How is the current race for artificial intelligence domination?

Many are asking this question as the United States, China, and Europe compete for global leadership in AI. The wave of innovation is cutting across the globe with companies adopting and deploying AI as digital transformation takes place.

The adoption of artificial intelligence in the retail sector is accelerating with the transformation in product management systems. Millennials continue to record the highest spending power in the consumer market and this in turn is influencing the adoption of artificial intelligence in retail.

There is hope for treatment of COVID-19 as the pandemic continues with scientists developing EIDD-2801, a drug that will change the approach of treating the virus. The reduction of damages in the lung section makes EIDD-2801 effective according to scientists with the drug expected for clinical trials.

These and more insights on our Weekly AI Update

Digital Transformation of the Global Economy

The United States reaped tremendous economic benefits from the last wave of digital innovation, becoming home to some of the world’s most successful tech companies, such as Amazon, Apple, Facebook, Google, Intel, and Microsoft.

Meanwhile, many parts of the world, including the European Union, paid an economic price staying on the sidelines. Recognizing that missing the next wave of innovation — in this case, AI — would be similarly problematic, many nations are taking action to ensure they play a large role in the next digital transformation of the global economy.

China, the European Union, and the United States are now emerging as the main competitors for global leadership in AI¹. Indeed, China, which achieved success in the Internet economy in part by shutting out U.S. firms, has clearly stated its ambition of achieving dominance in AI — both to increase its competitiveness in industries that have traditionally been vital to the U.S. and EU economies, and to expand its military power.

Moreover, the EU’s coordinated plan on AI states that its “ambition is for Europe to become the world-leading region for developing and deploying cutting-edge, ethical and secure AI.” The outcome of this race to become the global leader in AI will affect the trio’s future economic output and competitiveness, as well as military superiority.

Single-Image Super-Resolution

The primary aim of single-image super-resolution is to construct a high-resolution (HR) image from a corresponding low-resolution (LR) input. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR images.

Optimizing such metrics often leads to blurring, especially in high variance (detailed) regions. We propose an alternative formulation of the super-resolution problem based on creating realistic SR images that downscale correctly. We present a novel super-resolution algorithm² addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.

It accomplishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require training on databases of LR-HR image pairs for #supervisedlearning).

Data Network Effects

The concept of network effect (in general) is by now well understood: a flywheel type situation where a good or service becomes more valuable when more people use it. Many examples out there from the telephone system (the value of a phone increases if everyone has a phone) to Facebook to many marketplaces (with some nuances for the latter).

While they produce many of the same benefits, data network effects are more subtle and generally less well understood. Data network effects³occur when your product, generally powered by #machinelearning, becomes smarter as it gets more data from your users. In other words: the more users use your product, the more data they contribute; the more data they contribute, the smarter your product becomes.

The smarter your product is, the better it serves your users and the more likely they are to come back often and contribute more data — and so on and so forth. Over time, your business becomes deeply and increasingly entrenched, as nobody can serve users as well.

Google is a classic example of data network effect at play: the more people search, the more data they provide, enabling Google to constantly refine and improve its core performance, as well as personalize the user experience.

Customer Experience Innovation from Amazon

Amazon that sets the tone for so many aspects of customer experience is breaking down internal barriers and showing how other companies can do the same. Amazon, a leader in customer experience innovation, has taken things to the next level by reorganizing the company around its #AI and machine learning efforts.

Amazon’s approach to AI is called a flywheel⁴. In engineering terms, a flywheel is a deceptively simple tool designed to efficiently store rotational energy. It works by storing energy when a machine isn’t working at a constant level. Instead of wasting energy turning on and off, the flywheel keeps the energy constant and spreads it to other areas of the machine.

At Amazon, the flywheel approach keeps AI innovation humming along and encourages energy and knowledge to spread to other areas of the company. Amazon’s flywheel approach means that innovation around machine learning in one area of the company fuels the efforts of other teams. Those teams use the technology to drive their products, which impacts innovation throughout the entire organization.

