Original published in The San Francisco Chronicle (the cover article of Sunday's "Insight" section) What if the data tells you to be racist? Without the right precautions, machine learning -- the technology that drives risk-assessment in law enforcement, as well as hiring and loan decisions -- explicitly penalizes underprivileged groups. Left to its own devices, the algorithm will count a black defendant's race as a strike against them. Yet some data scientists are calling to turn off the safeguards and unleash computerized prejudice, signaling an emerging threat that supersedes the well-known concerns about inadvertent machine bias. Imagine sitting across from a person being evaluated for a job, a loan, or even parole.

Google started out as a simple search engine in 1998 and has turned into one of the most important and influential companies in the world. On Thursday, the company celebrates its 20th birthday, and although searching for something on the internet has commonly become "googling", the company itself has become a part of the everyday lives in more ways than one. Google was officially founded in September 1997 but September 1998 is generally seen as the date the company really started its now ubiquitous search engine. The search engine was created for about $100,000 by Sergey Brin and Larry Page, two Stanford PhD students, who wanted to challenge Altavista and Yahoo, the two most popular search engines in the earlier days of the internet. As its popularity grew, the company expanded to new territories.

We consider the problem of identifying intermediate variables (or mediators) that regulate the effect of a treatment on a response variable. While there has been significant research on this topic, little work has been done when the set of potential mediators is high-dimensional and when they are interrelated. In particular, we assume that the causal structure of the treatment, the potential mediators and the response is a directed acyclic graph (DAG). High-dimensional DAG models have previously been used for the estimation of causal effects from observational data and methods called IDA and joint-IDA have been developed for estimating the effects of single interventions and multiple simultaneous interventions respectively. In this paper, we propose an IDA-type method called MIDA for estimating mediation effects from high-dimensional observational data. Although IDA and joint-IDA estimators have been shown to be consistent in certain sparse high-dimensional settings, their asymptotic properties such as convergence in distribution and inferential tools in such settings remained unknown. We prove high-dimensional consistency of MIDA for linear structural equation models with sub-Gaussian errors. More importantly, we derive distributional convergence results for MIDA in similar high-dimensional settings, which are applicable to IDA and joint-IDA estimators as well. To the best of our knowledge, these are the first distributional convergence results facilitating inference for IDA-type estimators. These results have been built on our novel theoretical results regarding uniform bounds for linear regression estimators over varying subsets of high-dimensional covariates, which may be of independent interest. Finally, we empirically validate our asymptotic theory and demonstrate the usefulness of MIDA in identifying large mediation effects via simulations and application to real data in genomics.

The US government is only dabbling in artificial intelligence at the moment. It might make a larger commitment before long, however. A bipartisan group of senators (Brian Schatz, Cory Gardner and Rob Portman) have introduced an AI in Government Act that would increase federal AI adoption by both including AI in data-related plans and supplying the resources to make those plans a reality. Thankfully, this isn't just a question of throwing money at the problem -- it would have multiple government organizations shift more attention to the emerging technology. The General Services Administration would have additional powers to both research AI policy and provide relevant expertise to agencies.

Elon Musk On Joe Rogan AI Tech - Is he an Alien? Watch the moment when Elon Musk says "joking" that he is an Alien. De facto Elon Musk is a really singular person. How he is, the way he behaves and talks... he sometimes doesn't look like human. What if? Hope you like the video and don't forget to subscribe my channel Please Share, Support & Subscribe!!!

Watch the premiere episode of The Dr. Data Show, which answers the question, "What makes machine learning the coolest science?" This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover very best of machine learning and predictive analytics. Click here for more information and to sign up for future episodes of The Dr. Data Show Please note that viewing the video (above) is recommended, since it includes complementary visuals. Also, certain vocal inflections and gesticulations hold meaning. Some of the intented meaning is lost by reading this transcript rather watching the video.

Hi, I'm Tom Martin and today's LawDroid lesson is about AI and Chatbots. I'm the co-founder of the Vancouver chapter of Legal Hackers, and I've created LawDroid which is an AI legal assistant to help people solve their legal problems. In short it's a computer program that simulates a conversation with a person and a lot of people say that Chatbots are the new apps because they're on your mobile phone and they're proliferating like crazy. Well, intelligence is hard to define but in 1950 the Godfather of computers, Alan Turing, came up with a test and basically he said that if you're able to have a five minute conversation by text and you can't tell whether or not the other person who's talking to you is a computer or a person, the computer's passed the test. Actually he predicted that by the year 2000 the computer would win that test 30% of the time.

Yet the combination of these factors created a milestone in AI history, as it had a profound impact on real-world applications and the successful deployment of various AI techniques that have been in the works for a very long time, particularly neural networks.g I shared these remarks in various contexts during the course of preparing this article. The audiences ranged from AI and computer science to law and public-policy researchers with an interest in AI. What I found striking is the great interest in this discussion and the comfort, if not general agreement, with the remarks I made. I did get a few "I beg to differ" responses though, all centering on recent advancements relating to optimizing functions, which are key to the successful training of neural networks (such as results on stochastic gradient descent, dropouts, and new activation functions). The objections stemmed from not having named them as breakthroughs (in AI). My answer: They all fall under the enabler I outlined earlier: "increasingly sophisticated statistical and optimization techniques for fitting functions." Follow up question: Does it matter that they are statistical and optimization techniques, as opposed to classical AI techniques?

Every CEO should be able to reimagine their business and P&L with the help of AI. As a guide, I have identified 17 levers that CEOs can shape with the help of AI to boost their P&L. The framework I have developed (below) can be used both as a barometer for assessing the current state of your teams' efforts around AI and for spotting new opportunities. The 17 levers show how AI can help drive superior growth, increase return on capital, and manage both desirable and undesirable risks. Let's start by looking at growth.

Microsoft Corp. has announced a five-year, $40 million initiative aimed at harnessing the power of artificial intelligence to help children, protect refugees and displaced people, promote respect for human rights, and improve the effectiveness of disaster relief and recovery efforts. Announced at the company's annual IT event and in conjunction with the United Nations General Assembly meeting, the AI for Humanitarian Action is the third program of Microsoft's AI for Good initiative, which was launched in July 2017; the $25 million AI for Accessibility program and $50 million AI for Earth programs were announced this past May and December. Through the initiative, Microsoft will engage nongovernmental and humanitarian organizations in partnerships that leverage their expertise and the company's AI and data science know-how to develop new AI solutions that help forecast disasters and better target relief efforts; address the needs of children, including the provision of basic health services and the prevention of child trafficking; optimize the delivery of aid, supplies, and services to sixty-eight million displaced people, twenty-eight million of whom are refugees; and help monitor, detect, and prevent human rights abuses. Microsoft also announced the hiring of John Kahan as chief data analytics officer for the company's corporate, external, and legal affairs. In that role, Kahan will lead the company's efforts to promote the sustainable use of the planet's resources, improve opportunities for people with disabilities, protect human rights, strengthen humanitarian assistance, and increase the capacity of NGOs to respond to humanitarian disasters.