"The problem of giving rules for producing true scientific statements has been replaced by the problem of finding efficient heuristic rules for culling the reasonable candidates for an explanation from an appropriate set of possible candidates [and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.
Sure, world is crying out loud that big-data's biggest problem will be resources. Demand has skyrocketed and everyone in the world is going into tailspin in meeting that demands. Companies are going frantic and overspending to hire data scientists to secure themselves from any upcoming shortfall. This is nothing but a sign that world needs our robot algorithm friends to pacify some demand and increase credibility to new paradigms. Who could forget Steve Balmer's famous quote comparing Big Data as a Machine Learning problem.
The newly organized research project "MELLODDY" (Machine Learning Ledger Orchestration for Drug Discovery), involving ten large pharma companies and seven technology providers, is that kind of deals which can catalyze a transition of the pharmaceutical industry to a new level -- a "paradigm shift", as one might refer to it in terms of Thomas Kuhn's "The Structure of Scientific Revolutions". The project aims at developing a state-of-the-art platform for collaboration, based on Owkin's blockchain architecture technology, which would allow collective training of artificial intelligence (AI) algorithms using data from multiple direct pharmaceutical competitors, without exposing their internal know-hows and compromising their intellectual property -- for the collective benefit of everyone involved. While artificial intelligence (AI) already proved to be a groundbreaking thing in many industries (robotics, finance, surveillance, cyber security, self-driving cars to name just a few), drug discovery still seems like a hard case for machine learning practitioners. A major reason for that is the lack of quality data to train models properly. It might seem surprising, as pharmaceutical research generates enormous amounts of data daily.
RADIUS guest contributor Gary Grossman currently leads the Edelman AI Center of Excellence. As part of that, he led development of the 2019 Edelman Artificial Intelligence Survey that can be viewed here. Just how important is artificial intelligence (AI)? Microsoft's Chief Envisioning Officer, Dave Coplin, said recently that AI is "the most important technology that anybody on the planet is working on today." A PwC report estimates that global GDP will be 14 percent higher in 2030 as a result of AI--the equivalent of $15.7 trillion, which is more than the current output of China and India combined.
A breakthrough discovery shows that pterodactyls could fly from birth, something no other species before or since has been able to do. And British scientists said that the revelation has a'profound impact' on our understanding of the reptiles. The common belief was the pterodactyls, like birds and bats, only took to the air once they were fully grown. A new study shows pterodactyls could fly from birth, something no other species before or since can do. The findings have a'profound impact' on our understanding of reptiles Pterodactyls used both their arms and legs to push themselves off the ground during take-off, in a manoeuvre known as the'quadrupedal launch'.
Data is known to have been used to drive business performance since Taylor and Ford started measuring and optimising the output of assembly lines in the late 1800s. The importance of the analysis of data to support decision-making, referred to interchangeably as business or data analytics, has grown and continues to grow proportionally to our ability to store and process data. The volume, velocity and variety of data being processed in organisations has increased substantially. By one estimate, 90 per cent of the data that exists today has been produced over the last two years. This paradigm shift requires a fresh outlook.
Differential privacy is the gold standard in data privacy, with applications in the public and private sectors. While differential privacy is a formal mathematical definition from the theoretical computer science literature, it is also understood by statisticians and data experts thanks to its hypothesis testing interpretation. This informally says that one cannot effectively test whether a specific individual has contributed her data by observing the output of a private mechanism---any test cannot have both high significance and high power. In this paper, we show that recently proposed relaxations of differential privacy based on R\'enyi divergence do not enjoy a similar interpretation. Specifically, we introduce the notion of $k$-generatedness for an arbitrary divergence, where the parameter $k$ captures the hypothesis testing complexity of the divergence. We show that the divergence used for differential privacy is 2-generated, and hence it satisfies the hypothesis testing interpretation. In contrast, R\'enyi divergence is only $\infty$-generated, and hence has no hypothesis testing interpretation. We also show sufficient conditions for general divergences to be $k$-generated.
I always loved products and technology. But ever since I was a child, I was especially fascinated by these big inventions, powered by transformative technological revolution that changed - everything! So I felt extremely lucky, when about 20 years ago, at the beginning of my career, I was just in time for one of these revolutions: when the Internet happened. Through the connected PC, the world we lived in has been transformed from a "physical world" -- where we used to go to places like libraries, and use things like encyclopedias and paper maps, to a "digital world" -- where we consume digital information and services from the convenience of our home. What was especially amazing, was the rate and scale of this transformation.
Hypothetical Testing is an application of your statistical model to the questions from the real world. In the hypothetical testing, you first assume the result as an assumption. It is called the null hypothesis. After the assumption, you hold an experiment for testing this hypothesis. Then after based on the results of the experiment.
Folklore has it that during the American Revolution, George Washington was approached by an enquiring member of the press who asked: "George! What keeps you up at night?" It wasn't the Continental Congress, who even then seemed challenged when it came to accomplishing anything. His reply: "Their Spies!" Since that time – more than 240 years – we've amassed insights as to the early indicators of trusted insiders inclining toward the dark side. Notwithstanding those gains, the best we've generally been able to do is catch the spies after they've already hurt us.
Hypothesis Testing, as such an important statistical technique applied widely in A/B testing for various business cases, has been relatively confusing to many people at the same time. This article aims to summarize the concept of a few key elements of hypothesis testing as well as how they impact the test results. The story starts from hypothesis. When we want to know any characteristics about a population like the form of distribution, the parameter of interest(mean, variance etc.), we make an assumption about it, which is called the hypothesis of population. Then we pull samples from population, and test whether the sample results make sense given the assumption.