Among the signatories of the Asilomar Principles is Elon Musk, who recently drew attention for his comments at a meeting of the National Governors Association, where he called for a regulatory body to oversee AI development. In response, news organizations focused on his concerns that AI represents an existential threat. And his suggestion raised concerns with some AI researchers who worry that regulations would, at best, be unhelpful and misguided, and at worst, stifle innovation and give an advantage to companies overseas.

Every day, it seems, we read headlines about a new data breach or cyberattack. Then we talk about how to improve cybersecurity to prevent similar attacks from happening in the future. Chief among the issues to address is a lack of security personnel to fill vacant positions: How can we improve security if we don't have the people to perform the work? The IoT is imminent – and so are the security challenges it will inevitably bring. Get up to speed on IoT security basics and learn how to devise your own IoT security strategy in our new e-guide.

This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.

What machine learning and AI, in general, excel at (unlike human beings) is analysing millions of data points in real time to identify trends and, based on that, offering up "if this, then that" type conclusions. The inherent problem with that is it carries with it a self-reinforcing bias, because it assumes that what happened in the past will be repeated. Let's take the example of crime data. Black and minority neighborhoods with lower incomes are far more likely to be blighted with crime and anti-social behaviour than prosperous white ones. If you then use algorithms to shape laws, what will inevitably happen is that such neighbourhoods will be singled out for intensive police patrols, thereby increasing the odds of stand-offs and arrests.

China has recently announced their long-term goal to become #1 in A.I. by 2030. They plan to grow their A.I. industry to over $22 billion by 2020, $59 billion by 2025 and $150 billion by 2030. They did this same type of long-term strategic planning for robotics – to make it an in-country industry and to transform the country from a low-cost labor source to a high-tech manufacturing resource, and it's working. With this major strategic long-term push into A.I., China is looking to rival U.S. market leaders such as Alphabet/Google, Apple, Amazon, IBM and Microsoft. China is keen not to be left behind in a technology that is increasingly pivotal -- from online commerce to self-driving vehicles, energy, and consumer products.

Last month, a Senate committee created a proposal to allow autonomous vehicles onto the roads under specific safety and "tech neutral" requirements. Now that the bill has hit House of Representatives, the bipartisan Energy and Commerce Committee voted to send it along to the full chamber. The Safely Ensuring Lives Future Deployment and Research In Vehicle Evolution Act, or SELF DRIVE, is aimed at allowing companies like Uber and Google to test up to 100,000 autonomous vehicles across the country. While we're far from an actual bill, this seems like good forward movement. If driverless cars are ever going to gain a foothold on the road, they'll need to be as safe as (or safer that) current automobiles are.

Driscoll's is so secretive about its robotic strawberry picker it won't let photographers within telephoto range of it. But if you do get a peek, you won't see anything humanoid or space-aged. AgroBot is still more John Deere than C-3PO -- a boxy contraption moving in fits and starts, with its computer-driven sensors, graspers and cutters missing 1 in 3 berries. Such has been the progress of ag-tech in California, where despite the adoption of drones, iPhone apps and satellite-driven sensors, the hand and knife still harvest the bulk of more than 200 crops. Now, the $47-billion agriculture industry is trying to bring technological innovation up to warp speed before it runs out of low-wage immigrant workers.

Only 20 years ago, it would have sounded like the stuff of fantasy: a clean, green image of the 21st century, with Britons gliding along in electrified, no doubt self-driving, cars. Even now, the pledge to ban the sale of petrol and diesel cars and vans from 2040 has a bold, vaguely futuristic ring to it. That is its political genius. In fact, it reflects the current trajectory of the motor industry – while masterfully distracting us from the government's persistent failure in the rest of its plan to address the health crisis that must be tackled now: the air pollution that chokes our cities. The announcement may help to concentrate the minds of policymakers, consumers and car manufacturers on the need to press ahead with the switch and the huge changes that will be required.

In another paper, Computational Thinking is Critical Thinking: Connecting University Discourse, Goals, and Learning Outcomes (2016) points out that abstraction is common to both computational and critical thinking. The author (Kules) essentially makes the case for bridging the two types of thinking. The clear definition of computational thinking quoted below is helpful. Similarly, the Venn diagram below demonstrates the overlap between creative and critical thinking, which programming just happens to inform. There is'abstraction & simplification' right in the center. I'll be the first to admit, "abstraction" might be impotent if you can't appreciate the complexity of it.

WASHINGTON (Reuters) - An influential U.S. House Committee on Thursday approved a revised bipartisan bill on a 54-0 vote that would speed the deployment of self-driving cars without human controls and bar states from blocking autonomous vehicles.