Elon Musk might be well positioned in space travel and electric vehicles, but the world's second-richest person is taking a backseat when it comes to a brain-computer interface (BCI). New York-based Synchron announced Wednesday that it has received approval from the Food and Drug Administration to begin clinical trials of its Stentrode motor neuroprosthesis - a brain implant it is hoped could ultimately be used to cure paralysis. The FDA approved Synchron's Investigational Device Exemption (IDE) application, according to a release, paving the way for an early feasibility study of Stentrode to begin later this year at New York's Mount Sinai Hospital. New York-based Synchron announced Wednesday that it has received FDA approval to begin clinical trials of Stentrode, its brain-computer interface, beating Elon Musk's Neuralink to a crucial benchmark. The study will analyze the safety and efficacy of the device, smaller than a matchstick, in six patients with severe paralysis. Meanwhile, Musk has been touting Neuralink, his brain-implant startup, for several years--most recently showing a video of a monkey with the chip playing Pong using only signals from its brain.
Would you like to stay up to date with the latest robotics & AI research from top roboticists? The IEEE/RSJ IROS2020 (International Conference on Intelligent Robots and Systems) recently released their Plenary and Keynote talks in the IEEE RAS YouTube channel. Abstract: Computational modeling of cognitive development has the potential to uncover the underlying mechanism of human intelligence as well as to design intelligent robots. We have been investigating whether a unified theory accounts for cognitive development and what computational framework embodies such a theory. This talk introduces a neuroscientific theory called predictive coding and shows how robots as well as humans acquire cognitive abilities using predictive processing neural networks.
If you are from an IT background, you might have faced some situations where you want your system to perform certain tasks in the same way as our mind do. If a computer can perform such tasks with their own intelligence. In the World of AI ( Artificial Intelligence) & ML (Machine Learning), we want our machines to think like human Brain. As a Human Brain can learn things faster and can predict something on the basis of their past experiences, we want our machines to work in the same way. How Human Brain work, what are the core unit of Brain (Neuron)..? Let's find out .. The basic working unit of the brain Neurons, also known as nerve cells, send and receive signals from your brain.
Terence Mills, CEO of AI.io, a data science & engineering company that is delivering AI solutions in healthcare, travel and entertainment. We know that the future is bright, and lately, it's like someone has been turning up the contrast. We're fortunate to have the chance to lean into technology. For every misstep that the information age has bestowed upon us, we double down our efforts. We learn how to do better. After all, humans fall short when it comes to being infallible.
The tech world – as evidenced by billionaires taking 10 minute holidays to space, and that tiny little car that delivered the football onto the pitch during the Euros – is more advanced than ever before. Even the beauty industry is becoming more technologically minded, with the announcement of the world's first ever "connected fragrance" from Paco Rabanne. Released today, Phantom, a new perfume with an appropriately robot-shaped body, is a world-first from the luxury brand, using artificial intelligence to create a state of the art "Augmented Creativity" process. What that actually means is that the perfume works with the neuroscience of your scent receptors to change how you feel as well as how you smell. The team at Paco Rabanne developed a decidedly Black Mirror-sounding Science of Wellness programme for the release.
In this post, we summarise the final three invited talks from the International Conference on Machine Learning (ICML). These presentations covered: how machine learning can complement randomised controlled trials, encoding and decoding speech, and molecular science. Esther's work centres on the use of randomised controlled trials (RCT) and she runs policy experiments with the aim of understanding which policies work and which don't. Her work is particularly focussed on reducing poverty. Work of this type involves many causal questions, for which there are often many competing ideas. Such is the field that there is no real guidance for theory; experiments are needed to determine successful policies.
The aim of brain-computer interfaces (BCIs), also called brain-machine interfaces (BMIs), is to improve the quality of life and restore capabilities to those who are physically disabled. Last week, researchers at the Georgia Institute of Technology and their global collaborators published a new study in Advanced Science that shows a wireless brain-computer interface that uses virtual reality (VR) and artificial intelligence (AI) deep learning to convert brain imagery into actions. The brain-computer interface industry is expected to reach USD 3.7 billion by 2027 with a compound annual growth rate of 15.5 percent during 2020-2027 according to Grandview Research. "Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain–machine interfaces," wrote Woon-Hong Yeo at the Georgia Institute of Technology whose laboratory led the study in collaboration with the University of Kent in the United Kingdom and Yonsei University in the Republic of Korea. The AI, VR with BCI system was assessed on four able-bodied human participants according to a statement released on Tuesday by the Georgia Institute of Technology.
Are you a highly motivated researcher with an outstanding track record in Mathematics and its Applications in Science and Engineering? We offer a position at the interface of Dynamics and Deep Learning in the Applied Analysis group of the SACS cluster within the Department of Applied Mathematics (AM) at the University of Twente (UT). The challenge: You will actively develop your mathematical profile and seek connections between fundamental mathematical theory of dynamical systems, nonlinear analysis and the rising area of deep learning for data-driven model discovery. Based on a long-standing expertise and tradition of dynamical systems theory at the UT, well embedded in the Dutch NDNS cluster, our department is looking for a mathematician with a proven expertise in the broad area of dynamical systems, nonlinear analysis or approximation theory for deep neural networks. You show great passion in applying your novel methods to computational neuroscience, inverse problems in imaging or engineering applications driven by physics-informed machine learning for example within the multi-disciplinary research contexts at the UT, like the Digital Society Institute, the Technical Medical Centre or the MESA Institute for Nanotechnology.
Among various applications of AI technology in the pharmaceutical industry, some are viewed as most important and worth more depth of exploration. The first step in drug development is to understand the biological origin and mechanism of the disease, and then to determine suitable targets through high-throughput technologies such as shRNA screening and deep sequencing, and finally to find relevant patterns through a large number of diverse data sources. This is huge work and often presents an important challenge for traditional methods. Unlike traditional methods, AI can systematically analyze existing literature and data in just a few seconds. This real-time "omics" database analysis can more accurately understand pathological cells and molecular mechanisms, and it can be used for complex diseases such as neurodegenerative diseases.