"Long-term concerns about super-intelligent General AI should have little impact on current policy," the report Preparing for the Future of Artificial Intelligence reads. The administration is exploring how AI can bolster existing initiatives such as the Data Driven Justice and Police Data Initiative, both of which attempt to "provide law enforcement and the public with data that can better inform decision-making in the criminal justice system, while also taking care to minimize the possibility that AI might introduce bias or inaccuracies due to deficiencies in the available data," the report reads. The government should also explore ways to improve the understanding of and uses of AI in key agencies, the report says: "For example, Federal agencies should explore the potential to create DARPA-like organizations to support high-risk, high-reward AI research and its application, much as the Department of Education has done." Along with a call for AI training for federal employees, the proposal suggests an exchange model that would allow experts from federal and state governments to rotate among departments, "colleges and universities, Indian tribal governments, federally funded research and development centers, and other eligible organizations."

The two covered the ethical implications of machine learning, diversity in tech, neurodiversity, the collapse of funding for basic research, precision medicine, high-speed trading, cybersecurity, robots taking our jobs, internet regulation, space travel, and how Star Trek informed Obama's worldview. OBAMA: I think there's no doubt that developing international norms, protocols, and verification mechanisms around cybersecurity generally, and AI in particular, is in its infancy. Part of what makes this an interesting problem is that the line between offense and defense is pretty blurred. And at a time when there's been a lot of mistrust built up about government, that makes it difficult. When you have countries around the world who see America as the preeminent cyberpower, now is the time for us to say, "We're willing to restrain ourselves if you are willing to restrain yourselves."

Is artificial intelligence, or AI, a real threat to the world? It is, according to the Global Catastrophic Risks 2016 report issued by the Global Challenges Foundation.

President Barack Obama's administration believes that artificial intelligence can be a positive force in the United States, vastly improving specialized areas within health care, transportation, education and policing over the coming decades. In two reports released today, one day ahead of the White House Frontiers Conference, the Obama administration calls for long-term investments in AI research and a broad range of investigation into the ethics, security and uses of AI. The report also emphasizes the current limits of AI, noting that narrow AI systems have rapidly advanced over the past few years, but general-intelligence systems -- machines that learn and respond as humans do -- are decades away. "Long-term concerns about super-intelligent General AI should have little impact on current policy," the report Preparing for the Future of Artificial Intelligence reads. The report outlines a few areas of interest for the government to implement AI systems, including within policing and justice, a topic Engadget covered during AI Week 2016.

I'm currently writing a couple of papers which use CNNs and one thing I really struggle with is making nice looking diagrams that are actually clear. I've seen figures in other papers (for example, picturing convolutional layers as cubes, stuff like that) that seem to be very clear but I haven't really figured out 1) what they're using to make those and 2) there is not really an agreed upon style for enumerating and illustrating network design. Does anyone have any light to shed here, is there a drawing tool (LaTeX compatible) that is good for this kind of thing or is it just lots and lots of tikz?

It's been a rough few months for Mobileye, maker of the assisted driving platform used in cars from automaker Tesla. The two have been at odds over what caused recent Tesla accidents, with the car maker blaming Mobileye's system. Tesla is now developing its own platform for assisted driving, and Mobileye -- having denied responsibility for the Tesla accidents -- is moving on. It is designing a next-generation chip called EyeQ5, which will be used as the brains for fully autonomous cars by 2020. The chip will have a new 64-bit MIPS CPU from Imagination Technologies, which has been dealing with struggles of its own.

TDSP provides recommendations for managing shared analytics and storage infrastructure, including cloud file systems for storing datasets, databases, Big Data clusters (Hadoop, Spark), machine learning services, etc., both on the cloud and on-premises. This is where raw and processed datasets are stored, enabling reproducible analysis. It also avoids duplication, which could lead to inconsistencies and additional infrastructure costs. Scripts are provided to provision the shared resources, track them and allow each team member to connect to those resources securely. Our data science team uses the Microsoft Data Science Virtual Machine as our cloud development environment.

A couple of weeks ago I wrote an article on simple linear regression, which I would recommend reading before proceeding to read this one. Machine learning is a very interesting topic and I have been studying it on my free time. I hope this article sparks your interest in the subject or helps continue fuel it. In simple linear regression there is a one-to-one relationship between the input variable and the output variable. But in multiple linear regression, as the name implies there is a many-to-one relationship, instead of just using one input variable, you use several.

We've been hearing all artificial intelligence, all the time from the Customer Relationship Management (CRM) industry over the last several weeks. Microsoft is the latest to trumpet its AI capabilities for sales people with the general availability of Dynamics 365 coming on November 1st. Microsoft announced last summer that it was going to be combining its ERP and CRM into a unified solution, and this is the culmination of that announcement. Like many large organizations, Microsoft tends to deliver the news in waves -- it's coming, it's in beta, it's here. While the news smacks of "look at me too," Microsoft points out it has been working on AI long before its biggest competitors like Salesforce and Oracle, which recently announced their own AI capabilities at their respective customer conferences, Dreamforce and Oracle Open World. Microsoft has built in a couple of intelligence features into the release designed specifically for sales and service personnel.

Machine learning is revolutionising how companies are capitalising on Big Data to develop their marketing strategies. While the term encompasses a broad spectrum of technologies and approaches, in a marketing context it can be used to improve targeting, response rates and overall marketing ROI. To put it simply, machine learning involves the automated analysis of large volumes of data – such as consumer spending habits and purchasing behaviour, as well as demographic information – and using a mathematical algorithm and a computer to identify patterns and trends. The algorithm then tests predictions based on historical campaign data and learns from the predictions it gets right. With time, these algorithms become highly accurate as more data from campaign results is added.