Malaysia's strategic investment fund Khazanah Nasional Bhd is making a strategic investment of 100 million into New Jersey-based Fractal Analytics, a leading global provider of analytics, to accelerate its growth. In a press release, Khazanah executive director Chinta Bhagat commented: "The availability of large amounts of real-time information and the ability to analyse it at scale, will transform how the world makes decisions. Fractal is one of the companies leading this change and with our partnership, it can dramatically accelerate its programmatic investments and acquisitions." TA Associates, a Boston-based global private equity firm and AIMIA, the world's largest loyalty firm and Gulu Mirchandani of Onida are some of Fractal's current investors. Fractal Analytics co-founder and chief executive officer Srikanth Velamakanni said, the investment will allow the company to step up development of its artificial intelligence (AI) and deep learning-based software stack.

To a class of over 300 students at the Georgia Institute of Technology, there didn't seem to be anything unusual about the new teaching assistant, Jill Watson. They never met Ms. Watson, but she always responded to emails quickly and casually. Like any good TA, Ms. Watson's involvement was low-key but helpful. "She was the person –well, the teaching assistant– who would remind us of due dates and post questions in the middle of the week to spark conversations," student Jennifer Gavin told the Journal. Some students envisioned their TA as a young PhD hopeful.

If you're in Arizona, Google is hiring for a gig that could be a good alternative to doing Uber. The job entails test driving an autonomous car around the state for 20 an hour, six to eight hours a day. You'll still need to know how to actually drive to be able to take the wheel if needed. But since your role is testing out the big G's new technology, you're expected to provide the engineering team "concise written and oral feedback," submit daily reports and document any test or procedure performed.

Let's say that the average lifespan of a human being is L 70 years. The probability of dying this year, at any given age y, is p(y). Let's assume that the number of people that are y years old is M(y), and that p(y) is a monotonic decreasing function of age. If we have 7 billion human beings on Earth today, given these assumptions, how many will die this year? Now the question is: what is the lower and upper bounds for N (number of people who will die this year), regardless of the functions M() and p(), provided p() is monotonic decreasing, and that average age at death is L 70 years.

There is an ongoing war between hackers and information security systems. It usually plays out far from the public eye and happens constantly on servers all over the world. Millions of events take place on computers that could potentially signal malicious activity, and it is the job of carefully-designed software to decipher between the good and the bad. Unluckily for them, missing a single malicious event could mean hundreds of millions of dollars in damage to a company. A recent study from the Massachusetts Institute of Technology (MIT) suggests that the good guys in that fight may have found a significant advantage.

It is not often that you meet someone who looks at technology as a tool to make an impact on customers and in turn bring returns to business decisions. It is unfortunate that most of the business world puts customers second, but spend enormous amounts of money in acquiring them. The world of IT is not about information capture, it is about the "machine" learning continuously to understand the queries of clients or consumers – of the enterprise – when a transaction is made on the smart phone. Co-founder of Helpshift Abinash Tripathy is a technology proponent and believes that it can improve lives. But, he is vociferously against technology that focuses on trading – like ecommerce businesses that sell on discounts to capture consumer eye-balls.

When you get right down to it, developing vaccines is about data and luck. Scientists start with a set of variables--what drugs a virus responds to, how effectively, and for whom--and then it's a whole lot of trial and error until they stumble upon a cure. One of the most exciting possibilities in medical research right now is how technology like machine learning could help researchers rapidly process those enormous sets of data, more quickly leading to cures. This is already starting to happen: In a study published Wednesday in the journal Macromolecules, researchers from IBM and Singapore's Institute of Bioengineering and Nanotechnology reveal a breakthrough that could help prevent deadly virus infections. With the help of IBM super computer Watson, they hope their finding will soon make its way into vaccines.

MasterCard Inc. says new machine-learning technology has helped it quickly control three separate cyberattacks that targeted automated bank tellers, limiting the damage to about 100,000 each. The transaction-monitoring system, which also employs data visualization tools, caught the three attacks during the first two months of 2016, according to MasterCard. The company declined to identify the banks. The Safety Net system, rolled out globally late last year, analyzes more than 1.3 billion transactions per day involving MasterCard debit and credit accounts at banks, merchants and ATMs, using algorithms that assess customer behavior in real-time. In the three attacks this year, directed against two U.S. banks and one bank in South America, Safety Net identified anomalies such as large cash withdrawals or transactions outside the usual geographic area for a given account.

Artificial intelligence (AI) is an emotionally loaded term that strikes fascination into some and fear into others. But if we strip it of fantasy and ignore cyborgs and apocalypse, there is a near-term, practical side of AI that is already unfolding. Most humans can recognise a chair because they have learnt what a chair is – they can identify thousands of examples of chairs even if they have never seen that chair before. Instead of memorising every image of what a chair could be, humans learn what a chair is and then apply that to new images and examples of chairs. But how does a computer learn what a chair is?

As much as we love to fawn over artificial intelligence (AI), it's still not great at recognizing and parsing natural language. That's why Google is open sourcing its new language parsing model for English, which it calls'Parsey McParseface.' Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam, which has no clever backstory either. The overall AI model model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.