In 2017, MITSloan Management Review (MIT SMR) conducted a global survey of more than 3,000 executives, managers, and analysts on the current state of AI and its potential. The results reveal that there's still a significant gap between "ambition and execution."

Every year since 2001 we've picked what we call the 10 Breakthrough Technologies. People often ask, what exactly do you mean by "breakthrough"? It's a reasonable question--some of our picks haven't yet reached widespread use, while others may be on the cusp of becoming commercially available. What we're really looking for is a technology, or perhaps even a collection of technologies, that will have a profound effect on our lives.

Domestic power companies are racing to introduce digital technologies, including the "internet of things" and artificial intelligence, to cope with a changing business environment. Pressure to create new services is mounting amid intensifying competition following the April 2016 full liberalization of the retail electricity market and the growing use of renewable energy sources, including solar power. Last summer, Tokyo Electric Power Company Holdings Inc. started a project to set up a peer-to-peer electricity trading platform together with German power firm Innogy SE. The platform allows participating individuals to directly sell their surplus solar power to local supermarkets and companies via transactions using smartphones. The service is slated to begin in Germany as early as this month.

According to the report, Apple is seeking to lock down a long-term deal, securing several thousand metric tons a year, for a last five years. The move puts Apple in direct competition with other big players who are also looking for a similar agreement, and advantage. BMW, Volkswagen and Samsung's own battery division are thought to be engaged in similar negotiations for their own EV projects. It's clear from the piece that Apple is only seeking to secure material for batteries that go inside its consumer hardware. CEO Tim Cook has been open about his company's interest in the "autonomous systems" market, but wouldn't be drawn on what exactly was being worked on.

MIT researchers have developed a chip designed to speed up the hard work of running neural networks, while also reducing the power consumed when doing so dramatically – by up to 95 percent, in fact. The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation. The big advantage of this new method, developed by a team led by MIT graduate student Avishek Biswas, is that it could potentially be used to run neural networks on smartphones, household devices and other portable gadgets, rather than requiring servers drawing constant power from the grid. Because it means that phones of the future using this chip could do things like advanced speech and face recognition using neural nets and deep learning locally, rather than requiring more crude, rule-based algorithms, or routing information to the cloud and back to interpret results. Computing'at the edge,' as its called, or at the site of sensors actually gathering the data, is increasingly something companies are pursuing and implementing, so this new chip design method could have a big impact on that growing opportunity should it become commercialized.

Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that instance. For low class-label noise levels, especially when simple base learners are used, emphasis should be made on instances for which the disagreement rate is high. When more flexible classifiers are used and as the noise level increases, the emphasis on these uncertain instances should be reduced. In fact, at sufficiently high levels of class-label noise, the focus should be on instances on which the ensemble classifiers agree. The optimal type of emphasis can be automatically determined using cross-validation. An extensive empirical analysis using the beta distribution as emphasis function illustrates that vote-boosting is an effective method to generate ensembles that are both accurate and robust.

Among the young companies covered in the report, a number of them are innovatively bringing AI into our buildings in a way only start-ups can. These new entrants are currently exploiting the technology across a range of applications, from BIoT, energy management, security, real estate and property management and smart building to smart grid applications. B2B IoT startup Verdigris is one such company. The firm applies a rudimentary form of AI to building management, according to CEO Mark Chung. Verdigris' system listens to electrical signals to identify the type of equipment in the building, it can then create algorithms to offer predictive analysis and anomaly detection. "The main value proposition is to use the platform around energy efficiency and data layer to get better feedback around energy performance in a building," said Chung.

In the tech world in 2017, several trends emerged as signals amid the noise, signifying much larger changes to come. As we noted in last year's More Than Noise list, things are changing--and the changes are occurring in ways that don't necessarily fit into the prevailing narrative. While many of 2017's signals have a dark tint to them, perhaps reflecting the times we live in, we have sought out some rays of light to illuminate the way forward. The following signals differ considerably, but understanding them can help guide businesses in the right direction for 2018 and beyond. When a team of psychologists, linguists, and software engineers created Woebot, an AI chatbot that helps people learn cognitive behavioral therapy techniques for managing mental health issues like anxiety and depression, they did something unusual, at least when it comes to chatbots: they submitted it for peer review. Stanford University researchers recruited a sample group of 70 college-age participants on social media to take part in a randomized control study of Woebot. The researchers found that their creation was useful for improving anxiety and depression symptoms.

As compared to natural intelligence (NI), which describes the human capacity to perform both daily and complex tasks, artificial intelligence (AI) instead describes the completely autonomous behavior of computer systems. Equipped with the sensor technology to determine tasks that need to be performed, as well as any maintenance requirements, AI systems have become a routine technology that is incorporated into almost every device that we use and operate each day. Current advancements in AI have allowed for the exceptional perception and cognition abilities of systems like Siri, Alexa and Google Assistant to recognize our voices and immediately provide us with a wide range of information. The ultimate goal of factories is to mass-produce their products at a rapid speed while simultaneously minimizing production costs as much as possible. Innovative manufacturers have already implemented AI systems into their daily tasks in a number of ways, thereby allowing for factories to meet their global demands at an economically beneficial rate.

Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items. This work exposes a serious problem with the state of learning-to-rank algorithms, which is that they are based on convex proxies that lead to poor approximations. We then discuss the possibility of "exact" reranking algorithms based on mathematical programming. We prove that a relaxed version of the "exact" problem has the same optimal solution, and provide an empirical analysis.