PwC recently predicted that the economic impact of AI on our society will be close to 15 Trillion dollars by 2030 and that no sector will remain untouched. For this reason, on Wednesday March 14th, the Global Open Innovation Network hosted a Techmeeting on AI in Enterprise in San Francisco.

The panel was moderated by Chloe Rolland, Innovation Project Director at PRIME and featured

  • Summit Gupta, VP, AI, Machine Learning and HPC, IBM Cognitive Systems
  • Lionel Cordesses, Senior Team Manager in AI, Renault Innovation Lab
  • Raphael Bord, Strategy lead, Credit Agricole Residency Program in SV
  • Adam Bonnifield, VP Digital Transformation-Head of AI, Airbus

What is AI and Machine Learning?

In 2016, Stanford published a report [1] stating “AI can also be defined by what AI researchers do […] and is a branch of computer science that studies the properties of intelligence by synthesizing intelligence”, the definition most preferred by Lionel Cordesses from Renault. According to the Stanford report, AI is also “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.” In other words, AI is our attempt to make computers mimic humans.

Machine Learning is an application of artificial intelligence that gives machines access to data and lets them learn by themselves, without any rules. Deep Learning is a technique for implementing Machine Learning, generally based on architectures such as deep neural networks or recurrent neural networks. Deep Learning has enabled many practical applications of Machine Learning.

Deep Learning is used in consumer-facing applications such as Alexa and Siri, while other machine learning are more widely used for enterprise applications.

Voice is the next disruptor  

Voice is a game-changer in many industries. Voice is used best when it solves an interface problem; when it is cumbersome to have a screen for example. Voice allows people to interact with information in settings where they otherwise wouldn’t have and with machines in a whole new way. Most importantly though, as pointed out by Adam Bonnifield from Airbus, conversational assistants paired with other forms of artificial intelligence will be the most powerful.

Lionel from Renault shared an excellent example of an open innovation project. Customers of the new LEAF electric vehicle were requesting a way to ask their Amazon Echo the state of charge of their vehicle. Working out of their innovation lab in Sunnyvale, California, with a startup based out of France, Renault gave access to their data and system, to deploy an Alexa skill for all their customers in Japan.

Leveraging the power of startups

All panelists agreed that there is not really a big AI integrator on the market today and a rush to train employees how best to use AI within the enterprise. This is where collaborating with startups and the external environment can be useful. For this reason, it is important, according to the panelists to be as open as possible and trust the startup by giving them access so that they can come up with amazing applications and solutions.

For Credit Agricole, it is also important to make sure executives understand what AI is and what it is not, how it will disrupt the banking and finance sector and then prioritize different actions along a roadmap.

To illustrate the variety of AI applications in the enterprise, several startups pitched their solutions to the audience:

 

Join us for our next Techmeetings:

 

Smart Factory in Paris April 12

Cybersecurity in San Francisco April 16

Ai and Healthcare

[1] Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller.  “Artificial Intelligence and Life in 2030.” One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA,  September 2016. Doc: http://ai100.stanford.edu/2016-report.