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AI is sending people to jail--and getting it wrong

#artificialintelligence

AI might not seem to have a huge personal impact if your most frequent brush with machine-learning algorithms is through Facebook's news feed or Google's search rankings. But at the Data for Black Lives conference last weekend, technologists, legal experts, and community activists snapped things into perspective with a discussion of America's criminal justice system. There, an algorithm can determine the trajectory of your life. At the end of 2016, nearly 2.2 million adults were being held in prisons or jails, and an additional 4.5 million were in other correctional facilities. Put another way, 1 in 38 adult Americans was under some form of correctional supervision.


Face Recognition: What Can a Face Recognition App Be Capable Of and How to Make It Happen?

#artificialintelligence

There Is a Range of Tasks Your Face Recognition App Can Be Designed to Perform If You Use the Right Face Recognition Methods. The Facial Recognition technology has been one of those, gaining ground fastest over recent years and one that is still, obviously, pretty far from its heyday. Invented to, virtually, enhance, or rather, extend one of the 6 human senses, it is finding new, often, critically important (such as, for example, its role in the war on terror) uses and becoming more wide-spread globally by the day. According to Researchandmarkets.com, the total worth of the global Face Recognition software market is estimated to have constituted some USD 3.85 billion in 2017 and it is predicted to reach USD 9.78 billion in 2023, thus showing a nearly threefold growth. This can only mean that while giving those better equipped with Face Recognition apps an edge and an additional means of control, the rapidly developing Facial Recognition technology is also becoming a competitive factor for businesses in various industry sectors.


Face Recognition – Aniket Maurya – Medium

#artificialintelligence

Face Verification checks "is this the claimed person?". For example, in school, you go with your ID card and the invigilator verifies your face with the ID card. A mobile phone that unlocks using our face is also using face verification. It is 1:1 matching problem. Now suppose the invigilator knows everyone by their name.


On the Capacity of Face Representation

arXiv.org Machine Learning

Face recognition is a widely used technology with numerous large-scale applications, such as surveillance, social media and law enforcement. There has been tremendous progress in face recognition accuracy over the past few decades, much of which can be attributed to deep learning-based approaches during the last five years. Indeed, automated face recognition systems are now believed to surpass human performance in some scenarios. Despite this progress, a crucial question still remains unanswered: given a face representation, how many identities can it resolve? In other words, what is the capacity of the face representation? A scientific basis for estimating the capacity of a given face representation will not only benefit the evaluation and comparison of different face representations but will also establish an upper bound on the scalability of an automatic face recognition system. We cast the face capacity estimation problem under the information theoretic framework of capacity of a Gaussian noise channel. By explicitly accounting for two sources of representational noise: epistemic uncertainty and aleatoric variability, our approach is able to estimate the capacity of any given face representation. To demonstrate the efficacy of our approach, we estimate the capacity of a 128-dimensional DNN based face representation, FaceNet, and that of the classical Eigenfaces representation of the same dimensionality. Our experiments on unconstrained faces indicate that, (a) our proposed model yields a capacity upper bound of 5.8x$10^{8}$ for FaceNet and 1x$10^{0}$ for Eigenfaces at a false acceptance rate (FAR) of 1%, (b) the face representation capacity reduces drastically as you lower the desired FAR (for FaceNet; the capacity at FAR of 0.1% and 0.001% is 2.4x$10^{6}$ and 7.0x$10^{2}$, respectively), and (c) the empirical performance of FaceNet is significantly below the theoretical limit.


How is AI for video different from AI for images

#artificialintelligence

Extracting insights from video, or using AI technologies, presents an additional set of challenges and opportunities for optimization as compared to images. There is a misconception that AI for video is simply extracting frames from a video and running computer vision algorithms on each video frame. While you can certainly do that but that would not help you get the insights that you are truly after. In this blog post, I will use a few examples to explain the shortcomings of taking an approach of just processing individual video frames. I will not be going over the details of the additional algorithms that are required to overcome these shortcomings. Video Indexer implements several such video specific algorithms.


What is deep learning? - AI Applications - Intellectsoft Blog

#artificialintelligence

Intellectsoft has been offering AI-based software solutions, so we have started a series of blog posts to shed a light on what AI is, its applications, as well as how to implement it successfully in the enterprise. However, what it is and how it works still remains a subject significantly more complex than most users imagine. Join us, as we take a closer look at deep learning without going to the neighboring territories of mathematics and software engineering. Deep learning is a subfield of machine learning -- a type of data analysis that uses self-learning algorithms to analyse big data, learn from it, and eventually solve a problem, provide insights, or predict an outcome. Nevertheless, deep learning employs algorithms that are fundamentally more complex than those employed in machine learning.


Facial recognition is here. The iPhone X is just the beginning Clare Garvie

#artificialintelligence

I have a confession to make. I'm a privacy lawyer who researches the risks of face recognition technology – and I will be buying the new iPhone. Apple's next generation smartphone will use face recognition, thanks to infrared and 3D sensors within its front-facing camera. Reports indicate that the face scan and unlock system will be almost instantaneous and require no buttons to be pressed, being always "on" and ready to read your face. Android users can expect similar face unlock features as well.


Facial recognition is here. The iPhone X is just the beginning Clare Garvie

#artificialintelligence

I have a confession to make. I'm a privacy lawyer who researches the risks of face recognition technology – and I will be buying the new iPhone. Apple's next generation smartphone will unlock using face recognition, thanks to infrared and 3D sensors within its front-facing camera. Reports indicate that the face scan and unlock system will be almost instantaneous and require no buttons to be pressed, being always "on" and ready to read your face. Android users can expect similar face unlock features as well.


Facial recognition could soon be used to identify masked protesters

Mashable

"V for Vendetta" masks are a typical feature of many political protests since the eponymous dystopian movie came out in 2005 -- but what if facial recognition technology was able to identify the face behind the mask? SEE ALSO: Why the iPhone 8's facial recognition could be a privacy disaster We're not there yet, but researchers are slowly and steadily making highly-controversial steps in this direction. Academics from Cambridge University, India's National Institute of Technology, and the Indian Institute of Science used deep learning and a dataset of pictures of people in disguise to try to identify masked faces with an acceptable level of reliability. The research, published on the preprint server arXiv and shared in an AI newsletter, went viral after prominent academic and sociologist Zeynep Tufekci shared it on Twitter. Stressing that the paper "isn't that great", Tufekci nonetheless points out that it's the direction that's worrying, as oppressive and authoritarian states could use the tool to stifle dissent and expose anonymous protesters.


Apple entering 'trial production' of iPhone 8 handsets

Daily Mail - Science & tech

Apple has already begun test manufacturing of the three new iPhones it will unveil in September, it has been claimed. Previous reports have said the firm may be forced to delay its eagerly anticipated iPhone 8 until later in they year. However, it now appears the firm is back on track, with the handset expected to be revealed in September. According to Twitter leaker Benjamin Geskin, all three new 2017 iPhone models have begun trial production, including the iPhone 7s, iPhone 7s Plus, and iPhone 8. According to Twitter leaker Benjamin Geskin, all three new 2017 iPhone models have begun trial production, including the iPhone 7s, iPhone 7s Plus, and iPhone 8.