Election control problems are concerned with affecting the result of an election by modifying the structure of the election. Such election modifications could be either introducing some new candidates or voters or removing some existing candidates or voters from the election or partitioning candidates or voters [2, 27, 32, 42, 56, 57, 34, 35, 62]. We focus on the computational complexity of election control by adding and deleting candidates (that is, candidate control), for the case where the election involves only a few voters. From the viewpoint of computational complexity, voter control with few voters has not received sufficient study. We focus on very simple, practical voting rules such as Plurality, Veto, andt-Approval, but discuss several more involved rules as well. To analyze the effect of allowing only a small number of voters, we use the formal tools of parameterized complexity theory [21, 23, 38, 60]. From the viewpoint of classical complexity theory, most of the candidate control problems for most of the typically studied voting rules are NPhard. Indeed, candidate control problems are NPhard even for the Plurality rule; nonetheless, there are some natural examples of candidate control problems with polynomialtime algorithms. It turns out that for the case of elections with few voters, that is, for control problems parameterized by the number of voters, the computational complexity landscape of candidate control is much more varied and sometimes quite surprising.

When science fiction author Isaac Asimov devised his Three Laws of Robotics he was thinking about androids. He envisioned a world where these human-like robots would act like servants and would need a set of programming rules to prevent them from causing harm. But in the 75 years since the publication of the first story to feature his ethical guidelines, there have been significant technological advancements. Sci-fi author Isaac Asimov devised his Three Laws of Robotics he was thinking about androids. The three'Laws of Robotics' were devised by sci-fi author Isaac Asimov in a short story he wrote in 1942, called'Runaround'.

Artificial intelligence that can quickly identify patterns of risky behavior may be the only viable solution to protect health systems against an influx of ransomware attacks. The use of AI in the clinical environment has been well-documented as more health systems are turning to machine learning to improve oncology care, fight physician burnout, boost patient engagement and even reverse diabetes. But healthcare needs to use the power of machine learning to combat cybersecurity threats, according to a report (PDF) released by the Institute for Critical Infrastructure Technology. James Scott, a senior fellow at ICIT who authored the report, didn't mince words regarding the urgent need to protect patient information against cyberattacks, particularly ransomware, which has emerged as a critical threat to the industry over the past year. Scott noted that the healthcare industry "demonstrates lackadaisical cyber hygiene, finagled and Frankensteined networks, virtually unanimous absence of security operations teams and good ol' boys club bureaucratic board members flexing little more than smoke and mirror, cybersecurity theatrics as their organizational defense."

A lot of us have been looking at the recent WikiLeaks drop of Central Intelligence Agency (CIA) files related to hacking Internet of Things (IoT) and personal technology devices. It includes a lot of data, so it is easy to get lost in the woods with regard to what these tools do. But apparently they can break into devices and cast blame for it on the Russians, which I believe is problematic in a whole number of different ways. I think the real problem with this is the CIA's risk assessment process. I think this decision process problem is far larger than just the CIA and reflects on security in general. I also think that a deep learning tool like IBM's Watson could, if placed in the decision process, help prevent bad decisions like this.

The Bank of England has paired up with artificial intelligence and blockchain specialists in a bid to keep up to date with the fast-growing financial technology sector. The central bank is testing an artificial intelligence system with Canadian startup MindBridge AI to allow it to spot abnormalities in financial transactions and "explore the benefit of machine learning technology for analysing the quality of regulatory data input." It has also partnered with San Francisco-based startup Ripple, which opened an office in London last year, to trial a blockchain-based technology that would make cross-border payments and the movement of currencies more immediate. Blockchain is the technology which underpins crypto-currencies like bitcoin. The Bank said that its aim with Ripple is to "show how this kind of synchronisation might lower settlement risk and improve the speed and efficiency of cross-border payments."

A deal between Google's artificial intelligence firm DeepMind and the UK's NHS had serious "inadequacies", an academic paper has suggested. More than a million patient records were shared with DeepMind to build an app to alert doctors about patients at risk of acute kidney injury (AKI). The authors said that it was "inexcusable" patients were not told how their data would be used. Google's DeepMind said that the report contained "major errors". It told the BBC that it was commissioning its own analysis and rebuttal, which the authors said they welcomed. When the deal between London's Royal Free Hospital and DeepMind became public in February 2016, some three months after the data started to be collected, it caused controversy over the amount of patient information being shared and the lack of public consultation.

March 17, 2017 --Thousands of blasts from the past are teaching nuclear physicists new lessons about old physics. Scientists and film experts are teaming up to save a treasure trove of cold war-era nuclear test footage, teetering on the brink of decomposition. By digitizing the records and applying current video analysis, the team has learned we don't know quite as much as we thought we did about atomic bombs. The United States carried out hundreds of aboveground nuclear tests between 1945 and the early 1960s, when they were banned. Recording more than 10,000 videos from various angles, the government strived to glean every last bit of scientific information possible about the deadly new technology.

The U.S. Defense Advanced Research Projects Agency (DARPA) has come up with some crazy ideas in the past, and its latest idea is to create computers that are always learning and adapting, much like humans. Mobile devices, computers, and gadgets already have artificial intelligence features, with notable examples being Apple's Siri, Microsoft's Cortana, and Amazon's Alexa. But these devices can only learn and draw conclusions within the scope of information pre-programmed into systems. Existing machine-learning techniques don't allow computers to think outside the box, so to speak, or think dynamically based on the situations and circumstances. The goal of a new DARPA project is to create computers that think like biological entities and are continually learning.

Last month, when President Donald Trump toured a Boeing aircraft plant in North Charleston, South Carolina, he saw a familiar face in the crowd that greeted him: Patrick Caddell, a former Democratic political operative and pollster who, for forty-five years, has been prodding insurgent Presidential candidates to attack the Washington establishment. Caddell, who lives in Charleston, is perhaps best known for helping Jimmy Carter win the 1976 Presidential race. He is also remembered for having collaborated with his friend Warren Beatty on the 1998 satire "Bulworth." In that film, a kamikaze candidate abandons the usual talking points and excoriates both the major political parties and the media; voters love his unconventionality, and he becomes improbably popular. If the plot sounds familiar, there's a reason: in recent years, Caddell has offered political advice to Trump. He has not worked directly for the President, but at least as far back as 2013 he has been a contractor for one of ...

A page from the notebook of British mathematician and pioneer in computer science Alan Turing, the World War II code-breaking genius, is displayed in front of his portrait during an auction preview in Hong Kong Thursday, March 19, 2015. This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR. Alan Turing gives a talk at the London Mathematical Society in which he declares that "what we want is a machine that can learn from experience." Anticipating today's enthusiasm about machine learning and deep learning, Alan Turing described how intelligent machines will work: Let us suppose we have set up a machine with certain initial instruction tables, so constructed that these tables might on occasion, if good reason arose, modify those tables. One can imagine that after the machine had been operating for some time, the instructions would have altered out of all recognition, but nevertheless still be such that one would have to admit that the machine was still doing very worthwhile calculations.