Adrenaline Shots for Apple AI

  • Apple has been criticized for not doing enough in AI. Two recent announcements show the company is closing the gap.
  • In the past two weeks, the company has announced the hiring of Google’s AI head, and an AI partnership with IBM.
  • Google’s AI head (John Giannandrea) brings credibility to Apple AI, critical in recruiting, and is likely work on AI-powered interfaces and Apple’s self-driving car program.
  • IBM partnership allows iOS developers access to IBM Watson’s enterprise machine learning, and use it to make smarter AI apps.

Core ML 101. At WWDC 2017 Apple unveiled Core ML, a platform that allows developers to integrate machine learning into an app. The AI model runs locally on iOS and does not need the cloud. At the time of the announcement, Apple outlined 15 domains for which they have created ML models, such as face detection, text summarization, and image captioning.

IBM Watson and Apple announcement. Two weeks ago Apple and IBM announced they will integrate IBM Watson with Apple Core ML. Previously, developers could convert AI models built on other third-party platforms, like TensorFlow (Google) or Azure ML (Microsoft) into Core ML, and then insert that model into an iOS app. Now developers will be able to use Watson to build the machine learning model, convert it to Core ML, and then feed the data back to Watson’s cloud. The reason why this is important is it allows iOS developers to leverage Watson’s capabilities and ultimately improve the AI in iOS apps.

Watson works locally on iOS and improves apps. What’s unique about Core ML is it runs locally on mobile devices, meaning it doesn’t have to send data back to a server. This is different than other mobile AI approaches. Running locally is an advantage when the speed of AI is important, like image recognition in AR or natural language processing. What’s new is Watson will be able to “teach” Core ML to run the AI model built with Watson. Basically, Watson does the hard work of getting a usable AI model built and then teaches it to Core ML, who can then run the model locally on its own. The app can then send data on the model’s performance back to Watson, at any time, to be analyzed for available improvements.

Recent history of Apple and IBM. In July 2014, Apple and IBM partnered to create enterprise applications on iOS devices, leveraging IBM’s big data and analytics and Apple’s hardware-software integration. IBM started selling iPhones and iPads to clients that came with software and applications for enterprise designed with Apple’s help.

Summary of big tech’s machine learning services. 

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. From time to time, we will write about companies that are in our portfolio.  Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

Apple’s AI Coup

  • Apple has hired John Giannandrea who formerly served as Google’s head of AI and Search.
  • Given the industry’s shortage of AI talent, Giannandrea brings expertise along with credibility, critical in recruiting.
  • Giannandrea will likely be working on AI-powered interfaces that will replace the touchscreen and iOS, like augmented reality wearable. Separately, AI related to Apple’s self-driving car program (PAIL) will likely fall under Giannandrea.

What this means for Apple, recruiting more AI talent. It’s a win. Talent follows talent, and John Giannandrea will no doubt help to build Apple’s AI brand and enhance future recruiting efforts. His shared vision on privacy is good news for a company who claims to be the vanguard of user security. In the meantime, Google will maintain its strength in AI, given they are still an “AI first company” and have tremendous AI and deep learning horsepower with their Google Brain and DeepMind teams. Jeff Dean, the founder of Google Brain, has taken over as the head of their AI department in a “reshuffling” making AI a more central part of their business. Will Google employees follow in Giannandrea’s footsteps? There will probably be a few, but the competition is fierce, and this will not be the last major AI trade.

Why did Giannandrea come to Apple? Most likely – projects, pay, and privacy. As one of the most senior experts in arguably the most in-demand field in the world, the conversation around compensation was probably short. Giannandrea may be given freedom to work on projects he is more passionate about and have the chance to build something new. In an email obtained by the New York Times, Cook praised Giannandrea saying, “John shares our commitment to privacy and our thoughtful approach as we make computers even smarter and more personal. Our technology must be infused with the values we all hold dear.” That affinity for privacy may have steered him to Apple at a time when concerns have never been higher.

