HomePod Uniquely Positioned to Win Smart Speaker Market

In December, Apple will ship HomePod, a smart speaker with a unique focus on music. Don’t be fooled, however, by HomePod’s music-focused marketing; Apple has a grander vision than delivering a better sounding Echo. The company is making Siri a ubiquitous, ambient presence that connects and controls all your connected devices and services – and making a leap forward in the transition to voice-first computing.

The importance of natural language interface. The way humans interact with computers is changing. Today, we use our keyboards, mice, and touchscreens to interact with computers. In the future, we’ll simply rely on our voice, gestures, or even our thoughts. In the near-term, voice is quickly becoming a preferred interface. At Google I/O in may, CEO Sundar Pichai said that 20% of mobile queries are now made via voice search. Moreover, 42% of people in a MindMeld study said they have started using voice commands in the last six months. Natural language as a computing input is not only a more natural way to interact with our devices, but it can also be remarkably more efficient. When typing or clicking, users will be very brief, leaving the computer with little information to act on. Asking a verbal question, however, allows for more involved queries with which a machine can much more easily determine intent and deliver more specific information. This is one area in which Siri excels. Siri is able to process commands with multiple steps, such as, “make a note called Slide 4 in my Presentation Notes folder that says: change transition.” Users will also be able to say, “send directions to Steve’s house to my phone,” or, “turn on the TV and play the newest episode of Westworld.”  These functionalities are not unique to Siri, but Apple’s seamlessly integrated ecosystem of devices puts them in a position to employ voice-first computing in ways their competitors can’t match.

SiriKit is important for the future of the HomePod. Siri, which will be the AI brains inside HomePod, has recently extended an olive branch to developers with the introduction of SiriKit. SiriKit allows third-party developers to add voice capabilities to their apps. Consumers will be able to do a lot more with Siri than set a timer or ask for the weather. As Apple’s vibrant community of developers works to integrate voice into third-party apps, users will be able to get real work done with verbal inputs, marking a turning point in voice-first computing. However, lining up Siri against Alexa and Google Home reveals measurable gaps in ability early on – but the Siri we have come to know on our iPhones and the upcoming Siri that lives in HomePod with third-party integrations are two very different animals.

How does HomePod stack up? The smart speaker market has undergone impressive growth and rapid adoption in recent years, growing 62% in 2016 alone. When you use one, it’s easy to see why – the verbal interface is very natural and serves as a clear glimpse into the future of our interactions with computers. Meanwhile, this market continues to be flooded with products from new entrants, and from the continuing dominance of Amazon and Google.

Amazon’s Echo, released in November of 2014, costs $179. The Echo is part of a broader family of devices that also includes the Echo Show, Dot, Tap, and Look, each with their own distinctive features and price tags. Alexa-enabled devices command over 70% of the smart speaker market. Between licensing Alexa’s software to third-party hardware manufacturers, Amazon’s aggressive sales and marketing efforts, and allowing developers to augment user experience with Alexa Skills, the Echo family has solidified itself as the de facto voice platform of today. Alexa Skills, which integrate voice capability into an expansive range of third-party applications, are Amazon’s number one advantage going forward. While Amazon remains the market leader today, the sustainability of Amazon’s dominance comes into question going forward without an existing base of integrated phones.

The Google Home, which came along a full two years after Alexa, costs $129 and has a 24% market share. Google has opened its voice platform to various third parties, but does not have the exposure of Alexa Skills, or SiriKit. Google’s natural language processing, which is reported on extensively and tested in our research, is best in class, and may propel Google to the forefront of voice-first computing in the coming years.

In the small sliver of market share that remains, numerous alternatives have entered, hoping not to be left behind as voice becomes a major computing interface. Some prominent recent and upcoming entrants include the Alibaba Tmall Genie, Lenovo Smart Assistant (powered by Alexa), Harmon Kardon Invoke (powered by Cortana), and Samsung’s Bixby Assistant. With the underlying technology in its fledgling days, early leaders and laggards are bound to appear, but the core offerings remain fundamentally similar.

Apple is well-positioned for long-term success. As the technology improves, which our research suggests can happen quickly, competitors will converge, and the long-term winner will be the product that provides its user with a heightened experience and improved efficiency. We believe Apple is uniquely positioned to do so, as Apple’s device ecosystem delivers a frictionless experience, which will only get better with the adoption of voice-first computing.

Apple’s device ecosystem delivers a frictionless experience, which will only get better with the adoption of voice-first computing.

Interestingly, Apple has included an A8 chip in its HomePod, the same chip included in an iPhone 6. The A8 chip is much more powerful than the chips competing home assistants run on, which poses the question: what else is Apple planning with the HomePod?

