Top AI Trends To Watch In 2018

Artificial intelligence is changing the fundamental structure of every industry in areas ranging from agriculture to cybersecurity to commerce to healthcare, and more. We’re also interacting with technology in new ways, from giving voice commands to washer-dryers to playing advanced gesture-controlled video games.
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<p>Top AI Trends To 
Watch In 2018
China is racing ahead in AI. Deep learning is 
getting a make over. AI is coming to Cannabis 
tech. We look at 13 artificial intelligence 
trends reshaping industries and economies.
Artificial intelligence is changing the fundamental structure of 
every industry in areas ranging from agriculture to cybersecurity 
to commerce to healthcare, and more. We're also interacting 
with technology in new ways, from giving voice commands to 
washer-dryers to playing advanced gesture-controlled video 
games.
Governments are competing to establish superior AI research, 
seeing AI as a lever for greater economic influence and power.
We are also in the early stages of drastic shifts in the labor 
market. The hype around machine learning may start to fade 
 but that's because machine learning has already penetrated 
virtually every major piece of software, from calendar apps to 
search engines to sales management software.
AI is heating up across every industry
Equity deals Q4'12Q4'17
AI can now out-bluff world poker champions. A humanoid robot 
can do a perfect back flip and land on its feet. But despite these 
advances, AI algorithms are far from perfect in basic tasks that 
are easy for humans, such as understanding a scene in an image 
or recognizing a conversation's context.
Meanwhile, the promise of general AI  or artificial intelligence 
that can quickly learn new tasks without supervision  remains 
uncertain. Although a handful of companies like Vicarious 
Systems and Kindred have raised money to develop general AI, 
there is little evidence of specifics or real traction.
AI applications today focus on very narrow tasks. But together 
these narrow AI-driven tasks are reshaping businesses, 
markets, and industries.
We examined our database for the metrics and trajectories of 
thousands of AI companies globally to bring you 13 artificial 
intelligence trends our analysts will be watching in 2018. These 
range from China's ambitious plans to the emergence of capsule 
networks to 6-figure salaries for AI specialists.
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1New blue collar job  robot babysitters
2AI for X is  everywhere 
3China vies with US for global AI leadership 
4The future of defense turns on AI
5Cmo ests, Alexa?
6White-collar automation accelerates
7AI moves to the edge
8The emergence of 'capsule networks'
96-figure salaries in the AI talent wars
10The machine learning hype will die
11Amazon, Google, Microsoft dominate enterprise AI
12AI diagnostics gets the nod from regulators
13DIY AI is here
Table of 
contents
1
New blue collar job  
robot babysitters
Manufacturing jobs are notoriously vulnerable to being out-
sourced to developing countries where labor costs are cheaper.
But dropping industrial robot costs can sometimes also bring 
manufacturing bases closer to site of demand.
Recently, Chinese T-shirt manufacturer Tianyuan Garments 
Company signed a Memorandum of Understanding (MoU) with 
the Arkansas government to employ 400 workers at $14/hr at its 
new garment factory in Arkansas. Operations were scheduled to 
begin by the end of 2017.
Tianyuan's factory in Little Rock, Arkansas, will use sewing robots 
developed by Georgia-based startup SoftWear Automation to 
manufacture apparel for Adidas.
It appears much of the heavy lifting will be done by the robots 
with human workers taking over high-end jobs including robot 
maintenance and operation.
This means that the number and nature of manufacturing jobs will 
never equal 2008 numbers.
1
Counterintuitively, 
industrial robots and 
manufacturing jobs are 
both on the rise in the 
United States.
2
The Bureau of Labor Statistics includes different types of jobs 
under its definition of the manufacturing industry. Its outlook 
for quality control inspectors and assembler and fabricators, for 
instance, is negative due to the impact of automation.
For context, a 2012 DARPA contract awarded to previously-men-
tioned SoftWear Automation clearly states "complete production 
facilities that produce garments with zero direct labor is the 
ultimate goal."
But ever-changing consumer preferences and robots that are 
unable to adapt to drastic process changes stand in the way of 
complete automation.
This is reflected even in Amazon's highly automated warehouses.
