A wearable that gives users telekinesis-like abilities aims to let disabled individuals lead more normal lives. The wireless brain-machine interface gives them the power to control an electric wheelchair, interact with a computer or operate a small robotic vehicle - just by using brain signals. This fully portable, wireless, flexible scalp electronic system has been redesigned to give people more freedom without having to wear the traditional bulky hair electrode cap. A wearable that gives users telekinesis-like abilities aims to let disabled individuals lead more normal lives. The redesigned wearable was developed in collaboration with researchers from Georgia Institute of Technology, University of Kent and Wichita State University.

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We're possibly less than a decade away from self-conscious artificial intelligence capable of learning on its own, and this latest project gets us one step closer. Researchers OpenAI took an unusual approach at developing AI, one inspired by natural selection and competition. Put simply, they pit multiple AI agents against each other to compete for conflicting goals and found that it developed new sophisticated behavior in the long term. These AI agents are split between "hiders" / "seekers," and their roles are exactly as they sound. The hiders get a set time to hide, while the seekers are frozen, with each of the AI agents left on their own to discover ways to make use of their environment to achieve their goals.

Deep Learning has benefited primarily and continues to do so thanks to the pioneering works of Geoff Hinton, Yann Lecun and Yoshua Bengio in the late 1980s. Contributions of Yann Lecun, especially in developing convolutional neural networks and their applications in computer vision and other areas of artificial intelligence form the basis of many products and services deployed across most technology companies today. Here are a few of Yann's groundbreaking research papers that have contributed greatly to this field: The ability of neural networks to generalize can be greatly enhanced by providing constraints from the task domain. As a follow up to his widely popular work on back-prop, in this paper, Yann and his peers demonstrate how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the US Postal Service.

For decades, artificial intelligence scientists have sought to create intelligent machines by trying to study and replicate the structure and functionality of the human brain. Last week, researchers at AI research lab OpenAI introduced a more fundamental approach at developing AI, a project inspired by natural selection and competition, the simple rules that have led to the evolution of all living beings, including humans. The AI researchers pitted multiple AI agents against each other to compete for conflicting goals. They observed that the AI developed new and sophisticated behavior in the long term. While the project draws on existing AI techniques and concepts, it might provide new approaches and ideas to creating AI applications.

The concept of Artificial General Intelligence (AGI) that is able to carry out tasks and understand the world in the way that humans do has been around since 2005 when it was first mooted by Dr Ben Goertzel and Cassio Pennachin in their book Artificial General Intelligence. A new collaboration between network specialist Cisco and AI company SingularityNET brings practical AGI a step closer, with a commitment to developing applied technologies and customer solutions. SingularityNET's AGI technologies include a custom version of the OpenCog AGI engine, along with a variety of unique deep neural net technologies for vision, language and other data types, and a decentralized blockchain-based platform suited for deployment of AI technologies across all markets. "These corporate investments into AGI are occurring not only out of a desire to spur rapid progress toward important research and humanitarian goals, but also because AGI capability is expected to provide tremendous commercial benefit to whomever develops it," says Dr Goertzel. "This benefit may initially take the form of a generation of'Narrow AGI' systems that infuse general intelligence into products in specific vertical markets like, say, advertising, medical research, computer networking or financial analytics."

We've trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization--all without task-specific training. Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.

The first step ensures that important sentences will be extracted, which can be used to better condition the transformer. The second, abstractive step can thus generate a better summary during the inference phase. The researchers concluded that their extractive step significantly improve the performance of the summarization results.

Let's be clear, Artificial Intelligence, in particular in its latest development, deep learning that mimics the way the human mind works, first emerged in America. This gave the U.S. a huge head start over the rest of the world – including China, putting the U.S. firmly in the lead of the race for AI. What Americans didn't develop at home, they bought from Europe. In this respect, two British firms stand out with groundbreaking contributions to AI development: ARM and DeepMind. While all eyes are trained on the AI race between China and America, is there a role left for Europe?