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Big Blue is cool again according to investors.

NEW YORK: Here’s a vexing question for artificial mega-brain Watson: Why is IBM stock surging? Big Blue’s market value rose about $6 billion after the computer giant agreed on Thursday to buy Truven Health Analytics for $2.6 billion. Giving IBM’s artificial-intelligence platform more data to chew on is useful, but investors’ glee over an opaque addition to an enigmatic business effort is confusing.

Big Blue’s top line has been shrinking steadily for nearly four years. In the fourth quarter of 2015, all major divisions had declining sales, with overall revenue falling 8.5 percent compared with the same period a year earlier. Clients need less of IBM’s hardware, and its software and consulting businesses are faltering in competition with rivals’ cloud-based versions.

The upshot is a falling share price. It has dropped about 25 percent in the past four years, while the S&P 500 has risen about 40 percent.

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This is so true and even more importantly in the space of technology as we introduce more products and services in the AI space. Reason is because we are seeing the consumer’s buying patterns changing especially as consumers have more options around devices, services, and AI available to them.

As a result of more choices and AI sophistication; consumers are now & more so in the future will chose to buy things that “fit” more with their own style and personality today. And, this places pressures on companies to change/ expand their thinking on product innovation to include emotional thinking as well. Gone are the days of technology just being a machine/ devices designed to only process information and provide information insights only. Tech consumers today and in the future want technology that marries with their own sense of style and personalities. Therefore, corporate culture as a whole will need to change their thinking at all levels.

I once wrote an article about how people with outstanding academic achievement or technical brilliance can easily get hired, but brilliance will get them nowhere if they lack emotional intelligence and the ability to build strong working relationships. This is especially true in today’s highly competitive world where organisations rely heavily on interdependence to stay ahead of the game.

However, I have heard arguments against my claim from people who point out that there is no shortage of notoriously heartless CEOs lacking in EQ. While that argument might ring true to some extent, I find the reasons for that situation rather interesting. As well, it is essential to note that most CEOs with low EQ scores are not the best-performing business leaders.

First, let’s make it clear that we are talking about managers or C-level executives who have to climb the ladder themselves and not those who founded or inherited a business. In this case, I have found research showing that middle managers often stand out with the highest emotional intelligence scores in the workplace because companies generally promote high-EQ types to supervisory positions as they are level-headed and good with people. However, EQ scores tend to decrease as people move up further in the hierarchy.

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A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others.

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There is a need for a larger “official and governmental” review and oversight board for drones, robots, etc. due to the criminal elements; however, any review needs focus more on the immediate criminal elements that can use and is using this technology plus how to best manage it. Like guns; we may see a need for background check and registration & license to have drones and certain robots as a way to better vet and track who can own a drone or robot.

At AAAI-16, a panel discussed the safety that will be necessary when it comes to autonomous manned and unmanned aircraft. Here’s what you need to know.

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This agreement places Oxford in a very nice position.

Quantum transport measurements are widely used in characterising new materials and devices for emerging quantum technology applications such as quantum information processing (QIP), quantum computing (QC) and quantum sensors. Such devices hold the potential to revolutionise future technology in high performance computing and sensing in the same way that semiconductors and the transistor did over half a century ago.

Physicists have long used standard electrical transport measurements such as resistivity, conductance and the Hall effect to gain information on the electronic properties and structure of materials. Now quantum transport measurements such as the quantum Hall effect (QHE) and fractional quantum Hall effect (FQHE) in two-dimensional electron gases (2DEG) and topological insulators – along with a range of other more complex measurements – inform researchers on material properties with quantum mechanical effects.

The ultra low temperatures and high magnetic fields provided by Oxford Instruments’ TritonTM dilution refrigerator make it a key research tool in revealing the quantum properties of many materials of interest. SPECS’ Nanonis Tramea QTMS is a natural complementary partner to the Triton, with its fast, multi-channel measurements.

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IBM leads the way on AI — definitely makes sense and should given the years of research & funding spent on Watson. It would be really place IBM in a bad position not to be a leader in in AI especially since it has spent so many years on cognitive computing technology.

While Google and Facebook are taking the headlines with their advancements in Artificial Intelligence, another company is making some big strides behind the scenes. The ever resilient IBM has come up with an interesting strategy to garner attention for it’s cognitive computing technology “Watson “.

Here is Why IBM May Develop a Better AI than Google or Facebook Clapway

IBM HOLDS $5 MILLION CONTEST FOR AI

At a TED conference this past week, IBM has announced a $5 million contest for developers that can come up with some creative uses for their artificial intelligence. The details of the contest have not been released, but IBM has made it clear their cognitive computing technology should be far more revolutionary than that of Google or Facebook. What is known about the format of this competition is that developers will be given a lot of freedom towards their idea. The purpose isn’t necessarily to overcome a series of challenges and follow guidelines, but to produce an idea that will bring Watson to the forefront of AI.

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Neural networks have become enormously successful – but we often don’t know how or why they work. Now, computer scientists are starting to peer inside their artificial minds.

A PENNY for ’em? Knowing what someone is thinking is crucial for understanding their behaviour. It’s the same with artificial intelligences. A new technique for taking snapshots of neural networks as they crunch through a problem will help us fathom how they work, leading to AIs that work better – and are more trustworthy.

In the last few years, deep-learning algorithms built on neural networks – multiple layers of interconnected artificial neurons – have driven breakthroughs in many areas of artificial intelligence, including natural language processing, image recognition, medical diagnoses and beating a professional human player at the game Go.

The trouble is that we don’t always know how they do it. A deep-learning system is a black box, says Nir Ben Zrihem at the Israel Institute of Technology in Haifa. “If it works, great. If it doesn’t, you’re screwed.”

Neural networks are more than the sum of their parts. They are built from many very simple components – the artificial neurons. “You can’t point to a specific area in the network and say all of the intelligence resides there,” says Zrihem. But the complexity of the connections means that it can be impossible to retrace the steps a deep-learning algorithm took to reach a given result. In such cases, the machine acts as an oracle and its results are taken on trust.

To address this, Zrihem and his colleagues created images of deep learning in action. The technique, they say, is like an fMRI for computers, capturing an algorithm’s activity as it works through a problem. The images allow the researchers to track different stages of the neural network’s progress, including dead ends.

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