The promise of AI for DevOps in 2021

Heads on code

DevOps is a natural target for AI-driven efficiencies, as it involves frequently repeated processes that generate mountains of data. It seems reasonable to expect that, like other domains that require decisions to be made based on large volumes of data, AI will play an important role in DevOps, too. Definitions of AI vary considerably, so […]

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Top 10 Robotic Surgical Companies in 2021 all over the World

Robotic Surgical Companies

Medical robots form a rapidly growing sector of the medical devices industry. Regardless of whether utilized for home help, crisis response, negligibly invasive medical surgery, targeted therapy, or prosthetics, they are turning out to be increasingly more widely utilized these days, transforming medical care across the globe.

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Autonomous Cars and Minecraft have this in common  

Minercraft

Minecraft seems to be everywhere. Now over a decade old, the video game has reportedly attained more than 126 million monthly active players and has sold well beyond 200 million copies of the gaming software. Besides the game itself, there is plenty of merchandise to be had and lots of spin-offs […]

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Robotics & Neuroscience go hand in hand. You’d be surprised to know

Robotic hand holding a brain

In today’s date, modern robotic development is inspired by studying the human brain, so says Mikhail Lebedev, Academic Supervisor at HSE University’s Centre for Bioelectric interfaces. An article in Science Robotics called “Neuroengineering challenges fusing robotics and neuroscience” also talks about how neuroscience and robots are developing hand in hand, contributing to the progress in both fields to develop more advanced android robots.

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DevOps myths debunked

Truth maze

Leaders need not be DevOps experts, but they need to distinguish DevOps myths and realities to lead their digital transformation projects. As I indicated in a recent article, Engineering Practices Can Overcome DevOps Challenges, leaders need to set an inspiring DevOps directional vision for the organization, and proactively stimulate and sponsor team activities toward goals. […]

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Say hello to Azure Percept, Microsoft’s Latest Edge AI service

The tech giant has a trump card to leverage it’s AI abilities. Controlling elevators with voice commands, cameras that notify stores when to restock, video streams that keep a tab on parking space availability are some of the many things that are now possible, thanks to artificial intelligence and computing on the edge, via Azure Percept.

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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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How Edge AI Chipsets Will Make AI Tasks More Efficient

AI Chip

Artificial intelligence (AI) is an innovation powerhouse. It autonomously learns on its own and evolves to meet simple and complex needs, from product recommendations to business predictions. As more people and services produce data, more powerful AI is necessary to process it all. AI chipsets that use edge computing are the solution.

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I interviewed one of the World’s most advanced AI Systems: GPT3

GPT-3 conversation

I have been fascinated by computers in general since I was a kid. I can remember the computer lab when I was maybe 5 or 6 years old and we were being taught using a computer through the DOS prompt. I have seen computers and technology progress exponentially through time. In my humble opinion, Artificial Intelligence is going to shape our future more than we would like to acknowledge.

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Introducing Model Search: An Open Source Platform for Finding Optimal ML Models

Google Model Search

he success of a neural network (NN) often depends on how well it can generalize to various tasks. However, designing NNs that can generalize well is challenging because the research community’s understanding of how a neural network generalizes is currently somewhat limited: What does the appropriate neural network look like for a given problem? How deep should it be? Which types of layers should be used? Would LSTMs be enough or would Transformer layers be better? Or maybe a combination of the two? Would ensembling or distillation boost performance? These tricky questions are made even more challenging when considering machine learning (ML) domains where there may exist better intuition and deeper understanding than others.

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The Model’s shipped; what could possibly go Wrong?

Retrain button

In our last post we took a broad look at model observability and the role it serves in the machine learning workflow. This leads us to a natural question of: what should I monitor in production? The answer, of course, depends on what can go wrong. In this article we will be providing some more concrete examples of potential failure modes along with the most common symptoms that they exhibit in your production model’s performance.

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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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The adoption of Machine Learning in data driven SaaS products

Platform on Screen

SaaS solutions have been gaining popularity over recent years to the point where most software products are using SaaS based model. SaaS has been widely accepted by industry as it requires little to no installation, software can be instantly dispatched via cloud and cloud computing offers flexibility in computing power and resources.

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Is Hardware the Key to Advancing Natural Language Processing?

MIT SpAtten

Researchers at MIT have created an algorithm-based architecture called SpAtten that reduces attention computation and memory access in natural language processing (NLP) systems. If we think it’s hard to learn a new language, imagine the challenges hardware and software engineers face when using CPUs and GPUs to process extensive language data. Natural language processing (NLP) attempts to bridge this gap between language and computing.

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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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