Pat Brans

1 week ago | computerweekly.com | Pat Brans

With a large ecosystem of partners in France in both the public and private sectors, Microsoft already has a big stake in the country. But last May, the company announced it will be upping the ante with an investment of €4bn to accelerate the adoption of artificial intelligence (AI) and cloud technologies. The company said that much of the money will go towards developing a datacentre using the latest generation of technology and in training citizens on AI.

2 weeks ago | eetimes.eu | Pat Brans

Federated learning has great potential to improve AI training in cases where data is scarce and different sources are needed, or when multiple methods of data acquisition and labeling are required. Often, the parties contributing data don’t trust one another, so they must find ways to share information without compromising their intellectual property or the confidentiality of their customer data.

3 weeks ago | eetimes.eu | Pat Brans

Before edge AI can deliver a full range of new applications, there is work to be done to improve the way models operate in a constrained environment—without sacrificing accuracy. As an applied research institute in the field of integrated circuits, Fraunhofer IIS develops small-form–factor AI processors and optimized AI software to address the challenges posed by the edge environment.

1 month ago | eetimes.eu | Pat Brans

The race is on to gain a dominant position in the worldwide ecosystem coalescing around AI inference and training on edge devices. In Europe, an R&D investment project called Prevail has been working since 2021 to bolster Europe’s position in the global semiconductor industry by stimulating European innovation in edge AI.

1 month ago | eetimes.eu | Pat Brans

Because the edge is such a diverse environment, no single security architecture works in all cases. Researchers at Finland’s VTT are working on a set of tools to bridge the gap. The edge environment amplifies security risks. Adversaries can more easily reach AI models or data in remote locations and poison AI models, or tamper with them in other ways. When models are not trained locally, critical or private information taken from another source might be leaked from a model deployed on the edge.