Uber Technologies announced Monday creation of Uber AI Labs, a new division of Uber, based in San Francisco, dedicated to research in artificial intelligence and machine learning.
The ride-hailing company also announced the acquisition of the AI research startup Geometric Intelligence, whose 15 members will form the initial core of the AI Labs team.
Uber is in the business of using technology to move people and things in the real world. With all of its complexity and uncertainty, negotiating the real world is a high-order intelligence problem. It manifests in myriad ways, from determining an optimal route to computing when the car or UberEATS order will arrive to matching riders for uberPOOL.
It will also extend to teaching a self-driven machine to safely and autonomously navigate the world, whether a car on the roads or an aircraft through busy airspace or new types of robotic devices.
The deep learning approach has produced some astonishing results in recent years, especially as more data and more powerful computer hardware have allowed the underlying calculations to grow in scale. Deep-learning methods have matched, at times even surpassed, human accuracy in recognizing faces in images or identifying spoken words in audio recordings.
Google, Facebook, and other big companies are applying the approach to just about any task in which it is useful to spot a pattern in huge amounts of data, such as refining search results or teaching computers how to hold a conversation
The formation of Uber AI Labs will directed by Geometric’s founding CEO Gary Marcus, represents Uber’s commitment to advancing the state of the art, driven by the company;s vision that moving people and things in the physical world can be radically faster, safer and accessible to all.
Uber also hosts Uber Data Science that is looking for machine learning scientists to join its Machine Learning Platform, a group responsible for building and scaling the machine learning technology and intuition throughout the company.
The company aims to take its data culture to the next level through a combination of efficient, automation of common workflows, building products that supercharge the data scientist’s modeling process, and integrating prediction models into Uber infrastructure, allowing rapid deployment of our best ideas.
To date, Uber has been hard at work building a global transportation fabric that spans the globe, and data science lies at the heart of it. The Machine Learning Platform lies at the foundation of this plan with teams of cross-functional engineers, scientists and product professionals on some of the most high-leverage projects.