Ce diaporama a bien été signalé.
Le téléchargement de votre SlideShare est en cours. ×

Artificial Intelligence: investment trends and applications, H1 2016

Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Chargement dans…3
×

Consultez-les par la suite

1 sur 16 Publicité

Plus De Contenu Connexe

Diaporamas pour vous (20)

Les utilisateurs ont également aimé (20)

Publicité

Similaire à Artificial Intelligence: investment trends and applications, H1 2016 (20)

Plus récents (20)

Publicité

Artificial Intelligence: investment trends and applications, H1 2016

  1. 1. Russian Supercomputing Days September 2016 Peter Zhegin Artificial Intelligence Investment Trends & Applications
  2. 2. Table of content Summary and overview of data collection Presenter’s bio Definition of AI Overview of technology progress Investment trends M&A activity highlights Precedent investments by industry Implementation – case studies Conclusion: how to cope with AI disruption
  3. 3. Summary and overview of data collection Summary Defining AI as a set of technologies allows to describe it better. Core technologies, like computer vision and machine learning developed rapidly in the recent years. Investmentsin AI also grew, from 0.9% of all venture capital investmentsin 2012 to 3.1% in H1 2016. Cross-industrial business applications, as well as specific applications in Healthcare,Manufacturingand Industrial Services and Transportation/Logistics represent 57% of $1.6B invested in AI in H1 2016. Successful implementations of AI tech are already available in industries like Telecommunications, Banking and Manufacturing. Data collection Data on investment in AI was collected and analyzed by Flint Capital from diverse sources including Pitchbook and Crunchbase. 251 company in the US, the UK and Canada were identifiedvia key-word search and manually checked. We included only those companies which received venture capital financing and were privately held at the time of our research.
  4. 4. Peter Zhegin A venture capital professionaland an AI enthusiast Associate at Flint Capital, an internationalventure capital fund with exposure to cognitive technologies. Investmentsinclude: • CyberX – machine learningfor Industrial IoT, • Findo – an NLP-powered smart search engine, • Epistema – collaborative knowledge analytics platform, • AudioBurst – transcribes audio and understands the meaning of spoken words in real-time, and others… Co-lead at Russia.AI – a non profit initiative aiming to support Russian speaking AI entrepreneurs. Previously: Ozon.ru, ABRT Venture Fund. MSc in Management,Leeds UniversityBusiness School, MA in History, Moscow City Pedagogical University.
  5. 5. Artificial Intelligence (AI) may be considered as a set of several technologies Narrative definitions are too wide and they hardly describe what AI actually is Cognitive technologies comprising AI (2) 36% (1) Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements (Cambridge, UK: Cambridge University Press, 2010). Cited from: One Hundred Year Study on Artificial Intelligence (AI100),” Stanford University, accessed August 1, 2016. (2) Deloitte University Pres, http://www.theatlantic.com/sponsored/deloitte-shifts/demystifying-artificial-intelligence/257/ For example: ’Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment’ (1). AI as a set of technologies
  6. 6. 0 100 200 300 400 500 2015 Amazon’s Picking Challenge champion 2016 Amazon’s Picking Challenge champion Human worker Progress in technology is stunning Robots are quickly mastering difficult tasks # of items picked per hour by a robot vs. a human worker (3) (3) http://futurism.com/deep-learning-ai-leads-robot-to-victory-in-amazons-picking-challenge/ (4) http://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not?frsc=dg%7Cd The gap between a human and a robot is still wide 3x progress Error rate on ImageNet visual recognition challenge, % (4) 0% 5% 10% 15% 20% 25% 30% 2011 2012 2013 2014 2015 Goes beyond human level
  7. 7. Investments in AI have been growing rapidly in the last five years AI investments grew from 0.9% of world’s venture capital in 2012 to 3.1% in H1 2016 Venture capital investments in AI and other sectors, FY 2012 - H1 2016, Worldwide,$B and % (5) (5) AI excludes accelerator and incubator deals https://www.cbinsights.com/research-venture-capital-Q2-2016 , https://www.cbinsights.com/blog/artificial-intelligence-funding-trends/, https://www.cbinsights.com/blog/artificial-intelligence-funding-trends-q216/ 44.8 50.3 89.1 128.5 52.2 0.4 0.8 2.2 2.4 1.69 0.9% 1.5% 2.4% 1.8% 3.1% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 FY 2012 FY 2013 FY 2014 FY 2015 H1 2016 VentureinvestmentsinAIas%oftotal Ventureinvestments$B Other venture investments AI venture investments AI venture investments as % of total AI – 3% of VC invested in tech = $1.69B Annual investment in AI ~$2.4B
  8. 8. 24 AI tech companies were acquired or went public for the total disclosed value of $1.2B Selected AI tech companies acquired in H1 2016 Corporations are interested in getting AI tech in H1 2016 Acquirers of AI tech are very diverse in terms of industries and business models