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 Core ML Brings AI to the Masses

AI is one of the core themes on which we focus at Loup Ventures. And as analysts, we heard Google, Facebook, Amazon, and Apple emphasizing their focus on AI over the last several years. Google CEO Sundar Pichai has commented on each of the past three Google earnings calls that Google is transitioning from mobile-first to AI-first. Facebook has recently spent a lot of time and resources developing chat bots on its platform, and has utilized AI to create a better news feed, and improve photo recognition. Amazon uses AI extensively with recommendations, and is integrating third-party AI models into AWS. While Google, Facebook and Amazon are each making significant progress as it relates to AI, it’s worth noting that Apple was the first company of the four to embrace it.

Apple’s AI roots date back to the mid 1990s with handwriting recognition on the Newton. In June Apple announced Core ML, a platform that allows app developers to easily integrate machine learning (ML) into an app. Of the estimated 2.4m apps available on the App Store, we believe less than 1% leverage ML today – but not for long. We believe Core ML will be a driving force in bringing machine learning to the masses in the form of more useful and insightful apps that run faster and respect user privacy.

Apple’s history in ML. Apple’s history in ML dates back to 1993 with the Newton (a PDA Apple sold from 1993 to 1998) and its handwriting recognition software. While not a complete list, Apple has since used AI in the following areas:

  • Facial recognition in photos
  • Next word prediction on the iOS keyboard
  • Smart responses on the Apple Watch
  • Handwriting interpretation on the Apple Watch
  • Chinese handwriting recognition
  • Drawing based on pencil pressure on the iPad
  • Extending iPhone battery life by modifying when data is refreshed (hard to imagine that our iPhone batteries would be even worse if not for AI)

Core ML. Core ML was announced at Apple’s June 2017 WWDC conference. It’s a machine learning framework that sits below apps and third-party domain specific AI models, but above processing hardware inside of a Mac, iPhone, iPad, Apple Watch, or Apple TV.

Source: Apple

Core ML allows app developers to easily incorporate third-party AI models into their apps. App developers don’t need to be experts in AI and ML to deliver an experience powered by AI and ML within their app. In other words, Apple will take care of the technical side of incorporating ML, which allows developers focus on building user experiences.

At WWDC, Apple outlined 15 ML domains that can be converted to work on apps:

  • Real Time Image Recognition
  • Sentiment Analysis
  • Search Ranking
  • Personalization
  • Speaker Identification
  • Text Prediction
  • Handwriting Recognition
  • Machine Translation
  • Face Detection
  • Music Tagging
  • Entity Recognition
  • Style Transfer
  • Image Captioning
  • Emotion Detection
  • Text Summarization

What’s different when it comes to ML between Apple vs. Android? Google provides developers with TensorFlow compiling tools that make it easy for Android developers to integrate ML into their apps. Developer blogs suggest that Core ML makes it easier to add ML models into iOS apps, but we can’t compare the comparative ease of adoption. However, we can say they are different when it comes to speed, availability, and privacy.

  • Speed. ML on Apple is processed locally which speeds up the app. Typically, Android apps process ML in the cloud. Apple can process ML locally because app developers can easily test the hardware running the app (iOS devices). In an Android world, hardware fragmentation makes it harder for app developers to run ML locally.
  • Availability. Core ML powered apps are always available, even without network connectivity. Android ML powered apps can require network connectivity, which limits their usability.
  • Privacy. Apple’s privacy values are woven into Core ML; terms and conditions do not allow Apple to see any user data captured by an app. For example, if you take a picture using an app that is powered by Core ML’s vision, Apple won’t see the photo. If a message is read using an app powered by Core ML’s natural language processor, the contents won’t be sent to Apple. This differs from Android apps, which typically share their data with Google as part of their terms and conditions.

AI for the masses. In the years to come, iPhone users updating their favorite apps will experience a step function improvement in utility, but may never know that Core ML is behind the curtain making it all possible. We can all look forward to continually improving apps thanks to Core ML.

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.

Faceoff: Amazon Echo Show vs Google Home Part II

As a part of our continuing efforts to understand the ways and speed at which artificial intelligence enters our everyday lives, we reexamined two home assistants based on a study we performed in February.  The two most popular assistants, Google Home and Amazon Echo were put to the test, this time substituting the Echo with the Echo Show, which includes a 7″ touchscreen.

Methodology. For this experiment, we asked the same 800 queries of both the Echo Show and Google Home, similar to our first study. We graded the queries on two metrics:  First, did the device understand what we asked correctly? Second, did the device answer the query correctly? In our study, Amazon’s Echo Show understood 95.88% of the queries we asked and answered 53.57% of all queries correctly. Google Home understood 94.63% of the queries we asked, but was able to answer 65.25% correctly. Below, you can see the improvements that each home assistant made since our last set of queries.

