We have previously highlighted the role of robotics and artificial intelligence (A.I.) technologies in fighting the spread of COVID-19 in an article published on 15th May. In today’s post, we look at some of the leading companies in this space, and how they have contributed to fighting the pandemic, or are well-placed to benefit from economic, social and geo-political shifts borne out of the crisis.

Robots and automation in healthcare applications

The most visually obvious contribution of robotics and A.I. to combating COVID-19 has been the development of autonomous robots in healthcare – such as Omron’s LD-UVC, shown in Figure 1 below. Omron makes up 4.5% of RBTZ’s index (as at 21 August 2020). Their ground-breaking LD-UVC disinfects a particular premises by eliminating 99.9% of bacteria and viruses, both airborne and droplet, with a precise dosage of UVC energy1.

Reducing the risk of human exposure to the coronavirus is one application of robotics, while scaling up our capacity for clinical testing is another critical element of the fight.

Swiss Healthcare company, Tecan Group, which makes up 5.3% of RBTZ’s index (as at 21 August 2020), is a market leader in laboratory instruments, reagents and smart consumables used to automate diagnostic workflow in life sciences and clinical testing laboratories.

Automation is critical for countries attempting to scale up their COVID-19 testing capacity. Tecan is aiming to double production of its laboratory automation solutions and disposable pipette tip products, and has accessed emergency stockpiles to keep up with the massive demand.

Artificial intelligence: a critical tool

Californian company Nvidia makes up 9.4% of the index which RBTZ aims to track (as at 21 August 2020), making it the Fund’s largest holding. Nvidia is at the forefront of deep learning, artificial intelligence, and accelerated analytics. 

Nvidia was able to design and build the world’s seventh fastest supercomputer in three weeks, a task that normally takes many months, to be used by the U.S. Argonne National Laboratory to research ways to stop the coronavirus.

Supercomputers are proving to be a critical tool in many facets of responding to the disease, including predicting the spread of the virus, optimising contact tracing, allocating resources and providing decisions for physicians, designing vaccines and developing rapid testing tools.

Industrial robotics: re-tooling supply chains

Then there are companies and products that are helping us adapt to a post-COVID world and beyond.

Keyence Corporation, from Japan, positioned itself at the forefront of several key trends in an era of increasing factory automation. In the wake of the COVID-19 crisis, factories have never faced such an urgent need to replace humans with machines to keep production lines running.

Keyence specialises in automation systems for manufacturing, food processing and pharma – machine vision systems, sensors, laser markers, measuring instruments and digital microscopes. Think precision tools and quality control sensors that eliminate or detect infinitesimal assembly-line mistakes, improving throughput, and reducing wastage and costly shutdowns.

Its focus on product innovation and direct-sales model give it a competitive advantage, making it better able to adapt to new manufacturing processes and workflows while introducing high-value client solutions.

Keyence has maintained an operating profit margin >50%, has no net debt and managed to increase its dividend for the 2020 financial year, to become Japan’s third-largest company by market value.

Unmanned vehicles and drones: a new world order

One unfortunate consequence of the virus crisis has been the straining of international relations and a deterioration of the rules-based order. AeroVironment is a global leader in unmanned aircraft systems, or ‘drones’, and tactical missile systems. It is the number one supplier of small drones to the U.S. military. 

The Australian Defence Force is also an AeroVironment customer, with spending on drone and military technology expected to increase after the release of the 2020 Defence Strategic Update in July.

Beyond weapons systems, AeroVironment is also leading the evolution in stratospheric unmanned flight with the development of the Sunglider solar-powered high-altitude pseudo-satellite (HAPS), currently undergoing testing at Spaceport America in New Mexico. AeroVironment recently announced it was building a drone helicopter that will be deployed to Mars along with NASA’s Perseverence rover in 2021. The ‘Mars Helicopter’ will be the first aircraft to attempt controlled flight on another planet, in its mission searching for signs of habitable conditions and evidence of past microbial life.

Get exposure to the Robotics and Artificial Intelligence thematic

BetaShares Global Robotic and Artificial Intelligence ETF (ASX: RBTZ), invests in companies from across the globe that are exposed to the above thematic.

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For years, the sheer messiness of data slowed efforts to launch artificial intelligence (A.I.) and machine learning projects. Companies weren’t willing to wait a year or two while data analysts cleaned up a massive dataset, and executives sometimes had a hard time trusting the outputs of a platform or tool built on messy data.

Data pre-processing is a well-established art, and there are many tech pros out there who specialize in tweaking datasets for maximum validity, accuracy, and completeness. It’s a tough job, and someone has to do it (usually with the assistance of tools, as well as specialized libraries such as Pandas). But now IBM is trying to apply A.I. to this issue, via new data prep tools within AutoAI, itself a tool within the cloud-based Watson Studio.

