2020 vision: the top three trends in robotics
Stewart Goulding, managing director at precision drive system supplier Electro Mechanical Systems Ltd, explores some current trends that are set to appear in 2020.
Robots are everywhere — from robotic wearables, hands, and arms, to companion robots, medical devices, and even biomorphic drones that model the behaviour of bees.
Cobots in the workplace
Since arriving on the scene in the mid-2010s cobots, or collaborative robots, have taken the market by storm. Cobots offer a variety of opportunities for production lines, particularly to enable humans and robots to complement each other, all while working alongside one another safely. The new trend for these styles of robots is making them more accessible, with more cost-effective options now allowing for greater distribution and use.
In fact, cobots can reduce the human input on production by up to 50 per cent. With the current skills gap having cost UK organisations £6.3bn over the past 12 months, being able to integrate cobots and other robotic applications, has the potential to positively impact the economy.
In recent years, a significant focus has been placed on revolutionising non-invasive and minimally invasive surgery. As a result, a deluge of new surgical robots have become market-ready.
Due to more accurate diagnosis methods, the amount of non-invasive and minimally invasive surgeries has skyrocketed. This is putting an increasing strain, both physically and organisationally, on surgeons that carry out these procedures. Robot alternatives, therefore, offer an advantage to the public health service.
As such, these robots must be as accurate and reliable as possible to ensure that they can help ease the strain on the medical system. For example, endoscopy, which is a minimally invasive surgery that allows doctors to inspect the inside of a patient, is one procedure that robots have been developed to support.
Endoscopy robots must be compact and consistently precise. For this reason, when French company EndoControl was developing its new endoscopy ViKY system, it chose a range of FAULHABER brushless DC-motors, which help to achieve the required precision and consistency.
With a complimentary gearhead fitted these motors have a broad selection of reduction ratios available ranging from approximately 3:1 to 1500:1, which gives extensive adjustment of the speed and torque of the device. In the ViKY systems up to 700 mNm of precise movement was achieved using FAULHABER dirve systems.
These types of developments are crucial in ensuring medical facilities can cope with the rising number of surgeries, all while reducing fatigue, preserving surgeon wellbeing and avoiding burn out.
Robotics in agriculture
A recent market research study reports that the demand for agricultural robots will see an increase of 24.1 per cent by 2024. It is no wonder that more agricultural robotic applications are emerging, including biomorphic drones that model the behaviour of bees, which were demonstrated at UK-RAS.
Robots and drones could have a big effect on the effectiveness of farming. From drones that monitor and analyse crops, to automated tractors that can seed, fertilise and harvest, agricultural robot developments all mean that human labour can often now be devoted to more complex tasks.
In fact, some rural farms in China are starting to use heavy-duty industrial drones to water crops in hard to reach areas. The method is proving to be more fuel-efficient than transporting workers and the computer-controlled sprayers waste fewer resources.
So, whether it's across production lines, in surgical theatres or across vast agricultural fields, robotic applications are helping to provide innovative and reliable methods of working for all involved. Who knows what the future will hold in 2020 and beyond, but for now, advanced robots are here to stay.
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.