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Until February 2020, Amazon thought that the algorithms that controlled everything from their shelf space to their promoted products were practically unbreakable. For years they had used simple and effective artificial intelligence (AI) to predict buying patterns, and planned their stock levels, marketing, and much more based on a simple question: who usually buys what?

Yet as COVID-19 swept the globe they found that the technology that they relied on was much more shakable than they had thought. As sales of hand sanitizer, face masks, and toilet paper soared, sites such as Amazon found that their automated systems were rendered almost useless as AI models were thrown into utter disarray.

Elsewhere, the use of AI in everything from journalism to policing has been called into question. As long-overdue action on racial inequalities in the US has been demanded in recent weeks, companies have been challenged for using technology that regularly displays sometimes catastrophic ethnic biases.

Microsoft was recently held to account after the AI algorithms that it used on its MSN news website confused mixed-race members of girlband Little Mix, and many companies have now suspended the sale of facial recognition technologies to law enforcement agencies after it was revealed that they are significantly less effective at identifying images of minority individuals, leading to potentially inaccurate leads being pursued by police.

The past month has brought many issues of racial and economic injustice into sharp relief, says Rediet Abebe, an incoming assistant professor of computer science at the University of California, Berkeley. AI researchers are grappling with what our role should be in dismantling systemic racism, economic oppression, and other forms of injustice and discrimination. This has been an opportunity to reflect more deeply on our research practices, on whose problems we deem to be important, whom we aim to serve, whom we center, and how we conduct our research.

SEE ALSO: Artificial Intelligence Is on the Case in the Legal Profession

From the COVID-19 pandemic to the Black Lives Matter protests, 2020 has been a year characterized by global unpredictability and social upheaval. Technology has been a crucial medium of effecting change and keeping people safe, from test and track apps to the widespread use of social media to spread the word about protests and petitions. But amidst this, machine learning AI has sometimes failed to meet its remit, lagging behind rapid changes in social behavior and falling short on the very thing that it is supposed to do best: gauging the data fed into it and making smart choices.

The problem often lies not with the technology itself, but in a lack of data used to build algorithms, meaning that they fail to reflect the breadth of our society and the unpredictable nature of events and human behavior.

Most of the challenges to AI that have been identified by the pandemic relate to the substantial changes in behavior of people, and therefore in the accuracy of AI models of human behavior, says Douglas Fisher, an associate professor of computer science at Vanderbilt University. Right now, AI and machine learning systems are stovepiped, so that although a current machine learning system can make accurate predictions about behaviors under the conditions under which it learned them, the system has no broader knowledge.

The last few months have highlighted the need for greater nuance in AIin short, we need technology that can be more human. But in a society increasingly experimenting with using AI to carry out such crucial roles as identifying criminal suspects or managing food supply chains how can we ensure that machine learning models are sufficiently knowledgeable?

Most challenges related to machine learning over the past months result from change in data being fed into algorithms, explains Kasia Borowska, Managing Director of AI consultancy Brainpool.ai. What we see a lot of these days is companies building algorithms that just about do the job. They are not robust, not scalable, and prone to bias this has often been due to negligence or trying to cut costsbusinesses have clear objectives and these are often to do with saving money or simply automating manual processes, and often the ethical sideremoving biases or being prepared for changeisnt seen as the primary objective.

Kasia believes that both biases in AI algorithms and an inability to adapt to change and crisis stem from the same problem and present an opportunity to build better technology in the future. She argues that by investing in building better algorithms, issues such as bias and an inability to predict user behavior in times of crisis can be eliminated.

Although companies might previously have been loath to invest time and money into building datasets that did much more than the minimum that they needed to operate, she hopes that the combination of COVID and an increased awareness of machine learning biases might be the push that they need.

I think that a lot of businesses that have seen their machine learning struggle will now think twice before they try and deploy a solution that isnt robust hasnt been tested enough, she says. Hopefully the failure of some AI systems will motivate data scientists as well as corporations to invest time and resources in the background work ahead of jumping into the development of AI solutions we will see more effort being put into ensuring that AI products are robust and bias-free.

The failures of AI have been undeniably problematic, but perhaps they present an opportunity to build a smarter future. After all, in recent months we have also seen the potential of AI, with new outbreak risk software and deep learning models that help the medical community to predict drugs and treatments and develop prototype vaccines. These strides in progress demonstrate the power of combining smart technology with human intervention, and show that with the right data AI has the power to enact massive positive change.

