Large language models (LLMs) like GPT-4 have shown remarkable capabilities, but their tendency to "hallucinate" or generate incorrect information has been a major barrier to enterprise adoption. Cleanlab has launched the Trustworthy Language Model (TLM) to address this key challenge of reliability in generative AI. TLM augments existing LLMs by adding a trustworthiness score to every output, quantifying both the known uncertainties (aleatoric) that models are aware of as well as the unknown uncertainties (epistemic) that arise from lack of training data. This allows organizations to contain and manage hallucinations, enabling new use cases previously unsuitable for unreliable LLMs.More Accurate Outputs and Better Cost Savings Through rigorous benchmarking against GPT-4, Cleanlab has shown that TLM produces more accurate outputs overall. Crucially, TLM's trustworthiness scores are better calibrated than just using an LLM's self-evaluated confidence or output probabilities. This enables greater cost and time savings by prioritizing human review of low-scoring outputs.For applications with a required error rate tolerance, using TLM's trustworthiness scores to triage outputs for review catches more hallucinations under a fixed review budget compared to existing approaches. This unlocks new production use cases across medicine, law, finance and more.Avoiding Catastrophic Hallucinations The consequences of unchecked LLM hallucinations can be severe, as some organizations have already experienced. From airlines being forced to refund customers to law firms facing fines over fabricated citations, the risks of deploying unreliable LLMs are real. With TLM, teams can finally get the benefits of generative AI's capabilities while managing the reliability risks. By adding trustworthiness scoring, TLM is a key step towards responsibly deploying LLMs in the enterprise.I'm excited to see how TLM enables new generative AI applications! You can try out the TLM API for free or experiment in their interactive demo.

https://www.linkedin.com/posts/businessinsider_openai-destroyed-a-trove-of-books-used-to-activity-7193814561400012800-5b7m/ The link requires a Business Insider membership but the gist is that newly unsealed documents reveal that OpenAI deleted 2 huge datasets of books, estimated to contains more than a 100,000 published books, which were used to train its GPT-3 model.

Among the AI Doomers https://www.newyorker.com/magazine/2024/03/18/among-the-ai-doomsayers Can an A.I. Make Plans? https://www.newyorker.com/science/annals-of-artificial-intelligence/can-an-ai-make-plans A bit longer, but esp the first one a nice read and (for me) an interesting look under the hood of the stuff happening in AI outside of the main news.

Really good talk on the topic - highly recommend watching

"This void of emotional connection is precisely what romantic chatbots—and the deep-learning algorithms that power them—are trading in. Nearly half of all men, 45%, are expected to use AI for romance, marking a significant increase from the previous year, according to a new report from McAfee Research. Ads for AI girlfriends abound on TikTok, Instagram and Facebook. Sadly, demand is high." Very thought-provoking article with deep and very sad insights into the topic. https://client.13d.com/share/65ebb8f1dfb64