Results


What is hardcore data science – in practice?

@machinelearnbot

For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.


Don't fall for the AI hype: Here are the ingredients you need to build an actual useful thing

#artificialintelligence

Artificial intelligence these days is sold as if it were a magic trick. Data is fed into a neural net – or black box – as a stream of jumbled numbers, and voilà! It comes out the other side completely transformed, like a rabbit pulled from a hat. That's possible in a lab, or even on a personal dev machine, with carefully cleaned and tuned data. However, it is takes a lot, an awful lot, of effort to scale machine-learning algorithms up to something resembling a multiuser service – something useful, in other words.


Headlines for the Next 50 Years : Plastics Technology

AITopics Original Links

As micro-molding gives way to "nano-molding," processors will need creative answers to the problems of handling flyspeck-sized parts. Farms may replace oil wells as the source of new plastics. Biopolymers made from cornstarch or other renewable feedstocks will supple-ment petrochemical-derived polymers in a wide range of applications. What if you could change the color of every part right at the machine? Instant color changes may be part of the coming era of "mass customization."


A Survey of Robotic Musicianship

AITopics Original Links

The term'robotic musicianship' may seem like an oxymoron. The first word often carries negative connotations in terms of artistic performance and can be used to describe a lack of expressivity and artistic sensitivity. The second word is used to describe varying levels of an individual's ability to apply musical concepts in order to convey artistry and sensitivity beyond the facets of merely reading notes from a score. To understand the meaning of robotic musicianship, it is important to detail the two primary research areas of which it constitutes: Musical mechatronics, which is the study and construction of physical systems that generate sound through mechanical means;15 and machine musicianship, which focuses on developing algorithms and cognitive models representative of various aspects of music perception, composition, performance, and theory.31 Robotic musicianship refers to the intersection of these areas.


10 Ways Machine Learning Is Revolutionizing Manufacturing

#artificialintelligence

Bottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.


Artificial intelligence is now Intel's major focus

#artificialintelligence

At the forefront of these AI ambitions is a new platform called Nervana, which follows Intel's acquisition of deep-learning startup Nervana Systems earlier this year. Setting its sights on an area currently dominated by Nvidia's graphics processing unit (GPU) technology, one of the Nervana platform's main focuses will be deep learning and training neural networks – the software process behind machine learning that is based on a set of algorithms that attempt to model high-level abstractions in data. Google, for instance, is investing heavily in research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms, something it calls "Machine Intelligence". One way is in manufacturing, as intelligent computer systems replace certain human-operated jobs.


November Product Updates: Testing Our Way To 2017

Forbes

We've been running A/B tests on a small percentage of the mobile audience; testing new commenting and site socialization features, variations on UX treatments and relevancy matching on ad units, as well as some improvements aimed at streamlining page flow and better surfacing of related content. In the coming weeks, we'll be introducing improvements to how the CMS handles media, providing a simple, cohesive experience when adding media, and offering more granular search options. In November, ForbesConnect published the Forbes Healthcare app, a designated conference app for the Forbes Healthcare Summit in New York City. In December, we will continue our efforts to expand our business development plan in order to provide a light-weight networking platform for business schools.


Artificial Intelligence is now Intel's major focus

#artificialintelligence

With technology governing almost every aspect of our lives, industry experts are defining these modern times as the "platinum age of innovation"; verging on the threshold of discoveries that could change human society irreversibly, for better or worse. At the forefront of this revolution is the field of artificial intelligence (AI), a technology that is more vibrant than ever due to the acceleration of technological progress in machine learning - the process of giving computers with the ability to learn without being explicitly programmed - as well as the realisation by big tech vendors of its potential. One major tech behemoth fuelling the fire of this fast moving juggernaut called AI is Intel, a company that has long invested in the science and engineering of making computers more intelligent. The Californian company held an'AI Day' in San Francisco showcasing its new strategy dedicated solely to AI, with the introduction of new AI-specific products, as well as investments for the development of specific AI-related tech. And Alphr were in town to hear all about it.


Artificial intelligence is now Intel's major focus

#artificialintelligence

At the forefront of these AI ambitions is a new platform called Nervana, which follows Intel's acquisition of deep-learning startup Nervana Systems earlier this year. Setting its sights on an area currently dominated by Nvidia's graphics processing unit (GPU) technology, one of the Nervana platform's main focuses will be deep learning and training neural networks – the software process behind machine learning that is based on a set of algorithms that attempt to model high-level abstractions in data. Google, for instance, is investing heavily in research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms, something it calls "Machine Intelligence". One way is in manufacturing, as intelligent computer systems replace certain human-operated jobs.


How To Get Better Machine Learning Performance

#artificialintelligence

Machine Learning Performance Improvement Cheat Sheet Photo by NASA, some rights reserved. This cheat sheet is designed to give you ideas to lift performance on your machine learning problem. Outcome: You should now have a short list of highly tuned algorithms on your machine learning problem, maybe even just one. In fact, you can often get good performance from combining the predictions from multiple "good enough" models rather than from multiple highly tuned (and fragile) models.