pattern recognition


Artificial Intelligence in Health Care--Will the Value Match the Hype?

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Artificial intelligence (AI) and its many related applications (ie, big data, deep analytics, machine learning) have entered medicine's "magic bullet" phase. Desperate for a solution for the never-ending challenges of cost, quality, equity, and access, a steady stream of books, articles, and corporate pronouncements makes it seem like health care is on the cusp of an "AI revolution," one that will finally result in high-value care. While AI has been responsible for some stunning advances, particularly in the area of visual pattern recognition,1-3 a major challenge will be in converting AI-derived predictions or recommendations into effective action.


Toward artificial intelligence that learns to write code

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Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating. A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.


Facial Recognition with John Hershey, Machine Learning Researcher Anexinet %

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Is Facial Recognition a valuable public-safety tool or is it an infringement of our civil liberties? Also, name our new Podcast & win a prize! Links in the episode: STUDY: Facial feature discovery for ethnicity recognition San Francisco just banned facial-recognition technology SF Ban on Face Recognition – Acquisition of Surveillance Technology Facial recognition data collected by U.S. customs agency stolen by hackers Facial Recognition Software Wrongly Identifies 28 Lawmakers As Crime Suspects Does object recognition work for everyone? A new method to assess bias in CV systems Don't smile for surveillance: Why airport face scans are a privacy trap U.S. Customs and Border Protection says photos of travelers were taken in a data breach Chickens Prefer Attractive People


Examining The San Francisco Facial-Recognition Ban

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On May 14, 2019, the San Francisco government became the first major city in the United States to ban the use of facial-recognition technology (paywall) by the government and law enforcement agencies. This ban comes as a part of a broader anti-surveillance ordinance. As of May 14, the ordinance was set to go into effect in about a month. Local officials and civil advocates seem to fear the repercussions of allowing facial-recognition technology to proliferate throughout San Francisco, while supporters of the software claim that the ban could limit technological progress. In this article, I'll examine the ban that just took place in San Francisco, explore the concerns surrounding facial recognition technology, and explain why an outright ban may not be the best course of action.


Examining The San Francisco Facial-Recognition Ban

#artificialintelligence

On May 14, 2019, the San Francisco government became the first major city in the United States to ban the use of facial-recognition technology (paywall) by the government and law enforcement agencies. This ban comes as a part of a broader anti-surveillance ordinance. As of May 14, the ordinance was set to go into effect in about a month. Local officials and civil advocates seem to fear the repercussions of allowing facial-recognition technology to proliferate throughout San Francisco, while supporters of the software claim that the ban could limit technological progress. In this article, I'll examine the ban that just took place in San Francisco, explore the concerns surrounding facial recognition technology, and explain why an outright ban may not be the best course of action.


The new stage of the race for AI domination

#artificialintelligence

The new stage of the race for AI domination I. Let use your imagination (an ability only humans have). Imagine you are in the Library of Congress and need to find a book that contains one specific sentence. You may get lucky, but most probably you die before you find it. But you can hire thousands of interns, and they will be flipping through pages in books and comparing the sentence on a piece of paper you gave them with sentences in a book, and one will find the book rather soon. This is exactly what AI does these days, and will do for many years to come; AI is just an intern with thousand hands and eyes, and a brain strong just enough to learn some patterns, although for AI learning takes much more time than for a human intern.


UK to host world's first surveillance camera day

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The UK, which spends more than £2bn on video surveillance each year, is to mark National Surveillance Camera Day on 20 June as part of the National Surveillance Camera Strategy. The aim of the national event is to raise awareness about surveillance cameras and to encourage debate about the use of surveillance cameras in modern society by highlighting how they are used in practice, why they are used and who is using them. The initiative by the Surveillance Camera Commissioner (SCC) and the Centre for Research into Information, Surveillance and Privacy (Crisp) is also aimed at starting a nationwide conversation about how camera technology is evolving, especially around automatic face recognition and artificial intelligence (AI). The organisers hope that the resultant public debate will help inform policy-makers and service providers regarding societally acceptable surveillance practices and legitimacy for surveillance camera systems that are delivered in line with society's needs. As part of the initiative, the SCC is encouraging surveillance camera control centres to throw their "doors open" so that the public can see how they operate.


Boosting Supervision with Self-Supervision for Few-shot Learning

arXiv.org Machine Learning

We present a technique to improve the transferability of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. While recent approaches for self-supervised learning have shown the benefits of training on large unlabeled datasets, we find improvements in generalization even on small datasets and when combined with strong supervision. Learning representations with self-supervised losses reduces the relative error rate of a state-of-the-art meta-learner by 5-25% on several few-shot learning benchmarks, as well as off-the-shelf deep networks on standard classification tasks when training from scratch. We find the benefits of self-supervision increase with the difficulty of the task. Our approach utilizes the images within the dataset to construct self-supervised losses and hence is an effective way of learning transferable representations without relying on any external training data.


California could become first to limit facial recognition technology; police aren't happy

USATODAY - Tech Top Stories

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. SAN FRANCISCO – A routine traffic stop goes dangerously awry when a police officer's body camera uses its built-in facial recognition software to misidentify a motorist as a convicted felon. At best, lawsuits are launched. That imaginary scenario is what some California lawmakers are trying to avoid by supporting Assembly Bill 1215, the Body Camera Accountability Act, which would ban the use of facial recognition software in police body cams – a national first if it passes a Senate vote this summer and is signed by Gov. Gavin Newsom. State law enforcement officials here do not now employ the technology to scan those in the line of sight of officers.