Machines That Learn Language More Like Kids Do Children learn language by observing their environment, listening to the people around them, and connecting the dots between what they see and hear. Among other things, this helps children establish their language’s word order, such as where subjects and verbs fall in a sentence. In computing, learning language is the task of syntactic and semantic parsers. These systems are trained on sentences annotated by humans that describe the structure and meaning behind words. Parsers are becoming increasingly important for web searches, natural-language database querying, and voice-recognition systems such as Alexa and Siri. But gathering the annotation data can be time-consuming and difficult for less common languages. This “weakly supervised” approach — meaning it requires limited training data — mimics how children can observe the world around them and learn language, without anyone providing direct context. Visual learner The new parser is the first to be trained using video, Ross says.
Academy Sacred Geometry Webinar Series Session 1- October 11th - Mark Hanf, interview by Roger Green Mark Hanf and Roger Green Sacred Geometry and Tao and Feng Shui Leonardo's Mind: The Geometry of the Renaissance Mysteries from Antiquity Golden Proportions Tao is most often translated as "way" or "path". Deep within the ancient art and science of Geometry lies a beautiful and mysterious system of forms that has fascinated philosophers and sages from all spiritual traditions since antiquity. Participants are invited to access the golden key of Sacred Geometry and unlock secrets encoded in the art, architecture, science, and sacred symbols of China. We will also trace this lineage and explore clues from Ancient Egypt, Persia, Greece and the British Isles. We will discover how Artists and Architects throughout history, but especially during the Renaissance, have consciously integrated these proportions into their compositions so that they become vibrant and resonate with the greater whole. Michael Rice John Lloyd Dan Winter
Le Machine Learning expliqué avec du chocolat – N26 Magazine - Édition française Une courte intro Le Machine Learning (apprentissage automatique) est une discipline à part entière. Grâce à des algorithmes, les ordinateurs peuvent analyser des quantités de données très importantes et apprendre à prédire des comportements, des résultats ou des tendances… Tout cela pour permettre des prises de décisions. Le problème à résoudre Dans les bureaux de N26, il n’y a pas seulement des corbeilles de fruits frais. Les employés peuvent s’accorder quelques friandises, mises à disposition dans les cuisines situées à chaque étage de nos bureaux berlinois. Le choix est grand : des barres chocolatées au caramel, aux cacahuètes, au goût cookie ou à la noix de coco. La difficulté ici est de prévoir combien de paquets il faudra commander chaque semaine et comment les répartir entre chaque étage pour éviter que les placards restent vides, ou au contraire, qu’ils débordent. La méthode Que vont-ils faire ? Résultat Le jeudi 15 juin 2017, admettons qu’il faisait 20 degrés.
Phase Conjugate/Fractal Wave Mechanics of Perception/Bliss from Dan Winter Phase Conjugate Wave Mechanics as THE CAUSE of Consciousness/ Perception: - by Dan Winter, July 27 2015 (recording of radio interview this subject-below) Update: March 2016- Announcing our new IOS App- for Conjugate Brainwave Bliss Training! www.FlameinMind.com Dec 7: New Complete film of our excellent - Science of Bliss- Biologic Imperative- Course- from FractalU.com Update Dec.6, 2015: Download the PDF COURSE OUTLINE: Science of Bliss- Biologic Imperative- Todays Course- from FractalU.com Update Nov 8, 2015- FractalU.com -Advanced Fractal Physics Class Recording Nov 8 2015 140 Minutes Video Recording: Includes- Intro to Plasma Projection- the Shamans science with Sara - see www.sevenarrows.net The Original Fractal Physics course and film: www.fractalfield.com/conjugategravity Intro to Mark's Pure Geometric Physics Solutions: phxmarker.blogspot.fr/ Below: 1. Lehar Abstract
Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2018 In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. arXiv contains a veritable treasure trove of learning methods you may use one day in the solution of data science problems. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a “thumbs up” icon.
This Architect Fuses Art and Science by Hand Illustrating the Golden Ratio This Architect Fuses Art and Science by Hand Illustrating the Golden Ratio Rafael Araujo is a Venezuelan architect and illustrator who at the age of fifteen began to observe intelligent patterns in nature, giving rise to his interest in the golden ratio located in our natural environment. More than 40 years later, the results of this hobby is a collection of beautiful illustrations of nature made entirely by hand, equipped with a pencil, a compass, a ruler and a protractor. The artist's illustrations give his ability to represent the mathematical brilliance of the natural world, inciting the reunion of humans with nature. Illustrations that seem to come from a technological team, are made entirely by hand, mixing mathematical perfection with the artistic performance of Araujo. Most of us observe a simple butterfly flutter, the artist visualizes a complex mathematical framework that regulates movements subtle flight.
Machine learning will change jobs—impact on economy could surpass that of previous AI applications Machine learning computer systems, which get better with experience, are poised to transform the economy much as steam engines and electricity have in the past. They can outperform people in a number of tasks, though they are unlikely to replace people in all jobs. So say Carnegie Mellon University's Tom Mitchell and MIT's Erik Brynjolfsson in a Policy Forum commentary to be published in the Dec. 22 edition of the journal Science. Mitchell, who founded the world's first Machine Learning Department at CMU, and Brynjolfsson, director of the MIT Initiative on the Digital Economy in the Sloan School of Management, describe 21 criteria to evaluate whether a task or a job is amenable to machine learning (ML). "Although the economic effects of ML are relatively limited today, and we are not facing the imminent 'end of work' as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound," they write.
John Coltrane Draws a Picture Illustrating the Mathematics of Music Physicist and saxophonist Stephon Alexander has argued in his many public lectures and his book The Jazz of Physics that Albert Einstein and John Coltrane had quite a lot in common. Alexander in particular draws our attention to the so-called “Coltrane circle,” which resembles what any musician will recognize as the “Circle of Fifths,” but incorporates Coltrane’s own innovations. Coltrane gave the drawing to saxophonist and professor Yusef Lateef in 1967, who included it in his seminal text, Repository of Scales and Melodic Patterns. Where Lateef, as he writes in his autobiography, sees Coltrane's music as a "spiritual journey" that "embraced the concerns of a rich tradition of autophysiopsychic music," Alexander sees “the same geometric principle that motivated Einstein’s" quantum theory. Neither description seems out of place. Musician and blogger Roel Hollander notes, “Thelonious Monk once said ‘All musicans are subconsciously mathematicians.’ Related Content: