10 tips when unlocking AI for R&D

Elsevier’s four-part webinar series AI in innovation: Unlocking R&D with data-driven AI outlines the issues that can derail your AI projects and how to prepare yourself for these innovations — and those around the corner. In the first edition, a panel of AI and data experts explore the perils, pitfalls and promise of generative AI for R&D 새 탭/창에서 열기.

Moderated by Elsevier’s Commercial Director for Corporate Markets Zen Jelenje 새 탭/창에서 열기, the panel consisted of Elsevier’s VP of Data Science Life Sciences Mark Sheehan 새 탭/창에서 열기 and two experts from Elsevier’s SciBite: Director of Data Science & Professional Services Dr Joe Mullen 새 탭/창에서 열기 and Head of Ontologies Dr Jane Lomax 새 탭/창에서 열기. 

Do watch the whole episode 새 탭/창에서 열기 — a lot was covered. Meanwhile, here are some tips from the panel for navigating these changing times.

Tip #1: Get your data in order.

Tip #2: Don’t rush to a “solution.” Start by asking, “What’s the specific problem I want to solve?”

“You've got to remain focused on identifying what the problems are, and only then look at the ever-evolving solutions to solve those problems,” Joe says. 

“Instead of thinking of it as whether to invest in AI,” adds Zen, "you need to ask the question, ‘How does this improve my research?’”

Tip #3: Don’t consider LLMs as an all-in solution — especially for life sciences. (However, LLMs can still be part of the solution.)

Tip #4: Don’t underestimate scaling.

“One piece of advice: don’t underestimate the difficulty in being able to scale these types of technologies to production,” says Jane. “When we started with this three years ago, we ended up having to take a step back and first build the infrastructure and invest in the skills. We learned a lot through that process, but it was quite a learning curve. So, if you're investing in this, don’t overlook this. Come chat with us.” 

Tip #5: Think operationalization.

“New technology brings new holistic cost considerations,” Joe says. “There are costs associated with rolling out some of these larger models: monetary costs, time costs, disk and carbon footprint costs, and so on and so forth.” 

Tip #6: Get your hands dirty (while failing fast, learning fast and moving on).

“I read a McKinsey report the other day about whether you want to be a taker, a shaper or a maker in the AI space,” Mark says. “Are you going to wait until it’s fully cooked? Nothing wrong with that. And it can depend on the industry or your company’s appetite for risk and investment.” But for Elsevier, the road was clear: jump in now. 

“And definitely having the right team in place is important,” adds Zen. “And since some of the questions we try to address in scientific research are really specific to the domain, it's also harder to wait for somebody else to do it for us.”  

Tip #7: Think modularity.

“Our enrichment pipelines continue to become more automated and feature more of the latest AI technologies as we iterate,” says Mark. “And certainly, it's not the case that as soon as a new technology comes in, we throw out what we had before. It works well that we have a mix of rule-based technologies and machine learning technologies. And now we're exploring the latest Gen AI technologies. These can all be complementary.”

“We always try to find a way to integrate all these different pipelines, datasets and capabilities into what my team calls a Lego set,” Mark adds. “It's a great way to approach things in a modular and flexible way without getting too obsessed about the latest or greatest technologies.”

Tip #8: Stay on top of what’s happening.

It might be simpler to wait for others to fail and then adopt. But here you risk being left behind — and losing any competitive edge. As Joe points out: “Around 10 years ago, AI was beating humans at Space Invaders 새 탭/창에서 열기. Around five years ago, AI got better at Go 새 탭/창에서 열기. Just a few weeks ago, AI started beating humans in real-time drone racing 새 탭/창에서 열기. AI is evolving at such a pace, you need to keep yourself skilled up and aware of what's going on around you.

“And again, this is about getting your hands dirty. Reading a few articles and blogs isn’t enough. But it’s a difficult balance: keeping on top of things without getting sucked in, while just trying to identify those problems you want to solve.”

Tip #9: Keep humans in the loop. 

As the panel discussed, Subject Matter Experts (SMEs) remain essential to validate the output of any AI algorithm — and more so when it comes to LLMs. For instance, these SMEs can be deployed as prompt engineers to ask the right questions to the LLMs so the resulting output is easier to validate.  

“Prompt engineering is actually a skill that we should all have some appreciation and understanding of,” Joe says. “It's not as straightforward as some people might expect. You need to be able to relay your understanding of the world to an LLM … and this comes back again to the real importance of SMEs when applying it to scientific domains that really require some expertise.”

Tip #10: While waiting on regulatory decisions, aim to be responsible.

“If you ask me about the regulatory environment today, this webinar would be out of date in a month or so,” Mark says. And indeed: watch this space. But meanwhile, you should aim to be responsible. “Regulations are all about governments coming in saying we need to manage this space because we’re concerned about the future. But it could start with responsible AI where the actual practitioners go ‘How can we be responsible and ethical about how we approach this?’ And at Elsevier, we’ve really tried to bake this into our daily work from the start with our Responsible AI principles 새 탭/창에서 열기.”