Editor's note: This is an overview of a multi-part tutorial on data science for newbies. The author has given the series a different -- tongue-in-cheek -- title; take it in stride and recognize that the series' approach and content is a fresh look at getting started with various aspects of data science from a software engineering perspective. To do some serious statistics with Python one should use a proper distribution like the one provided by Continuum Analytics. Of course, a manual installation of all the needed packages (Pandas, NumPy, Matplotlib etc.) is possible but beware the complexities and convoluted package dependencies. The installation under Windows is straightforward but avoid the usage of multiple Python installations (for example, Python3 and Python2 in parallel).

Samsung's Bixby virtual assistant was a touted part of the Galaxy S8, but it initially missed being part of the flagship smartphone's April launch. However, Samsung will soon be bringing the full version of Bixby to Galaxy users. Bixby is expected to launch by late June in the U.S., the Wall Street Journal reported. For Samsung users, Bixby is the Galaxy's equivalent of similar voice assistant programs like Apple's Siri and Google's Assistant. With the service, which launched in Korea earlier this month, users can use Bixby to get information for topics like news, local locations and the weather.

LOS ANGELES, May 31 (Xinhua) -- "The ultimate power, in my opinion, is love, not AI," a top artificial intelligence (AI) expert and chief scientist of AI/ML (Google Cloud) Fei-Fei Li told Xinhua in a recent exclusive interview. In the Chinese language, the abbreviation of artificial intelligence, or AI, is pronounced the same as the character for "love." From the introduction of a second generation of its TensorFlow Processing Unit (TPU) to TensorFlow Research Cloud, almost all of Google's latest big announcements touched on AI in one way or another. Last year, the technology giant recruited Li, one of the most eminent women working on AI, to lead machine learning research for Google's cloud computing division. "Goal is to democratize AI for everyone!"

Beyond membership in the Pantheon of Famous Brits, Winston Churchill and George Orwell would seem to have little in the way of common ground. Orwell was a journalist and novelist. Churchill had money and pedigree; the young Orwell lived on the street and raised his own vegetables during World War II. Churchill's political leanings were conservative; Orwell flirted with communism until he witnessed the betrayal of his Republican comrades by Soviet agents in the Spanish Civil War. In "Churchill & Orwell: The Fight for Freedom," Thomas E. Ricks gets beyond these differences and finds the iron core of both men.

Microchip manufacturer Intel has invested heavily in the driverless car race with the latest US$15 billion (A$19.5bn) Mobileye develops sensors and intelligence technology behind automated driver-assistance systems and many self-driving cars. Its tech enables a car to "see" and understand the world. Other recent purchases include the deep learning tech company Nervana, microchip maker Movidius and automotive tech company Delphi. Intel is also working with the automotive companies BMW and Volkswagen to begin trials later this year. Intel is strategically putting together all the critical capabilities required to develop self-driving cars that can "see" and intelligently understand the world around us.

For people living with post-traumatic stress disorder (PTSD), erasing distressing memories is something that could only be dreamed of. But researchers believe they may have found a way of turning those dreams into reality. Scientists have pinpointed an enzyme in the brain that is critical in the storage of long-term memories. While the enzyme has only been studied in mice so far, the researchers are optimistic that it could be targeted to remove distressing memories in people with PTSD in the future. The enzyme, called acetyl-CoA synthetase 2, or ACSS2 'fuels' gene expression in the nucleus of nerve cells to turn on key memory genes after learning.

The New York Times published a provocative article on Friday, asking in the headline "Is China outsmarting America in A.I.?". But depending on how you define "outsmarting," and the context in which the question is asked, the answer might not even matter much. The answer might matter very much in terms of geopolitics and national security. Just like with supercomputing, quantum computing and other areas of deep computer science research, having better capabilities in artificial intelligence can arguably lead to an edge in areas like military, energy and climate science that can shift the world-power balance. But if we're talking about consumer or enterprise AI, then comparing China and the United States is kind of like comparing apples and oranges.

There is a 50 per cent chance that machines will outperform humans in all tasks within 45 years, according to a survey of more than 350 artificial intelligence researchers. AI will master many activities a lot sooner, though. Machines are predicted to be better than us at translating languages by 2024, writing high-school essays by 2026, driving a truck by 2027, working in retail by 2031, writing a bestselling book by 2049 and surgery by 2053. In fact, all human jobs will be automated within the next 120 years, say respondents. The survey, by the University of Oxford and Yale University, was sent to AI researchers who published in 2015 at one of two big conferences in the field – the Conference on Neural Information Processing Systems and the International Conference on Machine Learning. In total, 352 people responded.

Now, a quantitative study published on Friday in Nature Astronomy demonstrates that gender bias in astronomical research extends even to journal citations, which are an indicator of academic prestige and are linked with better access to grant money, speaking engagements, and professional advancement. Led by Neven Caplar, a PhD student at ETH Zürich's Institute of Astronomy, the new research found that papers with male lead authors were cited 10 percent more frequently than papers led by women, even after controlling for non-gender-specific disparities such as seniority, team size, publication date, field, and academic institution. The team reached this conclusion after using machine-learning to analyze a dataset of over 200,000 papers published between 1950 and 2015 in five influential journals: Astronomy & Astrophysics, The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society, Nature, and Science. In cases where first authors used their initials--a tactic women researchers disproportionately use to avoid gender bias--Caplar's team took extra measures to identify exceptions in publishing records that exposed authors' full names.

When equally qualified women are unequally represented, the astronomy community as a whole suffers, as does our understanding and exploration of worlds beyond our own. Subscribe to Science Solved It, Motherboard's new show about the greatest mysteries that were solved by science.