On July 13, the Open Innovation Club convened a TechMeeting on Machine Learning.

As explained in a previous article, Machine Learning is a field of Artificial Intelligence (AI) that explores the conception, analysis, and development of algorithms allowing a machine to learn from examples (data) and make predictions. Machine Learning can solve lots of problems and be applied to many industries from healthcare to retail, to finance. Most importantly, Machine Learning is becoming more available to every day engineers and democratizing across the enterprise

The event kicked off with a panel discussion moderated by Julien Brin, Strategist, and featuring Josh Neland, Senior Manager – Technology R&D at Accenture, and Vikram Lakshmipathy, Manager and Data Engineering & Analytics at GE Digital.

The panel addressed uses cases for the application of Machine Learning, such as the Industrial IoT. The panelists agreed that one of the major challenges for machine learning to be deployed in the enterprise is poor data quality (and machine learning algorithms need good quality data!).

While Machine Learning is a talent, it can also become a data war: researchers need lots of data to advance research, but they do not have access to the data.

Finally, the panelists talked about OpenAI and how it can help set standards in AI. OpenAI is a non-profit AI research company, associated with Elon Musk, that aims to promote and develop open-source friendly AI.

We then moved into a second panel discussion moderated by Nick Chapin, Venture Associate at SRI Ventures, and featuring Danny Lange, Head Machine Learning at Uber, Hussein Mehanna, Director of Engineering – Core Machine Learning at Facebook, and Alexis Roos, Senior Engineering Manager at Salesforce.

We started with a conversation on Machine Learning and Deep Learning. As Hussein Mehanna stressed, “Although those have been around for a while, it is the availability of great data quantities, the ever-increasing computer power and the fact that they can return sufficient ROI that have brought these fields back on the spotlight”. Smartphones can gather gigantic amounts of data, even more than they need, and as a consequence any startup launching an app will gather lots of data and be able to integrate Machine Learning capabilities much more easily than ten years ago. Data is king!

The panelists then talked about how they are introducing Machine Learning as a service within their company, trying to bring it to every corner of their organization. Their goal is for every engineer (especially the ones who don’t know about Machine Learning techniques) to be able to use Machine Learning platforms and build products.

Identifying potential synergies and business partnership opportunities between large companies and startups was also the purpose of this event. Many French, American and International corporations attended the event, among them: Renault, Schneider Electric, EDF, Ubisoft, Airbus, Apple, Flextronics, Hyundai, Oracle, SAP, Telefonica, Siemens and Verizon.

8 startups were invited to pitch their solutions covering a wide number of applications.

Here is the list of startups that presented:

Speech Recognition

• Maluuba
• Ava

Sounds Recognition

• Otosense

IoT, personalization and contextualization

• Neura

Neuro Technology

• Rhythm

Data Analytics and Big Data

• Trifacta

• Maana

Retail and E-commerce

• Gofind.ai