Artificial Intelligence and its Role in the Future of Mobile

White paper

DATE: AUG 2016

© Apppli Limited – All Rights Reserved - Apppli Limited Floor 7, 33 Cavendish Square, London W1G 0PW

WE UNDERSTAND MOBILE

DATE: AUG 2016

01OUTLINE

+

OutlineThis paper will explore artificial intelligence (AI), the field of building machines capable of automatically learning from data to iteratively improve their ability to solve complex tasks, as it applies to the mobile device ecosystem. The goal of this paper is to inform the reader on how the AI field has matured (particularly in the last decade) and how these core technologies render high-value opportunities in mobile tractable, sometimes for the first time.

02TABLE OF CONTENTS

+

Table of contents

1. Artificial intelligence

1.1. A brief history

1.1.1. Figure 1: a history of booms and busts

1.1.2. Figure 2: news/media mentions of key search terms, 150%+ YoY in last 12 months

1.2. A technical overview of AI and its constituent technologies

1.2.1. Figure 3: a framework for how AI systems work

1.3. What is AI good at solving and what is required to exploit its full potential?

2. How does AI fit into the mobile agenda

2.1. How has mobile changed the World?

2.1.1. Figure 4: US millennial behaviour on smartphones

2.2. What opportunities are there for AI in mobile? A case for context aware computation

2.3. Examples of context aware mobile products in the market

2.3.1. Google

2.3.2. Apple

2.3.3. Viv

2.3.4. Gluru

2.3.5. Snips

2.3.6. Microsoft Cortana

2.3.7. Amazon

3. Where might we be in 3 to 5 years time?

3.1. Connected everything

3.2. Healthcare

3.3. Autonomous vehicles

3.3.1. FIgure 5. News/media mentions of key autonomous vehicle search terms

3.3.2. Figure 6. Forecast UK production of connected and autonomous vehicles

3.3.3. Figure 7: investments (left) and exits (right) in mobile autonomy

4. Conclusion

03ARTIFICIAL INTELLIGENCE

+

1. Artificial intelligence 1.1. A brief history AI is a multidisciplinary field that focuses on building computer systems capable of performing increasingly human-like cognitive functions and tasks in the digital and physical world. The field kicked off in the 1950’s following a seminal paper by Alan Turing, Computing Machinery and Intelligence, in which Turing put forward the idea that machines are capable of thought. Figure 1 below summarises the ups and downs that the field of AI has undergone.

1.1.1. Figure 1: a history of booms and busts

The first two booms were driven by heavy government funding to build systems that would recapitulate the expert knowledge of professionals. However, progress repeatedly fell short of expectations. A report of academic research output in 1973 (Lighthill Report) concluded that "in no part of the field have discoveries made so far produced the major impact that was then promised". This resulted in research funding cuts and a loss of interest in the field. A key reason why AI under-delivered was that machines at the time had limited computational capacity, the universe of training data was small and the focus had been on rule-based expert systems that had difficulty facing problems they weren’t a priori coded to solve.

A new wave of excitement kicked off in the mid-90’s when IBM’s chess-playing Deep Blue beat a World Champion for the first time. This machine took advantage of significant computational output and training data. Shortly thereafter, Google was founded in 1998 with the mission of organising the World’s information and making it searchable - a task that would a few years later introduce machine learning to power search at scale. Moving into the new millennium, interest in AI accelerate in earnest as evidenced by press mentions (Figure 2), research output, private company financings and corporate investments. Several reasons have driven this trend, including technical advancements making AI models easier and faster to train using graphics processing units (GPUs) capable of parallel computing (see NVIDIA post here), as well as the proliferation of the Web and mobile devices, both of which have contributed to vast amounts of data creation.

