Elon Musk is terrified about humanity creating sentient AI like in the Terminator and The Matrix movies. What other myths exist about AI? And what are our biggest hopes on AI's promises? We asked these questions to actual AI experts, and this is how they responded. "The notion that AI will be so smart that it will take over our lives. At the end of the day, AI is just a tool. Pretty much like the shovel or kitchen knife, it can be used both in good ways and bad ways. But what we shouldn't forget is that the responsibility will always be with humans. So if an AI goes wrong, we have to find the people behind it and punish them, and not necessarily the technology on the front."

As 40% of people infected with COVID-19 are asymptomatic, if a patient is imaged for an unrelated health concern and doctors can identify COVID-19, we'll be in a much better position. In addition to identifying COVID-19 by viral detection and antibody response, we can also suspect viral infection indirectly through resting heart rate. Dr. Eric Topol explained in the "AI for Healthcare Keynote" that for a flu-like illness, the resting heart rate marker allows us to predict illness throughout the country from a wearable device like a Fitbit or Apple watch. Dr. Topol states that heart rate rises before a fever is present, so even if someone doesn't get a fever or experience symptoms, we can still detect that their body is fighting a virus. "Resting heart rate, with the analytics of AI for healthcare, can predict where an outbreak is likely to happen and that's a topic that doesn't get enough respect because people just think test, test, test and they don't understand that digital surveillance with AI can be very useful," said Dr. Topol. Pulse oximetry in wearable devices can also help us detect the virus's damage to the lungs. Dr. Topol thinks that the way to get ahead of this virus is simple: equip everyone with a wearable device that has a pulse oximeter and collects resting heart rate and body temperature. "Here we are in the US spending trillions of dollars. What we should be thinking about is: what can we arm each person with, so that we can help protect them?"

Shell has a broader strategy to embed AI across its operations, a move that has helped the oil giant lower costs and avoid downtime. Other oil-and-gas companies that have tapped AI to improve operations and reduce costs include Exxon Mobil Corp., BP PLC and Chevron Corp. "Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights which can keep us ahead of the competition," said Yuri Sebregts, Shell's chief technology officer, in an email. The initiative at Shell expands a 2019 yearlong pilot program with Udacity, based in Mountain View, Calif., that included about 250 Shell data scientists and software engineers. They picked up AI skills such as reinforcement learning, a type of machine learning where algorithms learn the correct way to perform an action based on trial-and-error and observations. Shell employees could use AI expertise, for example, to better predict equipment failures and automatically identify areas within a facility to reduce carbon emissions, said Dan Jeavons, Shell's general manager of data science.

On the day I met Sebastian Thrun in Palo Alto, the State of California legalized self-driving cars. Gov. Jerry Brown arrived at the Google campus in one of the company's computer-controlled Priuses to sign the bill into law. "California is a big deal," said Thrun, the founder of Google's autonomous-car program, "because it tends to be hard to legislate here." He said it with typical understatement. An idea that was in its technological infancy a decade ago, when Thrun and his colleagues were racing to develop a vehicle that could drive itself more than a few miles on a desert test course, was now being officially sanctioned by the country's most populous state.

Like anything in life, the best way to learn about anything is to get your feet wet. Watch some TedTalks on YouTube, read some blog posts, find forums and groups on social media platforms, and read some books on the subject. But, ultimately, you must be realistic as to whether the subject actually interests you or not. Before you do decide to take the plunge, complete some free courses on the subject or if possible, paid ones and see if it really is for you. Another good piece of advice is to find someone who has done what you are intending to do. Pick their brains and find out how they did it, and whether they would recommend it or not.

They are operated by Google technology and were linked to GPS satellite navigation technology. The route was programmed into the GPS navigation system. The car is able to drive without human assistance. It is fitted with video cameras, radar sensors and a laser range finder. Google already used normal cars to map out the predetermined journey so that the car would be able to get from A to B. Google already used normal cars to map out the area of the predetermined journeys around the city of California.

Active localization is the problem of generating robot actions that allow it to maximally disambiguate its pose within a reference map. Traditional approaches to this use an information-theoretic criterion for action selection and hand-crafted perceptual models. In this work we propose an end-to-end differentiable method for learning to take informative actions that is trainable entirely in simulation and then transferable to real robot hardware with zero refinement. The system is composed of two modules: a convolutional neural network for perception, and a deep reinforcement learned planning module. We introduce a multi-scale approach to the learned perceptual model since the accuracy needed to perform action selection with reinforcement learning is much less than the accuracy needed for robot control. We demonstrate that the resulting system outperforms using the traditional approach for either perception or planning. We also demonstrate our approaches robustness to different map configurations and other nuisance parameters through the use of domain randomization in training. The code is also compatible with the OpenAI gym framework, as well as the Gazebo simulator.

Twenty years from now we will speak all languages, recognise all faces, remember conversations and diseases that kill people today but will be detected much earlier now, thanks to Artificial Intelligence (AI) powered systems. In 50 years, it might be possible children born then will live to at least 200 years; and climate change will come to a halt! The world will be completely powered by alternate sources of energy instead of burning fossil fuels. In fact, Thrun, 51, who co-founded and runs three startups simultaneously, is working towards some of these goals himself. Udacity is for online learning, offering nano-degrees (short courses) in areas including drones and machine learning; Kitty Hawk Corp is making electric planes and flying cars while AI powered Cresta.ai is trying to automate repetitive jobs.

Mumbai: Sebastian Thrun is a man of many parts. The president and co-founder of e-learning company Udacity, is not only an innovator and computer scientist but also CEO of Kitty Hawk Corporation that makes flying cars and chairman of Cresta.ai--a Germany-born Thrun was earlier a Google VP and Fellow. At Google, he founded Google X and Google's self-driving car team. He is currently also an Adjunct Professor at Stanford University and at Georgia Tech.

When IBM's Deep Blue supercomputer won its famous chess rematch with then world champion Garry Kasparov in May 1997, the victory was hailed far and wide as a triumph of artificial intelligence. But John McCarthy – the man who coined the term and pioneered the field of AI research – didn't see it that way. As far back as the mid-60s, chess was called the "Drosophila of artificial intelligence" – a reference to the fruit flies biologists used to uncover the secrets of genetics – and McCarthy believed his successors in AI research had taken the analogy too far. "Computer chess has developed much as genetics might have if the geneticists had concentrated their efforts starting in 1910 on breeding racing Drosophila," McCarthy wrote following Deep Blue's win. "We would have some science, but mainly we would have very fast fruit flies."