What will he do? It’s easy to think about how Google uses AI (search, image rec., voice, etc.) but Apple’s use cases are more abstract. If you consider the user interfaces that will replace the touchscreen and iOS, like augmented reality wearables, it becomes more clear why AI is critical. Just as multi-touch was a core technology enabling the iPhone, AI will be a core technology enabling the operating systems of the future. For example, wearables like AR glasses or even AirPods will heavily rely on AI-driven functionality like image recognition, ambient listening, and smart notifications. In other words, these devices need to know what you want and when you want it. With our phones, we directly control the information that we want when we want it; in the future of computing, AI will anticipate the same information. We expect Giannandrea to address these opportunities as well as bolster Apple’s overall AI prowess, overseeing AI initiatives like Siri, Core ML, and the deliberately under-the-radar autonomy project.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. From time to time, we will write about companies that are in our portfolio.  Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

AI Is Pulling Off Its Own March Madness Upset

In what is becoming an annual tradition, we entered a bracket chosen by artificial intelligence into the Loup Ventures March Madness pool. (See our 2017 results here) Given this year has proven especially challenging to predict due to the uncanny number of upsets, Microsoft Bing Predicts’ bracket is in surprisingly good shape. Heading into the Final Four this weekend, it is tied for 2nd place out of 13 entrants. Here’s a look at its picks.

Results. To date, Bing has chosen 38 out of 64 games correctly, including the opening round. Bing was 3 of 4 in the opening round, 22 of 32 in the 1st Round, 8 of 16 in the 2nd Round, 3 of 8 in the Sweet Sixteen, and 2 for 4 in the Elite Eight.

It’s also important to note that if you look at Bing’s bracket now, it will show a different story because it re-picks winners for matches after each round. Even with this adjustment, it only picked 48 of 64 games correctly (75%, compared to 2017’s 72%) leading into tonight’s games.

Here are Bing’s results this year compared to last:

In 2018, Bing performed slightly worse in the 1st, 2nd, and Sweet Sixteen rounds. More importantly, Bing correctly picked 2 of the Final Four teams – no small feat for anyone, man or machine.

Methodology. The Bing Predicts algorithm factors in millions of data points in an effort to create the best predictive model. The algorithm looked at every college basketball game played in the last 16 years in an attempt to analyze correlations between measurable statistics and wins. The algorithm will give an output of the likelihood a team will win the game. It’s not meant to choose a certain winner, but the higher the percentage, the greater the disparity amongst the teams.

Walter Sun, an architect of the Bing Predicts algorithm, was asked by Wired Magazine about some of the important considerations in the algorithm. Defensive efficiency, strength of schedule, coaching rankings, and miles traveled were a few of the metrics that the algorithm measures.

Who will win the NCAA Championship? Below are the algorithm’s estimations for this weekend’s matchups.

Bing pits Michigan vs Villanova in the National Championship game on Monday night. This year has stumped even college basketball fanatics, and the AI bracket is no exception. At the end of the day, the algorithm is predicting the percentage chance each team has to win the game. It may be entirely accurate, but if Villanova has a 69% chance of winning, they also have a 31% chance of losing – thus is the nature of predicting something binary like the outcome of a sports game. What’s interesting, however, is that Bing picked these two teams to meet in this round at the beginning of the tournament with Kansas winning. Looking at it now, the algorithm gives Villanova the higher probability of winning. That’s quite the change of heart.

After a last-place finish in our pool last year, Microsoft’s algorithm has performed much better against us, whether by luck or by real improvement. If Kansas wins the NCAA tournament, there is a chance the AI bracket could win our pool. We’ll be watching this weekend to see if man will prevail over machine for another year.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. From time to time, we will write about companies that are in our portfolio.  Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

How To Think About Recent Volatility in Tech

Market decline does not change the mega growth opportunities. The heart rate of the market increased the past week because of fears of a trade war, Facebook data privacy, and broken market technicals, but the health of the market is unchanged and the health is good. Core underlying tech trends including artificial intelligence, robotics, big data, and autonomous transportation, will support continued growth.

Hold tech for the long-term. We believe that tech is essentially taking over the rest of the economy; therefore, investors should hold tech long term. Just as every company is now an internet company to some degree, we believe that eventually every company will be an AI company.

