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AI Model Fundamentally Cracks CAPTCHAs, Scientists Say

NPR Technology

A representation of the letter A, which can be used to crack CAPTCHAs. A representation of the letter A, which can be used to crack CAPTCHAs. Scientists say they have developed a computer model that fundamentally breaks through a key test used to tell a human from a bot. You've probably passed this test hundreds of times. Text-based CAPTCHAs, a rough acronym for Completely Automated Public Turing Test To Tell Computers and Humans Apart, are groups of jumbled characters along with squiggly lines and other background noise.

Winning Tasks That Neural Networks, Artificial Intelligence, and Machine Learning Perform


The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen, defines a neural network as – "...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs." Artificial neural networks are one of the most important tools used in machine learning. They are brain-inspired systems which are designed for reproducibility and the repetition of a test or complete experiment that we learn. Neural networks help us cluster and classify. Typically, a neural network is initially trained, or fed large amounts of data.

PyData New York City 2017 - YouTube


Keynote: Kerstin Kleese van Dam - Enabling Real Time Analysis & Decision Making Keynote: Thomas Sargent - Economic Models Keynote: Andrew Gelman - Data Science Workflow Andrew Therriault - Learning in Cycles: Implementing Sustainable Machine Learning Models... Jeff Reback - What is the Future of Pandas Chalmer Lowe - Pandas and Date Time Steve Dower - Why does Python need security transparency? Sudheesh Katkam - Simplifying And Accelerating Data Access for Python With Dremio and Apache Arrow Casey Clements - Money for Nothing Introducing Pennies, an Open Source Pythonic Pricing Package Noemi Derzsy - Data Science Keys to Open Up OpenNASA Datasets Tyler A. Erickson - Analyzing Petabytes of Earth Science Data with Jupyter and Earth Engine Nicole Carlson - Turning PyMC3 into scikit learn Leon Yin - Reverse image search engines using out of the box machine learning libraries Keith Ingersoll - Jupyter, R Shiny, and the Data Science Web App Landscape Ami Tavory - Getting Scikit Learn To Run ...

How machine learning can help protect life below water


They cover more than 70% of the earth's surface and the sheer size of the oceans makes tracking and measuring life under water an enormous task. New advances in satellite observation, open data and machine learning now allow us to process the massive amounts of data being produced. And they could not have come at a better time for protecting life under water, which is United Nations' Sustainable Development Goal No. 14 (SDG14). Last year was particularly challenging for most life underneath our world's oceans. Despite the dearth of bad news for the world's oceans, there is hope that 2017 can turn the tide for life under the sea thanks, in part, to machine learning.

Microsoft releases open-source toolkit to accelerate deep learning - The AI Blog


A toolkit used across Microsoft to achieve breakthroughs in artificial intelligence is generally available to the public via an open-source license, a team of researchers and software engineers announced today. "The 2.0 version of the toolkit is now in full release," said Chris Basoglu, a partner engineering manager at Microsoft. He has played a key role in developing Microsoft Cognitive Toolkit (previously known as CNTK). The full release of Microsoft Cognitive Toolkit 2.0 for use in production-grade and enterprise-grade deep learning workloads includes hundreds of new features incorporated since the beta to streamline the process of deep learning and to ensure the toolkit's seamless integration throughout the wider AI ecosystem. New with the full release today is support for Keras, a user-friendly open-source neural network library that is popular with developers working on deep learning applications.