Essentially, what is created in one part of Amazon acts as a catalyst for AI and machine learning growth in other areas. Amazon is no stranger to AI. The company was one of the first to use the technology to drive its product recommendations

How Companies Use and Organize AI

Artificial intelligence is reshaping business — though not at the blistering pace many assume. True, AI is now guiding decisions on everything from crop harvests to bank loans, and once pie-in-the-sky prospects such as totally automated customer service are on the horizon.

The technologies that enable AI, like development platforms and vast processing power and data storage, are advancing rapidly and becoming increasingly affordable.

The time seems ripe for companies to capitalize on AI. Indeed, we estimate that AI will add $13 trillion to the global economy over the next decade.

Despite the promise of AI, many organizations’ efforts with it are falling short. We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced #analytics⁵, and our data shows that only 8% of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process.

API for accessing NLP Models

OpenAI today announced the launch of an API for accessing new natural language processing models its researchers developed, including the recently released GPT-3. The company claims that, unlike most AI systems designed for one use case, the API provides a general-purpose “text in, text out” interface, allowing users to try it out on virtually any English language task.

The API is available in beta for free for the first two months, and only qualified customers will be provided access, according to OpenAI⁶— there’s a sign-up process. (Companies like Algolia, Koko, MessageBird, Sapling, Replika, Casetext, Quizlet, and Reddit, along with researchers at institutions like the Middlebury Institute, piloted it prior to launch.)

The company says the API will both provide a source of revenue to cover its costs and enable it to work closely with partners to see what challenges arise when AI systems are used in the real world.

“The field’s pace of progress means that there are frequently surprising new applications of AI, both positive and negative. We will terminate API access for obviously harmful use-cases, such as harassment, spam, radicalization, or astroturfing,” #OpenAI wrote in a blog post

Facebook’s Deep fake Detection Challenge

Facebook’s Deepfake Detection Challenge, in collaboration with Microsoft, Amazon Web Services, and the Partnership on AI, was run through Kaggle, a platform for coding contests that is owned by Google.

It provided a vast collection of face-swap videos: 100,000 deepfake clips, created by Facebook using paid actors, on which entrants tested their detection #algorithms⁷. The project attracted more than 2,000 participants from industry and academia, and it generated more than 35,000 deepfake detection models.

The best model to emerge from the contest detected deepfakes from Facebook’s collection just over 82 percent of the time. But when that algorithm was tested against a set of previously unseen deepfakes, its performance dropped to a little over 65 percent.

“It’s all fine and good for helping human moderators, but it’s obviously not even close to the level of accuracy that you need,” says Hany Farid, a professor at UC Berkeley and an authority on digital forensics, who is familiar with the Facebook-led project. “You need to make mistakes on the order of one in a billion, something like that.”

The Fourth Industrial Revolution

There’s widespread consensus that we’re in the throes of the fourth industrial revolution. Artificial intelligence and its sister technologies are transforming virtually every business. Yet with AI’s enormous potential comes great responsibility.

The majority (77%) of CEOs say that AI threatens to increase vulnerability and disruption to the ways they do business. Unfortunately, the call for responsible AI has taken a backseat for many companies. Only 25% of companies say that they definitely prioritize considering the ethical implications of an AI solution before investing in it, according to research by PwC.

In 2016, Amazon, Facebook, Google, DeepMind, Microsoft, and IBM came together to found the Partnership on Artificial Intelligence⁸to Benefit People and Society (Partnership on AI).

Since it was founded, the nonprofit coalition has amassed more than 100 partners, including members from industry, academia, and civil society. The partnership marks an important shift towards prioritizing responsible AI. But it’s merely a small step in the right direction.