Market undervalued. From a valuation perspective, our view is undervalued. The market has rallied back to the old highs, but the S&P is up only 3% per year over the past 17 years, compared to the previous 17 years (1983-2000) when it was up 17% per year.

Putting the size of tech into perspective. The tech sector’s growing clout is not just a U.S. story. Tech stocks have become so dominant in emerging markets that for the first time since 2004, the industry last year overtook finance as the biggest sector in the MSCI Emerging Markets Index. Tech had a 28% weighting near the end of 2017, more than double its level six years ago, according to data provided by MSCI. Facebook, Amazon, Netflix Inc. and Alphabet together account for a 7.8% weighting in the S&P 500, more than double from five years ago.

Company Updates:

Tesla. We remain positive on TSLA. Shares are down 20% in the past month mostly due to fears of another miss in Model 3 production. The recent stock dive is due to a combination of a Model X accident that is being investigated, Waymo’s partnership with Jaguar, which legitimizes a key competitor (the I-Pace electric SUV), growing concern among all companies testing self-driving vehicles amid the Uber fatality, and news that Moody’s has downgraded Tesla’s bonds to B3 from B2, citing significant shortfall in the Model 3 production rate and a tight financial situation. We continue to believe the Tesla story has the best risk-reward among tech companies over the next 5 years.

  • Model 3 production. We’re expecting another miss in Model 3 production in the March quarter but that does not change the story. There is more demand than supply for the Model 3 (about 400k preorders which is unheard of in automotive). It might take a year, but eventually, Tesla will get the Model 3 production right, and ramp output.
  • Model X accident. We see the recent Model X accident the same as accidents with gas cars. It is unlikely that the battery or Tesla’s advanced cruise control “autopilot” were to blame. Tesla disclosed that the autopilot feature properly functions 200 times a day on the same stretch of road where the accident happened.

Facebook. Limited upside to FB. Given the privacy issues, for the first-time advertisers have to think about Facebook as a liability. Separately, it’s unclear about how the recent privacy changes will impact Facebook’s ability to make money.

Nvidia. We remain positive on NVDA. Shares of NVDA dropped 11% in the past week following the announcement that they temporarily stopped autonomous testing, and in part because of the broader market sell off. While the company did not comment on timing, we expect testing to resume in the next 3 months. The big picture is the company is well positioned to capitalize on four mega trends, AI, autonomous cars, gaming, and blockchain through their dominance of GPU processors.

Apple. We remain positive on AAPL. Concern is emerging that iPhone demand in June will fall below Street expectations. We think iPhone demand over the next two quarters is not important to the story. What’s important is the share buyback, services, and the next iPhone.

  • Share buyback. Apple can add 4% per year to the stock price (assuming they use $40B of the $55B they generate in cash each year to buy back stock). Apple will give an update on the share buyback when they report the March quarter, likely late in April.
  • Bigger screen iPhone this fall. We expect Apple will announce a 25% bigger phone in the fall. This will be a positive for unit demand and average selling price.
  • Services. Services account for about 15% of revenue and are growing at 15-20% year over year. We believe this segment will continue to grow at a 15% or better rate over the next five years. This is important because the earnings multiple on shares of AAPL will likely increase as investors view the predictability of services are more attractive.

Google. We remain positive on GOOG. We expect the next six months to be rough for shares of GOOG as questions emerge about how the company uses data. Despite that negative potential, Google is too tightly woven into the fabric of the internet. The company is one of the best ways to invest in AI, given the company has a stated their intention to move from a mobile-first company to an AI-first company over the next several years. Lastly, the company has a stake in Waymo, the leading autonomous car company. We expect years of positive news to come from Waymo.

Amazon. We remain positive on AMZN. The company is best positioned for the future of retail. We see that future as a combination of both online and offline retail. Online sales account for about 15% of global retail, and in the future, we believe it will eventually reach 55% of sales. We also expect Amazon to do more with physical retail locations and we continue to believe the company will eventually acquire Target (TGT). The company’s AWS web hosting business is only 15% of revenue, but it is growing at greater than 30% for the next several years.

