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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? [\#kbinMeta](https://kbin.social/tag/kbinMeta)
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I rather avoid anything related to Red Hat. On the other hand, Debian 12 looks epic.
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I'll accept it if it's FOSS
Everything is Linux
github.com
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...
www.scientificamerican.com
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](https://arxiv.org/pdf/2309.12288v1.pdf)) **Ongoing Scaling Trends** * 10 years of remarkable increases in model scale and performance. * Expects next few years will make today's AI "pale in comparison." * Follows known patterns, not theoretical limits. **No Foreseeable Limits** * Skeptical of claims certain tasks are beyond large language models. * Fine-tuning and training adjustments can unlock new capabilities. * At least 3-4 more years of exponential growth expected. **Long-Term Uncertainty** * Can't precisely predict post-4-year trajectory. * But no evidence yet of diminishing returns limiting progress. * 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](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%. > > Source: [https://old.reddit.com/r/singularity/comments/16qdm0s/rain\_your\_language\_models\_can\_align\_themselves/](https://old.reddit.com/r/singularity/comments/16qdm0s/rain_your_language_models_can_align_themselves/)
[https://arxiv.org/abs/2309.11495](https://arxiv.org/abs/2309.11495) **Abstract** Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers are not biased by other responses; and (iv) generates its final verified response. In experiments, we show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata, closed book MultiSpanQA and longform text [https://i.imgur.com/TDXcdMI.jpeg](https://i.imgur.com/TDXcdMI.jpeg) [https://i.imgur.com/XfRVxJT.jpeg](https://i.imgur.com/XfRVxJT.jpeg) **Conclusion** We introduced Chain-of-Verification (CoVe), an approach to reduce hallucinations in a large language model by deliberating on its own responses and self-correcting them. In particular, we showed that models are able to answer verification questions with higher accuracy than when answering the original query by breaking down the verification into a set of simpler questions. Secondly, when answering the set of verification questions, we showed that controlling the attention of the model so that it cannot attend to its previous answers (factored CoVe) helps alleviate copying the same hallucinations. Overall, our method provides substantial performance gains over the original language model response just by asking the same model to deliberate on (verify) its answer. An obvious extension to our work is to equip CoVe with tool-use, e.g., to use retrieval augmentation in the verification execution step which would likely bring further gains. Source: [https://old.reddit.com/r/singularity/comments/16qcdsz/research\_paper\_meta\_chainofverification\_reduces/](https://old.reddit.com/r/singularity/comments/16qcdsz/research_paper_meta_chainofverification_reduces/)
medicalxpress.com
When the spinal cords of mice and humans are partially damaged, the initial paralysis is followed by the extensive, spontaneous recovery of motor function. However, after a complete spinal cord injury, this natural repair of the spinal cord doesn't occur and there is no recovery. Meaningful recovery after severe injuries requires strategies that promote the regeneration of nerve fibers, but the requisite conditions for these strategies to successfully restore motor function have remained elusive.
www.businessinsider.com
How Daron Acemoglu, one of the world's most respected experts on the economic effects of technology, learned to start worrying and fear AI.
www.canarymedia.com
In 2020, California had 500 megawatts of grid battery storage. Now, just three years later, it has over 5,000 megawatts.
themessenger.com
The mayor says they are paying less per hour than the city's minimum wage to lease the robot
www.businessinsider.com
How Daron Acemoglu, one of the world's most respected experts on the economic effects of technology, learned to start worrying and fear AI.
www.businessinsider.com
Will everything you learned in college be replaced by ChatGPT? The CEO of job site Indeed says it's not out of the question.
www.businessinsider.com
Will everything you learned in college be replaced by ChatGPT? The CEO of job site Indeed says it's not out of the question.
futurism.com
An investigation by Wired reveals the grisly complications of Neuralink brain implants in monkeys, including brain swelling and paralysis.
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the Lemmy/kbin instances stability during June 12-14 is goofily accurate 💀
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gimme chocolate!!
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agreed. I'm also rlly skeptical about LK-99.
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Space Colonization lore
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RIP Aaron. You were the hero we needed but lost. Fuck those prosecutors. I hope your dream comes true one day.
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Agreed. But it seems likely that the blackout will soon be extended since alot of people on YouTube advocated it.
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