Episode 1914 - May 30 - Tiếng Anh - Phần 3 của 3 - AI có thể lập kế hoạch không - Vina Technology at AI time - Lê Quang Văn | Podcast - Nhac.vn

Can an A.I. Make Plans? – Part 3 of 3

Today’s systems struggle to magine the future—but that may soon change.

By Cal Newport. The New Yorker. March 15, 2024.

In another example, the researchers asked GPT-4 to write a short poem in which the last line uses the same words as the first, but in reverse order. Furthermore, they specified that all of the lines of the poem needed to make sense in both grammar and content. For example:

Darkness requires light,

Toward this truth our imagination takes flight.

But let us not forget, as we take solace,

Light requires Darkness.

Humans can easily handle this task: the above poem, terrible as it is, satisfies the prompt and took me less than a minute to compose. GPT-4, on the other hand, stumbled. When Bubeck’s team asked it to attempt the assignment, the chatbot started its poem with the line “I heard his voice across the crowd”—an ill-advised decision that led, inevitably, to the nonsensical concluding line “Crowd the across voice his heard I.” To succeed in this poem-writing challenge, you need to think about writing your last line before you compose your first. GPT-4 wasn’t able to peer into the future that way. “The model relies on a local and greedy process of generating the next word, without any global or deep understanding of the task or the output,” the researchers wrote.

Bubeck’s team wasn’t the only one to explore the planning struggle. In December, a paper presented at Neural Information Processing Systems, a prominent artificial-intelligence conference, asked several L.L.M.s to tackle “commonsense planning tasks,” including rearranging colored blocks into stacks ordered in specific ways and coming up with efficient schedules for shipping goods through a network of cities and connecting roads. In all cases, the problems were designed to be easily solvable by people, but also to require the ability to look ahead to understand how current moves might alter what’s possible later. Of the models tested, GPT-4 performed best; even it was able to achieve only a twelve-per-cent success rate.

These problems with planning aren’t superficial. They can’t be fixed by making L.L.M.s bigger, or by changing how they’re trained. They reflect something fundamental about the way these models operate.

A system like GPT-4 is outrageously complicated, but one way to understand it is as a supercharged word predictor. You feed it input, in the form of text, and it outputs, one at a time, a string of words that it predicts will extend the input in a rational manner. (If you give a large language model the input “Mary had a little,” it will likely output “lamb.”) A.I. applications like ChatGPT are wrapped around large language models such as GPT-4. To generate a long response to your prompt, ChatGPT repeatedly invokes its underlying model, growing the output one word at a time.

To choose their words, language models start by running their input through a series of pattern recognizers, arranged into sequential layers. As the text proceeds through this exegetical assembly line, the model incrementally builds up a sophisticated internal representation of what it’s being asked about. It might help to imagine that the model has a vast checklist containing billions of possible properties; as the input text is processed by the model, it is checking off all of the properties that seem to apply. For example, if you provide GPT-4 with a description of a chessboard and ask it to make a move, the model might check off properties indicating that the input is about a game, that the game is chess, and that the user is asking for a move. Some properties might be related to more specific information, such as the fact that the board described in the input has a white knight on space E3; others might encode abstract observations, like the role that the white knight in space E3 is playing in protecting its king.

Once the input has been processed, the model must now apply what it’s learned to help select its next word.