The Vision AI Start-ups That Matter Most

Chris Rowen, PhD, FIEEE

CEO

Cognite Ventures

February 22, 2017

Outline

• The Vision AI Innovation Scene

• Quick Picture of Cognitive Computing Startups

• Embedded Vision and Cognitive Computing

• Embedded Vision Innovation Scene

• New Hardware for Embedded Vision

• Observations

• The List

Vision AI Innovation Scene

• Huge enthusiasm for cognitive computing aka artificial intelligence aka machine learning aka deep learning aka neural networks

• Transformation of all modeling and recognition of complex data, scenes and actions

• Applications from robotics to human-machine interface

• Profound changes in how we develop systems: programming labeling and training

• Massive investment by major cloud operators: Google, Baidu, Facebook, Microsoft, Amazon sucking up every deep learning PhD they can find

• Massive startup activity around the world

• Which startups are the most important and focused on serious cognitive computing?

From raw catalog of ~2200 AI startups, we’ve identified ~275 of particular interest – 75 of them in embedded vision

Typical omission: Exceed.ai’s chatbot platform helps brands increase revenue and customer loyalty by enabling personalized one-on-one conversations with their customers at scale through any messaging application.

Typical inclusion: drive.ai creates AI software for autonomous vehicles. It aims to build a hardware and software kit powered by artificial intelligence for carmakers.

What applications are at the heart of AI?

Autonomous Driving

Cyber Security

Surveillance

Generic deep

learning platforms

Medical imaging

Massive Scale

Sensor IoT – e.g. Ag

Legal Analysis

Advertising Automation

Drones

Toys

Robotics

Generic Data

Analytics

CRM

Language Translation

Financial modeling

Predictive Marketing

Geoanalytics

ERP

Retail Analytics Selection Criteria:

• Inherent huge data

• Embrace and tight focus on deep learning methods for modeling and classification

• Job listings and blogs show depth in machine learning

Quick Picture of Cognitive Computing Startups: What?

Cognitive Computing: 277

Embedded: 82

Vision: 125

Embedded Vision:74

• Almost ¾ of 276 startups focus on cloud software: CRM, logistics, predictive marketing

• Heavy emphasis on document and text processing in the cloud

• Plenty of vision/image processing in the cloud: non-real-time analysis

• Most of embedded includes vision. Rest is audio, voice and motion sensing

Quick Picture of Cognitive Computing Startups: Where?

CA, 67%

MA, 10%

NY, 5%

TX, 3% WA, 2%

CA MA

NY TX

WA PA

MI CT

DC NM

TN MD

FL OR

UT OH

CO DE

USA, 49%

UK, 21%

China, 8%

Canada, 5%

Germany, 3%

India, 2%

USAUKChinaCanadaGermanyIndiaJapanIsraelThe NetherlandsSouth KoreaFranceSwitzerlandSpainRussiaAustriaSouth AfricaSloveniaNorwayIrelandHungaryFinlandDenmarkBelgiumArgentina

Embedded Neural Network Product Segments

Autonomous Vehicles and Robotics

Monitoring, Inspection and Surveillance

Human-Machine Interface

Personal Device Enhancement

Vision

Multi-sensor: image, depth, speed Environmental assessment Full surround views

Attention monitoring Command interface Multi-mode ASR

Social photography Augmented Reality

Audio Ultrasonic sensing

Acoustic surveillance Health and performance monitoring

Mood analysis Command interface

ASR social media Hands-free UI Audio geolocation

Natural Language

Access control Sentiment analysis

Mood analysis Command interface

Real-time translation Local bots Enhanced search

Why Cognitive Vision Now?

