The virtual assistants that inhabit our smartphones are helpful, sure, but they're not going to pass the Turing test any time soon. They're designed for understanding specific commands and actions like checking on restaurant reservations or getting updates on the weather, rather than, say, carrying on an in-depth conversation with a human. But chatbots could soon become far more loquacious thanks to Facebook, which this morning released a startlingly lifelike chatbot that it's been developing, dubbed Blender, as an open-source resource for AI research. Facebook has been pouring money and resources into its Natural Language Processing technologies for a few years now and those efforts appear to have paid off. The company claims that Blender is the single largest open-source chatbot created to date.

Facebook AI Research (FAIR) today said it's open-sourcing wav2letter@anywhere, a deep learning-based inference framework that achieves fast performance for online automatic speech recognition in cloud or embedded edge environments. Wav2letter@anywhere is based on neural net-based language models wav2letter and wav2letter, which upon its release in December 2018, FAIR called the fastest open source speech recognition system available. Automatic speech recognition, or ASR, is used to turn audio of spoken words into text, then infer the speaker's intent in order to carry out a task. An API available on GitHub though the wav2letter repository is built to support concurrent audio streams and popular kinds of deep learning speech recognition models like convolutional neural networks (CNN) or recurrent neural networks (RNN) in order to deliver scale necessary for online ASR. Wav2letter@anywhere achieves better word error rate performance than two baseline models made from bidirectional LSTM RNNs, according to a paper released last week by eight FAIR researchers from labs in New York City and at company headquarters in Menlo Park.

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.

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