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.

Many young college graduates find the working world to be a scary place. That goes double in light of recent technological changes. As computers take over customer service, filing and other tasks associated with entry-level jobs, young people today may find it harder to gain a foothold. And in the long term, experts from Oxford University professors to Yale economist and Nobel laureate Robert Schiller have warned that automation and artificial intelligence could make many professions obsolete. That's not exactly the stuff of inspiration for young people who are at the very beginning of their careers.

So when it came to tapping historical and streaming data stores to improve operational efficiency, as well as predict and circumvent potential issues in its production facilities, the company went for the most cutting-edge machine learning technology on offer: IBM's Watson. Speaking at the recent Chief Analytics Officer Forum in Sydney, Woodside's principal data scientist, Elsa Jordan, shared with attendees the company's journey to build a data science capability from scratch in one year that could be utilised by employees right across the organisation. Woodside established its data science practice in January 2015 and as part of its approach, is running the largest commercial instance of the Watson advisor engine. "Our premise was think big, prototype small, scale fast," Jordan said. "Key to that was using machine learning algorithms. What appealed was that we could learn from history, predict from streaming data and keep on learning as new information becomes available."

Last week, the New York Times reported the first successful "limb reanimation" in a person with quadriplegia. Ian Burkhart, 24, had broken his neck as a teen in a diving accident. His spine was damaged at the fifth cervical vertebra, leaving him paralyzed from the shoulders down. Using nerve bypass technology that transmits his thoughts directly to his hand muscles, he has regained control over his right hand and fingers. This is the first time a brain-computer interface has been used to help an individual move his own hands.

About indico indico provides state-of-the-art machine learning algorithms for text and image analysis in the form of a simple to use web service. This, for the first time, enables companies to automatically extract meaningful insight from unstructured data regardless of their size or capability. Sentiment Analysis, Social Media Monitoring, Content Filtering, Content Classification, Recommendation, and Personalization are just some of the areas in which indico's customers are deploying its technology to improve business outcomes. Furthermore, indico's rapid customization capabilities have also enabled companies such as Mavrck, CO Everywhere, and interlinkONE to quickly develop compelling new solutions that weren't practical before.

REMEMBER the basic income grant South African labour unions, churches and NGOs campaigned for back in the 1990s and early "noughties", but on which Trevor Manuel's Treasury frowned on as a fiscal nonstarter? Silicon Valley A-lister Sam Altman thinks the US will have to adopt something like it within the next generation or two -- and he's not alone. Altman is founder and president of Y Combinator, the seed-stage tech investor that helped launch Airbnb and Dropbox. As co-chairman of OpenAI, he is working with Elon Musk to see that artificial intelligence, as it approaches and perhaps surpasses the human variety, benefits mankind. He believes that while technology will generate vast new wealth, it will in the process destroy much traditional employment without replacing it.

Dennis Mortensen, CEO and founder of x.ai, is on the cusp of revolutionising the way we arrange meetings with his AI Personal Assistant, Amy. Chris Sykes, CEO and founder of Volume Global, is working on Theodore, the IBM Watson-driven Virtual Consultant that could transform our experience of customer service. AIBusiness.org met up with Dennis and Chris to find out more about these exciting projects. In 2013, Dennis and his team at x.ai in New York decided it was time to cut out the'email ping pong' involved in setting up a meeting. Many have tried and failed to ultimately solve the problem, through extensions, plug-ins and various apps.

SAN FRANCISCO, CA--(Marketwired - Mar 28, 2016) - Umbo CV, an artificial intelligence start-up and provider of event recognition systems that think and learn like humans for the professional security industry, announced that it completed a 2.8 million round of seed funding led by AppWorks Ventures. Also participating were Mesh Ventures, Fortune Global 500 Wistron Corp. and Phison Electronics. Up to now, the security industry has grappled with ways to receive real-time notifications of intruders or incidents, having tested pre-defined algorithms-based systems that failed. Umbo CV aims to solve this problem. The company has developed algorithms to link images from multiple cameras, thus giving machines a more in-depth understanding of real-time occurrences.