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Future robots will be able to do the same. They will only need seconds instead of days.
mistakes by some powerful AI models causes extinction event
Quote from: hamdani yusuf on 27/10/2023 06:21:12Future robots will be able to do the same. They will only need seconds instead of days.But what instruction will you give your robot? And what will Marek's grandchildren do with their time on this earth?
No. The mistake will be made by a human who was hoping to benefit from the action. You can delegate authority but not responsibility.
I'm currently driving a rental car that defaults to "lane assist" whenever I switch it on. This is fine if I'm cruising along an otherwise empty highway, but it objects and resists me if I try to leave my lane without signalling.The least problem is that I gradually change my behavior and assume that I can change lanes any time, as long as I signal first. This can lead to a low-speed lateral impact with the guy the machine couldn't see and I didn't look for.The greater problem is that the machine delays or inhibits my response to an emergency that requires me to swerve quickly.The overriding rule is surely "don't hit anything, or if you must, hit the least animate object, it at the lowest possible closing speed, unless the animate object is vermin, but preferably don't hit a deer (they are vermin but very muscular)". Either way, I will be held liable, so I try to remember to disable the "assist" device before moving off.
The mistake made by AI will come from inaccurate data they were trained with, inaccurate data they are fed in, or the hyperparameters in their model structure.
Your conception of AGI finally clicks. The "tipping point" is when no additional human input is needed for the remainder of its future improvement.
Competition is a core part of human nature, and it can drive us to extraordinary feats. But when it goes wrong, the results can be devastating. Poker champion and science communicator Liv Boeree introduces us to "Moloch's trap" ? the dark force of game theory driving many of humanity's biggest social problems, which is now threatening to derail the AI industry.
The chip designs evolved to be better over time without sacrificing the competing agents.
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Market sentiment my arse. Greed, dear boy, pure and simple.
As AI-generated content fills the Internet, it?s corrupting the training data for models to come.
Public LLM leaderboard computed using Vectara's Hallucination Evaluation Model. This evaluates how often an LLM introduces hallucinations when summarizing a document. We plan to update this regularly as our model and the LLMs get updated over time.MethodologyTo determine this leaderboard, we trained a model to detect hallucinations in LLM outputs, using various open source datasets from the factual consistency research into summarization models. Using a model that is competitive with the best state of the art models, we then fed 1000 short documents to each of the LLMs above via their public APIs and asked them to summarize each short document, using only the facts presented in the document. Of these 1000 documents, only 831 document were summarized by every model, the remaining documents were rejected by at least one model due to content restrictions. Using these 831 documents, we then computed the overall accuracy (no hallucinations) and hallucination rate (100 - accuracy) for each model. The rate at which each model refuses to respond to the prompt is detailed in the 'Answer Rate' column. None of the content sent to the models contained illicit or 'not safe for work' content but the present of trigger words was enough to trigger some of the content filters. The documents were taken primarily from the CNN / Daily Mail Corpus. We used a temperature of 0 when calling the LLMs.We evaluate summarization accuracy instead of overall factual accuracy because it allows us to compare the model's response to the provided information. In other words, is the summary provided 'factually consistent' with the source document. Determining hallucinations is impossible to do for any ad hoc question as it's not known precisely what data every LLM is trained on. In addition, having a model that can determine whether any response was hallucinated without a reference source requires solving the hallucination problem and presumably training a model as large or larger than these LLMs being evaluated. So we instead chose to look at the hallucination rate within the summarization task as this is a good analogue to determine how truthful the models are overall. In addition, LLMs are increasingly used in RAG (Retrieval Augmented Generation) pipelines to answer user queries, such as in Bing Chat and Google's chat integration. In a RAG system, the model is being deployed as a summarizer of the search results, so this leaderboard is also a good indicator for the accuracy of the models when used in RAG systems.
https://www.microsoft.com/en-us/research/publication/orca-2-teaching-small-language-models-how-to-reason/Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs? reasoning abilities. Research on training small LMs has often relied on imitation learning to replicate the output of more capable models. We contend that excessive emphasis on imitation may restrict the potential of smaller models. We seek to teach small LMs to employ different solution strategies for different tasks, potentially different from the one used by the larger model. For example, while larger models might provide a direct answer to a complex task, smaller models may not have the same capacity. In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). More crucially, we aim to help the model learn to determine the most effective solution strategy for each task. We evaluate Orca 2 using a comprehensive set of 15 diverse benchmarks (corresponding to approximately 100 tasks and over 36,000 unique prompts). Orca 2 significantly surpasses models of similar size and attains performance levels similar or better to those of models 5-10x larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.
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