Transformation of Retail Sector by AI

Artificial Intelligence is transforming the retail industry. It is estimated that by 2025 the size of the AI software and systems industry is expected to reach US$35,870 million. AI will change the entire product and services cycle from manufacturing to post-sale customer communications. Both shoppers and retailers will benefit from this innovative, always learning technology.

The gradual shift of spending power to millennials has accelerated the growth of AI in the retail industry. This group of consumers prefers self-service when available and like to remain in control of online purchasing and other habits. In fact, studies have shown that 64% of millennials prefer self-service.

Whether they’re on an e-commerce site or using an app on their smartphone to help them shop, shoppers can easily access and use technology the moment they begin shopping. AI leverages that technology so that retailers can use it to their advantage.

Super Intelligence and Human Cognition

Full AI, or superintelligence, should possess the full range of human cognitive abilities. This includes self-awareness, sentience and consciousness, as these are all features of human cognition.

Oxford philosopher and leading AI thinker Nick Bostrom defines superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.”

Now AI only exists such that it specializes in one area. For instance, there’s AI that can beat the world chess champion⁹ in chess, but that’s the only thing it does. Even when scientists have built neural networks that mimic the intricate layers of how the brain understands, analyzes information and build concepts, they don’t know what exactly is going on in there, why neural networks are interpreting things in a certain way.

The failure to recognize the distinction between this intelligence and full AI could be contributing to Hawking and Musk’s existential worries, both of whom believe that we are already well on a path toward developing full AI.

Advancements in treating COVID-19

Scientists are hopeful that a new drug — called EIDD-2801 — could change the way doctors treat COVID-19. The drug shows promise in reducing lung damage, has finished testing in mice and will soon move to human clinical trials.

Researchers at the UNC-Chapel Hill Gillings School of Global Public Health are playing a key role in the development and testing of EIDD-2801.

The results of the team’s most recent study were published online April 6 by the journal Science Translational Medicine. The paper includes data from cultured human lung cells¹⁰ infected with SARS-CoV-2, as well as mice infected with the related coronaviruses SARS-CoV and MERS-CoV.

The study found that, when used as a prophylactic, EIDD-2801 can prevent severe lung injury in infected mice. EIDD-2801 is an orally available form of the antiviral compound EIDD-1931; it can be taken as a pill and can be properly absorbed to travel to the lungs.

When given as a treatment 12 or 24 hours after infection has begun, EIDD-2801 can reduce the degree of lung damage and weight loss in mice. This window of opportunity is expected to be longer in humans, because the period between coronavirus disease onset and death is generally extended in humans compared to mice.

Artificial Brain Synapses from MIT

MIT engineers developed a new chip comprised of memristors, or artificial brain synapses, that outperformed current designs. The memristor, made of silicon and silver and copper alloys, mimics the design of synapses in the human brain. The “brain-on-a-chip” is smaller than a piece of confetti.

The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several visual tasks, the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.

Their results, published today in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices — electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture.

Such brain-inspired circuits could be built into small, portable devices, and would carry out complex computational tasks that only today’s supercomputers can handle.

Machine Learning for Predictive Modeling

The U.S. Department of Energy said it would offer up to $30 million in grants for researchers specializing in using machine learning for predictive modeling and for the task of “decision support” in complex systems, like managing power grids

Advances in Artificial Intelligence (AI) technology have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment. In recent years, machines have surpassed humans in the performance of certain specific tasks, such as some aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue.

Although it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will reach and exceed human performance on more and more tasks.

As a contribution toward preparing the United States for a future in which AI plays a growing role, this report surveys the current state of AI, its existing and potential applications, and the questions that are raised for society and public policy by progress in AI.

Works Cited

¹Global Leadership in AI, ²Super Resolution Algorithm, ³Data Network Effects, ⁴Flywheel, ⁵AI and Advanced Analytics, ⁶OpenAI, ⁷Detection Algorithms, ⁸Partnership on Artificial Intelligence, ⁹World Chess Champion, ¹⁰Cultured Human Lung Cells