3 technological trends that will accelerate COVID-19 in 2021

The spending of 2020 under the shadow of a pandemic has affected what we need and expect from technology. For many, COVID-19 has accelerated the pace of digital transformation: while employees work from home, businesses have needed AI systems that enable remote work and computing power to support it.

The question is, how should companies focus their resources in 2021 to prepare for this changing reality and the new technology? Here are three trends that I predict will see great attention in 2021 and beyond.

AI must become practical

The progress in AI has already reached a point where it can add significant value to virtually any business. COVID-19 has caused a massive sense of urgency around digital transformations with the need for remote solutions. According to a report by Boston Consulting Group, more than 80% of businesses 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 the companies in McKinsey’s 2020 state of AI have had a significant impact on the bottom line. This is especially true in industries that have a physical-digital element. For example: there is a great need for remote-controlled, autonomous manufacturing facilities, refineries, or even, in the days of COVID-19, office buildings. While the underlying technology is there, achieving scalability remains a source of concern, and digital leaders will have to overcome the hurdle in 2021. Scalability barriers include a lack of disciplined approach, enterprise-wide mindset, credible partners, data liquidity and change management.

Part of the solution here is to create solutions that will be used 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 invents an autonomous car that only data scientists can drive, what’s the point?

Technology should empower the end user so that they can communicate and manipulate models without going through the finer points of datasets or code – in other words, the AI ​​will do the heavy work at the back, but a user-friendly explanation and UI empowers the end user. A facility manager can, for example, manage their global portfolio of buildings from a tablet sitting at a Starbucks. They can have full insight into operations, occupant experience and spending, with the ability to intervene in what would otherwise be an autonomous operation.

2. Solutions become more autonomous with deep learning

The deep learning pioneer, Dr. Geoffrey Hinton, recently told MIT Technology Review that deep learning can do ‘anything’ – that is, repeat all human intelligence. Deep neural networks have demonstrated extraordinary abilities to approach the most relevant subset of mathematical functions and promise to overcome reasoning challenges.

However, I believe that there is a step towards full autonomy that we must first overcome: what dr. Manuela Veloso at Carnegie Mellon calls symbiotic autonomy. With symbiotic autonomy, feedback and correction mechanisms are incorporated into the AI ​​so that humans and machines pass on to each other fluidly.

For example, instead of loud feedback (like thumbs up and thumbs down to power your 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 could explain why it recommended or performed certain actions.

With deep learning, neural networks approach complex mathematical functions with simpler, and the ability to take into account a growing number of factors and make smarter decisions with fewer computer resources, gives them the ability to become autonomous. I expect huge investment in research on these capabilities of deep neural networks across the board, from startups to top tech companies to universities.

This step towards fully autonomous solutions will be a critical step in implementing AI on a large scale. Imagine a company’s performance management system that can give you a single visibility and control over a global company that operates autonomous multiple facilities, workers and supply chains. It runs and learns on its own, but you can intervene and learn if it makes a mistake.

(The question of ethics in autonomous systems will be discussed here, but that is a topic for another article.)

3. The promise to cure future pandemics will accelerate research into quantum computers

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

First, researchers must simulate a new molecule while discovering drugs. It’s hugely challenging to do today’s high performance computers, but it’s a problem that lends itself to something on which quantum computers will ultimately perform. The quantum computer can eventually be linked to the “quantum system” that is the molecule, and simulate the binding energies and chemical transition strengths before anyone ever had to make a remedy.

However, AI and quantum computers have even more to offer than creating the vaccine. The logistics of manufacturing and delivering the vaccine are major computer 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 much promise, but breakthroughs are needed to grab investors’ attention. Technical visionaries can already begin to see how it will affect our future, especially with regard to the understanding of nanoparticles, the creation of new materials by molecular and atomic maps and the deeper composition of the human body.

The growth area I am most excited about is the intersection of research in these systems, which I think will start to combine more than the sum of their parts and yield results. Although there have been some connections between AI and quantum computing, or 5G and AI, all of these technologies can yield exponential results.

I’m especially excited to see how AI, quantum and other technologies will affect biotechnology, because it can be the secret of superhuman abilities – and what’s more exciting than that?

Usman Shuja is general manager at Honeywell.