Apple's new operating system for iPhones and iPads is about to land on your phone. And it will bring with it a host of new features that might make even old phones feel new – important, when the iPhone 7 is coming out the same week. Apple first unveiled iOS 10 at an event in June. There, it showed off how it had changed the lock screen and home screen to provide more information; allowed Siri to integrate with other apps; added artificial intelligence to Photos, the keyboard and many different apps; and more. It looks similar to the 6 and 6s.

As a technology researcher, I'm often asked if tech growth has peaked, or if there is more to come. After spending lots of time thinking about this question, I've decided that the tech industry is in for a sea change, leading to a potential tripling of demand for tech-related goods and services over the next decade. Here are my seven reasons to be bullish about technology's future, broken down by category. A major move to build out broadband wireless networks will provide the underlying infrastructure critical for innovation in communications, the Internet of Things (which will connect lots of formerly "dumb" electronics, like air conditioners and coffee makers) and self-driving automobiles. Meanwhile, "mesh networks" that connect nearby devices to one another in local grids will allow for a new wave of wireless innovation in densely packed cities and other communities.

Facebook uses machine learning (ML) for face recognition, Apple uses it to make Siri sound more human, and Googleminimizes energy use at data centers with the help of ML. Machine learning is a trendy element of artificial intelligence that is being successfully used in many industries. Regardless of the industry your business operates in, consider using ML to improve productivity and receive higher ROI. In traditional computer science we need to explain the task we wish to accomplish to the computer. For example, if we plan on creating a tool that calculates salaries, then we need to write a program that translates to the computer how to perform each operation in a language that it understands.

DataRPM delivers industry's only Cognitive Predictive Maintenance (CPdM) Platform for Industrial IoT. DataRPM uses patent pending Meta-Learning technology, integral component of Artificial Intelligence to automate predictions of asset failures. CPdM is a full stack platform available on cloud or on premises that connects to the data lake and then automatically runs multiple Machine Learning experiments, finds patterns & anomalies in data, identifies influencing factors & predictors, and builds an ensemble of predictive models to automate Predictive Maintenance. Not only does CPdM platform delivers descriptive, predictive, prescriptive analytics through Natural Language Discovery module but also provides a closed loop system by integrating these actionable insights through APIs to ERP, CRM, and CMS systems. The Meta Learning environment runs multiple live automated ML experiments on datasets, extracts data from every experiment, trains an ensemble of models on this meta data repository, applies models to predict the best algorithms and finally builds machine-generated and human verified Machine Learning models for Predictive Maintenance.

I just got back from a very good conference organized by startup.ml: Please read on for my to comments on part of one of the very good talks. Classic machine learning (especially as it is taught in classes) emphasizes a nice safe static environment where you are given some unchanging data and are asked to produce a nice predictive model one time. It is formally easier that casual inference or statistical inference as being right often is enough, no matter what the reason. Adversarial machine learning is the formal name for studying what happens when conceding even a slightly more realistic alternative to assumptions of these types (harmlessly called "relaxing assumptions").

Show a human any photograph and they'll able to predict what happens next with pretty decent accuracy. The woman riding her bike will keep on moving. The dog will catch the frisbee. The man is going to have a pratfall. It's such a basic skill that we don't consider the vast amount of information that is used to make these predictions -- concerning gravity, inertia, the nature of pratfalls, etc. -- and teaching computers to do the same is proving to be a key challenge in machine vision.

The conservative estimate of the brain being in the Zetta-scale of computing is conservative because it is leaving out a long list of the brain's known features that further increase the computational complexity of the brain. Besides the known ones, we still have the unknown ones to discover in the future. The estimate does not include known things like (as listed by Tim Dettmers) multi-neurotransmitter vesicles (which can be thought of as multiple output channels or filters, just as an image has multiple colors); glial cells (besides having an extremely abnormal brain [about one-in-a-billion], Einstein also had an abnormally high number of glial cells. The latest research suggests that astrocytes especially have an active role in brain neuroplasticity and communication. All this complexity and additional capabilities for computation probably pushes a normal brain to Yotta-scale computing, or even more.

Moore's Law posits that the number of transistors on a microprocessor -- and therefore their computing power -- will double every two years. It's held true since Gordon Moore came up with it in 1965, but its imminent end has been predicted for years. As long ago as 2000, the MIT Technology Review raised a warning about the limits of how small and fast silicon technology can get. The thing is, Moore's Law isn't really a law. Moore didn't describe an immutable truth, like gravity or the conservation of momentum.

MIT's Computer Science and Artificial Intelligence Laboratory is the largest on-campus laboratory as measured by research scope and membership. More than 250 companies have been hatched through CSAIL, including Akamai, iRobot, 3Com, and Meraki. CSAIL's research activities are divided into seven areas of emphasis: Artificial…

IBM is bringing the power of artificial intelligence to the US Open's official event application this year, enabling enhanced features such as a cognitive concierge. The app makes use of IBM's Watson AI technology to create a cognitive concierge that can understand natural language and help guide users through the event. The app understands questions such as, "Where can I park?" and "Where is the bathroom?" "Watson and its cognitive capabilities allow us to help the US Tennis Association publish video with subtitles and transcripts faster, as well as offer an experience that is more humanlike, such as asking questions in natural language about dining options and transportation to the US Open," said John Kent, technology program manager at IBM Worldwide Sponsorships. Artificially intelligent The Watson-powered features mostly make use of three key APIs provided by IBM: the Natural Language API, the Speech-to-Text API and the Watson Visual Recognition API.