Educator and entrepreneur Sebastian Thrun wants us to use AI to free humanity of repetitive work and unleash our creativity. In an inspiring, informative conversation with TED Curator Chris Anderson, Thrun discusses the progress of deep learning, why we shouldn’t fear runaway AI and how society will be better off if dull, tedious work is done with the help of machines. “Only one percent of interesting things have been invented yet,” Thrun says. “I believe all of us are insanely creative … [AI] will empower us to turn creativity into action.”
What is natural language generation, what should clients be doing with it, and what is its future? Get answers from Deloitte’s interview with Kris Hammond, chief scientist at Narrative Science.
Learn about the limitations of RNNs, how LSTMs work, and Gated Recurrent Units (GRUs).
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In this video, we will learn about Automatic text generation using Tensorflow, Keras, and LSTM. Automatic text generation is the generation of natural language texts by computer. It has applications in automatic documentation systems, automatic letter writing, automatic report generation, etc. In this project, we are going to generate words given a set of input words. We are going to train the LSTM model using William Shakespeare’s writings.
Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory. Generally, LSTM is composed of a cell (the memory part of the LSTM unit) and three “regulators”, usually called gates, of the flow of information inside the LSTM unit: an input gate, an output gate and a forget gate. Intuitively, the cell is responsible for keeping track of the dependencies between the elements in the input sequence. The input gate controls the extent to which a new value flows into the cell, the forget gate controls the extent to which a value remains in the cell and the output gate controls the extent to which the value in the cell is used to compute the output activation of the LSTM unit. The activation function of the LSTM gates is often the logistic sigmoid function. There are connections into and out of the LSTM gates, a few of which are recurrent. The weights of these connections, which need to be learned during training, determine how the gates operate.
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01:20 Text generation using TensorFlow, Keras, and LSTM
05:28 Get started with code
27:53 Build LSTM model and prepare x and y
42:25 LSTM model
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You might be familiar with NLP (especially if you are a subscriber of my channel). But do you know what is NLG?
In today’s video, I’ll explain the meaning of Natural Language Generation, and its relation with NLP.
NLG and NLP are closely related, since Speech Recognition is a subfield of NLP, or to be more precise, it is a subfield of computational linguistics. So if you are interested in this topic, in AI and/or machine learning, watch it right now! Also, don’t forget to leave your impressions about it and recommendations in the comments.
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A Markov Chain is a system that transitions between states using a random, memoryless process. Markov Chains are a great tool for simulating real-world phenomena and environments with computers. In this video, we’ll give a specific example of how to use Markov Chains in Natural Language Generation.
Watch this video to learn:
– What is a Markov Chain
– How are Markov Chains being used
– The reasons they’re useful for Natural Language Generation
In this segment, you will learn the basics of Natural Language Generation and the Integration between TIBCO Spotfire and Automated Insights’s Natural Language Generation Software Wordsmith.
Presentation by Catherine Henry (2017 Clearwater DevCon).
When teaching a subject through text it can be beneficial to evaluate the reader’s understanding; however, the creation of relevant questions and answers can be time-consuming and tedious. I will walk through how the implementation of NLP libraries and algorithms can assist in, and potentially remove altogether, the current necessity of an individual manually formulating these tests.
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.
– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python
All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial
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Markov chains are used for keyboard suggestions, search engines, and a boatload of other cool things. In this video, I discuss the basic ideas behind Markov chains and show how to use them to generate random text.
My code to generate text: https://github.com/unixpickle/markovchain
My code to generate line drawings: https://github.com/unixpickle/markovdraw
Nabil Hassein demonstrates how to train an “LSTM” neural network to generate text in the style of a particular author using Spell and ml5.js.
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“As creators of machine learning projects for art or otherwise, we have to take responsibility for what our programs produce and the impact that output has on people who interact with our creations. Given how common bias and oppression is in the world generally, many if not most datasets (including song lyrics) reflect that reality, and without countermeasures we as programmers are very likely to reproduce those harms. It is also worth explicitly noting that authorship and context matter, and identical words (or images, etc.) can assume completely different significance depending on who says them and when. I encourage everyone to take seriously the ethical aspects of the ml5.js documentation along with the technical material, and to consider your responsibility as a technologist to acknowledge and address the harm that the field of computing has too often caused for marginalized groups”
Nabil Hassein is a freelance technologist and educator based in Brooklyn, NY. He has previously worked as an infrastructure engineer at Khan Academy and a couple of startups, taught math and programming in both public schools and private settings, and occasionally writes and speaks. His website is https://nabilhassein.github.io.
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Professor Christopher Manning & PhD Candidate Abigail See, Stanford University
Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)
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==The Next Generation of the MicroPsi Framework== Joscha Bach, Humboldt University of Berlin, Germany
In this episode of AI Adventures, Yufeng interviews Google Research engineer Justin Zhao to talk about natural text generation, recurrent neural networks, and state of the art research!
