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Daily Archives: January 12, 2021
Open AI CLIP: learning visual concepts from natural language supervision
A few days ago OpenAI released 2 impressive models CLIP and DALL-E. While DALL-E is able to generate text from images, CLIP classifies a very wide range of images by turning image classification into a text similarity problem. The issue with current image classification networks is that they are trained on a fixed number of categories, CLIP doesn’t work this way, it learns directly from the raw text about images, and thus it isn’t limited by labels and supervision. This is quite impressive, CLIP can classify images with state of the art accuracy without any dataset-specific training. Read More
Machine learning at the speed of light: New paper demonstrates use of photonic structures for AI
As we enter the next chapter of the digital age, data traffic continues to grow exponentially. To further enhance artificial intelligence and machine learning, computers will need the ability to process vast amounts of data as quickly and as efficiently as possible.
Conventional computing methods are not up to the task, but in looking for a solution, researchers have seen the light—literally.
Light-based processors, called photonic processors, enable computers to complete complex calculations at incredible speeds. New research published this week in the journal Nature examines the potential of photonic processors for artificial intelligence applications. The results demonstrate for the first time that these devices can process information rapidly and in parallel, something that today’s electronic chips cannot do. Read More
Accelerating AI computing to the speed of light
Artificial intelligence and machine learning are already an integral part of our everyday lives online. … As the demands for AI online continue to grow, so does the need to speed up AI performance and find ways to reduce its energy consumption. Now a team of researchers has come up with a system that could help: an optical computing core prototype that uses phase-change material. This system is fast, energy efficient and capable of accelerating the neural networks used in AI and machine learning. The technology is also scalable and directly applicable to cloud computing. The team published these findings Jan. 4 in Nature Communications. Read More
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