Driving AI’s potential in organizations. As AI technologies standardize across industries, becoming an AI-fueled organization will likely be table stakes for survival.
And that means rethinking the way humans and machines interact within working environments. For some organizations, harnessing artificial intelligence’s full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. The Artificial Intelligence Wiki. AI Index 2018 Annual Report. Nine charts that really bring home just how fast AI is growing. With so much hype surrounding artificial intelligence today, it can be difficult to know where things actually stand.
Fortunately, a report (.pdf) issued by a group of AI policy researchers today collates a range of data that helps capture the state of the AI boom. The authors, from MIT, Stanford, and Harvard as well as nonprofits including OpenAI, look at investments, hiring, papers and patents, and even mentions of AI at government meetings. R2D3: Statistics and Data Visualization. Artificial Intelligence and Games – A Springer Textbook. New Theory Cracks Open the Black Box of Deep Learning. To Build Truly Intelligent Machines, Teach Them Cause and Effect. Google AI Blog: Google Duplex: An AI System for Accomplishing Real World Tasks Over the Phone. Factsheet: Artificial Intelligence for Europe. A European approach on AI will boost the European Union’s competitiveness and ensure trust based on European values.
The European Commission has already invested significant amounts to bring benefits to our society and economy. This factsheet provides an overview of AI in Europe. In its Communication "Artificial intelligence for Europe", the Commission puts forward a European approach to Artificial Intelligence based on three pillars: Algorithm Tips – Find tips for stories on algorithms. Deep Learning. Mapping the Brain to Build Better Machines.
Take a three year-old to the zoo, and she intuitively knows that the long-necked creature nibbling leaves is the same thing as the giraffe in her picture book.
That superficially easy feat is in reality quite sophisticated. The cartoon drawing is a frozen silhouette of simple lines, while the living animal is awash in color, texture, movement and light. How Canada has emerged as a leader in artificial intelligence. Academics, industry and government have joined together, setting the stage for Canada to become a research and development powerhouse in AI.
Governments can have a pretty dismal track record when it comes to predicting the next big thing. Tax dollars spent on visionary projects are often, it seems, tax dollars thrown away. Teachable Machine. Research Blog: Distill: Supporting Clarity in Machine Learning. Posted by Shan Carter, Software Engineer and Chris Olah, Research Scientist, Google Brain Team Science isn't just about discovering new results.
It’s also about human understanding. Scientists need to develop notations, analogies, visualizations, and explanations of ideas. Distill — Latest articles about machine learning. Distill. Google.ai. Facets - Visualizations for ML datasets. Better data leads to better models.
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive.
10 Free Must-Read Books for Machine Learning and Data Science. Machine Learning and Human Bias. Gartner Identifies Three Megatrends That Will Drive Digital Business Into the Next Decade. STAMFORD, Conn., August 15, 2017 View All Press Releases 2017 Emerging Technologies Hype Cycle Garners Insight From More Than 2,000 Technologies The emerging technologies on the Gartner Inc.
Hype Cycle for Emerging Technologies, 2017 reveal three distinct megatrends that will enable businesses to survive and thrive in the digital economy over the next five to 10 years. Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms are the trends that will provide unrivaled intelligence, create profoundly new experiences and offer platforms that allow organizations to connect with new business ecosystems.
The Emerging Technologies Hype Cycle is unique among most Gartner Hype Cycles because it garners insights from more than 2,000 technologies into a succinct set of compelling emerging technologies and trends. Hype Cycle for Emerging Technologies, 2017. The Evolution of Trust. LOOPY: a tool for thinking in systems. 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.
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. So that year, AP began working with an AI firm to train software to automatically write short earnings news stories. 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.
Guests were greeted with Translate-branded fortune cookies. 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. 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”. 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. 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. A Neural Network Playground. Um, What Is a Neural Network? 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. 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) . Intelligence matters: Artificial intelligence and algorithms.
Welcome back. 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. 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. 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. ML. Is Machine Learning, enough? Most managers’ jobs involve making predictions. A Visual Introduction to Machine Learning.