04

+

ARTIFICIAL INTELLIGENCE

1.1.2. Figure 2: news/media mentions of key search terms, 150%+ YoY in last 12 months

05

+

1.2. A technical overview of AI and its constituent technologies Generally speaking, the AI technology we have access to today is thought of as Artificial Narrow Intelligence (ANI) insofar as it is built to solve a specific task often in a single domain. ANI will be the focus of this paper given that it has real commercial relevance in the short to medium term. However, it is worth noting that several companies and research labs are making headway in architecting AI with more generalisable learning capabilities that are flexible to several problem types. This flavor of AI is popularly known as Artificial General Intelligence (AGI) and many believe it is the solution to truly powerful AI. The third and final step in the trajectory towards the pinnacle of AI is Artificial Superintelligence (ASI), which is the concept that machines can recapitulate the entirety of a human’s cognitive function and even outperform us on every task.

Within ANI, systems come in several technical flavors depending on the task at hand. The framework in Figure 3 is helpful to encapsulate the essence of how a learning system works.

ARTIFICIAL INTELLIGENCE

First, a learning system (“AI model”) is built to perceive, process and understand information in an environment (e.g. images, video or text). This input data can be structure (i.e. recorded and provided in a tabular format) or unstructured (i.e. without a predefined format or data model). On the basis this input data, the AI model is tasked with utilising specific features of the data as measureable properties to which it applies different weights to make a prediction. These predictions can range from “classify this image” to “what is the next state of the environment”. Once a prediction is made, the outcome is often judged by the user to ascertain whether it was good or bad. If the model performed well on this basis, the system can be rewarded; by contrast, if the model performs poorly, the weights or even the features themselves in the AI model need to be updated in an effort to improve its next prediction. The model may also write and read to a short term memory database in order to apply learnings to temporally sequential tasks such as dialogue. The feedback loop that involves a model making predictions, measuring results and updating model parameters is the key to machine learning, the process by which machines improve by seeing more data.

06

+

1.2.1. Figure 3: a framework for how AI systems work

ARTIFICIAL INTELLIGENCE

07

+

Several elements of this framework are important to point out. First, AI systems are built to perceive data in several modalities that reflect the way in which humans perceive the world. Today, these modalities really only include seeing (computer vision, natural language processing and understanding) and hearing (audio/speech recognition and dialogue systems). Today, smell, taste and to a large extent touch, are intractable.

Second, there are many types of AI models that are chosen as a function of input data type and the task at hand. These models include:

• Recommendation: observing user interaction data and using the similarities between user behaviour to personalise an experience (e.g. people who bought this also bought that).

• Classification and regression: making a discrete prediction using training data where the target prediction is either a categorical descriptor (classification, i.e. “dog”) or instead continuous (regression). These models are frequently used in computer vision, where a machine is tasked with analysing the content of images.

• Clustering: separating data points into similar groups based on intrinsic similarity of features, often while conducting data exploration, when there aren’t discrete labels to each data point to enable cookie cutter classification.

• Deep learning: multi-layered neural networks which discover important features in data automatically (as opposed to requiring hand crafting of features) and build their own rich representations of data. These models are powerful for classification and clustering especially for computer vision and natural language, but they require lots of data.

ARTIFICIAL INTELLIGENCE

08

+

• Reinforcement learning: agents take actions in an environment to maximise a designated reward. This is useful for exploring and subsequently exploiting optimal solutions to problems in simulated or real-world environments.

• Natural language processing/understanding: enabling machines to make use of and understand written language. This is useful for information retrieval, search, translation and any task where the human-computer interface is through text.

Third, these models are trained using three broad learning paradigms: supervised learning, unsupervised learning and reinforcement learning. The former requires the author to have example data specific to the task in question that is labeled with the correct answers that the model is expected to output (i.e. input/output pairs). This is often expensive to create because it requires human annotation (e.g. the ImageNet dataset took years). Unsupervised learning, on the other hand, does not require labelled data. Instead, the model discovers patterns in the data on its own accord to output predictions. In this case, there is no explicit error or reward signal to evaluate its solution. The third paradigm is reinforcement learning, where an agent takes actions in an environment and receives feedback as to whether these actions helped it maximise a target reward or were instead detrimental to reaching that goal. Here, an agent will balance exploration of the environment with exploitation of a strategy that optimises the goal. As such, unsupervised learning is different from both supervised and reinforcement learning paradigms in that it does not receive direct feedback or error readouts on its predictions.

