Seeing The Road Ahead: The Undervalued Self-Driving Asset of Data

This is the 4th in a series of notes based on our deep dive into computer perception for autonomous vehicles. Autonomy is a question of when? not if? In this series, we’ll outline our thoughts on the key components that will enable fully autonomous driving. See our previous notes (Computer Perception Outlook 2030What You Need To Know About LiDAR, The Importance of Cameras To Self-Driving Vehicles).

A self-driving car will only be as strong as the data it trains on.

The importance driving data will play in developing fully autonomous vehicles is often understated, and companies that possess large and high-quality driving data sets are much further ahead of their peers than some may think. Driving data is key because it is the core input that will train the artificial intelligence models that operate autonomous vehicles. The more data these models have, the more scenarios they can prepare for, and, in turn, the stronger the entire system becomes. However, obtaining good driving data is not an easy task, and it is virtually impossible to gather data on every single driving scenario in all types of weather conditions. Due to improvements in computer graphics technology, many are relying on simulated data to train their self-driving models.  In the paragraphs below, we dive deeper into the pros and cons of using simulated data versus real data and identify who are the data leaders among self-driving car companies.

Simulation vs. real data. We recently spoke to a computer vision expert at the University of Michigan about the difference between using simulated data versus real-world data. He believes that simulated training data is valuable because most of the data collected during on-road driving is innocuous. Real-time driving data is only very interesting when there is a critical event or an unusual scenario. Simulation data lets you test on critical events constantly. However, he also noted the AI in simulated data is still programmed by a human and those AI tend to act differently than humans on the road. All autonomous systems will train with some simulated data and will therefore require fine tuning to factor in the difference between human and machine driving styles. That said, we do not want to underestimate the value of real data and believe capturing non-programmed scenarios will play a key role in preparing AVs for all situations that may arise.

Waymo’s large data lead.  The more miles an autonomous vehicle drives, the more real data the system can capture, the more robust the system can become. Companies that are approved to test autonomous driving in California are responsible for recording and publishing the number of autonomous miles driven. As of April 1st, 52 companies have been issued permits to test autonomous vehicles in California, and as shown in the graph below, Waymo has driven 352,545 as of November 30th, 2017. As of February 2018, Waymo had announced they have driven over 5 million miles in total. This announcement came only ~3 months after they announced crossing the 4-million-mile mark. While testing takes place in many states other than California, these data points suggest that Waymo has a very large data lead over their peers, which may translate to a large lead in the race to full autonomy.

Tesla lurking in the shadows. While Tesla is one of the 52 companies approved to test autonomous vehicles in California, Tesla did not test on state roads in 2017. However, the company acknowledged in the report they filed with California DMV that Tesla conducts testing to develop autonomous vehicles via simulation, in laboratories, on test tracks, and on public roads in various locations around the world. Tesla also highlighted that they have a fleet of hundreds of thousands of customer-owned vehicles that test autonomous technology in “shadow-mode” during their normal operation. Shadow mode is a feature that runs in the background without actuating vehicle controls in order to provide data on how the features would perform in real-world and real-time conditions. This has allowed Tesla to gather billions of miles of passive real-world driving data to develop its autonomous technology. This data is extremely valuable in training autonomous vehicles to interact with the real world, and, in our eyes, makes Tesla one of the top contenders in the race for full autonomy.

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.

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.

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. 

Smart Speaker Satisfaction High, but It’s Early Days

We recently surveyed 520 US consumers about smart speakers and found that 89% of respondents were satisfied with them. A closer look at the results reveals the reason for this high satisfaction; early use cases are simple (Music, weather, general questions). While questions remain simple today, we expect what users demand from their smart speakers to become more complex. The survey covered smart speaker ownership, satisfaction levels, and common uses. Here are the key takeaways:

  • 31% of respondents own a smart speaker.
  • Amazon Echo dominates the market with 55% share, followed by Google Home at 23%. See more below.

  • 89% of smart speakers owners are either satisfied (59%) or very satisfied (30%) with their device.
  • Music, weather, and general knowledge questions dominate smart speaker usage. See more below.

In line with expectations. At roughly 1/3 of the U.S. population, smart speaker penetration is in line with our current estimations. Other than Cortana being slightly over-represented and Echo being slightly underrepresented, we believe the market share in the survey data also resembles the current landscape. In terms of smart speaker use cases, our survey finds the most common activities to be listening to music, getting the weather, and asking general questions. This is consistent with studies like the one from Quartz here.

It comes as no surprise, consequently, that 89% of respondents were satisfied or very satisfied with their smart speakers. This is due in large part to the relatively simple tasks that the majority of users demand of their devices. For example, Cortana scores a 57% on our comprehensive smart speaker test. On a standard report card, this is a failing grade, but Cortana is well suited to play your music from Spotify, tell you the weather, and answer any simple question you have, so it’s easy to see why the typical user would be plenty satisfied. Put simply, people aren’t using their smart speakers for anything all that smart. But we expect that to change.

Changing use over time. The top use cases for smart speakers today make sense because they are well defined and they work consistently. Benedict Evans put it well in a blog post early last year: “You can now use an audio wave-form to fill in a dialogue box – you can turn sound into text and text into a structured query, and you can work out where to send that query.” This works really well for simple ‘google-able’ questions or fetching info from a weather app, but as the use cases broaden, it is not always clear where to send a query. Just because calling up a Spotify playlist is a well built-out process doesn’t mean the same is true for a YouTube video or Podcast. It takes a huge amount of human time and energy to make these processes run smoothly. AI assistants are a new technology, so this is not a long-term concern, but until the voice ecosystem is more robust, users will have to settle for somewhat simple use cases.

The reason we are excited about smart speakers, however, involves the much wider use of voice as a computing input to remove friction. We believe the preferred interface for countless smart home devices and software services is not countless apps or small touchscreens, but your voice. This will involve drastically increasing the number of defined places you can send those queries and the number of connected devices in your life. Music, weather, and general questions won’t go away, but other activities will increasingly take place via voice. The desire for the voice interface is there. Smart speaker adoption is outpacing that of the smartphone, and the majority of users say they wouldn’t want to go back to their life without their smart speaker. We think it’s only a matter of time until voice cements itself into our everyday lives.

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.