AI 25发展趋势研究报告
AI 25发展趋势研究报告
WHAT’S NEXT IN AI?
Artificial Intelligence
Table of Contents
CONTENTS
NExTT framework 3
NECESSARY
Open-source frameworks 6 Edge AI 9 Facial recognition 12 Medical imaging & diagnostics 16 Predictive maintenance 18 E-commerce search 20
EXPERIMENTAL
Capsule Networks 23 Next-gen prosthetics 26 Clinical trial enrollment 28 Generative Adversarial Networks (GANs) 31 Federated learning 37 Advanced healthcare biometrics 40 Auto claims processing 43 Anti-counterfeiting 45 Checkout-free retail 50 Back office automation 53 Language translation 55 Synthetic training data 58
THREATENING
Reinforcement learning 62 Network optimization 66 Autonomous vehicles 70 Crop monitoring 73
TRANSITORY
Cyber threat hunting 75 Conversational AI 78 Drug discovery 81
NExTT FRAMEWORK
Artificial Intelligence Trends in 2019
TRANSITORY
Conversational
agents
Cyber threat hunting
Synthetic training data
E- NECESSARY
Open source frameworks
Facial recognition
Predictive Edge maintenance
computing
Medical imaging & diagnostics
commerce search
Drug discovery
Back office automation
Language translation Anti-counterfeit
Check-out free
retail
Advanced healthcare
Auto claims biometrics
Clinical trial processing
enrollment
Next-gen GANs
prosthetics
Federated
learning
Capsule Networks
EXPERIMENTAL
Crop monitoring
Reinforcement
Autonomous
learning
navigation
Network optimization
THREATENING
Low
MARKET S TRENGTH High
Application: Computer vision
Application: Natural language
processing/synthesis
Application: Predictive intelligence
Architecture
L o w
H i g h
I N D U S T R Y A D O P T I O N
Infrastructure
NExTT Trends
TRANSITORY NECESSARY
Advanced driver assistance
Telematics
Vehicle
connectivity On-demand Next gen HD
Lithium-ion
access infotainment
mapping
batteries AI p rocessor chips & software On-board
diagnostics AV sensors & sensor fusion Mobile Digital
marketing dealership
Additive
Industrial internet of
Usage-based manufacturing
things (IIoT)
insurance
Industrial
computer
Wearables and
Alternative
vision
exoskeletons powertrain
Driver
technology monitoring Flexible
Vehicle
Online Decentralized
assembly
lightweighting aftermarket production
lines parts
Predictive
maintenance Vehicle-to-everything
tech
g
Car vendin Automobile
machines
security
Virtual showrooms
Flying robotaxis Blockchain
EXPERIMENTAL
verification
THREATENING
TRANSITORY
Trends seeing adoption but
where there is uncertainty about market opportunity. As Transitory trends become more broadly understood, they may reveal additional opportunities and markets.
NECESSARY Trends which are seeing wide-
spread industry and customer implementation / adoption and where market and applications are understood.
For these trends, incumbents
should have a clear, articulated
strategy and initiatives .
EXPERIMENTAL
THREATENING Conceptual or early-stage trends with few functional products and which have not
seen widespread adoption. Experimental trends are already spurring early media interest and proof-of-concepts. Large addressable market forecasts and notable investment activity.
The trend has been embraced by early adopters and may be on the precipice of gaining widespread industry or customer adoption.
Low
MARKET STRENGTH
High
We evaluate each of these trends using the CB Insights NExTT framework.
The NExTT framework educates businesses about emerging trends and guides their decisions in accordance with their comfort with risk.
NExTT uses data-driven signals to evaluate technology, product, and business model trends from conception to maturity to broad adoption.
The NExTT framework’s 2 dimensions: INDUSTRY ADOPTION (y-axis): Signals include momentum of startups in the space, media attention, customer adoption (partnerships, customer, licensing deals).
MARKET STRENGTH (x-axis): Signals
include market sizing forecasts, quality and number of investors and capital, investments in R&D, earnings transcript commentary, competitive intensity, incumbent deal making (M&A, strategic investments).
I N D U S T R Y A D O P T I O N
L o w
H i g h
NExTT framework’s 2 d imensions
Industry Adoption (y axis)
Signals include:
momentum of
startups in the space
media attention
customer adoption
(partnerships, customer,
licensing deals)
Market Strength (x axis)
Signals include:
market sizing forecasts earnings
transcript
commentary
quality and number
competitive intensity of investors and
capital
investments in R&D incumbent deal making
(M&A, strategic investments)
Necessary
OPEN-SOURCE FRAMEWORKS
The b arrier t o e ntry i n A I i s l ower t han e ver b efore, t hanks t o open-
source software.
Google open-sourced its TensorFlow machine learning library in 2015.