ARTIFICIAL INTELLIGENCE

09

+

1.3. What is AI good at solving and what is required to exploit its full potential? It is worth prefacing this discussion by noting that AI is not always the best tool for solving a problem. These techniques often require complex models and lots of data. Thus, one should explore whether simple heuristics can be used to arrive at an approximate but good enough answer to the task at hand. That being said, given the explosion of data and penetration of mobile devices, AI is increasingly well suited to a large universe of tasks that involve high volume or high dimensional data that can be analysed using the panel of models described in the previous section. One way to analyse whether AI technologies are a suitable and effective solution to a given problem is to ask the following questions:

• Have existing non-AI approaches to solving the task reached a local maximum that is below what humans are capable of?

• Does the task require the analysis of unstructured data (e.g. textual data, images, video or audio) at scale such that manual processing within acceptable time limits is intractable?

• Are you looking for intrinsic patterns in data that can’t possible be entirely explored by hand?

• Is the task repetitive, structured and can it be accurately modeled?

• Does the task have measurable outcomes such that one can clearly assess performance?

• Is the data available in high volumes or are there sufficient input/output pairs for training?

ARTIFICIAL INTELLIGENCE

10

+

• Does the task involve some element of personalisation? For example, it’s often paramount to impress a delightful experience out of the box when users interact with products on mobile. AI can be used to this effect by learning preferences during onboarding or from similar user habits.

In order to exploit maximum value from AI-driven systems, it’s important to encourage user investment and intervention when outcomes are uncertain. Indeed, users are fickle and they churn through apps with no remorse: the average 90-day retention for apps is 25% (Localytics).

Returning expected value out of the box is key to building habits (see Nir Eyal), especially given that people are less likely to forgive machines than they are people (QZ). Employing a user-in-the-loop design model, where the user is asked to be involved in the live training of AI systems, is a powerful solution. Well known examples of this model in action include Gmail spam filters, which allow a user to confirm/reject spam labels, or Google Photos face and entity detection, which allow a user to remove incorrectly classified images returned by a search.

2. How does AI fit into the mobile agenda In this discussion, mobile is defined as a category of portable or autonomous devices that create, collect, compute and transmit data to other nearby devices or the cloud. Included within mobile are smartphones, laptops, tablets, internet-connected sensors (e.g. IoT) as well as autonomous agents on land/sea/air.

AI IN THE MOBILE AGENDA

11

+

2.1. How has mobile changed the World? It’s impossible to understate the importance of the launch of the Apple iPhone and Android operating system in 2007/8 and subsequent proliferation of mobile devices when considering the commercial maturity of AI. According to research by Cisco, mobile data traffic has grown 4,000-fold between 2005 (<1 petabyte per month) and 2015 (3.7 exabytes per month). By 2020, mobile networks are predicted to carry 30.6 exabytes per month (53% compounded annual growth 2015-2020). In 2015, global mobile devices and connections grew to 7.9 billion and are forecast to hit 11.6 billion by 2020, of which 8.2 billion will be handheld or personal mobile-ready devices and 3.2 billion will be machine to machine connections (e.g. IoT, remote monitoring devices).

As these trends unfold, we are on course to live in a world of ubiquitous computing where microprocessors are embedded in every physical object to enable them to create, consume and communicate information. Smartphones and phablets (phone/tablet hybrids) will dominate traffic consumption, growing from 76% in 2015 to 81% in 2020. Indeed, 90% of people in China access the web on mobile. Finally, the proportion of devices and connections that have advanced computing and multimedia capabilities with a minimum of 3G connectivity will grow from 36% in 2015 to 72% in 2020. Indeed, it will be these devices that create over 98% of mobile traffic by 2020 and do so over 3G/4G networks.

AI IN THE MOBILE AGENDA

12

+

All of these facts are important because smartphones are directly consumer facing and live at the very center of our personal and professional lives (Figure 4). Thus, the data they create, send and receive is particularly informative as raw ingredients to design increasingly powerful and engaging mobile user experiences.