Twitter. Limited upside to TWTR. About 14% of Twitters 2017 revenue came from selling data, growing at 18% y/y, compared to Twitter’s ad business that declined by 6%. Selling private data is a toxic label, and this could limit the upside to shares over the next year.

Disclaimer: We actively write about the themes in which we invest: virtual reality, augmented reality, artificial intelligence, and robotics. From time to time, we will write about companies that are in our portfolio.  Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make. 

Nvidia; Buy More GPUs, Save More Money

  • NVDA shares dropped 8% today on the announcement they temporary stopped autonomous testing and market sell off.
  • While the company did not comment on timing, we expect testing to resume in the next 3 months.
  • CEO Jensen Huang held a keynote today. The message was more GPU’s save money.
  • The company announced 5 new products targeted at AI, autonomy, and VR.

Nvidia started its GPU Technology Conference on Monday with their focus on AI & Deep Learning. On Tuesday, CEO Jensen Huang gave a keynote on Nvidia’s latest developments in a few key product categories, but Nvidia’s stock dropped by 8%. We attribute 2/3 of the decline to the company announcing they’ve temporary stopped autonomous vehicle testing until they receive a diagnostic report from the Uber accident last week, and 1/3 from the broader tech sell off.

Buy more GPUs, save more money. The message from Jensen Huang was if you buy Nvidia’s GPUs, you can save money. The idea is using an Nvidia architecture requires less hardware that consumes less energy.  That said, users of these systems are solving more advanced problems like AI, which will require an increase in total spending. We remain comfortable with our Nvidia revenue growth estimates of 31% in CY18 and 21% in CY19.

New Products. Nvidia made product announcements today:

  • RTX (AI and blockchain). On the workstation front, the company announced the Quadro GV100 with (real-time ray tracing) RTX Technology. The RTX technology offers a measurable improvement in rendering times.
  • DGX-2 (AI and blockchain). Nvidia also announced the DGX-2 platform for AI. The DGX-2 claims to be the first to offer 2 Petaflop single server deep learning system. Nvidia compared this platform to a traditional hyperscale center, which would have 300 dual-core CPUs and cost about $3M. The DGX-2 is may seem expensive at $399K, but it takes up 1/60th of the space and consumers 1/18th of the power.
  • Isaac SDK (AI) Adding artificial intelligence to robots for perception, navigation, and manipulation. Nvidia showed a video of one of its first Isaac projects, a two-wheeled delivery robot named Carter.
  • Drive Orin (Autonomy). Drive Orin is comparable to two Drive Pegasus supercomputers while being smaller in physical size. Jensen shared that many customers were including two Drive Pegasus chips in each vehicle, and it made sense to package the same computing power into one product.
  • Drive Constellation (Autonomy). This is a datacenter solution used to test and validate self-driving vehicles in virtual reality.
  • Project Wakanda (VR). See details below.

Nvidia committed to an autonomous future. Given the Tempe accident, Jensen spent most of the self-driving segment of the keynote talking about the importance of safety, and why fully-autonomous future means safer transportation. He also reiterated this his belief that self-driving cars are “probably the hardest computing problem that the world has ever encountered”.  The company did not give a timeline of when testing will resume, but given the hold is based on the Uber review, we would expect it to take a few months. Autonomous simulation will play an increasingly important role in the future. Drive Constellation can run thousands of virtual worlds, each while running thousands of scenarios in order to collect more data. For example, 10,000 constellations can simulate about 3 billion miles in a year, significantly more than 5-10 millions driven each year by the current fleets of test vehicles.

Project Wakanda hints at the future of relationship between man and machine. Nvidia closed the keynote by unveiling what has been dubbed “Project Wakanda.” Similar to what we saw in Black Panther, Jensen showed a driver operating a vehicle in virtual reality, while sitting in a fully-equipped cockpit. Next, a third screen appeared and showed a driverless car at a remote location.

While Nvidia didn’t share much about its direction with this project, Jensen did share that he views virtual reality as a way to provide humans with teleportation – augmented with autonomous machines.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. From time to time, we will write about companies that are in our portfolio. Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.