When you prompt an LLM, the entire conversation acts as a prompt driving the reply. Thus your prompt can push the LLM in unforeseen, unintended directions.
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Ultimately, the AI knowledge system will be enriched or impoverished by human decisions, not by discovering a superintelligence.
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Just as the AI industry is becoming more analytical and realistic about how best to use AI, some users are falling in love with chatbots. We need to know more.
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One change is that Sam Altman, CEO of OpenAI, the maker of ChatGPT-5, seems to be acknowledging that the AI bubble might soon burst.
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Perhaps it is worth keeping in mind that bots not only don’t buy things themselves but may be chasing away humans who do.
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We will be dealing with problems around non-conscious AI that some people suppose is conscious long before we deal with conscious AI — if we ever do.
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A reality check for LLMs is inevitable, and with OpenAI, “Project Strawberry,” and the new hype cycle — thanks to Chollet and others — it’s already here.
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In yesterday’s post, I talked about the fact that AI’s don’t understand the work they’re doing. That makes the goal — to make them think like people — elusive. This brings us to the second problem, which ended up spawning an entire field, known as “Explainable AI.” Neural networks not only don’t know what they’re doing when they do it, they can’t in general explain to their designers or users why they made such-and-such a decision. They’re a black box; in other words, they are obstinately opaque to any attempts at a conceptual understanding of their decisions or inferences. How does that play out? It means, for example, that, with image recognition tasks like facial recognition, the network can’t explain Read More ›
This story was #5 in 2022 at Mind Matters News in terms of reader numbers. As we approach the New Year, we are rerunning the top ten Mind Matters News stories of 2022, based on reader interest. In “Google dismisses engineer’s claim that AI really talked to him” (June 14, 2022), our News division looks at what happened when software engineer Blake Lemoine, now ex-Google, became convinced that the large language program he tended to was a person. Google engineer Blake Lemoine was working with LaMDA (Language Model for Dialogue Applications), a large language program which motors through trillions of words on the internet to produce coherent answers using logic. Along the way, he convinced himself that the program is Read More ›
Google engineer Blake Lemoine was working with LaMDA (Language Model for Dialogue Applications), a large language program which motors through trillions of words on the internet to produce coherent answers using logic. Along the way, he convinced himself that the program is sentient: Lemoine, who works for Google’s Responsible AI organization, began talking to LaMDA as part of his job in the fall. He had signed up to test if the artificial intelligence used discriminatory or hate speech. As he talked to LaMDA about religion, Lemoine, who studied cognitive and computer science in college, noticed the chatbot talking about its rights and personhood, and decided to press further. In another exchange, the AI was able to change Lemoine’s mind about Read More ›
Artificial intelligence is an oxymoron. Despite all the incredible things computers can do, they are still not intelligent in any meaningful sense of the word. Decades ago, AI researchers largely abandoned their quest to build computers that mimic our wondrously flexible human intelligence and instead created algorithms that were useful (i.e., profitable). Despite this understandable detour, some AI enthusiasts market their creations as genuinely intelligent. For example, a few months ago, Blaise Aguera y Arcas, the head of Google’s AI group in Seattle, argued that “statistics do amount to understanding.” As evidence, he cites a few exchanges with Google’s LaMDA chatbot. The examples were impressively coherent but they are still what Gary Marcus and Ernest Davis characterize as “a fluent spouter of bullshit” because computer algorithms Read More ›
I’ve written before about how computer algorithms are like Nigel Richards, the New Zealander who has won multiple French-language Scrabble tournaments even though he does not understand the words he is spelling. Computers can similarly manipulate words in many useful ways — e.g., spellchecking, searching, alphabetizing — without any understanding of the words they are manipulating. To know what words mean, they would have to understand the world we live in. They don’t. One example is their struggles with the Winograd schema challenge — recognizing what it refers to in a sentence. Another example is the inability to answer simple questions like, “Is it safe to walk downstairs backwards if I close my eyes?” A third type of example is the brittleness of language translation programs. Yet another Read More ›