Digital computers have transformed work in almost every sector of the economy over the past several decades. We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a general purpose technology, like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities, there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be suitable for ML (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent end of work as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.
Artificial intelligence has become one of the biggest technological developments in business in recent years, but the field is still largely shrouded in uncertainty. While expectations run sky-high, what are businesses actually doing now? Anew report by BCG and MIT's Sloan Management Review aims to demystify AI in business and take stock of current industry adoption. The report is based on a global survey of more than 3,000 executives and in-depth interviews with more than 30 technology experts and executives. Its goal is to present a realistic baseline that allows companies to compare their AI efforts and ambitions and to provide guidance for things to come.
There is no question that artificial intelligence (AI) is presenting huge opportunities for companies to automate business processes. However, as you prepare to insert machine learning applications into your business processes, I’d recommend that you not fantasize about how a computer that can win at Go or poker can surely help you win in the marketplace. A better reference point will be your experience implementing your enterprise resource planning (ERP) or another enterprise system. Yes, effective ERP implementations enhanced the competitiveness of many companies, but a greater number of companies found the experience more of a nightmare. The promised opportunity never came to fruition. Why am I raining on the AI parade? Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. But eventually, the outputs of most automated processes require people to do something. As most managers have learned the hard way, computers can process data just fine, but that processing isn’t worth much if people are feeding them bad data in the first place or don’t know what to do with information or analysis once it’s provided.
The Internet of Things (IoT) has been a long time coming, but as with so many software and cloud-driven markets today, the curve from hand-waving to pervasive adoption is set to be remarkably steep. Network-driven markets increasingly tend to be pretty close to winner takes all (think Google in Search, Apple in phones, Facebook in social, Snapchat in dogear-driven Augmented Reality) which makes timing and effective, community-driven execution all the more important. Which brings us to IBM. Wait. What? IBM? OK bear with me here.
From Amazon and Facebook to Google and Microsoft, leaders of the worlds most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). But what is AI? Why is it important? And why now? While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience.
Executives predict that in 2016 artificial intelligence will assume a very important role in enterprise technology, but except for a few enterprises, artificial intelligence still seems to be a thing of the future.
Artificial intelligence is our most powerful technology, and in the coming decades it will change everything in our lives. If we get it right it will make humans almost godlike. If we get it wrong... well, extinction is not the worst possible outcome. Surviving AI is a concise, easy-to-read guide to what's coming, taking you through technological unemployment (the economic singularity) and the possible creation of a superintelligence (the technological singularity).
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