  9. 9. 531 263 189 170 107 102 99 61 33 24 24 16 15 Cross-industrial Consumer products/Services Healthcare Infrastructure Transportation/Logistics Manufacturing/Industrial services Retail/Commerce Finance/Insurance/Legal Agriculture Aerospace Security/Defense Education Construction/Maintenance/Utilities Cross-industrial business functions like HR or Marketing attract AI investments Healthcare, transportation,and manufacturing are approached by AI investors as well Venture capital investments in AI by industry, H1 2016, US,UK, and Canada, $M (6) (6) Data from Flint Capital Applications of AI in Marketing, HR, Business Intelligence, Sales, and Administrative functions AI in manufacturing processes and analytical applications in manufacturing, warehousing Self-driving cars for consumer and business purposes, route-planning software Medical imaging, surgery robotics, analytical software Core technologies e.g. computer vision or NLP Consumer applications: toys & games, photo editing, household robotics Cross-industrial Consumer Healthcare Infrastructure Transportation Manufacturing Every industry uses AI in its own way
  10. 10. Data – the key component of healthcare and AI’s target 95% of investments in AI that helps to collect, analyze and predict AI in Healthcare, sub-segments & examples, H1 2016, US, UK, and Canada, $M (7) 97 50 33 8 1 Data digitalisation/Interpretation Analycal processes augmentation Data-related processes automation Manual+Cognitive processes augmentation Interaction/Communication automation Exoatlet* Visiongate Restoration robotics (7) Data from Flint Capital * When here and further a company is marked by ‘*’ – the company was not included in the sample pf H1 2016 investments and is used as an example
  11. 11. 103 4 Manual processes automation Analycal processes augmentation Transportation – self-driving cars is the key topic >95% of investments in Transportation segment were allocated to self-driving cars AI in Transportation, sub-segments & examples, H1 2016, US, UK, and Canada, $M (8) NAMI-Yandex-KamAz* Clearmetal Zoox (8) Data from Flint Capital
  12. 12. 57 43 3 Analycal processes augmentation Manual processes automation Data digitalisation/Interpretation Manufacturing/Industrial services are balancing between analytics and robotizing AI is applied almost equally to analytical and operational elements of production process AI in Manufacturing/Industrial services, sub-segments & examples, H1 2016, US, UK, and Canada, $M (9) Seegrid (9) Data from Flint Capital RoboCV* Senseye CyberX*
  13. 13. Verdigris BRAIQ Astrobotic AI applications are almost endless… Aerospace, Utilities, Productivity, Interfaces and many more Findo*
  14. 14. Telco's, banking, steel production, consumer services – AI seems to be industry agnostic Case-studies of AI tech implementations (10) http://eprints.lse.ac.uk/64516/1/OUWRPS_15_02_published.pdf , (11) SDBA Group presentation, 2016 (12) https://yandexdatafactory.com/case-studies/ydfs-recommender-system-to-decrease-steelmaking-costs-at-magnitogorsk-iron-and-steel-works/ (13) Findo’s presentation, 2016 Telefonica Retail bank Magnitogorsk Iron & Steel Works By Blue Prism (UK) By SBDA Group (RU) By Yandex Data Factory (RU) Telefónica O2 automated 15 core processes including SIM swaps, credit checks, and others, representing about 35 percent of all back office. FTEs had been reduced on the automated processes by a few hundred. UK-based people were redeployed to other service areas and the business continued to grow (10). ML enabled the bank to send to cli ents very targeted messages releva nt to their real life events. As a result, usage of some services increased e.g. from 1% to 6% for paying parking fines online and from 41% to 74% for mobile top-up services (11). Yandex Data Factory created a rec ommender system, integrated into MMK’s software, that helps to reduce ferroalloy use by an average of 5%. This equates to annual savings of more than $4m in production costs (12). Several industries have already started harvesting fruit of AI implementations Consumers By Findo (RU/US) Uses DL and train generative statis tical models on texts. AI helps with automatic tagging and allows users to search by description, not keywords (13).
  15. 15. Several factors contributes to the success of AI, one has to find a way to exploit it Factors contributingto AI development and ways to exploit it (13) Jasnam S. Sidhu, and David Moloney, Price Waterhouse Coopers Presentation at Digital Catapult’s event, London, September 2016 Factors moving AI forward How to benefit from AI (13) Ø Progress in technology; Ø Growing investments; Ø Interest and resources of the leading corporations; Ø Cross-industrial and cross-functional character. Ø Track new companies / products in AI; Ø Define clear priorities on what to look at; Ø Develop a relevant strategy; Ø Build a relevant talent pool; Ø Experiment with AI. AI is here to stay, paying attention to it is curtail for success of a business
  16. 16. Thankyou! Please feel free to let me knowif youwouldlove to knowmoreaboutAI applications,marketsandinvestmenttrends and/or youare developinganAIstartup pz@flintcap.com checkfor insightsandupdates

×