A Deep Learning Primer – The Reality May Exceed the Hype
A Deep Learning Primer – The Reality May Exceed the Hype
Deep learning based AI will drive the next phase of disruption for TMT. Leveraging hyperscale data centers and ubiquitous mobile devices, AI systems that allow computers to interpret ambiguous inputs and find optimal solutions are enhancing human-machine interfaces, enabling autonomous machines to execute complex tasks, and addressing previously intractable analytic challenges. However, the prerequisites for leadership in AI are rare. There may be as few as 50 true experts in deep learning, concentrated in a small number of organizations. Only a few hyperscale cloud operators have the computing power critical to addressing the most promising opportunities. Similarly, deep learning systems work best with massive data sets – a big advantage for big consumer cloud franchises. Finally, experience matters – learning systems improve iteration by iteration. The top player is GOOGL, which employs 40% of the world’s top AI data scientists and has dozens of initiatives based on the technology. MSFT runs a distant second, with FB, IBM, and AMZN further behind. AAPL has the most at risk, as it struggles to catch up in a technology that could threaten its ecosystem. A raft of startups, most tightly focused on a particular application, have emerged, and many more will come, particularly as core technology is contributed to open source solutions and as the leaders begin to offer deep learning platforms as a service.
“The impact of AI on tech is like the impact of electricity was on industry” – Andrew Ng, Chief Scientist, Baidu
Deep learning is the next phase for the Cloud-Mobile Era. Deep learning technology is poised to threaten the app-focused smartphone paradigm, to revolutionize data analysis, to establish new systems control paradigms, and to enable entirely new classes of applications while displacing traditional services across the economy. In this, Deep learning will create larger, more immediate and more foundational business opportunities than other hyped innovations, such as VR, wearables and the IoT, while leaving TMT players without strength in the technology extremely vulnerable.
Optimal solutions to ambiguous problems. We see four major vectors for deep learning systems development. 1. Allowing computers to cope with ambiguous input data, such as natural language, speech, raw images, or gestures. 2. Enabling the autonomous operation of machines – e.g. self-driving cars, drones, robots, or smart homes. 3. Performing highly complex analysis of very large data sets, thus supporting medical/scientific research, economic forecasting, crime/fraud prevention, resource allocation, and other significant analytic bottlenecks. 4. Enhancing personal services – anticipating user needs, offering insight, increasing personalization, adding context awareness, and performing tasks.
What will change? AI could make the touch GUI/App interface model obsolete, not just by simplifying user input – voice assistants Siri, Google Now, Cortana and Alexa are all AI-based and can get MUCH better – but by carrying a user’s context across all devices, anticipating needs as much as responding to them. AI could dramatically improve applications – e.g. shopping applications that really know your tastes, customer service that can anticipate problems, CRM systems that can accurately qualify leads, analytics that cope with messy data and find unexpected insights, or quantitative models that adjust to changing conditions as they happen – and enable new ones that have yet to be articulated.The prerequisites for success are hard to gather. 1. Talent – The acceptance of the deep learning approach is relatively recent and there is still a dearth of experienced talent in the field – there may be as few as 50 real experts in deep learning worldwide. 2. Data – The more data, the better the AI. 3. Infrastructure – Deep learning is extremely computationally intensive, requiring access to massive computing resources. 4. Experience – Deep learning is a fundamentally iterative process without obvious shortcuts. These requirements greatly advantage a relatively small set of leaders. Smaller companies will either work with these companies, or will remain tightly focused on narrow applications.
GOOGL – The giant of deep learning. #1 GOOGL likely has 40% of the top minds in deep learning, with hundreds of younger researchers working under them. They are attracted to an organization that has been working on AI from its inception and that offers the world’s most powerful computing infrastructure, an ocean of data both broad and deep, reach to more than a billion consumers, and a commitment by senior management to use deep learning to address inspiring problems. GOOGL leads in all four vectors of development, with major work long underway in natural language processing, image recognition, predictive search, autonomous machines, medical research, deep learning platforms as a service, and many other projects.
The next 4, in order. #2 MSFT has long standing investments in deep learning – Cortana, Kinect, HoloLens, and other cutting edge product areas. It has a sizeable team able to leverage a strong computing infrastructure and an excellent base of data. #3 FB has been very aggressive, hiring experts of its own, even poaching talent from GOOGL. It has the platform and the data to support a major AI push, and has made deep learning “bots” the cornerstone of its strategy for monetizing messaging. #4 IBM is vocal about its Watson deep learning initiative and has AI pioneer Yoshua Bengio leading the effort. Its focus is necessarily more industrial, lacking the consumer data of its rivals. #5 AMZN is characteristically secretive about its research, but its recommendation engines, Alexa and drone program suggest significant investment. Obviously it has the necessary data and computing platform.
AAPL – At risk? AAPL is playing catch up in AI, acquiring talent in deals for Siri, Perceptio, VocalIQ, and Emotient, and posting dozens of deep learning job openings. Its extreme secrecy, reluctance to capture user data, and focus on local device computing rather than cloud-based data centers are all significant impediments to progress, and its results to date with learning driven initiatives – e.g. Siri, maps, music recommendations, etc. – have been disappointing. We believe AI systems have the potential to subvert AAPL’s app-based interface model, to lessen the importance of the smartphone itself, and to erode the substantial switching barriers around the AAPL ecosystem.
The others. BIDU poached GOOGL’s Andrew Ng to lead its deep learning efforts, and is considered ahead of Chinese rivals BABA and Tencent. All three benefit from an aggressive government initiative to develop deep learning talent in the Chinese university system. Meanwhile, a raft of deep learning startups have launched, typically with a sharp vertical or functional focus that minimizes the disadvantages of their scale. Many of these small companies have been acquired in recent months, for their talent as much as their products.