algorithm


Use deep learning on data you already have

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Clean, labeled data requires data analysts with a combination of domain knowledge, and infrastructure engineers who can design and maintain robust data processing platforms. Looking toward narrow AI systems, much of the recent excitement involves systems that combine deep learning with additional techniques (reinforcement learning, probabilistic computing) and components (memory, knowledge, reasoning, and planning). Many current systems based on deep learning require big compute, big data, and big models. While researchers are seeking to build tools that are less dependent on large-scale pattern recognition, companies wanting to use deep learning as a machine learning technique can get started using tools that integrate with their existing big data platforms.


How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms

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Repeat Step 1 by recalculating the distance matrix, but this time merge the Bottlenose & Risso's Dolphins into a single object with length 3.3m. Then, we repeat Step 1 -- recalculate the distance matrix, but now we've merged the Pilot & Killer Whales into a single object of length 7.0m. Then, we repeat Step 1 and compute a new distance matrix, having merged the Bottlenose & Risso's Dolphins with the Pilot & Killer Whales. Then, it's back to Step 1 -- compute the distance matrix, having merged the Humpback & Fin Whales.



Top 5 open-source tools for machine learning - JAXenter

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Some have been a little worried about the machine learning arms race leaving the world's top universities bereft of AI talent. Helpfully, the getting started section with TensorFlow has a ML for beginners section as well as a section for experts. Their getting started page is pretty well structured for deep learning beginners and walks newcomers through the initial steps with some problem sets. Microsoft's Cognitive Toolkit is a deep-learning toolkit for training algorithms to learn like the human brain.


Parley 'SnotBot' expedition brings machine learning and artificial intelligence to whale research - IT Peer Network

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In more than five years with Intel Labs, I've had the good fortune to be involved with a team of extremely bright people focused on researching large-scale machine learning and data mining techniques. This week's Parley SnotBot expedition, parts of which will be captured live by National Geographic, will build on the work being done by the Mind's Eye Lab, a research initiative arising out of a collaboration between Intel Labs and the Princeton Neuroscience Institute. Applied to drone video, machine learning can learn and remove the distortions caused by water, bringing out whale color patterns, speckling, and shape. While this week's expedition focuses on humpback whales, the same technologies can be applied to other animals to generate deeper insights than would be possible even with direct observations in the field.


Using AI to Break Detection Models

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This week at Black Hat, one researcher hopes to contribute to the discipline by showing off a new automated AI agent that probes the data science behind machine learning malware detection models and looks for mathematical weaknesses. "All machine learning models have blind spots. The agent essentially inspects an executable file and uses a sequence of file mutations to test the detection model. The idea of machine learning and AI hardening is generally gaining momentum with data scientists and security specialists of late.


Senior Research Scientist - Machine Learning

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The machine learning team is currently building novel technologies for data integration and machine learning on large scale graphs. We are seeking candidates with expertise in machine learning, graph algorithms, deep learning using highly structured data, active learning, online learning, manifold and spectral learning, large scale graph algorithms, distributed algorithms, or frequent graph pattern mining. Data61 are the largest data innovation group in Australia, a connector that brings together technology innovators, businesses and universities to transform Australian industry and to help solve our greatest challenges. The "reset password" link will open in a new browser window.


Artificial intelligence is not as smart as you (or Elon Musk) think

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It represented one of those defining technological moments not unlike IBM's Deep Blue beating chess champion Garry Kasparov, or even IBM Watson beating the world's greatest Jeopardy champions in 2011. Former MIT robotics professor Rodney Brooks, who was one of the founders of iRobot and later Rethink Robotics, reminded us at the TechCrunch Robotics Session at MIT last week that training an algorithm to play a difficult strategy game isn't intelligence, at least as we think about it with humans. Gil Pratt, CEO at the Toyota Institute, a group inside Toyota working on artificial intelligence projects including household robots and autonomous cars, was interviewed at the TechCrunch Robotics Session, said that the fear we are hearing about from a wide range of people, including Elon Musk, who most recently called AI "an existential threat to humanity," could stem from science-fiction dystopian descriptions of artificial intelligence run amok. Physicist Stephen Hawking and philosopher Nick Bostrom also have expressed reservations about the potential impact of AI on humankind -- but chances are they are talking about a more generalized artificial intelligence being studied in labs at the likes of Facebook AI Research, DeepMind and Maluuba, rather than the more narrow AI we are seeing today.


how-ai-will-become-omnipresent

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We had to go through a whole process of development and discovery, and, as a result of computer experts working hand in hand with domain experts over the course of 15 to 20 years, computers and specialized software were developed to suit different needs. Most people now are familiar with conversion rate optimization (CRO), where site operators try to maximize conversions by testing new ideas for design, messaging, user experience, and more. The operator sets parameters and goals, but the AI decides the combination of ideas, always trying to find a better answer and better results against that goal. And just like computerization, AI enablement will only be fully achieved once all of us can be considered AI experts by today's standards.


Core Differences between Artificial intelligence and Machine Learning -Big Data Analytics News

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It is among the major fields of Computer Science that cover robotics, machine learning, expert systems, general intelligence and natural language processing. The national security system uses data on AI systems, which then presents accurate problems that the nation might face. Reading texts and deciding whether it's a compliment or a complaint, finding out how the genre of music would affect the mood of the listener or composing themes of its own are offered by systems working around Machine Learning and Neural Networks. This has lead to the innovative prospect of Natural Language Processing (NLP), on which work begun and still is being done.