Mike Lewis Facebook Ai Research !!

Mike Lewis - ai.facebook.com

Mike Lewis. Mike Lewis is a Research Scientist at Facebook AI Research in Seattle working on natural language processing. Previously he was a postdoc at the University of Washington, working with Luke Zettlemoyer on search based structured prediction. Mike Lewis is a Research Scientist at Facebook AI Research in Seattle working on natural language processing. Previously he was a postdoc at the University of Washington, working with Luke Zettlemoyer on search based structured prediction. He completed his PhD at the University of Edinburgh (advised by Mark Steedman) based on combining distributional and logical approaches to semantics. Mike has a Masters degree from the University of Oxford, and won a Best Paper award at EMNLP 2016.

We introduce kNN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a k-nearest neighbors (kNN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and…

We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making. Rather than directly selecting micro-actions, our agent first generates a latent plan in…

Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce…

The Stanford Natural Language Processing Group

Mike Lewis is a scientist at Facebook AI Research, working on connecting language and reasoning. Previously he was a postdoc at the University of Washington, developing search algorithms for neural structured prediction, and has a PhD from the University of Edinburgh on combining symbolic and distributed representations of meaning. Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other's reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance.

Mike Lewis is a scientist at Facebook AI Research, working on connecting language and reasoning. Previously he was a postdoc at the University of Washington, developing search algorithms for neural structured prediction, and has a PhD from the University of Edinburgh on combining symbolic and distributed representations of meaning. He has won an Outstanding Submission Award at the 2014 ACL Workshop on Semantic Parsing, Best Paper at EMNLP 2016 and Best Resource Paper at ACL 2017. His work has been extensively covered in the media, with varying levels of accuracy, everywhere from New Scientist to the front page of The Sun.

Mike Lewis | OpenReview

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Facebook AI researchers have cloned Bill Gates’ voice with uncanny

Researchers at Facebook Inc. have managed to clone Microsoft Corp. Bill Gates’ voice so well you won’t be able to tell it's machine-generated speech.Sean Vasquez and Mike Lewis at Facebook AI by James Farrell

Researchers at Facebook Inc. have managed to clone Microsoft Corp. Bill Gates’ voice so well you won’t be able to tell it’s machine-generated speech.

Sean Vasquez and Mike Lewis at Facebook AI Research said Monday they’ve been working on trying to mimic human speech for some time, something that’s clearly difficult given that even the arguably most well-known speaking machine of Stephen Hawking still sounded very much like a machine.

It seems now progress has been made, and if you listen to the clone of Gates (pictured), you’ll agree. It sounds like him, and you’d be hard-pressed to tell the difference from the machine and his real voice.

Here the machine says, as Gates, “The glow deepened in the eyes of the sweet girl.” Here it clones the words, “Write a fond note to the friend you cherish.” What’s perhaps uncanny about the last sentence is how the machine gets right Gates’ unmistakable rising inflection when saying “cherish.”

The technology used to do this, called MelNet, can be used to copy human intonation. Gates’ voice and many others’ voices have so far been reproduced with such perfection. The cloned audio was taken from various Ted Talks, said Vasquez and Lewis.

The researchers said that up until recently, the reason why text-to-speech software hasn’t worked very well is that it used waveform recordings. These show how sounds change in scale in a matter for seconds. If you hear that word “cherish” uttered by Gates, the tone shifts quite a lot. The deep-learning machine when trying to mimic a person must guess all these small shifts, no easy task.

Vasquez and Lewis said they managed to clone voices much more accurately by using something called a spectrogram to train the machine.

“The temporal axis of a spectrogram is orders of magnitude more compact than that of a waveform, meaning dependencies that span tens of thousands of timesteps in waveforms only span hundreds of timesteps in spectrograms,” said the researchers. “This enables our spectrogram models to generate unconditional speech and music samples with consistency over multiple seconds.”

There are some setbacks, though. The team said that though they can reproduce a sentence almost perfectly, it won’t be able to replicate “intonation to indicate changes in topic or mood as stories evolve over tens of seconds or minutes.” Still, when it comes to human and computer interaction, the team said, this technology could be transformative in terms of conversations that involve only short phrases.

