Challenges Facing AI in Science and Engineering

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An exciting possibility offered by artificial intelligence (AI) is its ability to solve some of the most difficult and important problems facing the fields of science and engineering. AI and science complement each other very well, with the former looking for patterns in data and the latter focusing on uncovering the fundamentals that give rise to those patterns.

As a result, AI and science are likely to massively unleash the productivity of scientific research and the pace of engineering innovation. For example:

Biology: AI models such as DeepMind's AlphaFold provide the ability to discover and catalog the structure of proteins, allowing professionals to unlock countless new drugs and drugs. Physics: AI models emerge as the best candidates for addressing crucial challenges in achieving nuclear fusion, such as real-time predictions of future plasma states during experiments and improving equipment calibration . Medicine: AI models are also great tools for medical imaging and diagnostics, with the potential to diagnose conditions like dementia or Alzheimer's far earlier than any other known method. Materials science: AI models are very good at predicting the properties of new materials, discovering new ways to synthesize materials, and modeling the performance of materials under extreme conditions.

These major profound technological innovations have the potential to change the world. However, to achieve these goals, data scientists and machine learning engineers face significant challenges to ensure their models and infrastructure achieve the change they want to see.

Explainability

A key part of the scientific method is being able to interpret and explain both the workings and the outcome of an experiment. This is essential to allow other teams to repeat the experiment and verify the results. It also allows non-experts and members of the public to understand the nature and potential of the results. If an experiment cannot be easily interpreted or explained, then there is probably a major problem in further testing a discovery and also in popularizing and commercializing it.

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When it comes to AI models based on neural networks, we also need to treat inferences as experiments. Even though a model technically generates inference based on patterns it has observed, there is often a degree of randomness and variance that can be expected in the output in question. This means that to understand the inferences of a model, one must be able to understand the intermediate steps and the logic of a model.

This is a problem faced by many AI models that leverage neural networks, as many currently serve as "black boxes": the steps between input and output of a piece of data are not labeled, and there is no possibility of explaining "why". revolved around a particular inference. As you can imagine, this...