With LakeFlow, Databricks users will soon be able to build their data pipelines and ingest data from databases like MySQL, Postgres, SQL Server and Oracle, as well as enterprise applications like Salesforce, Dynamics, Sharepoint, Workday, NetSuite and Google Analytics.
In a way, getting data into a data warehouse or data lake should indeed be table stakes because the real value creation happens down the line.
The first is LakeFlow Connect, which provides the connectors between the different data sources and the Databricks service.
It’s fully integrated with Databricks’ Unity Data Catalog data governance solution and relies in part of technology from Arcion.
Databricks is rolling out the LakeFlow service in phases.
Now rebranded as Mosaic AI, the platform has become integral to Databricks’ AI solutions.
Today, at the company’s Data + AI Summit, it is launching a number of new features for the service.
Databricks is launching five new Mosaic AI tools at its conference: Mosaic AI Agent Framework, Mosaic AI Agent Evaluation, Mosaic AI Tools Catalog, Mosaic AI Model Training, and Mosaic AI Gateway.
And we’ve also found in our internal AI applications, like the assistant applications for our platform, that this is the way to build them,” he said.
But when you really pushed people, they were using Open AI.
Not only is Apple rebranding AI to “Apple Intelligence” for its purposes, but it’s also integrating the new AI features in iOS 18 in a more practical way.
Image Credits: ApplePresented in this way, some of the new Apple Intelligence features don’t even feel like AI, they just feel like smarter tools.
If you want to make your writing more concise or summarize an email, Apple Intelligence can help.
Outside of a few features — like Genmoji, which is just silly — Apple Intelligence feels boring and practical.
Apple Intelligence will launch in beta this fall.
When you’re looking for a startup idea that could slow climate change, you might become an expert at home energy assessments.
Instead, the startup has put together a small team of engineers to create its own AI model specialized in home energy assessments using machine learning.
The company uses open data, such as satellite images, as well as its own training data set with millions of photos and energy assessments.
In the company’s first tests, its home energy assessments have been accurate within 5% of old-fashioned assessments.
The startup has now raised €4.7 million ($5.1 million at today’s exchange rate) with Racine² leading the round and a non-dilutive investment from Bpifrance.
Google has developed a new AI tool to help marine biologists better understand coral reef ecosystems and their health, which can aid in conversation efforts.
The project began by inviting the public to listen to reef sounds via the web.
By crowdsourcing this activity, Google was able to create a library of new fish sounds that were used to fine-tune the AI tool, SurfPerch.
Although bird sounds and reef recordings are very different, there were common patterns between bird songs and fish sounds that the model was able to learn from, they found.
The project continues today, as new audio is added to the Calling in Our Corals website, which will help to further train the AI model, Google says.
The company first started working with Mr. Bricolage, a popular DIY retailer in France.
Dealt operates a white-label platform for Mr. Bricolage so that it can upsell its own clients with services.
After that, retailers like Mr. Bricolage can provide services and generate new revenue lines as they take a cut on each transaction.
Other Dealt clients include Fnac-Darty, Orange, E.Leclerc, Conforama, Boulanger, 3Suisses and Rue du Commerce.
Some of these retailers work with Dealt to provide services that aren’t necessarily related to a new purchase.
Every company, large or small, needs to choose software, and the bigger the company, the more complex the exercise.
Some have internal tools and processes to help narrow down the list of possible vendors and eventually make a selection.
“Taloflow replaces homegrown technology and software selection processes that can last weeks or months,” the startup’s CEO and co-founder, Louis-Victor Jadavji, told TechCrunch.
“Unlike Gartner or G2, which offer mostly generic insights, Taloflow creates tailored reports for specific use cases,” he said.
Taloflow has built large language models that sift through publicly available information and speed up the time and cost of generating the base reports.
Powerhouse venture capital firm Andreessen Horowitz is promoting Jennifer Li to general partner after six years at the firm.
She’s being tapped to help invest the new $1.25 billion Infrastructure fund managed by longtime a16z general partner Martin Casado.
The Infrastructure fund is part of the fresh $7.2 billion that the Silicon Valley VC giant just raised.
Li has been an investing partner on the Infrastructure team for a while, which means she was already writing checks and taking board seats.
Plus she’s one of only four GPs on the Infrastructure team.
That means there’s a market for a layer between companies and Large Language Models (LLMs) — something companies can use to pick LLMs easily without needing to commit for all time to one platform.
That’s the market Langdock is targeting with its chat interface that sits between LLMs and a company.
“Companies don’t want to have a vendor lock-in on just one of those LLM providers,” Lennard Schmidt, co-founder and CEO of Langdock, told TechCrunch.
In addition to the chat interface, the company also offers security, cloud and on-premises solutions.
In contrast, Langdock’s chat interface works for a broader range of use cases and can be used by any kind of staff.
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.
