Thought some of you might be interested in this, reads very well for Apple’s future if it’s even partially correct……
Rule on……. John > > https://qz.com/1003672/what-apples-silicon-business-might-have-to-do-with-its-machine-learning-desires/?utm_source=YPL&yptr=yahoo > > <https://qz.com/1003672/what-apples-silicon-business-might-have-to-do-with-its-machine-learning-desires/?utm_source=YPL&yptr=yahoo> > > Apple just created the largest installed base of AR-capable devices > Written by Jean-Louis Gassée <data:#> Editor, Monday Note > At last week’s Worldwide Developers Conference (WWDC), Apple made an > unusually large number of hardware and software announcements. Today we’ll > look at a potential connection between Apple’s silicon design strengths and > its just-unveiled augmented reality and machine learning applications > development tools. > > When Apple introduced its 64-bit A7 processor > <https://en.wikipedia.org/wiki/Apple_A7> in Sept. 2013, they caught the > industry by surprise. According to an ex-Intel gent who’s now at a > long-established Sand Hill Road venture firm, the competitive analysis group > at the imperial x86 maker <https://en.wikipedia.org/wiki/X86> had no idea > Apple was cooking a 64-bit chip. > > As I recounted in a Sept. 2013 Monday Note titled “64 bits. It’s Nothing. You > Don’t Need It. And We’ll Have It In 6 Months > <https://mondaynote.com/64-bits-it-s-nothing-you-don-t-need-it-and-we-ll-have-it-in-6-months-1d394641e97a>,” > competitors and Intel stenographers initially dismissed the new chip. They > were in for a shock: Not only did the company jump to the head of the race > for powerful mobile chips, but Apple also used its combined control of > hardware and software to build what Warren Buffett refers to as a wide “wide > moat > <https://signalvnoise.com/posts/333-warren-buffett-on-castles-and-moats>”: > > In days of old, a castle was protected by the moat that circled it. The wider > the moat, the more easily a castle could be defended, as a wide moat made it > very difficult for enemies to approach. > > The industry came to accept the idea Apple has one of the best, if not the > best, silicon design team; the company just hired Esin Terzioglu > <http://fortune.com/2017/05/30/apple-qualcomm-esin-terzioglu/>, who oversaw > the engineering organization of Qualcomm’s core communications chips > business. By moving its smartphones and tablets—hardware and software > together—into the 64-bit world, Apple built a moat that’s as dominant as > Google’s superior Search, as unassailable as the aging Wintel > <https://en.wikipedia.org/wiki/Wintel> dominion once was. > > I think we might see another moat being built, this time in the fields of > augmented reality <https://en.wikipedia.org/wiki/Augmented_reality> (AR), > machine vision <https://en.wikipedia.org/wiki/Machine_vision> (MV), and, more > generally, machine learning <https://en.wikipedia.org/wiki/Machine_learning> > (ML). > > At last week’s WWDC <https://developer.apple.com/wwdc/>, Apple introduced > ARKit (video here <https://developer.apple.com/videos/play/wwdc2017/602/>), a > programming framework that lets developers build augmented reality into their > applications. The demos (a minute into the video) are enticing: A child’s > bedroom is turned into a “virtual storybook”; an Ikea app lets users place > virtual furniture in their physical living room. > > As many observers have pointed out, Apple just created the largest installed > base of AR-capable devices. There may be more Android devices than iPhones > and iPads, but the Android software isn’t coupled to hardware. The wall > protecting the massive Android castle is fractured. Naturally, Apple was only > too happy to compare the 7% of Android smartphones running the latest OS > release to the 86% of iPhones running iOS 10. > > Apple also introduced CoreML > <https://developer.apple.com/documentation/coreml>, an application framework > that integrates “trained models > <https://developer.apple.com/documentation/coreml/converting_trained_models_to_core_ml>” > into third-party apps. Unlike with ARKit, there were no fun CoreML demos; > CoreML implementations will be