Algorithms modeled loosely on the brain have helped artificial intelligence take a giant leap forward in recent years. Those algorithms, in turn, have advanced our understanding of human intelligence while fueling discoveries in a range of other fields. MIT founded the Quest for Intelligence to apply new breakthroughs in human intelligence to AI, and use advances in AI to push human intelligence research even further. This fall, nearly 50 undergraduates joined MIT's human-machine intelligence quest under the Undergraduate Research Opportunities Program (UROP). Students worked on a mix of projects focused on the brain, computing, and connecting computing to disciplines across MIT.
HEALTHCARE providers across the Asia Pacific (APAC) region are building systems that are artificially intelligent, powered by data, and capture and analyze information in real time to produce actionable insights. There are also several technology companies in the healthcare space, creating data-driven solutions to support customers and improve their experience. It is therefore surprising that there are no laws to specifically regulate how all of these businesses manage data and use it to build their offering, usually with some degree of AI-built in. True, there are personal data protection laws in Singapore, Malaysia, and other countries in the APAC, but none are particularly sensitive to the complexities of data this sector. This is something that has suddenly become a question consumers and businesses are asking in light of the new announcement by the Department of Health and Social Care in the UK.
China's state-run press agency has welcomed its first female AI anchor who will join its growing team of virtual presenters. The female AI newsreader will make her professional debut during the upcoming meetings of the country's national legislature and top political advisory body in March, according to Xinhua at a press conference on Tuesday. Modelled after the agency's flesh-and-blood journalist Qu Meng, the AI newsreader was jointly developed by Xinhua and search engine company Sogou.com China's state-run press agency Xinhua on Tuesday unveiled its first female AI anchor, Xin Xiaomeng, who will join its growing team of virtual presenters The female AI anchor is modelled after agency's flesh-and-blood journalist Qu Meng. 'Hello everyone, I am the world's first female AI presenter developed by Xinhua News Agency and Sougu.
Technology has rapidly evolved over the years and has transformed every industry vertical. Healthcare, which is a complex and regulated industry, has also evolved in critical synchronisation with technology. Since the digital age has dawned upon the world, it has become crucial to address the elephant in the room, and direct the evolution of healthcare technology. According to Deloitte, the global healthcare expenditure is going to increase to $10.059 trillion by 2022. The IBEF has also speculated the Indian healthcare market to reach $372 billion by 2022.
At first glance, the two rows of portraits at the top of this article just look like a bunch of average-looking people. The catch is, none of them exist. All of these faces are fakes, put together by artificial intelligence. To be more precise, these faces are created by a generative adversarial network (GAN) developed by Nvidia, using deep learning techniques to produce realistic portraits out of a database of existing photos. Head over to the This Person Does Not Exist website to see for yourself: every time you refresh the page, you get a new face.
Hi everybody, so i wanted to learn to do pruning in deep neural network (specifically on Tensorflow), but the only thing i have found is the library of tensorflow, that it relies in applying masks to the different operations. So i wanted to ask which method have you used, for example, i tried to modify the cpkt files that have the weights of the network, but i haven't found a correct way to do it.
The roles of machine learning engineer vs. data scientist are both relatively new and can seem to blur. However, if you parse things out and examine the semantics, the distinctions become clear. While a scientist needs to fully understand the, well, science behind their work, an engineer is tasked with building something. But before we go any further, let's address the difference between machine learning and data science. It starts with having a solid definition of artificial intelligence.
Upgrade Android Studio (I have version 3.3). Download Bazel just as Google tells you to. However, you don't need MSYS2 if you already have other things like Git Shell -- or maybe I already have MinGW somewhere, or who knows. Upgrade Android Studio (I have version 3.3). Download Bazel just as Google tells you to.
Intel is working on a new transistor called MESO that could be 10 to 30 times more efficient than existing transistors, a potential game-changer for the industry (see our main article here). It could help solve many of the world's biggest problems, spurring AI efforts that could help everything from fighting climate change to improving waste management. We interviewed Intel's Amir Khosrowshahi, CTO of AI, and Ian Young, Senior Fellow and circuit designer and lead researcher on the MESO project. Khosrowshahi, who is supposed to be focused on product development and thus on projects with impact within the next 2 to 5 years, says he's more excited about MESO than any other project right now -- even though it could take 10 years to get to market. Young's team wrote a paper about MESO for Nature, published in December.
My journey into machine learning began in the summer of 2016. It all started at a barbecue party at the home of my fiancé's aunt and uncle's in northern Stockholm. I was sitting outside at a garden table together with the older men of her family. These are old and tough Finish men, her granddad (96 years old) fought in the war against the Russians. As you can imagine, as the new kid on the block, I was keeping a low profile and my mouth shut.