Artificial Intelligence Industry: An Overview by Segment by Emerj AI Research

2. Today’s artificial intelligence
market is not easy to quantify.

3. Besides the lack of consensus on a coherent
definition for “artificial intelligence” as a term, the
field’s nascent stage of development makes it
difficult to carve out silos or hard barriers of where
one industry or application ends, and another
begins.

4. If you’re interested in how developments in
machine learning and AI might impact your own
company or then keeping an eye on trends of
industry and application growth is pertinent.

5. We hope that this presentation will be a good
jumping off point to some of the most thought-out
assessments of AI and it’s “segments” as we could
collate from the web.

6. We’ll begin by looking through existing efforts to break
up and categorize the AI market, and we’ll end by
pulling together what we believe to be the key
meta-trends and insights.

7. CB Insights Various AI Reports (2016)

8. CB Insights is a New York-based research firm with
under 200 employees, specializing in tech
intelligence from various soures, to include venture
capital, startups, patents, partnerships, and new
media. They seem to have created some of the most
relevant graphics and information about the broad AI
market.

9. In the graphic above, CB Insights highlights the acquisitions and fundraising deal
frequency across various industries. Their own research shows healthcare
topping the list, with marketing / advertising and business intelligence trailing
only slightly in 5-year deal volume.
Link to Research

10. Noteable Quotes
AI in healthcare accounted for 15% of all equity
deals to artificial intelligence startups in 2015.
Smart money VCs have backed companies
including Lumiata, SigTuple, Deep Genomics and
twoXAR.

12. The previous graphic displays funding activity from major investment firms over
the last 5 years. CB Insights shows Intel, Google, and GE topping the list. The data
in this illustration seems to be derived from the number of companies funded
rather than number of dollars funded (though there is likely a correlation
between the two). Knowing who is investing in what companies (and which
technologies) is useful knowledge for other industry leaders who want to
understand where the “smart money” is finding a home in the AI market.

13. The graphic above is particularly interesting because it seems to
demonstrate that “long shot” AI platforms and core technologies have
garnered significantly more investment than companies focusing on
specific technology problems of the next few years.

14. In the days of the internet, there were battles between Netscape, Yahoo,
Microsoft, and others, in order to determine who would “win the web.” It might be
said that Google came out as the winner of web search and consumer web
applications broadly speaking. Investors are interested in investing the same
kind of game-changer technology in AI, which seems to give broadly-focused
companies like Sentient Technologies and Vicarious Systems some of their allure.

15. It seems prudent to keep an eye on where these aspiring AI “industry standard”
companies focus their efforts, as those domains will almost invariably gather
more momentum and excitement as this nascent field looks to new leaders vying
to become global AI platforms / solutions for direction and focus.

16. VentureScanner AI Industry
Assessment (March 2016)

18. The AI Sector Map previously featured breaks down into 13 broad categories, with
the total number of active companies in that sector in parentheses. The results
from VentureScanner’s March 2016 report is as follows:
• Deep learning/machine learning (general) (123 companies)
• Deep learning/machine learning (apps) (260 companies)
• NLP (gen) (154 companies)
• NLP (speech rec) (78 companies)
• Computer Vision/Image Rec (gen) (106 companies)
• Computer Vision/Image Rec (apps) (83 companies)
• Gesture control (33 companies)
• Virtual personal assistants (92 companies)
• Smart robots (65 companies)
• Rec engines and collaborative filtering (60 companies)
• Context aware computing (28 companies)
• Speech to speech translation (15 companies)
• Video automatic content recognition (14 companies)
Link to Research

19. Other charts on the VentureScanner report page show funding by AI category (by
far most are in machine learning (ML) apps, followed by NLP); venture investing
in AI (most in ML apps, followed by NLP); AI total funding annually (accelerating
since 2010); average funding by AI category (most in ML apps, followed by smart
robots and gesture control); average age of technology by AI category (speech to
speech translation oldest, followed by gesture control, video content
recommenders, and speech recognition); among others.
A total of 910 companies were accounted for in this report. VentureScanner’s
same “sector analysis” for the previous year counted 633 companies.