• Computing and communication driven by new data in/out

• CMOS sensors trigger imaging explosion

• 99% of of captured raw data is pixels (dwarfs sounds and motion)

1010 sensors x 108 pixels/sec = 1018 raw pixels/sec

• Rapid growth of vision-based products and services

• Starting 2015: more image sensors than people

• New Age: Making sense of pixels requires computer cognition 0

2E+09

4E+09

6E+09

8E+09

1E+10

1.2E+10

1.4E+10

1.6E+10

1.8E+10

2E+10

1990 1995 2000 2005 2010 2015 2020

World Population

Three-year sensorpopulation

Vision

0

10

20

30

40

50

2011 2012 2013 2014 2015 2016Imag

eN

et

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-1 E

rro

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Year

Rapid Progress on Accuracy

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0 5 10 15 20 25

Imag

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GMACs per image

Bounded Compute Load

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0 50,000,000 100,000,000 150,000,000

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Model Coefficients

Models Getting More Manageable

• Computer vision is big, obvious NN domain

• Many related tasks: classification, localization, segmentation, object recognition, captioning, generation

• Huge computation in embedded inference

• Vision is fundamentally hard – even for humans!!

• Example: ImageNet Classification: • 1000 categories

• 120 species of dogs

23.5

24

24.5

25

25.5

0 2 4 6 8

Imag

eN

et

Top

-1 E

rro

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GMACs

Optimization Doubles Efficiency

ResNet 50,101

Cadence

Tibetan mastiff Shih-Tzu Norwegian elkhound

Embedded Vision Startup Scene: What and Where? • Identified 74 startups focused on machine learning for embedded vision

• Half in US, >30% in CA. Half doing robots, drones and cars

0

5

10

15

20

25

30

35

Cognitive EV Startup Countries

COMIPATXMA

Surveillance

Vehicles

Human-Machine Interface

Drones and Robots

Silicon

0

5

10

15

20

Application for Cognitive EV Startups

Some Examples

DeepGlint (格灵深瞳) [China]

• 18M in 3 Rounds, most recent June, 2014

• Deep Glint focuses 3D computer vision and machine learning technologies, to provide automatic human trajectory analysis solutions to banks and shopping centers.

Mashgin [US]

•$620k in 3 Rounds, most recent September, 2015

•Mashgin is building a self-checkout kiosk that uses 3D reconstruction, computer vision and deep learning to identify items.

FiveAI [UK]

$2.7M Seed on July, 2016

We're building the world's most reliable autonomous vehicle software stack to solve the most difficult problem of all - delivering a solution that's safe in complex urban environments, without any driver involvement.

Embedded Vision Drives Hardware

Need scale and efficiency: • Conventional wisdom - deep neural

networks much less efficient than hand-tuned feature recognition methods (but more effective)

• Convolutional neural networks allow • High parallelism

• Low bit resolution

• Structured, specialized architectures

• Manageable memory bandwidth

• ~1000x energy improvement over GP CPU may compensate for efficiency gap

10

100

1,000

10,000

10 100 1,000 10,000 100,000

GM

AC

S P

er W

att

GMACs

Neural Network Platforms

Vision DSP core 1 Vision DSP core 1 cluster Vision DSP core 2

Vision DSP core 2 cluster Embedded GPU core cluster Data Center GPU 1 cluster

Embedded GPU cluster FPGA 1 FPGA 2

Convolutional neural network (CNN) engine CNN engine cluster Data Center GPU 2 cluster

Data Center GPUs

FPGAs

Embedded GPUs

Vision DSPs

Vision + NN DSPs

CNN engines

GP

CP

U

Hardware Startups for Embedded Vision • Modest number of startups reflects challenging funding for silicon

• Rapid pace of change in basic neural network algorithms dictates programmability

• Major semis and IP providers investing heavily in vision and deep learning

Name Description Website Country State BrainChip Spiking Neuron Adaptive Processor www.brainchipinc.com USA CA

Cambricon Device and cloud processors for AI www.cambricon.com China

Cerebras Systems Specialized next-generation chip for deep-learning applications cerebras.net USA CA

Deep Vision Low-power silicon architecture for computer vision www.deepvision.io USA CA

