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How does artificial intelligence (AI) interact with robotics?

Artificial intelligence and machine learning involves software programs classifying data and making decisions about things in the real world. Robotics involves manipulating objects in the real world. This creates the ability to manipulate the real world using a combination of machine learning and robotics. At the same time, artificial intelligence programs can use data from the real world acquired through robotics to improve their performance. AI/machine learning have a symbiotic relationship. One obvious application of AI to robotics is in computer vision. Computer vision allows robots and drones to navigate the real world more accurately. This has other applications for people: One experimental robot could remind people about ordinary things such as leaving milk outside of the refrigerator. Robots are already used in manufacturing, but usually in pre-programmed tasks. Robots could learn tasks with machine learning by being shown by human...
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Why Self-Driving Cars Must Be Programmed to Kill

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Self-driving cars are already cruising the streets. But before they can become widespread, car makers must solve an impossible ethical dilemma of algorithmic morality. When it comes to automotive technology, self-driving cars are all the rage. Standard features on many ordinary cars include intelligent cruise control, parallel parking programs, and even automatic overtaking—features that allow you to sit back, albeit a little uneasily, and let a computer do the driving. So it’ll come as no surprise that many car manufacturers are beginning to think about cars that take the driving out of your hands altogether (see “ Drivers Push Tesla’s Autopilot Beyond Its Abilities ”). These cars will be safer, cleaner, and more fuel-efficient than their manual counterparts. And yet they can never be perfectly safe. And that raises some difficult issues. How should the car be programmed to act in the event of an unavoidable accident? Should it minimize the loss of life, even if it means sacrif...
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Decentralization is likely to become one of the pillars that influences the next decade of artificial intelligence(AI). The friction between decentralized and centralized models is going to be of the existential challenges of the next years of AI. Continuing relying on centralized models is likely to increase the gap between large companies and countries with the resources to develop AI solutions and the rest of the market. The current centralized nature of AI models introduces a “rich get richer” vicious cycle in which only companies with access to large, labeled datasets and data science talent can benefit from the promises of AI. Understanding the centralization challenges of AI solutions is far from trivial as they range from purely philosophical to practical implementations. If we visualize the traditional lifecycle of an AI solution we will see a cyclical graph that connects different stages such as model creation, training, regularization, etc. My t...
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