20. TechEmergence Executive Consensus
on AI Trends (June 2016)

22. Our own recent AI industry research involved polling over 30 AI startup founders
and company executives (with firms as small as six people and as large as 600).
Our first consensus question asked about AI consumer technology applications
in the coming five years.
The vast majority of companies interviewed had nothing to do with chat bots or
personal assistants, yet over a third of all executive responses expressed
confidence in chat bots as the most influential consumer AI technology in the
coming give years.
It’s important to note that the question of technology trends was presented in an
open-ended fashion, and categories (such as “smart objects / environment”,
“virtual agents”, etc) were applied after analyzing individual responses.
Link to Research

23. O’Reilly State of Machine Learning 2.0
(December 2015)

25. This article was written by Bloomberg’s Shivon Zilis, and it breaks down the
“Machine Intelligence Ecosystem” into a number of categories and sub-
segments.
Shivon’s graphic lists dozens of companies, though it seems clear that many
more had to be left out due to limitations of the size of this graphic. In this case,
we see a different and distinct set of segments than in other research graphics,
as well as the inclusion of non-profits (i.e.: OpenAI) and open source technologies
(i.e.: Caffe).
Link to Research

26. Noteable Quotes
The two biggest changes I’ve noted since I did this
analysis last year are (1) the emergence of
autonomous systems in both the physical and
virtual world and (2) startups shifting away from
building broad technology platforms to focusing
on solving specific business problems.

27. Shivon’s “machine learning landscape” from 12 months prior (December
2014) is available on Medium, with the graphic below:

28. Comet Labs AI and Robotics Startup
Landscape (February 2016)

29. Comet Labs is a venture fund focused specifically on artificial
intelligence-oriented technologies, and they’ve done a good deal of their own
homework in assessing the industry at large. Their own effort to map the land-
scape of AI and robotics startups can be in the next slide:

31. The synopsis of their brief findings can be found below:

32. While this graphic does draw attention to various discrete industries and compa-
nies, it seems to be a bit more broadly focused and includes a number of startup
but also a handful of companies like Yaskawa (founded over 100 years ago), Ap-
ple (Siri) and Nvidia, which don’t seem to belong in a graphic labeled as
“startups.”
That discretion aside, the article also doesn’t make it clear how many companies
were assessed, or how and why the industry delineations were drawn out (where
is “natural language processing” or “assistants” or “marketing / advertising”?)

33. The graphic does help draw attention to some of the major application areas of AI
(and mirrors Comet Labs’ own logo), but doesn’t appear as comprehensive or to
draw conclusive enough insights about the current “industry breakdown” to
warrant serious merit.
In the Comet Labs article in which those graphics were featured, there are
interesting and useful examples given of the ongoings of specific startups within
the various sectors highlighted.
(Note:We recently interviewed Comet Labs’ Managing Director Saman
Farid, and the episode will air on our podcast soon).

34. BCC Research Smart Machines Market
Report (May 2014)

35. BCC Research presents their growth forecasts for 2019-2024 (in millions)
below:

36. The graphic featured on the previous slide is from the Siemens’ corporate blog. It
is interesting to note that BCC predicts the highest aggregate 5-year growth rate
in the area of digital assistants, which seems to corroborate with our own 5-year
AI trends executive consensus.
Link to Research

37. Nvidia’s Report on GPU Usage
(January 2016)

38. Graphics processing unit (GPU) is a specialized electronic circuit, designed to
rapidly manipulate and alter memory to accelerate the creation of images in a
frame buffer intended for output to a display (credit). Nvidia is one of the world’s
leading GPU manufacturers, and they have chronicled and visualized the growth
of GPU-related sales into various industry segments, as seen in the next slide:

