Brown University researchers have developed a new image analysis technique to distinguish two key cancer cell types associated with tumor progression. The approach could help in pre-clinical screening of cancer drugs and shed light on a cellular metamorphosis that is associated with more malignant and drug-resistant cancers. The epithelial-mesenchymal transition, or EMT, is a process by which more docile epithelial cells transform into more aggressive mesenchymal cells. Tumors with higher numbers of mesenchymal cells are often more malignant and more resistant to drug therapies. The new technique combines microscopic imaging with a machine learning algorithm to better identify and distinguish between the two cell types in laboratory samples.

Google scientists have created a computer program that uses basic reasoning to learn to navigate the London Underground system by itself. The same Artificial Intelligence (AI) agent could also answer questions about the content of snippets of stories and work out family relationships by looking at a family tree. Scientists predict that in future a similar approach could pave the way for virtual assistants that would be able to instantaneously scour the internet to answer questions and carry out instructions with precision. Herbert Jaegar, a computer scientist at the University of Bremen, said: "I think this can be described as rational reasoning. They [the tasks] involve planning and structuring information into chunks and re-combining them."

This article was written By Henri Waelbroeck, Head of Research at Portware. Execution management has matured from laying the foundation for electronification by automating repetitive workflows to extracting progressively more value from the infrastructure as it evolves. Each generation in execution management technology has pushed automation one level higher in the decision hierarchy. The FM London Summit is almost here. Today, we are seeing the dawning of the next generation of execution management systems: one where AI works with the trader to combine the best of quantitative optimization (at speed and at scale) and the trader's domain knowledge.

In an interview in Vox, Marc Andreessen asserted that Vinod Khosla "has written all these stories about how doctors are going to go away…And I think he is completely wrong." Mr. Khosla was quick to respond via Twitter: "Maybe @pmarca [Mr. Andreessen] should read what I think before assuming what I said about doctors going away." He included a link to his detailed "speculations and musings" on the topic. It turns out that Mr. Khosla believes that AI will take away 80 percent of physicians' work, but not necessarily 80 percent of their jobs, leaving them more time to focus on the "human aspects of medical practice such as empathy and ethical choices." That is not necessarily much different than Mr. Andreessen's prediction that "the job of a doctor shifts and becomes a higher-level, more important job that pays better as the doctor becomes augmented by smarter computers."

There's a lot of movement in the tech space today, as developments in AI, machine learning and now deep learning are coming at a pace best described as rapid-fire. There's a substantial amount of buzz around that last term, though--the newest to the group of powerhouses with the potential to change everything. Let's examine what exactly makes deep learning so promising and explore what it means for the enterprise. Deep learning falls under the umbrella of artificial neural networks (ANNs), which, essentially, are clusters of virtual neurons created to learn from data sans human supervision. If this sounds a whole lot like what you know of machine learning, that's because it is--both techniques extract statistics and classify results after looking through large amounts of data.

"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?