Artificial Intelligence

Past Presentations

The Move to AI: From HFT to Laplace Demon

The race for low latency data continues. 10 years ago, Flashboys were helping HFT make money with low-latency infrastructures. Today, hedge funds build AI brains pumping hundreds of sources of data in real-time, seeking ubiquity to build Laplace Demons.

Eric Horesnyi CEO @streamdata.io
Albert Bifet Associate Professor @Telecom ParisTech
Empowering Apps with Microsoft Cognitive Services

Microsoft Cognitive Services let you build apps with powerful algorithms using just a few lines of code. They work across devices and platforms such as iOS, Android, and Windows, keep improving, and are easy to set up. Join us to learn about those APIs (including Bing search APIs) and see how you...

Blazej Kotelko Senior Program Manager
Tools to Put Deep Learning Models in Production

While there are a lot of machine learning frameworks and libraries available, putting the models in production at large scale is still a challenge. I’d like to talk about how we took on the challenge of supporting the data scientists with their efforts by making it easy to put their models in...

Sahil Dua Developer at Booking.com; Open Source Contributor in DuckDuckGo, GitHub and Pandas
Models in Minutes not Months: AI as Microservices

Companies are redefining their businesses by building models and learning from data. Whether it is using data science to predict their best sales and marketing targets, automating digital customer interactions using bots, or reducing waste in logistics and manufacturing - Artificial Intelligence...

Sarah Aerni Director, Data Science @Salesforce Einstein
AI in the Asset Management Industry

In the Financial industry, Artificial Intelligence has been one of the sophisticated techniques used by early adopters to manage multiple assets. Those early adopters are Quantitative Hedge Funds, around since the 80s and managing today an estimated USD 940 billion. After presenting the main...

Antoine Pichot Quantitative Researcher @Systematica Investments
Machine Intelligence at Google Scale

The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn't scale for production service, so you need distributed training on the cloud eventually, or take advantage of...

Guillaume Laforge Developer Advocate at Google Cloud and PMC Chair for Apache Groovy

Interviews

Sarah Aerni Director, Data Science @Salesforce Einstein

Models in Minutes not Months: AI as Microservices

I cannot go to any Data Conference and not hear about the Einstein Platform. Why?

Salesforce is democratizing AI with Einstein. Any company and any business user should be able to use AI, regardless of size.

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