Deephi Compressed CNN networks and processors www.deephi.com China

Graphcore Graph-oriented processors for deep learning www.graphcore.ai UK

Isocline Ultra-low power NN inference IC design based on flash+analog+digital www.isosemi.com USA TX

KNUPATH Ultra-scale processor ICs for vision and ML www.knupath.com USA TX

Leapmind Embedded deep learning platform www.leapmind.io Japan

Reduced Energy Microsystems

Lowest power silicon for deep learning and machine vision www.remicro.com USA CA

Tenstorrent Deep learning processor: designed for faster training and adaptability to future algorithms

www.tenstorrent.com Canada

ThinCI vision processing chips www.thinci.com USA CA

The Cognitive Embedded Vision List Abundant Robotics

Accelerated Dynamics

AIMotive

Airware

AKA

Alchera Technologies

Algocian

Anki

Argo AI

Auro Robotics

Blue Vision Labs

BrainChip

Cambricon

Cerebras Systems

Clearpath Robotics

CloudMinds

Cognitive Pilot

Comma AI

Deep Vision

Deep Vision

DeepGlint

Deephi

DeepScale

Drive.ai

Emotibot

Emovu

Emteq

Evolve Dynamics

Face++

FiveAI

Graphcore

Horizon Robotics

Intuition Robotics

Iris Automation

Isocline

Kindred

Kneron

KNUPATH

Leapmind

Lily Camera

Machines with Vision

Mashgin

Memkite

Minieye

Momenta

MorpX

Nauto

Netradyne

Neurala

Novumind

Noxton Analytics

nuTonomy

Osaro

Oxbotica

Pilot AI Labs

Quanergy

Reduced Energy Microsystems

RobArt

RoboCV

Rokid

Scortex

Shield AI

Skydio

Sportcaster

Tenstorrent

TeraDeep

ThinCI

Third Eye Systems

Universal Robotics

Velodyne

Viz

White Matter

Zero Zero Robotics

Zoox

Observations

1. Yes, the cognitive startup space is extremely lively, but demand exceeds supply – many under-served niches

2. Deep learning disrupts embedded vision – massive retraining of people and retooling of solutions

3. New technology enables new business models, especially

• new services – e.g. “emotion as a service”

• cloud-device hybrids – e.g. base layers trained in cloud, transfer learning in device

• human augmentation – e.g. guided labeling tools, remote supervision data programming

• autonomous agents – e.g. knowledge structures in support of task reasoning (AIBrain)

4. Expertise and grit gets funded

Sources and Caveats

• Crunchbase: Comprehensive database of startups but needs lots of sifting: includes unfunded dreams, research programs, acquired companies, etc.

• Shivon Zilis and James Cham in the Harvard Business Review: “The State of Machine Intelligence, 2016”

• MMC Ventures: “Artificial Intelligence in the UK: Landscape and learnings from 226 startups”

• 机器之心选出全球最值得关注的100家人工智能公司: (“The heart of the machine selected the world's most noteworthy 100 artificial intelligence companies”)

Caveats:

• Somewhat subjective selection based on estimated degree on concentration on machine learning and neural network methods

• The scene is constantly changing, with companies appearing and disappearing daily

• Hard to get every link and summary correct

• Send me corrections and suggestions: rowen@cogniteventures.com

The Whole Cognitive Computing List www.cogniteventures.com/the-cognitive-computing-startup-list 4Paradigm

