The viewers asked a lot of great questions, but, due to time constraints, host Malcolm Moore from JMP wasn’t able to get to all of them during the event. We felt that many in the greater JMP community shared these questions and would be interested in the answers.
Q: While increasing the number of scientists who have data skills means faster innovation and delivery of products to market, it can also mean increasing the risk of making wrong decisions. Do you agree? If so, how can we mitigate that risk?
A: Analytics is a power tool that can be used to “see things that couldn’t be seen without it, much like a telescope or microscope,” as one panelist said, but like any tool, risks arise from improper use. In larger organizations, data scientists or statisticians within internal Centers of Excellence can guide process experts in best practices of analytic techniques. However, the greatest risk comes from bypassing the process experts entirely in favor of an overreliance on machines to make good decisions. Careful consideration of the results of any analytic endeavor, by process experts, is the best way to mitigate that risk. On the other hand, it is also equally important to consider the risks of doing nothing or maintaining the status quo, such as losing the opportunity to increase innovation capacity or to speed products to market.
Q: Am I the only one who finds that pro-innovation bias gets in the way of a sensible discussion of the basics, such as defining the problem, GIGO, DOE, and measurement systems?
A: This question received many likes during the event. The answer: No, you are not the only one. Evidently, this sentiment is shared among many. It would seem that, often, a rush to innovate causes experimenters to neglect the basics, as you’ve described them, in favor of a try-and-see or one-factor-at-a-time approach to development. When this happens, the best advice is to raise this point and emphasize the value of these basics during discussions with colleagues when planning experiments.
Q: I have been asked to predict commercially successful inventions from patent and nonpatent literature. Are ML and AI experts often asked do this?
A: David Hand addressed this question during the discussion. Data scientists are often asked to embark on this type of exercise. (In particular, patent data is a frequent subject for analytics, as seen in this presentation at a recent JMP Discovery Summit). David mentioned a similar situation in which he was tasked with creating a model to predict the success of start-up companies, which he described as “very, very difficult.” However, two new features in JMP Pro 16 – Sentiment Analysis and Term Selection – make just such an exercise significantly easier. (This short video demonstrates how these features work.)
Q: Many feel the true goal of machine learning and AI is to have the technology signal us on how to optimize and/or address signal deviations, such as in sensor data patterns. How far are we from meeting that goal?
A: This question was also touched upon by the panel. Each member emphasized the value gained by leveraging ML and AI to augment the understanding of processes or patterns that couldn’t have been seen without these methods, but also the importance of process experts to interpret these findings. We are at a point now where machines can optimize, detect, interpret, signal and even decide what actions to take, but there are massive risks in allowing them to implement these autonomously by removing specialists from the equation. By the way, Functional Data Explorer in JMP Pro is a great machine learning tool for analyzing sensor data.
Q: Have you experienced pushback over the fact that something was done a different way historically, specifically in the manufacturing environment? If so, what did you do to help persuade people to use the new tools and features available?
A: Pushback is a common response to anyone trying to implement change. The advice on overcoming it is to think big, start small, and broadcast your successes. Machine learning can be used to solve big problems or revolutionize entire industries, but it can also help with small things that are under the radar for most people. And while your company, department or team may be resistant to change, there may already be success stories elsewhere in your company or close to home that will resonate with your colleagues. You can find a collection of such stories here. Often, the fear of change may be driving the pushback. If that’s the case, STIPS, JMP’s free online course that can help scientists and engineers overcome their fears, may prove useful.
Q: I feel there is some confusion around the differences and overlaps between the terms AI and unsupervised ML, as well as supervised ML and predictive/inferential analytics. A clearer set of definitions would be welcome.
A: Indeed! To add to what the panelists discussed during the event, this page from SAS does a great job in explaining the differences and overlaps between these terms. In short:
Artificial intelligence: the broad science of building machines to mimic human abilities.
Machine learning: a specific subset of AI relating to the techniques by which machines are trained to learn.
Supervised learning: the development of models to classify, explain or predict outcomes in data using data with known outputs.
Unsupervised learning: comparing or differentiating data points based on patterns or underlying structures within the data itself, without the use of response variables.
Predictive/inferential analytics: using supervised or unsupervised learning to make predictions or infer knowledge and understanding about the world around us (manufacturing processes, product developments, weather patterns, stock markets, etc.).
Q: What’s the best way to bring the data experts and the “physical” experts (e.g., sensor development) together? Very often, the first lacks understanding of physical artifacts, while the other lacks understanding of what ideal data should look like.
A: Optimally, the data experts and physical experts are the same people. JMP delivers the power of analytics to the scientists and engineers that are in the best position to understand their individual processes, but who don’t necessarily have a background in data science. Where possible, statisticians and those with data science backgrounds serve in advisory or overseer roles to support those process experts in their efforts.