Businesses are beginning to see the benefits of using chatbots for their consumer-facing products, according to a survey by Oracle. The survey included responses from 800 decision makers including chief marketing officers, chief strategy officers, senior marketers, and senior sales executives from France, the Netherlands, South Africa, and the UK. When asked which emerging technologies they are already using and which they intended to implement, 80% of respondents said they already used or planned to use chatbots by 2020. Chatbots are interactive software platforms that reside in apps, live chat, email, and SMS and can behave in a human-like manner. Additionally, the survey shows that business leaders and decision makers are turning to the broader umbrella of automation technologies, which includes chatbots, for things like sales, marketing, and customer service.

Today our focus has been on KPIs, ERP, WMS, TMS, YMS, EDI, The Cloud, S and OP, 3 D Printing, IoT, IoE, Drones: Same Hour/Day/Time Delivery to Customers, Cyber Security, Theft, Government Regulations, E-Commerce, Omni-Channel, Modeling/Simulation, Risk Management, Tracking, Traceability, Re-shoring, Robotics, et al, but…what about Artificial Intelligence or AI technologies? AI is a controversy of deep, lasting dimensions. Will machines learn to think like humans…and then outthink us? If AI Technologies Can Think & Act Like "Us" Where do "We" Go? The application of AI technologies has created the ability to understand, store and use product information in an entirely new way.

Fassbender also cautions against colonisation, while Kurzel speaks about mainstream snobbishness towards the gaming community.

Is it too soon to do reviews and forecasts for the New Year? I noticed another contributor to SA has offered a forecast for Palo Alto (NYSE:PANW) and its prospective performance for the coming year. Several brokerage analysts have offered their evaluation of the year ahead for IBM (NYSE:IBM). The old year is passing at a frightful pace, and it means that all valuation metrics need to be adjusted for the year ahead. One of the interesting things about IBM is that its headline metrics are not expected to change much in 2017, with earnings expected to rise and revenues expected to show marginal shrinkage, how much credence should investors place in the consensus? How should investors look at prospects for IBM's dividend? How should investors value a company without growth, or will there be growth in the future? Of course, I do not hold all the answers. But I think as 2017 approaches, it might be worthwhile for me to express my point of view about what to expect going forward. I think that IBM is most likely to miss and/or guide down for a variety of reasons that I outline in the foregoing article.

Ten years after its U.S. launch, Amazon Web Services (AWS) opened its first Canadian datacentre, located in Montreal, at the beginning of December to much fanfare. In this video interview (two more interviews, on cloud security and the democratization of IT, can be seen here and here), ITWC CIO Jim Love and Amazon.com CTO Werner Vogels discuss AI services and experimentation (hint: they have little, if anything, to do with Hollywood AI) during the company's Dec. 8 AWS Canada (Central) launch event in Toronto. The new Canada Region is currently available for multiple services, including Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service (Amazon S3), and Amazon Relational Database Service (Amazon RDS). Regions are Amazon Web Services' way of describing its own data centre model.

Tesla has been working on enhancing its Autopilot self-driving feature, and the upcoming update might make its cars even safer than human-driven vehicles. The update will cap the top speed to the preset speed limit when the vehicle's Autosteer function is used, TechCrunch reported citing sources. Autosteer is a Tesla feature that controls the wheel and maintains the car's lane. If speed limits could be imposed using the updated software, it would make vehicles less prone to accidents. A Purdue University study in 2008 found that most drivers on U.S. roads don't follow speed limits, which is one of the major causes of accidents.

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one--learning how to get a GPU server online suitable for deep learning--and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.

Learning to learn by gradient descent by gradient descent

My last interview for this year is with Steve Lohr. Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting. We discussed Big Data and how it influences the new Artificial Intelligence awakening. Steve Lohr: Both Google and Microsoft are contributing their tools to expand and enlarge the AI community, which is good for the world and good for their businesses.

Editor's note: The following is an interview with Columbia University Professor Andrew Gelman conducted by Marketing scientist Kevin Gray, in which Gelman spells out the ABCs of Bayesian statistics. Andrew Gelman: Bayesian statistics uses the mathematical rules of probability to combines data with "prior information" to give inferences which (if the model being used is correct) are more precise than would be obtained by either source of information alone. Classical statistical methods avoid prior distributions. In classical statistics, you might include in your model a predictor (for example), or you might exclude it, or you might pool it as part of some larger set of predictors in order to get a more stable estimate. These are pretty much your only choices.