2.1.1. Figure 4: US millennial behaviour on smartphones

AI IN THE MOBILE AGENDA

13

+

2.2. What opportunities are there for AI in mobile? A case for context aware computation At their core, mobile devices are built to deliver instantly gratifying experiences to users regardless of their time and place. Mobile devices offer us the convenience of perpetual access to the information, products and services we love most, ranging from the Web, email, multimedia and apps. Thankfully, smartphones have constantly become better, faster and cheaper at each yearly release cycle. For example, Apple’s A8 chip in the iPhone 6 is 50x faster than the original iPhone chip, and its GPU is 84x faster. The A9 from the iPhone 6S has 70% more CPU performance and 90% more GPU performance than the A8. Importantly, smartphones are equipped with a growing diversity of sensors monitoring and logging a range of parameters that track the device’s use and environmental context. The newest iPhone 6S, for example, includes a proximity sensor, ambient light sensor, 12MP Camera with OIS, accelerometer, gyroscope, compass, barometer, NFC for Apple Pay, Touch ID fingerprint scanner and a pressure sensitive display (e.g. sensors in iPhones). Leveraging the data produced by these sensors with the CPU/GPU computational power on the device opens a wealth of opportunity for predictive, AI-powered applications to become the new normal.

AI IN THE MOBILE AGENDA

14

+

In this environment where plentiful devices have the ability to communicate with one another and enrich our ability to describe what’s going on in the physical and digital world, AI is well positioned to reach its potential. In the developed world, we’re already regularly interacting with at least two mobile devices: a smartphone and a laptop. Some of the services we use across these devices are the same, while others might be different but interoperate through shared user profiles or application programming interfaces. The opportunity for AI is to create rich models that describe how and in what context we use certain devices, applications and services in order to abstract away needlessly repetitive interactions with technology. This vision of context aware computation is arguably the biggest opportunity for AI in mobile and extends far deeper than framing context as a function of location alone. Indeed, AI needs to be involved at several levels, ranging from analysing location, user state (work or social), action intent, as well as relevant content or information required to complete an action. AI is required because these problems are inherently complex, must be solved in the background in near real-time, don’t have obvious answers that could otherwise be approximated with heuristics and are best modelled using probability distributions and inference. As several cycles of predictions and real-world outcomes are collected, it’s possible to improve the probabilistic modelling of each of these problems such that a context-aware system has a far deeper and accurate understanding of reality. These user models can also be employed for security purposes whereby they lock down a device if it’s usage patterns deviate from that recorded from its true owner’s habitual use. There are several high-value opportunities of context-aware computation (Bolchini et al. 2007) that will most likely make it to market. These include (a) adapting user interfaces, (b) tailoring the set of application-relevant data, (c) increasing the precision of information retrieval, (d) facilitating service discovery, (e) making the user interaction implicit, or (f) building smart environments. As discussed previously, AI systems are well positioned to solve problems in search, predictions, recommendations, text analytics and the automated analysis of large volumes of high dimensional data, all of which are relevant to context awareness. There are a number of examples of mobile products using these principles in the commercial setting today.

AI IN THE MOBILE AGENDA

15

+

2.3. Examples of context aware mobile products in the market 2.3.1. Google Google released Google Now, an intelligent personal assistant, on Android in July 2012 and iOS in April 2013. The application uses a card interface to present the user with contextually relevant information for their personal lives derived from their content integration partners (e.g. Spotify, Zipcar, Indeed, Skyscanner, transport apps, cinema, public transport, calendar, news). The product aims to help a user stay on top of what they need to do and important information they need while on the go. Anecdotal user feedback is that Google Now mostly presents stock charts, news, weather and instructs a user to ‘go home’ at strange times. It appears that there is still a ways to go before predictions are up to par with user expectation, which is certainly true for all digital assistants.

In December 2015, Google released their most recent Android OS, Marshmallow, in which Google Now On Tap was introduced. With this release, Now is weaved throughout the OS such that it can retrieve contextually relevant information by reading the content of a current screen (such as Hangouts, web, email…). This is conceptually similar to the approach taken by Snips (discussed below) and has yet to blow users away.

