The business world continues to shape deep learning’s future.
What’s new: Commerce is pushing AI toward more efficient consumption of data, energy, and labor, according to a report on trends in machine learning from market analyst CB Insights.
What they think: The report draws on a variety of sources including records of mergers and acquisitions, investment tallies, and patent filings. Among its conclusions:
- Consumers are increasingly concerned about data security. One solution may be federated learning, the report says. Tencent’s WeBank is developing this approach to run credit checks without removing consumers’ data from their devices. Similarly, Nvidia’s Clara allows hospitals to share diagnostic models trained on patient data without compromising the data itself.
- AI’s success so far has relied on big data, but uses in which large quantities of labeled data are hard to come by require special techniques. One solution to this small data problem is to synthesize training examples, such as faux MRIs that accurately portray rare diseases. Another is transfer learning, in which a model trained on an ample dataset is fine-tuned on a much smaller one.
- Businesses can have a tough time finding the right models for their needs, given the shortage of AI specialists and the variety of neural network architectures to choose from. One increasingly popular solution: AI tools that automate the design of neural networks, such as Google’s AutoML.
- Demand for AI in smartphones, laptops, and the like is pushing consumer electronics companies toward higher-efficiency models. That helps explain Apple’s purchase of edge-computing startup Xnor.ai in January.
We’re thinking: It’s great to see today’s research findings find their way into tomorrow’s commercial applications. The road from the AI lab to marketplace gets busier all the time.