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An AI backed by Elon Musk just 'evolved' to learn by itself

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Most of today's artificial intelligence (AI) systems rely on machine learning algorithms that can predict specific outcomes by drawing on pre-established values, but now researchers from OpenAI, a company funded by no less than Elon Musk and Peter Thiel, who are trying to democratise AI for "human good" just discovered – literally – that a machine learning system they created to predict the next character in the text of reviews from Amazon evolved into an unsupervised learning system that could learn how to read sentiment. That's a pretty big deal, and it's also something that, at the moment, even the researchers themselves can't explain. "We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment," said OpenAI in a blog. According to the post OpenAI's neural network was able to train itself and analyse sentiment accurately by classifying Amazon's reviews as either positive or negative – and it then generated follow on text that fit with the sentiment. The AI the team used was what's known as a multiplicative long short-term memory (LSTM) model that was trained for a month, processing 12,500 characters a second using Nvidia Pascal GPU's – which Nvidia's own CEO gifted to Elon Musk last year – with "4,096 units on a corpus of 82 million Amazon reviews to predict the next character in a chunk of text."


Unsupervised sentiment neuron

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Our system beats other approaches on Stanford Sentiment Treebank while using dramatically less data. The number of labeled examples it takes two variants of our model (the green and blue lines) to match fully supervised approaches, each trained with 6,920 examples (the dashed gray lines). Our L1-regularized model (pretrained in an unsupervised fashion on Amazon reviews) matches multichannel CNN performance with only 11 labeled examples, and state-of-the-art CT-LSTM Ensembles with 232 examples. We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment. We believe the phenomenon is not specific to our model, but is instead a general property of certain large neural networks that are trained to predict the next step or dimension in their inputs.


How DeepMind's Latest AI Hints at Machines That Think More Like Us

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I once asked a deep learning researcher what he'd like for Christmas. Nerd jokes aside, the lack of so-called "labeled" training data in deep learning is a real problem. Deep learning relies on millions upon millions of examples to tell the algorithm what to look for--cat faces, vocal patterns, or humanoid things strolling on the street. A deep learning algorithm is only as good as the data it's trained on--"garbage in, garbage out"--so accurately gathering and labeling existing data is essential. For the human researchers tasked with the job, carefully parsing the training data is a horrendously boring and time-consuming process.


OpenAI sets benchmark for sentiment analysis using an efficient mLSTM

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Because the model was trained to be generative, it was also able to output reviews with preset sentiments. The table below is pulled from the paper and shows a random assortment of examples for both positive and negative reviews. These results are cool, but if you're totally new to this, let's take a few steps back. Even before machine learning, engineers interested in classifying sentiment would employ relatively dumb heuristics like keyword search to get the job done. However, with these methods, a sentence like, "I hope you're happy," could easily be misinterpreted as having a positive connotation simply because it possesses the word happy.


Deep Learning for NLP, advancements and trends in 2017 - Tryolabs Blog

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To create a summary, two different people will use different words and sentence orders, and both summaries will probably be considered as valid. Thus, a good summary does not necessarily have to be a sequence of words that match a sequence in the training dataset as much as possible. Knowing this, the authors avoid the standard teacher forcing algorithm, which minimizes the loss at each decoding step (i.e. for each generated word), and they rely on a reinforcement learning strategy that proves to be an excellent choice.