Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of A.I. where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more. AI for Good is a movement in which institutions are employing AI to tackle some of the world's greatest economic and social challenges. For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels.[1] The Air Operations Division (AOD) uses AI for the rule based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[2]

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.

The traditional insurance business model is going to fundamentally and permanently change from what was invented in the Lloyd's coffee shop in 1668 and which has been the basis of insurance ever since: Risk Mitigation via Indemnity and minimising interactions with customers. The next 10 years will see unprecedented change in the insurance industry. Traditional insurance companies selling and servicing the old style product model are being replaced by IT enabled, risk management companies selling profitable, long term contracts for valuable services delivered as RMAAS (risk management as a service). The experience of other industries offers a stark warning to the insurance industry: banks suffering death by a thousand cuts from tech companies in payments, cards, lending and now open banking, show the way it will go. Even software is sprinting to a cloud based, software as a service (SAAS) model.

Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection -- and even to predict and prevent crimes. While today's basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are.

Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data. Stream processing applications, however, typically require near real-time response using limited resources. In particular, it becomes essential that the underpinning query language has favourable computational properties and that stream processing algorithms are able to keep only a small number of previously received facts in memory at any point in time without sacrificing correctness. In this paper, we propose a recursive fragment of temporal Datalog with tractable data complexity and study the properties of a generic stream reasoning algorithm for this fragment. We focus on the window validity problem as a way to minimise the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time.

In the last few years, Artificial Intelligence (AI) has gained momentum in every industry. Healthcare, transportation, manufacturing, logistics, retail, and more have started to use AI-based applications to improve productivity and performance. According to PwC, Artificial Intelligence will contribute $15.7 trillion to the global economy by 2030. So businesses can reap huge benefits from investing in AI. The MIT Sloan Management Review's 2017 Artificial Intelligence Global Executive Study and Research Project found that 85% of executives believe that AI will help their businesses gain or sustain competitive advantage.

Click to learn more about author Dr. Michael Zeller. As volume, velocity and variety of data – often referred to as the "three V's of Big Data" – continue- to increase, many businesses are unprepared to match the influx of this data with tools for collection and analysis. Even more surprising is that most organizations are not in a position to truly leverage their own operational data for more intelligent decision making, which is the means by which data converts into business value. Basically, the current, standard business processes mostly leverage hard-coded rules and make fixed, pre-defined decisions. This classical "rules-based" approach is in many cases ill-suited for today's Cloud-centric, data-driven enterprise environment where customers demand a more personalized response of IT systems.

Artificial intelligence in cybersecurity has recently made several headlines: "The Future of Cybersecurity: Artificial Intelligence" (Cybersecurity.CIOReview), "How AI is the Future of Cybersecurity" (Infosecurity Magazine) and "AI in Cyber Security Market to Grow at an Exorbitant Pace" (P&S Market Research). These headlines make seasoned cybersecurity professionals wary. We've seen other emerging technologies receive similar attention, and we've seen many of them fail to live up to their expectations. In this article, we will build a real-world perspective on AI in cybersecurity. We will explore where scepticism regarding AI in cybersecurity is justified, how the technology can provide tangible value, and what to look for in an AI-driven cybersecurity provider.

New Zealand Prime Minister Jacinda Ardern said those are the things she has obsessed over the most as a new parent, and that the experience of focusing on such basic needs for her baby girl has helped her appreciate why people with young families may not find time for politics. "So it's our job to make sure that we are serving the needs of people, regardless of whether they have time to engage with what we're doing or not," she said. "That's something that's been really amplified for me." Ardern spoke on Thursday at her bungalow in Auckland as she prepared to return to the capital, Wellington, after six weeks of leave following the birth of her daughter, Neve. Ardern, 38, is just the second elected world leader in recent history to give birth while holding office, and her story has provided inspiration for working mothers around the world.

AUCKLAND, New Zealand – New Zealand Prime Minister Jacinda Ardern said Thursday the world is watching closely to see if recent optimism over North Korea leads to the elimination of its nuclear weapons program. In an interview with The Associated Press, Ardern also said New Zealand is among many nations concerned about the tit-for-tat trade war between China and the U.S. She said the idea of New Zealand becoming a republic is not a priority for her government. Ardern spoke with the AP at her home in Auckland as she prepared to return to the capital, Wellington, after six weeks of leave following the birth of her daughter, Neve. Ardern, 38, is just the second elected world leader in recent history to give birth while holding office.