While understanding and trusting models and their results is a hallmark of good (data) science, model interpretability is a serious legal mandate in the regulated verticals of banking, insurance, and other industries. This talk presents several approaches beyond the error measures and assessment plots typically used to interpret deep learning and machine learning models and results.
As we have explained on multiple occasions, AI has and will have an impact on many industries. Of course, with this development, the question that all the people working in those industries is the same: "what will happen to my job?" Have you ever ask yourself this question: How will AI affect the demand for human Labor.
Last week, Elon Musk confirmed that his company, NeuraLink, is working on the development of a brain-machine interface (BMI). For the last couple of years, Musk has made no secret of his fears of artificial intelligence advancing to the point where it overtakes its human creators. Musk believes that in order to safeguard the human race against a superintelligent machine apocalypse, we must essentially become them: cyborgs whose minds are internally augmented by advanced computer systems. Along the way, he believes, this transition will spawn countless benefits -- and profitable industries -- for humans.
In my previous blog on artificial intelligence (AI), I dealt with the general characteristics of AI and machine learning. Thanks to complex virtual learning techniques, machines are now able to perform a wide range of physical and cognitive tasks. And the efficiency and accuracy of their work is expected to increase as AI systems advance through machine learning, big data and increased computational power.
Artificial Intelligence (AI) has become the latest buzzword in the IT industry. Everything from the dishwasher and fridge to TVs and cars has become connected due to the Internet of Things (IoT), and with AI, many think that these products are going to think like human beings. Computers have certainly become more intelligent. In 2016, Google's AI software called AlphaGo finally beat one of the top Go players in the world, a feat that had been thought to be impossible as Go is very complex.