“Our goal over time is to make a Llama-powered Meta AI be the most useful assistant in the world,” said Joelle Pineau, Vice President AI Research.
Most notably, Meta’s Llama families, built as open-source products, represent a different philosophical approach to how AI should develop as a wider technology.
But Meta is also playing it more cautiously, it seems, especially when it comes to other generative AI beyond text generation.
Ironically — or perhaps predictably (heh) — even as Meta works to launch Llama 3, it does have some significant generative AI skeptics in the house.
It’s not generative AI,” he said.
Such is the case with Anthropic and its latest research which demonstrates an interesting vulnerability in current LLM technology.
Of course given progress in open-source AI technology, you can spin up your own LLM locally and just ask it whatever you want, but for more consumer-grade stuff this is an issue worth pondering.
But the closer we get to more generalized AI intelligence, the more it should resemble a thinking entity, and not a computer that we can program, right?
If so, we might have a harder time nailing down edge cases to the point when that work becomes unfeasible?
Anyway, let’s talk about what Anthropic recently shared.
The market for high-level quantum computer science — which applies quantum principles to manage complex computations in areas like finance and artificial intelligence — appears to be quickening its pace.
In the latest development, a startup out of San Sebastian, Spain, called Multiverse Computing is announcing that it has raised €25 million (or $27 million) in an equity funding round led by Columbus Venture Partners.
The funding, which values the startup at €100 million ($108 million), will be used in two main areas.
“Multiverse’s exceptional team will soon apply their unparalleled capability to deliver quantum and quantum-inspired software solutions also within the life sciences and biotechnology markets, where Columbus Venture Partners will help to identify unmet market needs and high-profile industrial partners,” Javier Garcia, a partner at Columbus Venture Partners, in a statement.
Others competing in the same space include the Alphabet spinout Sandbox AQ, Quantum Motion, and Classiq.
Chinese tech companies are gathering all sorts of resources and talent to narrow their gap with OpenAI, and experiences for researchers on both sides of the Pacific Ocean can be surprisingly similar.
The parallel glimpse into their typical day reveals striking similarities, with wake-up times at 9 a.m. and bedtime around 1 a.m.
Both start the day with meetings, followed by a period of coding, model training and brainstorming with colleagues.
Besides building its own LLM in-house, Alibaba has been aggressively investing in startups such as Moonshot AI, Zhipu AI, Baichuan and 01.AI.
Facing competition, Alibaba has been trying to carve out a niche, and its multilingual move could become a selling point.
If accurate, this latest injection of capital would value Moonshot AI at $2.5 billion – the largest single funding round for Chinese LLM developers on public record.
Co-leading the round are e-commerce giant Alibaba and HongShan – the VC firm formerly known as Sequoia China, according to South China Morning Post.
Other in the round included Chinese “super app” Meituan and Xiaohongshu (sometimes called China’s answer to Instagram), according to Chinese tech blog LatePost.
Pink Floyd Fan turned AI trailblazerYang Zhilin had a long list of achievements to his credit even before starting Moonshot.
The total amount raised by China’s AI firms amounted to roughly $2 billion, 70% less than the year before.
In fact, an April 2023 Arize survey found that 53% of respondents planned to deploy LLMs within the next year or sooner.
The H100 GPU from Nvidia, a popular choice for LLMs, has been selling on the secondary market for about $40,000 per chip.
One source estimated it would take roughly 6,000 chips to train an LLM comparable to ChatGPT-3.5.
That source estimated that the power consumption to run ChatGPT-3.5 is about 1 GWh a day, or the combined daily energy usage of 33,000 households.
Power consumption can also be a potential pitfall for user experience when running LLMs on portable devices.
The Amazon CEO said that the company is building a more “generalized and capable” large language model to power Alexa. This would give Alexa the ability to recognize and summarize…