This year has revealed the full scope of AI, laying bare the challenges that developers face alongside the potential for tremendous benefits. Building datasets that encompass the broadest scope of human experience may be challenging, but it will also make machine learning more equitable, more useful, and much more powerful. Its an opportunity that those in the field should be keen to corner.

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How Coronavirus and Protests Broke Artificial Intelligence And Why Its A Good Thing - Observer

New developments and opportunities are opening up in artificial intelligence, says Paul Budde.

I RECENTLY followed a "lunch box lecture", organised by the University of Sydney.In thetalk, Professor Zdenka Kuncic explored the very topical issue of artificial intelligence.

The world is infatuated with artificial intelligence (AI), and understandably so, given its super-human ability to find patterns in big data as we all notice when using Google, Facebook, Amazon, eBay and so on. But the so-called general intelligence that humans possess remains elusive forAI.

Interestingly, Professor Kuncic approached this topic from a physics perspective. By viewing the brains neural network as a physical hardware system, rather than the algorithm-based software as for example AI-based research used insocial media.

Her approach reveals clues that suggest the underlying nature of intelligence is physical.

Basically, what this means is that a software-based system will require ongoing input from software specialists to make updates based on new developments.Her approach, however, is to look at a physical system based on nanotechnology and use these networks as self-learning systems, where human intervention is no longer required.

Imagine the implications of the communications technologies that are on the horizon, where basically billions of sensors and devices will be connected to networks.

The data from these devices need to be processed in real-time and dynamic decisions will have to be made without human intervention. The driverless car is, of course, a classic example of such an application.

The technology needed to make such a system work will have to be based on edge technology in the device out there in the field. It is not going to work in any scaled-up situation if the data from these devices will first have to be sent to the cloud for processing.

Nano networks are a possible solution for such situations. A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometres at most in size), which at the moment can perform only very simple tasks such as computing, data storing, sensing and actuation.

However, Professor Kuncik expects that new developments will see expanded capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information.

Professor Kuncik concentrates, in her work, on electromagnetics for communication in the nanoscale.

This is commonly defined as the 'transmission and reception of electromagnetic radiation from components based on novel nanomaterials'.

Professor Kuncik mentioned this technology was still in its infancy. She was very upbeat about the future, based on the results of recent research and international collaboration. Advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas.

From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for the emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power forinput energy.

The researchers are looking at the output of these nanonetworks rather than the input. The process is analogue rather than digital. In other words, the potential output provides a range of possible choices, rather than one (digital) outcome.

The trick is to understand what choices are made in a nanonetwork and why.

There are two main alternatives for electromagnetic communication in the nanoscale the one as pursued by Professor Kuncik the other one being based on molecular communication.

Nanotechnology could have an enormous impact on for example the future of 5G. If nanotechnology can be included in the various Internet of Things (IoT) sensors and devices than this will open an enormous amount of new applications.

It has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nano radio.

Second, graphene-based nano-antennas have been analysed as potential electromagnetic radiators in the terahertz band.

Once these technologies are further developed and commercialised, we can see a revolution in edge-computing.

Paul Buddeis an Independent Australia columnist and managing director ofPaul Budde Consulting, an independent telecommunications research and consultancy organisation. You can follow Paul on Twitter@PaulBudde.

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Artificial intelligence is on the rise - Independent Australia

While artificial intelligence systems continue to make huge strides forward, they're still not particularly good at dealing with chaos or unpredictability. Now researchers think they have found a way to fix this, by teaching AI about physics.

To be more specific, teaching them about the Hamiltonian function, which gives the AI information about the entirety of a dynamic system: all the energy contained within it, both kinetic and potential.

Neural networks, designed to loosely mimic the human brain as a complex, carefully weighted type of AI, then have a 'bigger picture' view of what's happening, and that could open up possibilities for getting AI to tackle harder and harder problems.

"The Hamiltonian is really the special sauce that gives neural networks the ability to learn order and chaos," says physicist John Lindner, from North Carolina State University.

"With the Hamiltonian, the neural network understands underlying dynamics in a way that a conventional network cannot. This is a first step toward physics-savvy neural networks that could help us solve hard problems."

The researchers compare the introduction of the Hamiltonian function to a swinging pendulum it's giving AI information about how fast the pendulum is swinging and its path of travel, rather than just showing AI a snapshot of the pendulum at one point in time.

