The first patient living with Elon Musk's Neuralink'brain chip' implant wants the world to know how'amazing' and'rewarding' his clinical trial with the tech has been. Just four months ago, 30-year-old Noland Arbaugh went under the knife for the experimental surgery that would allow him to control computers with his mind. 'I'm really excited to keep going,' Arbaugh, who has been paralyzed from his neck's fourth vertebra down since college, said of his role in Neuralink's human trial. But while the tech company's brain-computer interface has allowed him to race his stepfather in Nintendo's Mario Kart, navigate a computer cursor and more -- all with just his thoughts alone -- technical hurdles still plague the brain chip's functioning. A report on Arbaugh's Neuralink trial said that approximately 85 percent of the chip's tendril-like connections to his brain have come loose, forcing Neuralink staff to retool the system on its software side, as FDA approves trials on a second patient.

Neuralink is one step closer to selling brain implants that can transmit human thought. The neurotechnology company in May announced that it had received approval from the U.S. Food and Drug Administration (FDA) to launch its first in-human clinical trial. A statement on its Twitter account said the approval "represents an important first step that will one day allow our technology to help many people." Cofounded by Elon Musk in 2016, Neuralink plans to implant devices in human brains that would allow people with neurological disorders to control computers or robotic limbs with their minds. Musk has said he also wants to "achieve a sort of symbiosis with artificial intelligence" and possibly enable telepathic communication with the device.

It would be useful for machines to use computers as humans do so that they can aid us in everyday tasks. This is a setting in which there is also the potential to leverage large-scale expert demonstrations and human judgements of interactive behaviour, which are two ingredients that have driven much recent success in AI. Here we investigate the setting of computer control using keyboard and mouse, with goals specified via natural language. Instead of focusing on hand-designed curricula and specialized action spaces, we focus on developing a scalable method centered on reinforcement learning combined with behavioural priors informed by actual human-computer interactions. We achieve state-of-the-art and human-level mean performance across all tasks within the MiniWob++ benchmark, a challenging suite of computer control problems, and find strong evidence of cross-task transfer. These results demonstrate the usefulness of a unified human-agent interface when training machines to use computers. Altogether our results suggest a formula for achieving competency beyond MiniWob++ and towards controlling computers, in general, as a human would.

While the design and development of contemporary AI systems has been largely results-oriented, there are also scenarios where it could be advantageous if models learned to do things "as a human would" to help with everyday tasks. That's the premise of the new DeepMind paper A Data-driven Approach for Learning To Control Computers, which proposes agents that can operate our digital devices via keyboard and mouse with goals specified in natural language. The study builds on recent developments in natural language processing, code production, and multimodal interactive behaviour in 3D simulated worlds that have enabled the generation of models with remarkable domain knowledge and desirable human-agent interaction capabilities. The proposed agents are trained on keyboard and mouse computer control for specific tasks with pixel and Document Object Model (DOM) observations, and achieve state-of-the-art and human-level mean performance across all tasks on the MiniWob benchmark. MiniWob is a challenging suite of web-browser-based tasks for computer control, ranging from simple button clicking to complex formfilling.

Scientists affiliated with the University of Melbourne and Synchron, Inc. published earlier this week in the Journal of NeuroInterventional Surgery the first-in-human study of Stentrode, a wireless neuroprosthesis that uses machine learning and a stent. What makes the Stentrode technology unique is that it is a stent that records brain activity inside a blood vessel in the brain. It is implanted through the jugular vein so there is no need for open brain surgery. The technology platform originated from the University of Melbourne, in a collaborative effort with the Royal Melbourne Hospital, the Florey Institute of Neuroscience and Mental Health, Monash University, and Synchron, Inc.. A brain-computer interface (BCI) enables two-way communications between the biological brain and a machine.

A mind-reading chip that let you control a computer by just thinking has been unveiled at a conference in China. Dubbed Brain Talker, works by picking out small electrical pulses in the brain and quickly decoding them into signals that a computer can interpret. The chip could be used to control computers, smartphones and other devices, its creators say. It also has potential medical, education, security and entertainment applications, they add. However, the information released so far on the chip and exactly how it operates is limited.