machine learning

The Canadian division of information and communication technology company Ericsson is entering a new partnership with Concordia University to allow employees to expand their skill set.

Employees will enhance their artificial intelligence (AI) and machine learning (ML) skills in this specialized program by working with a team of researchers and graduate students from the university.

They’ll be presented with lectures and projects that discuss big data, programming, ML, deep learning, and infrastructure, in a program promised to stray away from a traditional learning environment.

It will instead allow students to bring the challenges they face at work to class and have professors provide tailored concepts that answer these challenges.

The collaboration will give Ericsson employees a new set of resources to create AI projects in a market that’s continuously developing.

“Technology changes on the front lines quickly, and ensuring our workforce has the right skill set is critical for our industry and business to stay competitive in a global economy,” said Paul Baptista, the head of Montreal research and development for Ericsson Canada, in a press release.

The program advances cross-sector partnerships and will assist the companies 5G and wireless domain experts with AI and ML tools.

“The collaboration of our researchers and graduate students with experts from industry will provide players on all sides focused time and effort to foster innovation and creative solutions that will expedite the use of AI driving the economy,” said Mourad Debbabi, dean of the university’s Gina Cody School.

The institutions have a history of collaboration. In 2019, the two, along with the Natural Sciences and Engineering Research Council of Canada, created a security program to strengthen cybersecurity for future networks. A second collaboration came in 2020. Along with ENCQOR 5G, they created a new program to improve 5G network performance through cloud, AI, and edge computing technologies.

MobileSyrup has reached out to Ericsson for more information regarding when the program will begin and how employees can sign up. An update will be provided once the information is received.

Image credit: Shutterstock

Source: Ericsson

Researchers from Rutgers University in New Jersey say they can use smartphone data and machine learning to detect cannabis intoxication.

The project started as a proof-of-concept way to passively detect cannabis use rather than rely on existing testing measures like blood, urine or saliva tests. The researchers published their findings in the Drug and Alcohol Dependence journal (via CTV News).

The study involved an experiment with 57 young adults who reported using cannabis at least twice a week. Researchers asked participants to complete three surveys a day over 30 days. The survey asked about how high participants felt at a given time, when they had last used cannabis and the quantity consumed. Participants reported a total of 451 episodes of cannabis use.

Additionally, researchers asked participants to download a smartphone app that analyzed GPS data, phone logs, accelerometer data and other smartphone sensor data and usage statistics.

The researchers found that when looking at the time of day, a machine learning algorithm could detect an episode of cannabis use with 60 percent accuracy. With just the smartphone data, the algorithm had an accuracy of 67 percent.

But with both time-of-day data and sensor data combined, the algorithm accurately predicted cannabis use with 90 percent accuracy.

The researchers said that GPS and accelerometer sensor data were the most important in detecting cannabis use — the study found that participants didn’t travel as far while high, while the accelerometer could be used to measure body movements.

While certainly interesting results, there could be potential concerns with applying the algorithm in real-world scenarios. For example, bias in the algorithm (unintentional or otherwise) could skew results. Another problem could be the accuracy — 90 percent is impressive, but if you fall in the 10 percent where the algorithm gets it wrong, that could cause problems.

The researchers say that this is the first study to examine how smartphone sensors could help detect cannabis intoxication. However, some of the researchers were involved in a similar 2018 study that investigated if smartphone data could be used to detect heavy drinking episodes.

Source: Drug and Alcohol Dependence Via: CTV News