In her first public remarks since her Jan. 31 confirmation, Transportation Secretary Elaine Chao said she planned to take a look at guidance from the previous administration on safety measures for makers of self-driving cars, which some have criticized as being too lenient. Chao told the National Governors Association on Sunday the current administration would consult the assembled state leaders "and other stakeholders as we update and amend [the list of guidelines] to ensure it strikes the right balance," adding she believes autonomous vehicle safety could increase substantially, Reuters reported. "There's a lot at stake in getting this technology right," she said, citing research findings that 94 percent of auto crashes were a result of human error. She also urged finding ways for the technology to "develop … in a way that will not dampen the basic creativity and innovation of our country," and emphasized the need to avoid a "patchwork" of state laws limiting the technology's nationwide growth. Within the self-driving vehicle corner of the tech world, Chao has won a warm welcome.

Despite job automation threatening to cause chaos in the labour market, a group of Sri Lankan Ministers of Parliament have said that their jobs are safe in the knowledge that stupidity cannot be automated. Bandula Gunawardena said, "Sri Lankans were the first to coin the term artificial intelligence after they realised that the people they elected to run the country lacked real intelligence. I am glad to have been cited as a case study of this. Recently there has been a lot of talk about job automation. While certain sectors of the civil service will be automated we are confident it will be limited to skilled areas which involve a lot of processing power and use of high-level thinking."

Conducting surveillance missions using sensor networks is essential for many civilian and military applications, such as disaster response [1], border patrol [2], force protection [3], [4], combat missions [5], and traffic management [6]. One main task in these missions is to collect data regarding the operational environment and then obtain intelligence information from the data. Because the sensors used to collect data are often spatially distributed, extracting data patterns becomes critical to obtain accurate knowledge about the underlying activities. The existing work on identifying data patterns from spatially distributed sensors is focused on developing probabilistic reasoning techniques without recognizing the specific data association or data patterns. Existing approaches for multitarget state estimation can be characterized by two features: a data-to-target assignment algorithm, and an algorithm for single target state estimation under preexisting data-to-target associations. With unknown data association, probabilistic data association (PDA) [7] and multiple hypothesis tracking (MHT) [8] are two common approaches where dense measurements are available. In the study of traffic patterns, the existing research is focused on estimating traffic density and smart routes [6] without analyzing the data pattern to obtain better knowledge of traffic information.

Recidivism prediction instruments (RPI's) provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This paper discusses several fairness criteria that have recently been applied to assess the fairness of recidivism prediction instruments. We demonstrate that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups. We then show how disparate impact can arise when a recidivism prediction instrument fails to satisfy the criterion of error rate balance.

Hypothesis testing is an important cognitive process that supports human reasoning. In this paper, we introduce a computational hypothesis testing approach based on memory augmented neural networks. Our approach involves a hypothesis testing loop that reconsiders and progressively refines a previously formed hypothesis in order to generate new hypotheses to test. We apply the proposed approach to language comprehension task by using Neural Semantic Encoders (NSE). Our NSE models achieve the state-of-the-art results showing an absolute improvement of 1.2% to 2.6% accuracy over previous results obtained by single and ensemble systems on standard machine comprehension benchmarks such as the Children's Book Test (CBT) and Who-Did-What (WDW) news article datasets.

Bribery in elections is an important problem in computational social choice theory. However, bribery with money is often illegal in elections. Motivated by this, we introduce the notion of frugal bribery and formulate two new pertinent computational problems which we call Frugal-bribery and Frugal- $bribery to capture bribery without money in elections. In the proposed model, the briber is frugal in nature and this is captured by her inability to bribe votes of a certain kind, namely, non-vulnerable votes. In the Frugal-bribery problem, the goal is to make a certain candidate win the election by changing only vulnerable votes. In the Frugal-{dollar}bribery problem, the vulnerable votes have prices and the goal is to make a certain candidate win the election by changing only vulnerable votes, subject to a budget constraint of the briber. We further formulate two natural variants of the Frugal-{dollar}bribery problem namely Uniform-frugal-{dollar}bribery and Nonuniform-frugal-{dollar}bribery where the prices of the vulnerable votes are, respectively, all the same or different. We study the computational complexity of the above problems for unweighted and weighted elections for several commonly used voting rules. We observe that, even if we have only a small number of candidates, the problems are intractable for all voting rules studied here for weighted elections, with the sole exception of the Frugal-bribery problem for the plurality voting rule. In contrast, we have polynomial time algorithms for the Frugal-bribery problem for plurality, veto, k-approval, k-veto, and plurality with runoff voting rules for unweighted elections. However, the Frugal-{dollar}bribery problem is intractable for all the voting rules studied here barring the plurality and the veto voting rules for unweighted elections.

The word "Disagree" is seen on the hand of Julia Grabowski during a town hall meeting for Republican U.S. Senator Bill Cassidy in Metairie, Louisiana. News about President Donald Trump -- including an apparently neglected vegetable garden that once belonged to former first lady Michelle Obama -- is inescapable. As The New York Times' Farhad Manjoo wrote, "he is no longer just the message. In many cases, he has become the medium." Mental health professionals in the U.S. have reported that the all-encompassing coverage of the president has induced anxiety and depression, or post-election stress, in many of their patients.

The Federal Trade Commission today announced the agenda for its March 9, 2017, FinTech Forum focusing on the consumer implications of two rapidly developing technologies: artificial intelligence and blockchain. The forum, which is the third in an ongoing event series, will take place from 9:00 a.m. to approximately 12:30 p.m. Pacific Time in Berkeley, California. The event will bring together industry representatives, consumer advocates, government officials, and others with expertise regarding these technologies. The half-day forum will feature two panel discussions. The first panel will focus on the potential benefits and risks for consumers with the use of artificial intelligence, which involves the capability of machines to mimic human thinking or actions such as learning and problem solving, in consumer products or services in fields including personalized financial services.

Can Wearables Help You Reach Immortality?

It would make it a misdemeanor to fly them without permission over correctional facilities such as jails and prisons and military facilities. Under the plan, it would be a felony to use a drone to deliver drugs or contraband to a correctional facility and to have a drone capable of firing a bullet or being used as a weapon.