Home Artificial Intelligence The 7 Biggest Artificial Intelligence (AI) Trends In 2022

The 7 Biggest Artificial Intelligence (AI) Trends In 2022

by Bernard Marr
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In 2022, we will see artificial intelligence continue along the path to becoming the most transformative technology humanity has ever developed. According to Google CEO Sundar Pichai, its impact will be even greater than that of fire or electricity on our development as a species.

This may seem like a very ambitious claim, but considering it is already being used to help us tackle climate change, explore space, and develop treatments for cancer, the potential is clearly there.

The full scale of the impact that giving machines the ability to make decisions – and therefore enable decision-making to take place far more quickly and accurately than could ever be done by humans – is very difficult to conceive right now.

But one thing we can be certain of is that in 2022 breakthroughs and new developments will continue to push the boundaries of what’s possible. Here’s my pick of the key areas and fields where those breakthroughs will occur in 2022

The augmented workforce

There have always been fears that machines or robots will replace human workers and maybe even make some roles redundant. However, as companies navigate the process of creating data and AI-literate cultures within their teams, we will increasingly find ourselves working with or alongside machines that use smart and cognitive functionality to boost our own abilities and skills.

In some functions, such as marketing, we’re already used to using tools that help us determine which leads are worth pursuing and what value we can expect from potential customers.

In engineering roles, AI tools help us by providing predictive maintenance – letting us know ahead of time when machines will need servicing or repairing. In knowledge industries, such as law, we will increasingly use tools that help us sort through the ever-growing amount of data that’s available to find the nuggets of information that we need for a particular task.

In just about every occupation, smart tools and services are emerging that can help us do our jobs more efficiently, and in 2022 more of us will find that they are a part of our everyday working lives.

Bigger and better language modeling

Language modeling is a process that allows machines to understand and communicate with us in language we understand – or even take natural human languages and turn them into computer code that can run programs and applications. We have recently seen the release of GPT-3 by OpenAI, the most advanced (and largest) language model ever created, consisting of around 175 billion “parameters”- variables and datapoints that machines can use to process language.

OpenAI is known to be working on a successor, GPT-4, that will be even more powerful. Although details haven’t been confirmed, some estimate that it may contain up to 100 trillion parameters, making it 500 times larger than GPT-3, and in theory taking a big step closer to being able to create language and hold conversations that are indistinguishable from those of a human. It will also become much better at creating computer code.

AI in cybersecurity

This year the World Economic Forum identified cybercrime as potentially posing a more significant risk to society than terrorism. As machines take over more of our lives, hacking and cybercrime inevitably become more of a problem, as every connected device you add to a network is inevitably a potential point-of-failure that an attacker could use against you.

As networks of connected devices become more complex, identifying those points of failure becomes more complex. This is where AI can play a role, though. By analyzing network traffic and learning to recognize patterns that suggest nefarious intentions, smart algorithms are increasingly playing a role in keeping us safe from 21st-century crime. Some of the most significant applications of AI that we will see develop in 2022 are likely to be in this area.

AI and the Metaverse

The metaverse is the name given for a unified persistent digital environment, where users can work and play together. It’s a virtual world, like the internet, but with the emphasis on enabling immersive experiences, often created by the users themselves. The concept has become a hot topic since Mark Zuckerberg spoke about creating it by combing virtual reality technology with the social foundations of his Facebook platform.  

AI will undoubtedly be a lynchpin of the metaverse. It will help to create online environments where humans will feel at home at having their creative impulses nurtured. We will also most likely become used to sharing our metaverse environments with AI beings that will help us with tasks we’re there to do, or just be our partner for a game of tennis or chess when we want to relax and unwind.

Low-code and no-code AI

A big barrier to the adoption of AI-driven efficiency in many companies is the scarcity of skilled AI engineers who can create the necessary tools and algorithms. No-code and low-code solutions aim to overcome this by offering simple interfaces that can be used, in theory, to construct increasingly complex AI systems.

