[CG] ChatGPT can't make simple generalizations

James Davenport found an article that shows how simple-minded ChatGPT happens to be. If it can find an appropriate reasoning method stored in its immense volume of stored data, it can seem to be a genius. But if the problem requires a simple transformation of that reasoning method, it can be very stupid or horribly wrong. Observation: There are three safe and dependable ways of using LLMs: 1. Translate languages (including computer notations) to and from equivalent forms in other languages. As we have seen, Wolfram, Kingsley Idehen, and others have successfully used LLMs to provide English-like front ends to their systems. 2. Use LLMs with a relatively small corpus of closely related data, such as user manuals for some equipment or the complete corpus of a single author to support Q/A sessions about what that author said or wrote. 3. Use LLMs with a larger amount of data about a fairly large field to generate hypotheses (guesses) about topics in that field, and then use the 70+ years of work in AI and Computer Science to test, evaluate, and correct whatever the LLMs generate. All three of these methods can be run on a good laptop computer with a disk drive that holds the data (a couple of terabytes would be sufficient). The laptop could be extended to a larger systems for supporting the workload of a large corporation. But the monstrous computational systems used by Google, OpenGPT, and others is an irresponsible waste of hardware, electricity, water, and other resources. The European Union is already putting restrictions on companies that are trying to emulate Google, OpenGPT, and other wasteful systems. And by the way, there are hints coming from Google employees who are becoming disillusioned about the value of processing more and bigger volumes of data. When a system cannot do simple reasoning and generalization, it can never be truly intelligent. Adding more power to a stupid system generates larger volumes of stupidity. John ---------------------------------------- From: "James Davenport' via ontolog-forum" <ontolog-forum(a)googlegroups.com> Sent: 7/9/24 10:13 PM There’s a good article today in the Financial Times, showing that, while ChatGPT can solve well-known puzzles (Monty Hall etc.), that’s because it has seen the solution, and it can’t even solve alpha-converted variants. The conclusion is good. A computer that is capable of seeming so right yet being so wrong is a risky tool to use. It’s as though we were relying on a spreadsheet for our analysis (hazardous enough already) and the spreadsheet would occasionally and sporadically forget how multiplication worked. Not for the first time, we learn that large language models can be phenomenal bullshit engines. The difficulty here is that the bullshit is so terribly plausible. We have seen falsehoods before, and errors, and goodness knows we have seen fluent bluffers. But this? This is something new. https://www.ft.com/content/7cb55561-8315-487a-a904-d5a92f37551d?desktop=tru… _________________ From: John F Sowa I received the following reply in an offline note: Anonymous: ChatGPT is BS. It says what is most likely to come next in our use of language without regard to its truth or falsity. That seems to me to be its primary threat to us. It can BS so much better than we can, more precisely and more effectively using statistics with a massive amount of "test data," than we can ever do with our intuition regarding a relatively meager amount of learning. That is partly true. LLMs generate a text that is derived by using probabilities derived from a massive amount of miscellaneous texts of any kind: books, articles, notes, messages, etc. They have access to a massive amount of true information -- more than any human could learn in a thousand years. But they also have a massive amount of false, misleading, or just irrelevant data. Even worse, they have no methods for determining what is true, false, or irrelevant. Furthermore, they don't keep track of where the data comes from. That means they can't use information about the source(s) as a basis for determining reliability. As I have said repeatedly, whatever LLMs generate is a hypothesis -- I would call it a guess, but the term BS is just as good, Hypotheses (guesses or BS) can be valuable as starting points for new ways of thinking. But they need to be tested and evaluated before they can be trusted. The idea that LLM-based methods can become more intelligent by using massive amounts of computation is false. They can generate more kinds of BS, but at an enormous cost in hardware and in the electricity to run that massive hardware. But without methods of evaluation, the probability that random mixtures of data are true or useful or worth the cost of generating them becomes less and less likely. Conclusion: Without testing and evaluation, the massive amounts of computer hardware and the electricity to run it is a massive waste of money and resources. John