Tesla Working on Full Self-Driving Mode, Extending AI Lead 

Tesla Model car

Tesla’s goal to release its level 5 Full Self Driving (FSD) mode autopilot capability in 2021 was deemed unrealistic by the CEO of competitor Waymo in a recent interview.  Tesla is the only autonomous vehicle manufacturer using real-time cameras, rather than pre-mapped Lidar (Light Detection and Ranging) to guide vehicle movement. Tesla also…

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Asimov’s three Laws of Robotics and AI Autonomous Cars 

Robot driver

Perhaps one of the most well-known facets about robots is the legendary set of three rules proffered by writer Isaac Asimov. His science fiction tale entitled The Three Laws was published in 1942 and has seemingly been unstoppable in terms of ongoing interest and embrace.   

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Prank Gone Wrong? This AI-Powered Robot Dog is shooting paintballs

Paintball robot

A New York-based start-up, MSCHF (mischief) has mounted a compressed air paint gun on one of Boston Dynamic’s $75,000 Spot robots and will apparently link its controls to a public website. Spot is the company’s robotic dog machine that can perform tricks like dancing, parkour, etc. Boston Dynamics has received several million views on YouTube with the clips of this futuristic dog going viral. But pranksters at MSCHF have opposing views.

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Symbio emerges from stealth with $30 million to automate industrial assembly

Engineer fixing robotic arm

Emeryville, California-based industrial robotics startup Symbio Robotics today emerged from stealth with $30 million in funding. The company says the capital will be put toward further developing its technology as it looks to sign new customers.

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Top 10 Robotic Scientists making it big in the 21st Century

Top 10 robotic scientists

Today, we are experiencing an evolved description of robotics that includes the development, creation and use of bots that explore Earth’s hardest conditions, robots that assist law-enforcement and even guide in every facet of healthcare. All this was possible only because of pioneer robotics scientists who worked day and night to establish their ideas in form of robots. In order to celebrate their victory, Analytics Insight has listed the top 10 robotic scientists who are making a difference in the 21st century.

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Misclassifying a Snowman as a Pedestrian is troublesome for AI Autonomous Cars 

Child sculps a snowman

We misclassify a lot of things, all the time, daily, and at any moment. You are waiting in a restaurant for a friend to come and have lunch with you. Your eyes are scanning the people that are entering the busy eatery. Assume that it is a cold day and raining or snowing, which means that most of those coming into the restaurant are wearing heavy clothes and generally covered up. It would be quite easy to spot someone that appeared to be your friend, based perhaps on their height and overall shape, yet once they removed their coat and hat, presumably by now seeing clearly the face of the person, you would realize it is not the person you were waiting for.

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Impact of Robotic Technology on Healthcare Industry

Robot Healthcare

The whole idea of robots in hospitals is not new for healthcare professionals. The idea of using robotic technologies in healthcare evolved in 1985. It started when healthcare planned to transform robots into precise machinery for surgery. “The idea came into reality in early 2000 with the invention of the DaVinci robot. Hospital management software and robotic technologies are proof of how technologies have evolved.” According to research conducted by Credence, the global market of medical robotics will grow to USD 20 billion by 2023.  

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Scaling for Robot Intelligence

Robot intelligence

RIOS CorporationJust now·3 min readBy Matt ShafferTechnologically, the last 30 years or so have been shaped by advancements in computation, and the ability to build machines that can make decisions independent of human operators is a direct result of this progress. With the growing global demand for machines that can perform labor, intelligent automation will bring about the real changes needed to deliver at scale. Though historically, robotic systems with embedded intelligence are inherently more difficult to build with reliability because they operate in the real world — a world with less regularity and more unpredictable consequences than the carefully-designed frameworks of the digital world. Given the challenges, it is not surprising to consider that factory automation is still largely driven by human workers who perform tasks that are often repetitive, but difficult to automate.Machine learning is most effective at scale, where the experiences of many systems can be aggregated.Automation is non-trivial, but it is not due to the fact that research cannot solve a lot of these problems — but that it only became a possibility more recently. There are certainly quite a few reasons for this, some of which have to do with the hardware and computational advancements, and others that revolve around data. But there is another interesting theory going around that is articulated by Sara Hooker in “The Hardware Lottery”. She postulated that research directions in the field of machine learning are often explored due to software and hardware available at the time, rather than being motivated by the most promising ideas. This theory is aligned with our premise at RIOS that advancing the capabilities of robots is heavily dependent on both specialized hardware and software that must coevolve.Robots in the real world have traditionally been programmed in isolation on a single task, rather than leveraging collective knowledge as in simulated environments..Today, we are reaching an inflection point, and there is a monumental opportunity to develop custom hardware and software systems that enable robots to take on increasingly open-ended tasks without the need of reprogramming for each new instruction. We can do this by taking the lessons of the internet to apply data at scale to robotics. By strategically designing systems with the intent of learning from them, and building the infrastructure to support information sharing, we can adapt more quickly to new tasks and master the ones we are already familiar with. The real promise of applying machine learning to robotics is not teaching a single robot to learn for itself, but to aggregate experience from a vast network of robots so that they can improve at scale.A core tenet of what we do at RIOS is to build machines with this idea in mind. Like hardware, skills and behaviors should be transferable across platforms when possible, and each deployed system should be able to share what it has learned with other systems. At a high level, you can think of this as storing knowledge rather than just data to reduce the need for retraining. The result is a class of robots that can do a variety of tasks and address new challenges with less development time. By building distributed robots that continuously learn from both their environment and the collective experience of others, we can help push intelligent robotics forward at scale much in the same way that the information economy benefited from the web.The next generation of technological progress is starting to favor organizations that can rapidly assemble the best technologies of the web-era and use them to take fields like robotics in new directions. In many parts of the world, where labor shortages exist or workers are subjected to poor conditions, this couldn’t come at a better time. Moreover, what used to be a long lead-time in deploying new systems or developing solutions is disappearing as robots can reuse not just hardware, but prior knowledge when taking on new tasks. As more robots fill empty roles in factories, we’ll start wonder how we lived without them, and eventually forget they are doing the most thankless of work for us without any complaints.Matt Shaffer is the RIOS Director of Artificial Intelligence and is the architect behind the brain of our robots. This article is a shortened version of Matt’s article Scaling Artificial Intelligence for Robotics in 2021.