If neural networks understand the Hamiltonian flow so where the pendulum is, in this analogy, where it might be going, and the energy it has then they are better able to manage the introduction of chaos into order, the new study found.

Not only that, but they can also be built to be more efficient: better able to forecast dynamic, unpredictable outcomes without huge numbers of extra neural nodes. It helps AI to quickly get a more complete understanding of how the world actually works.

A representation of the Hamiltonian flow, with rainbow colours coding a fourth dimension. (North Carolina State University)

To test their newly improved AI neural network, the researchers put it up against a commonly used benchmark called the Hnon-Heiles model, initially created to model the movement of a star around a sun.

The Hamiltonian neural network successfully passed the test, correctly predicting the dynamics of the system in states of order and of chaos.

This improved AI could be used in all kinds of areas, from diagnosing medical conditions to piloting autonomous drones.

We've already seen AI simulate space, diagnose medical problems, upgrade movies and develop new drugs, and the technology is, relatively speaking, just getting started there's lots more on the way. These new findings should help with that.

"If chaos is a nonlinear 'super power', enabling deterministic dynamics to be practically unpredictable, then the Hamiltonian is a neural network 'secret sauce', a special ingredient that enables learning and forecasting order and chaos," write the researchers in their published paper.

The research has been published in Physical Review E.

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Artificial Intelligence Can't Deal With Chaos, But Teaching It Physics Could Help - ScienceAlert

Next Monday will see the launch of the very first digital couture fashion week, taking the place of a schedule of physical shows which typically sees the fashion elite decamp to Paris. Instead, a number of the prestigious brands which sit in the couture category will host digital offerings to press, clients and fans. And, in response to this unique new model, British fashion house Ralph & Russo has entered brand new territory for the world of couture by using artificial intelligence to model the new collection.

"I have always been fascinated by the digital space, and specifically by artificial intelligence as a medium," creative director Tamara Ralph tells us. "While AI as a dimension is something I have always been interested in exploring, really its the recent developments in the world, and subsequent limitations, that made me feel now is the right time to do so."

Courtesy of Ralph & Russo

Following months of enforced lockdown in countries across the world and with social distancing still in place, the ability to host a physical fashion show to a crowd this month is not plausible. Ralph & Russo has therefore used this opportunity of a digital-only format to explore an entirely new world.

"During times of change like this, I feel it's so important to not only evolve but to also be innovative. I was so excited by the prospect of not only doing something new for our brand, but for couture as a category something that would really push the limits. And while avatars in and of themselves might not be new, I dont feel its something that has been fully realised within couture, given its inherent nature of old world craftsmanship, techniques and level of detail."

When it came to creating the brand's avatar, Ralph says that she had very a clear idea of who she wanted the woman to be and what she was to represent.

"The process of choosing and building our avatar, named Hauli which is a traditional Swahili name, synonymous with strength and power was quite layered and complex, but incredibly exciting. It all began with sourcing inspiration, as you would for a collection, to inform what she would look like and how she would be brought to life. I wanted to create a beautiful Black woman, who was inspired by and an embodiment of the spirit of inspirational women changing lives in all four corners of the world.

"She represents the brand and all of the values that we stand for. She is the ultimate modern woman; fusing both old and new, elegance and strength, and stands with the brand as a positive force for change. Once these details were ascertained, we then worked closely with an agency to develop a base plate for Hauli, which then slowly progressed into the final avatar."

Courtesy of Ralph & Russo

Ralph explains that turning Hauli into a formed avatar wearing the collection was a long process where each level of detail and the intricacy of the couture pieces are painstakingly replicated on screen, for example each flower or jewel is individually placed on her form: "In short, it takes a village, but was well worth the effort."

This forced opportunity to explore the digital may have come about as a result of the recent crisis but, of course, the lack of a physical show is not the only limitation that fashion houses have found as a result of the Covid-19 pandemic. With ateliers forced to shut and staff made to stay home, designing and creating the physical collections became a challenge. At Ralph & Russo, this meant finding a whole new way of working.

"Like all brands, we of course faced challenges during this pandemic, which meant having to approach our design process in a different way. For instance, procuring fabrics and materials from our partners in France and Italy became difficult with shipping restrictions, especially with those countries being amongst the hardest hit. So, instead utilised as much as could from our in-house library of fabrics and materials. We're fortunate to have a large room full of rolls upon rolls of beautiful fabrics to choose from however, so this did not limit us at all in terms of creativity.

