It seems to me that AI won't completely replace jobs (but will do in 10-20 years). But will reduce demand because oversaturation + ultraproductivity with AI. Moreover, AI will continue to improve. A work of a team of 30 people will be done with just 3 people.
Why are the microblogs not working properly?
I rather avoid anything related to Red Hat. On the other hand, Debian 12 looks epic.
I'll accept it if it's FOSS
Hello 👋, Just came across this project, and saw the app being advertised as open source, like on this page where it's advertised as "100% open source". Looking at the app repo though, the licensing...
Hello 👋, Just came across this project, and saw the app being advertised as open source, like on this page where it's advertised as "100% open source". Looking at the app repo though, the licensing...
AI is poised to revolutionize our understanding of animal communication
AI is poised to revolutionize our understanding of animal communication
Training AI models like GPT-3 on "A is B" statements fails to let them deduce "B is A" without further training, exhibiting a flaw in generalization. (https://arxiv.org/pdf/2309.12288v1.pdf)
Ongoing Scaling Trends
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10 years of remarkable increases in model scale and performance.
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Expects next few years will make today's AI "pale in comparison."
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Follows known patterns, not theoretical limits.
No Foreseeable Limits
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Skeptical of claims certain tasks are beyond large language models.
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Fine-tuning and training adjustments can unlock new capabilities.
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At least 3-4 more years of exponential growth expected.
Long-Term Uncertainty
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Can't precisely predict post-4-year trajectory.
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But no evidence yet of diminishing returns limiting progress.
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Rapid innovation makes it hard to forecast.
TL;DR: Anthropic's CEO sees no impediments to AI systems continuing to rapidly scale up for at least the next several years, predicting ongoing exponential advances.
Paper: https://arxiv.org/abs/2309.07124
Abstract:
> > > Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, the so-called finetuning step. In contrast, aligning frozen LLMs without any extra data is more appealing. This work explores the potential of the latter setting. We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guide backward rewind and forward generation for AI safety. Notably, RAIN operates without the need of extra data for model alignment and abstains from any training, gradient computation, or parameter updates; during the self-evaluation phase, the model receives guidance on which human preference to align with through a fixed-template prompt, eliminating the need to modify the initial prompt. Experimental results evaluated by GPT-4 and humans demonstrate the effectiveness of RAIN: on the HH dataset, RAIN improves the harmlessness rate of LLaMA 30B over vanilla inference from 82% to 97%, while maintaining the helpfulness rate. Under the leading adversarial attack llm-attacks on Vicuna 33B, RAIN establishes a new defense baseline by reducing the attack success rate from 94% to 19%. > >
agreed. I'm also rlly skeptical about LK-99.
Space Colonization lore
kbin.social is well funded and will probably be around for many years to come
RIP Aaron. You were the hero we needed but lost. Fuck those prosecutors. I hope your dream comes true one day.
Agreed. But it seems likely that the blackout will soon be extended since alot of people on YouTube advocated it.
