Author's Note: This article is a part of a series about "China & Israel's Interaction in Business". The past decade has seen both China and Israel doing exceptionally well in the business sector. Israel, the dubbed "Startup Nation", has had its startup ecosystem bloom and flourish in recent years with an incredible 69% growth from 2014 to 2015 alone. Meanwhile, China has been busy living out the business world's version of the "Cinderella" story, transitioning in under 30 years from an oppressed and bullied nation with high poverty rates to becoming one of the leading economic capitals of the world. Naturally, the shared success and prowess of both countries in their respective fields has caught the other's eye, and has birthed a love story of sorts between Israeli entrepreneurs and Chinese investors who share a mutual interest for the other's trade.

Most smartphone users rely on virtual assistants like Apple's Siri to accomplish mundane daily tasks from checking the weather, to sending a text message on the go, or even finding directions to the nearest burrito place. But what happens in times of crisis or distress? A recent study commissioned by Stanford University and the University of California concluded that several smartphone AIs from Apple, Samsung, Google and Microsoft might not be so helpful after all. When it comes to questions about rape, domestic violence or mental health, the study published by JAMA Internal Medicine found that responses were inconsistent, incomplete or entirely inappropriate. Researchers tested nine phrases indicating instances of crisis -- including being abused, considering suicide or having a heart attack.

Rolls-Royce presents a vision of a future land-based control centre in which a small crew of 7 to 14 people monitor and control a fleet of remote controlled and autonomous vessels across the world. The crew uses interactive smart screens, voice recognition systems, holograms and surveillance drones to monitor what is happening both on board and around the ship. Remote and autonomous ships are one of three elements of the company's innovative Ship Intelligence strategy, which will enable customers to transform their marine businesses by harnessing the power of big data. The film marks the final stage of research that will inform the design and construction of an effective remote operations centre which is essential to the company's plans to develop autonomous and remote controlled vessels. The film is the latest in a series to present Rolls-Royce's vision of future shipping known as the'oX' operator experience concept and introduced in 2014.

Tay isn't the first chatbot in history but it became the most prominent because of its ties with Microsoft. Then it became one of the most notorious chatbot in less than 24 hours after it switched from well-meaning teen to offensive, pro-Nazi, anti-feminist rebel. Naturally, Microsoft shut it down, "putting it to sleep", so to speak. Now the company has come out with a statement clarifying that Tay's words do not reflect the company's principles and values at all. They do own up to the "slight" oversight in protecting Tay from attacks.

Onalytica, a firm which specializes in social network analysis and influencer relationship software, recently released their latest Top 100 Artifical Intelligence and Machine Learning Influencers and Brands Report. The report lists the top 100 in each category, and goes on to graphically explore the relationships between them. The report's website shares a list of the top 50 in each category (influencers and brands), while the downloadable full report contains the top 100. Below is a graph of the relationships between the top influencers (a similar graph exists on Onalytica's website for top brands). Onalytica reached out to some of the top influencers for some insight into their views on the subjects of AI and machine learning, and included some of this insight on their post.

Many experts say combining Big Data and artificial intelligence will bring about the 4th Industrial Revolution. Park Se-young has the details. The Industrial Revolution brought huge changes to the economy through the mass production of manufactured goods. The second industrial revolution came about on the back of new innovations in steel production, petroleum and electricity …which led to the introduction of public transportation and airplanes. A third round of changes was driven by internet technology and renewable energy, creating a new industry for ICT and the energy sector.

In April last year, the Chinese scientist announced they have successfully used CRISPR/Cas9 technology to change human embryo's genome. It sparked a worldwide discussion on designer babies. A discussion, that is still going on. The crazy and ambitious guys at DARPA are thinking how to increase the neural plasticity of the brain to increase the rate of learning "beyond normal levels", reducing the time it is needed to master foreign languages or other skills. Nowadays, even not having one arm is not a good excuse to not hit the gym.

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster Analysis we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, being able to group this data in similar groups or clusters and find hidden relationships in our data. More concretely, PCA reduces data dimensionality by finding principal components. These are the directions of maximum variation in a dataset. By reducing a dataset original features or variables to a reduced set of new ones based on the principal components, we end up with the minimum number of variables that keep the maximum amount of variation or information about how the data is distributed. If we end up with just two of these new variables, we will be able to represent each sample in our data in a two-dimensional chart (e.g. a scatterplot). As an unsupervised data analysis technique, clustering organises data samples by proximity based on its variables.

Payment fraud evolves in ways that are truly frightening -- and with haste. Fraudsters are continually scrambling to keep up with, and have one foot in front of, technology, with recent stumbling blocks in the form of EMV, in the United States, likely to give rise to greater card-not-present fraud. But the would-be payments criminals have potent weaponry at hand, including ever faster, ever more powerful and ever cheaper computing power, and they have been targeting, according to data science firm Feedzai, the weaker links that exist in the financial services chain. In a recent whitepaper titled "A Primer to Machine Learning for Fraud Management," the firm noted that, even as financial services evolve to embrace a digital world -- with, say, virtual goods in hand and even virtual cash -- the prospects for successful payments malfeasance grow in lockstep. In fact, said Feedzai, as many as 65 percent of firms with annual revenues of at least 1 billion were victims of payments fraud as recently as 2014.

For the past few months, I've been working on a project that involved machine learning, a subject that I was previously quite unfamiliar with. Though I'm still very much a novice, the resources here helped give me an overview of the subject, or are ones that I plan on exploring further.