Marc Andreessen once said that software was eating the world. The point he was trying to make was that software increasingly represents the value in most economic processes. In this sense, where software was previously one small component of a complex process that involved lots of human input and complex non-digital systems, it now represents the bulk of the intelligence and value-add. Think about cars before Tesla. You had carburetors, fuel injection systems, spark plugs, complex mechanical transmission systems, cooling systems, pumps and much more.

A pair of artificial intelligence experts from Cornell University have joined a nationwide effort to ensure the nightmare science fiction scenarios -- the ones involving corrupted human-killing computers -- don't become a reality. "We are in a period in history when we start using these machines to make judgments," researcher Bart Selman, a professor of computer science at Cornell, explained in a news release. Joseph Halpern, a professor of computer science at Cornell and also a "decision theory" expert, says providing an artificial intelligent agent with as much information as possible will make these difficult decisions more manageable. Scientists at Georgia Tech have been working to instill human values by teaching robots fairy tales.

A total of 51 institutions submitted a Letter of Intent (LOI) to the Fund in Inaugural Competition 2. Of these, 30 were invited to submit a full proposal. Ultimately, 29 proposals were submitted, representing a total Fund request of 2.38 billion. The proposals underwent a multilevel peer review process that included an evaluation of scientific merit and strategic relevance by external experts, and merit assessment by expert review panels, based on three selection criteria. Each proposal was assigned to one of four review panels composed of leading Canadian and international experts. Subsequently, proposals underwent a strategic review by the selection board, which is composed of distinguished Canadian and international leaders.

In this special guest feature, Pallab Deb, Vice President and Global Head, Analytics at Wipro Limited, outlines how you can gain benefit from using your gut business instincts by combining them with extreme data insights. Pallab Deb in his current role heads the Wipro's Analytics service line which delivers state of the art analytics solutions to a global clientele. With his extensive experience in Information Technology, Pallab has not only held multiple leadership roles in Connected Enterprise Services (CES) service line of Wipro, client engagements & strategic alliances but also has led sales teams on complex consulting, system integration and outsourcing deals in North America and Europe in High Tech, Manufacturing, Retail & Consumer Goods, Life Sciences and Utilities industries delivering on aggressive sales growth targets for seven consecutive years. Organizations are tantalizingly close to a vast amount of data that can prove meaningful to business. There is a prodigious amount of text, visual and audio information flowing across media reports, company filings, government records, surveys, research documents, social media, messaging applications, blogs, email, IVR, machine logs, contracts, ERP, POS, CRM, MES, IoT, etc. Somewhere, within that blur of rapidly flowing real-time data, is the insight that could change your business.

Continuing our series of articles on Innovation we recently talked to Mike Zimmerman, Founder of BuildingIQ, about the approaching 3rd Step change in Building Energy Management (BEMS) technology. The 1st step change in BEMS technology was of course the move to DDC controls. Since then we have seen a 2nd wave of innovation in analytics; where companies have started to extract useful data using open protocols and analyze it to help report on performance or identify operating issues. But the limitation of this approach is that it only provides a static "point in time" view of the building. Additionally, any issues identified still require human intervention to address.

Q-learning is a simple and powerful tool in solving dynamic problems where environments are unknown. It uses a balance of exploration and exploitation to find an optimal solution to the problem. In this paper, we propose using four basic emotions: joy, sadness, fear, and anger to influence a Qlearning agent. Simulations show that the proposed affective agent requires lesser number of steps to find the optimal path. We found when affective agent finds the optimal path, the ratio between exploration to exploitation gradually decreases, indicating lower total step count in the long run

Two self-sailing ships have been travelling across the Bering Sea, off the coast of Alaska. The boats are operated by Saildrone, a company that is creating robotic sailboats that can travel without sailors for up to eight months. These autonomous vessels can collect details on water temperature, salinity and ecosystem information that would be difficult and expensive to collect by person. Saildrone is a company creating robotic self-driving sailboats that can travel without sailors for up to eight months. The Saildrone boats have been used by scientists and engineers from the National Oceanic and Atmospheric Administration (NOAA) to collect valuable information about the Alaskan coast.

We consider machine learning techniques to develop low-latency approximate solutions to a class of inverse problems. More precisely, we use a probabilistic approach for the problem of recovering sparse stochastic signals that are members of the $\ell_p$-balls. In this context, we analyze the Bayesian mean-square-error (MSE) for two types of estimators: (i) a linear estimator and (ii) a structured estimator composed of a linear operator followed by a Cartesian product of univariate nonlinear mappings. By construction, the complexity of the proposed nonlinear estimator is comparable to that of its linear counterpart since the nonlinear mapping can be implemented efficiently in hardware by means of look-up tables (LUTs). The proposed structure lends itself to neural networks and iterative shrinkage/thresholding-type algorithms restricted to a single iterate (e.g. due to imposed hardware or latency constraints). By resorting to an alternating minimization technique, we obtain a sequence of optimized linear operators and nonlinear mappings that converge in the MSE objective. The result is attractive for real-time applications where general iterative and convex optimization methods are infeasible.

Recently I wrote about an interesting new project that's helping to deepen our understanding of the cosmos. A team from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) are working on a prototype of an army of 5,000 robots that can head out into the galaxy. The machines, which are known as ProtoDESI, come in'platoons' of 10 robots and are designed to help us enhance the accuracy of the Dark Energy Spectroscopic Instrument (DESI). DESI is intended to provide a 3D map of the universe and allow scientists to further explore things like dark matter. A second team, from University College London, are using machine learning techniques in their search for habitable worlds.

A computer's victory over a human go master this past March reminds us of the pending "singularity" -- the rapidly approaching moment in time when artificial intelligence overtakes human intelligence. Machines will learn, and we won't be their teachers. Are we prepared for it? Can we prepare for it? Many futurists declare it inevitable, probably within a generation, maybe less.