AI Update: COVID-19 AI Tools and AI Breakthroughs

Original article can be found here (source): Artificial Intelligence on Medium

ARTIFICIAL INTELLIGENCE

AI Update: COVID-19 AI Tools and AI Breakthroughs

New COVID-19 AI Tools and Testing for AI Reasoning and Deep Learning Flaws

Artificial intelligence is supporting the fight against COVID-19 outbreak with Nanox from Israel and Canadian based BlueDot using algorithms to analyze the virus.

Photo Credit: Forbes

By using data and analytics, governments across the globe are facilitating quarantine and mitigation efforts¹ to stop the spread of COVID-19.

There is more in store for AI: AI is treating heart conditions and with biotechnology applications, health care is transforming with AI-augmented solutions.

These and more insights on the weekly AI updates

AI is leading the fight against COVID-19

AI detected the coronavirus long before the world’s population really knew what it was.

On December 31st, a Toronto-based startup called BlueDot identified the outbreak in #Wuhan, several hours after the first cases were diagnosed by local authorities.

The BlueDot team confirmed the info its system had relayed and informed their clients that very day.

Thanks to the speed and scale of AI, BlueDot was able to get a head start over everyone else. If nothing else, this reveals that AI will be key in forestalling the next coronavirus-like outbreak.

BlueDot isn’t the only startup harnessing AI and machine learning to combat the spread of contagious viruses. One Israel-based medtech company, Nanox, has developed a mobile digital X-ray system that uses AI cloud-based software to diagnose infections and help prevent epidemic outbreaks.

It incorporates a vast image database, radiologist matching, diagnostic reviews and annotations, and also assistive artificial intelligence systems, which combine all of the above to arrive at an early diagnosis.

Nanox announced a $26 million strategic investment, led by Foxconn.

It also signed an agreement this week to supply 1,000 of its Nanox Systems to medical imaging services across Australia, New Zealand, and Norway.

Coronavirus be warned.

Food Industry AI Applications

AI is changing the way we live by finding its way into the most personal aspects of our lives. One of the best examples of how close #AI actually got to humans is its implementation in the food industry.

If artificial intelligence can successfully predict whether a flavor tastes good, imagine what else it could do to improve our lives. Another way AI is going to change what we eat is through data-based diet planning.

Even though AI-guided diets are still in the early stages, the technology is developing quickly and we could soon be testing #software for personalized diet plans.

AI-based diet planning programs² would rely on machine learning and data analytics to create meal plans for your specific digestive system. AI would analyze the user’s metabolism and digestive system to create an ideal meal plan for their needs.

A change like this could potentially save millions of lives by preventing diabetes, heart disease, and other conditions caused by malnutrition.

Personalization of diet plans increases the probability (by 20%) on #data applied models that users will meet their original weight loss goal.

Thus this created data model predicts with 72% probability that users will actually lose the expected weight.

AI and diet planning — what could the future bring?

Testing AI Reasoning and Solutions

A new #dataset reveals just how bad AI is at reasoning³ — and suggests that a new hybrid approach might be the best way forward.

Known as CLEVRER, the data set consists of 20,000 short synthetic video clips and more than 300,000 questions and answer pairings that reason about the events in the videos.

Each video shows a simple world of toy objects that collide with one another following simulated physics. In one, a red rubber ball hits a blue rubber cylinder, which continues on to hit a metal cylinder.

The data set, created by researchers at Harvard, DeepMind, and MIT-IBMWatson AI Lab are meant to help evaluate how well AI systems can reason.

When the researchers tested several state-of-the-art computer vision and natural language models with the data set, they found that all of them did well on the descriptive questions but poorly on the others.

The team then tried a new AI system that combines both deep learning and symbolic logic. Symbolic systems used to be all the rage before they were eclipsed by machine learning in the late 1980s.

But both approaches have their strengths: deep learning excels at scalability and pattern recognition; symbolic systems are better at abstraction and reasoning.

Biotechnology and Medicine transformation by AI

One of the more interesting and useful applications of artificial intelligence technology has been in the world of biotechnology and medicine, where now more than 220 startups (not to mention universities and bigger pharma companies) are using AI to accelerate drug discovery⁴ by using it to play out the many permutations resulting from drug and chemical combinations, DNA and other factors.

Now, a startup called Turing — which is part of the current cohort at YCombinator due to present in the next Demo Day on March 22 — is taking a similar principle but applying it to the world of the building (and “discovering”) new consumer packaged goods products.