One advantage the Amazon Echo Show has when it comes to understanding queries is that we have the ability to confirm the data using Amazon’s companion app.  This app gives the user a live feed of what Amazon Echo Show heard.  Google Home does not offer a transcript of what it’s home assistant device picked up.  Because of this, it was difficult to tell if Google Home understood the queries but couldn’t answer them, or if it truly had a harder time understanding queries. Since we were unable to see exactly how well Google Home understood our queries, we assumed that if Google Home responded that it was unable to perform a certain function, then it had understood the query correctly.  For example, if we asked, “Hey Google, send a text to John” and received a response “Sorry, I can’t send texts yet,” then the query would be marked as understood correctly, but answered incorrectly.

Results. Both home assistants showed increased performance across the board.  This time the Google Home outperformed the Echo in total number of correct answers by nearly 12 percentage points, up from a 5 point performance gap in our February results.  While each digital assistant has its strengths and weaknesses, Google Home outperformed its rival in 3 of the 5 query categories by a surprising margin.  This is significant because it shows not only rapid improvement, but outperformance of Amazon who has both a 2-year head start and a near 70% market share vs. Google’s 24% share of the home assistant market, according to eMarketer.

Both Home Assistants Notably Improved in Navigation. The most dramatic increase for both assistants was in navigation. In February, over 90% of navigation questions were answered with: “I can’t help you with that.” Today, navigation is the best category for both the Google Home and the Echo Show, with the Google Home answering 92% of queries correctly, and the Echo Show answering 63% of queries correctly.

Echo Show: Screen adds to experience, but software upgrades drive improvement. The Echo Show’s camera and touchscreen allow it to make video calls, monitor your security cameras, visually display some forms of information, and introduces new use cases with Alexa Skills that incorporate a screen. For instance, you can say, “Alexa, show me the trailer for the new Spiderman movie,” or scroll through recommendations for local pizzerias. While this adds to the user experience, the addition of the screen itself isn’t driving all of the improvement that we are seeing with Alexa. Instead, numerous software updates have increased the way Alexa can contribute to our daily lives. The Echo Show had a near 20% improvement in its ability to answer both local questions (“Where can I find good barbecue?”), and respond to commands (“Cancel my 2:00 p.m. meeting tomorrow”). Both of these changes are driven by software improvements, not the addition of the screen.

Google Home: Quickly adding features to pass Alexa. Google Home improved its local and commerce results by 46 percentage points and 24 percentage points, respectively. This represents a broadening of its skills along with high navigation, information, and local scores. Google Home also supports up to 6 different user accounts, meaning your whole family can get personalized responses when you say, “Okay Google, what’s on my calendar today?” Google Home will recognize your voice and read your upcoming events. Separately, commerce is an area that was previously dominated by Amazon, but Google is now at parity, mainly due to its superior ability to understand more diverse natural language. While Alexa still has a larger database of add-on skills, Google Home outperformed in our set of queries.

Future home assistant competition looks intense. While Amazon and Google are the current frontrunners in the home assistant race, they are facing competition from several notable future entrants:

  • Apple HomePod (expected December 2017)
  • Alibaba Tmall Genie (released August 8th, 2017)
  • Microsoft Invoke (expected Fall 2017)
  • Lenovo Smart Assistant (utilizing Alexa, expected Fall 2017)
  • HP Cortana Speaker
  • Samsung Vega

Persisting Problems of Home Assistants. While home assistants continue to make noticeable improvements, we still believe that they are in the early innings of a platform that will become an important part of computing in the future. That being said, there are small, technologically reasonable improvements that we would like to see from these products. Our main complaint is the lack of integration with devices to make use of information or take further action. In most cases, the fastest way to get information to a user is on a screen – it’s hardly convenient to have a list of 10 restaurant recommendations read to you one at a time. Instead, you should be able to call up information verbally and have it sent to your smartphone, computer screen, or television. The Echo is able to interact with your phone via the Alexa app. Google Home can control a Chromecast. Both are able to control certain smart home devices. There is clear progress being made on this front, but it remains a key obstacle to the devices’ effectiveness. Another shortcoming that persists is unsatisfactory natural language processing, an added barrier to widespread use. Both assistants were selective in the way you had to phrase a question in order for it to be answered correctly. For example, Google Home will understand, “What is a gigawatt?” but cannot process, “Google gigawatt.” or, “Tell me what a gigawatt is.” In order for digital assistants to reach widespread adoption, users need to interact with them seamlessly.

Overall, we were impressed by the improvement that took place in a few short months and remain optimistic that the technology will continue to advance at this pace going forward.  As new players enter the space and homes become more connected, the technology in these devices will be increasingly important in our everyday lives.  Later this year we will track the further progress made by the Echo and the Home, and compare them to some of the new entrants set to arrive by the end of 2017.

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.