both “everywhere” and less explicit than AR. > Architecturally, CoreML is a foundation for sophisticated machine vision and > natural language processing apps: > > > Courtesy: Apple Developer Documentation. (Provided by author) > As far as we know, all of this runs on the Ax processors in recent iPhones > and iPads. But a recent, unsubstantiated Bloomberg rumor has aroused > interest: “Apple Is Working on a Dedicated Chip to Power AI on Devices > <https://www.bloomberg.com/news/articles/2017-05-26/apple-said-to-plan-dedicated-chip-to-power-ai-on-devices>.” > > Auxiliary chips that run to the side of the main processor, dedicated to a > specific set of operations, have (almost) always existed. The practice > started with FPUs <https://en.wikipedia.org/wiki/Floating-point_unit> > (Floating Point Processors). High-precision Floating Point > <https://en.wikipedia.org/wiki/Floating-point_arithmetic> operations, mostly > for scientific and technical applications, demanded too much from the main > CPU <https://en.wikipedia.org/wiki/Central_processing_unit> (central > processing unit), slowing everything down. Such operations were offloaded to > specialized, auxiliary FPUs. > > Later, we saw the rise of GPUs > <https://en.wikipedia.org/wiki/Graphics_processing_unit>, graphic processing > units, dedicated to demanding graphics operations, simulations, and games. > Because of the prevalence of graphics-intensive apps and the demand for > reactive, no-lag interactions, GPUs are now everywhere, in PCs, tablets, and > smartphones. Companies such as Nvidia <https://en.wikipedia.org/wiki/Nvidia> > made their name and fortune building a range of high-performance GPUs. > > These are impressive machines. Stripped of the logic (transistors) needed for > the complex logic operations of general purpose CPUs, GPU hardware resources > are dedicated to a narrow set of tasks performed at the highest possible > speed. Think of a track car with none of the amenities of a road vehicle, > running much faster but unsuited for everyday road and city use. > > Such was the performance of GPUs that some financial institutions > experimented with machines that harnessed hundreds of GPUs in order to > execute complex predictive models in near real-time, giving them a putative > trading advantage. > > A similar thought arose with the need to run complicated convolutional neural > networks <https://en.wikipedia.org/wiki/Convolutional_neural_network> and > related ML/AI computations. This led Google to design its TPU > <https://en.wikipedia.org/wiki/Tensor_processing_unit> (tensor processing > unit) to better run its TensorFlow algorithms > <https://en.wikipedia.org/wiki/TensorFlow>. > > Back to the dedicated AI chip rumor: It’s an attractive story that comes from > a source (Bloomberg) that has a record of eerily accurate predictions mixed > with a few click-baiting bloopers. > > Let’s indulge in a bit of speculation. > > Tentatively dubbed Apple Neural Engine (ANE), this hypothetical chip fits > well with Apple’s tradition of designing hardware for its software, following > Alan Kay’s <https://en.wikiquote.org/wiki/Alan_Kay> edict: “People who are > really serious about software should make their own hardware.” > > Couple Apple’s AR and ML announcements with the putative ANE chip and we have > an integrated whole that sounds very much like the Apple culture and silicon > muscle we’ve already witnessed, a package that would further strengthen the > company’s moat, its structural competitive advantage. > > It’s an attractive train of thought, although possibly a dangerous one, in > the vein of “It’ll work because it’d be great if it did.” That perfunctory > precaution taken, I’d give it more than an even chance of becoming reality in > some form. Or your Monday Note subscription money back. > > This post originally appeared at Monday Note > <https://mondaynote.com/apple-silicon-and-machine-learning-1ea34ccab246>. > Learn how to write for Quartz Ideas > <https://qz.com/635686/the-complete-guide-to-writing-for-quartz-ideas/>. We > welcome your comments at id...@qz.com <mailto:id...@qz.com>.
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