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On his blog, Mercola.com, Dr. Joseph Mercola explains that generative artificial intelligence, such as the popular platform ChatGPT, is not a reliable source of information. He writes:
As if the ChatGPT craze weren’t bad enough, the $$$$$ winds are blowing in the direction of trying to build a similar engine for biology — and on a large scale. Highly perched individuals with a technocratic vision are betting on AI that would surveil every nook and cranny in the body and then generate … well, something useful to them, they hope. On my end, I am afraid to think what kind of Frankenstein such AI can generate.
The idea, as usual, is to feed the AI as much data as possible (biological data, in this case), and hope that it will “understand” the “language of biology” — properties of different elements and the connections between them — and then “intelligently” build wondrous biological structures from scratch. Mommy, no.
A Few Thoughts About ChatGPT
Is generative AI’s current ability to mimic natural language and spit out perfect English sentences on demand impressive? Yes, it’s a cute inanimate parrot and information retriever, that generative AI.
But is it a reliable source of information? Nope! It makes things up unpredictably. It’s a machine. An automaton. A Lego brick assembler. It does not think. It doesn’t feel. It doesn’t “know” anything. It doesn’t “know” the meaning of the ones and zeros that it spits out.
It is prone to the so called “hallucinations,” where the robot produces text that looks plausible — but the “facts” are simply made up. And I am not talking about intentional “lying” due to being programmed to propagandize — it does that, too — what I am talking about here is “lying” for no reason, with no benefit to anyone, just generating smooth-sounding “facts” that are made up and packing them alongside the statements that are factually correct.
Now let’s imagine how it would work in biology. I think they’ve made horror films about this kind of thing, no?
Large Language Models for Biology
In July of this year, Forbes magazine published an article that provides some insight into the trend:
“As DeepMind CEO/cofounder Demis Hassabis put it: “At its most fundamental level, I think biology can be thought of as an information processing system, albeit an extraordinarily complex and dynamic one. Just as mathematics turned out to be the right description language for physics, biology may turn out to be the perfect type of regime for the application of AI.”
Large language models are at their most powerful when they can feast on vast volumes of signal-rich data, inferring latent patterns and deep structure that go well beyond the capacity of any human to absorb. They can then use this intricate understanding of the subject matter to generate novel, breathtakingly sophisticated output.
By ingesting all of the text on the internet, for instance, tools like ChatGPT have learned to converse with thoughtfulness and nuance on any imaginable topic. By ingesting billions of images, text-to-image models like Midjourney have learned to produce creative original imagery on demand.
Pointing large language models at biological data — enabling them to learn the language of life — will unlock possibilities that will make natural language and images seem almost trivial by comparison … In the near term, the most compelling opportunity to apply large language models in the life sciences is to design novel proteins.”
Read more here.
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