Amazon's collaborative warehouse robots perform much of 
the heavy lifting, while workers focusing on delicate tasks like 
"picking" items off shelves and slotting them into separate orders.
Robots are still less-than-perfect at gripping, picking, and  
handling items in unstructured environments. Amazon already 
uses over 100,000 robots in various warehouses, but at the same 
time is creating thousands of new jobs for humans in its new 
fulfillment centers.
3
AI for X is  everywhere
Artificial intelligence is everywhere. Or more exactly, machine 
learning is everywhere. Machine learning refers to the training of 
algorithms on large data sets so that they learn how to identify 
and generate desired patterns. Over time, the algorithms  pro-
vided with the correct parameters by their human creators  get 
better at their tasks.
This tech can basically be used to do anything, provided there is 
data to train the software on and a desired outcome in mind.
So: UK's IntelligentX wants to introduce the world's first AI- 
brewed beer.
DeepFish in Russia is using neural networks to identify, well, fish. 
It merges radar technology with AI to differentiate between fish 
and noise in radar images.
Sweden's Hoofstep raised VC money to bring deep learning-based 
behavioral analysis to horses.
Are you vegan, gluten-free, or allergic to soy? New York's Prose 
wants to use AI for made-to-order hair products. It raised $7.57M 
from well-known VCs including Forerunner Ventures, Lerer 
Hippeau Ventures, and Maveron.
2
The 'AI for X' trend is 
unstoppable. From 
brewing beer to tending 
to cannabis buds, 
machine learning is 
doing it all.
4
AI is also coming to cannabis tech. DeepGreen uses computer 
vision to identify the gender and health profile of cannabis  
plants. Weedguide raised $1.7M to use AI for personalized  
weed recommendations.
From hobbies to revenue-generating ideas to simply taking 
things too far, we expect to see more out-of-the-box "AI for X" in 
2018. More broadly, the prevalence of this trend and increasingly 
absurd-seeming examples reveal that machine learning is not 
an exotic technology. Rather, it is one of the building blocks for 
modern software and applications.
5
China vies with US for 
global AI leadership
Despite a mere 9% share of deals going 
to AI startups globally, China's AI startup 
scene took nearly 50% of dollars going to 
AI startups globally in 2017, surpassing 
the United States for the first time for 
share of dollars.
China is aggressively executing a thoroughly-designed vision for 
AI. In some areas of AI, China is clearly beating the US.
The Chinese government is promoting a futuristic artificial 
intelligence plan. It encompasses everything from smart 
agriculture and intelligent logistics to military applications and 
new employment opportunities growing out of AI.
Part of the resources are going to innovative China-based 
startups developing AI in industries ranging from healthcare  
to media.
3
6
In fact, China accounts for a mere 9% share of deals going to AI 
startups globally. But China's AI startups took 48% of all dollars 
going to AI startups globally in 2017, surpassing the United States 
for the first time for share of dollars.
To put this proportion in perspective, in 2016, China accounted for 
only 11.3% of global funding.
China dominates global AI funding
US vs. China total equity funding to startups in 2017
7
The United States still dominates globally in terms of the number 
of AI startups and total equity deals. But it is gradually losing its 
global deal share.
The United States is losing its global AI  
deal share
Equity deal share, 20132017
Chinese companies' R&D efforts are reflected in their  
patent activity.
Chinese companies seem to be overtaking their US counterparts 
in AI patent applications. Based on basic keywords searches 
of title and abstract, AI-related patent publications in China are 
surging far ahead of patents being published in these spaces by 
the US Patent and Trademark Office.
In deep learning, for example, patents published in China are 6x 
what they are in the US. (Note: The patent filing process involves a 
significant time-lag before the publishing of patent applications.)
AI-related patent pub-
lications explode in China
Based on keyword searches of title  
and abstract, 20132017
Source: epo.org
8
Two prominent technologies fueling China's AI growth are facial 
recognition and AI chips. The former advances the government's 
ambitious country-wide surveillance plans, while the latter is a 
direct challenge to US-made chips.