Using machine learning to simulate different combinations of ingredients plus desired outcomes to figure out optimal formulations for different goods (hence the “Turing” name, a reference to Alan Turing’s mathematical model, referred to as the Turing machine), Turing is initially addressing the creation of products in-home care (e.g. detergents), beauty and food and beverage.

Turing’s founders claim that it is able to save companies millions of dollars by reducing the average time it takes to formulate and test new products, from an average of 12 to 24 months down to a matter of weeks.

Emergency Preparations for COVID-19

Global cases of COVID-19 surpassed 170,000 today. As President Trump signs into law an $8.3 billion emergency aid package to address the crisis, the chief of the World Health Organization (WHO) said yesterday that this is “a time for pulling out all the stops.”

AI and big data played a significant role in China’s response to COVID-19, according to a #WHO report compiled by about a dozen outside health professionals and released last month.

The assessment finds that swift action by Chinese authorities to limit travel and quarantine entire cities potentially kept hundreds of thousands of people from being infected.

It’s unclear to what extent facial recognition⁵ played a role in enforcement of public safety in China, but one researcher mentioned that China is making strides on COVID-19 through “good old social distancing and quarantining, being very effectively done because of that on-the-ground machinery at the neighborhood level facilitated by AI and big data.”

China quarantined 50 million people in cities like Wuhan and used WeChat and Alipay to track people’s movement and keep infected individuals from traveling. The government also deployed facial recognition and thermal sensors in drones and helmets.

Photo Credit: Venturebeat

AI is treating Atrial Fibrillation

Heart conditions pose risks to patients and through AI technology; Doctors can examine patient conditions and diagnose problems early.

Atrial fibrillation is one such condition affecting the health of most patients, and culminates in a stroke.

New AI solutions enable medical doctors to detect heart anomalies even when patients show good signs.

Using Deep Learning for Fake Social Media Accounts

Facebook is opening up about the behind-the-scenes tools it uses to combat fake account creation on its platforms, and the company says it has a new artificial intelligence-powered method known as Deep Entity Classification (DEC) that’s proved especially effective⁶.

DEC is a machine learning model that does not just take into account the activity of the suspect account, but it also evaluates all of the surrounding information, including the behaviors of the accounts and pages the suspect account interacts with.

Facebook says it has reduced the estimated volume of spam and scam accounts by 27 percent.

So far, DEC has helped Facebook thwart more than 6.5 billion fake accounts that scammers and other malicious actors created or tried to create last year.

A vast majority of those accounts are actually caught in the account creation process, and even those that do get through tend to get discovered by Facebook’s automated systems before they are ever reported by a real user.

Still, Facebook estimates that around 5 percent of all 2.89 billion monthly active users currently on the platform are fake accounts belonging to what Facebook considers violators of its terms of service. That’s where DEC comes in.

The goal is to combat the ways malicious actors replicate genuine behavior.

Automation from AI

Automation and #AI-based experiences are so pervasive in our lives that we are now demanding and expecting our work environment to match similar experiences — a.k.a. consumerization.

Think of Alexa, GoogleHome, Siri, Nest, Ring, Blink, Hue, Netflix, Amazon Video, and smart TVs and refrigerators, to name just a few, all in the palm of your hand on a smartphone.

With all of this on a 4G network, imagine the possibilities with an even faster and broader 5G network coming our way soon.

So with all this “smartness” around us, I often wonder how these advancements are changing the very nature of our work and our personal lives.

The perspective is that AI and automation will continue to get ever so sophisticated to a point where the work we do as a people will need to evolve to become more human and empathetic to drive customer intimacy⁷, employee or citizen advocacy and, ultimately, how we collectively make society and our environment better.

The age of AI and automation will test and shape the fabric of our society.

We are already being tested by seemingly contradictory quandaries such as: “I want transparency, but I don’t trust the ever-growing sources of information,” or “I want personalization, but my privacy cannot be compromised.”

Using AI to Transform the Education System

Artificial intelligence is a major influence on the state of education today, and the implications are huge. AI has the potential to transform how our education system operates, heighten the competitiveness of institutions, and empower teachers and learners of all abilities.

The opportunities for AI to support education are so broad that recently Microsoft commissioned research on this topic from IDC to understand where the company can help.

