# Scientific Discovery

### The Silent Rockstar of BigData: Machine Learning - AnalyticsWeek

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.

### Pharma Companies Join Forces to Train AI for Drug Discovery Using Blockchain

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.

### Age of AI -- The Paradigm Shift to Natural UI

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.

### How to do Hypothesis Testing : A Beginner Guide For Data Scientist

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.

### To Catch A Spy: The Emergence of Artificial Intelligence

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.

### A General Guidance of Hypothesis Testing – Towards Data Science

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.