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Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money. Second, these deep neural networks will be high performers on their task, but cannot easily generalize to other, related tasks, or they need large amounts of data to do so. In this blog post, Yann LeCun and Ishan Misra of Facebook AI Research (FAIR) describe the current state of Self-Supervised Learning (SSL) and argue that it is the next step in the development of AI that uses fewer labels and can transfer knowledge faster than current systems. They suggest as a promising direction to build non-contrastive latent-variable predictive models, like VAEs, but ones that also provide high-quality latent representations for downstream tasks.

OUTLINE:
0:00 — Intro & Overview.
1:15 — Supervised Learning, Self-Supervised Learning, and Common Sense.
7:35 — Predicting Hidden Parts from Observed Parts.
17:50 — Self-Supervised Learning for Language vs Vision.
26:50 — Energy-Based Models.
30:15 — Joint-Embedding Models.
35:45 — Contrastive Methods.
43:45 — Latent-Variable Predictive Models and GANs.
55:00 — Summary & Conclusion.

Paper (Blog Post): https://ai.facebook.com/blog/self-supervised-learning-the-da…telligence.
My Video on BYOL: https://www.youtube.com/watch?v=YPfUiOMYOEE

ERRATA:

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Women constitute a mere 22 per cent or less than a quarter of professionals in the field of AI and Data Science.

There is a troubling and persistent absence of women when it comes to the field of artificial intelligence and data science. Women constitute a mere 22 per cent or less than a quarter of professionals in this field, as says the report “Where are the women? Mapping the gender job gap in AI,” from The Turing Institute. Yet, despite low participation and obstacles, women are breaking the silos and setting an example for players out in the field of AI.

To honour their commitment and work done, we have listed some of the women innovators and researchers who have worked tirelessly and contributed significantly to the field of AI and data science. The list below is provided in no particular order.

The brainchild behind and the founder of The Algorithmic Justice League (AJL), Joy Buolamwini, has started the organisation that combines art and research to illuminate the social implications and harms of artificial intelligence. With her pioneering work on algorithmic bias, Joy opened the eyes of the world and brought out the gender bias and racial prejudices embedded in facial recognition systems. As a result, Amazon, Microsoft, and IBM all halted their facial recognition services, admitting that the technology was not yet ready for widespread usage. One can watch the famous documentary ‘Coded Bias’ to understand her work. Her contributions will surely pave the way for a more inclusive and diversified AI community in the near future.

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The Seoul Metropolitan Government (SMG) is the first local government in Korea to establish a metaverse platform, which has emerged as a contactless communication channel in the post-pandemic era, to start providing a new-concept public service by using the platform in its administration.

The SMG plans to establish “Metaverse Seoul” (tentatively named), a high-performance platform, by the end of next year, and create a metaverse ecosystem for all areas of its municipal administration, such as economic, cultural, tourism, educational and civic service, in three stages from next year.

Starting with the pilot program of a Bosingak Belfry virtual bell ringing event at the end of this year, the SMG will consecutively provide various business support facilities and services, including the Virtual Mayor’s Office, Seoul FinTech Lab, Invest Seoul and Seoul Campus Town, on its metaverse platform.

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Sensors introduce an important new method to spot bio-marker for brain diseases Accurate timings of when brain signals fire demonstrated for the first time by the Sussex scientists, which has implications for tracking the onset of brain disease The quantum brain sensors could present a more efficient and accurate alternative to EEG and fMRI scanners.

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The CEO of Tesla and SpaceX is not only the world’s richest person, but he’s also worth more than Warren Buffet and Bill Gates combined! Stay tuned to find out what other billionaires think of Elon Musk and subscribe to Futurity.

#elonMusk #jeffBezos #tesla.

Here at Futurity, we scour the globe for all the latest tech releases, news and info just so you don’t have to! Covering everything from cryptocurrency to robotics, small startups to multinational corporations like Tesla and Jeff Bezos to Elon Musk and everything in between!

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Scientists have been able to trap antimatter particles using a combination of electric and magnetic fields. Antiprotons have been stored for over a year, while antimatter electrons have been stored for shorter periods of time, due to their lower mass. In 2011, researchers at CERN announced that they had stored antihydrogen for over 1,000 seconds.

While scientists have been able to store and manipulate small quantities of antimatter, they have not been able to answer why antimatter is so rare in the universe. According to Einstein’s famous equation E = mc2, energy should convert into matter and antimatter in equal quantities. And, immediately after the Big Bang, there was a lot of energy. Accordingly, we should see as much antimatter as matter in our universe, and yet we don’t. This is a pressing unsolved mystery of modern physics.

According to Einstein’s equations, as well as other modern theories of antimatter, antimatter should be exactly the same as ordinary matter, with only the electric charges reversed. Thus, antimatter hydrogen should emit light just like ordinary hydrogen does, and with exactly the same wavelengths. In fact, an experiment showing exactly this behavior was reported in early 2020. This was a triumph for current theories, but meant no explanation for the universe’s preference of matter was found.

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Sushil Reddy is no stranger to long-distance electric bicycles rides, having broken the Guinness World Record back in 2016 with a 7,424 km (4,613 mile) ride across India. Since then he’s set his sights on solar power, performing several more long-distance solar-powered electric bike rides. Now he’s halfway through a 10,460 km (6,500 mile) ride around the US on a custom-built solar-powered electric bike as part of the SunPedal Ride project.

As the SunPedal Ride project explained:

“The SunPedal Ride is an outreach project started by Sushil Reddy in 2016. The idea is to have conversations about clean energy and sustainable mobility via endurance journeys undertaken on zero tail-pipe emission vehicles. Each edition of The SunPedal Ride is a new challenge which is executed by a team and supported by a group of sponsors/partners to spread the message via public interactions. A medium of a zero tail-pipe emissions vehicle is used in each edition of The SunPedal Ride.”

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November 27, 2021 — A few years ago, a little known Montreal XR company was putting its patented technical chops into building out what many think is the “last interface”, one that Facebook (ahem, Meta’s) CEO Mark Zuckerberg is betting the social media giant’s future on. Today, that Montreal company, and some pretty impressive Canadian innovators, find themselves thick in the middle of the Metaverse’s battle of the titans.

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Blazing along at space-record speeds that would get it from Earth to the Moon in under an hour, NASA

Established in 1958, the National Aeronautics and Space Administration (NASA) is an independent agency of the United States Federal Government that succeeded the National Advisory Committee for Aeronautics (NACA). It is responsible for the civilian space program, as well as aeronautics and aerospace research. It’s vision is “To discover and expand knowledge for the benefit of humanity.”

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