Three key players here are China-based unicorns Megvii  
(dba Face++) and SenseTime, and startup CloudWalk (the  
latter a recipient of a $301M grant from the Guangzhou  
Municipal Government).
In 2017, around 55 cities in China were part of a plan called 
Xue Liang or "sharp eyes." Footage from surveillance cameras 
in public and private properties will be processed centrally to 
monitor people and events. Media reports suggest that this may 
eventually power China's Social Credit System: a metric to gauge 
the "trustworthiness" of its citizens.
China invests heavily in facial recognition 
technology
All deals, including grants, 2013 - 2017
Startup Megvii already has access to 1.3 billion face data 
records on Chinese citizens and is backed by Chinese insurance 
companies (Sunshine Insurance Group), government entities 
(Russia-China investment group), and corporate giants (Foxconn, 
Ant Financial).
9
Two investors in the company, Alibaba Group 
(through Ant Financial) and Foxconn, partnered with 
Hangzhou city in China in 2016 for the "City Brain" 
project, using AI to analyze data from surveillance 
cameras and social feeds.
Ant Financial separately uses facial recognition for 
payments at Alibaba-owned retail stores.
The United States and China are also competing on 
dominance in AI chip technology.
In July 2017, the Chinese government said it 
planned to reach parity with the United States on 
artificial intelligence by 2020 and become the world 
leader by 2030. One government-backed project is 
to create a chip that has 20 times the performance 
and energy efficiency of NVIDIA's GPUs. Chinese 
company Cambricon is pledging to make one billion 
processing units in the next three years and is 
developing chips specifically for deep learning.
Key Chinese tech giants like Baidu and JD are 
investing in AI companies abroad, including in the US.
Recently, Baidu and JD.com backed ZestFinance, 
and Tencent backed NY-based ObEN. Some start-
ups like WuXi NextCODE and Pony.ai are operating 
in both countries, further blurring competitive lines.
Despite scrutiny of Chinese companies seeking 
partnerships or investments in the US, there are 
more Chinese investments in AI startups in the US, 
than vice-versa.
Cross-border AI investments are on the rise
Equity deals, 2013 - 2017
10
4 The future of defense turns 
on AI
The battlefield is moving to data centers.
As far back as in 2014, Amazon built a custom cloud computing 
service for the CIA, meeting stringent compliance and regulatory 
requirements for sensitive data.
In Q4'17, AWS opened these tools to other government customers 
outside the intelligence community.
Amazon has also acquired two AI-cybersecurity companies  
Harvest.ai and Sqrrl  for securing sensitive data in the cloud.
Whether it's Amazon or a host of new startups catering to 
government clients, AI promises to be the backbone of new 
government-sponsored cybersecurity efforts.
In the Cold War, governments talked about their "missile gaps," 
or their disadvantages relative to rivals in terms of nuclear 
warheads. Now, governments increasingly rate their gaps in terms 
of cyber capabilities. As a result, the worlds of cybersecurity and 
traditional defense are merging.
The AI-cybersecurity 
market is getting 
increasingly crowded. 
Some of the startups 
boast a roster of 
government clients 
hoping to stay one step 
ahead of hackers.
11
Data breaches bring the risks into sharp relief: from the  
Equifax leak of millions of citizens' social security numbers to 
WannaCry ransomware to Russian meddling in elections in the 
US and elsewhere.
US government organizations received one of the lowest scores 
for cybersecurity in a 2017 analysis by SecurityScorecard (a 
New York-based company backed by Intel Capital and Moody's 
Corporation, among others). The analysis included "552 local, 
state, and federal government organizations, each with more than 
100 public-facing IP addresses."
Cybersecurity poses a real opportunity for the deployment 
of AI algorithms, since attacks are constantly-evolving and 
defenses frequently face previously-unknown types of malware. 
Presumably, AI would have an edge here given its ability to 
operate at scale and sift through millions of incidents to identify 
anomalies, risks, and signals of future threats.
The market is now flooded with new cybersecurity companies 
trying to leverage machine learning to some degree.
A total of 134 startups have raised $3.65B in equity funding in the 
last 5 years. About 34 of them raised equity for the first time last 
year to compete in a market still dominated by larger companies 
like Cybereason, CrowdStrike, Cylance, and Tanium  each with 
$900M+ valuations.