40. While GPU usage is by no means a causal influence on the applications of AI in a
given industry, it provides a certain amount of context on industry growth (Nvid-
ia claims to have sold GPUs to nearly 100 times more companies in 2015 than in
2013, a significant leap). Even in 2015, we can see that higher education (which
we can presume to imply university research) is the largest consumer of Nvidia’s
GPU technology.
It can be presumed that the shrinking relative percentage of sales to “Higher Ed”
will continue as more companies in all sectors begin adapting machine learning
into their regular processes.
Link to Research

41. Noteable Quotes
In just two years, the number of companies
NVIDIA collaborates with on deep learning has
jumped nearly 35x to over 3,400 companies.
Industries such as healthcare, life sciences,
energy, financial services, automotive,
manufacturing, and entertainment will benefit by
inferring insight from mountains of data.

42. Other Vertical Breakdowns
There are a number of other attempts to value and properly segment AI
verticals. The following slides feature a few worth considering:

43. Markets and Markets breaks out AI verticals into the following main
categories in their 2020 AI Forecast:
Media & Advertising
Finance
Retail
Healthcare
Automotive & transportation
Agriculture
Law
Oil & gas

44. Tractica’s recent AI Applications in Enterprise report breaks down
technology forecasts as follows:
Cognitive Computing
Machine Learning
Deep Learning
Predictive APIs
Natural Language Processing
Image Recognition
Speech Recognition
Other AI Technologies
Others

45. Takeaways on Artificial Intelligence
Market Segmentation

46. The amount of reliable information about the AI market is less than ideal, and far
less quantified than more mature and established markets. Regardless, there
is still insight to be gained, and below are outlined some of the most important
takeaways from an assessment of previous efforts to segment the AI market.

47. 1 – Healthcare, Marketing, and Finance
Consistently Appear as Areas of AI
Focus
1

48. CB Insights claims that healthcare has been the domain of greatest deal flow in
AI. Google’s DeepMind is honed in on healthcare, IBM set it’s sights on healthcare
years ago (and continues to burrow into that market), and many of the biggest
“broad AI” players like Ayasdi are jumping into the healthcare market.
Healthcare also offers a kind of noble “flavor” that other application areas do not.
AI companies who begin by working on Wall Street may be perceived as simply
profit-driven, or possibly helping the wrong party, while a company devoting
itself to curing disease or improving treatment (even if for the exact same profit
motive) may be viewed in a different light.

49. It’s our contention that companies like DeepMind who are interested in moving
towards strong AI will have to move forward with “friendly” steps into noble fields
like medicine in order to dispell some of the fear around progress towards
machines that may (one day) become more intelligent than humans.
Marketing and finance also represent huge areas of AI focus. Sentient
Technologies’ Aware software promises to deliver better conversion rates for
eCommerce vendors, and Cortica‘s myriad applications for eCommerce and
marketing will be fleshed out in the coming months and years.

50. All three of these commonly targeted AI segments – health, marketing and fi-
nance – involve a tremendous amount of high-volume information, and all three
segments are nearly infinite in size. I’m of the belief that the convoluted sales
cycles and market forces in health care will lead to finance, eCommerce, and
marketing leaping ahead in terms of relative AI adoption and innovation, though
only the future will tell. What seems certain is that these three fields will be
among the biggest domains of focus for AI firms, and these application areas are
themselves likely to spawn many scientific innovations in AI itself.

51. 22 – Segmenting Markets is Qualitatively
Messy, but Might Clear Up in Time

52. Determining segments is a high-level art and science; it is a creative task that
requires a well-informed perspective. A market can hypothetically be “sliced” in
an infinite number of ways (depending on one’s purpose), but accurately boxing
applications into specific “verticals” involves a lot of grey area...

