OpenAI research has a short introduction on their newest research "Learning to Communicate". There are many trends that I watch for in the field of Deep Learning. Two trends that are related and I believe going to be very promising areas are language learning and multi-agent communication. If you have not been watching, this week has had a tremendous release of papers involving the former and culminating with OpenAI's post, stitching it all together! Let me explain though what transpired in this amazing week.

As is so often the case, the giants of the Internet are chasing the same sparkly vision of the future: chatbots. In the coming months and years, these companies promise, you'll chat with Internet services in much the same way you now chat with friends and family. Bots will instantly answer questions, respond to requests, and even anticipate your needs. While chatting with some some old college pals about an upcoming reunion, you'll ask an OpenTable bot for restaurant recommendations. Google's New Allo Messaging App Gets Its Edge From AI Google Has Open Sourced SyntaxNet, Its AI for Understanding Language Facebook Open Sources Its AI Hardware as It Races Google Google's New Allo Messaging App Gets Its Edge From AI Google's New Allo Messaging App Gets Its Edge From AI But a major challenge remains: building chatbots that can actually chat.

Google has just open-sourced its'Parsey McParseface' AI tool and it understands English Google has open-sourced its neural network framework, SyntaxNet to include new language parsing model for English, which it calls'Parsey McParseface.' This is a tool that developers can use to analyze English text. In other words, developers will be able to swindle with the underlying technology powering Google's powerful natural language software so that apps, voice assistants and robots can better comprehend what English-speaking users want. "One of the main problems that makes parsing so challenging is that human languages show remarkable levels of ambiguity," Google says in a blog post. "It is not uncommon for moderate length sentences--say 20 or 30 words in length--to have hundreds, thousands, or even tens of thousands of possible syntactic structures. A natural language parser must somehow search through all of these alternatives, and find the most plausible structure given the context."

Artificial intelligence and machine learning are going thoroughly open source, with some of the biggest tech companies contributing projects to the community. Recently, I covered Google's decistion to open source a program called TensorFlow. It's based on the same internal toolset that Google has spent years developing to support its AI software and other predictive and analytics programs. Now, in a follow-on move, Google is open sourcing SyntaxNet, which is natural-language understanding software that can automatically parse sentences. SyntaxNet is part of its TensorFlow open source machine learning library, and is hardened and tested by Google.

A TensorFlow implementation of the models described in Andor et al. (2016). At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. We are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework for TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you, and that you can use to analyze English text. So, how accurate is Parsey McParseface?

As is so often the case, the giants of the Internet are chasing the same sparkly vision of the future: chatbots. In the coming months and years, these companies promise, you'll chat with Internet services in much the same way you now chat with friends and family. Bots will instantly answer questions, respond to requests, and even anticipate your needs. While chatting with some some old college pals about an upcoming reunion, you'll ask an OpenTable bot for restaurant recommendations. But a major challenge remains: building chatbots that can actually chat.

Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam, which has no clever backstory either. The overall AI model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Combining machine learning and search techniques, Parsey McParseface is 94 percent accurate, according to Google. It also leans on SyntaxNet's neural-network framework for analyzing the linguistic structure of a sentence or statement, which parses the functional role of each word in a sentence.

Google has just released two powerful natural language understanding tools for free, open-source use by anyone. These tools allow machines to read and understand English text (such as text you type into a browser to do a Google search). SyntaxNet is a "syntactic parser" -- it allows machines to parse, or break down, sentences into their component parts of speech and identify the underlying meaning). And the Parsey McParseface program implements SyntaxNet in English (it learned from an annotated collection of old newswire stories called The Penn Treebank Project). Here's an example of how it parses and analyzes an English sentence:Using deep neural networks, SyntaxNet is implemented in Google's TensorFlow (see Google open-sources its TensorFlow machine learning system).

How machines deal with comprehending human languages is called Natural Language Understanding (NLU), and revolutionary changes in this technology have given us the many virtual assistants we have today. However, NLU still has many obstacles to go through due to the ambiguous nature of the countless languages all over the world. Now, Google claims they're cutting through these difficulties as they announced the open sourcing of a neural network software developed with TensorFlow, SyntaxNet, together with…Parsey McParseface, apparently an English parser. Parsing, in linguistics, is the breaking down of sentences into their component parts to define what each part means. Experts assert that this is a first key component in NLU systems.