In the last decade we have seen more robotics innovation becoming real products and companies than in the entire history of robotics. Furthermore, the greater Silicon Valley and San Francisco Bay Area is at the center of this'Cambrian Explosion in Robotics' as Dr Gill Pratt, Director of Robotics at Toyota Research Institute described it. In fact, two of the very first robots were developed right here. In 1969 at Stanford, Vic Sheinman designed the first electric robot arm able to be computer controlled. After successful pilots and interest from General Motors, Unimation acquired the concept and released the PUMA or Programmable Universal Machine for Assembly.

The just-issued World Robotics Report announced an all-time high of 517,385 new industrial robots installed in 2021 in factories around the world, representing 31% year-on-year growth. That brought the current stock of operational robots around the globe to about 3.5 million, a new record. Toyota has created a 6-foot-10-inch basketball-shooting robot named Cue that uses sensors on its ... [ ] torso to judge the distance and angle of the basket and uses motorized arms and knees to execute set shots. This robot record was reached half a century after the development of SHAKEY, the world's first "mobile intelligent robot." According to the 2017 IEEE Milestone citation, it "could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers."

Ever since disruptive technologies like artificial intelligence, robotics, machine learning, etc. made their debut in modern society, the world has turned upside down. Today, everything starting from the way we wake up by alarms and the automatic option that turns off the light when we go to sleep are powered by technology. Even though many disruptive technologies might also emerge in the future, some of the moments of history and some of the'firsts' have a remarkable spot in human minds. They carry the scientists' hard work and passion to deliver a futuristic solution to humankind. To celebrate their efforts, Analytics Insight has listed remarkable moments of disruptive technologies that were photographed.

From Shakey the Robot to self-driving cars, from the personal computer to personal assistants on our phones, the Defense Advanced Research Projects Agency (DARPA) has led the development of integrated artificial intelligence (AI) systems for more than half a century. From the earliest days of AI, it was apparent that a robust, generally intelligent system should include a complete set of capabilities: perception, memory, reasoning, learning, planning, and action; and when DARPA initiated AI research in the 1960s, ambitious projects such as Shakey the Robot went after the complete package. As DARPA realized the challenges, they backed away from the ultimate goal of integrated AI and tried to make progress on the individual problems of image understanding, speech and language understanding, knowledge representation and reasoning, planning and decision aids, machine learning, and robotic manipulation. Yet, even as researchers struggled to make progress in these subdisciplines, DARPA periodically resurrected the challenge of integrated intelligent systems and pushed the community to try again. In the 1980s, DARPA's Strategic Computing Initiative took on challenges of integrated AI projects such as the Autonomous Land Vehicle and the Pilot's Associate.

In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the ASNet architecture to make it more expressive, while still remaining invariant to a range of symmetries that exist in PPDDL problems. We also present a thorough experimental evaluation of ASNets, including a comparison with heuristic search planners on seven probabilistic and deterministic domains, an extended evaluation on over 18,000 Blocksworld instances, and an ablation study. Finally, we show that sparsity-inducing regularisation can produce ASNets that are compact enough for humans to understand, yielding insights into how the structure of ASNets allows them to generalise across a domain.

Modern robots are not unlike toddlers: It's hilarious to watch them fall over, but deep down we know that if we laugh too hard, they might develop a complex and grow up to start World War III. None of humanity's creations inspires such a confusing mix of awe, admiration, and fear: We want robots to make our lives easier and safer, yet we can't quite bring ourselves to trust them. We're crafting them in our own image, yet we are terrified they'll supplant us. But that trepidation is no obstacle to the booming field of robotics. Robots have finally grown smart enough and physically capable enough to make their way out of factories and labs to walk and roll and even leap among us.

Modern robots are not unlike toddlers: It's hilarious to watch them fall over, but deep down we know that if we laugh too hard, they might develop a complex and grow up to start World War III. None of humanity's creations inspires such a confusing mix of awe, admiration, and fear: We want robots to make our lives easier and safer, yet we can't quite bring ourselves to trust them. We're crafting them in our own image, yet we are terrified they'll supplant us. But that trepidation is no obstacle to the booming field of robotics. Robots have finally grown smart enough and physically capable enough to make their way out of factories and labs to walk and roll and even leap among us.

This blog will take a thorough dive into the timeline of AI, beginning from the very start, the 1940s. The term "Artificial Intelligence" was first coined by the father of AI, John McCarthy in 1956. But the revolution of AI began a few years in advance, i.e. the 1940's. Around 37% of industries have implemented AI in some form, which is a 270% increase for the past 4 years. AI has taken multiple forms over the years.

Developed at the Artificial Intelligence Center of the Stanford Research Institute (SRI) from 1966 to 1972, SHAKEY was the world's first mobile intelligent robot. According to the 2017 IEEE Milestone citation, it "could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers."

Developed at the Artificial Intelligence Center of the Stanford Research Institute (SRI) from 1966 to 1972, SHAKEY was the world's first mobile intelligent robot. According to the 2017 IEEE Milestone citation, it "could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers." In November 1963, Charles Rosen, head of the AI group at SRI, wrote a memo in which "he proposed development of a mobile'automaton' that would combine the pattern-recognition and memory capabilities of neural networks with higher-level AI programs," according to Nils Nilsson in his book The Quest for Artificial Intelligence. In April 1964, SRI submitted to the Advanced Research Projects Agency (ARPA) at the U.S. Department of Defense, a proposal for research in "Intelligent Automata," which it claimed would ultimately lead to "the development of machines that will perform tasks that are presently considered to require human intelligence."