The Future of Mobile Applications

John Canny UCB EECSMarc Davis UCB School of Information & Yahoo! Research Berkeley

The Business

• There are 6.5 billion people on earth - only about 1.2 billion in “developed” countries

• They will buy 800 million mobile phones this year - one person in eight on the planet

• That’s 4x PC or TV unit sales

• Fraction of smartphones should reach 40% by 2009 - most common “computer”

800m

200m

What kind of computer is it?

This year’s Smartphone (free with service contract)• 150-200 MHz ARM processor • 32 MB ram• 2 GB flash (not included)

Windows-98 PC that boots quickly!

Plus:• Camera• AGPS (Qualcomm/Snaptrack)• DSP cores, OpenGL GPU• EV-DO (300 kb/s), Bluetooth

200 mips

What’s Coming

In the past, the platform was driven by voice+messaging

Now the high end is driven by video, gaming, location,…

The result is diversification of the platform, and sudden jumps in performance, e.g. Qualcomm has 4 platforms:

1. Value platform (voice only)

2. …

3. …

4. Convergence platform (MP3 player, gamer, camera,…) several timesthe performance of today’s high-end

Log P

time

PC

The Inevitable…

In response to MIT’s $100 laptop, Microsoft last month proposed the cell phone computer for developing countries:

Bollywoodon demandclick here

Back to the future, which is…

Using Context• Location, time, BT neighborhood,…• Community• User History

Harnessing Content• Text, Images, Video + Metadata • Speech Recognition• Computer Vision

What’s wrong today…

Did you ever try to find a neighborhood restaurant using a mobile browser…

and find it while you were in the same neighborhood?

In a car you might end up in the next county…

Luckily a house stopped thisdriver before they got into serioustrouble.

Context-Awareness

Context-awareness is the holy grail for next generation mobile applications:• Location (e.g., video store) heavily shapes the user’s likely actions. • The system can present streamlined choices – “here are your top-10 video suggestions with clickable previews”.• For users this is very convenient.• For vendors,…

Context-Awareness and Pro-Activity

Knowledge of user background and context provide great opportunities for pro-active services:

• “It’s 7pm and you’re in San Francisco, would you like me to find a nearby restaurant?”

Context-Awareness and Pro-Activity

Knowledge of user background and context provide great opportunities for pro-active services:

• “It’s 7pm and you’re in San Francisco, there is a table available two blocks away at Aqua. Would you like me to book it?”

Context-Awareness and Pro-Activity

Knowledge of user background and context provide great opportunities for pro-active services:

• “It’s 7pm and you’re in San Francisco, there is a table available two blocks away at Aqua, and they have a special on Salmon in parchment for $28. Would you like me to book a table, and order the special?”

Context-Awareness and Recognition

Consider now a speech recognizing version of this application:

• “It’s 7pm and you’re in San Francisco, there is a table available two blocks away at Aqua, and they have a special on Salmon in parchment for $28. Would you like me to book a table, and order the special?”

User: Yes or No

Context-Awareness and Activity

People’s actions are part of larger wholes called activities.

When you plan an evening out it may include:• Going for coffee • Seeing a movie• Eating dinner

- planned and coordinated by the phone

• Sharing photos on your cameraphone• Scoring your date in real-time *

It’s a social platform!

Activity-Based Design

Activity-based design creates chains of services (BigTribe) or menus of related actions. More examples:

• Planning a trip: hotel, car, events• Going back to school: housing, books etc.• Shopping for holiday gifts• Moving house• Hobbies: Needlepoint,…

Monster truck racing

Some of these services exist, but activity analysis supports automatic discovery and customization of them.

Sociotechnical Systems

• The problems of the internet are not purely technological

• Need to conduct sociotechnical analysis and design of large scale internet systems and applications at the intersection of media, technology, and people

• Leverage media metadata created by context-aware devices, content analysis, and communities

Signal-to-Symbol Problems

• Semantic Gap– Gap between low-

level signal analysis and high-level semantic descriptions

– “Vertical off-white rectangular blob on blue background” does not equal “Campanile at UC Berkeley”

Signal-to-Symbol Problems

• Sensory Gap– Gap between how an object appears and what it is– Different images of same object can appear

dissimilar– Images of different objects can appear similar

Computer Vision and Context

• You go out drinking with your friends• You get drunk• Really drunk• You get hit over the head and pass out• You are flown to a city in a country you’ve never been to with

a language you don’t understand and an alphabet you can’t read

• You wake up face down in a gutter with a terrible hangover• You have no idea where you are or how you got there• This is what it’s like to be most computer vision systems—

they have no context

• Context is what enables us to understand what we see

Campanile Inspiration

MMM: Mobile Media Metadata Idea

• Leverage the spatio-temporal context and social community of media capture in mobile devices– Gather all automatically available information at the point

of capture (time of capture, spatial location, collocated phone users, etc.)

– Analyze contextual metadata and media to find similar media that has been captured before

– Use patterns in previously captured media/metadata to infer the content, context, and community of newly captured media

– Interact with users to augment system-supplied metadata for captured media

MMM: Mobile Media Metadata Projects

Mobile Media Metadata

Davis, Canny, et al.

