2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning.

Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements. Large language models (LLMs) have shown impressive capabilities not just in natural language understanding, but also in “reasoning” tasks, such as reading comprehension. 

In the coming year, I expect generative AI to continue to evolve quickly and play a big role across various industries. With many companies strapped for resources – including talent and capital – and looming financial uncertainty, we will also see more organizations leverage AI and automation to drive cost efficiencies. 

Here are three key AI trends we’ll likely see in 2023.

Large language models (LLMs) will help people be more creative

We’re likely going to see more solutions like GitHub Co-Pilot, where LLMs can assist programmers in meaningful ways. These tools won’t get everything right, but they will help solve that initial writer’s block. When you’re staring at a blank page, it’s often hard to get started. But if you can describe the prompt or problem to the model and it outputs based on what it’s told, this can provide a good starting point to work from. Prompt engineering (i.e., instructing models using the right starting text) will become a new way of writing computer programs, leveraging natural language.

People thought the big hold-up for AI would be creativity, but ironically, it may be the reverse. It may be that AI will actually help us become more creative – by seeding us with initial ideas that we can build upon and refine.

Natural language processing (NLP) + object recognition will bring search to the next level

While most people write scrapers today to get data off websites, this may soon be replaced by further advancements in NLP. Progress has been made to a point where soon you can describe in natural language what you want to extract from a given web page and the machine pulls it for you. 

For example, you could say, “search this travel site for all the flights from San Francisco to Boston and put all of them in a spreadsheet, along with price, airline, time, and day of travel.” It’s a hard problem, but we could actually solve it in the next year.

In health care, I think we’ll be able to predict – automatically – the notes and documentation a doctor might write for a given diagnosis or treatment, which would be a huge achievement. It could save frontline health care workers valuable time that could instead be directed to patient care.

Generally, we’ll be able to tie object detection – where we train algorithms to predict what is in an image – to natural language processing. This would be a big step forward as it will allow us to describe what output we want and the machine figures out how to build a classifier to deliver it. For example, you could say “Does this image contain an animal with four feet and tails?” and that would be “programming” a classifier. 

While we can do this to some extent now, it will become more advanced in the coming year and allow us to go one level deeper – only describing attributes of what we want to find rather than providing labeled examples of the object itself. We may also develop new methods for combining prompt engineering and supervised labeled examples into a coherent whole. 

Businesses leveraging AI to do more with less during challenging times will win in the long term

Microsoft CEO Satya Nadella recently said, “Software is ultimately the biggest deflationary force.” And I would add that out of all software, AI is the most deflationary force. 

Deflation basically means getting the same amount of output with less money, and the way to accomplish that is largely through the use of automation and AI. AI allows you to take something that costs a lot of human time and resources and turn it into computer time, which is dramatically cheaper – directly impacting productivity. 

While many companies are facing budget crunches and labor shortages in a volatile market, it’s important for organizations to prioritize AI and automation efforts in order to get back on track and realize cost savings and productivity enhancements in the future.

It’s exciting to see the rapid pace of progress being made in AI to solve some of the most complex problems in industries like healthcare, including clinician burnout and administrative headaches. As the AI community makes further progress in areas such as ML, NLP, and computer vision, we’ll also see more productivity and quality improvements for programmers and technical teams. This will directly influence organizations and their success on a broader scale. 

In these times of economic uncertainty – and beyond – AI is one of the most powerful technical solutions we can leverage across industries to overcome challenges and continue to innovate and evolve as a society.