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Facebook AI researchers' MelNet AI sounds like Bill Gates | VentureBeat

To demonstrate MelNet’s prowess, researchers created a website with music and voice samples made by the AI system. Facebook AI research scientist Mike Lewis and AI resident Sean Vasquez There’s no going back to the 2019 playbook, particularly for benefits. Learn what employees expect in the new normal, and how you can keep a competitive edge.

A pair of Facebook AI researchers used TED Talks and other data to make AI that closely mimics music and the voices of famous people, including Bill Gates. MelNet is a generative model that uses spectrogram visuals of audio for training data instead of waveforms. Doing so allows for the capture of multiple seconds of timesteps from audio, then creates models that can generate end-to-end text-to-speech, unconditional speech, and solo piano music. MelNet was also trained to generate multi-speaker speech models.

Using spectrograms instead of waveforms allows for the capture of timesteps for several seconds. Well-known synthesizers of voices like Google’s WaveNet rely on waveforms instead of spectrograms for training AI systems.

“The temporal axis of a spectrogram is orders of magnitude more compact than that of a waveform, meaning dependencies that span tens of thousands of timesteps in waveforms only span hundreds of timesteps in spectrograms,” Facebook AI researchers said in a paper explaining how MelNet was created. “Combining these representational and modelling techniques yields a highly expressive, broadly applicable, and fully end-to-end generative model of audio.”

To demonstrate MelNet’s prowess, researchers created a website with music and voice samples made by the AI system. Facebook AI research scientist Mike Lewis and AI resident Sean Vasquez published a MelNet paper on arXiv earlier this month.

In order to generate AI that sounds like George Takei, Jane Goodall, and luminary AI scholars like Daphne Koller and Dr. Fei-Fei Li, researchers trained MelNet using a number of data sets including voice recordings of more than 2,000 TED Talks.

The Blizzard 2013 data set of 140 hours of audiobooks trains MelNet’s single speaker-speech skills, and the VoxCeleb2 data set of more than 2,000 hours of speech with more than 100 nationalities and a variety of accents, ethnicities, and other attributes hones the model’s multi-speaker speech function.

Creating MelNet also meant solving for other challenges such as producing high fidelity audio and the reduction of information loss.

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Improving Semantic Parsing for Task Oriented Dialog - Facebook Research

Facebook Conversational AI arashe@fb.com Panupong Pasupat Stanford University ppasupat@cs.stanford.edu Sonal Gupta Facebook Conversational AI sonalgupta@fb.com Rushin Shah Facebook Conversational AI rushinshah@fb.com Mrinal Mohit Facebook Conversational AI mrinalmohit@fb.com Mike Lewis Facebook AI Research mikelewis@fb.com Luke Zettlemoyer %PDF-1.5%¿÷¢þ24 0 obj>endobj 25 0 obj> /W [ 1 3 1 ] /Index [ 24 221 ] /Info 22 0 R /Root 26 0 R /Size 245 /Prev 168601 /ID [] >>streamxœcbd`àg`b``8 "9|ÁìÉxL®‘

Facebook’s AI system can speak with Bill Gates’s voice | MIT Technology

At least, not until today. Enter Sean Vasquez and Mike Lewis at Facebook AI Research, who have found a way to overcome the limitations of text-to speech systems to produce remarkably lifelike Machine speech is something of a disappointment. Even the best text-to-speech systems have a mechanical quality and lack the basic changes in intonation that humans use. Stephen Hawking’s much copied speech system is a case in point.

That’s something of a surprise given the huge advances in machine learning in recent years. Surely the techniques that have worked so well in recognizing faces and objects and then producing realistic images of them should work equally well with audio. Not really.

At least, not until today. Enter Sean Vasquez and Mike Lewis at Facebook AI Research, who have found a way to overcome the limitations of text-to speech systems to produce remarkably lifelike audio clips generated entirely by machine. Their machine, called MelNet, not only reproduces human intonation but can do it in the same voice as real people. So the team trained it to speak like Bill Gates, among others. The work opens the possibility of more realistic interaction between humans and computers, but it also raises the specter of a new era of fake audio content.

First some background. The slow progress on realistic text-to-speech systems is not from lack of trying. Numerous teams have attempted to train deep-learning algorithms to reproduce real speech patterns using large databases of audio.

