The 7 NLP Techniques That Will Change How You Communicate in the Future (Part I)

Human languages are ambiguous; thus there is a high level of complexity in representing, learning, and using linguistic / situational / contextual / word / visual knowledge towards the human language.

Deep LearningMost of these NLP technologies are powered by Deep Learning – a subfield of machine learning.

In contrast, deep learning’s learned features are easy to adapt and fast to learn.

The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. On the model side, it covers word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, and convolutional neural networks, as well as some recent models involving a memory component.

They allow deep learning to be effective on smaller datasets, as they are often the first inputs to a deep learning architecture and the most popular way of transfer learning in NLP. The most popular names in word embeddings are Word2vec by Google and GloVe by Stanford.

We then go through each position t in the text, which has a center word c and context words o. Next, we use the similarity of the word vectors for c and o to calculate the probability of o given c. We keep adjusting the word vectors to maximize this probability.

Then we skip one of these words and try to learn a neural network that gets all terms except the one skipped and predicts the skipped term.

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How will machine learning shape the future of writing?

Machine learning is a widely used application of AI that allows programmes to learn from extensive datasets without being programmed manually.

It can replace, as the paragraph itself implies, certain writing tasks being automated, leading to job loss for low-cost/low-skilled writers.

Imagine this: it takes almost half a lifetime for a human being to read enough to be able to pick up the art of writing and then actually write and get published, let alone be exceptionally adept in it.

Human labour has value, and that is why we still patronise such labour.

If you cannot differentiate the text written by a human author from that written by a machine, would you be willing to pay for it as much as you did before?

Human creativity, apart from following others and learning certain strategies, also requires raw feelings and emotions.

The only hope I see for the near future is collaboration between machines and human writers where, rather than competing with each other, both would complement each other’s skills and continue to produce great reads.

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Blue the robot could be the AI-powered workhorse of the future

Blue, a new robot from UC Berkeley, aims to break that mold with the help of AI. Blue looks a little bit like a child’s drawing of a robot: it’s made from bulky, 3D-printed parts, and it has a pair of humanoid robot arms with pincers for hands.

Pieter Abbeel, the roboticist leading the project, wants to change this, and he says Blue has been built from the ground up to take advantage of recent improvements in AI. “The fact that AI is becoming more capable gave us an opportunity to rethink how to design a robot,” Abbeel tells The Verge.

PR2, a popular research robot built by Willow Garage that also has a pair of arms and pincers, set researchers back around $400,000.

The Elon Musk-founded research lab OpenAI has done similar work using robot hands, and Google is also exploring AI-training for robots.

The robot is being built in small batches right now, but Abbeel hopes to scale up, eventually moving to outsourced manufacturing to produce larger numbers.

Offering a cheaper robot will make them more widely available, boosting the output of robot research.

Abbeel hopes that Blue will provide a blueprint for what the home robot of the future could look like: something that is low cost, flexible, and plays well with humans

https://berkeleyopenrobotics.github.io/

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