Artificial intelligence came alive in the '80s with many startups, governments, and large enterprises deploying new systems that executed tasks typically performed by human experts. These were largely rule-based systems that encoded behaviors in rules instead of using the strict procedural logic of traditional programming languages. Then, as memory became more affordable, systems were able to handle much more computationally intense tasks, such as machine learning, planning and scheduling, and natural language understanding. Now in the age of big data, many believe AI has completely changed the tech landscape, but in some ways, as the Talking Heads song goes, it's the "same as it ever was." What remains the same are the core elements of an intelligent application.

Universities will get £17m to help them develop pioneering robotics and AI as part of the plans to support the'booming' sector, which is behind smartphone voice and touch recognition technology and digital assistants such as the iPhone's Siri. AI also forms the bedrock of video games and music and film recommendations services, as well as improving online customer services, and is used in fraud detection tools used by banks. Among the projects supported with the money from the Engineering and Physical Sciences Research Council (EPSRC) is a move by the University of Manchester to develop autonomous robots for hazardous environments such as nuclear facilities. Researchers at Imperial College London will use some of the funds to try and make advances in the field of surgical micro-robotics. A major review of AI will also be carried out by Professor Dame Wendy Hall, Regius Professor of Computer Science at the University of Southampton, and BenevolentTech chief executive Jerome Pesenti.

Stand aside Asimov, in the real world, we need real laws to manage advances in artificially intelligent robots. I've been talking about this for a while now and I'm quite happy to see that governments, who are typically very slow in acting on technology, are actually taking decisions before AI enabled robots and software become truly able to learn and go beyond their own programming. This is likely a reaction to the upcoming movement in the car industry (BMW, Ford, Chinese transportation drone companies, etc...) to put millions of self-driven cars and drones on the streets around the world on or about the year 2020 (3 years from now). There is obvious fear about how people will react when the first self-driven car will occur or when the first AI enabled industrial machine hurts someone. What happens when people panic because the economy can't sustain itself with too many efficient AI's doing a better job than any human can, taking their place in the world?

In January, I started a weekly newsletter on the impact automation (AI and robotics) is having on jobs. I did this mostly because I wanted to keep better track of what's happening in this space, and I thought that a weekly publication deadline might help me do this. I'm only six weeks in, and there have already been so many developments in the field that I have to be selective about what I include. Writing about this stuff week on week, several trends have emerged that paint a picture of a world waking up to the huge changes automation is going to bring. Here are some of the ways these trends have unfolded so far in 2017.

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.

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."

Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovative target location learning approach, where numerous machine learning approaches, including K-means clustering and supported vector machines (SVM), are used to learn the data pattern across a list of spatially distributed sensors. To enable the accurate data association from different sensors for accurate target localization, appropriate data pre-processing is essential, which is then followed by the application of different machine learning algorithms to appropriately group data from different sensors for the accurate localization of multiple targets. Through simulation examples, the performance of these machine learning algorithms is quantified and compared.

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.

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.