AI News, Data Science at Slush 2016

Data Science at Slush 2016

Slush, Europe’s leading startup event, took place in Helsinki from November 30th to December 1st.

Thousands of attendees including startups, investors, tech companies, and researchers came together to get a glimpse of the latest developments in a massive range of fields.

Futurice was there in force: our Futucafe went on tour, providing good coffee and a meeting space for attendees, and we launched the Chilicorn Fund as a way to make the world a slightly better place.

IBM had a large presence at the conference, mostly centered around their Watson APIs and Bluemix cloud services - these tools provide machine learning services that developers can integrate in their applications.

This idea of machine learning as a service was also reflected right down to the startup level, with Finnish company Valohai aiming to be to machine learning what Github is to Git: a provider independent set of tools for rapidly deploying machine learning.

arXiv sees new research papers on deep learning and other AI topics released daily, while GitHub allows researchers and companies to share their code freely.

Google and other companies are open-sourcing their machine learning toolkits, but without their access to the data it is impossible for others to train models with the same accuracy.

Solving this challenge could be the difference between a future of intelligent systems that we can interact with in familiar ways, and one of faceless algorithms controlling the world around us.

Finnish startup Jenny aims to improve customer support by building a bot that learns from conversation logs how to respond to common queries.

Teqmine is using machine learning to index patents, so that people can find similar work before investing in a new idea or product.

No human doctor can keep up with that pace, so we need systems that can analyse and aggregate this huge knowledge base to help doctors decide on the best courses of action.

To that end, IBM is partnering with Tekes to create a center of excellence for healthcare in Helsinki, where their Watson machine learning tools will be used to tackle big data problems in health.

He gave a number of examples where ubiquitous sensors and machine learning can improve success rates of experiments by detecting or preventing human error.

It was great to see innovative use cases for machine learning, and I expect we'll continue to see more startups follow this trend in the next few years.

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