12
New AI-cybersecurity startups enter market 
dominated by unicorns
1st equity deals, 2013 - 2017
Even a traditional consulting firm like Accenture has been scaling 
its technology in AI-cybersecurity to better serve federal govern-
ment clientele. A notable deal here is startup Endgame, which 
has clients like the US Air Force. Endgame sold its government 
services division to Accenture.
The intelligence community's venture arm In-Q-Tel funded 
Anomali, Interset, and Cylance in 2016. UK's Darktrace claims its 
system has over 3,000 worldwide deployments, including use by 
government agencies. Colorado-based Logrhythm works with the 
US Air force, NASA, and defense contractor Raytheon.
Other top defense contractors are investing here as well. 
Lockheed Martin was an early investor Cybereason (currently 
valued at over $900M). In 2017, Boeing backed Texas-based 
cybersecurity startup SparkCognition through its venture  
arm HorizonX.
13
Cmo ests, Alexa?
Alexa has unleashed a voice revolution.
Voice-enabled computing was all the buzz at the Consumer 
Electronics Show in 2018. Hardly any IoT device was without 
integration into the Amazon Echo or Google Home.
Samsung is now working on its own voice assistant, Bixby. It 
wants all of its products to be internet-connected and have 
intelligence from Bixby by 2020.  LG made all of its appliances 
in 2017 WiFi-enabled. Over 80 LG products now integrate with 
Google Home.
Although Amazon had an early lead in voice computing, it has 
fallen behind in terms of language support.
Amazon announced last quarter that it'll start shipping its Alexa-
powered speakers to around 80 countries. But on the downside, 
it expects users around the globe to either interact in English, 
German, or Japanese.
Google Home is available in English, German, French, and 
Japanese. Apple's HomePod currently is available in English only, 
despite its plans of launching soon in Germany and France.
5
Amazon Echo and 
Google Home domi-
nate the smart home 
speaker market. But 
non-English speaking 
markets are currently 
underserved by the big 
tech companies.
14
Google has a significant advantage over Amazon here. Google 
Assistant for Android phones in available in English, French, 
German, Italian, Japanese, Korean, Spanish, and Portuguese. 
Its speech recognition capabilities  used for speech-to-text 
conversions and voice searches  extends to 119 languages.
The Spanish smart home market is currently underserved by big 
tech companies, despite being one of the most widely spoken 
languages in the world after Mandarin.
In China, Alibaba reported that its Chinese-speaking Tmall Genie 
(its version of Amazon Echo) has sold over 1 million units since 
officially launching in July 2017.
In 2018, we will continue to see voice assistants battle it out for 
dominance in non-English speaking markets.
15
White-collar automation 
accelerates
A growing wave of AI-infused Expert Automation & Augmentation 
Software (EAAS, pronounced /z/) platforms will usher in a new 
era of AI-assisted or AI-enhanced productivity.
This AI-enhanced productivity is threatening jobs at the more 
clerical end of the white-collar spectrum.
The EAAS market map below highlights some of the startups 
building expert automation & augmentation software across a 
number of professions and industries ranging from lawyers to 
journalists to wealth managers to traders to consulting, and more.
For instance, artificial intelligence has huge potential to reduce 
time and improve efficiency in legal work. On the litigation side, 
natural language processing (text analytics) can summarize 
thousands of pages of legal documents within minutes. This 
is a task that might take a human counterpart several days to 
complete. Meanwhile, it also reduces the probability of error.
As AI platforms become more efficient and commercialized, this 
will impact the fee structure of external law firms that charge by 
the hour.
6
White collar workers 
 including lawyers, 
consultants, financial 
advisors, journalists, 
traders, and more  
will face the effects 
of AI as much as 
blue-collar laborers.
16
Programmers are not immune. Early-stage startups are focused 
on AI-based software testing, debugging, and basic frontend 
development. One of the top rounds last year went to UK-based 
DiffBlue, which is developing AI to automate traditional coding 
tasks like bug fixing, custom code development, and translating 
code from one programming language to another.
