In search of the ‘live’ AI killer appĪll the most exciting talk around the future and potential of AI centers around the capability of deep learning yet most of the anticipated ‘killer apps’ of Machine Learning involve latencies, throughput and accuracy levels which the current generation of neural networks are not yet capable of networks for which even the central defining frameworks and methodologies are still in flux.įor instance, the most promising of the research papers which I read daily offer systems with accuracy or efficacy in the high nineties. Whether these sectors would welcome a shift in role from passenger to prime mover is debatable. This applies particularly to the cut-stricken US military, whose AI tenders have slanted towards economical ‘off-the-shelf’ solutions in the last 6-7 years. However these sectors are not only more secretive than the general assembly of current academic research, but are also benefitting directly from this cross-pollinated frenzy of activity.
If business becomes chary of continuing commitment to the huge body of pre-commercialized research currently under way, it may fall to more reliable or self-motivated sectors such as military research (drones and automated weapons systems) and data analysis (Google and other major players in online and mobile advertising) to pick up the slack. Self-driving technologies face years - if not decades - of regulatory hurdles, and any globalized race-to-the-bottom in this respect seems likely to be undermined by the scandal of headline-grabbing setbacks, if history is any indication.Ī withdrawal of commercial interest in autonomous vehicles would directly affect some of the most active AI research sectors, including image recognition and video analysis, as well as having an indirect effect on related sectors such as security and facial recognition. Some of the main commercial engines of the massive resurgence of interest in AI over the last five years are already threatened by legislative, political and economic factors. It’s a grim prospect, since the bursting of the AI bubble would recall the AI winter of the 1970s. If the current groundswell of interest in artificial intelligence should peak and then abate, the field risks to suffer the same kind of ‘false dawn’ which virtual reality experienced in the early 1990s, as business gradually realizes that the enabling technologies and milestones might be a decade or more away.
In the AI research and application sphere there’s evident tension between the need to maintain excitement and funding, and the embarrassing truth that state-of-the-art neural networks are still extraordinarily experimental - or even merely conceptual. Does AI research need its own 'RealPlayer' to bridge the PR gap between research and applicability?
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