The weather’s getting hotter — but not quite as hot as the generative AI space, which saw a slew of new models released this week, including Meta’s Llama 3.
In other AI news, Hyundai-owned robotics company Boston Dynamics unveiled an electric-powered humanoid follow-up to its long-running Atlas robot, which it recently retired.
And Rebecca and Sean report on layoffs at Tesla , which they say hit high performers and gutted some departments.
AnalysisGoogle Cloud bets on generative AI: Ron writes about how Google Cloud is investing heavily in generative AI, as evidenced by the string of announcements during Google’s Cloud Next conference earlier in the month.
Generative AI in health: Generative AI is coming for healthcare — but not everyone’s thrilled.
Why vector databases are having a moment as the AI hype cycle peaks GenAI spurs demand for vector search startups, but database giants are also taking noteVector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie.
“Working with visual search and robotics at Amazon was when I really looked at vector search — I was thinking about new ways to do product discovery, and that very quickly converged on vector search,” Clark told TechCrunch.
“I think the same is likely to happen with vector databases,” Zaitsev told TechCrunch.
“Our pitch is, ‘we do advanced vector search in the best way possible.’ It is all about specialization.
At some point, users will face limitations if vector search is a critical component of your solution.”
Allison Cohen is the senior applied AI projects manager at Mila, a Quebec-based community of more than 1,200 researchers specializing in AI and machine learning.
One of the projects I managed involved building a dataset containing instances of subtle and overt expressions of bias against women.
I learned firsthand why this process is fundamental to building responsible applications, and also why it’s not done enough — it’s hard work!
What advice would you give to women seeking to enter the AI field?
How can investors better push for responsible AI?
How many AI models is too many?
We’re seeing a proliferation of models large and small, from niche developers to large, well-funded ones.
And let’s be clear, this is not all of the models released or previewed this week!
Other large language models like LLaMa or OLMo, though technically speaking they share a basic architecture, don’t actually fill the same role.
The other side of this story is that we were already in this stage long before ChatGPT and the other big models came out.
Welcome to Startups Weekly — your weekly recap of everything you can’t miss from the world of startups.
Despite this general downturn, certain segments like generative AI continue to attract significant funding, indicating a selective yet substantial interest in specific AI applications.
AI investment is slowing down for a few reasons, like the crowded market and the steep costs of building big AI models.
Investors are getting pickier and want to see real, solid returns instead of just throwing money at hopeful growth.
(That isn’t stopping them from raising billion-dollar funds focusing on AI, of course.)
New AI models from Meta are making waves in technology circles.
Meta’s new Llama models have differently sized underlying datasets, with the Llama 3 8B model featuring eight billion parameters, and the Llama 3 70B model some seventy billion parameters.
The company’s new models, which were trained on 24,000 GPU clusters, perform well across benchmarks that Meta put them up against, besting some rivals’ models that were already in the market.
What matters for those of us not competing to build and release the most capable, or largest AI models, what we care about is that they are still getting better with time.
While Meta takes an open-source approach to AI work, its competitors are often prefer more closed-source work.
Today, Webflow announced that it acquired Intellimize, a startup leveraging AI to personalize websites for unique visitors.
The majority of the Intellimize team — around 50 people — will join Webflow.
Vlad Magdalin, the CEO of Webflow, said Intellimize was a natural fit for Webflow’s first-ever acquisition because its product meets a need many Webflow customers share: personalizing and optimizing their websites.
Intellimize will continue to be sold standalone to non-Webflow customers, but it’ll increasingly link to — and integrate with — Webflow services.
— personalization product efforts at Webflow.
Last week, Meta started testing its AI chatbot in India across WhatsApp, Instagram, and Messenger.
Meta confirmed that it is restricting certain election-related keywords for AI in the test phase.
When you ask Meta AI about specific politicians, candidates, officeholders, and certain other terms, it will redirect you to the Election Commission’s website.
But just like other AI-powered systems, Meta AI has some inconsistencies.
This week, the company rolled out a new Llama-3-powered Meta AI chatbot in more than a dozen countries, including the U.S., but India was missing from the list.
The tools would be part of a wider set of proposals Ofcom is putting together focused on online child safety.
Consultations for the comprehensive proposals will start in the coming weeks with the AI consultation coming later this year, Ofcom said.
AI researchers are finding ever-more sophisticated ways of using AI to detect, for example, deep fakes, as well as to verify users online.
It found that 32% of the kids reported that they’d seen worrying content online, but only 20% of their parents said they reported anything.
Among children aged 16-17, Ofcom said, 25% said they were not confident about distinguishing fake from real online.
As I wrote recently, generative AI models are increasingly being brought to healthcare settings — in some cases prematurely, perhaps.
Hugging Face, the AI startup, proposes a solution in a newly released benchmark test called Open Medical-LLM.
Hugging Face is positioning the benchmark as a “robust assessment” of healthcare-bound generative AI models.
It’s telling that, of the 139 AI-related medical devices the U.S. Food and Drug Administration has approved to date, none use generative AI.
But Open Medical-LLM — and no other benchmark for that matter — is a substitute for carefully thought-out real-world testing.
