Cuban was in conversation with Charlie Kirk, the 24-year-old founder of Turning Point USA, a right-wing non-profit organization aimed at promoting conservative political ideas among high school students. Cuban does not identify as Republican or Democrat: "I don't belong to any political party and I never will," Cuban told Kirk. Amid a wide-ranging debate about what the scope of government should be, Cuban argued that the government should be involved in funding artificial intelligence research. In November, the billionaire warned that the United States should not allow countries like China and Russia to pull ahead in terms of developing artificial intelligence. China's government has said publicly it plans to be the global leader in artificial intelligence by 2030 and Russian President Vladimir Putin has said, "the one who becomes the leader in this sphere will be the ruler of the world."

The United States could lose its economic and political standing in the world if it doesn't develop a comprehensive, high-priority plan for artificial intelligence. That's the stark warning from a new report issued today by the Center for a New American Security (CNAS), a think tank in Washington, DC. World order: Other nations, including China, India, France, and the UK, have embraced artificial intelligence by announcing major national initiatives. Many experts recommend that the US follow suit, but the Trump White House has taken a surprisingly hands-off approach. Falling behind in AI development and implementation would present a risk for U.S. global economic and military leadership.

"Artificial intelligence is a technology that transcends borders," Rep. Will Hurd (R-Texas) said during a House hearing on the technology earlier this year. "We have allies and adversaries -- both nation-states and individual hackers -- who are pursuing artificial intelligence with all they have because dominance in artificial intelligence is a guaranteed leg up in the realm of geopolitics and economics." To ensure that the U.S. maintains an advantage in this critical area, the Defense Advanced Research Projects Agency has launched the Artificial Intelligence Exploration program to develop new, game-changing AI technologies for U.S. national security. Through a series of short-term AIE Opportunities that use "other transaction authority," DARPA hopes to establish the feasibility of new AI concepts within 18 months of award. "The intent is to get researchers on contract quickly to test the value and feasibility of innovative concepts," DARPA Deputy Director Peter Highnam said.

Federal cuts limit Iowa's access to health insurance enrollment services CORALVILLE -- The University of Iowa Hospitals and Clinics has become the first to employ new technology -- developed by a company rooted in the university's research engine -- that uses artificial intelligence to diagnose an eye disease. On June 12, the Diabetes and Endocrinology Center at UI Health Care-Iowa River Landing in Coralville began using technology developed by IDx, the Coralville-based medical diagnostics company. The device, which received approval from U.S. Food and Drug Administration in April, was developed to diagnose diabetic retinopathy, a diabetes complication that can cause blindness. Called IDx-DR, the device uses software and a retinal camera to take images of a patient's retina. From there, the AI analyzes the patient's images "the same way I do as a clinician" to determine if the patient has the condition, said Dr. Michael Abramoff, president and director of IDx and UI Health Care ophthalmologist.

Prediction and explanation are key objects in supervised machine learning, where predictive models are known as black boxes and explanatory models are known as glass boxes. Explanation provides the necessary and sufficient information to interpret the model output in terms of the model input. It includes assessments of model output dependence on important input variables and measures of input variable importance to model output. High dimensional model representation (HDMR), also known as the generalized functional ANOVA expansion, provides useful insight into the input-output behavior of supervised machine learning models. This article gives applications of HDMR in supervised machine learning. The first application is characterizing information leakage in ``big-data'' settings. The second application is reduced-order representation of elementary symmetric polynomials. The third application is analysis of variance with correlated variables. The last application is estimation of HDMR from kernel machine and decision tree black box representations. These results suggest HDMR to have broad utility within machine learning as a glass box representation.

Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data ML may surpass density functional theory in computational speed and chemical accuracy. However, the most accurate machine learning methods require optimized 3D molecular geometries, limiting their applicability for high-throughput screening. We show that near-optimal results for large polymeric molecules can be obtained without optimized 3D geometry, and that trained model weights can be used to improve performance on related tasks.

We worry about robots stealing jobs and leaving huge swaths of the population with nothing to do but collect universal basic income and watch Simon's Cat videos. But some robots are going to take the jobs no person could or should do. If a Thai soccer team gets trapped in a cave a decade from now, we'll send a team of robots through the dangerous waters to get the boys, not a team of divers who could get hurt or killed. And if Jeff Bezos or Elon Musk ever gets around to building a space colony, they're not going to send humans who actually have to breathe to set up the first protective structures -- they'll send autonomous robots. We can already get a glimpse of how we can benefit when robots do inhuman work.

One of the things that we learned from the DARPA Robotics Challenge is that it's useful for robots to have legs to walk, but it's even more useful for robots to be versatile and adaptable, with multimodal locomotion capabilities that they can deploy depending on the situation. At the DRC, we saw all kinds of different designs, but one of the more unique approaches came from the University of Bonn, in Germany, with their robot Momaro. Momaro used a "centaur" design, with four legs that had wheels on the bottom (like a wheeled quadruped) coupled to a humanoid upper torso with a head and arms. It was the top-ranked European robot in the DRC, completing an almost perfect run in just 34 minutes. We've since been wondering whether the centaur design would inspire other disaster robots, and now we know the answer is yes.

Like most people in high tech these days, Uncle Sam is upping his investment in AI. The Defense Advanced Projects Research Agency (DARPA) announced a new program on Friday that will add an estimated $100 million a year to its current sending on machine learning. The AI Exploration program will initially spend about $10 to 20 million to fund a set of feasibility studies to generate ideas worth pursuing. They are expected to spawn larger projects that will eventually bump DARPA's total estimated spending on AI in its various forms to as much as $400 million annually. "We hope we will have a conference next summer focusing on what we will do in AI," said Steven Walker, DARPA's director, speaking at an event here on the agency's Electronics Resurgence Initiative.

Following the launch of the Law Society's Public Policy Technology and Law Commission to examine the use of algorithms in the justice system, and its call for evidence, it is worth considering the interface between lawtech which uses algorithms and the tightening global regulatory framework for data protection and privacy. The quote from Google's research director Peter Norvig, 'We don't have better algorithms. We just have more data,' invites interrogation of the value chain linking AI software and data assets: does the algorithm or the data generate more value for a professional services organisation, or is the added value created by determining the right blend for each context? And can software achieve that? At the commission launch, data scientist Professor Sofia Olhede focused on data quality rather than quantity – returning to the'garbage in, garbage out' principle that is too often forgotten or ignored.