RNNs in TensorFlow: https://goo.gl/ss5dEY
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What if I told you, a machine wrote the script for this video and a robot spoke the voice over? At Deloitte we use artificial intelligence combined with Natural Language Processing and Natural Language Generation to automatically analyze, interpret and identify the most significant data.
For more information click here / Weitere Informationen gibt es hier: https://deloi.tt/2ZxWDb4
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Get more information about Deloitte on our website / Besucht auch unsere offizielle Website für News, aktuelle Studien, Trends, Stellenangebote und Infos rund um Deloitte:
● Website: https://www2.deloitte.com/de/
● Karriere: https://www.deloitte.com/de/karriere
VPI Conversational Virtual Agents powered by Artificial Intelligence
It’s time to reshape the way we think about customer telephone self-service. Stop scripting. Start conversing. Stop disappointing. Start delighting. Watch to learn how VPI’s conversational virtual agents in the cloud can help you effectively automate a wider variety of inbound and outbound call types while lowering costs and improving customer experience.
Ο Πάνος Τραχανιάς είναι καθηγητής στο Τμήμα Επιστήμης Υπολογιστών του Πανεπιστημίου Κρήτης και στο Ίδρυμα Τεχνολογίας και Έρευνας (ΙΤΕ/FORTH).Είναι επικεφαλής στο Εργαστήριο Υπολογιστικής Όρασης και Ρομποτικής του ΙΤΕ, όπου επιβλέπει και ασχολείται ενεργά με την έρευνα και με χρηματοδοτούμενα έργα πάνω σε γνωσιακά συστήματα και ευφυή αυτόνομα ρομπότ. Στην ομιλία του μας μιλάει για το πως τα έξυπνα ρομπότ και η τεχνητή νοημοσύνη έχουν την δυνατότητα να ενισχύσουν την δημιουργικότητα και τις γνωστικές μας ικανότητες.
Panos Trahanias is a Professor with the Department of Computer Science, University of Crete, Greece and the Foundation for Research and Technology–Hellas (FORTH). Ηe is the head of the Computational Vision and Robotics Laboratory, engaged in research and RTD projects in cognitive systems and intelligent autonomous robots. Prof. In his talk he is talking about, how smart robots and artificial intelligence have the potential to enhance the creativity and cognitive abilities.
Panos Trahanias is a Professor with the Department of Computer Science, University of Crete, Greece and the Foundation for Research and Technology – Hellas (FORTH). In the past he has been with the Department of Electrical and Computer Engineering, University of Toronto, Canada, and was a consultant to SPAR Aerospace Ltd., Toronto. Since 1993 he is with the University of Crete and FORTH. Currently, he is the head of the Computational Vision and Robotics Laboratory, engaged in research and RTD projects in cognitive systems and intelligent autonomous robots. Prof. Panos Trahanias has pursued many successful European- and Nationally-funded projects and has received RTD grants from industry. He has published extensively in the areas of visual perception, intelligent robots, human-robot interaction and robotic cognition. He has served in the Program Committees of many International Conferences in computer vision, computer graphics and robotics, and has been chair of Eurographics 2008.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Simulation of mechanisms of coding, data processing in biological systems at the molecular level provides promising tools for next generation cognitive systems with the capability of real-time response to the environmental signals. In my research, I apply biological coding to define algorithms for solving NP hard problems. In this TED Talk I’ll present the results and provide insights and applications for molecular sensors and implications for next generation of artificial intelligence.
Dr. Tara Karimi is a multi-disciplinary scientist who has devoted her academic life to learning biological systems and applying the natural principles to the outside world. Dr. Karimi holds two PhDs in veterinary science and biochemistry and has completed several post doc projects in tissue engineering, genetic engineering, molecular and developmental biology, stem cell research, and regenerative medicine.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Today we’re joined by Ahmed Elgammal, a professor in the department of computer science at Rutgers, and director of The Art and Artificial Intelligence Lab. In my conversation with Ahmed, we discuss:
• His work on AICAN, a creative adversarial network that produces original portraits, trained with over 500 years of European canonical art.
• How complex the computational representations of the art actually are, and how he simplifies them.
• Specifics of the training process, including the various types of artwork used, and the constraints applied to the model.
The complete show notes for this episode can be found at twimlai.com/talk/265.
Guo talks about how artificial intelligence will revolutionize how we approach the most basic questions of political philosophy. Our history of assumptions about human nature, existence, and civil society will be turned on its head and artificial intelligence will play a drastic role in how our world changes. Chelsea Guo is a senior at Yale completing a four-year joint B.S./M.S. degree in Molecular, Cellular, and Developmental Biology and a B.A. in Political Science. At Yale, she is a first-year counselor in Pauli Murray College, an undergraduate researcher at the Yale Stem Cell Center, the president of the Women’s Leadership Initiative, and the AI Discussion Group leader for Yale Effective Altruists. Her interests lie at the intersection of science, technology, philosophy, and political theory. After graduating, she plans to pursue a Ph.D. in political theory and a J.D. in international law. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx