Don't miss the livestream on April 21st at 7.45 pm (CET) – right here! Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is an effort to built machines which can learn from their environment, from mistakes and from people. Machines that can learn is the field within aritificial intelligence which is the most observed today. Dr. Werner Huber (Manager Highly Automated Driving BMW Group) "Human or Robot – who will control our future vehicle?"

A team of researchers from Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT, together with startup PatternEx has developed an artificial intelligence system, AI2, which is the best so far at detecting cyber-attack. What makes this tool unique is the use of human intuition and machine learning abilities. It has been tested on 3.6 billion log lines triggered over a period of three months by millions of users. This new system is thrice more accurate in detecting the cyber attack. The systems used previously were either virtual-machine based systems of exclusively operated by humans.

You're still on your own when it comes to cleaning the porcelain throne or scooping up after kitty. But one day you could hand the laundry over to a machine learning-powered ironing robot, developed by researchers at Columbia University, that can iron shirts, skirts and more. Robots can easily manipulate rigid objects like coffee cups or computer parts. But they're often confounded by soft, flexible objects, like clothes, that change shape as they're handled. "Robotic ironing is a very challenging task," said Yinxiao Li, lead author on a paper set to be published at the IEEE International Conference on Robotics.

One of the most useful things to do with machine learning is inform assumptions about customer behaviors. This has a wide variety of applications: everything from helping customers make superior choices (and often, more profitable ones), making them contagiously happy about your business, and building loyalty over time. Increasingly, it's not enough to simply let your customers pick and choose from the products and services options offered. Customers expect intelligent recommendations and for you to chart courses of action with minimal room for ambiguity or misinterpretation. Sounds straightforward enough.. how do you actually make it happen?

Machine learning has come of age in public health reporting according to researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis. They have found that existing algorithms and open source machine learning tools were as good as, or better than, human reviewers in detecting cancer cases using data from free-text pathology reports. The computerized approach was also faster and less resource intensive in comparison to human counterparts. Every state in the United States requires cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. Typically, however, busy health care providers submit cancer reports to equally busy public health departments months into the course of a patient's treatment rather than at the time of initial diagnosis.

The increasing number of genome-wide assays of gene expression available from public databases presents opportunities for computational methods that facilitate hypothesis generation and biological interpretation of these data. We present an unsupervised machine learning approach, ADAGE (analysis using denoising autoencoders of gene expression), and apply it to the publicly available gene expression data compendium for Pseudomonas aeruginosa. In this approach, the machine-learned ADAGE model contained 50 nodes which we predicted would correspond to gene expression patterns across the gene expression compendium. While no biological knowledge was used during model construction, cooperonic genes had similar weights across nodes, and genes with similar weights across nodes were significantly more likely to share KEGG pathways. By analyzing newly generated and previously published microarray and transcriptome sequencing data, the ADAGE model identified differences between strains, modeled the cellular response to low oxygen, and predicted the involvement of biological processes based on low-level gene expression differences. ADAGE compared favorably with traditional principal component analysis and independent component analysis approaches in its ability to extract validated patterns, and based on our analyses, we propose that these approaches differ in the types of patterns they preferentially identify. We provide the ADAGE model with analysis of all publicly available P. aeruginosa GeneChip experiments and open source code for use with other species and settings. Extraction of consistent patterns across large-scale collections of genomic data using methods like ADAGE provides the opportunity to identify general principles and biologically important patterns in microbial biology. This approach will be particularly useful in less-well-studied microbial species.

About these poems: Self Published Amazon Poems By Marco Essomba (@marcoessomba) a Network and Security Expert, Self Confessed Geek, CTO/Cofounder of AMPS Intl, a provider of world class F5 Consulting Services enabling organisations in banking, retail, finance, insurance, and technology sector to design, implement, support, and optimize their Application Delivery Infrastructure (ADI) (www.amps-global.com). This poem is part of a collection of encrypted poems for which only the author has the keys. But for those who are brave, a long journey to attempt to decrypt the new digital transformation and information technology related topics based on the author real life experience in the network and security field and career spanning more than 10 years. In the spirit of open source poetry, all feedback (including bad ones) will be much appreciated. Keep reading those poems, for you may learn something about life on earth.

Facebook never ceases to amaze us with the speed at which it releases its new features. The latest promise to make your videos super smart, with embedded facial recognition and auto-captioning features. Recent reports reveal that Facebook is working on technology that will help users have their profiles automatically tagged on videos, just like they do on photos. This new functionality is the result of further improvements in Facebook's proprietary technology, which could help users look for their photos and footage on every material shared with them and also trace specific moments in videos that may include them. Also, Facebook is bringing more goodies to video uploads, helping users generate captions automatically, without having to upload their own subtitle files.

Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription. Under Armour is one of the many companies to have worked with IBM's Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc. The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom. A 32-year-old woman who is training for a 5km race could use the app to create a personalized training and meal plan based on her size, goals, lifestyle.

Neither humans nor AI have proved overwhelmingly successful at maintaining cybersecurity on their own, so why not see what happens when you combine the two? That's exactly the premise of a new project from MIT, and it's achieved some pretty impressive results. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a new platform called AI2 that can detect 85 percent of attacks. It also reduces the number of "false positives" -- nonthreats mistakenly identified as threats -- by a factor of five, the researchers said. The system was tested on 3.6 billion pieces of data generated by millions of users over a period of three months.