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Gómez’s research is grounded in the computational music field, where she contributes to the understanding of the way humans describe music and the methods in which it’s modeled digitally.
What I liked at the time from machine learning was its modelling capabilities and the shift from knowledge-driven to data-driven algorithm design — e.g.
There’s also PHENICX, a large European Union (EU)-funded project I coordinated on the use of music; and AI to create enriched symphonic music experiences.
What advice would you give to women seeking to enter the AI field?
They should learn about the working principles and limitations of AI algorithms to be able to challenge them and use them in a responsible way.
Even when some or all of those are addressed, there remains the question of what happens when a system makes an inevitable mistake.
We can’t, however, expect consumers to learn to program or hire someone who can help any time an issue arrives.
Thankfully, this is a great use case for LLMs (large language models) in the robotics space, as exemplified by new research from MIT.
“LLMs have a way to tell you how to do each step of a task, in natural language.
It’s a simple, repeatable task for humans, but for robots, it’s a combination of various small tasks.
Spotify has carved out a business for itself in music streaming, podcast entertainment and audiobooks.
Starting with a rollout in the U.K., Spotify is testing the waters for an online education offering of freemium video courses.
Mohit Jitani, the London-based product director for the education business, said in an interview that pricing choices were part of what it’s testing.
Around half of Spotify Premium subscribers have listened to education or self-help themed podcasts, Spotify says.
Offering educational content aimed at running a business, or improving your music production, fits with that.
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Accenture announced today that it would acquire the learning platform Udacity as part of an effort to build a learning platform focused on the growing interest in AI.
While the company didn’t specify how much it paid for Udacity, it also announced a $1 billion investment in building a technology learning platform it’s calling LearnVantage.
While it could also offer more general technology training, the company made clear that it is particularly interested in offering training to get workers up to speed on AI.
As for Udacity, which was founded in 2011, it gave the usual kinds of statements a company makes when it gets acquired by a much larger organization like Accenture.
That is, it believes that it can reach more people and help them acquire skills at part of the larger entity.
A new startup called Ema out of San Francisco thinks it’s a lot more than just a passing fancy.
“Our goal is to build a universal AI employee,” Surojit Chatterjee, the CEO and co-founder, said in an interview.
Accel, Section 32 and Prosus Ventures are co-leading, and Wipro Ventures, Venture Highway, AME Cloud Ventures, Frontier Ventures, Maum Group and Firebolt Ventures also participating.
He himself has some 40 patents to his name in areas like machine learning enterprise software and ad tech.
Most recently, he was VP of engineering at Okta where he oversaw data, machine learning and devices; and prior to that he was also at Google, where he was engineering lead for data and machine learning where he focused on privacy and safety.
Of the professions in danger of being replaced by AI, language teacher is certainly up there.
One of those is Loora, which leans on conversational AI to teach English to students.
“The idea for Loora [came from] our frustration with language learning,” Mor told TechCrunch in an email interview.
Quite a few English learning platforms offer features along those lines, including OpenAI-backed Speak, Preply (which recently doubled down on AI tech) and ELSA.
Loora also intends to launch an enterprise service, broadening beyond its current customer base of 15,000 app users.
Tomorrow.io just released the results from its first two radar satellites, which, thanks to machine learning, turn out to be competitive with larger, more old-school forecasting tech on Earth and in orbit.
Weather prediction is complex for a lot of reasons, but the interplay between high-powered but legacy hardware (like radar networks and older satellites) and modern software is a big one.
Space is, of course, the obvious place to invest, but weather infrastructure is prohibitively big and heavy.
Tomorrow.io’s plan is to create a new space-based radar infrastructure with a modern twist.
“We are working on providing real time precipitation data anywhere in the world, which we believe is a game changer in the field of weather forecasting,” Chowla said.
When Anomalo’s co-founders left Instacart in 2018, they thought they could put machine learning to work to solve data quality problems inherent in large data sets.
Five years later, the company’s idea is even more relevant as data quality takes center stage with large language models.
Today, the startup announced a $33 million Series B, equaling their 2021 Series A and bringing the total raised to $72 million, according to the company.
As companies store increasingly large amounts of data in cloud storage and data warehouses like Databricks and Snowflake, this need has only become more pronounced, he says.
SignalFire led the $33 million Series B investment with participation from strategic investor Databricks Ventures.
