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AI Predictions


Despite a tough year for many, US companies are accelerating plans to implement artificial intelligence (AI). Another 54% are heading there fast. And they've moved way beyond just laying the foundation. Many are reaping rewards from AI right now, in part because it proved to be a highly effective response to the challenges brought about by the COVID-19 crisis. In fact, most of the companies that have fully embraced AI already report seeing major benefits.

Council Post: AI: Failed Promise Or A Case Of Unrealistic Expectations?


Following bold claims about the business benefits and even world-saving power of artificial intelligence (AI), it's not surprising that talk of broken promises is growing. However, has AI failed to deliver, or are we setting our expectations too high? Recently, consternation has centered around Covid-19 as some initial AI-assisted breakthroughs quickly fizzled out. For many, frustration stretches back much further, with a 2010 study showing 38% of organizations lacked the understanding of how to use analytics to make better and faster decisions. Today, fewer than 25% of global organizations have developed an enterprise-wide AI strategy.

AI helps triage tuberculosis patients on chest x-ray


An artificial intelligence (AI) model can accurately distinguish between active and healed tuberculosis on chest x-rays, according to a study published August 3 in Radiology. A team of researchers trained a deep-learning algorithm on thousands of x-rays from patients with active and healed tuberculosis and found the model accurately differentiated between the two. The model performed better than pulmonologists and as well as radiologists, which suggests it could be beneficial in countries fighting tuberculosis with poor resources and few specialists. "The network may help radiologically triage patients with active tuberculosis by excluding healed tuberculosis in high-burden countries and may assist in monitoring the activity of mycobacterial diseases that require long-term treatment," wrote a team led by Dr. Soon Ho Yoon, PhD, of Seoul National University College of Medicine. Previous studies have shown that AI models can outperform human experts in detecting tuberculosis on chest x-rays, but the networks in those studies were not trained on chest x-rays of treated patients with lung damage, so little is known about how an AI model might perform in countries with a high burden of the disease, according to the authors.

Covid-19: Travel rules set to change and Wales to decide on 'vaccine passports'

BBC News

As a charity says thousands of tenants fell into debt during the pandemic, one woman tells us about her constant fear of eviction. StepChange says 10% of private renters have fallen into arrears, owing nearly £800 each on average, and is calling for emergency support as the furlough scheme and Universal Credit uplift end. The government says unprecedented action has helped keep people in their homes and it's right for measures to be lifted as the economy reopens.

Deep learning helps predict new drug combinations to fight COVID-19


The existential threat of COVID-19 has highlighted an acute need to develop working therapeutics against emerging health threats. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data needs time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) to ask: how can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

The Anti-Tech Dystopia of "Dune"


Life during the Covid-19 pandemic would be even more difficult without the Internet and automation, making this year timely for the upcoming "Dune" film to portray a distant future where humanity is devastated by our dependence on machines. Director Dennis Villeneuve is set to release his adaptation of the science fiction epic on October 22, both on HBO Max and in movie theaters. The release was delayed from last December to make it safer for people to view and hear the space fantasy in theaters. WarnerMedia aims to distribute Villeneuve's vision of the first "Dune" novel in two films, but has not yet scheduled a release date for the second film after the first half is released. Frank Herbert's "Dune" novel begins in the far distant future, thousands of years after humans were enslaved by robots, fought a revolutionary crusade and banned artificial intelligence with a new anti-tech religion.

AI Anywhere: Scaling Artificial Intelligence across the enterprise


The best use cases for AI in the last one year were found in combating the effects of the pandemic. The healthcare industry built numerous applications of AI for faster diagnosis, forecasting the spread and pattern of the disease, tracking people and their recovery, developing drugs, vaccinations, and managing the logistics. Supply chain and retail companies have used AI and cognitive automation to overcome the threats of Covid-19, as consumers faced significant challenges in shifting from physical to online mode. Besides healthcare and retail, businesses of all shapes and sizes across several sectors have adopted AI to pursue higher productivity and enhanced customer experiences. However, managing the entire charter of an organisation gets tough as AI permeates through a multitude of functions.

How Is Artificial Intelligence Transforming the Food Industry?


Some people call this Artificial Intelligence (AI), but the reality is this technology will enhance us. So instead of AI, I think we'll augment our intelligence, quoted by Ginni Rometty, CEO (Chief executive officer) of IBM. The business of selling food to customers is being disturbed to a level not since the last pandemic, over 100 years ago. So, it's not true that the crisis accelerated the adoption of technology in the manner that is occurring today with Artificial Intelligence (AI) in the food industry. It is increasingly possible that our food system was ill-prepared (antifragile) for this Covid-19 induced crisis.

Clustering City Nightlife using Machine Learning


Everyone knows how Covid-19 pandemic devastated the nightlife industry with social distancing, lockdowns, mask-wearing and early curfews. These nightlife spaces were shuttered because they had been deemed non-essential services and places of easy transmission for the coronavirus. Now that central and state governments in India have eased the restrictions people can finally enjoy a breather, commemorating a special occasion or just spending time with friends over food and drinks. In a city like Pune, which boasts a happening nightlife scene, there's always a party happening somewhere or the other. Widely known as the "IT hub of India", "Automobile and Manufacturing hub of India" and "Oxford of the East", Pune is known for its lifestyle, pleasant weather and just… everything good.

Physicists Simulate Artificial Brain Networks with New Quantum Materials


Isaac Newton's groundbreaking scientific productivity while isolated from the spread of bubonic plague is legendary. University of California San Diego physicists can now claim a stake in the annals of pandemic-driven science. A team of UC San Diego researchers and colleagues at Purdue University have now simulated the foundation of new types of artificial intelligence computing devices that mimic brain functions, an achievement that resulted from the COVID-19 pandemic lockdown. By combining new supercomputing materials with specialized oxides, the researchers successfully demonstrated the backbone of networks of circuits and devices that mirror the connectivity of neurons and synapses in biologically based neural networks. Like biologically based systems (left), complex emergent behaviors--which arise when separate components are merged together in a coordinated system--also result from neuromorphic networks made up of quantum-materials-based devices (right).