Google Is Betting On The Right Long-Term Trends

Following the company’s Q2 earnings release, Google shares are down 3%, based on higher traffic acquisition costs (TAC). As a percentage of revenue, TAC increased to 11.1%, up from 8.8% a year ago. We think this is a classic example of investors looking at the near-term bumps rather than the long-term positives. We saw several positive themes in the quarter:

  1. Revenue growth has been stable over last 5 quarters. Google’s revenue grew 21% y/y. Over the last five quarters, revenue has grown between 20-22%, even though there has been anticipation that revenue growth would slow.
  2. AI is having a positive impact on Google. Sundar Pichai began his portion of the earnings call by saying: “Google continues to lead the shift to AI driven computing.” This was the third consecutive earnings call in which Sundar touched on AI during his commentary. In Q1 of this year, he said: “I’m really happy with how we are transitioning to an AI-first company.” In Q4 of 2016, Sundar stated: “Computing is moving from mobile-­first to AI­-first with more universal, ambient and intelligent computing that you can interact with naturally, all made smarter by the progress we are making with machine learning.” Google mentioned “AI” or “Machine Learning” 18 times during the Q4’16 call, 24 times on the Q1’17 call, and 21 times on the Q2’17 call. The focus on AI is important because AI will empower Google to have better, more targeted search results for consumers, higher ROI for advertisers (through Google’s smart bidding platform), lay the groundwork for natural language processing (the future of Google Home and Assistant), and improve computer vision-based search.
  3. Google remains heavily invested in the AR/VR theme. Google Lens, a computer vision platform driven by machine learning, is the foundation of Google’s future in Augmented Reality. Google is taking the long-term approach to Google Lens, as new computing form factors emerge (ie. AR Glasses) that lend themselves to input methods more natural than taking out a phone and snapping a picture. In addition, Google shared that by year end, there will be 11 Daydream-ready devices on the market. Most notable, Samsung’s Galaxy S8 and S8+ are Daydream-ready.

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 the Future of Voice Search Affects Marketers Today

Written by guest author Lindsay Boyajian at Conductor

Since Amazon announced its acquisition of Whole Foods, the running joke across social media has been, “Jeff Bezos said to Alexa, ‘Buy me something on Whole Foods,’ and Alexa bought Whole Foods.”

This quip highlights the shortcomings that plague voice search. Today, voice recognition technology is very much flawed and often falls short in delivering on the user’s intent.

Despite its weaknesses, voice search is promising to be the user input of tomorrow. The major tech companies are investing heavily in the technology— Apple has Siri, Amazon has Alexa, Google has Google Assistant, and Microsoft has Cortana. Even with the technology in its nascency, Google reports 20 percent of queries on its mobile app and Android devices are voice searches.

And thanks to artificial intelligence and machine learning, voice search is improving quickly. It improves with every user interaction, becoming more apt at understanding user intent. With the technology advancing, more users will adopt voice search, fueling the growth cycle.

The work that is going into voice recognition technology today will power the next evolution in computing— augmented reality.

Augmented Reality & Voice Search

Augmented reality (AR) represents a new computing paradigm. Augmented reality overlays digital assets on the real-world environment. The technology promises to change how users interact with the digital world.

Soon, everything from office activities to shopping will be experienced through augmented reality. For instance, a shopper will be able to put on a lightweight pair of AR glasses to visualize in 3D what different couches will look like in her home. Some AR experiences like this are already offered today through head-mounted devices like Hololens and Meta. However, these devices are only available to developers and still have their limitations. They are not ready for mass consumer adoption.

The principal user input for augmented reality devices (excluding hardware input accessories like keypads and clickers) is gesture and voice. The issue with gesture controls is user discomfort and fatigue. Many experts agree that voice will be the primary input for these devices.

As the augmented reality space matures so will the importance of voice search.

The tech company with the most advanced voice recognition technology will have an advantage in augmented reality computing.

Optimizing Organic Search for the Future of Voice Search

Although mass consumer adoption of AR hardware is still years away, brands that optimize for voice search early will lead in organic and search marketing when the technology becomes ubiquitous.

Voice search behavior differs from traditional search patterns. Consumers approach voice search using natural, more conversational language. The queries are often longer and delivered as questions.

The result for marketers is that content optimized only for keywords will falter, while content that delivers value and matches the intent of the user will see improved organic search performance. To do this, marketers need to develop a deeper understanding of their customers to deliver content that provides relevant and timely value. This approach to marketing is known as customer first marketing.

Customer first marketing is not new. More and more brands are quickly adopting a customer-centric marketing approach. Relevant and contextual content drives traffic, fosters customer engagement, and builds loyalty. The rise of voice search and its link to the future of augmented reality only makes adopting a customer first marketing strategy even more advantageous for brands and marketers.

This piece originally appeared on LinkedIn. For more, follow Lindsay Boyajian on Twitter and LinkedIn

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.