3 tech trends COVID-19 will accelerate in 2021

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

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 believe will garner massive 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-19 has triggered 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 reached or exceeded their target value.

Many AI projects are small-scale – less than a quarter of companies in McKinsey’s 2020 AI State reported a significant impact on their bottom line. This is especially true in industries that have a physical-digital element. For example: there is a great need for autonomous and remote-controlled manufacturing facilities, refineries or even, in the COVID-19 era, office buildings. While the underlying technology exists, achieving scalability remains a concern and digital leaders will need to overcome this hurdle in 2021. Barriers to scalability include a lack of a disciplined approach, a mindset to scale of the company, credible partners, data liquidity and change management.

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

The technology needs to empower the end user so they can interact with and manipulate models without having to go through the finer points of datasets or code – in other words, the AI ​​will do the heavy lifting. on the back-end, but a user-friendly explanation and user interface empowers the end user. For example, a facilities management manager can manage their overall building portfolio from a tablet installed in a Starbucks. They can have full visibility into operations, occupant experience and spending, with the ability to step into what would otherwise be a stand-alone operation.

2. Solutions become more autonomous through deep learning

Deep learning pioneer Dr Geoffrey Hinton recently told the MIT Technology Review that deep learning is capable of “anything” – that is, replicating all human intelligence. Deep neural networks have demonstrated extraordinary abilities to approximate the most relevant subset of mathematical functions and promise to overcome reasoning challenges.

However, I believe that there is a step towards total autonomy that we must first achieve: what Dr Manuela Veloso of Carnegie Mellon calls symbiotic autonomy. With symbiotic autonomy, feedback and correction mechanisms are incorporated into AI so that humans and machines pass information to each other in a fluid manner.

For example, instead of difficult comments (like thumbs up and thumbs down to fuel your Netflix queue), the symbiotic autonomy might feel like a chat 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, with the program being able to explain why it recommended or performed certain actions.

With deep learning, neural networks approximate complex mathematical functions with simpler ones, and the ability to take into account an increasing number of factors and make smarter decisions with fewer computing resources gives them the ability to become autonomous. I plan to invest heavily in researching these deep neural network capabilities at all levels, from startups to top tech companies to universities.

This step towards fully autonomous solutions will be a critical step towards implementing AI at scale. Imagine an enterprise performance management system that can give you a single layer of visibility and control within a global business that operates multiple facilities, employees and supply chains independently. It works and learns on its own, but you can step in and teach when it makes a mistake.

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

3. The promise of curing future pandemics will accelerate research in quantum computing

Quantum computers have the computing power to handle complex algorithms due to their abilities to process solutions in parallel, rather than sequentially. Let’s think about how this might affect vaccine development and delivery.

First, when discovering drugs, researchers have to 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 excel at. The quantum computer could eventually be mapped to the “quantum system” that is the molecule and simulate binding energies and chemical transition forces before anyone even has to make a drug.

However, AI and quantum computing have even more to offer than the creation of the vaccine. The logistics of vaccine manufacturing and delivery are massive IT challenges – which of course makes them ripe for a solution that combines quantum computing and AI.

Quantum machine learning is an extremely new and very promising field, but breakthroughs are needed to gain the attention of investors. Tech visionaries can already begin to see how this is going to impact our future, especially when it comes to understanding nanoparticles, the creation of new materials through molecular and atomic maps, and compositional insight more deep of the human body.

The area of ​​growth that I’m most passionate about is the intersection of research into these systems, which I believe will begin to combine and produce results more than the sum of their parts. While there have been some connections between AI and quantum computing, or 5G and AI, all of these technologies working together can produce exponential results.

I’m especially excited to see how AI, quantum, and other technologies will influence biotechnology, as this could be the secret to superhuman abilities – and what could be more exciting than that?

Usman Shuja is Managing Director at Honeywell.