In what is being called the start of the cognitive era for computing, IBM's new Watson computer program is able to mine data to think and solve problems. Significantly, it learns from its mistakes. Watson is already being used by businesses to deliver a competitive advantage. Examples include ANZ, one of Australia's largest banks, the energy company Woodside, and Melbourne's Deakin University. While artificial intelligence (AI) has benefits, there are also concerns.

It should come as no surprise that Satya Nadella put Cortana and AI at the center of his Microsoft Build 2016 keynote outlining Microsoft's future strategy. Two years ago, in his first letter to Microsoft employees, he identified machine learning as a key technology pillar that would enable a future where "computing will become even more ubiquitous and intelligence will become ambient." The big news from Microsoft Build is that Nadella is banking on "conversations as a platform" -- basically, advanced natural language processing -- and the new class of application Microsoft calls the "bot." Yes, an embarrassing episode has already occurred with the Twitter chat bot, Tay, which was taught racist palaver by mischievous users, but at least the incident demonstrated that bots actually learn. Microsoft officially introduced the Microsoft Bot Framework today as part of the Cortana Intelligence Suite, formerly known as the Cortana Analytics Suite.

Introduction to Bots A BOT is the most basic example of a weak AI that can do automated tasks on your behalf. Chat bots were one of the first automated programs to be called as'bots'. Web crawlers used by Search Engines like Google are a perfect example of sophisticated and advanced BOT.

The power of the cloud is a bit fuzzy to most of us, but Microsoft wants to improve that by giving developers a series of API tools. The suite, dubbed Cognitive Services, empowers developers to make their software far smarter, including tools for trainable speech-to-text processing and a quality of object recognition verging on actual magic. Under the slogan of "Give your apps a human side," Cognitive Services is a collection of APIs for developers to use in their applications. Two examples demoed at the Build conference include a brand-new object recognition engine, which is likely to replace Project Oxford. To demo what this API can do, Microsoft created Captionbot.ai, which is a tremendously addictive (and science-fiction-grade awesome).

Previous articles have detailed the ways in which they represent the next great leap in computer technology, allowing the possibility of things like vastly improved machine learning, artificial intelligence, and a number of other things straight out of science fiction. The problem has always been building them, but one major breakthrough just made getting there much easier. But to begin, you need to understand a bit about how quantum computers work. Now, research published in Science Advances details the creation of the first quantum Fredkin gate, a key component in making the quantum circuits that are required for quantum computers. Researchers from Griffith University and the University of Queensland used linear optics to perform the first demonstration of the quantum Fredkin gate. The Fredkin gate, or the controlled swap gate, is a three-qubit gate where, depending on the state of the control qubit, the quantum states of the two target qubits are swapped.The Fredkin gate typically requires a circuit of five logic operations to be implemented.

Lawrence Livermore's new supercomputer system uses 16 IBM TrueNorth chips developed by IBM Research (credit: IBM Research) Lawrence Livermore National Laboratory (LLNL) has purchased IBM Research's supercomputing platform for deep-learning inference, based on 16 IBM TrueNorth neurosynaptic computer chips, to explore deep learning algorithms. IBM says the scalable platform processing power is the equivalent of 16 million artificial "neurons" and 4 billion "synapses." The brain-like neural-network design of the IBM Neuromorphic System can process complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips, says IBM. The technology represents a fundamental departure from computer design that has been prevalent for the past 70 years and could be incorporated in next-generation supercomputers able to perform at exascale speeds -- 50 times faster than today's most advanced petaflop (quadrillion floating point operations per second) systems. The TrueNorth processor chip was introduced in 2014 (see IBM launches functioning brain-inspired chip).

When Apple launched the Apple II in 1977, it was still far from certain that consumers would want or need a home computer. While hobbyists were tinkering with computers they built and programmed themselves, Apple saw a need for something easier, and introduced the Apple II. This article, republished from the May 1, 1978, edition of Computerworld, sets the scene at the time and describes how a young Apple Computer was looking to the future. With more than 100,000 units sold, the personal computer market is finally being recognized as much larger then the original hobby market, a spokesman stated, observing that there have already been forecasts of 2 billion in sales by 1985. Apple, which introduced a self-contained personal computer (Apple II) in June 1977, believes the personal computer will make home life better for middle-income families.

The Amazon Echo has quietly become the hottest smart home product on the market. Suddenly, every tech company wants to integrate its products with Amazon's customizable virtual assistant, Alexa. The integrations keep coming, at such a pace that it can be hard to know exactly which products work with Alexa. Amazon's website tries to keep on top of things, but its interface can be tough to navigate. It also (understandably) underplays the fact that you don't need an Echo to take advantage of Alexa and all the devices she can control.

Robots are already working in our everyday lives and have changed the way that some industries operate. The future of robotics will change how we live forever. Manufacturing companies have used robots for well over two decades already, and it has proven to be successful. Society is ready for the change as robot sales increased 29-percent in 2014 totaling 229,261 units. Soon, robots could look and function similar to humans.

As big data use cases proliferate in telecom, health care, government, Web 2.0, retail etc there is a need to create a library of big data workload patterns. . We have created a big data workload design pattern to help map out common solution constructs. There are 11 distinct workloads showcased which have common patterns across many business use cases. It essentially consists of matching incoming event streams with predefined behavioural patterns & after observing signatures unfold in real time, respond to those patterns instantly. Let's take an example: In registered user digital analytics scenario one specifically examines the last 10 searches done by registered digital consumer, so as to serve a customized and highly personalized page consisting of categories he/she has been digitally engaged.