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3 technological trends that COVID-19 will accelerate in 2021



Spending 2020 in the shadow of a pandemic has affected what we need and expect from technology. For many, COVID-19 accelerated the pace of digital transformation: because employees worked from home, companies needed AI systems that made remote work and computing power easier to support.

The question is, how should companies focus their resources in 2021 to prepare for this changed reality and new technologies on the horizon? Here are three trends that I think will get a lot of attention in 2021 and beyond.

1. AI must become practical

Advances in AI have already reached a point where they can add significant value to virtually any business. COVID-1

9 has created a huge sense of urgency around digital transformations with the need for remote solutions. According to a report by the Boston Consulting Group, more than 80% of companies plan to accelerate their digital transformation, but only 30% of digital transformations have achieved or exceeded their target value.

Many artificial intelligence projects are small-scale – less than a quarter of companies in the state of artificial intelligence in 2020 at McKinsey report significant impact. This is especially true for industries that have a physical digital element. For example: There is a great need for remote-controlled, autonomous production facilities, refineries or even, at the time of COVID-19, office buildings. As long as core technology is in place, achieving scalability remains a challenge and digital leaders will have to overcome this barrier in 2021. Barriers to scalability include a lack of discipline, enterprise-wide thinking, reliable partners, data liquidity and change management.

Part of the solution here is to create solutions that will be managed by someone who is not necessarily a data scientist, so that more people who are experts in the field can manage the programs they need. If Tesla invented an autonomous car that only data scientists can drive, what’s the point?

Technology must enable the end user to interact and manipulate models without having to move through the finer points of datasets or code – in other words, the AI ​​will lift heavily on the back end, but a convenient explanation and the user interface allows the end user. For example, a CEO of facilities can manage his global portfolio of buildings from a tablet that sits at Starbucks. They can have full visibility of operations, passenger experience and costs, with the possibility of interfering in what would otherwise be an autonomous operation.

2. Decisions become more autonomous with deep learning

Dr. Jeffrey Hinton, a pioneer in deep learning, recently told the MIT Technology Review that deep learning will be able to do “everything.” to reproduce the whole human intellect. Deep neural networks have demonstrated exceptional abilities to bring together the most appropriate subset of mathematical functions and promise to overcome the challenges of reasoning.

However, I believe that there is a step towards full autonomy that we must first conquer: what Dr. Manuela Veloso in Carnegie Mellon calls symbiotic autonomy. With symbiotic autonomy, feedback and correction mechanisms are incorporated into AI so that humans and machines transmit information smoothly.

For example, instead of hard feedback (like thumbs up and thumbs down feeding the Netflix queue), symbiotic autonomy may seem like a discussion with your phone’s virtual assistant to determine the best route to a destination. Interactions with these forms of AI would be more natural and conversational, and the program can explain why it recommends or performs certain actions.

With deep learning, neural networks bring complex mathematical functions closer to simpler ones, and the ability to take into account the growing number of factors and make smarter decisions with fewer computing resources gives them the ability to become autonomous. I expect large investments in research into these capabilities of deep neural networks, from start-ups to top technology companies to universities.

This step towards fully autonomous solutions will be a critical step towards the application of AI on a large scale. Imagine an enterprise performance management system that can give you a single window of visibility and control in a global enterprise that operates multiple facilities, workers and supply chains autonomously. He works and learns on his own, but you can intervene and teach when he makes a mistake.

(The issue of ethics in autonomous systems will come into play here, but that’s the subject of another article.)

3. The promise of a cure for future pandemics will accelerate research in quantum computing

Quantum computers have the computing power to handle complex algorithms because of their ability to process solutions in parallel rather than sequentially. Let’s think about how this can affect the development and delivery of vaccines.

First, during the discovery of a drug, researchers must simulate a new molecule. This is extremely difficult to do with today’s high-performance computers, but it is a problem that lends itself to something that quantum computers will eventually outperform. The quantum computer can eventually be transformed into the “quantum system” that is the molecule, and simulate the binding energy and strength of the chemical transition before anyone even has to make a drug.

However, AI and quantum calculations can offer even more than the creation of a vaccine. The logistics of vaccine production and delivery are huge computational challenges – which, of course, makes them ready for a solution that combines quantum computing and AI.

Quantum machine learning is an extremely new field with so many promises, but breakthroughs are needed to attract the attention of investors. Technical visionaries can now begin to see how this will affect our future, especially in terms of understanding nanoparticles, creating new materials through molecular and atomic maps, and gaining a deeper insight into the human body.

The area of ​​growth that I am most excited about is the crossroads of research in these systems, which I believe will begin to combine and yield results in more than the sum of their parts. Although there are some links between AI and quantum computing or 5G and AI, all of these technologies working together can lead to exponential results.

I am especially excited to see how AI, quantum and other technologies will affect biotechnology, as this may be the secret of superhuman capabilities – and what could be more exciting than that?

Usman Shuja is the general manager at Honeywell.

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