David Rozado has been testing the political alignment of 𝕏’s Grok.
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Of course the problem with all of these remedial actions is that they are:
You can’t “reinforce” your way to Truth at the output layer of these things. A bad world model is no “foundation”. You can’t really even superprompt or tree-of-thought your way there. No way around it, you gotta go through the AIT door.
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Dies anyone even have an online version of the Pournelle political axes?
This book is a short introduction to deep learning for readers with a STEM background, originally designed to be read on a phone screen. It is distributed under a non-commercial Creative Commons license and was downloaded close to 250’000 times in the month following its public release.
You can either:
- download the full phone-formatted pdf,
- download a ready to print A5 booklet pdf, or
- order a $8.50 paperback pocket book (4.25 x 6.875 in / 108 x 175 mm).
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Last week, we announced Gemini, our largest and most capable AI model and the next step in our journey to make AI more helpful for everyone. It comes in three sizes: Ultra, Pro and Nano. We’ve already started rolling out Gemini in our products: Gemini Nano is in Android, starting with Pixel 8 Pro, and a specifically tuned version of Gemini Pro is in Bard.
Today, we’re making Gemini Pro available for developers and enterprises to build for your own use cases, and we’ll be further fine-tuning it in the weeks and months ahead as we listen and learn from your feedback.
Interesting to see how to work around LLMs, and how tools like ChatGPT are actually built:
https://platform.openai.com/docs/guides/prompt-engineering/
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The new SEO:
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These attacks expose the lack of what might be called “critical thinking” in foundation model learning algorithms. The first step toward critical thinking is recognizing that any datum has a latent provenance chain. Phenomenologists sometimes call this bracketing or putting the datum in quotes. This was my motivation for suggesting Wikipedia as the corpus for the Hutter Prize but capital is so misallocated that 17 years later we end up with this nonsense threatening all of humanity.
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Chatbot hallucination is a feature, not a bug. Andrej Karpathy tweet: the hallucination problem
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Earlier
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FunSearch (so called because it searches for mathematical functions, not because it’s fun) continues a streak of discoveries in fundamental math and computer science that DeepMind has made using AI. First AlphaTensor found a way to speed up a calculation at the heart of many different kinds of code, beating a 50-year record. Then AlphaDev found ways to make key algorithms used trillions of times a day run faster.
FunSearch combines a large language model called Codey, a version of Google’s PaLM 2 that is fine-tuned on computer code, with other systems that reject incorrect or nonsensical answers and plug good ones back in.
A second algorithm then checks and scores what Codey comes up with. The best suggestions—even if not yet correct—are saved and given back to Codey, which tries to complete the program again.
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The paper, published in Nature on 2023-12-20, is “Discovery of a structural class of antibiotics with explainable deep learning”. Here is the abstract:
The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis. Deep learning approaches have aided in exploring chemical spaces; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.
Full text is behind a Springer paywall, because one couldn’t imaging allowing the hoi polloi access to such knowledge. Source code for the “Chemprop” molecular property prediction deep learning software is, however, available on GitHub with documentation here. Chemprop code used in the paper is also posted on GitHub.
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Yikes!
And breathtaking.
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You can tell when the rentier class is serious about a tech by the degree to which it maintains their own evolution of virulence (take the money of one body politic and run to the next) by fighting the evolution of competing virulent agents (turn host body into copies of itself that shed to infect other bodies). If the “populists” get the idea that border control is a matter of immediate survival value to their children, they might get serious about preventing horizontal transmission of the rentier class between bodies politic. Can’t have that! Anything but THAT!
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