This website uses cookies. If you continue to browse the site, we assume you are fine with how we use cookies on the web. Read More

{ Markettagged:True , MatchedLanguageCode:True }
Making machines smarter with Berkeley’s RISELab

By connecting people and devices faster than ever before, 5G is on track to transform industry and society as a whole. Realizing the potential of this change will require players from around the world to come together to define joint goals for progress and share knowledge. In partnership with a range of organizations, Ericsson is doing exactly that. It is a founding member of The University of California (UC) Berkeley’s Real-time Intelligence with Secure Execution Lab (RISELab); a collaborative research lab that is leading innovation in the fields of big data analytics and machine intelligence.

people having discussion

An innovative partnership

Ericsson Research has a long-standing collaboration with UC Berkeley. The partners have worked together on projects including UC Berkeley's pioneering AMPLab, which utilized machine learning, cloud computing and crowdsourcing to develop a new big data analytics platform.

Now, the two organizations are furthering their partnership with RISELab. The project is a five year collaboration that aims to develop technologies that allow machines to interact intelligently and securely with their environment in real time. This is a critical area of expertise that will have far-reaching implications for organizations across all industries in the future.

The project is a response to the changes that are being wrought by the growth of wireless technologies: we carry sensors everywhere, artificial intelligence is becoming a practical reality and from our homes to our cars, our world is becoming increasingly programmable. As such, the loop between data generation, computation and actuation is closing – creating huge opportunities.

Through leveraging the expertise of the multi-disciplinary researchers involved in the project, RISELab aims to address these opportunities and develop platforms that will provide enterprises with new business models with a range of applications. It is hoped that the collaboration will discover critical advancements in secure real-time decision making.

people taking photos

Exploring the possibilities of 5G

Key challenges in the fields of big data and machine intelligence include enabling real-time decisions on confidential data without revealing it, and developing useable privacy-preserving mechanisms for aggregate information from private data. With the growth of 5G and the Internet of Things (IoT) making digital infrastructures a critical part of society, overcoming these challenges is increasingly important.

5G and its influence on the IoT will be core factors in the research at RISELab, and with extensive experience of cyber security and the protection of digital assets, Ericsson is ideally placed to offer its expertise in the field.

As part of the RISELab collaboration, UC Berkeley, Intel, Honeywell and Ericsson have launched the 5G Innovators Initiative (5GI2), which brings together leading technology companies, industry leaders and academics to explore, test and innovate with 5G network distributed edge technology in the US.

Together, the partners are focusing on the IoT and investigating the network, cloud and 5G requirements of different applications that may have the power to transform society – from autonomous driving and drone surveillance of hazardous environments to smart cities and connected healthcare.

Securing the IoT future

Today's complex cyber challenges require a holistic approach to business models, technologies and use cases in order to meet the demand for secure IoT businesses. Ericsson's sponsorship of RISELab and its role in 5GI2 are examples of the ways we are using our approach and commitment to innovation to ensure the ongoing security of new platforms and systems.

Want to find out more?

To find out more about our collaboration with UC Berkeley and RISELab:

Read ‘Ericsson co-sponsors a new Berkeley lab for machine learning’