Much like the way web design and no-code UI tools now let users create web pages and other interactive systems simply by dragging and dropping graphical elements together, no-code AI systems will let us create smart programs by plugging together different, pre-made modules and feeding them with our own domain-specific data.

Technologies such as natural language processing and language modeling (see above) mean that soon it may be possible to use nothing more than our voice or written instructions. All of this will play a key role in the ongoing “democratization” of AI and data technology.

Autonomous vehicles

AI is the “brains” that will guide the autonomous cars, boats, and aircraft that are set to revolutionize travel and society over the coming decade. 2022 should be a year to remember when we look back in the future and contemplate with horror the fact that we thought it was normal that 1.3 million people died of traffic accidents every year, 90% of which were caused by human error!

As well as increasingly effective autonomous cars – Tesla says its cars will demonstrate full self-driving capability by 2022, although it’s unlikely they will be ready for general use. Its competitors include Waymo (created by Google), Apple, GM, and Ford, and any of them can be expected to announce major leaps forward in the next year.

The year will hopefully also see the first autonomous ship crossing the Atlantic, as the Mayflower Autonomous Ship (MAS), powered by IBM and designed in partnership with non-profit ProMare, will once again attempt the journey (having been forced to turn back during its initial attempt this year).

Creative AI

We know that AI can be used to create art, music, poetry, plays, and even video games. In 2022, as new models such as GPT-4 and Google’s Brain redefine the boundaries of what’s possible, we can expect more elaborate and seemingly “natural” creative output from our increasingly imaginative and capable electronic friends.

Rather than these creations generally being demonstrations or experiments to show off the potential of AI, as is the case now, in 2022, we will increasingly see them applied to routine creative tasks, such as writing headlines for articles and newsletters, designing logos and infographics.

Creativity is often seen as a very human skill, and the fact we are now seeing these capabilities emerging in machines means “artificial” intelligence is undeniably coming closer in terms of scope and function to the somewhat nebulous concept we have of what constitutes “real” intelligence.

Overcoming AI Challenges with IBM’s AI Ladder

With $16 trillion up for grabs by 2030, there’s a race to be leaders and pioneers in the brave new world of AI and automation. Across every industry, we see an acceleration in the rollout of smart, cognitive systems that promise improved customer experience and streamlined more efficient business processes.  

Inevitably this leads to the dreaded FOMO – fear of missing out – which in turn leads to badly executed ideas, wastage, and missed opportunities. For me, warning signs flash when I get the impression that leadership teams, often chasing hype and buzzwords, are approaching the task of digital transformation from a tech-first perspective rather than a problem-first or customer-first perspective.

I believe that almost any business can benefit from the application of artificial intelligence (AI). However, any business that sets off on such a mission is setting itself up for failure if it doesn’t start by focusing on putting together a strategy for AI and, specifically, data, which is the fuel of all AI engines.

Fortunately, a great deal of the groundwork has been done on this already – strategic, mission-oriented thinking from many of the leaders in the field of business technology is readily available. One such leader is IBM – a name that has been synonymous with cutting-edge computing for over 100 years.

For the last decade or so, however, it has built a reputation as a leading supplier of AI-as-a-service solutions, as well as the cloud and hybrid cloud infrastructures that make it possible for any organization, regardless of its size, to join the AI revolution.

The AI Ladder

Setting out to integrate AI into business operations can seem like a daunting task. Where do you start? We know AI can level up every aspect of a business’s performance, from marketing to research and development, manufacturing, customer services, and any number of back-office functions such as finance, logistics, and HR. Understanding where an organization will get the best bang-for-buck is essential.

Then there are issues of governance, compliance, regulation, and trust – it’s vital to have a complete understanding of the implications as they relate to your business, data, and customers, but again, none of this comes without cost.