Healthcare and education are considered to be some of the 
industries least at risk of automation due to the dynamic nature 
of tasks. These fields also require a high level of emotional intelli-
gence. But in education, for instance, AI startups are beginning to 
provide ancillary services like paper grading, language coaching, 
and feedback on writing.
17
AI moves to the edge
AI is getting decentralized.
Intelligence on a device  like a smartphone or a car or even 
a wearable device  gives it the ability to process information 
locally and respond quickly to situations, instead of communicat-
ing with a central cloud or server.
For instance, an autonomous vehicle has to respond in real-time 
to what's happening on the road. Decisions are time-sensitive and 
latency could prove fatal.
Another case for edge AI would be training your personal AI 
assistant locally on your device to recognize your unique accent 
or identify faces.
2017 was marked by big tech companies making huge leaps in 
edge computing.
7
2017 saw a major push 
for bringing AI to the 
edge, i.e. to smaller 
devices and sensors 
operating closer to the 
periphery of computing 
networks. In other 
words, AI may live 
within your earphones 
rather than being 
housed in the cloud or 
in your smartphone.
18
AI on the edge reduces response times
A few examples of emerging edge AI applications
Apple released its A11 chip with a "neural engine" for iPhone 8 
and X. Apple claims it can perform machine learning tasks at up 
to 600B operations per second. It powers new iPhone features 
like FaceID, which scans a user's face with an invisible spray of 
light, without uploading or storing any user data (or their face) in 
the cloud.
As the dominant processor in many data centers, Intel has had to 
play catch-up with massive acquisitions. Recently Intel released 
an on-device vision processing chip called Myriad X (initially 
developed by Movidius, which Intel acquired in 2016).
Myriad X promises to take on-device deep learning beyond 
smartphones to devices like baby monitors and drones.
Google proposed a similar concept with its "federated learning" 
approach, where some of the machine learning "training" can 
happen on your device. It's testing out the feature in Gboard, the 
Google keyboard.
AI on the edge reduces latency. But unlike the cloud, edge has 
storage and processing constraints.
More hybrid models will likely emerge with intelligent edge 
devices communicating with each other and a central server.
19
The emergence of 'capsule 
networks'
Neural networks have different architectures. A popular one in 
deep learning today is known as convolutional neural networks.
Now a new architecture, capsule networks, has emerged and 
promises to outperform convolutional neural networks (CNNs)  
on multiple fronts.
CNNs, despite their success, have shortcomings that may lead 
to lack of performance or even security gaps. Researchers are 
looking for ways to improve AI algorithms and overcome these 
drawbacks.
The example below shows a very basic illustration. A CNN  
would identify individual features and mistake the second  
image to be a face.
Challenges of convolution neural networks
One of the pioneering researchers in deep learning, Google's 
Geoffrey Hinton, published a research paper in 2017 that intro-
duces the concept of "capsule networks," also known as CapsNet.
Deep learning has 
fueled the majority 
of the AI applications 
today. It may now get 
a makeover thanks to 
capsule networks.
8
Illustration source: 
jhui.github.io
20
The paper is still in the review stage, and will need to be tested in 
practical scenarios. But the promise it holds has generated a lot 
of buzz in the media and tech community.
Without getting into technical details, capsule networks would 
allow AIs to identify general patterns with less data and be less 
vulnerable to incorrect results. For example, these networks 
would more easily identify that when features on a face are 
rearranged it is no longer a face. This is something convolutional 
networks are not good at.
Promise of capsule networks
Another issue with CNNs is that they cannot handle different 
variations of input data. For instance, you have to train the 
algorithm with images of the same object from different angles  
or viewpoints for a CNN to identify all variations. As a result,  
it would require a large volume of training data to cover all 
possible variations.
Capsule networks claim to outperform CNNs here. They would 
require less training data, and would take relative positions and 
orientation of an object into consideration without needing to be 
trained exhaustively on variations.
Hinton's paper also claims that capsule networks have been 
tested against some sophisticated adversarial attacks (tampering 
with images to confuse the algorithms) and was found to 
outperform convolutional neural networks.
