What Is ChatGPT Doing... and Why Does It Work?

Good piece by Stephen Wolfram,  below the intro: 

What Is ChatGPT Doing... and Why Does It Work?

It’s Just Adding One Word at a Time

That ChatGPT can automatically generate something that reads even superficially like human-written text is remarkable, and unexpected. But how does it do it? And why does it work? My purpose here is to give a rough outline of what’s going on inside ChatGPT—and then to explore why it is that it can do so well in producing what we might consider to be meaningful text. I should say at the outset that I’m going to focus on the big picture of what’s going on—and while I’ll mention some engineering details, I won’t get deeply into them. (And the essence of what I’ll say applies just as well to other current “large language models” [LLMs] as to ChatGPT.)

The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.”

So let’s say we’ve got the text “The best thing about AI is its ability to”. Imagine scanning billions of pages of human-written text (say on the web and in digitized books) and finding all instances of this text—then seeing what word comes next what fraction of the time. ChatGPT effectively does something like this, except that (as I’ll explain) it doesn’t look at literal text; it looks for things that in a certain sense “match in meaning”. But the end result is that it produces a ranked list of words that might follow, together with “probabilities”:

And the remarkable thing is that when ChatGPT does something like write an essay what it’s essentially doing is just asking over and over again “given the text so far, what should the next word be?”—and each time adding a word. (More precisely, as I’ll explain, it’s adding a “token”, which could be just a part of a word, which is why it can sometimes “make up new words”.) ... '

Google Research on Language Models

Good insight into what this is all about,  and what they are doing today and beyond.  Below just the intro, following:

Google Research, 2022 & beyond: Language, vision and generative models

WEDNESDAY, JANUARY 18, 2023

Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the Google Research community  ... 

Today we kick off a series of blog posts about exciting new developments from Google Research. Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in the series.

I’ve always been interested in computers because of their ability to help people better understand the world around them. Over the last decade, much of the research done at Google has been in pursuit of a similar vision — to help people better understand the world around them and get things done. We want to build more capable machines that partner with people to accomplish a huge variety of tasks. All kinds of tasks. Complex, information-seeking tasks. Creative tasks, like creating music, drawing new pictures, or creating videos. Analysis and synthesis tasks, like crafting new documents or emails from a few sentences of guidance, or partnering with people to jointly write software together. We want to solve complex mathematical or scientific problems. Transform modalities, or translate the world’s information into any language. Diagnose complex diseases, or understand the physical world. Accomplish complex, multi-step actions in both the virtual software world and the physical world of robotics.

We’ve demonstrated early versions of some of these capabilities in research artifacts, and we’ve partnered with many teams across Google to ship some of these capabilities in Google products that touch the lives of billions of users. But the most exciting aspects of this journey still lie ahead!

With this post, I am kicking off a series in which researchers across Google will highlight some exciting progress we've made in 2022 and present our vision for 2023 and beyond. I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models. Over the next several weeks, we will discuss novel developments in research topics ranging from responsible AI to algorithms and computer systems to science, health and robotics. Let’s get started!  ... '  

Towards Helpful Robots: Grounding Language in Robotic Affordances

In Google Blog,   what and how can we get things done with commands?    How will the language models help.  

Towards Helpful Robots: Grounding Language in Robotic Affordances

Tuesday, August 16, 2022  ...Posted by Brian Ichter and Karol Hausman, Research Scientists, Google Research, Brain Team

Over the last several years, we have seen significant progress in applying machine learning to robotics. However, robotic systems today are capable of executing only very short, hard-coded commands, such as “Pick up an apple,” because they tend to perform best with clear tasks and rewards. They struggle with learning to perform long-horizon tasks and reasoning about abstract goals, such as a user prompt like “I just worked out, can you get me a healthy snack?”

Meanwhile, recent progress in training language models (LMs) has led to systems that can perform a wide range of language understanding and generation tasks with impressive results. However, these language models are inherently not grounded in the physical world due to the nature of their training process: a language model generally does not interact with its environment nor observe the outcome of its responses. This can result in it generating instructions that may be illogical, impractical or unsafe for a robot to complete in a physical context. For example, when prompted with “I spilled my drink, can you help?” the language model GPT-3 responds with “You could try using a vacuum cleaner,” a suggestion that may be unsafe or impossible for the robot to execute. When asking the FLAN language model the same question, it apologizes for the spill with "I'm sorry, I didn't mean to spill it,” which is not a very useful response. Therefore, we asked ourselves, is there an effective way to combine advanced language models with robot learning algorithms to leverage the benefits of both?

