Artificial intelligence has the opportunity to affect almost every aspect of how we live and consume. I struggle to think of a single industry that could not be transformed through AI technologies, leading to huge gains in effectiveness or efficiency. Yet it's sometimes hard to see a place for AI in particular businesses or organizations, especially if there's an established product, workflow, and way of thinking. Because of this, it can be easy to overlook the opportunities afforded by some of the most revolutionary technologies of our age. To see these opportunities, it is important to remember that powerful AI methods have not been around for very long.

BOT TO THE FUTURE: Mastercard wants to make commerce'more conversational' Mastercard has unveiled plans to launch artificial intelligence (AI) bots for its merchant and bank partners, allowing consumers to use chat, messaging and natural language interfaces to shop and manage their finances. Mastercard KAI, the payments giant's bot for banks, will allow consumers to ask the bot questions about their accounts, review purchase history, monitor spending levels and receive contextual offers. Meanwhile, the Mastercard Bot for Merchants will allow consumer to shop and transact on messaging platforms and then check out with the Masterpass global digital payment service. According to research firm Gartner, nearly US$2bn in online sales will be performed exclusively through mobile digital assistants by the end of 2016. Kiki Del Valle, SVP at Mastercard, explained to NFC World the company was aiming to make commerce "more conversational by combining secure digital payments and artificial intelligence technology".

Towards this end, researchers at MIT are investigating ways of making artificial neural networks more transparent in their decision-making. As they stand now, artificial neural networks are a wonderful tool for discerning patterns and making predictions. But they also have the drawback of not being terribly transparent. The beauty of an artificial neural network is its ability to sift through heaps of data and find structure within the noise. This is not dissimilar from the way we might look up at clouds and see faces amidst their patterns.

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

In the course of this three-part series on the challenges and opportunities for enterprise machine learning, we have worked to define the landscape and ecosystem for these workloads in large-scale business settings and have taken an in-depth look at some of the roadblocks on the path to more mainstream machine learning applications. In this final part of the series, we will turn from pointing to the problems and look at the ways the barriers can be removed, both in terms of leveraging the technology ecosystem around machine learning and addressing more difficult problems, most notably, how to implement the human side of machine learning in an organization. For now, however, let's start looking at solutions at the top of the technology side with the sheer performance and workflow possibilities. Logically, if we want to reduce the cycle time for machine learning radically, it makes sense to attack the most time-consuming tasks. As we noted previously, data scientists spend most of their time collecting and cleaning data, so it makes sense to focus effort on simplifying and expediting this task.

Social psychologists have long known that humans make snap judgements about each other based on nothing more than the way we look and, in particular, our faces. We use these judgements to determine whether a new acquaintance is trustworthy or clever or dominant or sociable or humorous and so on. These decisions may or may not be right and are by no means objective, but they are consistent. Given the same face in the same conditions, people tend to judge it in the same way. And that raises an interesting possibility.

I have been using Lean Six Sigma (LSS) to improve business processes for the past 10 year and am very satisfied with its benefits. Recently, I've been working with a consulting firm and a software vendor to implement a machine learning (ML) model to predict remaining useful life (RUL) of service parts. The result which I feel most frustrated is the low accuracy of the resulting model. As shown below, if people measure the deviation as the absolute difference between the actual part life and the predicted one, the resulting model has 127, 60, and 36 days of average deviation for the selected 3 parts. I could not understand why the deviations are so large with machine learning. After working with the consultants and data scientists, it appears that they can improve the deviation only by 10% through data cleansing.

Sergey Levine is an assistant professor at UC Berkeley whose research is focused on the thing our parents used to make such a fuss over, whenever we made stupid mistakes or should have known to avoid this or that and how on earth we could be so clueless. He has a keen interest in teaching common sense. It might sound frivolous or a fool's errand, but it's in line with the pursuit you begin to hear a lot about these days among experts focused on machine learning and computer vision and the like. Okay, so we want to get robots to a state where they're useful and able to operate and mingle among humans and be an everyday fixture of everyday life. Those machines don't have the benefit of decades of acquired knowledge -- schooling, social skills acquired through interactions and so forth -- which means someone needs to program responses that fit scenarios.

Even if self-driving cars aren't part of our daily lives yet, vehicles are becoming internet-connected at a rapid pace. Gartner predicts that one fifth of all autos on the road, and great majority of new vehicles being produced worldwide will have wireless network connectivity by 2020. Yet, few organizations have access to use the data generated by these vehicles today. That's where Otonomo, a startup based in Herzliya, Israel comes in. The company's systems gather up driver and vehicle data from disparate automakers and original equipment manufacturers.

DB: Imagine what the next 5 years will bring: The term "mobile computing" will eventually become a dated term, replaced by "ubiquitous computing" as the internet will be everywhere. These changes include the transportation we take on land, in the air, and at sea; the clothes and devices we wear, sensors at work, at home, in our environment, and (if we chose) in us for medical purposes as well. DB: Also right behind and coupled with the Internet of Everything: 3D mass fabricators enabling individuals to affordably "print" and modify at the molecular level tangible substances based on digital designs. Maker Faires around the world already exist showcasing the early stages of what 3D fabricators can do in the hands of artists, engineers, and hobbyists. As Co-Chair of the IEEE Committee focused on Artificial Intelligence and Innovative Policies, I firmly believe exponential changes like the era we're in offer great opportunities for society -- as well as great challenges.