“We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources,” Rob Thomas, General Manager of IBM Data and AI, wrote in a statement.“The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building ML models and experiments faster.”

In addition to data cleanup, AutoAI includes a number of other tools for building A.I. and ML algorithms, including ones that set optimal hyperparameters (which are the parameters with values set before the machine’s learning begins). There’s also IBM Neural Networks Synthesis, or NeuNetS, which creates customized neural networks (users are asked to optimize for either speed or accuracy).

IBM is competing fiercely with Google (which is plunging into the ML-automation game with AutoML Video and AutoML Tables, with other tools surely on the way) and Microsoft (which has automation and recommendation tools built into its Azure Machine Learningplatform) to claim the attention of companies interested in the A.I./ML market. If that wasn’t enough of a crowded landscape, Amazon is plunging heavily into the enterprise-automation game with Amazon Personalize, which streamlines everything from mobile-app development to email marketing.

Of course, the rise of A.I./ML automation could lead to a new host of problems. Sure, having tech professionals build bespoke algorithms and tools in-house is a painstaking process with a fair amount of risk (if you fail, you’ve burned tons of time and resources), but there’s the reasonable expectation that you’ll have something tailored to your needs, based on reliable data and math. When you begin to automate these processes, you risk obfuscating at least a portion of the data and logic behind dashboards—which might lead some to question the output of the work.

Then again, many firms can’t afford to even begin an internal, customized A.I./ML program; in that context, these automated solutions are the best (and perhaps only) bet if they want to get into this particular game.

For tech professionals, these new tools are yet another sign that the A.I./ML market is maturing. Those professionals who understand how these tools work—as well as the underlying logic and theories—will have their pick of positions, as companies desperate for A.I./ML talent are willing to pay enormous salaries and benefits. Although these technologies might seem daunting, there are a number of resources designed to give you a solid education; check them out.

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If you listen to the tech pundits (and many tech-firm CEOs), artificial intelligence (A.I.) and machine learning (ML) will define the future. And they’re certainly not wrong: virtually every industry is interested in how A.I. and ML can streamline business processes and boost profits.

These are clearly areas worth any tech professional’s time and attention. But for students and new graduates, the prospect of plunging into the study of artificial intelligence is no doubt intimidating; the associated technologies (not to mention the underlying mathematics) are often hideously complicated. Nonetheless, it’s important to familiarize yourself with A.I. and ML if you want to “future proof” your career.

A.I. Beginner

If you’re totally new to the idea of A.I.—as in, the only thing you know about it is what you’ve seen in sci-fi movies such as “The Terminator”—that’s totally okay, because there are lots of resources out there that will give you basic-level instruction in A.I. theories and practice.

For instance, Hacker Noon offers a lovely breakdownof A.I. from a programmer’s perspective, including the industry’s “Holy Grail”: Artificial General Intelligence, or AGI (which some other resources call “General Artificial Intelligence”). KDNuggets also has a rundown of the basic terms and the technologies involved.

If you want to get to know some of the tools that actually make A.I. work, start off with Google’s three-hour introduction to deep-learning fundamentals. Since it’s Google, the materials inevitably focus on the company’s open-source software library, TensorFlow, that’s used in machine-learning applications (such as neural networks).

Google also offers a machine-learning “crash course” with 25 lessons and 40+ exercises, designed to take roughly 15 hours to complete.You don’t need to know a lot to start off with it, but it’s definitely a smoother process if you have some knowledge of programming basics, Python, and intro-level Algebra.

A.I. Mid-Level

Once you’ve learned a bit about the particulars of artificial intelligence, check out IBM’s developerWorks, which includes a number of articles and tutorials on everything from neural networks to building Internet of Things (IoT) apps with Apple’s Swift and Watson (IBM’s A.I. platform). Of course, you have to keep in mind that IBM’s resources are slanted toward Watson, because the company wants developers to use its products; nonetheless, there is quite a bit of good material here about the fundamentals of A.I.

In a similar vein is Microsoft’s AI School, which offers lessons in everything from text analytics and object recognition to custom neural-network models. Yes, there’s an emphasis on Microsoft products, but there’s also quite a bit on “universal” A.I. skills. Take a look at this once you’ve learned some basic A.I. and machine learning concepts.

A.I. Advanced

If you’re truly ready to get your hands dirty with artificial intelligence and machine learning, swing over to OpenAI, the kinda-nonprofit foundation (it’s complicated) devoted to creating an ethical framework for A.I. development. OpenAI hosts what it calls “Gym,” a toolkit for developing and comparing reinforcement algorithms, as well as a set of models and tools for training A.I. and ML. There’s also a handy, very extensive tutorial in deep reinforcement learning (i.e., deep RL).

