Application of hypothesis testing is predominant in Data Science. It is imperative to simplify and deconstruct it. Like a crime-fiction story, hypothesis testing, based on data, leads us from a novel suggestion to an effective proposition. Hypothesis originates from the Greek work hupo (under) and thesis(placing). It means an idea made from limited evidence. It is a starting point for further investigation.

If you could construct a sexual partner that was faithful, beautiful, and responsive to your every wish, would you? It's a question Aimee van Wynsberghe, co-founder of the Foundation for Responsible Robotics, thinks a lot about. In July 2017, she and fellow ethicist Noel Sharkey published a report (pdf), Our Sexual Future with Robots, that delved into the state of the robot sex industry and its future. Quartz met van Wynsberghe, a professor of robotics and ethics at the Delft University of Technology in the Netherlands, on a trip to London in a busy café, just before she headed to the Science Museum's Robots exhibition, to discuss how close humanity is to sex and even love with robots, and the risks involved. The interview is edited and condensed for clarity. Quartz: Your report mainly deals with "precursors" to sex robots. How are the dolls and devices that already exist connected to possible robots of the future?

Not much about Makoto Koike's adult life suggests that he would be a farmer. Trained as an engineer, he spent most of his career in a busy urban section of Aichi Prefecture, Japan, near the headquarters of the Toyota Motor Corporation, writing software to control cars. Koike's longtime hobby is tinkering with electronic kits and machines; he is not naturally an outdoorsy type. Yet, in 2014, at the age of thirty-three, he left his job and city life to move to his parents' cucumber farm, in the greener prefecture of Shizuoka. "I thought I was getting old," Koike told me.

Summary: What are the real threats of job loss from real and AI enhanced virtual robots? How do we position ourselves and our children to succeed in this new environment? Data Scientists Automated and Unemployed by 2025! is the title of an article we wrote almost exactly a year ago. If you thought that job loss due to automation was going to be restricted to traditional industries you'll need to think again. It's clear this is going to encompass jobs we thought until recently were immune from automation.

Elon Musk's grand plan of moving beyond passenger cars to truly revolutionize transportation just got a bit grander. In addition to developing an electric 18-wheeler that Tesla plans to unveil next month, Musk wants to make the thing drive itself. Tesla is working with Nevada authorities to begin testing a robo-rig prototype at some point in the not-too-distant future. "Our primary goal is the ability to operate our prototype test trucks in a continuous manner across the state line and within the States of Nevada and California in a platooning and/or Autonomous mode without having a person in the vehicle," Tesla's Nasser Zamani told officials with the Nevada Department of Motor Vehicles, according to Reuters. Assuming Tesla can figure out how to make battery tech work for long-haul trucking (no easy feat), adding autonomy to the equation makes perfect sense.

Jennifer Zhu Scott is the co-founder and principal of Radian Blockchain Ventures and Radian Partners, focusing on private investment in the Artificial Intelligence, Blockchain, and renewable energy sectors. Prior, she was head of business development and strategy in APAC for Thomson Reuters and led the firm's speech-to-text, deep search, and video-indexing project. She co-founded one of the first education companies in China and exited before moving to UK as a senior advisor to the education subsidiary of Daily Mail & General Trust. How is Blockchain technology currently applied? First of all, I think we should demystify it. The Blockchain essentially is a decentralised database with a variety of applications, but not universal applications.

We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. Major distinguishing features of OpenML benchmark suites are (a) ease of use through standardized data formats, APIs, and existing client libraries; (b) machine-readable meta-information regarding the contents of the suite; and (c) online sharing of results, enabling large scale comparisons. As a first such suite, we propose the OpenML100, a machine learning benchmark suite of 100~classification datasets carefully curated from the thousands of datasets available on OpenML.org.

The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.

LONDON (Reuters) - Artificial intelligence robots are turbo-charging the race to find new drugs for the crippling nerve disorder ALS, or motor neurone disease. The condition, also known as Lou Gehrig's disease, attacks and kills nerve cells controlling muscles, leading to weakness, paralysis and, ultimately, respiratory failure. There are only two drugs approved by the U.S. Food and Drug Administration to slow the progression of ALS (amyotrophic lateral sclerosis), one available since 1995 and the other approved just this year. About 140,000 new cases are diagnosed a year globally and there is no cure for the disease, famously suffered by cosmologist Stephen Hawking. "Many doctors call it the worst disease in medicine and the unmet need is huge," said Richard Mead of the Sheffield Institute of Translational Neuroscience, who has found artificial intelligence (AI) is already speeding up his work. They analyze huge chemical, biological and medical databases, alongside reams of scientific papers, far quicker than humanly possible, throwing up new biological targets and potential drugs.

Last month, we published Part 1 of a series of posts designed to highlight the Top 10 use cases from the IBM Machine Learning Hub. In particular, we're eager to share scenarios that can stretch our understanding about machine learning -- compliments of our collaborations with data scientists from across our client base. The goal of the series is to look beyond the usual set of machine learning use cases that come to mind when considering a particular sector. For example, Part 1 focused on government but looked beyond the role of ML in sentencing and parole to explore use cases driving vital improvements to regional and municipal agencies. Hospital and clinics assess their own effectiveness in many ways -- from average patient stay to ER wait times to direct surveys of service.