53. Consider the following:
CB Insights does not have “natural language processing” (NLP) listed in their vertical break-
down of AI deals, though there are a great many companies working on NLP itself, not just
NLP for eCommerce, but for smart homes, etc...
AI can be an entire business (RocketFuel) or a LOT of what a company does (Yahoo) or some
of what a company does (IBM) – how much AI does a company or deal need to be directly
involved in order to get placed in an “AI Industry” category?
CometLabs’ industry breakdown doesn’t mention marketing / finance at all, despite the fact
that this is one of the most prevalent application areas of AI today and in the near future
The same company could easily be called “eCommerce” by one analyst, “Marketing” by
another, and “both” by yet another, an issue that is terribly difficult to work around with an
increasingly large number of possible segments

54. This is not to say that any of the completed AI industry landscapes are “wrong” (I
happen to like CB Insights’ breakdown as a good general breakdown, and BCC has
a nice simple segmentation as well), but that fuzzy edges will always be present
in this kind of work, and that a consensus across research firms is unlikely
(indeed, a consensus within any one research firm seems unlikely).

55. The “grey area” around and within a specific AI segment may make it more valu-
able to focus on one industry at a time, firmly identifying the grey areas and the
direct applications, without having a thousand points of “fuzzy” overlap between
12 or 20 discrete industries. For example, if one is just analyzing “AI for Business
Intelligence,” one can construct a relatively rigorous set of classifying rules to
determine what does and does not get couched under the header of “AI” and
“Business Intelligence.” Much of our future work here at TechEmergence will
involve specific industry deep dives of this kind.

56. 33 – No One Knows What Will “Take
Off,” and This Influences the
Behavior of the Industry

57. At a recent VentureBeat conference, Robert Stephens (former founder of Geek
Squad) mentioned that the world of AI chat bots is at the phase that the internet
was at in 1994. We don’t know were applications will pick up, or take off, and in
many fields, use cases are still taking shape as “experiments” and not as direct
and succinct drivers of business value.

58. Hence, many companies are “feeling out” where to apply these technologies
(going from “AI platform” to solving specific business problems). Conveying the
value of AI has proven difficult for many vendors. Companies like Sentient Tech-
nologies have confusingly broad demo videos (see their explainer video here)
that could leave one wondering, “What will this do for my business?” IBM has
even been criticized for it’s inability to convey the value of Watson to clients.

59. This “not knowing what will be a big deal” has – we believe – resulted in a
number of observable behaviors and patterns in the AI industry:
Consistently huge bets in the future “Google of AI”, ‘industry standard’ AI platforms (that bat-
tlefield is still wide open, and it’s uncertain how it will congeal, or if it will continue to
fragment over time)
Companies with broad application areas beginning to explain their technology in “non-PhD”
language, and in terms that programmers and CEOs can understand (a good example here is
Ayasdi – their technology is tremendously complex but they explain their value proposition
and use cases in this explainer video and this use case video)
AI vendors and large company executives are both looking to the “cool kids” (hottest new AI
companies with tens of millions in funding, and the biggest AI tech firms like Facebook,
Google, Amazon) for determining future trends, and where they should go next. Since many
AI vendors and large organizations don’t have “traction” in terms of real ROI, they must look
to where they believe traction to exist.

60. 44 – Specifics Up for Grabs, but
Optimism Abounds

61. Despite the disagreements about specific industry or domain application and
adoption, the rumble on the tracks (from all directions) seems to liken AI not to a
specific tool for a few specific jobs, but as an entirely different (and in large part
unimaginable) paradigm of work, research, and productivity.

62. Twenty years ago, Bill Gates locked himself away to contemplate “the internet”
and decide how important it would be for Microsoft’s future. Thank goodness he
took action. Today, Microsoft’s CEO and Google’s CEO have both expressed their
extreme commitment to bringing AI into the core of their business and their plans
for growth. It’s not just Silicon Valley companies on the bandwagon anymore, and
we could never have prepared for the exact roll-out of massive changes brought
about by the internet. Similarly, it is unlikely that we’ll be able to foresee many
of the most powerful applications of AI in business and personal life – but we can
keep our ears to the tracks.

63. Thanks for viewing our presentation. If you’d like to stay ahead of the curve about
cutting-edge research trends and insights in the field of artificial intelligence, be
sure to stay connected on social media by clicking the icons below:
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