Facebook took major steps to announce its all out committment to Chatbots. The first is a a chatbot training ground called ParlAI--a play on words which stems from its primarily French-speaking researchers. Moreover, Facebook is sharing ParlAI with the world as an open source tool. Facebook is offering the training software so that developers and researchers can use it to train their chatbot "agents."

Facebook has gone a step further in exploring its AI capabilities. The company has released a one-stop-shop platform that can utilize advanced artificial intelligence to make machines act smarter. The framework named ParlAI (pronounced as par-lay) will allow researchers to build conversational AI systems and to combine varied machine dialog approaches. This will encourage dialog research where new tasks and training algorithms can be submitted by researchers in a single, unified and shared repository. The ParlAI framework will allow developers to build smarter and more articulate Chatbots that would not be easily confused by an unexpected question.

One of the long-term goals in AI is to develop intelligent chat bots that can converse with people in a natural way. Existing chat bots can sometimes complete specific independent tasks but have trouble understanding more than a single sentence or chaining subtasks together to complete a bigger task. More complex dialog, such as booking a restaurant or chatting about sports or news, requires the ability to understand multiple sentences and then reason about those sentences to supply the next part of the conversation. Since human dialog is so varied, chat bots must be skilled at many related tasks that all require different expertise but use the same input and output format. To achieve these goals, it is necessary to build software that unifies these tasks, as well as the agents that can learn from them.

Facebook has recently released a platform dubbed ParlAI that has 20 language data sets built in, along with question-and-answer examples aiming to aid developers in building conversational AI systems, by giving them a simple framework to train and test their chatbots. Antoine Bordes from Facebook's AI research lab FAIR said, "ParlAI is trying to develop the capacity for chatbots to enter long-term conversation."

Facebook has released a platform that could combine different advances in artificial intelligence and make machines a lot more articulate. The framework, called ParlAI, offers researchers a simpler way to build conversational AI systems, and to combine different approaches to machine dialogue. The framework should make it easier for developers to build chatbots that aren't so easily stumped by an unexpected question. A common criticism of the chatbots released to date, including those available via Facebook, is that they are too narrowly focused and too easily confused. The long-term hope is that ParlAI will help advance the state of the art in natural language research by reducing the amount of work required to develop and benchmark different approaches.

Most of us talk to our computers on a semi-regular basis, but that doesn't mean the conversation is any good. We ask Siri what the weather is like, or tell Alexa to put some music on, but we don't expect sparkling repartee -- voice interfaces right now are as sterile as the visual interface they're supposed to replace. Facebook, though, is determined to change this: today it unveiled a new research tool that the company hopes will spur progress in the march to create truly conversational AI. The tool is called ParlAI (pronounced like Captain Jack Sparrow asking to parley) and is described by the social media network as a "one-stop shop for dialog research." It gives AI programmers a simple framework for training and testing chatbots, complete with access to datasets of sample dialogue, and a "seamless" pipeline to Amazon's Mechanical Turk service.

Facebook Artificial Intelligence Research (FAIR) today announced plans to launch a testing environment in which AI researchers and bot makers can share and iterate upon each other's work. While the initial focus is on open-sourcing the dialogue necessary to train machines to carry on conversations, other research on ParlAI will focus on computer vision and fields of AI beyond the natural language understanding required for this task. The combination of smarts from multiple bots and bot-to-bot communication will also be part of research carried out on ParlAI. Learn how developers and brand marketers are using AI to grow their businesses, at MB 2017 on July 11-12 in SF. We cut through the hype to show how marketers are achieving real ROI.

The only trouble: They didn't really know how to chat. As Facebook and so many other Silicon Valley players trumpet the benefits of software that can carry on a conversation--apps that book your plane flights or manage your bank account through SMS-like dialogue--the technology still lags behind. In recent years, using what are called deep neural networks, companies like Facebook, Google, and Microsoft have fashioned services that can reliably identify faces and objects in photos, recognize voice commands on smartphones, and translate from one language to another. But building bots that can truly carry on conversations is still proving elusive. It's an undertaking that requires a far more varied array of AI techniques; researchers are still trying to figure out how the different approaches all fit together, or whether they'll really work at all.

If you've been paying any attention to what Facebook is up to lately, you'll know that artificial intelligence and conversational chat bots are two of the most important initiatives for the company. Today, the Facebook Artificial Intelligence Research group (FAIR) is announcing a new initiative that bridges the two. A new online "lab" will let anyone test and use publicly-available datasets to test their own AI dialog systems. The new system, called ParlAI, is FAIR's attempt to make smarter AI bots that aren't as single-minded as many of the ones available now. By pulling from the 20 different datasets that Facebook is making available, the hope is that people will build multi-purpose dialog systems that pull from all of the various data sets.

ParlAI (pronounced "par-lay") is a framework for dialog AI research, implemented in Python. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, WebQuestions, SimpleQuestions, WikiMovies, QACNN & QADailyMail, CBT, BookTest, bAbI Dialog tasks, Ubuntu Dialog, OpenSubtitles, Cornell Movie and VQA-COCO2014. Included are examples of training neural models with PyTorch and Lua Torch, with batch training on GPU or hogwild training on CPUs. Using Theano or Tensorflow instead is also straightforward. Our aim is for the number of tasks and agents that train on them to grow in a community-based way.