For those of you who are not familiar with computational science – it is the discipline at the intersection of domain science, computer science, applied mathematics, and engineering. In a nutshell, it is the link between everything you touch and feel (natural and human-made) and rapidly growing computing.
The most challenging science and technology problems whether related to life sciences or physical sciences fall into the grand challenge category in the simple 2×2 classification of AI Application Landscape Heatmap below and posted on LinkedIn.
These problems are hard and yet to be solved as the problem dimensionality (one can think of number of parameters to explore all the solution space) is extremely high and in turn would need a lot of experimentation and data. AI, machine learning, and good statistical science can help in reducing experiments, but they cannot create more information than what is contained in the data. This is where computational science plays an important role in bridging domain science knowledge with AI/ML tools. If you think of an equation, theory, or laws (E = mc2; Newton’s laws of motion or theory of general relativity; laws of thermodynamics), they are the compact representations of our knowledge of the universe and underlying processes. Blackbox models like AI/ML or many statistical tools throw away all this knowledge and relearn all the basic interactions as they are trained on available data. This is where for these grand challenge problems it is important to incorporate all available knowledge into efficient processes where only new things are learnt and thus reducing the dimensionality of the grand challenge problems. This in turn would make solutions to these problems more accessible to AI and ML.
If we look past all the debate of the value of the AI investments or whether AI will become sentient and reach human intelligence or superintelligence, what is fundamentally happening is enormous investments in computing hardware with unprecedented explosion of computing power. The hardware (computing, memory bandwidth, storage, wired and wireless networking) is getting exponentially cheaper by the day and more power efficient. This is leading to ubiquitous computing where we have powerful computing devices in our hand (a supercomputer not so long ago) and connected to the backend mega servers at lower costs than ever before. This trend is only going to continue as mentioned in the recent AMD’s Advancing AI Event (https://youtu.be/zCOqJx3Yst4?si=X_pPgYr3WGJVKnMY); the expected investments in AI are going to cross $500B by 2030 and currently the market is projected to have an incredible CAGR of 80%.
If I translate that to increase in computational power, that is greater than 80% rate as computing is getting lot cheaper by the day. This means that this is the 2007 internet moment for computational science. The hardware is coming, and the question is how can we leverage decades of efforts in computational science in building the knowledge, the tools, the algorithms, the connections between computing and real world, and leverage advances in AI/ML and the associated ecosystem to rapidly create ecosystems to create value to the society via both natural and physical sciences. This will span innovations in health sciences, energy, materials, weather and climate, sustainability, defense, and in short across all sectors.
This also means that computational thinking is an important skill for the modern workforce – the ability to translate problems and processes into streamlined instructions to the ever-increasing computing that is going to be all around us. As I have posted recently, AI is already enabling rapid multiplication of effort of well thought out constructs by removing the drudgery of lot of copy and paste coding and debugging and also synthesizing information across the disciplines as the natural world does not have the silos of the traditional academic disciplines. The folks who can see this future and adapt will be on the right side of the disruption and the others will be left behind.