That's the year in which artificial intelligence will be able to perform any intellectual task a human can perform, according to one survey of experts at a recent AI conference. Anything and everything any person has ever done in all of history -- all of it doable, by 2050, by intelligent machines. But what can AI do today? How close are we to that all-powerful machine intelligence? I wanted to know, but couldn't find a list of AI's achievements to date.

Facial recognition is nothing new, however this is now extending beyond humans into the world of domestic cattle. Whilst'smart' cattle monitoring is more commonplace, existing systems largely require the use of physical tracking devices. Facial recognition technology will eliminate the stress of fitting these devices, allowing easy monitoring of an entire heard with minimal interaction. This is set to enable individual monitoring of group behaviour, early detection of lameness and accurate recording of feeding habits. Although hailed as the future of farming, the extent to which AI will change the daily operations of the traditional family farm is yet to be seen.

This article originally appeared on Real Money on March 6, 2017. Given how much several tech giants throw around the terms "artificial intelligence," "machine learning" and "deep learning," it's easy to assume that they're all trying to do similar things. And in some cases, the companies are indeed using AI to tackle problems that have much in common, particularly when it comes to making sense out of real-life sounds, images and dialogue. But a closer look shows that tech giants are at times not only tackling very different challenges, but employing different AI techniques to do so. IBM (IBM), which has reportedly set a goal of obtaining $10 billion in annual revenue related to its Watson AI platform by 2023, is a particular outlier -- not seen as a leader when it comes to certain large-scale, consumer-focused AI solutions, but very much one when it comes to AI offerings that require a measure of industry-specific expertise.

When you hear "artificial intelligence" or "machine learning," what comes to mind? A complicated technology that demands deep domain experience or a degree to use? This was once the way technology worked; only a select few had access. But innovation has a funny way of changing things. What might seem out of reach today can become widely accessible tomorrow -- just look at the GPS system, or drones. Machine learning has made it so that marketing automation platforms can be predictive -- able to learn, think and act without explicit instructions.

For a decade, one-to-one sales and marketing has lingered in twilight sleep. Still, marketers and salespeople see it coming. Soon, they realize, data intelligence will fill in the details. We'll know the right companies to target, when to do so, what messaging to use, and with whom to share it. The image may be out of focus, but the ingredients are in place.

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How well can future health events of patients be predicted from EHR data, at various lengths of time in advance? And how can such predictions improve human health? This talk answers the first question via an approach called high-throughput machine learning, and it speculates about answers to the second question. In particular, this talk argues that many healthcare applications require not just accurate prediction, but accurate prediction by causally-faithful models. Causal discovery from observational data is already a major research direction in machine learning and statistics, and this talk discusses new approaches across the spectrum from when "we know all the relevant variables" to when "we know only one relevant variable" for the task at hand.

At IBM Edge 2016, a team of developers and data scientists presented a practical study that evaluated the efficiency of training a TensorFlow model in a distributed mode. A use case featured high-resolution images of lymph nodes used for possible cancer detection. Relying on a distributed model of TensorFlow and high-performing nature of the OpenPOWER infrastructure, the demonstrated system can accelerate medical data analysis--depending on the number of GPUs and nodes in its cluster. The particular subjects of the research were how training time decreases when the cluster grows and whether the accuracy of the results is affected by the distributed nature of the computations.

What if you couldn't fact check the investment data you wanted to buy, but the data was verified by a cryptographically proven, immutable blockchain? One major bank that is solely responsible for managing an estimated 11% of all the world's financial assets is exploring just such a possibility. Following a State Street report published last week on the long-term value of blockchain and other technologies, the bank's executive vice president of global exchange, Lou Maiuri, elaborated on how his group is experimenting with new ways to capitalize on blockchain tech. In conversation with CoinDesk, Maiuri explained how combining artificial intelligence and blockchain could lead to new revenue streams derived from valuable client data. "Anyone who's not looking at these pools of data will be arbitraged, will lose out."

MIT SMR and Boston Consulting Group are collaborating on a new research initiative, Artificial Intelligence & Business Strategy, to explore the most important business opportunities and challenges for managers posed by AI. The field of artificial intelligence (AI) is finally yielding valuable smart devices and applications that do more than win games against human champions. According to a report from the Frederick S. Pardee Center for International Futures at the University of Denver, the products of AI are changing the competitive landscape in several industry sectors and are poised to upend operations in many business functions. MIT Sloan Management Review and the Boston Consulting Group have joined forces to find out. Our new research initiative, Artificial Intelligence & Business Strategy, explores the most important business opportunities and challenges from AI. Artificial intelligence covers a diverse set of abilities, much like Howard Gardner's breakdown of human intelligence into multiple intelligences.