The problem with this approach, say Vasquez and Lewis, is with the type of data. Until now, most work has focused on audio waveform recordings. These show how the amplitude of sound changes over time, with each second of recorded audio consisting of tens of thousands of time steps.

These waveforms show specific patterns at a number of different scales. During a few seconds of speech, for example, the waveform reflects the characteristic patterns associated with sequences of words. But at the scale of microseconds, the waveform shows characteristics associated with the pitch and timbre of the voice. And at other scales, the waveform reflects the speaker’s intonation, the phoneme structure, and so on.

Another way of thinking about these patterns is as correlations between the waveform at one time step and at the next time step. So for a given time scale, the sound at the beginning of a word is correlated with the sounds that follow.

Deep-learning systems ought to be good at learning these types of correlations and reproducing them. The problem is that the correlations act over many different time scales, and deep-learning systems can study correlations over only limited time scales. That’s because of a type of learning process they employ, called backpropagation, which repeatedly rewires the network to improve its performance on the basis of the examples it sees.

The repetition rate limits the time scale over which correlations can be learned. So a deep-learning network can learn correlations in audio waveforms over long time scales or short ones, but not both. That’s why they perform so badly at reproducing speech.

Vasquez and Lewis have a different approach. Instead of audio waveforms, they use spectrograms to train their deep-learning network. Spectrograms record the entire spectrum of audio frequencies and how they change over time. So while waveforms capture the change over time of one parameter, amplitude, spectrograms capture the change over a huge range of different frequencies.  

This means the audio information is packed more densely into this type of data representation. “The temporal axis of a spectrogram is orders of magnitude more compact than that of a waveform, meaning dependencies that span tens of thousands of timesteps in waveforms only span hundreds of timesteps in spectrograms,” say Vasquez and Lewis.

That makes the correlations more accessible to a deep-learning system. “This enables our spectrogram models to generate unconditional speech and music samples with consistency over multiple seconds,” they say.

And the results are impressive. Having trained the system using ordinary speech from TED talks, MelNet is then able to reproduce the TED speaker’s voice saying more or less anything over a few seconds.  The Facebook researchers demonstrate its flexibility using Bill Gates’s TED talk to train MelNet and then use his voice to say a range of random phrases.

This is the system saying “We frown when events take a bad turn” and “Port is a strong wine with a smoky taste.” Other examples are here.

There are some limitations, of course. Ordinary speech contains correlations over even longer time scales. For example, humans use changes in intonation to indicate changes in topic or mood as stories evolve over tens of seconds or minutes. Facebook’s machine does not yet seem capable of that.

So while MelNet can create remarkably lifelike phrases, the team has not yet perfected longer sentences, paragraphs, or entire stories. That does not seem like a goal that is likely to be reached soon.

Nevertheless, the work could have a significant impact on human-computer interaction. Many conversations involve only short phrases. Telephone operators and help desks in particular can get by with a range of relatively short phrases. So this technology could automate these interactions in a way that is much more human-like than current systems.

And as ever, there are potential problems with natural-sounding machines, particularly those that can mimic humans reliably. It doesn’t take much imagination to dream up scenarios in which this technology could be used for mischief. And for that reason, it is yet another AI-related advance that raises more ethical questions than it answers.

Facebook Research at ACL 2020

Another core focus area for Facebook AI is cross-lingual research, where models are trained in one language and then used for other languages without additional training data. We’re presenting XLM-R , our state-of-the-art model that performs cross-language tasks with 100 different languages, including low-resource languages.

About Me - Millicent Li

I’m an incoming AI Resident at Facebook AI Research, where I’ll be working with with Marjan Ghazvininejad and Mike Lewis on natural language processing research. I recently graduated from the University of Washington with my undergraduate degree in computer science.

‪Mike Lewis‬ - ‪Google Scholar‬

2014. Question-answer driven semantic role labeling: Using natural language to annotate natural language. L He, M Lewis, L Zettlemoyer. Proceedings of the 2015 conference on empirical methods in natural language …. , 2015. 124. 2015. A corpus of natural language for visual reasoning. A Suhr, M Lewis, J Yeh, Y Artzi. mike lewis facebook ai research

mike lewis facebook ai research

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