To help businesses navigate this tricky set of obstacles, IBM has created what it calls the AI Ladder. As the name suggests, it’s a step-by-step framework that orders and explains the most pressing challenges that must be tackled at every stage of the process a company goes through during its evolution to the state of a smart enterprise.

Andrew Brown, General Manager for Technology at IBM UK & Ireland, sums it up neatly: “A hybrid cloud and AI strategy allow businesses to leverage their existing investments while adding new technologies rapidly and seamlessly for digital advantage. We assist organizations with a prescriptive approach by architecting AI into their data fabric, applications, and processes, enabling them to turn data into insight, making applications more client centric and processes more agile.”

One statistic that IBM highlights is that 81% of business leaders admit that they don’t understand the data and infrastructure requirements for adopting AI in their organizations. The ladder is an attempt to formalize the learning process that needs to be followed in order to fix this.   

It has four steps – collect, organize, analyze and infuse.

To take a bit of a closer look into what is meant by each of them, collect refers to the process of “making data simple and accessible.” This means breaking down siloes that may have traditionally been a barrier to effective, organization-wide exploitation of data resources, fully understanding the range of data that’s available (and how to find or create the data that isn’t), and how to leverage tools that bring AI and smart automation to the job of collecting, organizing and storing data itself.

“Organise” means to “create a business-ready analytics foundation.” Once you’ve worked out what data you need and how you’re going to collect it, you’re ready to tackle issues such as data quality, how to catalog and store data so it will be ready for use when it’s needed, and identifying issues around access and governance of your data.

The most valuable data is often human data – and this is usually personal data, meaning it comes with a high burden of regulation and compliance. Initiating automated processes to ensure all of your data is stored and accessed appropriately is an essential step of the ladder that companies can’t afford to skip.

“Analyse” requires businesses starting to work with AI to learn to “build and scale AI with trust and transparency.” This is where we develop and deploy the models and algorithms that we use to wring insights from the data we’ve collected and organized in steps one and two.

If we’ve done it the right way, hopefully, it leads to insights that we can use to improve our products and services, create internal efficiencies, or even create entirely new business models based on smart, data-driven analytics.

The final step on the ladder is “infuse,” which IBM defines as learning to “operationalize AI throughout the business.” Here we come to the tricky subject of building company-wide cultures of data and tech-literacy.

This means building an understanding of how data flows through an organization – not just within one particular AI or analytics use case, but how the process of generating insights in one area can be scaled out across an entire business so best practices are adopted, from shop floor to boardroom, and lessons learned can be applied anywhere, regardless of where the initial work was done.

Andrew Brown shares further insight into IBM’s approach: “We’re focused on our client’s core capabilities to create digital advantage – delivered on an open ‘build once, deploy anywhere’ platform. And AI is an absolutely essential part of those capabilities.

Whether you apply AI to automate processes to reclaim time for higher-value work, make data-driven predictions, secure business with real-time threat insights, or modernize your environment for agility. The ability to architect for digital advantage, integrating across an open technology ecosystem, is crucial to success.”

To illustrate this concept, IBM uses the example of a customer in the energy production business that digitized the knowledge of the expert teams of engineers responsible for the company’s success. This information can then be disseminated to employees anywhere in the organization, where their insights can be re-used to drive efficiency and innovation. In this case, AI is used to interpret the experts’ knowledge and experience in a way that makes it valuable far beyond the domain where it was originally conceived.

Putting the ladder to work

The ladder is a great framework for approaching the initial challenges of adopting AI into a business. A very pertinent point the authors make is that “AI is not magic.” Although there have been a lot of heady promises about what can be achieved with AI, and sometimes it’s sold as a one-size-fits-all solution for companies that want to modernize and leverage technology, it still requires a great deal of thought and planning before positive results can be expected.

Working with companies all over the world on their own AI transformations, I have seen first-hand how important a solid strategy and framework is to success. Once that’s in place, the real fun of applying human imagination and ingenuity to the task of creating AI-driven growth and success.

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