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A Viga T Ing R T Ificia L N Te Ll Igence July 24, 2018 Semiconductor Get real with artificial intelligence (AI) "Seriously, do you think you could actually purchase one of my kind in Walmart, say in the next 10 years?" NTELLIGENCE I "You do?! You'd better read this report from RTIFICIAL RTIFICIAL cover to cover, and I assure you Peter is not being funny at all this time." A ■ Fantasies remain in Star Trek. Let’s talk about practical AI technologies. ■ There are practical limitations in using today’s technology to realise AI elegantly. ■ AI is to be enabled by a collaborative ecosystem, likely dominated by “gorillas”. ■ An explosion of innovations in AI is happening to enhance user experience. ■ Rewards will go to the problem solvers that have invested in R&D ahead of others. Analyst(s) AVIGATING AVIGATING Peter CHAN T (82) 2 6730 6128 E [email protected] N IMPORTANT DISCLOSURES, INCLUDING ANY REQUIRED RESEARCH CERTIFICATIONS, ARE PROVIDED AT THE Powered by END OF THIS REPORT. IF THIS REPORT IS DISTRIBUTED IN THE UNITED STATES IT IS DISTRIBUTED BY CIMB the EFA SECURITIES (USA), INC. AND IS CONSIDERED THIRD-PARTY AFFILIATED RESEARCH. Platform Navigating Artificial Intelligence Technology - Semiconductor│July 24, 2018 TABLE OF CONTENTS KEY CHARTS .......................................................................................................................... 4 Executive Summary .................................................................................................................. 5 I. From human to machine .......................................................................................................10 II. Mimicking the brain with today’s computing hardware ..........................................................17 III. Enabling a collaborative ecosystem of AI ............................................................................30 IV. Investment Strategy............................................................................................................55 V. Finding “gorillas” ..................................................................................................................58 Appendix A: Neuromorphic computing .....................................................................................59 Appendix B: Quantum computing .............................................................................................60 Appendix C: Memristor ............................................................................................................63 Appendix D: 5G network ..........................................................................................................66 Company Briefs .......................................................................................................................71 2 Sector Note Navigating Artificial Intelligence│Semiconductor│July 24, 2018 Global Semiconductor Get real with artificial intelligence ■ “General AI”, as exemplified by Lt. Commander Data is far from reality for a long time. ■ Our existing technologies are still incredibly inadequate even for “Narrow AI”. ■ New processors and memories are quickly sprouting to address AI applications. ■ Rewards will go to the problem solvers that have invested in R&D ahead of others. ■ We screen worthy players in the AI space by the principle of “The Gorilla Game”. No, this report is not written by an “iAnalyst” Some may cast sympathetic eyes on us, thinking the miserable equity analysts may soon be displaced by increasingly brainy machines. Those peers who care less about strategic analysis and critical thinking may be at a disadvantage competing with machines that are more efficient at looking backwards for patterns in data and newsflow. Nevertheless, machines cannot figure out something from nothing. With the available technologies, machines still have difficulty forming concepts autonomously, even very narrow concepts. Practical AI The advancement of AI is bounded by the advancement of cognitive science. We cannot mimic the brain if we do not understand it sufficiently. In fact, new computing hardware developed in recent years operate much closer to the brain than ever. We believe technologies today can enable machines to perform the cognitive functions of sensation (by sensors), concept (by deep learning), and discretionary intent (by inference). These may constitute the opportunity space of AI in the next 10 years, in our view. How do machines learn? Until now, the best-known method to teach machines a concept is “deep learning”, which mimics how the brain processes information. Deep learning is commonly accomplished through feeding massive amounts of data to train a deep neural network (DNN), which consists of a stack of discrete layers of nonlinear processing nodes. After