Open-source frameworks for AI are a two-way street: It makes AI accessible to everyone, and companies like Google, in turn, benefit from a community of contributors helping accelerate its AI research.
Hundreds o f u sers c ontribute t o T ensorFlow e very m onth o n G itHub (a software development platform where users can collaborate).
Below are a few companies using TensorFlow, from Coca-Cola to eBay to Airbnb.
Facebook released Caffe2 in 2017, after working with researchers from Nvidia, Qualcomm, Intel, Microsoft, and others to create a “a lightweight and modular deep learning framework” that can extend beyond the cloud to mobile applications.
Facebook also operated PyTorch at the time, an open-source machine learning platform for Python. In May’18, Facebook merged the two under one umbrella to “combine the beneficial traits of Caffe2 and PyTorch into a single package and enable a smooth transition from
fast prototyping to fast execution.”
The number of GitHub contributors to PyTorch have increased in
recent months.
Theano is another open-source library from the Montreal Institute for Learning Algorithms (MILA). In Sep’17, leading AI researcher Yoshua Bengio announced an end to development on Theano from MILA as these tools have become so much more widespread.
“The s oftware e cosystem s upporting d eep learning r esearch h as b een e volving q uickly, and has now reached a healthy state: open- source software i s t he n orm; a v ariety
of f rameworks a re a vailable, s atisfying needs spanning from exploring novel ideas t o d eploying t hem i nto p roduction; and strong i ndustrial p layers a re b acking different s oftware s tacks i n a s timulating competition.”
- YOSHUA BENGIO, IN A MILA ANNOUNCEMENT
A number of open-source tools are available today for developers to choose from, including Keras, Microsoft Cognitive Toolkit, and Apache MXNet.
EDGE AI
The n eed f or r eal-time d ecision m aking i s p ushing A I c loser t o the edge.
Running AI algorithms on edge devices —like a smartphone or a car or even a wearable device —instead of communicating with a central cloud or server gives devices the ability to process information locally and respond more quickly to situations.
Nvidia, Q ualcomm, a nd A pple, a long w ith a n umber o f e merging s tartups, are f ocused o n building chips exclusively for A I workloads a t the “edge.”
From consumer electronics to telecommunications to medical imaging, edge AI has implications for every major industry.
For example, an autonomous vehicle has to respond in real-time to what’s happening on the road, and function in areas with no internet connectivity. Decisions are time-sensitive and latency could prove fatal.
Big tech companies made huge leaps in edge AI between 2017-2018.
Apple released its A11 chip with a “neural engine” for iPhone 8, iPhone 8 Plus, and X in 2017, claiming it could perform machine learning tasks
at up to 600 billion operations per second. It powers new iPhone features like Face ID, running facial recognition on the device itself to unlock the phone.
Qualcomm launched a $100M AI fund in Q4’18 to invest in startups “that share the vision of on-device AI becoming more powerful and widespread,” a move that it says goes hand-in-hand with its 5G vision.
As the dominant processor in many data centers, Intel has had to play catch-up with massive acquisitions. Intel released an on-device vision processing chip called Myriad X (initially developed by Movidius,
which Intel acquired in 2016).
In Q4’18Intel introduced the Intel NCS2 (Neural Compute Stick 2), which is powered by the Myriad X vision processing chip to run computer vision applications on edge devices, such as smart home devices and industrial robots.
The CB Insights earnings transcript analysis tool shows mentions of edge AI trending up for part of 2018.
Microsoft said it introduced 100 new Azure capabilities in Q3’18 alone, “focused on both existing workloads like security and new workloads like IoT and edge A I.”
Nvidia recently released the Jetson AGX Xavier computing chip for edge computing applications across robotics and industrial IoT.
While AI on the edge reduces latency, it also has limitations. Unlike the cloud, e dge has s torage a nd p rocessing c onstraints. M ore hybrid m odels will emerge that allow intelligent edge devices to communicate with each other and a central server.
FACIAL RECOGNITION
From unlocking phones to boarding flights, face
recognition is going mainstream.
When it comes to facial recognition, China’s unapologetic push towards surveillance coupled with its AI ambitions have hogged the media limelight.
As the government adds a layer of artificial intelligence to its surveillance, startups are playing a key role in providing the government with the underlying technology. A quick search on the CB Insights platform for face recognition startup deals in China reflect the demand for the technology.
Unicorns like SenseTime, Face++, and more recently, CloudWalk,
have emerged from the country. (Here’s our detailed report on China’s surveillance efforts.)
But e ven i n t he United S tates, i nterest i n t he t ech i s s urging, a ccording t o the CB Insights patent analysis tool.
Apple popularized the tech for everyday consumers with the introduction of facial recognition-based login in iOS 10.
Amazon is selling its tech to law enforcement agencies.
Academic institutions like Carnegie Mellon University are also working
on technology to help enhance video surveillance.
