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 rate of digital transformation: as employees worked from home, companies needed AI systems that would facilitate remote work and the computing power to support them.

The question is: how should companies concentrate their resources in 2021 to prepare for this changed reality and new technologies on the horizon? Here are three trends that I anticipate will receive massive attention in 2021 and beyond.

1. AI must become practical

AI progress has already reached a point where it can add significant value to virtually any business. COVID-19 unleashed an enormous 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 have reported a significant impact on financial results. This is especially true in sectors that have a physical-digital element. For example: There is a great need for autonomous factory facilities operated remotely, refineries, or even, at the time of COVID-19, office buildings. As long as the underlying technology is there, achieving scalability remains a concern and digital leaders will have to overcome that barrier in 2021. Scalability barriers include a lack of disciplined approach, corporate mindset, reliable partners, data liquidity and change management .

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

Technology needs to empower the end user so that he can interact and manipulate models without having to crawl over the finer points of data sets or code – in other words, AI will do the heavy lifting on the back end, but a friendly explanation and the UI empowers the end user. For example, a facility management executive can manage his global building portfolio from a tablet sitting at a Starbucks. They can have full visibility of operations, occupant experience and expenses, with the ability to intervene in what would otherwise be an autonomous operation.

2. Solutions become more autonomous with deep learning

Deep learning pioneer, Dr. Geoffrey Hinton, recently told the MIT Technology Review that deep learning will be able to do “everything” – that is, replicate all human intelligence. Deep neural networks have demonstrated extraordinary abilities to bring together 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, from Carnegie Mellon, calls symbiotic autonomy. With symbiotic autonomy, feedback and correction mechanisms are incorporated into AI so that humans and machines pass information between them in a fluid way.

For example, instead of direct feedback (like thumbs up and down feeding your Netflix queue), symbiotic autonomy can 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 colloquial, with the program able to explain why it recommended or performed certain actions.

With deep learning, neural networks bring complex mathematical functions closer together with simpler functions, and the ability to consider an increasing number of factors and make smarter decisions with fewer computing resources gives them the ability to become autonomous. I anticipate a major investment in researching these deep neural network skills across the board, from startups to leading technology companies and universities.

This step towards fully autonomous solutions will be a critical step for the implementation of AI at scale. Imagine an enterprise performance management system that can provide a single visibility and control panel in a global company that operates multiple facilities, employees and supply chains autonomously. It works and learns on its own, but you can intervene and teach when you make a mistake.

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

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

Quantum computers have the computational power to handle complex algorithms due to their ability to process solutions in parallel, rather than sequentially. Let’s think about how it can affect vaccine development and distribution.

First, during the discovery of the drug, researchers must simulate a new molecule. This is tremendously challenging to do with today’s high-performance computers, but it is a problem that lends itself to something in which quantum computers will eventually excel. The quantum computer could eventually be mapped to the “quantum system” that is the molecule and simulate binding energies and chemical transition intensities before anyone even had to make a drug.

However, AI and quantum computing have even more to offer besides creating the vaccine. The logistics of manufacturing and delivering the vaccine are huge computational challenges – which, of course, makes them ripe for a solution that combines quantum computing and AI.

Quantum machine learning is an extremely new field with so many promises, but advances are needed to get investors’ attention. Visionaries of technology can already begin to see how this will impact our future, especially with regard to understanding nanoparticles, creating new materials through molecular and atomic maps and envisioning the deeper constitution of the human body.

The area of ​​growth with which I am most excited is the intersection of research in these systems, which I believe will begin to combine and produce results more than the sum of its parts. Although 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 am particularly excited to see how AI, quantum and other technologies will influence biotechnology, as this may be the secret to superhuman capabilities – and what could be more exciting than that?

Usman Shuja is general manager of Honeywell.