French AI startup Mistral is introducing new AI model customization options, including paid plans, to let developers and enterprises fine-tune its generative models for particular use cases.
Mistral has released a software development kit (SDK), Mistral-Finetune, for fine-tuning its models on workstations, servers and small datacenter nodes.
For developers and companies that prefer a more managed solution, there’s Mistral’s newly launched fine-tuning services available through the company’s API.
Compatible with two of Mistral’s models for now, Mistral Small and the aforementioned Mistral 7B, Mistral says that the fine-tuning services will gain support for more of its models in the coming weeks.
Lastly, Mistral is debuting custom training services, currently only available to select customers, to fine-tune any Mistral model for an organization’s apps using their data.
With the launch of TC’s AI newsletter, we’re sunsetting This Week in AI, the semiregular column previously known as Perceptron.
But you’ll find all the analysis we brought to This Week in AI and more, including a spotlight on noteworthy new AI models, right here.
The group published an open letter on Tuesday calling for leading AI companies, including OpenAI, to establish greater transparency and more protections for whistleblowers.
(Reward models are specialized models to evaluate the outputs of AI models, in this case math-related outputs from GPT-4.)
Should generative AI replace most knowledge workers within three years (which seems unrealistic to me given AI’s many unsolved technical problems), economic collapse could well ensue.
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.
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.
Meta has released the latest entry in its Llama series of open source generative AI models: Llama 3.
Meta describes the new models — Llama 3 8B, which contains 8 billion parameters, and Llama 3 70B, which contains 70 billion parameters — as a “major leap” compared to the previous-gen Llama models, Llama 2 8B and Llama 2 70B, performance-wise.
In fact, Meta says that, for their respective parameter counts, Llama 3 8B and Llama 3 70B — trained on two custom-built 24,000 GPU clusters — are are among the best-performing generative AI models available today.
So what about toxicity and bias, two other common problems with generative AI models (including Llama 2)?
The company’s also releasing a new tool, Code Shield, designed to detect code from generative AI models that might introduce security vulnerabilities.
NeuBird founders Goutham Rao and Vinod Jayaraman came from PortWorx, a cloud native storage solution they eventually sold to PureStorage in 2019 for $370 million.
When they went looking for their next startup challenge last year, they saw an opportunity to combine their cloud native knowledge, especially around IT operations, with the burgeoning area of generative AI.
Rao, the CEO, says that while the cloud native community has done a good job at solving a lot of difficult problems, it has created increasing levels of complexity along the way.
“We’ve done an incredible job as a community over the past 10 plus years building cloud native architectures with service oriented designs.
At the same time, large language models were beginning to mature, so the founders decided to put them to work on the problem.
The Linux Foundation today announced the launch of the Open Platform for Enterprise AI (OPEA), a project to foster the development of open, multi-provider and composable (i.e.
modular) generative AI systems.
Now, OPEA’s members are very clearly invested (and self-interested, for that matter) in building tooling for enterprise generative AI.
Domino offers a suite of apps for building and auditing business-forward generative AI.
And VMWare — oriented toward the infrastructure side of enterprise AI — last August rolled out new “private AI” compute products.
And more AI companies are receiving investments than ever before, with 1,812 AI startups announcing funding in 2023, up 40.6% versus 2022, according to the Stanford HAI report.
“There’s been a more deliberate approach by investors in evaluating AI investments compared to a year ago.
According to a PitchBook report compiled for TechCrunch, VCs invested $25.87 billion globally in AI startups in Q1 2024, up from $21.69 billion in Q1 2023.
Despite the general malaise within AI investor circles, generative AI — AI that creates new content, such as text, images, music and videos — remains a bright spot.
“We’ll soon be evaluating whether generative AI delivers the promised efficiency gains at scale and drives top-line growth through AI-integrated products and services,” Kumar said.
Offered as an answer of sorts to OpenAI’s Sora, Google’s Imagen 2 and models from the growing number of startups in the nascent generative AI video space, Adobe’s model — a part of the company’s expanding Firefly family of generative AI products — will make its way into Premiere Pro, Adobe’s flagship video editing suite, sometime later this year, Adobe says.
Like many generative AI video tools today, Adobe’s model creates footage from scratch (either a prompt or reference images) — and it powers three new features in Premiere Pro: object addition, object removal and generative extend.
The lack of release time frame on the video model doesn’t instill a lot of confidence that it’ll avoid the same fate.
And that, I’d say, captures the overall tone of Adobe’s generative video presser.
Adobe’s clearly trying to signal with these announcements that it’s thinking about generative video, if only in the preliminary sense.
Prominent generative AI startups in healthcare include Ambience Healthcare, which is developing a generative AI app for clinicians; Nabla, an ambient AI assistant for practitioners; and Abridge, which creates analytics tools for medical documentation.
The broad enthusiasm for generative AI is reflected in the investments in generative AI efforts targeting healthcare.
Collectively, generative AI in healthcare startups have raised tens of millions of dollars in venture capital to date, and the vast majority of health investors say that generative AI has significantly influenced their investment strategies.
But both professionals and patients are mixed as to whether healthcare-focused generative AI is ready for prime time.
Generative AI might not be what people wantIn a recent Deloitte survey, only about half (53%) of U.S. consumers said that they thought generative AI could improve healthcare — for example, by making it more accessible or shortening appointment wait times.