More direct competitors to Google’s Now product are other home screen launcher applications such as Cover (acquired by Twitter in 2014), Aviate (acquired by Yahoo in 2014 and inactive since 2015), EverythingMe (NB. shut down in 2015), Scout (formerly Bento - description of the product here), Quixey that pull data from a user’s installed apps to serve the right ones for the right time.

AI IN THE MOBILE AGENDA

16

+

As Sundar Pichai (Google’s CEO) mentioned in his shareholder letter this past May, Google delved further into the digital assistant world by releasing Google Assistant. It goes beyond Google Now in that it provides a persistent, cross-device and contextual experience to power a suite of Google products including Google Home (similar to Amazon Echo, discussed below) and Allo (messaging). Interestingly, it was also announced that 20% of queries that are made via the Google Android app are voice queries vs. text, suggesting that some users might be adopting new avenues of human-computer interaction.

2.3.2. Apple Apple do not currently produce a stand alone application targeted at making content predictions around user context. However, they are making a push into contextual data mining and information retrieval via Spotlight Search (as part of their operating system, iOS) and their Contacts client. In the former case, iOS10 users are now presented with a page that suggests apps, people, locations and content from installed apps before a user enters a search query. The focus is first on contacts and applications. It’s not entirely clear how these results are returned and prioritised. On the apps front, it seems most likely that recency of use is the most highly weighted parameter. For contacts, it appears to be a combination of messaging services and calendar appointments. The Contacts app queries structured data from the default Mail client to suggest additions (e.g. telephone number, address, email). While this is a small feature, it shows the interoperability of iOS applications that pass data between each other.

AI IN THE MOBILE AGENDA

17

+

In mid-2016, Apple announced the launch of SiriKit, which enables iOS10 app developers to integrate with Siri such that users can use voice as an interface to get things done with their content and services. SiriKit extends Siri’s support for iOS messaging, photo search and phone calls, as well as ride booking and personal payments. An integrated application collects a user’s intent as recognized by Siri and connects the intent to an intent handler. The handler resolves the intent by checking whether the intent parameters can be fulfilled (e.g. "book an Uber in 5 minutes" - is an Uber available?) and creates a response message asking the user to confirm the intent resolution (SeekingAlpha). However, Siri cannot understand any intent within 3rd party applications, only those limited to this list.

More broadly, Apple is finally making a push into AI by purchasing startups (e.g. VocalIQ for dialogue systems) and hiring for the Siri team (see this job ad). According to these sources, the goal is to help Siri “move, understand, plan, learn, speak, and remember.” Use cases cited in these sources include “a safer way to use your iPhone in your car, calling up your favorite movie on AppleTV and safely navigating a map”.

AI IN THE MOBILE AGENDA

18

+

2.3.3. Viv Viv is a startup set up by the original founders of Siri (acquired by Apple in 2010), the first digital assistant to make it onto the iOS platform. The aim of Viv is to build an intelligent interface to all connected devices and enable developers to add new logic to Viv (similar to Amazon Alex described below). The company launched publicly in 2016 and takes a different approach to Apple’s Siri or Microsoft’s Cortana, which the team calls a “dynamically evolving cognitive architecture system”. In short, Viv listens to a user query (using Nuance’s speech recognition technology), understands intent by parsing action and concept objects to create a sequential plan of action on the fly. Of note, Viv leverages an ecosystem of 3rd party services to draw knowledge to fulfil the user query. If we’re to depend on digital assistants for every possible use case we can imagine, this approach is certainly more scalable than hard-coding connections between predefined words/phrases, domain expertise and ontologies.

AI IN THE MOBILE AGENDA

19

+

2.3.4. Gluru Gluru is a London-based machine learning company focused on predicting the intent behind user actions from their cloud-connected enterprise collaboration tools (e.g. Google Drive, DropBox, email, calendar) in order to deliver contextually-relevant file recommendations. It is also developing a new-age task manager given that existing ones do not capture how daily tasks are executed because users are only sporadically creating, updating and completing tasks. Manually created tasks are also often just reminders and do not contain enough contextual information to allow an AI to complete them. Instead Gluru's intention lifecycle (GIL) automatically creates and updates structured context rich tasks by processing the semistructured stream of information coming from user connected cloud services and smart devices. This allows Gluru's intentions inference engine (GIIE) to semantically understand what users are trying to accomplish and to suggest or automate a possible execution. Gluru combines the power of natural language processing, deep learning, semantic analysis and context awareness to accomplish this.