The findings illustrate the strategic nature of AI in education and highlight the need for technologies and skills to make the promise of AI a reality.

The results showed almost universal acceptance among educators that #AI is important for their future — 99.4% said AI would be instrumental to their institution’s competitiveness within the next three years, with 15% calling it a “game-changer.”

Nearly all are trying to work with it too — 92% said they have started to experiment with the technology.

Yet, on the other hand, most institutions still lack a formal data strategy or practical measures in place to advance AI capabilities, which remains a key inhibitor.

The finding indicates that although the vast majority of leaders understand the need for an AI strategy.

Building AI Applications

There is a perception that AI is complex and beyond our abilities. This is not accurate.

Building AI systems⁸ and projects is becoming simpler with OpenAI Gym, a project supported by industry experts including Elon Musk and Peter Thiel emerging.

From developing games and walking support, the OpenAI Gym encourages participation in building AI solutions in our communities.

AI course curriculum’s teach students to create self-driving cars by exposing them to real-world applications.

Tech companies such as Tesla and Google are hiring data scientists and machine learning engineers because of the increasing demand.

2D-3D Image Conversion by AI

The AI research labs at Facebook, Nvidia, and startups like Threedy.ai have at various points tried their hand at the challenge of 2D-object-to-3D-shape conversion. But in a new preprint paper, a team hailing from Microsoft Research detail a framework that they claim is the first “scalable” training technique for 3D models from 2D data.

They say it can consistently learn to generate better shapes than existing models when trained with exclusively 2D images, which could be a boon for video game developers, eCommerce businesses, and animation studios that lack the means or expertise to create 3D shapes from scratch⁹.

In contrast to previous work, the researchers sought to take advantage of fully featured industrial renderers — i.e., software that produces images from display data. To that end, they train a generative model for 3D shapes such that rendering the shapes generates images matching the distribution of a 2D dataset.

The generator model takes in a random input vector and generates a continuous voxel representation (values on a grid in 3D space) of the 3D object.

Then, it feeds the voxels to a non-differentiable rendering process, which thresholds them to discrete values before they’re rendered using an off-the-shelf renderer.

Photo Credit: Venturebeat

Applications of Machine Learning and AI for Public Benefit

The team of MIT and Harvard researchers built a neural network (an algorithm inspired by the brain’s architecture) and trained it to spot molecules that inhibit the growth of the Escherichia coli bacterium using a dataset of 2,335 molecules for which the antibacterial activity was known — including a library of 300 existing approved antibiotics and 800 natural products from plant, animal and microbial sources.

They then asked the network to predict which would be effective against Ecoli but looked different from conventional #antibiotics. This produced a hundred candidates for physical testing and led to one (which they named “halicin” after the HAL 9000 computer from 2001: A Space Odyssey) that was active against a wide spectrum of pathogens — notably including two that are totally resistant to current antibiotics and are therefore a looming nightmare for hospitals worldwide.

There are a number of other examples of machine learning for the public good rather than private gain. One thinks, for example, of the collaboration between Google DeepMind and Moorfields eye hospital. But this new example is the most spectacular to date because it goes beyond augmenting human screening capabilities to aiding the process of discovery¹⁰.

AI is the future of Augmented Healthcare

Ada Health was founded nine years ago, hardly anyone was talking about combining artificial intelligence and physician care — outside of a handful of futurists.

But the chatbot boom gave way to a powerful combination of AI-augmented healthcare which others, like Babylon Health in 2013 and KRY in 2015, also capitalized on.

Co-founded with Daniel Nathrath and Dr. Martin Hirsch, the startup initially set out to be an assistant to doctors rather than something that would have a consumer interface. At the beginning, Novorol said they did not talk about what they were building as an #AI so much as it was pure machine learning.

Years later, Ada is a free app, and just like the average chatbot, it asks a series of questions and employs an algorithm to make an initial health assessment. It then proposes the next steps, such as making an appointment with a doctor or going to an emergency room. But Ada’s business model is not to supplant doctors but to create partnerships with healthcare providers and encourage patients to use it as an early screening system.

Works Cited

¹Quarantine and MitigationAI-Based Diet Plan, ³AI Bad Reasoning, ⁴Machine-Learning Applications, ⁵Facial Recognition, ⁶Fake Social Media Accounts, ⁷Consumerization Demands, ⁸Building AI Systems, ⁹2D-3D Image Conversion, ¹⁰Innovative Solutions

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