Top 4 Myths About Leveraging AI in the Lab

Myth #1: AI will replace Scientists

While there are some truly remarkable applications of AI, these systems are generating outputs that are still very much within the domain of knowledge they were trained upon and struggle with anything beyond that domain. In R&D, where innovation often requires novel ideas and approaches that diverge from existing knowledge, the unique ability of humans to think abstractly, ask new questions, and draw from a wide array of experiences is crucial and will remain so even as AI continues to advance. So, there will always be a need for scientists.

Myth #2: You can use AI with only data from a few experiments

Many people think that if you have data from a few experiments, you can give it to an AI, and it will immediately understand the nature of the chemistry in your problem and be able to tell you what to do next. The reality is that to get to the type of AI we are seeing in other business units today, you may need months but more likely years of diverse examples from across the R&D ecosystem to train the AI on before it will be reliable enough for all scientists to use at scale. For example, most of the AI tools people think of today have been trained on hundreds of millions or more examples accumulated over many years.

Myth #3: AI can perform miracles

Organizations often expect AI to come up with something they never would have thought of. While AI is great at pointing you in the right direction to look for novel answers or in recommending small changes to what you’ve done in the past, it can’t tell you something it’s never seen before without a high likelihood of nonsensical proposals.  The most pragmatic way to approach AI in R&D is to expect it to propose recommendations that are reasonable and based on historical data that already exists. The key function of the AI then is to enhance the so-called ‘network effect’ between researchers — the work that any individual does today, informs the potential ideas (i.e. the proposals generated by an AI) of all their colleagues tomorrow. This is where the greatest opportunity for accelerated innovation exists today.  

Myth #4: AI doesn’t require human intervention

Unless you have millions, or billions of examples to train the AI on, you will need to help guide it yourself. This is especially true for Chemistry and Materials Science R&D, where domain expertise plays such a significant role. Early AIs behave like toddlers; they need constant input and correction, explained in diverse ways so they understand the guardrails and constraints. While this can take time in the beginning, the investment in teaching the AI domain knowledge will pay off immensely down the road.

Will AI Actually be Useful in the Lab?

While the journey to AI may take longer in R&D than some people realize, it brings with it an enormous number of benefits in productivity, efficiency, and insight—many of which can be realized in as little as a few months. The key is taking the right steps now to ensure your organization is positioned for success. To learn more about the critical steps you must take to successfully implement AI, download Albert’s comprehensive eBrief: Artificial Intelligence in R&D – Critical Path to Success.