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.

Through this Request for Information (RFI), NITRD NCO seeks input from the public, including those directly performing Artificial Intelligence (AI) research and development (R&D) and directly affected by such R&D, on whether the strategic plan should be revised and, if so, the ways in which it may be improved. This includes suggestions as to the addition, removal, or modification of strategic aims, comments as to existing strategic aims as well as their past or future implementation by the Federal government. The public input provided in response to this RFI will inform NITRD NCO and the Select Committee in updating the National Artificial Intelligence Research and Development Strategic Plan. Interested persons are invited to submit comments on or before 11:59 p.m. (ET) on October 26, 2018. Instructions: Response to this RFI is voluntary.

AI and cyber security firm Darktrace has raised $50 million in funding at a valuation of $1.65 billion. The investment is led by European private equity firm Vitruvian Partners with participation from existing investors KKR and 1011 Ventures. Darktrace uses artificial intelligence and machine learning algorithms to automatically detect cyber threats to its customers' networks. The Cambrige-headquartered business claims that its Antigena product can respond automatically to cybersecurity issues within two seconds. Its roster of customers includes eight major international airports including London Gatwick.

Context and Knowledge: The details mentioned above are the tools that when implemented rightly help to leverage the benefits of AI and machine learning for cybersecurity. But, to understand the data sets and the role of each entity, it is essential to know the context of the datasets, such as the devices, applications, and users. Context is added to the datasets to make sense of the available data. For example, if you know that your device needs to respond to a particular network at specific time intervals, then it is normal behavior but in case if the response is erratic, then it may be a result of an attack.

A new digital accelerator aims to transform the way the NHS accesses its specialists. Artificial Intelligence (AI) has long since left its science fiction roots behind and has become immersed in manufacturing, supply chain, retail and business. However, one space where it is set to really make a tangible, life-saving impact is in healthcare. Already there are applications in place that are providing essential care to people in underserved or developing areas, improving patient data care and management, and transforming diagnoses and patient care. According to research undertaken by Frost & Sullivan, AI in the healthcare market is predicted to hit $6.16 billion by 2022.

TIANJIN, CHINA – The award-winning film "Tree" offers a rare multisensory virtual reality experience of becoming a rainforest tree from a seedling in just 15 minutes. Reading a short speech into a microphone analyzes your voice and tells who you are -- your sex, height, skull type, personality and emotional state. A high-resolution video camera identifies you from 500 meters away. These computational imaging and artificial intelligence-driven technologies were all on display at the World Economic Forum's conference in Tianjin, China, last week. Some 2,500 leaders from politics, academia and a range of business sectors, including many startups, from around the world gathered in the city for the Annual Meeting of the New Champions 2018 -- often dubbed the "Summer Davos" -- where they experienced these advanced technologies and discussed how those such as AI and blockchain will influence society in the coming years.

During SpaceX's BFR / BFS lunar event, Japanese billionaire Yusaku Maezawa discusses why he wants to take six to eight artists with him to the moon. The newest designs for SpaceX's Big Falcon Rocket and Big Falcon Spaceship, which were unveiled at SpaceX's headquarters on Monday, Sept. 17, 2018. Rather, it was how much it looks like spaceships of our imaginations. Landing vertically with three big fins, the BFR, as the rocket has been dubbed, looks somewhat like so many of those finned spaceships landing on distant planets in old science-fiction movies -- not like the more ungainly craft seen in films like in "2001: A Space Odyssey" or even in real life, like the lunar lander that delivered Neil Armstrong to the moon in 1969. As turns out, Musk, the billionaire who is CEO of the rocket maker as well as Tesla, the electric car maker, said looks played a role, not just functionality, in the design. "There's more than one way to solve this problem," he said.

"If you want a job for the next few years, work in technology. If you want a job for life, work in cybersecurity." The field of cybersecurity will soon face some dire, but somewhat conflicting, views on the availability of qualified cybersecurity professionals over the next four or five years. Global Information Security Workforce Study from the Center for Cyber Safety and Education, predicts a shortfall of 1.8 million cybersecurity workers by 2022. The cybersecurity workforce gap will hit 1.8 million by 2022.

We consider the problem of detecting the presence of the signal in a rank-one signal-plus-noise data matrix. In case the signal-to-noise ratio is under a certain threshold, we propose a hypothesis testing based on the linear spectral statistics of the data matrix. The error of the proposed test matches that of the likelihood ratio test, which minimizes the sum of the Type-I and Type-II errors. The test does not depend on the distribution of the signal or the noise, and it can be extended to an adaptive test, which does not require the knowledge on the value of the signal-to-noise ratio, but performs better than random guess. As an intermediate step, we establish a central limit theorem for the linear spectral statistics of rank-one spiked Wigner matrices for a general spike.

Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interaction based on multivariate Wiener filtering is analyzed. It is shown that for a vast and important class of interactions, that respect flow conservation, the topology of the interactions can be exactly recovered. The class of problems where reconstruction is guaranteed to be exact includes power distribution networks, dynamic thermal networks and consensus networks. The efficacy of the approach is illustrated through simulation and experiments on consensus networks, IEEE power distribution networks and thermal dynamics of buildings.