Today, many companies now process huge amounts of data using artificial intelligence (AI). If the data that fuels AI algorithms is unrepresentative of society, however, these programs essentially learn and adopt our biases. More organisations are now opting to employ algorithmic decision-making in order to reduce bias and improve operations. Nevertheless, it is possible for these algorithms to share many of the same vulnerabilities found in a human decision-making process. Indeed, the interim report Bias in Algorithmic Decision Making released in July this year supports this.

A team of Italian mathematicians, including one who is also a neuroscientist from the Champalimaud Centre for the Unknown (CCU), in Lisbon, Portugal, has shown that artificial vision machines can learn to recognize complex images spectacularly faster by using a mathematical theory that was developed 25 years ago by one of this new study's co-authors. Their results have been published in the journal Nature Machine Intelligence. During the last decades, machine vision performance has exploded. For example, these artificial systems can now learn to recognise virtually any human face - or to identify any individual fish moving in a tank, in the midst of a large number of other almost identical fish which are also moving. The machines we're talking about are, in fact, electronic models of networks of biological neurons, and their aim is to simulate the functioning of our brain, which is as good as it gets at performing these visual tasks - and this, without any conscious effort on our part.

Disclaimer: I would like it to be made very clear that I am absolutely 100% open to the idea that I am wrong about anything in this post. I don't only accept but explicitly request arguments that could convince me I am wrong on any of these issues. If you think I am wrong about anything here, and have an argument that might convince me, please get in touch and present your argument. I am happy to say "oops" and retract any opinions presented here and change my course of action. As the saying goes: "When the facts change, I change my mind. I plan on releasing it on the 1st of July. Before criticizing my decision to do so, please read my arguments below. If you still think I'm wrong, contact me on Twitter @NPCollapse or by email (thecurioushacker@outlook.com) and convince me. For code and technical details, see this post. UPDATE: My mind has been changed, and I plan on not releasing. See my update post here that explains my reasoning. UPDATE 2: This post is now part 1 in a series of ...

This is a clip from a conversation with Yann LeCun on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2NJiCov If you enjoy these, consider subscribing, sharing, and commenting below. Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning.

In Grenoble, France, one company is aiming to make an impact in the field which is so visibly dominated by multi-billion dollar corporations. We caught up with the company's Business Unit Director responsible for introducing their products to the Automotive market, Stéphane Cordova, to find out more, ahead of their attendance at AutoSens Detroit in May. The company's approach to "Supercomputing on a chip" has evolved from a the business origins providing components and software services to data centres, where high speed and reliability as well as low power consumption and significantly reduced heat generation were all key factors in processor component design. What helped you decide to commit to exhibiting at AutoSens again? Kalray's technology will be at the heart of autonomous driving.

Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founding father of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast. OUTLINE: 0:00 - Introduction 1:11 - HAL 9000 and Space Odyssey 2001 7:49 - The surprising thing about deep learning 10:40 - What is learning?

With an artificial intelligence revolution overtaking the workforce, it's clear that how work is done is going to change. That brings with it worry, especially for employees concerned about job security. But with the right skills, AI and automation don't have to represent a loss. In fact, the coming rush of automation represents opportunity for many. Katherine LaVelle, managing director of talent and organization for Accenture, said, "The majority of CEOs that we talked to said there will be an increase in jobs as a result of AI."

Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF) framework is proposed for clustering high-dimensional data. Specifically, RFA-LCF integrates the robust flexible CF by clean data space recovery, robust sparse local-coordinate coding and adaptive weighting into a unified model. RFA-LCF improves the representations by enhancing the robustness of CF to noise and errors, providing a flexible constraint on the reconstruction error and optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a sparse projection to recover the underlying clean data space, and then the flexible CF is performed in the projected feature space. RFA-LCF also uses a L2,1-norm based flexible residue to encode the mismatch between the recovered data and its reconstruction, and uses the robust sparse local-coordinate coding to represent data using a few nearby basis concepts. For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates. By updating the local-coordinate preserving data, basis concepts and new coordinates alternately, the representation abilities can be potentially improved. Extensive results on public databases show that RFA-LCF delivers the state-of-the-art clustering results compared with other related methods.

It began with Mark Zuckerberg's leaked email on the future of Facebook. Whether it be through an augmented google glass-like device or within a fully immersed VR headset such as the Occulus Quest - One thing is for sure. Facebook is taking VR very seriously, and if I can't be so bold myself, it's their primary focus. In his email, dated 2014, Mark talks of acquiring Unity, a gold standard video game and development engine. His reason, to facilitate the exponential growth of VR/AR content by enabling video game developers to create content for their AR/VR platform.

Technology is and has always been a crucial part of finance. From the first promissory notes (banknotes) in the Netherlands and China, there was a race with counterfeiters that parasitically undermined trust. As in political communication, technology is the message, rather than merely "a tool": when it comes to money, trust is not just instrumental, it is fundamental. With cashless payments being the norm and social media platforms weαving an additional layer of involvement in our social data web – Amazon, Google, Facebook, Apple – Artificial Intelligence (AI) is already in our wallets, business, and financial affairs. In a non-western setting, one may refer to the Chinese "social rating" system, which allows the state to value and evaluate social behaviour patterns, creating a link to individual credit rating. That is a far-reaching "Panopticon" structure that would be unthinkable without AI.