They continue to target computer networks and damage their infrastructure. Now, a combined team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx has developed a powerful artificial intelligence system called AI2 which works significantly better than any existing cyber-attack detection system. The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. Researchers have found that new tool can detect cyber-attacks with 85% accuracy which is roughly three times better than the previous benchmark. Moreover, it reduces the number of'false positives' – an event wrongly identified as threat – by a factor of 5. Conventional security systems are either virtual machine-based or humanly operated but none of them has proven overwhelmingly successful at encountering cyber-attacks.

Visitors to Australia's federal parliament are often surprised by the robust verbal confrontation between the government and the opposition – technically known as questions without notice, more commonly as question time. A theatrical highpoint of every sitting day, question time is part intellectual cage fight, part kindergarten spat – and all psychological warfare. Political journalists watch the hour-long question time as drought-stricken farmers view the clouds. They look for signs, they read the climate. But what if you were interested in facts?

Gradescope, which has graded millions of exam questions, has made the grade itself. The company has raised a 2.6 million round of funding from Freestyle Capital, Bloomberg Beta, Reach Capital and the House Fund. Existing investor K9 Ventures also participated. Dave Samuel from Freestyle will be joining Manu Kumar from K9 Ventures on Gradescope's board. The company, started as a side project at the University of California Berkeley in 2012, makes a software that helps science and engineering professors and teaching assistants grade exam questions on handwritten tests.

AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo's success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer -- independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It's not implausible to imagine that one day a "strategist in a box" could autonomously develop and execute a business strategy.

IT and analytics managers struggling with all the data flooding into their organizations may find it hard to ignore the increased marketing push machine learning tools are getting from technology vendors. And for good reason: Running automated algorithms designed to learn on their own as they churn through large data sets can accelerate data mining and predictive analytics applications -- and give users information they might not get otherwise. But companies looking to take advantage of machine learning often face a substantial learning curve. For starters, a lot of big data infrastructure technologies -- Hadoop, the Spark processing engine and related open source software in particular -- typically underlie machine learning efforts. In many cases, that means building a suitable data processing and management architecture from scratch.

The final homework assignment for CS545 Machine Learning was to implement a K-means clustering algorithm to cluster and classify the OptDigits data. The raw data looks something like the figures to the left. So these instances are fields of 0's whereby some 0's have been flipped to be 1's such that the image is recognizable (to humans) as a handwritten digit. For the K-means classifier, we ran 2 different experiments. The first expeiment used 10 centroids (one per digit), the second used 30 centroids to see if it could find clusters where the handwritten digits were different enough to notice differences.

Niall Dunne is the Chief Sustainability Officer for BT, working with BT's Chief Executive, Chairman and executive management team to bring the company's purpose, to use the power of communications to make a better world, to life. Before joining BT in 2011, Niall was Managing Director in Europe, the Middle East and Africa (EMEA) at Saatchi & Saatchi. Prior to that, Dunne was an executive at Accenture, where he helped establish the company's climate change and sustainability practice. Dunne has written and spoken about the power of communications to tackle major social, environmental and economic problems. Niall was vice chair of the WEF's Global Agenda Council on Sustainable Consumption 2012-14 and joined the WEF Global Agenda Council on Climate Change in 2014.

Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay. The acquisition of WhatAapp by Facebook in 2014 for a hefty 22 Billion price tag made it clear and promising as TechCrunch noticed it one year later. But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less next to the Internet, without a direct impact, except their increasing audience. The recent surge of interest in Bots and AI is changing the game and we'll be witnessing the second major fragmentation of the Internet.

A new artificial intelligence (AI) system developed by MIT researchers promises to offer increased threat detection capabilities and reduce false positive rates, boosting incident response and productivity in the security world. The team, based at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), detailed in the paper AI2: Training a big data machine to defend [PDF], how the new platform achieves three times higher prediction capabilities, and is able to deliver significantly fewer false positive rates than current analytics models. The team showcased the AI2 platform last week at the IEEE International Conference on Big Data Security, and released the study to the public earlier today. The paper explains how the tool combines AI with'analyst intuition' to create a learning model whereby intermittent human analyst feedback is layered into a continuous unsupervised machine learning system. "You can think about the system as a virtual analyst," commented CSAIL research scientist Kalyan Veeramachaneni, who designed AI2 alongside PatternEx chief data scientist and former CSAIL researcher, Ignacio Arnaldo. "It continuously generates new models that it can refine in as little as a few hours, meaning it can improve its detection rates significantly and rapidly," he added.

First there was Artificial Intelligence (AI), then came machine learning... neural networks and finally cognitive computing technology. But then came multilingual cognitive computing technology. Cogito Studio is a product for developing customised semantic applications for text analytics, including information analysis, categorisation and extraction. Developed by Expert System in the US state of Maryland, Cogito Studio combines a cocktail of AI algorithms for simulating the human ability to read and understand language (semantics) and deep learning techniques (machine learning) to help optimise the creation of applications that are advanced, intelligent and intuitive.