"The problem of giving rules for producing true scientific statements has been replaced by the problem of finding efficient heuristic rules for culling the reasonable candidates for an explanation from an appropriate set of possible candidates [and finding methods for constructing the candidates]."
– B. Buchanan, quoted in Lindley Darden. Recent Work in Computational Scientific Discovery.
Hypothesis testing is a statistical approach that assists researchers in determining the validity of their theories. It is frequently used in statistics and data science to determine whether an event has occurred or will occur based on previous happenings. Let's understand Hypothesis Testing with an example. There is one Pharma Company which produces Vaccine A and you need to take 2 doses of that vaccine to get fully immune to the virus. Millions of people have already taken 2 doses of Vaccine A. After a few days the same company comes up with a Vaccine B which gives faster results, they claim that only 1 dose of this vaccine is enough to get fully immune to the virus.
Usually when we think of computers, we probably imagine glowing displays, interconnected networks sharing digital information, and more software applications than anyone one person could ever come close to using -- but that's only part of computing's story. Analog computers, and later mechanical computers, were an integral part of humanity's pursuit of scientific discovery, fueled by our desire to anticipate future events and outcomes. For a species that conquered the entire world thanks to our larger brains and toolmaking prowess, it's no surprise that we've been using artificial tools to augment and enhance our intelligence as far back as our history goes -- and probably even longer than that. From the careful positioning of stones in England, to the soaring water clocks of China's Song Dynasty to the precise arrangement of mechanical gears in the visionary inventions of Blaise Pascal and Charles Babbage, analog and mechanical computers have served our forebearers well and helped them not just survive but thrive by transcending the bounds of our biology. In Salisbury Plain in the south of England, a collection of about 100 massive and roughly even-cut stones form a pair of standing rings whose purpose is lost to history, but whose construction began before the invention of the wheel and took at least 1,500 years to complete, and possibly even longer.
Say you're driving with a friend in a familiar neighborhood, and the friend asks you to turn at the next intersection. The friend doesn't say which way to turn, but since you both know it's a one-way street, it's understood. That type of reasoning is at the heart of a new artificial-intelligence framework – tested successfully on overlapping Sudoku puzzles – that could speed discovery in materials science, renewable energy technology and other areas. An interdisciplinary research team led by Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science in the Cornell Ann S. Bowers College of Computing and Information Science, has developed Deep Reasoning Networks (DRNets), which combine deep learning – even with a relatively small amount of data – with an understanding of the subject's boundaries and rules, known as "constraint reasoning." Di Chen, a computer science doctoral student in Gomes' group, is first author of "Automating Crystal-Structure Phase Mapping by Combining Deep Learning with Constraint Reasoning," published Sept. 16 in Nature Machine Intelligence.
"VICReg could be used to model the dependencies between a video clip and the frame that comes after, therefore learning to predict the future in a video." Humans have an innate capability to identify objects in the wild, even from a blurred glimpse of the thing. We do this efficiently by remembering only high-level features that get the job done (identification) and ignoring the details unless required. In the context of deep learning algorithms that do object detection, contrastive learning explored the premise of representation learning to obtain a large picture instead of doing the heavy lifting by devouring pixel-level details. But, contrastive learning has its own limitations.
Many people believe that the process for achieving breakthrough innovations is chaotic, random, and unmanageable. Breakthroughs can be systematically generated using a process modeled on the principles that drive evolution in nature: variance generation, which creates a variety of life-forms; and selection pressure to select those that can best survive in a given environment. Flagship Pioneering, the venture-creation firm behind Moderna Therapeutics, uses such an approach, which it calls emergent discovery. It involves prospecting for ideas in novel spaces; developing speculative conjectures; and relentlessly questioning hypotheses. On November 30, 2020, Moderna Therapeutics announced that Phase III clinical trials for its messenger RNA vaccine demonstrated 95% protective efficacy against the SARS-CoV-2 virus that had killed almost 1.5 million people worldwide in the previous 10 months. A relative upstart in the Covid-19 vaccine race and a company that few people had heard of before the pandemic, Moderna looked to be an overnight success. But as its CEO, Stéphane Bancel, has noted, that success was 10 years in the making. Far from a one-and-done stroke of luck, the vaccine was the product of a repeatable process that has been used countless times by the company from which Moderna emerged: Flagship Pioneering, a venture-creation firm based in Cambridge, Massachusetts, whose mission is to conceive, make, and commercialize breakthrough innovations in previously unexplored domains of the life sciences. The misconception about the Moderna case, as with many other breakthrough innovations, is understandable. Breakthrough innovations are typically seen as the result of chaotic, random, and unmanageable efforts--the product of pure serendipity or the inspiration of a rare visionary. That view, we believe, is deeply flawed. From our different vantage points (Afeyan has spent the past three decades starting ventures based on breakthrough science and technology, and Pisano has studied innovation processes during the same period), we have come to realize that breakthroughs tend to emerge from a relatively well-defined process modeled on the basic principles that drive evolution in nature: variance generation, which creates a variety of life-forms, and selection pressure to select those that can best survive and reproduce in a given environment. The approach, called emergent discovery, is a structured and disciplined process of intellectual leaps, iterative search and experimentation, and selection.
Kevin Yager (front) and Masafumi Fukuto at Brookhaven Lab's National Synchrotron Light Source II, where they've been implementing a method of autonomous experimentation. In the popular view of traditional science, scientists are in the lab hovering over their experiments, micromanaging every little detail. For example, they may iteratively test a wide variety of material compositions, synthesis and processing protocols, and environmental conditions to see how these parameters influence material properties. In each iteration, they analyze the collected data, looking for patterns and relying on their scientific knowledge and intuition to select useful follow-on measurements. This manual approach consumes limited instrument time and the attention of human experts who could otherwise focus on the bigger picture.