搜索“Microsoft”

ot looks like a product. You type something in and you get magic back! But the magic might not be useful, in that form, and it might be wrong. It looks like product, but it isn’t. Microsoft’s failed and forgotten attempt to bolt this onto Bing and take on Google at the beginning of last year is a good microcosm of the problem. LLMs look like better databases, and they look like search, but, as we’ve seen since, they’re ‘wrong’ enough, and the ‘wrong’ is hard enough to manage, that you can’t just

匹配字段正文

s to your context if you use Android, but that’s a distinct minority in the USA (while even less of the Android base than the iPhone base would be able to do any of this locally). Microsoft’s AI PCs have some of this context, especially for a work context, but the smartphone is the primary device with all the real context for most people now, not the PC. Does Meta have that context? Some of it, maybe. There will be an interesting anti-trust conversation here, at some point. But the point of leve

匹配字段正文

g at this on another axis: with any new technology, we begin by trying to make it fit the problems we already have, while the incumbents try to make it a feature (hence Google and Microsoft spraying LLMs all over their products in the last year). Then startups use it to unbundle the incumbents (to unbundle search, Oracle or Email), but meanwhile, other startups try to work out what we could build that would be truly native to the new technology. That comes in stages. First, Flickr had an iPhone

匹配字段正文

00) chips in one server (connected with NVLink) + comparatively high network bandwidth of 500Mb/s - 100Gb/s; As a close reference for this claim, a paper called Varuna released by Microsoft in 2022 demonstrates how to scale distributed training across 100+ spot instances for models with up to 200B parameters (without InfiniBand connections): Unlike Google's proprietary efforts, SWARM parallelism is open-source, allowing anyone to begin distributed training across clusters. Although it is primari

匹配字段正文

FB When it comes to artificial intelligence, what are we actually creating? Even those closest to its development are struggling to describe exactly where things are headed, says Microsoft AI CEO Mustafa Suleyman, one of the primary architects of the AI models many of us use today. He offers an honest and compelling new vision for the future of AI, proposing an unignorable metaphor — a new digital species — to focus attention on this extraordinary moment. (Followed by a Q&A with head of TED Chr

匹配字段正文

a SQL regulator, and the same point applies when we read that FTX had a spreadsheet with eight different balance sheets. We don’t call for a spreadsheet regulator, and demand that Microsoft stop this from happening again. That would be the wrong level of abstraction. This, I think, is the challenging in talking about ‘AI Ethics’, or in writing laws to regulate ‘AI’. There are lots of ways that bad people can do bad things with software, and there are lots of ways that people can screw up with so

匹配字段正文

an afford them. This is precisely what is already happening in AI – it’s why you can use state of the art generative AI not just at low cost but even for free today in the form of Microsoft Bing and Google Bard – and it is what will continue to happen. Not because such vendors are foolish or generous but precisely because they are greedy – they want to maximize the size of their market, which maximizes their profits. So what happens is the opposite of technology driving centralization of wealth

匹配字段正文

ng development timeline, size, training strategies, training data, hardware, and more; Sparks of artificial general intelligence: Early experiments with GPT-4: Early analysis from Microsoft Research on the capabilities of GPT-4, the current most advanced LLM, relative to human intelligence; The AI revolution: How Auto-GPT unleashes a new era of automation and creativity: An introduction to Auto-GPT and AI agents in general. This technology is very early but important to understand—it uses intern

匹配字段正文

argument structure of a sentence, and is often described as answering “Who did what to whom”. CoNLL-2004 & CoNLL-2005 Sentence similarity: also known as paraphrase detection MRPC (MicRosoft Paraphrase Corpus): It contains pairs of sentences extracted from news sources on the web, with annotations indicating whether each pair is semantically equivalent; QQP (Quora Question Pairs) STS Benchmark: Semantic Textual Similarity Sentence Acceptability: a task to annotate sentences for grammatical accept

匹配字段正文