Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
While artificial intelligence may be powering Siri, Google searches, and the advance of self-driving cars, many people still have sci-fi-inspired notions of what AI actually looks like and how it will affect our lives. AI-focused conferences give researchers and business executives a clear view of what is already working and what is coming down the road. To bring AI researchers from academia and industry together to share their work, learn from one another, and inspire new ideas and collaborations, there are a plethora of AI-focused conferences around the world. There's a growing number of AI conferences geared toward business leaders who want to learn how to use artificial intelligence and related machine learning and deep learning to propel their companies beyond their competitors. So, whether you're a post-doc, a professor working on robotics, or a programmer for a major company, there are conferences out there to help you code better, network with other researchers, and show off your latest papers.
We are stepping into an avant-garde period, powered by advances in robotics, the adoption of smart home appliances, intelligent retail stores, self-driving car technology etc. Machine leaning is at the forefront of all these new-age technological advancements. The development of automated machines which have the capability match up to or maybe even surpass the human intelligence in the coming time. Machine learning is undoubtedly the next'big' thing. And, it is believed that most of the future technologies will be hooked on to it. Machine learning is given a lot of importance because it helps in prophesying behavior and spotting patterns that humans fail to predict.
As the calendar reaches its last month in 2019, the bot is hot. Research firm Tractica LLC has forecast that a combination of cloud-computing and robotic hardware, software and services will propel global revenue in the cloud robotics field from single digits to in excess of $170 billion within the next five years. This is about a lot more than having robots deliver concierge services or burritos. Bots are having a major impact on how over 1-billion active Instagram users channel posts to reach target audiences. And "Grinch bots" are reportedly dominating online traffic to retailer login pages this week to elbow out human shoppers for the best deals.
Every week, we publish a selection of AI-related content that is trending on Twitter. To be in the loop, you can find us on Twitter @AITimeJournal and subscribe to our newsletter! This week's tweets are featured in no particular order, and they are by: These industrial #robots work in an @Audi factory. As #AI is empowering other digital technologies, the immediate future will experience significant transformation in the adoption of #emergingtech like #Cloud, #CyberSecurity, #IoT, #Edge, #5G & #Blockchain in India to propel growth in digital economy.https://t.co/mntIyTnEWu Top 25 #AI Influencers to Follow on Twitter by 2019 https://t.co/VmkEhbbhng
Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with minor adversarial or even accidental perturbations. Consequences of such errors can be disastrous and even potentially fatal as shown by the recent Tesla autopilot crash. Thus, there is an urgent need for formal analysis systems that can rigorously check neural networks for violations of different safety properties such as robustness against adversarial perturbations within a certain L-norm of a given image. An effective safety analysis system for a neural network must be able to either ensure that a safety property is satisfied by the network or find a counterexample, i.e., an input for which the network will violate the property.
Toyota has, over the years, earned a reputation for producing the beige Corollas that always seem to clog the fast lane. Yet this is the same company that still offers in many of its cars the rapidly dying stickshift option so many enthusiasts ask for. Moving towards the autonomous era, Toyota is taking a refreshingly open view to how the self-driving car will keep the driver engaged. The philosophy falls under the "automation with a human touch" banner. Advanced driver aids, says the company, are designed to enhance the human experience, not replace it.
The Government is to amend road traffic legislation to allow for the testing of self-driving vehicles on Irish roads. So what has the State got to give the autonomous driving world? It seems that Irish motorists' pain is the automotive industry's potential gain. Self-driving vehicles use a combination of video and radar to feed data to the self-driving programmes. Both the cameras and the radars have shown to work reasonably well on the dry and well-marked highways of certain US states such as California.
Technology has upended one business after another across the United States. To cite only the most recent developments: Lyft and others have utterly changed personal transportation, and Airbnb has done the same for hospitality. And in January 2018, the first Amazon Go store opened, sans checkout clerks, promising similar upheaval for grocers. What is happening is fairly well understood, if initially underestimated. Digitization and other technological advances are exposing the vulnerabilities in every industry, particularly retail. And now, logistics companies are starting to feel the heat. Our new research has turned up five trends that offer startling indicators of impending change for the trucking, rail, warehousing, and logistics companies that move America's merchandise. Start with autonomous trucks (ATs), which will change the cost structure and utilization of trucking--and with that, the cost of consumer goods. Sixty-five percent of the nation's consumable goods are trucked to market.
Deep generative models have recently shown great promise in imitation learning for motor control. Given enough data, even supervised approaches can do one-shot imitation learning; however, they are vulnerable to cascading failures when the agent trajectory diverges from the demonstrations. Compared to purely supervised methods, Generative Adversarial Imitation Learning (GAIL) can learn more robust controllers from fewer demonstrations, but is inherently mode-seeking and more difficult to train. In this paper, we show how to combine the favourable aspects of these two approaches. The base of our model is a new type of variational autoencoder on demonstration trajectories that learns semantic policy embeddings.
Ai has come a long way. Just after WW2, there was a preconception that developing Artificial Intelligence would lead to something like an'Attack of the Zombie Robots', and that AI could only be a bad thing for humanity. Fortunately, we have come a long way from the old sci-fi view of AI, and we even have robotics used in surgery, but there is still a lingering feeling that AI and robotics are threatening in some way, and one of those ways is'bias'. AI is very much part of the fourth industrial revolution, which also includes cyber-physical systems powered by technologies like machine learning, blockchain, genome editing, and decentralized governance. The challenges that we face in developing our use of AI, are tricky ethical ones for the most part, which need a sensitive approach.