Speed: GPUs have a high number of cores, which makes them well-suited for parallel processing tasks such as matrix operations. This makes them faster for certain types of machine learning tasks, such as training deep neural networks. Cost-effectiveness: Training large machine learning models can require a lot of computational resources, and using GPUs can be more cost-effective than using CPUs for these tasks, as they can process large amounts of data much faster. Large-scale training: Training deep neural networks requires a lot of data and computational power, which makes GPUs ideal for this type of work. By using GPUs, researchers and practitioners can train much larger and more complex models than they would be able to with CPUs alone.

A few weeks ago at Davos, US executives warned that China may be winning the so-called AI race with Europe. In another recent article, Bloomberg pointed out that countries are rushing to not be left behind. The author also correctly pointed out that there's still a long way to go before AI will be commercially viable. In its vision for AI, launched last December, the European Commission has described its concerns with the position of AI in this race, which some have claimed Europe already lost. In my opinion, speaking of a'race' is both wrong and dangerous to begin with.

Lungs cancer is one of the most common cancer among men and second most in women. In U.S alone lung cancer is responsible for more then 29 percent of cancer deaths. The general symptoms of lung cancer are coughing in blood, pain in chest and shortness in breath. For the prevention of this cancer and to detect it as early as possible, a team of researches from New York has developed an artificial intelligence tool that analyzes patients' lung tissue. It also specify the cancer types, and even identify the altered genes driving abnormal cell growth.