Tim Burton's new movie about an orphanage for fantastically gifted kids is expected to debut at the head of its class at the box office this weekend, expelling last week's victor, "The Magnificent Seven," from the top of the charts. "Miss Peregrine's Home for Peculiar Children," from 20th Century Fox and Chernin Entertainment, is expected to gross 25 million to 30 million in its opening in the U.S. and Canada through Sunday, according to people who have reviewed prerelease audience surveys. That should be enough to unseat Sony and MGM's western remake (starring Denzel Washington and Chris Pratt) that bowed to 35 million and is expected to drop about 50% in its second go-around. "Miss Peregrine," adapted from the popular fantasy novel by Ransom Riggs, cost 110 million to make. Though it boasts high interest and parallels to the X-Men and Harry Potter series, it remains to be seen if the film will prove popular enough to start a Hollywood franchise.

"Modern Machine Learning is devoted to deriving value from data, not jamming the airlocks." The above quote from Arthur C. Clark's HAL 9000 vision is about to become a reality, thanks to Big Data enabled Machine Learning (ML). With modern computers exhibiting exceptional performance and storage devices being available within easy budgets, global businesses are increasingly indulging in Machine Learning models to tackle the petabytes of Big Data. In the recent years, Machine Learning solutions have brought Big Data to the mainstream business workflow. Big Data has signaled a new era of information, where by credible estimates, about 50-75 billion connected devices have entered the daily lives of people.

This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Hearables are here: Apple held its "Special Event" and, among other things, officially killed the iPhone's 3.5-millimeter earbud jack, replacing it with 159 wireless AirPods. But then I read Mike Elgan's paean to this development in Computerworld. Elgan says that AirPods are actually artificial intelligence hardware. "The biggest thing going on here is the end of'dumb speaker' earbuds, and the mainstreaming of hearables -- actual computers that go in your ears," he says.

Making the artificial feel more natural is one of the goals of computer scientists these days. Last week in New York, innovators from IBM, Foursquare, Microsoft, and other companies discussed what may lie ahead in the commercial development of artificial intelligence. From baby steps in machine learning to longer strides in interacting with humans, AI has gotten better at interpreting the world--though the technology is still decades away from some of the stuff of science fiction. NYU Future Labs and ff Venture Capital co-hosted last Tuesday's forum, which led off with a keynote address from Minerva Tantoco (pictured above), senior adviser with Future\Perfect Ventures. She started her first AI company while still in college, and her early efforts in the field included getting a computer to parse a sentence in English into its grammatical parts.

We may be decades from unlocking the true power of quantum computing, but D-Wave is promising to offer a taste of the future with its significantly upgraded quantum processor. When it is released early next year, the Canadian firm's new quantum chip will be able to handle some 2,000 quantum bits (qubits), roughly double the usable number found in the processor in the existing D-Wave 2X system, and be capable of solving certain problems 1,000x faster than its predecessor. D-Wave machines are multi-million dollar computers that crunch data using "quantum transistors", tiny loops of niobium cooled to close to absolute zero by liquid helium. Only a handful of such systems are in use, run by Google and the Universities Space Research Association, Lockheed Martin and Los Alamos National Laboratory. However, D-Wave also offers access to its quantum computers via a cloud service.

We've had talking dolls since Edison's time. Now, not only do the toys talk back, but using AI and cloud-based systems, they remember, record, transcribe and even email parents. Hello Barbie is just one example of animated and "cognitized" children's toys and objects that are increasingly entering our homes. When combined with personal assistants such as Amazon's Echo and Apple's Siri, childhood interactions, general inquisitiveness and simple play are being transformed. Is this a good thing?

IBM's Project DataWorks in action: A NYC-based startup that develops clean energy projects in American inner cities used Project DataWorks to build a cognitive application that performs a comprehensive energy audit of individual properties to simulate energy savings and ultimate determine the correct mix of high-efficiency technology to reduce each customer's energy consumption. IBM's Project DataWorks uses Watson Analytics to analyze and create complex visualizations IBM's Project DataWorks uses Watson Analytics and natural language processing to analyze and create complex visualizations with one line of code – like this one, which illustrates correlations between product purchases by customers of a sporting goods store. IBM's Project DataWorks helps users access and gain insights from the 90% of unstructured data that goes untapped by organizations (according to IDC). The Console pictured here provides a snapshot that categorizes and previews an organization's data assets for easy access while also providing a full audit trail that allows users to understand who else on their team is interacting with the data and how.

Microsoft is quickly pivoting to position itself as a leader in artificial intelligence. In his second keynote on the subject this year, CEO Satya Nadella yesterday (Sept. Quartz caught up with Nadella after he hopped off stage, to talk about the progress of his quest to make machines that assist humanity in a transparent way. You started talking earlier in the year saying we need to create transparent machines, ethical machines, accountable machines. What has been done since then, what is concrete?

Starting today, Google will rely more heavily on artificial intelligence when it translates language. The new method, called Google Machine Neural Translation, cuts down errors by 80% compared to its current algorithm, and is nearly indistinguishable from human translation on standardized tests, the company said. It's a radical change from how Google translates now, which is called phrase-based translation, and is standard for the industry. Under this method, an algorithm cuts up a sentence, like one entered by a high-schooler trying to game their homework, and attempts to match words or phrases to a large dictionary. The new method takes that same large dictionary and uses it to train two neural networks.