RESPONSE


This is when robots will start beating humans at every task

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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. 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. Language translation could outpace human performance by 2024, responses indicated, and robots may be able to write better high-school-level essays than humans in 2026. Ultimately, the researchers found AI could automate all human tasks by the year 2051 and all human jobs by 2136.


Facebook's Just Revealed That Its Chatbots Can Negotiate as Well as Humans

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Over time, the bots learned to go beyond simply mimicking humans and instead became more unpredictable with their responses. To test the model's effectiveness, Facebook created scenarios with a hypothetical set of objects. Facebook used an "end-to-end" training model, which means the process could be altered to give the algorithm other goals similar to the one in the study. In an email, Dhruv Batra, a Facebook visiting researcher who worked on the project and also teaches computer science at the Georgia Institute of Technology, told Inc. that Facebook doesn't have any plans to implement the technology into its product yet.


How Watson works - myth busting at IBM InterConnect 2017

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Have you ever wondered how Watson, IBM's AI works? Lastly there's empathy where Watson has tone analysis, emotion analysis and can provide personality insights. Watson's tone analyzer for example uses psycholinguistics, emotion analysis and language analysis to assess tone. Now Expressive SSML and Voice Transformation SSML bring life and a human lilt to computed voices.


Machine Learning: An In-Depth Guide - Overview, Goals, Learning Types, and Algorithms

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Once these data subsets are created from the primary dataset, a predictive model or classifier is trained using the training data, and then the model's predictive accuracy is determined using the test data. As mentioned, machine learning leverages algorithms to automatically model and find patterns in data, usually with the goal of predicting some target output or response. In a nutshell, machine learning is all about automatically learning a highly accurate predictive or classifier model, or finding unknown patterns in data, by leveraging learning algorithms and optimization techniques. The columns in this case, and the data contained in each, represent the features (values) of the data, and may include feature data such as game date, game opponent, season wins, season losses, season ending divisional position, post-season berth (Y/N), post-season stats, and perhaps stats specific to the three phases of the game: offense, defense, and special teams.


Facebook built the perfect chatbot but can't give it to you yet

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Facebook's experimental assistant, offered inside the company's Messenger app, shows the value of having a true digital butler in your pocket. Weizenbaum had suggested back in 1964 that something like this could make Eliza smarter, and within weeks it worked for M. Lebrun remembers being surprised after thanking the assistant for ordering movie tickets. Even if M were to automatically turn down the most complex of user queries, though, the sheer variety of their requests makes the goal of having algorithms take over from human trainers harder to reach. A technique called deep learning has recently made machine learning more powerful (memory networks are an example).


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It seemed like yesterday when things like automated social media posts, blog content, and chatbots were something laughable, not fully able to compete with human intelligence. When you factor in the potential breadth of these systems, including the obsolete cost of hiring actual writers, and the increased accuracy of keyword inclusion and optimization, the new industry of SEO and AI will be immensely powerful. Even though this technological development sacrifices human perspective and insight, the return is going to provide content providers with articles that could potentially lead to the top of search results almost every time. This means that updating automated responses and search terms will occur instantaneously -- cycling through which ones will achieve the highest ROI in a matter of seconds.


Machine Learning: An In-Depth Guide - Overview, Goals, Learning Types, and Algorithms

#artificialintelligence

Once these data subsets are created from the primary dataset, a predictive model or classifier is trained using the training data, and then the model's predictive accuracy is determined using the test data. As mentioned, machine learning leverages algorithms to automatically model and find patterns in data, usually with the goal of predicting some target output or response. In a nutshell, machine learning is all about automatically learning a highly accurate predictive or classifier model, or finding unknown patterns in data, by leveraging learning algorithms and optimization techniques. The columns in this case, and the data contained in each, represent the features (values) of the data, and may include feature data such as game date, game opponent, season wins, season losses, season ending divisional position, post-season berth (Y/N), post-season stats, and perhaps stats specific to the three phases of the game: offense, defense, and special teams.


The Morning After: Friday, December 30 2016

Engadget

There's also donut-shaped Mars shelters, and our take on Apple's 2016: it's not a positive one. While Amazon only just recently started delivering to real customers via drone, it has even bigger ideas. A patent filing reveals a system where they take off from floating blimps stocked with commonly requested products. The adult industry and CES have a longer relationship than you might know. From 1984 to 1998 porn was a part of CES, until AVN split off for its own concurrently running show.


Why Morgan Stanley remains overweight on consumer stocks in 2017

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In a chat with ET Now, Amay Hattangadi, ED, and Swanand Kelkar, ED, discuss Morgan Stanley Investment Management's outlook for 2017 Edited excerpts: In your large portfolio, you are overweight on consumer stocks now and distinctly much higher than what the MSCI allocation would be. Why is it that you are so bullish again in a pocket where it is difficult to predict how the growth will shape up? Amay Hattangadi: When we look at consumers in India, we look at it as a combination of consumer staples, consumer discretionary and the consumer lending segment within financials. When we say we are overweight on consumer stocks, it is a combination of these sectors and this was one growth engine for the last two or three years. At a time when the corporate capex cycle was anaemic, there was surplus capacity in India and Indian industry was operating at about 70% overall capacity utilisation.


4 trends in security data science for 2017

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Get started with deep learning and neural networks with "Fundamentals of Deep Learning," by Nikhil Buduma. Security data science is booming--reports indicate that the security analytics market is set to reach $8 billion dollars by 2023, with a growth rate of 26%, thanks to relentless cyber attacks. If you want to stay ahead of emerging security threats in 2017, it is important to invest in the right areas. In March 2016, I wrote a piece on the 4 trends to be aware of for 2016; for my 2017 trends post, Cody Rioux from Netflix joins me, bringing his platform perspective. Our goal is to help you formulate a plan for every quarter of 2017 (i.e., 4 trends for 4 quarters).