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Locus Robotics raises $150 million to scale its warehouse robotics platform

Locus Robotics

Locus Robotics, a Wilmington, Massachusetts-based warehouse robotics startup, today announced it has raised $150 million in series E funding at a $1 billion post-money valuation. The company says the funding will allow it to accelerate product innovation and global expansion. Locus expects that in the next four years, over a million warehouse robots will be installed and that the number of warehouses using them will grow tenfold.

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SEER: Simulative Emotional Expression Robot

SEER Robot

“SEER” is a humanoid robotic head developed as an artistic work by Takayuki Todo. It explores the significance of gaze and facial expression in the sphere of human-machine research. Takayuki Todo is interested in how people establish an emotional relationship with humanoid robots. As the discipline of robotics has shown for years, a realistic similarity to the human form alone is not able to break down the distance between a human and a machine.

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The origin of Robot Arm Programming Languages

Robot Arm

This short blog post is about the origin of languages for describing tasks in automation, in particular for industrial robot arms. Three people who have passed away, but were key players were Doug Ross, Victor Scheinman, and Richard (Lou) Paul, not as well known as some other tech stars, but very influential in their fields. Here, I must rely not on questions to them that I should have asked in the past, but from personal recollections and online sources.

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3D Scene understanding with TensorFlow 3D

3D Scene understanding

The growing ubiquity of 3D sensors (e.g., Lidar, depth sensing cameras and radar) over the last few years has created a need for scene understanding technology that can process the data these devices capture. Such technology can enable machine learning (ML) systems that use these sensors, like autonomous cars and robots, to navigate and operate in the real world, and can create an improved augmented reality experience on mobile devices. r.

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AI autonomous Cars might showcase the wisdom of the Einstein Clock Paradox 

Einstein Paradox Clock

Are you familiar with the famous twin’s paradox that was proffered by Einstein? It’s quite a hoot. The topic focuses on the foundational nature of time and clocks. Einstein brought up the topic while conceiving the theories of relativity, though historians point out that the thought experiment can be traced to a 1911 paper from scientist Paul Langevin. In any case, let’s jump into the details and put aside the historical origins.

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Karakuri, the British Tesla of kitchen and food robotics is here

Karakuri

From picking to plate, the food industry is heavily reliant on human labour and has relatively little automation. In a move to revolutionise the food and hospitality industry, London-based startup Karakuri has ambitious goals to bring robots and AI to the food industry finally.

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My thoughts on the AI Revolution: Hype, scams, and big brother

Character in Mask

What is behind Artificial Intelligence?
Artificial intelligence can be defined as a science that models intelligent human behavior. This definition may have one significant drawback — the concept of intelligence is difficult to explain in principle. The problem of defining artificial intelligence comes down to the problem of defining intelligence in general: is it something in common, or does this term combine a set of disparate abilities, and even more as individual or even collective abilities?

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Artificial Intelligence — Agents and Environments

Agents - Environments

Agent and Environment are two pillars in Artificial Intelligence, our aim is to build intellectual agents and work in an environment. If you consider broadly agent is the solution and environment is the problem. In simple terms, even starter or researcher can understand that and is defined Agent as game and Environment as ground.

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100 uses of mobile robots – part one

Robot snow track

What are mobile robots used for? A question that, given the title of this piece, has many possible answers, at least 100 uses of robots in fact. The applications of robotics are vast and expanding, with huge implications for the future of mankind, the planet—and beyond. Over the last 50 years, we’ve become familiar with the idea of a range of robot applications, with widespread use in factories and laboratories, but today, thanks to sensors, actuators, and AI, advanced robots are popping up everywhere.

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