OpenAI is likely something you won’t want to explore until you’re comfortable with the basics of artificial intelligence and machine learning, but it’s an excellent source of tools andresearch knowledge. Bookmark it.

If your mathematics knowledge is advanced (i.e., you’re deeply familiar with includes linear algebra, multivariate differential calculus, probability theory, and statistics) and you have a computer-science background, andyou’re intimate with data structures and algorithms, check out Bloomberg’s Foundations of Machine Learning, a free online course. It is, in a word, comprehensive.

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For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast. Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, A.I. is surely one of its driving forces.

It is an especially exciting time for a researcher like me. When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face. But thanks to the growth of big data, advances in algorithms like neural networks and an abundance of powerful computer hardware, something momentous has occurred: A.I. has gone from an academic niche to the leading differentiator in a wide range of industries, including manufacturing, health care, transportation and retail.

I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.

I call this approach “human-centered A.I.” It consists of three goals that can help responsibly guide the development of intelligent machines.

First, A.I. needs to reflect more of the depth that characterizes our own intelligence. Consider the richness of human visual perception. It’s complex and deeply contextual, and naturally balances our awareness of the obvious with a sensitivity to nuance. By comparison, machine perception remains strikingly narrow.

Sometimes this difference is trivial. For instance, in my lab, an image-captioning algorithm once fairly summarized a photo as “a man riding a horse” but failed to note the fact that both were bronze sculptures. Other times, the difference is more profound, as when the same algorithm described an image of zebras grazing on a savanna beneath a rainbow. While the summary was technically correct, it was entirely devoid of aesthetic awareness, failing to detect any of the vibrancy or depth a human would naturally appreciate.

That may seem like a subjective or inconsequential critique, but it points to a major aspect of human perception beyond the grasp of our algorithms. How can we expect machines to anticipate our needs — much less contribute to our well-being — without insight into these “fuzzier” dimensions of our experience?

Making A.I. more sensitive to the full scope of human thought is no simple task. The solutions are likely to require insights derived from fields beyond computer science, which means programmers will have to learn to collaborate more often with experts in other domains.

Such collaboration would represent a return to the roots of our field, not a departure from it. Younger A.I. enthusiasts may be surprised to learn that the principles of today’s deep-learning algorithms stretch back more than 60 years to the neuroscientific researchers David Hubel and Torsten Wiesel, who discovered how the hierarchy of neurons in a cat’s visual cortex responds to stimuli.

Likewise, ImageNet, a data set of millions of training photographs that helped to advance computer vision, is based on a project called WordNet, created in 1995 by the cognitive scientist and linguist George Miller. WordNet was intended to organize the semantic concepts of English.

Reconnecting A.I. with fields like cognitive science, psychology and even sociology will give us a far richer foundation on which to base the development of machine intelligence. And we can expect the resulting technology to collaborate and communicate more naturally, which will help us approach the second goal of human-centered A.I.: enhancing us, not replacing us.

Imagine the role that A.I. might play during surgery. The goal need not be to automate the process entirely. Instead, a combination of smart software and specialized hardware could help surgeons focus on their strengths — traits like dexterity and adaptability — while keeping tabs on more mundane tasks and protecting against human error, fatigue and distraction.

Or consider senior care. Robots may never be the ideal custodians of the elderly, but intelligent sensors are already showing promise in helping human caretakers focus more on their relationships with those they provide care for by automatically monitoring drug dosages and going through safety checklists.

These are examples of a trend toward automating those elements of jobs that are repetitive, error-prone and even dangerous. What’s left are the creative, intellectual and emotional roles for which humans are still best suited.

No amount of ingenuity, however, will fully eliminate the threat of job displacement. Addressing this concern is the third goal of human-centered A.I.: ensuring that the development of this technology is guided, at each step, by concern for its effect on humans.

Today’s anxieties over labor are just the start. Additional pitfalls include bias against underrepresented communities in machine learning, the tension between A.I.’s appetite for data and the privacy rights of individuals and the geopolitical implications of a global intelligence race.

Adequately facing these challenges will require commitments from many of our largest institutions. Universities are uniquely positioned to foster connections between computer science and traditionally unrelated departments like the social sciences and even humanities, through interdisciplinary projects, courses and seminars. Governments can make a greater effort to encourage computer science education, especially among young girls, racial minorities and other groups whose perspectives have been underrepresented in A.I. And corporations should combine their aggressive investment in intelligent algorithms with ethical A.I. policies that temper ambition with responsibility.

No technology is more reflective of its creators than A.I. It has been said that there are no “machine” values at all, in fact; machine values are human values. A human-centered approach to A.I. means these machines don’t have to be our competitors, but partners in securing our well-being. However autonomous our technology becomes, its impact on the world — for better or worse — will always be our responsibility.

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