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Machine Learning - M2M - AI

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How Companies Are Already Using AI. Executive Summary A survey by Tata Consultancy Services reveals that while some jobs have been lost to machine intelligence, that’s not the major way companies are using AI today.

How Companies Are Already Using AI

Companies are more likely to be using AI to improve computer-to-computer tasks while employing the same number of people. The 170-year-old news service Associated Press offers a case in point. In 2013, demand for quarterly earnings stories was insatiable, and staff reporters could barely keep up. The Great A.I. Awakening. Four days later, a couple of hundred journalists, entrepreneurs and advertisers from all over the world gathered in Google’s London engineering office for a special announcement.

The Great A.I. Awakening

Guests were greeted with Translate-branded fortune cookies. Their paper slips had a foreign phrase on one side — mine was in Norwegian — and on the other, an invitation to download the Translate app. The Future of Work. Explicit cookie consent. THERE IS SOMETHING familiar about fears that new machines will take everyone’s jobs, benefiting only a select few and upending society.

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Such concerns sparked furious arguments two centuries ago as industrialisation took hold in Britain. People at the time did not talk of an “industrial revolution” but of the “machinery question”. First posed by the economist David Ricardo in 1821, it concerned the “influence of machinery on the interests of the different classes of society”, and in particular the “opinion entertained by the labouring class, that the employment of machinery is frequently detrimental to their interests”. Thomas Carlyle, writing in 1839, railed against the “demon of mechanism” whose disruptive power was guilty of “oversetting whole multitudes of workmen”. Deep Learning Solutions for Enterprise. Here’s how a neural network works. Daniel Smilkov and Shan Carter at Google put together this interactive learner for how a neural network works.

Here’s how a neural network works

In case you’re unfamiliar with the method: It’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. A Neural Network Playground. Seldon - Open Source Machine Learning for Enterprise.

Algorithm Visualizer. BigML.com is Machine Learning for everyone. Deep Learning. H2O.ai (0xData) - Fast Scalable Machine Learning. Shivon Zilis - Machine Intelligence. Machine Intelligence in the Real World (this pieces was originally posted on Tech Crunch) .

Shivon Zilis - Machine Intelligence

I’ve been laser-focused on machine intelligence in the past few years. I’ve talked to hundreds of entrepreneurs, researchers and investors about helping machines make us smarter. In the months since I shared my landscape of machine intelligence companies, folks keep asking me what I think of them — as if they’re all doing more or less the same thing.

Intelligence matters: Artificial intelligence and algorithms. Welcome back.

Intelligence matters: Artificial intelligence and algorithms

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How Long Until Computers Have the Same Power As the Human Brain? — AI Revolution. AI Revolution. Where Computers Defeat Humans, and Where They Can’t. Photo ALPHAGO, the artificial intelligence system built by the Google subsidiary DeepMind, has just defeated the human champion, Lee Se-dol, four games to one in the tournament of the strategy game of Go.

Where Computers Defeat Humans, and Where They Can’t

Why does this matter? After all, computers surpassed humans in chess in 1997, when IBM’s Deep Blue beat Garry Kasparov. So why is AlphaGo’s victory significant? Like chess, Go is a hugely complex strategy game in which chance and luck play no role. Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced — Humanizing Technology. Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced I’ve spent the last few weeks learning, well, about deep learning.

Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced — Humanizing Technology

I’ve parsed through the internet, read a ton, and tried to get a sense of where we are as a community. I wanted to put together a resource for someone who’s interested in getting up to speed as quickly as possible with the best of the best resources that I can find, from doing research, looking for funding, and learning about the various open-source frameworks to following along with excellent tutorials. Rogue wave ahead. Sailing history is rife with tales of monster-sized rogue waves — huge, towering walls of water that seemingly rise up from nothing to dwarf, then deluge, vessel and crew.

Rogue wave ahead

Rogue waves can measure eight times higher than the surrounding seas and can strike in otherwise calm waters, with virtually no warning. ML. Is Machine Learning, enough? A Visual Introduction to Machine Learning. Finding better boundaries Let's revisit the 240-ft elevation boundary proposed previously to see how we can improve upon our intuition.

A Visual Introduction to Machine Learning

Clearly, this requires a different perspective. By transforming our visualization into a histogram, we can better see how frequently homes appear at each elevation. While the highest home in New York is ~240 ft, the majority of them seem to have far lower elevations.