Illustration source: 
jhui.github.io
21
Hackers can introduce small variations to fool a CNN. 
Researchers at Google and OpenAI have demonstrated this with 
several examples.
One of the more popular examples is from a 2015 paper. As can 
be seen below, a small change that is not readily noticeable to the 
human eye means the image results in a neural network identify-
ing a panda as a gibbon, a type of ape, with high confidence.
22
6-figure salaries in the AI 
talent wars
China is hiring AI experts. 
Some of the salaries listed are nearly $567-624K for a senior 
machine learning researcher at BMW China, and $315-410K for 
ML experts at various other companies.
The jobs are listed on Liepin, a recruitment platform that is itself a 
unicorn startup from China. 
According to a recent Tencent report, the estimated number of 
qualified researchers currently in the field is 300,000, including 
students in relevant research areas. Meanwhile, companies likely 
require a million or more AI specialists for their engineering needs.
In the US, a Glassdoor search for "artificial intelligence" shows 
over 32,000 jobs currently listed, with several salary ranges well 
into the 6 digits.
Big tech companies are scooping up the crme-de-la-crme of AI 
with competitive salary packages.
The demand for AI 
talent is far outpacing 
the availability of 
skilled researchers. 
Top AI researchers 
can now mint money 
in the millions.
9
23
Deepmind Technologies (acquired by Google in 2014) reported 
in its financial statement last year that "staff costs and other 
related costs" accounted for 104.8M. A quick LinkedIn search 
puts the staff number at 415. Assuming this as team size in 2016, 
and discounting other expenses, this puts the average employee 
salary for the team at 252,000 (around $350,000 per annum).
Adding to this, AI researchers at big tech corporations are also 
leaving to start their own companies.
Andrew Ng left Baidu to launch a $175M AI fund. The chief 
technology officer of stealth AI chip startup Groq worked at the 
hardware engineering unit for the Tensor Processing Unit at 
Google and later at Google X.
The CTO and co-founder of chip startup Horizon Robotics was a 
previous Baidu employee who led the image recognition team at 
the Baidu Deep Learning Institute.
With talent bleeding to startups, the talent war becomes even 
more intense.
Source: Companies House, United Kingdom
24
The machine learning hype 
will die
First came big data, then the cloud, then the machine learning frenzy.
We reached peak machine learning in 2017. Incubators alone 
housed more than 300 AI startups, up 3x from the previous year.
Last year investors poured in over $15.2B in funding to AI startups 
across industries. It was a 141% jump in funding from 2016.
Over 1,100 new AI companies have raised their first rounds of 
equity funding since 2016. To put this in perspective, that's more 
than half the historic number of AI startups that have ever raised 
an equity round.
Machine learning will 
soon become the new 
normal. And the 1,100+ 
new AI startups that 
emerged after 2016 will 
need robust business 
models to stay alive.
10
25
AI sees 141% funding jump in 2017
Equity deals, 2013  2017 (excluding hardware-focused 
robotics startups)
But the hype will soon die.
The normalization of machine learning will make investors picky 
about the AI companies they fund.
As Frank Chen of a16z put it, "in a few years, no investors  
are going to be looking for AI startups." It will be "assumed" 
 that startups are using the necessary AI algorithms to power 
their products.
We are already seeing this happen in many industries.
Machine learning is inseparable from IIoT. We need AI to make 
sense of the vast amounts of data collected from machines and 
sensors, and process them in real-time. Almost all cybersecurity 
companies use machine leaning to some extent today. In addition 
to this, big tech companies are offering a suite of machine 
learning solutions to enterprises.
Top investors are carefully gauging startups using AI. For instance, 
diagnostics startup Freenome was sent 5 unlabeled blood 
samples for analysis with its AI-powered algorithms before a16z 
decided to back the company.
26
Amazon, Google, 
Microsoft dominate 
enterprise AI
As companies struggled to integrate machine learning into 
their products, startups emerged to provide ML-as-a-service.
Now, as big tech companies like Google, Amazon, Salesforce, 
and Microsoft improve their enterprise AI offerings, they will 
make smaller companies and funding to the space obsolete.