a DNN completes its training, the learned concept could then be used for inference, which basically is the process of machine drawing statistically-sound conclusions. Off-the-shelf hardware just not good enough CPU, GPU, FPGA, and ASIC are the computing hardware commonly used for deep learning and inference today. Each has its own shortcomings in executing AI applications gracefully; similarly, every existing type of memory has its own shortcomings too. Pressured by the enormous data volume and complicated algorithms in AI applications, some interim improvements have been done to boost performance-power ratio. However, the gap between a brain and a supercomputer is still ridiculously huge today (Figure 1). Creating collaborative ecosystem of AI Engineers around the world are racing ahead to innovate new computing architectures and develop novel memories to improve the execution of AI applications. Engineers also need to upgrade communication technologies to minimise latency of data transmission and fortifying security to deal with increasing risk exposure. As these technological advancements mature and expand in scale, a collaborative ecosystem of AI may flourish together, driving a super growth cycle in the semiconductor space (Figure 6). Finding “gorillas” In retrospect, we see the technology investment theory proposed by Geoffrey Moore in “The Gorilla Game” still shining. Although the AI market is at the inception stage, leading technology companies have already invested heavily for several years. Given their abundant financial resources, huge talent pools, and strong market position, we expect big players to become even bigger, by “The Gorilla Game”. Long live Geoffrey Moore! Figure 1: Comparing the brain to a supercomputer Analyst(s) Peter CHAN T (82) 2 6730 6128 E [email protected] SOURCES: Editor (Jun 2018), Artificial Intelligence vs. Human Intelligence, educba.com IMPORTANT DISCLOSURES, INCLUDING ANY REQUIRED RESEARCH CERTIFICATIONS, ARE PROVIDED AT THE END OF THIS REPORT. IF THIS REPORT IS DISTRIBUTED IN Powered by EFA THE UNITED STATES IT IS DISTRIBUTED BY CIMB SECURITIES (USA), INC. AND IS CONSIDERED THIRD-PARTY AFFILIATED RESEARCH. EFACustomEntityStatement Platform Navigating Artificial Intelligence Semiconductor│July 24, 2018 KEY CHARTS AI is driving innovations Moore’s Law has reached its end. Process technology migration alone can no longer satisfy the performance, power, and area efficiency required to commercialise AI with economic feasibility. Engineers around the world are developing novel technologies to address the specific needs of AI applications in computing, memory, communication, and security. The AI era will be a super cycle For nearly two decades, the mobile devices ecosystem has been driving semiconductor demand growth, but now the mobile era has reached maturity. We believe AI applications will drive the next growth curve, a super cycle for the semiconductor market, lasting longer than the PC and smartphone cycles combined. The might of this super cycle may not be evident for another few years, as the associated business models and regulations also need to be ready to unleash the massive proliferation of AI applications. Much opportunities ahead We are still at the inception stage of AI. Scientists and Rule-based AI Narrow AI General AI engineers have a long way to go before they could achieve General AI, as exemplified by Lt. Command Data. Even for Unsupervis Narrow AI, many problems need to be solved before more Unsupervis Self-aware Rule-based Supervised ed context- AI applications can be commercialise to the user’s ed narrow unsupervise inference learning aware learning d learning satisfaction, and be economically feasible. For investment learning in the AI space, we should uncover the problem solvers who adopt the right strategy, invest ahead of time, and Before 2017 2017 - 2025 2025 and beyond execute successfully. “The Gorilla Game” still reigns We believe the best investment strategy for AI-related public equities still goes back to the classic quote from “The Gorilla Game”, “Hold gorilla stocks long term. Sell only when a viable substitution product emerges”, said Geoffrey Moore. SOURCES: (from the top) 1. J. Hennessy & D. Patterson (2018), Computer Architecture: A Quantitative Approach; 2. C. Handy (2015), The Second Curve: Thoughts on Reinventing Society; 3. M. Hu (Nov 2017), 4 Facts Every Exec Must Know: A.I. and Enterprise Automation; 4. G. Moore (1998), The Gorilla Game 4 Navigating Artificial Intelligence Semiconductor│July 24, 2018 Get real with artificial intelligence Executive Summary A brief history of artificial intelligence The concept of artificial intelligence (AI) began before the Dark Ages, in myths, fictions, and philosophies, though vocabularies signifying man-made
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