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.


Machine Learning: An In-Depth, Non-Technical Guide - Part 1

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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.


Branding Once Meant Logos. Today, It Means AI

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After all, consider that bees' markings are instantly distinctive to each other and across species as a biological imperative. The idea of the brand goes from optimistic (bees were a brand, man!) Will we ever start to see distinct, convincing personalities emerge as brands--or at least a few decent archetypes? Because right now, I don't think that most of us could really distinguish a Siri response from a Cortana response from an Alexa response, beyond the specific voice.


Deep Learning for Chatbots, Part 2 โ€“ Implementing a Retrieval-Based Model in Tensorflow

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A positive label means that an utterance was an actual response to a context, and a negative label means that the utterance wasn't โ€“ it was picked randomly from somewhere in the corpus. Each record in the test/validation set consists of a context, a ground truth utterance (the real response) and 9 incorrect utterances called distractors. Before starting with fancy Neural Network models let's build some simple baseline models to help us understand what kind of performance we can expect. The Deep Learning model we will build in this post is called a Dual Encoder LSTM network.