To handle more-natural spoken interactions, Alexa must track references through several rounds of conversation. If, for instance, a customer says, "How far is it to Redmond?" and after the answer follows up by saying, "Find good Indian restaurants there", Alexa should be able to infer that "there" refers to Redmond. We call the task of reference tracking "context carryover," and it's a capability that is currently being phased in to the Alexa experience. At this year's Interspeech, the largest conference on spoken-language understanding, my colleagues and I will present a paper titled "Contextual Slot Carryover for Disparate Schemas," which describes our solution to the problem of slot carryover, a crucial aspect of context carryover. "Domain" describes the type of application -- or "skill" -- that the utterance should invoke; for instance, mapping skills should answer questions about geographic distance.

What happens when you put a neural network in charge of a rules-based marketing automation solution? Would the AI emerge victorious, or does the human driver still have a thing or two to show the talented mimicker? It's an interesting question, to be sure, but more importantly, and it's an experiment that the folks at SAS – which still uses rules-based approaches in some of its analytics offerings -- actually ran, and the results might surprise you. "It beat our system," SAS Executive Vice President Oliver Schabenberger said during the SAS Analytics Experience conference held last week in San Diego, California. The result forced Schabenberger, who also holds the title of CTO and COO, to inquire about the cause. "Why is it the AI system works better than what our best minds can put together?" he said.

CHINA'S "Sputnik moment" came on May 27th 2017. On that day an algorithm thrashed Ke Jie, the world's best player of Go, an ancient and demanding Chinese board game. Mr Ke's defeat by AlphaGo, an artificial intelligence (AI) system developed by DeepMind, a British firm that had been bought by Google, was as much a blow to China's psyche as the Soviet satellite was to America's self-esteem in 1957. Within months, China announced ambitious plans to dominate AI by 2030. Kai-Fu Lee thinks it will succeed. He is well placed to judge.

I first entered the informatics field in the late 1980s, at the tail end of the first era of artificial intelligence (AI) in medicine. Initial systems focused on making medical diagnoses using symbolic processing, which was appropriate for a time of relatively little digital data, both for individual patients and healthcare as whole, and underpowered hardware. Systems like MYCIN [1], INTERNIST-1/QMR [2], and DXPLAIN [3] provided relatively accurate diagnostic performance, but were slow and difficult to use. They also provided a single likely diagnosis, which was not really what clinicians needed. Because of these shortcomings, they never achieved significant real-world adoption, and their "Greek Oracle" style of approach was abandoned. There was also some early enthusiasm for neural networks around that time [5], although in retrospect those systems were hampered by lack of data and computing power.

Deep Learning is a subset of Machine Learning,which itself a subset of Artificial Intelligence. Artificial Intelligence (AI): Any algorithm or technique that allows computing devices to make decisions or solve problems that previously required humans to perform them manually can be called an AI algorithm or technique. An AI can be either a huge stack of simple if-else statements or a very complex algorithm. If you have studied Artificial Intelligence in your school or college,you would read something titled "Rule Based Systems", which is nothing but a collection of IF THEN statements,to perform a task. An example of a Rule based system is MYCIN,which is developed to identify bacteria causing severe infections and to recommend antibiotics by Stanford,which has basically nothing but IF THEN statements.

Upgrade your inbox and get our Daily Dispatch and Editor's Picks. CHINA'S "Sputnik moment" came on May 27th 2017. On that day an algorithm thrashed Ke Jie, the world's best player of Go, an ancient and demanding Chinese board game. Mr Ke's defeat by AlphaGo, an artificial intelligence (AI) system developed by DeepMind, a British firm that had been bought by Google, was as much a blow to China's psyche as the Soviet satellite was to America's self-esteem in 1957. Within months, China announced ambitious plans to dominate AI by 2030. Kai-Fu Lee thinks it will succeed.

Eager to use artificial intelligence, some companies may be unnecessarily complicating easy business problems. What's going on: Companies are over-using complex AI techniques when they would be better served with simpler approaches. Rule-based systems, for instance, show their work, thus allowing non-experts to pop the hood and see why an algorithm is misbehaving, unfair or biased.

Abstract--Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention. A two-track competition of agents for the game will take place in the 2018 CIG conference. In this paper, we develop a genetic algorithm that builds rulebased agents by determining the best sequence of rules from a fixed rule set to use as strategy. In three separate experiments, we remove human assumptions regarding the ordering of rules, add new, more expressive rules to the rule set and independently evolve agents specialized at specific game sizes. As result, we achieve scores superior to previously published research for the mirror and mixed evaluation of agents. Game-playing agents have a long tradition of serving as benchmarks for AI research. However, traditionally most of the focus has been on competitive, perfect information games, such as Checkers [1], Chess [2] and Go [3]. Cooperative games with imperfect information provide an interesting research topic not only due to the added challenges posed to researchers, but also because many modern industrial and commercial applications can be characterized as examples of cooperation between humans and machines in order to achieve a mutual goal in an uncertain environment. In this paper, we address a particularly interesting cooperative game with partial information: Hanabi [4].

I was lucky to be selected to the Square Kilometre Array Big Data Africa School funded by the SKA and the Development of Africa With Radio Astronomy (DARA). It was held in Cape Town, South Africa. Big Data is a cloudy idea. Easy to know when you have it, hard to describe. I like thinking of it as data that is sufficiently large such that it is difficult to draw information from it "easily".

Abstract--Drought forecasting and prediction is a complicated process due to the complexity and scalability of the environmental parameters involved. Hence, it required a high level of expertise to predict. In this paper, we describe the research and development of a rule-based drought early warning expert systems (RB-DEWES) for forecasting drought using local indigenous knowledge obtained from domain experts. The system generates inference by using rule set and provides drought advisory information with attributed certainty factor (CF) based on the user's input. The system is believed to be the first expert system for drought forecasting to use local indigenous knowledge on drought. The architecture and components such as knowledge base, JESS inference engine and model base of the system and their functions are presented. The intricate complexity of drought has always been a stumbling block for drought forecasting and prediction systems [1]. This is mostly due to the web of environmental events (such as climate variability) that directly/indirectly triggers this environmental phenomenon. There are six broad categories of drought: meteorological, climatological, atmospheric, agricultural, hydrologic and water drought [1]. Nevertheless, irrespective of the category of drought, there is a consensus amongst scientist that drought is a disastrous condition of lack of moisture caused by a deficit in precipitation in a certain geographical region over some time period [2]. The effect of drought can be quantified based on the frequency, duration and intensity in the affected region subject to established timescales.