Google released Cloud AutoML. Customers can bring their 
own data to train the algorithms to suit their specific needs.
Amazon began selling AI-as-a-Service with "Amazon AI" 
under its AWS banner. Amazon AI's goal is to serve big and 
Investors poured in 
$1.8B into enterprise 
AI startups in 5 
years. Now Amazon, 
Microsoft, and Google 
may make smaller 
companies obsolete.
11
27
small-time developers that want AI without the upfront costs or 
hassle. It unveiled offerings that will work like an API and allow 
any developer to access Lex (the NLP inside Alexa), Amazon Polly 
(speech synthesis), and Amazon Rekognition (image analysis).
In Q4'17, Amazon expanded its services to include video 
recognition, audio transcriptions, and sentiment analysis. AWS 
has a massive footprint. Its fourth quarter revenue alone was $5B, 
up 44% quarter-to-quarter.
Microsoft is competing neck and neck with Amazon. Salesforce 
and other companies like Oracle are also not far behind.
28
AI diagnostics gets the 
nod from regulators
US regulators are looking at approving AI for use in  
clinical settings.
The promise of AI in diagnostics is early detection and  
increased accuracy.
Machine learning algorithms can compare a medical image with 
those of millions of other patients, picking up on nuances that a 
human eye may miss. It can do in seconds what a human would 
take hours to complete.
Consumer-focused AI monitoring tools like SkinVision  which 
uses computer vision to monitor suspicious skin lesions  are 
already in use. But a new wave of healthcare AI applications will 
institutionalize ML capabilities in hospitals and clinics.
This month AstraZeneca announced a partnership with Alibaba 
subsidiary Ali Health to develop applications including AI-assisted 
screening and diagnostics in China.
This comes on the heels of GE and Nvidia's partnership to bring 
deep learning capabilities to GE's medical imaging devices, and 
It will soon 
be routine for 
machine learning 
to play a role in 
medical imaging 
and diagnostics.
12
29
Google DeepMind's advances in using AI to diagnose  
eye diseases.
Big names Google DeepMind, IBM, GE, and Alibaba make this a 
tough market for smaller startups to compete in. But this hasn't 
stopped new companies from venturing into the space.
Healthcare is the hottest area of AI startup investment as our 
heatmap at the beginning of the report showed. Much of this 
growth is fueled by medical imaging & diagnostics companies.
The increasingly crowded healthcare AI 
space
1st equity deals, 2013 - 2017
One of the first FDA approvals was for startup Arterys. Its cloud 
computing platform was approved for analyzing cardiac images, 
reportedly based on a series of tests for accuracy and speed of 
diagnosis. It is now applying for FDA approval for AI in oncology.
Another startup, MedyMatch, is using deep learning to detect 
intracranial hemorrhage from CT scans. The FDA recently gave 
it a "breakthrough device designation" to expedite the process of 
bringing the product to market.
The biggest bone of contention in a high-stakes industry like 
healthcare is who takes responsibility for misdiagnosis by AI 
software. The current wave of applications are all geared to assist 
radiologists and physicians, as opposed to serving as a final 
verdict on diagnosis.
30
DIY AI is here
You don't need a PhD in computer science or mathematics to 
build your own AI.
Between open source software libraries, hundreds of APIs and 
SDKs, and easy assembly kits from Amazon and Google, the 
barrier to entry is lower than ever before.
Google launched an "AI for all ages" project called AIY (artificial 
intelligence yourself).
The first product was a do-it-yourself voice recognition kit for 
Raspberry Pi. From making mid-80s intercoms with intelligent 
assistants inside of them, to making voice assistants sound like 
characters from Dr. Who, users are knocking themselves out 
creating new AI-based inventions.
Google also launched a vision kit with neural network software 
programs so users can make algorithms to identify dogs and cats, 
or to match emotion to facial expressions.
Amazon has launched DeepLens, a $249 deep learning-based 
video camera. Amazon is offering $7,500 for the winners of 
its first DeepLens hackathon, which involves building machine 
learning projects.
Make your voice 
assistant sound 
like Dalek from Dr. 
Who. Or build your 
own AI camera.
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