UC Berkeley

Context-Aware Face Recognition

Context-Aware Face Recognition

• Face recognition alone - 43% accurate(state of the art computer vision)

• Context analysis alone - 50% accurate(Face prediction from contextual data on the phone)

• Context+Content analysis - 60% accurate

Figure 1. (Top) Subjects with frontal pose, (Bottom) Same

Context-Aware Place Recognition

• Image analysis alone - 30% accurate

• Context analysis alone - 55% accurate

• Context+Content analysis - 67% accurate

MMM2: Context to Community

Photo Share Guesser

1 2 3 4 5 6 7 8 9 100.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

SFA prediction

Baseline

Photo Level of Interest (LOI) Browser

PhotoCat: Context-Aware Photo Browser

Technologies

We have been developing core technologies for context and content mining for the last 5 years:

• Accurate, scalable personalization (used in MMM2)• Algorithms for integration of personal and context information, and for activity discovery• Methods to preserve privacy while mining user location history and online behavior

We’ve also worked with a company (BigTribe) through 3 funded NSF SBIRs, to migrate these ideas into products.

Harnessing Large, Mixed Content Sources

Early access to an XML Content-Base (Mark Logic CIS)

We built an efficient location+metadata server from diverse well- and poorly-structured data sources.

• Street data comes from XML Census data (Tiger/GML).• Restaurant data is from the Open

Directory, partly structured.• Addresses converted to

LAT/LONG by the database. • The map you see is produced

entirely using XQuery (SVG).

Picante Playa

Location Content-Base

A native XML engine supports efficient tree traversal.

The location C-B uses R-treeorganization as its XML schema

The result is that our “software” spatial database has the same efficiency as custom spatial databases.

i.e. not only are data types extensible,but also the types of query that areefficiently supported.

Context-Aware Design – Glaze

Designing new context-aware apps works best “in the wild.” We are doing a participatory design experiment with 20 AGPS phones this spring.

Users carry the phones with them everywhere, be able to use some seed applications, and otherwise create their own “micro-apps” through noun-verb composition.

Perceptual Interfaces - Vision

We needed continuous mouse input for map browsing, so we developed TinyMotion, a software mouse for cameraphones.

By moving the camera against any background, real-time image motion estimation provides mouse coordinates.Also great for games – demo in BID lab

Perceptual Interfaces - Vision

Cameraphones are capable of much more. Right now, the vision algorithms available include:

• Motion• Barcodes• OCR text (business cards etc.)

Coming soon:• Face recognition• Building or streetscape recognition

Perceptual Interfaces - Speech

Speech recognition technology has improved steadily in the last ten years, particularly in noisy environments.

Speech was never a good match for office environments.

But the mobile playing field is completely different.

Mobile users often need their eyes and hands free, and the phone will always have a voice channel for telephony.

Speech on Mobile Phones

Restricted speech recognition is available on many phones.

Large-vocabulary recognition just appeared on cell phones last year (Samsung P207). Its a huge step. It enables the next generation of mobile speech-based apps:

• Message dictation• Web search• Address/business lookup• Natural command forms

(no need to learn them)…

Most of this technology was developed in the US by VoiceSignal Technologies.

Research in Mobile Speech

We are developing a state-of-the-art (continuous acoustic model) recognizer for SmartPhones.

The goals are:• To provide an open platform for next-generation, speech-

based interfaces on mobile devices.• To support integration of contextual knowledge in the

recognizer. • To allow efficient exploration of the higher levels of dialog-

based interfaces.

Speech for Developing Regions

Speech is an even more important tool in developing regions.

Literacy is low, and iconic (GUI) interfaces can be hard to use.

Unfortunately, IT cannot help most of these people because they lack even more basic skills – fluency in a widely-spoken language like English or Mandarin.

This project focuses on teaching English in an ecologically appropriate way.

Speech-based phones are ideal for this.

Speech for Developing Regions

Speech (with headset) allows students to learn while working.

It leaves their eyes and hands free, and engages their minds during tedious, manual work.

Some game motifs:• Safari: hear sound & say the name in English• Karoake: in English• Listen and summarize: BBC, cricket etc. • Treasure hunt: leave LB clues in English• Adventure games: dialog-driven scenarios

In Summary,The Future of Mobile is:• Using Context• Harnessing Content• Context Proactivity• C/P Content sharing• Perceptual: Speech/Vis• 5 Billion new users

Upcoming…

Special issue of ACM Queue magazine on context-aware and perceptual interfaces (summer 06?) JFC guest Ed.

Workshop on Mobile Applications

Planning an event on campus later this semester.

Send mail to jfc@cs.berkeley.edu if interested.

Demos and Posters Today

You can see the projects discussed here in the BID lab (Berkeley Institute of Design) open house, 2-4pm:

• Tinymotion camera mouse• Glaze location service design• Speech recognition for cell phones• English-Language learning with cell phones

+ a dozen other projects

The lab is in 354-360 Hearst Mining Bldg.

Thanks to the sponsors of this work:

and..

Acknowledgements