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Machine to Machine Communication in the AI Age

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Today, in order to have two machines talking to each other, one need to use a lot of human effort. Engineers are required to discover services and APIs, read their docs, write the integration code and maintain the integration as software evolves. These are the most boring engineering tasks that are delegated to cohorts of support engineers.

And that’s just about the engineering, whereas people also required to sign contracts and terms of service and pay bills. Common situation is that the upfront labour costs to integrate surpass the running costs of using the API for a couple of years.
Moreover, for many cases, such as AI services like machine translation, you need frequently swap service providers to have a cutting edge of technology. With the manual peer-to-peer API integration that’s just economically infeasible.

Veröffentlicht in: Technologie
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Machine to Machine Communication in the AI Age

  1. 1. Machine to Machine Communication in the AI Age Konstantin Savenkov Intento, Inc. Data Science Weekend, Moscow, 2017
  2. 2. It’s not about SkyNet It’s about business and should be solved Data Science Weekend, Moscow, 2017
  3. 3. New Digital Economy MAIN FACTORS cheaper information search cheaper communication MAIN OUTCOMES more scale digital transformation more room in value chains 1995-… Data Science Weekend, Moscow, 2017
  4. 4. New AI Economy 2016-… MAIN FACTOR cheaper decision making MEANING data collection, storage and processing risk management and prediction decision delegation, implementation and monitoringData Science Weekend, Moscow, 2017
  5. 5. New AI Economy 2016-… EFFICIENCY for business built around decisions: mortgage, insurance etc AI TRANSFORMATION business processes rebuilt around decision-making automation NEW MARKETS more room in value chains Data Science Weekend, Moscow, 2017
  6. 6. Decision-making in XX few trusted information sources in-house analysts and experts few trusted partners on pre-paid contracts planning everything ahead Data Science Weekend, Moscow, 2017
  7. 7. Decision-making in XXIreal-time information discovery outsourced analysts and experts on-demand services with instant gratification in situ decision making COOPERATION more better faster cheaper Data Science Weekend, Moscow, 2017
  8. 8. Would machines cooperate like we humans do, but better? Data Science Weekend, Moscow, 2017 ?
  9. 9. Machine cooperation TODAY few hardwired integrations no machine-readable documentation custom pre-paid contracts and ToS Data Science Weekend, Moscow, 2017
  10. 10. How many humans does it take to make two programs talk? Client App Service Provider Discover API Read API docs Write integration code Maintain integration $394Bn System Integration Market* $35Bn Custom Software Integration Market* *2017 projection by Gartner Data Science Weekend, Moscow, 2017
  11. 11. How many humans does it take to make two programs talk? Client App Service Provider Discover API Read API docs Write integration code Maintain integration + legal + deals Data Science Weekend, Moscow, 2017
  12. 12. Typical solutions Custom integrations in private cloud IPaaS (cloud integration for public services) Crowdsourcing (service platforms) are based on manual directories Unlikely to work for “50Bn connected devices by 2020” Data Science Weekend, Moscow, 2017 (to avoid custom p2p integrations)
  13. 13. Like it was in early web Personal bookmarks Curated directories Automated search but… Data Science Weekend, Moscow, 2017
  14. 14. APIs are not documents, delivery means not reading, but interacting HERE COMES AI! Data Science Weekend, Moscow, 2017
  15. 15. Two interesting projects Viv Labs, acquired by Samsung in 2016: synthesises middleware code for third-party services integrated to the platform Microsoft/Cambridge DeepCoder: combines code samples from other programs to reach its goal Data Science Weekend, Moscow, 2017
  16. 16. Two interesting projects Viv Labs, acquired by Samsung in 2016: synthesises middleware code for third-party services integrated to the platform Microsoft/Cambridge DeepCoder: combines code samples from other programs to reach its goal Intento: API Integration Platform, powered by “artificial engineers” THREE! Data Science Weekend, Moscow, 2017
  17. 17. INTENT Client software translate CONTEXT text to Intento Service Platform accepts requests with an intent and its context, routes them to the appropriate API, receives the answer and translates it back to the intent domain. Service providers Microsoft Cognitive Services IBM Watson Google Cloud Services … Indie providers System of Intents Routing & Integration Billing Data Science Weekend, Moscow, 2017
  18. 18. Focused on AI Services machine translation image recognition voice synthesis OCR et ceteraWHY? multiple providers per intent arms race between service providers context-based relevance (currenly)
  19. 19. (DEMO)
  20. 20. What it has to do with Machine to Machine Communication? powered by “artificial engineers”, not manual ideally, any new public service is integrated to the platform in a matter of seconds, much like Google does to websites “standard library” of intents may be used as a lingua franca for “I need to to do…” between machines
  21. 21. Please, Oh Please! If you are interested in either using the Intento Platform or submitting your APIs, fill out the form at https://inten.to/api-platform
  22. 22. Q&A Konstantin Savenkov CEO Intento <ks@inten.to> Data Science Weekend, Moscow, 2017

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