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- AI Speech App Passes $1b Valuation
AI Speech App Passes $1b Valuation
PLUS: Police use DNA phenotyping and facial recognition, sparking misidentification concerns.
Welcome! Today we're covering:
Funding: Voice AI startup ElevenLabs hits unicorn status with $80M raise
Economics: MIT study on AI job replacement overlooks impact of generative models
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News & Insights
ElevenLabs Latest AI Unicorn After $80M Raise
Voice AI startup ElevenLabs recently secured an $80 million Series B, propelling the company into the unicorn realm, with its valuation soaring past the billion-dollar mark. The funding, led by heavyweight investors including Andreessen Horowitz and Sequoia Capital, is a testament to the burgeoning investor confidence in AI technologies. ElevenLabs, which specializes in replicating voices across various languages, is on the brink of launching innovative products like a dubbing studio and a voice library marketplace, aiming to revolutionize content interaction and accessibility.
Despite a general slowdown in venture capital, AI firms like ElevenLabs continue to defy trends, drawing substantial investments early this year. With nearly $50 billion funnelled into generative AI and AI startups in 2023 and a solid start to the current year, the sector appears resilient to economic headwinds. ElevenLabs, which began its journey in 2022, has now accumulated over $100 million in funding, reflecting a rapid ascent in the competitive AI landscape. crunchbase
Why does this matter?
As ElevenLabs scales new heights, consider the potential implications of hyper-realistic voice AI on the authenticity of digital interactions. The technology could redefine identity verification processes, where voice replication might both pose new security challenges and offer innovative solutions.
New MIT CSAIL Study On AI Job Replacement Ignores Generative AI
Researchers at MIT CSAIL suggest that the economic feasibility of AI-driven job replacement is more limited than the alarming forecasts suggest. Neil Thompson, a co-author, emphasizes the slower and more nuanced transition, stating, "We find that only 23% of the wages being paid to humans for doing vision tasks would be economically attractive to automate with AI." techcrunch
While this number is lower than we’ve otherwise seen, the study is far from comprehensive, focusing only on visual analysis jobs and not on the impressive capabilities of generative models like GPT-4. What was then a headline about job security actually demonstrates how much AI can replace, with nearly 1/4 of wages at risk without factoring in generative AI.
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Around The World
IBM Watson Co-Creator's Early Attempt to Develop AI Tutoring Fails Due to Technological and Practical Implementation Challenges.
Microsoft Hires Director of Nuclear Development Acceleration to Power AI Revolution, Signaling a Bold Move into Atomic Energy Integration for Technological Advancement.
SAM: Segment Anything Model introduces a versatile AI framework capable of analyzing diverse data types for precise segmentation, revolutionizing how machines understand and process complex information.
My TED talk is finally live!! I proposed the recipe for the "Foundation Agent": a single model that learns how to act in different worlds. LLM scales across lots and lots of texts. Foundation Agent scales across lots and lots of realities. If it is able to master 10,000 diverse… twitter.com/i/web/status/1…
— Jim Fan (@DrJimFan)
Jan 22, 2024
Cops Used DNA To Predict A Suspect’s Face—and Tried To Run Facial Recognition On It
In a groundbreaking yet contentious move, East Bay police detectives combined DNA phenotyping with facial recognition to generate a lead on a decades-old case. The suspect’s visage, crafted from DNA evidence by Parabon NanoLabs, was not just a scientific sketch but a detailed 3D rendering, complete with phenotypically predicted features and witness-informed artistic touches. This novel approach, however, has sparked debate among civil liberties experts and privacy advocates who caution against the potential risks of misidentification inherent in such unproven methods.
As law enforcement agencies navigate the complex intersection of genetic science and digital identification, the implications of using algorithmically created faces in facial recognition systems are becoming increasingly apparent. While the technique may offer a glimmer of hope for cold cases, it also underscores the urgent need for stringent oversight, given the current lack of federal guidelines governing the use of such investigative tools. Without it, the risk of error in suspect identification looms large, raising serious concerns about the consequences for individuals mistakenly targeted by these advanced yet imperfect technologies. wired
Written by Isaac R. Ward, Casey Clifton, and Alex Brogan.
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