ABEJA

Abundant Robotics

Accelerated Dynamics

Affectiva

AIBrain

AiDO

AIMotive

Airware

AKA

Alchera Technologies

Algocian

Algorithmic Intuition

Alpha I

Amplero

Anki

Argo AI

Arimo

Arimo

Arya.ai

Atomwise

Auro Robotics

Aurora AI

Automat

Avalon AI

Aylien

Bay Labs

Behavox

Behold.ai

Benevolent

BenevolentAI

Birds.ai

Bloomsbury AI

Blue Vision Labs

BMLL Technologies

BrainChip

Butterfly Networks

Calipsa

Camio

Capio

Captricity

CareSkore

Cartoaware

Celaton

Cerebellum Capital

Cerebras Systems

Cirrascale

Citrine Informatics

Clarifai

Clear Metal

Clearpath Robotics

CloudMedx

CloudMinds

Cogitai

Cognative

Cognicor

Cognitive Pilot

Comma AI

Content Technologies

Cortexica

Cortica

Cortical.io

CrossingMinds

CrowdAI

CrowdFlower

Cyberlytic

CyCorp

Cylance

Cyra

DarkTrace

Datalogue

Dataminr

DataRobot

Deep Genomics

Deep Instinct

Deep Vision

Deep Vision

Deep Vision

Deep6 Analytics

DeepGlint

Deephi

Deepomatic

DeepScale*

DeepSense.io

DeepVu

Descartes Labs

Digital Reasoning

DigitalGenius

Ditto Labs

Drive.ai

DroneDeploy

Eloquent Labs

Emotech Ltd

Emotibot

Emotient

Emovu

Emteq

Enlitic

Evolve Dynamics

Eyeris

Face++

Fashwell

FeatureSpace

FiveAI

FoodVisor

Grail Inc.

Graphcore

Graphistry

Greedy Intelligence

GridSpace

H2O

Hocrox

Horizon Robotics

Hubino

Hyperverge

ICarbonX

Idio

Imagia

Imubit

Indico

Insilico Medicine

Intelligent Voice

Intelnics

Intuition Robotics

Iris Automation

iSentium

Isocline

JukeDeck

Kaggle

Keen Research

Kheiron Medical

Kimera

Kindred

Kneron

KNUPATH

KONUX

Last Mile Technologies

LastMile

Leapmind

Level 6 AI

Leverton

Lexalytics

Lily Camera

Linguamatics

Loop AI

Luminist

Luminoso

Lunit

Maana

Machines with Vision

Mad Street Dan

Marax AI

Mashgin

Matroid

MEDANN

MedWhat

Memkite

Mentat Innovations

micropsi

MindMeld

Minds.ai*

MINDSET

Minieye

Mobvoi

Momenta

MorpX

Nara Logics

Nauto

Neo AI

Netra

Netradyne

Neural Painting

Neurala

Neurence

NNaisense

Novumind

Noxton Analytics

Nudgr

Numenta

Numerate

nuTonomy

Oncora Medical

OpenAI

OpenCapacity

Orbital Insight

Osaro Oseven Oxbotica Pat Phrasee Pilot AI Labs PitStop Pixoneye Planet Pop Up Archive Preferred Networks Prowler.io pulseData Quanergy Rainbird Technologies RapidMiner Ravn Systems Re:infer Realeyes Recursion Pharmaceuticals Reduced Energy Microsystems Replika Resnap Retechnica RobArt RoboCV Rokid Sage Senses Scaled Inference Scortex Seamless.AI Seldon Semantic Machines SenseTime Sensifai Sentenai Senter Sentient Sentisum Sentrian

Shield AI

Sight Machine

SigOpt

Siwa

Skindroid

Skydio

Skymind

Sonalytic

SoundHound

SpaCy

SparkBeyond

SparkCognition

Speechmatics

Sportcaster

Tamr

Tend

Tenstorrent

TeraDeep

Terrabotics

Terraloupe

TheySay

ThinCI

Third Eye Systems

TickAI

Tractable

TUPU

Twenty Billion Neurons

TypeScore

Unisound

Universal Robotics

Valoosa

Velodyne

Vertex.ai

Vicarious

Visii

Visio Ingenii

Viz

Volley

Vuno

Wave Computing

White Matter

Xihelm

YITU Technology

Zephyr Health

Zero Labs

Zero Zero Robotics

Zest Finance

Zoox

*Chris Rowen has an interest

neural network technology and applications