and leaders as the world makes giant leaps under our watchful eyes. It is an education that instills in us an ability to learn and a love for learning, for lifenot only the latest professional skills or the highest level of digital savvy, but everything that is needed to explore, grow, and overcome. If you are lucky, you will also develop a sense of purpose and meaning in your lifefinding fulfillment and satisfaction in whatever you pursue. And that is how we prepare for the future.

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AI, the Future of Work, and the Education That Prepares You ... - City University of New York