Courtesy of Ralph & Russo

"Another challenge is not being able to work with my design and atelier teams in person, as we spend most days in the lead up to couture together, going over designs, making tweaks, fitting on a model, and so on. I was worried to see how this would all take shape in this new era of working remotely. However, again we just had to be agile and flexible, and become accustomed to working just as closely, but in a different way. Meetings became video calls, where we would share illustrations with one another digitally, and we also got into the routine of mailing our swatches to one another wherever possible. Overall, I think we shifted our way of working quite well and quite quickly we had to."

This shift has led to many temporary changes within the way we work and indeed in the way fashion collections are presented, but, as with much that has pivoted as a result of the pandemic, some of these changes might be here to stay. Ralph thinks this is particularly the case for the modernisation of the fashion show.

Courtesy of Ralph & Russo

"I think the merging of the two worlds [of the traditional and the digital] has become necessary, and the pandemic has accelerated this. The current climate has cemented the importance of digital as a channel, and how critical it is for a brand to regularly be activating digitally with unique content to keep their audience engaged. This was, of course, always crucial for us, but its true value has really come into view in these past few months. Ive also really enjoyed and embraced this as a learning experience; the opportunities within this space are truly limitless and allow for so much creativity.

"Making digital a priority will absolutely be our new norm, and I do think this will extend across the entire industry, even beyond couture and into other product categories. I still believe, however, that there is incredible value and a place for engaging with your audience, clients and friends of the brand in real life as well, so I think the future will be about trying to strike that ideal balance of both worlds and doing so as responsibly as possible."

See the full unveiling of Ralph & Russo's autumn/winter 2020 couture collection at 4pm on Monday 6 July on the brand's channels.

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Exclusive: Ralph & Russo brings artificial intelligence to the couture world - harpersbazaar.com

Last fall, NASA unveiled the new suits that Artemis astronauts will wear when they take humanitys first steps on the lunar surface for the first time since way back in 1972. The look of theA7LB pressure suit variants that accompanied those earlierastronauts to the Moon, and later to Skylab, has since gone on to signify for many the definitive, iconic symbol of humanitys most ambitiously-realized space dreams.

With Artemis 2024 launch target approaching, NASAs original Moon suit could soon be supplanted in the minds of a new generation of space dreamers with the xEMU, the first ground-up suit made for exploring the lunar landscape since Apollo 17s Eugene Cernan and Harrison Schmitt took humanitys last Moon walk (to date). Unlike those suits, the xEMUs design is getting an assist from a source of "brain" power that simply wasnt available back then: artificial intelligence.

Specifically, AI is reportedly crunching numbers behind the scenes to help engineer support components for the new, more versatile life support system thatll be equipped to the xEMU (Extravehicular Mobility Unit) suit. WIRED reports that NASA is using AI to assist the new suits life support system in carrying out its more vital functions while streamlining its weight, component size, and tolerances for load-bearing pressure, temperature, and the other physical demands that a trip to the Moon (and back) imposes.

Recruiting AI isnt just about speed though speed is definitely one of the perks to meeting NASAs ambitious 2024 timeline and all that lies beyond. The machines iterative process is 100 or 1,000 times more than we could do on our own, and it comes up with a solution that is ideally optimized within our constraints, Jesse Craft, a senior design engineer at a Texas-based contractor working on the upgraded version of the xEMU suit, told WIRED.

But in some instances, AI even raises the bar for quality, as Craft also noted. Were using AI to inspire design, he explained. We have biases for right angles, flat surfaces, and round dimensions things youd expect from human design. But AI challenges your biases and allows you to see new solutions you didnt see before.

So far, NASA is relying on AI only to design physical brackets and supports for the life support system itself in other words, not the kind of stuff that might spell life or death in the event of failure. But that approach is already paying off by cutting mass without sacrificing strength, yielding component weight reductions of up to 50 percent, according to the report.

Even at 1/6 the gravity that astronauts experience back on Earth, that kind of small weight savings here and there can add up to make a big difference on the Moon. And even a slight slimming down cant hurt the xEMUs chances at perhaps becoming a new standard bearer in space fashion, as Artemis captivates a new generation with its sights set on the stars.

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Artificial intelligence helping NASA design the new Artemis moon suit - SYFY WIRE