Cyber criminals are quite adept at stealing data, money and privacy. No network is off limits as they exploit any point of weakness they find in businesses, homes, institutions, automobiles, utility networks and other portals. And their tactics evolve faster than security professionals can manage them. The question is, can we leverage technologies such as artificial intelligence (AI) and cognitive computing to win the war against cyber criminals? Cybersecurity experts shared their thoughts on this topic.

The Draper Satellite Image Chronology Competition (Chronos) ran on Kaggle from April to June 2016. This competition, which was novel in a number of ways, challenged Kagglers to put order to time and space. That is, given a dataset of satellite images taken over the span of five days, the 424 brave competitors were required to determine their correct order. The challenge, which Draper hosted in order to contribute to a deeper understanding of how to process and analyze images, was a first for Kaggle--it allowed hand annotation as long as processes used were replicable. While the winners of the competition used a mixture of machine learning, human intuition, and brute force, Damien Soukhavong (Laurae), a Competitions and Discussion Expert on Kaggle, explains in this interview how factors like the limited number of training samples which deterred others from using pure machine learning methods appealed to him.

The focus of this talk is scalable machine learning using the H2O R and Python packages. H2O is an open source distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). The core machine learning algorithms of H2O are implemented in high-performance Java; however, fully featured APIs are available in R, Python, Scala, REST/JSON and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine. H2O currently features distributed implementations of generalized linear models, gradient boosting machines, random forest, deep neural nets, dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), and anomaly detection methods, among others.

We are on a mission to build smart AI to tackle complex social and collaboration problems. Our team of Aalto researchers consists of world-class experts in network optimization and social network analysis. Building upon years of research and expertise, our ultimate goal is to develop technology that helps individuals cope with complex communication and collaboration problems that arise in social settings - in business environments as well as in everyday life. The solutions we envision take the form of chat-bots, i.e., electronic personal assistants that integrate gracefully into modern collaboration platforms (such as Slack, Skype for Business, or Facebook Messenger).

Software agents are nothing new, but we're now entering a new wave of innovation relating to bots and artificial intelligence that has the potential to impact our lives at home and at work, and change how businesses operate. New applications for'intelligent' automation are springing up everywhere, with the potential to affect all of the processes and engagements that make organisations work… but where does it make sense to prioritise your efforts? From work automation to augmentation, what approaches are right for your use cases today? On July 21 Neil Ward-Dutton, Angela Ashenden and Craig Wentworth of MWD Advisors presented a free webinar that gave a hype-free overview of this fast-moving space. If you've got a free membership account you can watch the replay below.

Last month I was reading about Autoencoders for Collaborative filtering. Using Autoencoders for Collaborative filtering is a fairly recent idea and it proven to be very effective beating all state-of-art SVD based methods. Actually the idea is pretty simple, instead of using linear function for Matrix Factorization (dot product of the two latent factor matrices, use some complex non-linear function of the two matrices so as to capture more complicated dependencies. Autoencoders are a way to do so. Matrix Factorization based CF minimizes the following objective: Note that in the objective function only known/observed user-rating pairs (set O) are considered.

Data scientists have a variety of different skills that they bring to bear on Big Data projects. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight. Machine learning can be applied to data to help businesses quickly find clusters of similar objects (e.g., identify segments of customers) and to predict outcomes (e.g., identify customers who are at-risk of churning). While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning.

Telefónica Open Future_, Telefónica's startup accelerator that helps the best entrepreneurs grow and build successful businesses, and PAPIs.io Artificial Intelligence (AI) has a track-record of improving the way we make decisions. So why not use it to decide which startups to invest in, and take advantage of all the startup data that is available? The AI Startup Battle, powered by PreSeries (a joint venture between BigML and Telefónica Open Future_), is a unique experience you don't want to miss, where you'll witness real-world and high-stakes AI. As an early stage startup, you will enjoy a great opportunity to secure seed investment, and get press coverage in one of the technology capitals of the world.

The system is called Recognition and will be running for three months in London up to 27 November 2016, both online and in a small exhibition at the Tate. A nice touch is that it will also take in feedback from matching selections made by viewers themselves at the art museum on Millbank. Created by Fabrica, it's the winner of the IK Prize 2016 for digital innovation, awarded by Tate Britain, in partnership with Microsoft. You can see it in action above, in the match of LS Lowry's Industrial Landscape (from 1955) with a recent construction image of Changi Airport in Singapore… Apparently, Recognition employs multiple artificial intelligence technologies. For example, there is natural language processing to interpret image captions and text, analysing context and subject matter.