Tesla's Model S sedan, the car company's flagship vehicle, was first shown as a prototype in 2009, has been on sale since 2012, and, barring one small change to remove the fake grille at the front, has looked exactly the same for nearly a decade. This is notable because most manufacturers fully redesign their cars every four to six years to keep them fresh--and to keep buyers buying. For Tesla, tech upgrades are the selling point. The company pushes software updates several times a year, adding features like summon, where a car pulls in and out of a garage with nobody inside, or camper-mode, for sleeping in the car with the heating on. Tesla's biggest claim is that one day, all the cars it's currently building will be capable of full-self driving.

Tesla's Model S sedan, the car company's flagship vehicle, was first shown as a prototype in 2009, has been on sale since 2012, and, barring one small change to remove the fake grille at the front, has looked exactly the same for nearly a decade. This is notable because most manufacturers fully redesign their cars every four to six years to keep them fresh--and to keep buyers buying. For Tesla, tech upgrades are the selling point. The company pushes software updates several times a year, adding features like summon, where a car pulls in and out of a garage with nobody inside, or camper-mode, for sleeping in the car with the heating on. Tesla's biggest claim is that one day, all the cars it's currently building will be capable of full-self driving.

As response speed and first-call resolution have become a norm in call centre interaction (and overall bottom line), the most forward-thinking companies are beginning to deploy technologies like chatbots to support customer needs. Amazon's Alexa is a solid showcase of the demand for more efficient customer service through innovation, but how does the industrial sector, historically rooted in outdated systems, equip their human call centre agents with the tools to handle all issues a customer might have? There are two widely accepted types of chatbots. One is AI driven and is completely based on learning and adapting its gained knowledge to "optimize" response. The technology is impressive, but can be too complicated to solve simple tasks.

Discussing the broad impact of AI on the enterprise, Jon points out that we are already seeing AI in action, if we know where to look. "Some say AI has been hidden away for a long time, but actually it hasn't. There are already AI applications in place in our normal daily activity. This year we have seen IBM Watson, Microsoft's Cortana and other personal assistant technology raise their marketing approach, yet Google Search and chatbots have been in use for some time. Part of what The AI Summit demonstrated was the extraordinary level of application and uptake that is already happening".

It is "well known" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of "instrumental variables" sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a path or trek can be computed directly from the parameter values associated with each edge in the path or trek; (4) the association of two variables produced by multiple treks can be computed from the parameters associated with each trek; and (5) the independence of two variables conditional on a third implies the corresponding independence of the sums of the variables over all units conditional on the sums over all units of each of the original conditioning variables.These properties are exploited in search procedures. It is also known that properties (2)-(5) do not hold for all Bayes nets with binary variables. We show that (1) holds for all Bayes nets with binary variables and (5) holds for all singly trek-connected Bayes nets of that kind. We further show that all five properties hold for Bayes nets with any DAG and binary variables parameterized with noisy-or and noisy-and gates.

Autonomous agents in real-time strategy (RTS) games lack an integrated framework for reasoning about choke points and regions of open space in their environment. This paper presents an algorithm which partitions the environment into a set of polygonal regions and computes optimal choke points between adjacent regions. This representation can be used as a component for AI agents to reason about terrain, plan multiple routes of attack, and make other tactical decisions. The algorithm is tested on a set of popular maps commonly used in international Starcraft competitions and evaluated against answers made by human participants. The algorithm identified 97% of the choke points that the participants found and also identified a number of bottlenecks that human participants did not recognize as choke points.