How can artificial intelligence (AI) draw on principles from nature to solve complex problems? When it comes to recognizing patterns in large amounts of data, AI is faster and more capable than humans. However, it has difficulties when it has to make connections or deal with uncertainties and fuzziness. Through evolution, development, and learning, nature has developed much more practical problem-solving solutions. Yaochu Jin, the Alexander von Humboldt Professor of Artificial Intelligence at Bielefeld University since the autumn, is looking at how such principles can be transferred to AI. The Humboldt Professor will be continuing his previous research on nature-inspired artificial intelligence at Bielefeld University and looking for applications of nature-inspired and self-organized AI. 'My goal is to understand and borrow successful mechanisms from nature and transfer them into artificial intelligence for problem-solving,' says Jin.

Yaochu Jin is doing research on nature-inspired intelligent technological systems that organize themselves in a changing environment. How can artificial intelligence (AI) draw on principles from nature to solve complex problems? When it comes to recognizing patterns in large amounts of data, AI is faster and more capable than humans. However, it has difficulties when it has to make connections or deal with uncertainties and fuzziness. Through evolution, development, and learning, nature has developed much more practical problem-solving solutions. Yaochu Jin, the Alexander von Humboldt Professor of Artificial Intelligence at Bielefeld University since the autumn, is looking at how such principles can be transferred to AI.

Machine learning (ML) is a branch of artificial intelligence that studies the use of data and algorithms to allow computers to imitate the way humans learn and become smart over time. IBM's Arthur Samuel coined the term'machine learning' in 1959. We experience machine learning around us in our daily lives. For instance, Netflix recommendations are based on an ML model. There are different types and sub-fields of machine learning.

Advanced computing enables us to make connections like never before. We can teach machines to detect cancer, identify cybersecurity threats before they happen, and optimize business operations – the possibilities are endless. So how can machines continue to learn, without the dedication of abundant resources? Deep learning, a sub-mechanism of machine intelligence (MI), is a computing process that enables machines to find patterns in data. Deep learning sifts through data and makes connections, identifying odd trends that would be unfindable by the human senses.

During the three years I've spent researching and writing about shyness, one of the most common questions people ask is about the relationship between shyness and technology. Are the internet and the cellphone causing our social skills to atrophy? I often hear this from parents of shy teenagers, who are worried that their children are spending more time with their devices than with their peers. The shy aren't necessarily antisocial; they are just differently social. They learn to regulate their sociability and communicate in indirect or tangential ways.

IN A former leatherworks just off Euston Road in London, a hopeful firm is starting up. BenevolentAI's main room is large and open-plan. In it, scientists and coders sit busily on benches, plying their various trades. The firm's star, though, has a private, temperature-controlled office. That star is a powerful computer that runs the software which sits at the heart of BenevolentAI's business.

IN A former leatherworks just off Euston Road in London, a hopeful firm is starting up. BenevolentAI's main room is large and open-plan. In it, scientists and coders sit busily on benches, plying their various trades. The firm's star, though, has a private, temperature-controlled office. That star is a powerful computer that runs the software which sits at the heart of BenevolentAI's business.

Artificial intelligence is no longer the stuff of the future. It is becoming an increasingly common part of our everyday lives, from the watches we wear that monitor our heart rate to the robots that vacuum our floors without any direction from us. Yet we have traditionally thought of artificial intelligence as similar to a robot that has a mind of its own and can act independently. While these robots would certainly qualify, they are not the only examples. Google -- the leader in almost all things Internet-related -- has introduced RankBrain, which is a machine learning algorithm.

Artificial intelligence is no longer the stuff of the future. It is becoming an increasingly common part of our everyday lives, from the watches we wear that monitor our heart rate to the robots that vacuum our floors without any direction from us. Yet we have traditionally thought of artificial intelligence as similar to a robot that has a mind of its own and can act independently. While these robots would certainly qualify, they are not the only examples. Google - the leader in almost all things Internet-related - has introduced RankBrain, which is a machine learning algorithm. It is an example of ANI, just like the spam filters in your email or the "recommended products" feature on Amazon.