2.3.5. Snips Snips is Paris-based startup whose mission is to write AI that will make technology disappear. More precisely, the mission is to model user context and actions so as to predict what they’ll do next in addition to the information or applications they’ll need in order complete these actions/tasks.

AI IN THE MOBILE AGENDA

20

+

Their first product was is a mobile keyboard replacement which is meant to serve the user with addresses important to them while on the go using a mix of addresses parsed from the user’s calendar, copied to clipboard or predicted from historical use. The performance of this product was quite poor - better results are achieved by simply looking up addresses the app pulls from the calendar integration - and the user retention is also low. The next product was a smartphone app that connected email, contacts and calendar to enable search across these data sources. It was also meant to predict likely next actions during the day on the basis of a user’s habits it had detected through location monitoring. Again, performance was poor as hardly any predictions were made and it wasn’t intuitively obvious how a user should engage with the search feature. More recently, the company has worked on an Android app that reads language from messaging app screens to parse out locations to quickly suggest transport routes. The series of products built by Snips shows how challenging it can be to develop mobile-based assistants for the consumer market given these are a class of products that have delivered poorly in the past and user experience expectations are rather high.

2.3.6. Microsoft Cortana Microsoft’s voice-based digital assistant application, Cortana, is baked into the Window’s Mobile and PC operating system and running on Android as of 2015. The app is able to set reminders, recognise speech and answer questions using information retrieved from the Bing search engine. It also contains a Notebook, which stores personal information such as interests, location data, reminders and contacts that are used by Cortana and editable by the end user.

AI IN THE MOBILE AGENDA

21

+

2.3.7. Amazon Amazon launched a voice-enabled wireless speaker, Echo, in mid-2015 after several years of development. It was the #1 seller across all $100+ products on Amazon.com on Black Friday that year. The use case is focused on the home. The product features a digital assistant, Alexa, who is always listening out for a ‘wake word’ uttered by its user. Once ‘on’, Alexa listens out for questions or commands that she is capable of fulfilling, mostly around information retrieval, entertainment (music) and product purchasing requests that can be fulfilled via a user’s Amazon Prime account. The brains of Alexa sit on Amazon Web Services (‘Alexa Voice Service’) and the speech recognition, information retrieval and Q&A technology was developed in collaboration with three acquisitions the company made: Evi, Yap and IVONA.

Similar to the Viv approach, Amazon created a 3rd party developer ecosystem centered around Alexa to extend her capabilities (or ‘skills’). Amazon offers a collection of APIs, tools, documentation and code samples so developers can add voice actioned skills to their applications running Alexa. For example, Amazon released the Smart Home Skill API, such that developers can write custom skills using Amazon’s voice interaction model to handle customer requests to control connected home appliances. Alexa converts the user’s speech into a directive and sends that to the developer’s skill adapter that works with the relevant device’s proprietary control systems. Examples include using Alex to control a home thermostat or lighting. As of mid-2016, there were over 1000 skills built and shipped by developers on Alexa.

AI IN THE MOBILE AGENDA

22

+

3. Where might we be in 3 to 5 years time?

It is certainly an exciting time to be building AI. It’s clear that AI technologies are powerful tools for solving complex problems in today’s hyper-networked, always-on, data and sensor rich world where computation is widely accessible and cheap. In addition to becoming more digitally-obsessed, it seems as though we also have less patience and have less ‘time to waste’. We take technology and information access for granted, just as we do electricity. It just has to be there. We often find it difficult to function properly without it. In this World of ubiquitous connectivity and computation, there are several fascinating opportunities to deploy AI at scale.

