It can walk, run and even jump - even though it is essentially blind. MIT has revealed a video of its latest robot it hopes will one day be able to'feel' its way around much like a person in a pitch black room. It has no cameras or external environmental sensors, instead relying on software and touch sensors in its limbs to predict its surroundings. It could one day be sent into the world's most dangerous environments, such as nuclear power plants, where conventional cameras and sensors are unable to operate. 'There are many unexpected behaviors the robot should be able to handle without relying too much on vision,' says the robot's designer, Sangbae Kim, associate professor of mechanical engineering at MIT told MIT News.

Northern California weather is pretty temperate. You can get by with a lack of central air conditioning, which is good news since nary a single place I've lived in the Bay Area has had it. But in the summer, there are sweltering days where the heat feels inescapable. While I don't wish I had central air, I do wish I had something better than a box fan for those hottest weeks, something that would follow me around while perfectly streaming air at my dew-drenched face. What I want is a robotic fan.

Machine learning algorithms are being applied to advance DevOps via analytics software that Electric Cloud is making available as a software-as-a-service (SaaS) application. Electric Cloud CTO Anders Wallgren said ElectricFlow DevOps Foresight is made possible because of the massive amounts of data that Electric Cloud is able to acquire providing an application release and continuous delivery platform-as-a-cloud service. Electric Cloud applies predictive analytics against all the data it collects to identify and contrast patterns, which it then uses to generate recommendations to specific DevOps teams based on what's occurring in their local environment, said Wallgren. ElectricFlow DevOps Foresight is able to generate a risk score metric that predicts the probable outcome of a release before being deployed in a production environment. Based on that score, he said, a DevOps team can decide to go ahead and deploy that code or hold it back for further testing.

The second annual Baidu AI Developers Conference, officially known as Baidu Create 2018, opened in Beijing today. Baidu unveiled China's first cloud-to-edge AI chip, Kunlun, and many other upgraded versions of Baidu's AI products this morning on the first day of this two-day event. Li Yanhong, known as Robin Li, the founder and CEO of Baidu, introduced Baidu's latest research achievements in artificial intelligence (AI) field. Started in 2013, the autonomous driving project was mainly lead and developed by the Baidu Research Institute. At the 2017 Baidu World Congress in November last year, Robin Li stated that Baidu's Level 4 self-driving bus "Apolong" would be mass-produced by July 2018.

Well, the Chinese startup is happy to announce that they've started to deliver the ES8 all-electric SUVs to its customers. One of the USPs of the ES8 is that the SUV has something called NOMI artificial intelligence technology, claimed to be the world's first in-car AI system for production vehicles. The NOMI can see her owner's arrival and quickly setup the seats and steering wheel position. She can also do things like take a selfie via roof mounted camera or play the desired music, when asked to do so. Another USP is that the ES8 features swappable battery technology, where the SUV can go into a swapping station to get its entire battery pack swapped within 3 minutes.

Scientists have created an artificial intelligence (AI) based computer programme that can accurately predict where emitted radioactive material will eventually land, over 30 hours in advance. "Our new tool was first trained using years of weather-related data to predict where radioactivity would be distributed if it were released from a particular point," said Takao Yoshikane from The University of Tokyo in Japan. "In subsequent testing, it could predict the direction of dispersion with at least 85 per cent accuracy, with this rising to 95 per cent in winter when there are more predictable weather patterns," said Yoshikane.When a nuclear power plant accident occurs and radioactive material is released, it is vital to evacuate people in the vicinity as quickly as possible, according to the research published in the journal Scientific Reports. However, it can be difficult to immediately predict where the emitted radioactivity will settle, making it impossible to prevent the exposure of large numbers of people, the researchers said. Using weather forecasts on the expected wind patterns, the tool enables evacuation plans and other health-protective measures to be implemented if another nuclear accident like in 2011 at the Fukushima Daiichi Nuclear Power Plant were to occur. The team created a system based on a form of artificial intelligence called machine learning, which can use data on previous weather patterns to predict the route that radioactive emissions are likely to take.

Chinese internet giant Baidu's co-founder Robin Li has revealed new technologies such as an unmanned bus, upgraded digital assistant, and smart chip at the firm's second artificial intelligence conference. Li introduced Apolong bus, developed with King Long United Automotive Industry, at its Baidu Create 2018 event today. The vehicle can operate without driver's participation after entering the desired destination, and this is why it has no steering wheel nor driver's seat. "We can think of autonomous driving as a four-wheeled robot," Li said, adding that it can help in daily activities such as logistics and cleaning, but in the future, also in security work, disaster relief, and agriculture. Baidu also released other AI-related products such as a digital assistant DuerOs 3.0, and smart chip called Kunlun, which it suggested to be China's first cloud-to-edge AI chip.

As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools enabled by machine learning. With this in mind, a team of researchers from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building. As a proof-of-concept, the team is working with staff at Berkeley Lab's Molecular Foundry, to demonstrate the concepts of Science Search on the images captured by the facility's instruments. A beta version of the platform has been made available to Foundry researchers.

When a nuclear power plant accident occurs and radioactive material is released, it is vital to evacuate people in the vicinity as quickly as possible. However, it is difficult to predict where the emitted radioactivity will settle, making it impossible to prevent the exposure of large numbers of people. A means of overcoming this difficulty has been presented in a new study reported in the journal Scientific Reports by a research team at The University of Tokyo Institute of Industrial Science. The team has created a computer program that can accurately predict where emitted radioactive material will eventually land over 30 hours in advance, using weather forecasts on the expected wind patterns. This tool enables evacuation plans and other health-protective measures to be implemented in the event of a nuclear accident like the 2011 Fukushima Daiichi Nuclear Power Plant disaster.

Technology has changed how we used to do things. When it comes to transportation, we have come a long way of exploiting fuels like petrol and diesel to harnessing electricity and solar energy. Our resources have become purer that does not affect the ecosystem and also producing more efficiency than ever. In the forthcoming years, there are chances of many improvements in the way we commute. Artificial Intelligence is nothing but creating a mind so powerful that it can correct its own mistake to give an optimized work output.