In the first case of its kind, artificial intelligence (AI) will be present throughout an entire U.S. court proceeding, when it helps to defend against a speeding ticket.
San Francisco-based DoNotPay has developed “the world’s first robot lawyer” – an AI that can be installed on a mobile device. The company’s stated goal is to “level the playing field and make legal information and self-help accessible to everyone.”
The AI company has earlier created something similar earlier, they have in the past used AI-generated form letters and chatbots to help secure and recovers people’s fund for onboarding wifi that failed to work.
Many people have reacted to this new innovation citing that it may be injurious to lawyers’ legal business, particularly lawyers who have no knowledge about artificial intelligence.
The eerie new capabilities of artificial intelligence are about to show up inside a courtroom — in the form of an AI chatbot lawyer that will soon argue a case in traffic court.
That’s according to Joshua Browder, the founder of a consumer-empowerment startup who conceived of the scheme.
Sometime next month, Browder is planning to send a real defendant into a real court armed with a recording device and a set of earbuds. Browder’s company will feed audio of the proceedings into an AI that will in turn spit out legal arguments; the defendant, he says, has agreed to repeat verbatim the outputs of the chatbot to an unwitting judge.
The biggest obstacle is that each robotics lab has its own idea of what a conscious robot looks like. There are also moral implications to building robots that have consciousness. Will they have rights, like in Bicentennial Man?
Considerations about conscious robots have been the domain of science fiction for decades. Isaac Asimov wrote several novels, including I, Robot, that examined the implications from the perspectives of law, society, and family, raising a lot of moral questions. Experts in ethical technology have considered and expanded upon these questions as scientists like those in the Columbia University lab work toward building more intelligent machines.
Science fiction has also brought us killer machines like in The Terminator, and conscious robots sound like a good way to have some. Humans might learn bad ideas and act upon them, and there is no reason to believe that robots will not fall into the same trap. Some of science’s greatest minds have warned against getting carried away with artificial intelligence.
The US Supreme Court on Monday rejected a bid by NSO Group to block a WhatsApp lawsuit accusing the Israeli tech firm of allowing mass cyberespionage of journalists and human rights activists.
The Supreme Court denied NSO’s plea for legal immunity and ruled that the case, which targets the company’s Pegasus software, can continue in a California federal court, a court filing showed.
Pegasus gives its government customers—which have allegedly included Mexico, Hungary, Morocco and India—near-complete access to a target’s device, including their personal data, photos, messages and location.
In this #webinar, Dr Vincenzo Sorrentino from the Department of Biochemistry and Healthy Longevity Translational Research Programme at the Yong Loo Lin School of Medicine, shared about his research on the relationship between metabolism, nutrition and proteostasis and their impact on health and ageing, and engaged in discussion about the role of mitochondrial proteostasis in ageing and related diseases.
Register for upcoming #HealthyLongevity #webinar sessions at https://nus-sg.zoom.us/webinar/register/7916395807744/WN__sypkX6ZSomc7cGAkK3LbA
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References:
Closing video source: https://www.youtube.com/watch?v=sycgL3Qg_Ak.
Disclaimer: The opinions and advice expressed in this webinar are those of the speakers and do not represent the views and opinions of the organizers and National University of Singapore or any of its subsidiaries or affiliates. The information provided in this webinar is for general information purposes only as part of a general discussion on public health. The information is not intended to be a substitute for professional medical advice, diagnoses or treatment; and cannot be relied on in place of consultation with your licensed healthcare provider. All Rights Reserved.
All of the proceedings of this webinar, including the presentation of scientific papers, are intended for limited publication only, and all property rights in the material presented, including common-law copyright, are expressly reserved to the speaker or NUS. No statement or presentation made is to be regarded as dedicated to the public domain.
Greg Yang is a mathematician and AI researcher at Microsoft Research who for the past several years has done incredibly original theoretical work in the understanding of large artificial neural networks. Greg received his bachelors in mathematics from Harvard University in 2018 and while there won the Hoopes prize for best undergraduate thesis. He also received an Honorable Mention for the Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student in 2018 and was an invited speaker at the International Congress of Chinese Mathematicians in 2019.
In this episode, we get a sample of Greg’s work, which goes under the name “Tensor Programs” and currently spans five highly technical papers. The route chosen to compress Tensor Programs into the scope of a conversational video is to place its main concepts under the umbrella of one larger, central, and time-tested idea: that of taking a large N limit. This occurs most famously in the Law of Large Numbers and the Central Limit Theorem, which then play a fundamental role in the branch of mathematics known as Random Matrix Theory (RMT). We review this foundational material and then show how Tensor Programs (TP) generalizes this classical work, offering new proofs of RMT. We conclude with the applications of Tensor Programs to a (rare!) rigorous theory of neural networks.
Patreon: https://www.patreon.com/timothynguyen.
Part I. Introduction.
00:00:00 : Biography.
00:02:36 : Harvard hiatus 1: Becoming a DJ
00:07:40 : I really want to make AGI happen (back in 2012)
00:09:00 : Harvard math applicants and culture.
00:17:33 : Harvard hiatus 2: Math autodidact.
00:21:51 : Friendship with Shing-Tung Yau.
00:24:06 : Landing a job at Microsoft Research: Two Fields Medalists are all you need.
00:26:13 : Technical intro: The Big Picture.
00:28:12 : Whiteboard outline.
Part II. Classical Probability Theory.
00:37:03 : Law of Large Numbers.
00:45:23 : Tensor Programs Preview.
00:47:25 : Central Limit Theorem.
00:56:55 : Proof of CLT: Moment method.
01:02:00 : Moment method explicit computations.
Part III. Random Matrix Theory.