3.1. Connected everything A 2014 study by the Pew Research Center canvassed 2,558 technology builders to get their views on what 2025 would look like. A quasi-uniformly accepted future was one where mobile, wearable and embedded computing devices are all collecting and passing data between each other and interacting with a user as instructed by a globally-trained, cloud-based knowledge base powered by AI. This universal fabric would in essence capture the collective digital and physical experiences of all users, such that AI can best instruct devices to work for us in the background, anticipating our needs. The seeds of this technology are already planted, insofar as Apple and Google have and continue to build out their own connected ecosystems of devices spanning the smartphone, watch, laptops, home entertainment systems and the car. These ecosystems are mindful of where we are, what we’re doing, where we’re going, who we’re communicating with and what information we care about using.

THE FUTURE OF AI IN MOBILE

23

+

While closed ecosystems enable companies like Apple and Google to optimise their product suite, they fall short of the longer term idea of a fully interoperable data and device environment that is centered around the user as opposed to the hardware/software manufacturer. Open ecosystems are more powerful, as exemplified by Tencent’s WeChat in China, first released in 2011. Today, WeChat is a cross-platform messaging service used by over 760 million people worldwide every day to pay rent, locate parking, manage their finance and invest, make a doctor’s appointment, date, hail a ride, donate to charity, communicate with their friends, order and pay at restaurants, book hotels, send money, and more (Bloomberg). Thus, it’s not inconceivable to imagine a future where products and services, both digital and physical, become interconnected and interoperable such that cover all aspects of our lives. By learning how we engage in this World and what we seek to optimise while doing so, it’s possible to build AI to automate and abstract away unnecessarily tedious interactions with digital products.

3.2. Healthcare

Today’s dominant paradigm in the provision of healthcare services is largely one of reactive damage fixing versus proactive early detection and preemptive action taking. The development of therapeutics is particularly challenging because biology is complex and governed by evolution. Drug discovery is therefore expensive, highly regulated and slow. As a case in point, big pharma is today feeling the pain of a drier development pipeline than in years past. What’s more, health systems are generally overburdened, underfunded and understaffed.

Consider today’s clinical model. A patient presents into the hospital when they feel something is wrong. The doctor must conduct a battery of tests as well as consult the (available) medical records of the patients in order to derive a diagnosis. These tests address a single (often late-stage) time point, at which moment little can be done to reverse damage (e.g., in the case of cancer). If the patient presents to a doctor in an unfamiliar medical system or speciality, the doctor often does not have access to the patient’s medical history given the fragmentation of records.

THE FUTURE OF AI IN MOBILE

24

+

The solution in an age of ubiquitous computing is instead to longitudinally monitor physiology and lifestyle in real time, learn the normal state and predict when dangerous changes might occur. If it’s possible to track (with consent) everything a person does, the conditions they are predisposed to (via genetic and other tests) and the physiological state of their body, it should become feasible to create AI that acts as a guardian angel for our health. Consider that the devices many of interact with on a daily basis are able to track our movements, vital signs, exercise, sleep and even reproductive health. Fitbit, a market leader in wearable health, has seen its active user base grow from half a million in 2012 to 17 million in 2015 (Statista). On a large population level, therefore, there is the possibility to interrogate data sets that have never before existed in order to glean insights into how nature and nurture influence the genesis and development of disease. We could predict disease onset and outcome, understand which condition a patient likely suffers from and how they’ll respond to various therapeutic modalities.

There are plentiful applications for AI here, ranging from processing and analysis multimodal signals, detecting anomalies, multivariate classifiers, deep learning on molecular interactions and so forth. Importantly, this future is already unfolding with companies like Sano (monitoring biomarkers in blood using sensors and software), Zebra Medical (deep learning systems for analysing medical imaging and providing diagnostic support), Deep Genomics (predicting how genetic variation influence health and disease), Flatiron Health (infrastructure for clinics and hospitals to store and process oncology data) and Google (recently filed a patent covering an invention for drawing blood without a needle, a small step toward wearable sampling devices).

Thus, as we develop a great diversity of mobile/wearable physiological sensors (2010 review here), it will be possible to envision a future where early detection of health conditions can occur in in near real time, driving down cost of care over a patient’s lifetime while consequently improving outcomes.

