This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Hundreds of millions of years of evolution have blessed our planet with a wide variety of lifeforms, each intelligent in its own fashion. Each species has evolved to develop innate skills, learning capacities, and a physical form that ensure its survival in its environment. But despite being inspired by nature and evolution, the field of artificial intelligence has largely focused on creating the elements of intelligence separately and fusing them together after development. While this approach has yielded great results, it has also limited the flexibility of AI agents in some of the basic skills found in even the simplest lifeforms.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Hundreds of millions of years of evolution have blessed our planet with a wide variety of lifeforms, each intelligent in its own fashion. Each species has evolved to develop innate skills, learning capacities, and a physical form that ensure its survival in its environment. But despite being inspired by nature and evolution, the field of artificial intelligence has largely focused on creating the elements of intelligence separately and fusing them together after development. While this approach has yielded great results, it has also limited the flexibility of AI agents in some of the basic skills found in even the simplest lifeforms.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Hundreds of millions of years of evolution have blessed our planet with a wide variety of lifeforms, each intelligent in its own fashion. Each species has evolved to develop innate skills, learning capacities, and a physical form that ensure its survival in its environment. But despite being inspired by nature and evolution, the field of artificial intelligence has largely focused on creating the elements of intelligence separately and fusing them together after development. While this approach has yielded great results, it has also limited the flexibility of AI agents in some of the basic skills found in even the simplest lifeforms.

This article is part of "the philosophy of artificial intelligence," a series of posts that explore the ethical, moral, and social implications of AI today and in the future Last week, I wrote an analysis of "Reward Is Enough," a paper by scientists at DeepMind. As the title suggests, the researchers hypothesize that the right reward is all you need to create the abilities associated with intelligence, such as perception, motor functions, and language. This is in contrast with AI systems that try to replicate specific functions of natural intelligence such as classifying images, navigating physical environments, or completing sentences. The researchers go as far as suggesting that with well-defined reward, a complex environment, and the right reinforcement learning algorithm, we will be able to reach artificial general intelligence, the kind of problem-solving and cognitive abilities found in humans and, to a lesser degree, in animals. The article and the paper triggered a heated debate on social media, with reactions going from full support of the idea to outright rejection. Of course, both sides make valid claims.

Last week, I wrote an analysis of Reward Is Enough, a paper by scientists at DeepMind. As the title suggests, the researchers hypothesize that the right reward is all you need to create the abilities associated with intelligence, such as perception, motor functions, and language. This is in contrast with AI systems that try to replicate specific functions of natural intelligence such as classifying images, navigating physical environments, or completing sentences. The researchers go as far as suggesting that with well-defined reward, a complex environment, and the right reinforcement learning algorithm, we will be able to reach artificial general intelligence, the kind of problem-solving and cognitive abilities found in humans and, to a lesser degree, in animals. The article and the paper triggered a heated debate on social media, with reactions going from full support of the idea to outright rejection.

This article is part of "the philosophy of artificial intelligence," a series of posts that explore the ethical, moral, and social implications of AI today and in the future Last week, I wrote an analysis of "Reward Is Enough," a paper by scientists at DeepMind. As the title suggests, the researchers hypothesize that the right reward is all you need to create the abilities associated with intelligence, such as perception, motor functions, and language. This is in contrast with AI systems that try to replicate specific functions of natural intelligence such as classifying images, navigating physical environments, or completing sentences. The researchers go as far as suggesting that with well-defined reward, a complex environment, and the right reinforcement learning algorithm, we will be able to reach artificial general intelligence, the kind of problem-solving and cognitive abilities found in humans and, to a lesser degree, in animals. The article and the paper triggered a heated debate on social media, with reactions going from full support of the idea to outright rejection. Of course, both sides make valid claims.

Mr. Skybetter and Ms. Cuan join a line of working artists who have experimented with technology to break new ground in dance. The pioneer was the choreographer Merce Cunningham who, working with the electronic artist Thecla Schiphorst, used a software program called LifeForms that could sketch movement. "Trackers" (1991) was Cunningham's first dance made with LifeForms, and roughly a third of the movement was created on the computer. Using the software opened up "possibilities of working with time and space that I had never thought of before," he said at the time. By the end of the 20th century, motion capture, wearable tech and virtual reality had arrived on the scene.

While it may be unlikely that we will have superintelligent computers or robots like the ones in films such as Terminator or I, Robot anytime soon, Daeyeol Lee believes that investigating the differences between human intelligence and artificial intelligence can help us better understand the future of technology and our relationship with it. "It may eventually be possible for humans to create artificial life that can physically replicate by itself, and only then will we have created truly artificial intelligence," Lee says. "Until then, machines will always only be surrogates of human intelligence, which unfortunately still leaves open the possibility of abuse by people controlling the AI." In his new book, Birth of Intelligence (Oxford University Press, 2020), Lee traces the development of the brain and intelligence from self-replicating RNA to different animal species, humans, and even computers in order to address fundamental questions on the origins, development, and limitations of intelligence. Lee is a Bloomberg Distinguished Professor of Neuroeconomics who holds appointments in the Krieger School of Arts and Sciences and the School of Medicine.

After successfully synthesizing artificial DNA strands from Baker's yeast using nothing but laboratory equipment and human ingenuity, scientists working as part of the so-called Synthetic Yeast Project (SC2.0) are now ready to move onto their next endeavor: The synthesis of artificial human DNA. Set to be released in just two years, the first artificial lifeforms that mimic the DNA found in nature and replicate it for biotechnology purposes could end up unleashing a cascade of human tampering with nature. Synthetic DNA, researchers say, can now be manufactured in the lab and swapped out with the natural DNA in living organisms, resulting in the creation of new synthetic organisms that can be programmed to do all sorts of things that they otherwise wouldn't do naturally. Though the team that worked on the Baker's yeast only synthesized 30 percent of the Baker's yeast DNA that it was working with, it won't be long until the entire DNA set is replicated and apportioned for use. And since this DNA closely resembles that of human DNA, the sky's the limit when it comes to the clinical potential of synthetic DNA.