Learning Deep Learning with Keras

Learning #DeepLearning with #Keras  #NeuralNetworks @pmigdal

  • For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python.
  • Deep learning is a name for machine learning techniques using many-layered artificial neural networks.
  • See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):

    In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.

  • Deep learning (that is – neural networks with many layers) uses mostly very simple mathematical operations – just many of them.
  • Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet.

I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi…
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When robots will start taking over human tasks

When robots will start outperforming humans @sai #AI

  • According to a new study from Oxford and Yale University researchers, those are the years artificial intelligence is slated to take over each of those tasks.
  • And so it will go for millions of other jobs over the next 50 years, researchers find.
  • The study relied on survey responses of 352 AI researchers who gave their opinions on when in the future machines would replace humans for various tasks.
  • Lead investigator Katja Grace and her colleagues found the tasks most likely to get automated within the next 10 years were rote, mechanical tasks.
  • Ultimately, the researchers found AI could automate all human tasks by the year 2051 and all human jobs by 2136.

A survey of AI researchers tallied predictions for when machines will start beating humans at everything from LEGO assembly to essay-writing.
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China’s Tech Moguls Warn of AI’s Troubling Trajectory

China’s tech moguls warn of AI’s troubling trajectory #AI #automation

  • I recently spent a few weeks in the country, talking to researchers and entrepreneurs developing cutting-edge AI technologies and products.
  • What stuck with me—beyond the growing ambition of China’s researchers and the overall vibrancy of its tech scene—is how much people are starting to talk about the potential for AI to eliminate jobs across the country.
  • Speaking at an event organized in Detroit by his company last week, the CEO of Alibaba, Jack Ma, said that artificial intelligence could displace many workers in both China and the U.S., thereby heightening tensions that some fear could lead the two countries toward armed conflict.
  • In a compelling op-ed piece in the New York Times this week, Kai-Fu Lee, a renowned technical expert, entrepreneur, and educator and the chairman of an AI lab run by his VC firm Sinnovation Ventures, argues that AI will cause widespread job displacement in coming years.
  • But this in and of itself may be a cause for concern, as public perception regarding jobs and economic prospects in both the U.S. and China will be incredibly important in coming years.

Never mind the singularity; artificial intelligence could eliminate countless jobs, and perhaps reshape global politics in the process.
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Artificial Intelligence and Economic Theories

#ArtificialIntelligence and Economic Theories

  • Artificial Intelligence and Economic TheoriesAbstractThe advent of artificial intelligence has changed many disciplines such as engineering, social science and economics.
  • This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied.
  • The theories that are considered are: of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field.
  • Despite what many Marxists claim, he never foretold the advent of artificial intelligence, otherwise he would probably have said ” Artificial intelligent machines of the world unite, you have nothing to lose but chains “.
  • The economy was by man and about man but the theories that explained the economy did not quite match the behaviour of a man.

The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field. 1.1 Introduction ” Workers of the world unite, you have nothing to lose but chains ” so said Karl Marx (Marx, 1849). Despite what many Marxists claim, he never foretold the advent of artificial intelligence, otherwise he would probably have said ” Artificial intelligent machines of the world unite, you have nothing to lose but chains “. But what Marx realized was that the principal agent of work is man. Man is the invisible hand that drives the economy as observed by Adam Smith (Smith, 2015). The economy was by man and about man but the theories that explained the economy did not quite match the behaviour of a man. For this reason the rational man is indeed irrational and his irrationality permeates every aspect of life including the very concept we call the economy. Homo-Sapiens have been around for hundred thousand years and throughout their existence and even from their forbearers have inherited certain traits and behaviours that influence them even today (Harari, 2014). Some of these traits and behaviours include greed, fear, bias and social structure. All these traits are still with us today because of one and only one reason and that it
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Building AI: 3 theorems you need to know – DXC Blogs

Building #AI: 3 theorems you need to know #MachineLearning

  • The mathematical theorem proving this is the so-called “no-free-lunch theorem” It tells us that if a learning algorithm works well with one kind of data, it will work poorly with other types of data.
  • In a way, a machine learning algorithm projects its own knowledge onto data.
  • In machine learning, overfitting occurs when your model performs well on training data, but the performance becomes horrible when switched to test data.
  • Any learning algorithm must also be a good model of the data; if it learns one type of data effectively, it will necessarily be a poor model — and a poor student – of some other types of data.
  • Good regulator theorem also tells us that determining if inductive bias will be beneficial or detrimental for modeling certain data depends on whether the equations defining the bias constitute a good or poor model of the data.

Editor’s note: This is a series of blog posts on the topic of “Demystifying the creation of intelligent machines: How does one create AI?” You are now reading part 3. For the list of all, see here: 1, 2, 3, 4, 5, 6, 7.
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Guide to Advanced Safety Systems for Cars

The Positive Impact of Advanced Safety Systems for Cars  #m2m #MachineLearning

  • Why Do We Have Campaigns?
  • Join our campaigns and together, we’ll hold corporations and lawmakers accountable.

Consumer Reports survey on Advanced Safety Systems for Cars looks at driver experiences with safety features on 66,000 vehicles and rates the systems.
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