THE FUTURE OF AI IN MOBILE

25

+

3.3. Autonomous vehicles

Perhaps one of the most exciting areas of mobile is that of autonomous ground, aerial and water-based vehicles. These notably include self-driving car technology as well as drones, both for commercial purposes (e.g. automated deliveries, transport fleets, inspection of vast areas) and personal transport. Figure 5 below charts the relative change in news and media mentions in the field, showing how interest has grown over time and particularly since 2015.

3.3.1. Figure 5. News/media mentions of key autonomous vehicle search terms

THE FUTURE OF AI IN MOBILE

26

+

The value chain of autonomous agents is complex. Generally speaking, it involves:

• Sensing the environment: a variety of sensors are attached to an autonomous agent, including digital video cameras, radar, ultrasound, LiDAR, IMS, GPS and audio, providing data feeds that together explain the environmental context of the agent.

• Perception: understand which entities (i.e. cars, people, obstacles, road signs and markings, sidewalks, traffic lights) are present in the agent’s environment and what they are currently doing.

• Agent intention modelling: building up models for how the perceived entities behave in a variety of situations, i.e. what they will do given what they are and where they are.

• Planning and decisions: using the intention modelling to then forecast how the agent should behave in any given circumstance.

• Trajectory determination: how the agent in question should modulate its direction, speed and position based on the above.

• Control software: passing on the commands to make sure those decisions are executed so the agent does what it’s meant to.

Many of these steps are only solvable using AI technology, particularly computer vision, probabilistic modelling and reinforcement learning. As vehicles that hit the production line become increasingly connected and equipped with technologies that enable aspects of autonomy (Figure 6), they present a large commercial opportunity for deploying mobile AI products and services. These include the core technologies themselves, as well as experiences within the vehicle ecosystem (e.g. entertainment, remote vehicle control and telemetry analysis).

THE FUTURE OF AI IN MOBILE

27

+

3.3.2. Figure 6. Forecast UK production of connected and autonomous vehicles

Source: KPMG, 2015

The market for autonomous ground, water and aerial agents has expanded significantly in 2015 and 2016. The charts below tracking investments (left) and exits (right) in terms of deal number (yellow line) and capital deployed (blue bars) for both startups and larger corporates. Notable transactions include the $1bn acquisition of Cruise Automation (advanced highway cruise control) by General Motors and the $200m financing round into Zoox (autonomous taxi fleet). This market is only going to grow - as Mark Platshon, an investor at BMW i-Ventures said, “One billion dollars is cheap to make sure you are in the game.” (WSJ).

THE FUTURE OF AI IN MOBILE

28CONCLUSION

+

3.3.3. Figure 7: investments (left) and exits (right) in mobile autonomy

4. Conclusion

Although the field of AI research and its commercial applications have seen several ups and downs since the 1950s, the last decade has witnessed significant progress of AI-powered digital products serving millions to billions of people. The proliferation of connected devices, interoperable software and hardware, the velocity of data creation and consumer demand for experiential digital products are together creating momentum to drive the infusion of AI into every connected device. Mobile computing devices will only become more substantial components to this future.

29

+

For further information on Ampersand & Ampersand and our studies on AI in mobile, please visit www.3amp.co or email info@3amp.co

+44 (0) 20 71127100

ARTIFICIAL INTELLIGENCE AND ITS ROLE IN THE FUTURE OF MOBILE

We are a multi-award winning team of creatives, engineers and visionaries united by a single goal: to produce the best possible work, and in doing so, to define ourselves and our clients as disruptors.

Mobile is a global phenomenon, like our team, our clients and our ambition. We’re a cosmopolitan business in a cosmopolitan city, excited by innovation and excited by opportunities to do things differently. We want to be impactful and we believe our work and our culture speak to that desire.

We see other people doing the same-old and we see same and old. We embrace curiosity, difference and the questions no one else asks. The present and the future are about disruption: new business models, differentiated services, betterment through collaboration. At the same time we believe that innovation must be protected, nurtured and allowed to evolve. We want, do, think, walk and talk difference.

We are collaborators with values, committed to research and development, to finding better answers, to blending rigour with magic to accelerate strategic innovation and create stand-out products and campaigns for our clients

As featured in:

DATE: AUG 2016