The controversial technology could help cut the carbon footprint of daily living.

We know the pandemic has forced many to work from home (WFH) and as a result driven up demand for products like PCs and home IT equipment. There has also been a large reduction in commuting to and from workplaces, which many have enjoyed and vowed to continue even after lockdowns are lifted.

These changes ought to benefit the planet by reducing greenhouse gas emissions and other pollution. We should consider the impact of the extra demands placed on data infrastructures to handle this upsurge in remote working, however. It takes energy to move all that data back and forth, although arguably this would happen whether workers are at the office or at home.

Data center businesses have blossomed during the pandemic, with an uptick in demand for their services. These include work-related services as well as home entertainment. Netflix has reported record consumption, although the rise has flattened recently, perhaps as content has become exhausted.

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Plus, how AI can help deal with the threats.

Forget enterprise digital transformation. For better or worse, we are digitizing the world, and it’s changing everything. The opportunities to make things better are tremendous; we can save wasted energy and natural resources, democratize access to all kinds of services, improve standards of healthcare, prevent avoidable accidents, and accelerate our transition to renewable energy. By connecting everything, and introducing AI into the mix, we can gain insights we would otherwise never detect. Moreover, we can see the effects of our own behavior and use the analysis to identify ways to improve. Earlier technologies could never have done all this for us.

But there is always an “on the other hand,” and in this case the issues relate mostly to privacy and cybersecurity. We are putting more information about ourselves than ever before into the hands of data scientists. While we can expect better shopping experiences, we are at the same time disclosing insights into ourselves, our activities, and our preferences as individuals. And if we are not giving away information directly, everything we do online (and we are almost permanently online) reinforces the accuracy of any and every inference made by the AIs that constantly watch from the cloud.

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The electronics industry should adopt data-driven planning methods.

For many companies, supply-chain management has become a major challenge as the pandemic has continued to disrupt all our lives. As lifestyles have become home-based, for work and leisure, demands have shifted from services to products: materials and tools for lockdown projects, gaming and video equipment, and extra work-from-home IT. There is a global shortage of shipping containers and ships to carry them. As a result, shipping costs have increased sharply. It could take a long time for container costs to return to pre-pandemic levels. Added to that, the spread of the virus has disrupted and depleted workforces, resulting in backlogs and delays.

On top of the misery came the recent blockage of the Suez Canal, adding several days of delay as the backlog was cleared. And, of course, there were domino effects at ports around the world, as cargo was unable to move into or out of the system. The problem has raised questions about the future of super-large container ships and strengthens the argument for using larger numbers of smaller vessels.

Far-flung supply chains, designed to enhance competitiveness and minimize costs, are now under threat and will likely need to change. The world is simply too impatient to wait for things to return to normal. Moreover, there are strong calls for a “new normal” that should, at the very least, strive for environmental sustainability.

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How AI and lab-on-a-chip pave the way for economical solutions.

We could be moving into the end game with the Covid-19 pandemic, at least as far as the severest effects are concerned. Clearly, the virus and its mutations are here to stay, and the future will be about protecting us through immunization and developing better treatments. The fact that effective vaccinations have become available only a year after the pandemic was recognized is remarkable. It’s partly due to the speed with which researchers have been able to do the data crunching needed to model and understand how best to attack the virus.

In the past, the computations involved in sequencing the virus DNA would have taken vast quantities of computer time and prolonged development of the vaccine. Cloud computing using AI accelerators has dramatically shortened the time to complete the technical work involved in creating the vaccines now being rolled out.

It would be great if we could harness our technologies to create an early warning system when clusters of unusual diseases or events occur anywhere in the world. That’s exactly what organizations like BlueDot are doing right now. Indeed, BlueDot says it spotted the cluster of unusual pneumonia cases in Wuhan in December 2019 that we now know was the coronavirus. To monitor the spread of infectious diseases around the world, it analyzes a huge number of variables, not only official public health data but also climate information, international travel patterns, animal and insect population data, and others. This relies on the ability of AI to detect patterns, and exceptions to those patterns, hidden within the enormous body of information. By sifting through the reports and data points collected every few minutes, 24 hours a day, from sources around the world, using techniques like machine learning and natural language processing, BlueDot brings a small number of cases to the attention of experts for further investigation. Only with AI do we have a hope of finding those cases.

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