In “Do As I Can, Not As I Say: Grounding Language in Robotic Affordances”, we present a novel approach, developed in partnership with Everyday Robots, that leverages advanced language model knowledge to enable a physical agent, such as a robot, to follow high-level textual instructions for physically-grounded tasks, while grounding the language model in tasks that are feasible within a specific real-world context. We evaluate our method, which we call PaLM-SayCan, by placing robots in a real kitchen setting and giving them tasks expressed in natural language. We observe highly interpretable results for temporally-extended complex and abstract tasks, like “I just worked out, please bring me a snack and a drink to recover.” Specifically, we demonstrate that grounding the language model in the real world nearly halves errors over non-grounded baselines. We are also excited to release a robot simulation setup where the research community can test this approach.

With PaLM-SayCan, the robot acts as the language model’s “hands and eyes,” while the language model supplies high-level semantic knowledge about the task.

A Dialog Between User and Robot, Facilitated by the Language Model

Our approach uses the knowledge contained in language models (Say) to determine and score actions that are useful towards high-level instructions. It also uses an affordance function (Can) that enables real-world-grounding and determines which actions are possible to execute in a given environment. Using the the PaLM language model, we call this PaLM-SayCan.

Our approach selects skills based on what the language model scores as useful to the high level instruction and what the affordance model scores as possible.

Our system can be seen as a dialog between the user and robot, facilitated by the language model. The user starts by giving an instruction that the language model turns into a sequence of steps for the robot to execute. This sequence is filtered using the robot’s skillset to determine the most feasible plan given its current state and environment. The model determines the probability of a specific skill successfully making progress toward completing the instruction by multiplying two probabilities: (1) task-grounding (i.e., a skill language description) and (2) world-grounding (i.e., skill feasibility in the current state).

There are additional benefits of our approach in terms of its safety and interpretability. First, by allowing the LM to score different options rather than generate the most likely output, we effectively constrain the LM to only output one of the pre-selected responses. In addition, the user can easily understand the decision making process by looking at the separate language and affordance scores, rather than a single output.

PaLM-SayCan is also interpretable: at each step, we can see the top options it considers based on their language score (blue), affordance score (red), and combined score (green).

Training Policies and Value Functions

Each skill in the agent’s skillset is defined as a policy with a short language description (e.g., “pick up the can”), represented as embeddings, and an affordance function that indicates the probability of completing the skill from the robot’s current state. To learn the affordance functions, we use sparse reward functions set to 1.0 for a successful execution, and 0.0 otherwise.

We use image-based behavioral cloning (BC) to train the language-conditioned policies and temporal-difference-based (TD) reinforcement learning (RL) to train the value functions. To train the policies, we collected data from 68,000 demos performed by 10 robots over 11 months and added 12,000 successful episodes, filtered from a set of autonomous episodes of learned policies. We then learned the language conditioned value functions using MT-Opt in the Everyday Robots simulator. The simulator complements our real robot fleet with a simulated version of the skills and environment, which is transformed using RetinaGAN to reduce the simulation-to-real gap. We bootstrapped simulation policies’ performance by using demonstrations to provide initial successes, and then continuously improved RL performance with online data collection in simulation.   ... ' 

Can We Trust Our Impressive AI Language Models?

As usual an excellent, heavily linked and considerable and serious piece by Irving, below just an intro, ldo ink-through...

Irving Wladawsky-Berger

A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects .... 

Can We Trust Our Impressive AI Language Models?

One of the key findings of the 2022 AI Index Report was that large language models (LLMs) are setting records on technical benchmarks thanks to advances in deep neural networks and computational power that allows them to be trained using huge amounts of data. LLMs are now surpassing human baselines in a number of complex language tasks, including English language understanding, text summarization, natural language inference, and machine translation.

A.I. Is Mastering Language. Should We Trust What It Says?, a recent NY Times Magazine article by science writer Steven Johnson, took a close look at one such LLM, the Generative Pre-Trained Transformer 3, generally referred to as GPT-3. GPT-3 was created by the AI research company OpenAI. It’s been trained with over 700 gigabytes of data from across the web, along with a large collection of text from digitized books. “Since GPT-3’s release, the internet has been awash with examples of the software’s eerie facility with language - along with its blind spots and foibles and other more sinister tendencies,” said Johnson. .... ' 

What have Language Models Learned?

 Interesting

What Have Language Models Learned?

Large language models are making it possible for computers to write stories, program a website and turn captions into images.

One of the first of these models, BERT, is trained by taking sentences, splitting them into individual words, randomly hiding some of them, and predicting what the hidden words are. After doing this millions of times, BERT has “read” enough Shakespeare to predict how this phrase usually ends: