Our effort is toward unifying GAN and DRL algorithms into a unifying AI model (AGI or general-purpose AI or artificial general intelligence which has general-purpose applications to: (A) offline learning (of stored data) like GAN in (un/semi-/fully-)SL setting such as big data analytics (mining) and visualization; (B) online learning (of real or simulated devices) like DRL in RL setting (with/out environment reward) such as (real or simulated) robotics and control; Our core proposal is adding an (generative/predictive) environment model to the actor-critic (model-free) architecture which results in a model-based actor-critic architecture with temporal-differencing (TD) error and an episodic memory. The proposed AI model is similar to (model-free) DDPG and therefore it's called model-based DDPG. To evaluate it, we compare it with (model-free) DDPG by applying them both to a variety (wide range) of independent simulated robotic and control task environments in OpenAI Gym and Unity Agents. Our initial limited experiments show that DRL and GAN in model-based actor-critic results in an incremental goal-driven intellignce required to solve each task with similar performance to (model-free) DDPG. Our future focus is to investigate the proposed AI model potential to: (A) unify DRL field inside AI by producing competitive performance compared to the best of model-based (PlaNet) and model-free (D4PG) approaches; (B) bridge the gap between AI and robotics communities by solving the important problem of reward engineering with learning the reward function by demonstration;

Pope Simulator will let you step in to the shoes of one of the most powerful people on Earth to see if you can bring about world peace. Polish publishers, Ultimate Games, haven't set a launch date for the game but have uploaded a preview of the software to Steam and say it is'coming soon'. A new trailer for the game opens with white smoke coming from a chimney, signifying the election of a Pope - likely a cutscene before the game begins with you as the new Pontiff. The stunning trailer gives some insight into how the user will step into papal life and live out the daily duties of the Pope - including prayer and blessings. This is just an illustrative trailer as Ultimate Games have only just started working on the new simulator game - saying it will be some time before it is released.

Robot maker Boston Dynamics is lending the service of its robotic dog Spot during the coronavirus pandemic. The four-legged machine is helping healthcare workers at Brigham and Women's Hospital of Harvard University treat coronavirus patients remotely to limit the risk of contracting the virus themselves. A custom mount and attachment for a notepad has been added to its design, allowing doctors and other healthcare workers to video conference with patients in testing tents outside of the hospital. The process usually needs up to five employees, but with Spot, the Massachusetts hospital is able to reduce their staff's exposure to the coronavirus and conserve personal protective equipment (PPE). This project has opened up a new world of telemedicine for Boston Dynamics, as it is now exploring ways to use Spot robots in collecting vitals needed to fight the pandemic. Spot is helping healthcare workers at Brigham and Women's Hospital of Harvard University treat coronavirus patients remotely to limit the risk of contracting the virus themselves Brigham and Women's Hospital of Harvard University, located in Boston, has been using Spot for the last week as a'mobile telemedicine platform' to assist in treating people with the illness.

Housed at TED and supported by leading social impact advisor The Bridgespan Group, The Audacious Project is a collaborative funding initiative that's catalyzing social impact on a grand scale by convening funders and social entrepreneurs, with the goal of supporting bold solutions to the world's most urgent challenges. Among this year's carefully selected change-makers is Jim Collins and a team at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), including co-principal investigator Regina Barzilay. The funding provided through The Audacious Project will support the response to the antibiotic resistance crisis through the development of new classes of antibiotics to protect patients against some of the world's deadliest bacterial pathogens. "The work of Jim Collins and his colleagues is more relevant now than ever before," says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. "We are grateful for the commitment from The Audacious Project and its contributors, to both support and foster the research around AI and drug discovery, and to join our efforts in the School of Engineering to realize the potential global impact of this incredible work."

Machine learning has several applications in diverse fields, ranging from healthcare to natural language processing. Dr. Ragothanam Yennamalli, a computational biologist and Kolabtree freelancer, examines the applications of AI and machine learning in biology. Machine Learning and Artificial Intelligence -- these technologies have stormed the world and have changed the way we work and live. Advances in these areas have led to many either praising it or decrying it. However, for a computational person like me, they are not new words. AI and ML, as they're popularly called, have several applications and benefits across a wide range of industries.

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved -- in some cases, down to low triple digits.

The graph represents a network of 1,863 Twitter users whose tweets in the requested range contained "iot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 22 April 2020 at 11:55 UTC. The requested start date was Wednesday, 22 April 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 19-hour, 11-minute period from Tuesday, 21 April 2020 at 04:49 UTC to Wednesday, 22 April 2020 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved--in some cases, down to low triple digits.

Rizwan Malik had always had an interest in AI. As the lead radiologist at the Royal Bolton Hospital, run by the UK's National Health Service (NHS), he saw its potential to make his job easier. In his hospital, patients often had to wait six hours or more for a specialist to look at their x-rays. If an emergency room doctor could get an initial reading from an AI-based tool, it could dramatically shrink that wait time. A specialist could follow up the AI system's reading with a more thorough diagnosis later.

Our comprehensive guide to how chatbots will develop in 2020 and beyond. Artificial intelligence is the hottest talking point for business users looking to improve their efficiency, deliver new ideas and take the next steps in the transition to a digital enterprise. AI and chatbots are helping democratise business, empower startups and help build new partnerships, something that every organisation needs to prepare for. "Every business is a technology business" was one of the mantras of the decade just concluded. Every company across every vertical and market started working and communicating with smartphones, using cloud services to open up their data and adopted as-a-service solutions to reduce the cost of doing business and broaden their business base and the opportunities for workers. Ten years ago, specialists were needed to manage databases and build websites. Now anyone with a plan can build an entire company out of off-the-shelf parts, sell across the world without leaving their desk. They can pick advice from a huge range of sources to grow the business and partner with a massive range of organisations to deliver whatever they sell. Now as we move into the 2020s, enterprises and startups alike are taking the next step, adopting AI and bringing smart services into their organisations. It has already started with chatbots and analytics tools, but is already expanding to business-enabling technology, using a mix of machine learning, deep learning, computer vision, natural language processing, machine reasoning (MR), and deep or strong AI. Companies will continue to deploy AI for intelligent robotic process automation, computer vision tasks, and machine learning applications.