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NASA is preparing studies on passenger comfort for future air taxis, which could revolutionise urban transportation.
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What’s in store:
Rain AI welcomes top Apple chip exec.
Meta comes up with a translation tool for lesser-spoken languages.
Around the web: Massachusetts rideshare drivers gain benefits and $32.50 minimum pay under new Uber and Lyft settlement.
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Read Time: 6 minutes
AI STARTUP
Rain AI has welcomed Jean-Didier Allegrucci, a former Apple chip executive, to lead its hardware engineering.
This is the second major hire this month for the startup which aims to revolutionise semiconductor design for AI.
Allegrucci, known for his work on Apple processors, that replaced intel in iPhones and Macs, will focus on developing more energy-efficient chips at Rain AI.
He’ll collaborate with Amin Firoozshahian, a former Meta architect.
Here’s what’s happening:
Jean-Didier Allegrucci joins Rain AI to lead hardware engineering.
Focus on developing energy-efficient chips using in-memory compute.
Backed by industry leaders and exploring brain-inspired technology.
Allegrucci’s new challenge
Backed by OpenAI co-founder Sam Altman and Y Combinator, Rain AI is exploring in-memory compute technology.
This method, inspired by the human brain, processes data where it’s sorted, promising significant energy savings.
Despite the challenges around its economic and environmental feasibility, in-memory computing is pursued by industry giants like Intel, TSMC, and Samsung.
Efficient chips are key to broadening AI’s applications across devices.
According to Rain AI CEO William Passo, Rain AI's compute-in-memory technology is set to unlock the potential of modern AI models, making them faster, cheaper, and more advanced.
Turning AI dreams into silicon reality, one chip at a time.
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On This Day
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META
Meta is enhancing its translation capabilities on Facebook and Instagram to include 200 lesser-spoken languages worldwide.
The No Language Left Behind (NLLB) project aims to make these languages more accessible, but experts suggest consulting native speakers to refine the tool.
Meta’s NLLB project recently announced they’ve scaled their technology to support these languages.
The project includes European languages like Scottish Gaelic, Galician, Irish, Ligurian, Bosnian, Icelandic, and Welsh.
Meta classifies these as "low resource" languages, with less than one million sentences available for AI training.
Experts recommend Meta involve native speakers and language specialists to improve the service.
The current tool still needs work to achieve better accuracy.
How it works:
Meta trains its AI using data from the Opus repository, an open-source platform with a collection of authentic text.
Contributors are experts in natural language processing (NLP), enabling computers to translate and understand human language.
Meta also uses mined data from sources like Wikipedia to create a multilingual language model. This AI can translate between languages without relying on English.
200 languages, one goal
William Lamb, a professor of Gaelic ethnology and linguistics at the University of Edinburgh, points out that Meta’s Scottish Gaelic translations are still not very good.
He suggests that Meta should consult native speakers to improve the tool.
However, most native speakers are older and do not use computers, making this challenging.
Lamb recommends Meta collaborate with the BBC, which produces high-quality online content in Gaelic.
Meta's translations have improved by 44% since the first model was released in 2020.
When fully implemented, there will be more than 25 billion translations daily on Facebook and Instagram.
The NLLB team uses a benchmark of human-translated sentences to evaluate translation quality, filtering out toxic words and phrases.
Alberto Bugarín-Diz, a professor of AI at the University of Santiago de Compostela in Spain, believes linguists and tech experts should work together to refine datasets.
He notes that using Wikipedia data can be advantageous, as it covers almost every aspect of human life, but stresses the need for legal compliance in data usage.
Lamb remains sceptical about recommending Meta's tool due to current inaccuracies.
Bugarín-Diz, however, argues that user engagement is essential for Meta to invest in improvements, recognising that understanding the tool's weaknesses is key before widespread use.
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