The university was granted a patent around “hallucinating facial features”—a method to help law enforcement agencies identify masked suspects by reconstructing a full face when only the periocular region of the face is captured. Facial recognition may then be used to compare the “hallucinated face” to images of actual faces to find ones with a strong correlation.
But the tech i s not without g litches. A mazon was in t he news for reportedly misidentifying some Congressmen as criminals.
Smart cameras outside a Seattle school were easily tricked by a WSJ reporter who used a picture of the headmaster to enter the premises, when the “smile to unlock feature” was temporarily disabled.
“Smile to unlock” and other such “liveness detection” methods offer an added layer of a uthentication.
For instance, Amazon was granted a patent that explores additional layers of security, including asking users to perform certain actions like “smile, blink, or tilt his or her head.”
These actions can then be combined with “infrared image information, thermal imaging data, or other such information”
for more robust authentication.
Early commercial applications are taking off in security, retail, and consumer electronics, and facial recognition is fast becoming a dominant form of biometric authentication.
MEDICAL IMAGING & DIAGNOSTICS
The FDA is greenlighting AI-as-a-medical-device.
In April 2018, the FDA approved AI software that screens patients
for diabetic retinopathy without the need for a second opinion from
an expert.
It was given a “breakthrough device designation” to expedite the process of bringing the product to market.
The software, IDx-DR, correctly identified patients with “more than mild diabetic retinopathy” 87.4% of the time, and identified those who did not have it 89.5% of the time.
IDx is one of the many AI software products approved by the FDA for clinical commercial applications in recent months.
The FDA cleared Viz LVO, a product from startup Viz.ai, to analyze CT scans and notify healthcare providers of potential strokes in patients. Post FDA clearance, Viz.ai closed a $21M Series A round from Google Ventures and Kleiner Perkins Caufield & Byers.
The FDA also cleared GE Ventures-backed startup Arterys for its Oncology AI suite initially focused on spotting lung and liver lesions.
Fast-track r egulatory a pproval o pens u p n ew c ommercial p athways f or over 80 AI imaging & diagnostics companies that have raised equity financing since 2014, accounting for a total of 149 deals.
On the consumer side, smartphone penetration and advances in image recognition are turning phones into powerful at-home diagnostic tools.
Startup Healthy.io’s first product, Dip.io, uses the traditional urinalysis dipstick to monitor a range of urinary infections. Users take a picture
of the stick with their smartphones, and computer vision algorithms calibrate the results to account for different lighting conditions and camera quality. The test detects infections and pregnancy-related complications.
Dip.io, which is already commercially available in Europe and Israel, was cleared by the FDA.
Apart from this, a number of ML-as-a-service platforms are integrating with FDA-approved home monitoring devices, alerting physicians when there is an a bnormality.
PREDICTIVE MAINTENANCE
From manufacturers to equipment insurers, AI-IIoT can save incumbents millions of dollars in unexpected failures.
Field a nd f actory e quipment g enerate a wealth o f data, yet unanticipated equipment failure is one of the leading causes of downtime in manufacturing.
A r ecent G E s urvey o f 450 f ield s ervice a nd I T d ecision m akers f ound
that 70% of companies are not aware of when equipment is due for
an upgrade or maintenance, and that unplanned downtime can
cost companies $250K/hour.
Predicting w hen e quipment o r i ndividual c omponents w ill f ail
benefits asset insurers, as well as manufacturers.
In predictive maintenance, sensors and smart cameras gather a continuous stream of data from machines, like temperature and pressure. T he quantity and varied formats of real-time data generated make m achine l earning a n i nseparable c omponent o f I IoT. O ver t ime, t he algorithms can predict a failure before it occurs.
Dropping costs of industrial sensors, advances in machine learning algorithms, and a push towards edge computing have made predictive maintenance more widely a vailable.
A leading indicator of interest in the space is the sheer number of big tech companies and startups h ere.
Deals to AI companies focused on industrials and energy, which includes ML-as-a-service platforms for IIoT, are rising. Newer startups are competing with unicorns like C3 IoT and Uptake Technologies.
GE Ventures was an active investor here in 2016, backing companies including Foghorn Systems, Sight Machine, Maana, and Bit Stew Systems (which it later acquired). GE is a major player in IIoT, with its Predix analytics platform.
Competitors include Siemens and SAP, which have rolled out their own products (Mindsphere and Hana) for IIoT.
India’s Tata Consultancy announced that it’s launch ing predictive maintenance and AI-based solutions for energy utility companies.
Tata claimed that an early version of its “digital twin” technology —replicating on-ground operations or physical assets in a digital
format
for monitoring them —helped a power plant save ~$1.5M per gigawatt per year.
Even b ig t ech c ompanies l ike M icrosoft a re e xtending t heir c loud a nd edge analytics solutions to include predictive maintenance.