Goto

Collaborating Authors

Results


Facebook details wav2vec, an AI algorithm that uses raw audio to improve speech recognition

#artificialintelligence

Automatic speech recognition, or ASR, is a foundational part of not only assistants like Apple's Siri, but dictation software such as Nuance's Dragon and customer support platforms like Google's Contact Center AI. It's the thing that enables machines to parse utterances for key phrases and words and that allows them to distinguish people by their intonations and pitches. Perhaps it goes without saying that ASR is an intense area of study for Facebook, whose conversational tech is used to power Portal's speech recognition and who is broadening the use of AI to classify content on its platform. To this end, at the InterSpeech conference earlier this year the Menlo Park company detailed wave2vec, a novel machine learning algorithm that improves ASR accuracy by using raw, untranscribed audio as training data. Facebook claims it achieves state-of-the-art results on a popular benchmark while using two orders of magnitude less training data and that it demonstrates a 22% error reduction over the leading character-based speech recognition system, Deep Speech 2. Wav2vec was made available earlier this year as an extension to the open source modeling toolkit fairseq, and Facebook says it plans to use wav2vec to provide better audio data representations for keyword spotting and acoustic event detection.


A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.


Building Jarvis

#artificialintelligence

My personal challenge for 2016 was to build a simple AI to run my home -- like Jarvis in Iron Man. My goal was to learn about the state of artificial intelligence -- where we're further along than people realize and where we're still a long ways off. These challenges always lead me to learn more than I expected, and this one also gave me a better sense of all the internal technology Facebook engineers get to use, as well as a thorough overview of home automation. So far this year, I've built a simple AI that I can talk to on my phone and computer, that can control my home, including lights, temperature, appliances, music and security, that learns my tastes and patterns, that can learn new words and concepts, and that can even entertain Max. It uses several artificial intelligence techniques, including natural language processing, speech recognition, face recognition, and reinforcement learning, written in Python, PHP and Objective C. In this note, I'll explain what I built and what I learned along the way. Diagram of the systems connected to build Jarvis.


15 examples of artificial intelligence in marketing

#artificialintelligence

Artificial intelligence (see the Wikipedia definition), specifically machine learning, is an increasingly integral part of many industries, including marketing. Here are a whole bunch of case studies and use cases, as a complete primer for AI in our industry. Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription. Uniting information from diverse datasets is a common use of AI.


How AI and machine learning tech can aid your startup strategy

#artificialintelligence

Since the past 4-5 years, we have seen a change in the shopping behavior of users, both online as well as offline. It has resulted from user's reviews and recommendations about the products ranging from fashion to home to technology, all thanks to social websites like Facebook, Pinterest, Instagram and many other global as well as regional social sites. Social commerce is a term, coined by Yahoo in 2005, as a set of online shopping tools that take into account the user liking patterns, sharing reviews, information and advices on products, as per their usages, thus affecting the sales of those products. There are two types of social commerce strategies -- one is offsite where the e-retailer brings in the social angle from external social platforms, separate from their own websites, thus enhancing the sales and second is onsite social commerce platform where the website/platform uses its own channel to enhance sales based on content, context, and reviews etc. AI and ML Tech comes into play after these reviews and recommendations have been provided by the users and then placing the same in front of potential buyers for better decision making. Artificial and machine learning technologies have been used by giants like Google, Microsoft, Facebook, and Apple for more than a decade to enhance their platforms for better user experience which can now be seen to be mandatory adaptation for most of the internet based businesses, not only as it shows better ROI, but also open countless doors for future digital opportunities.


Building Task-Oriented Dialogue Systems for Online Shopping

AAAI Conferences

We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner. As a pioneering work, we show what & how existing NLP techniques, data resources, and crowdsourcing can be leveraged to build such task-oriented dialogue systems for E-commerce usage. To demonstrate its effectiveness, we integrate our system into a mobile online shopping app. To the best of our knowledge, this is the first time that an AI bot in Chinese is practically used in online shopping scenario with millions of real consumers. Interesting and insightful observations are shown in the experimental part, based on the analysis of human-bot conversation log. Several current challenges are also pointed out as our future directions.


Artificial Intelligence in marketing (15 examples)

#artificialintelligence

Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription. Uniting information from diverse datasets is a common use of AI. Under Armour is one of the many companies to have worked with IBM's Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.


Building Jarvis

#artificialintelligence

My personal challenge for 2016 was to build a simple AI to run my home -- like Jarvis in Iron Man. My goal was to learn about the state of artificial intelligence -- where we're further along than people realize and where we're still a long ways off. These challenges always lead me to learn more than I expected, and this one also gave me a better sense of all the internal technology Facebook engineers get to use, as well as a thorough overview of home automation. So far this year, I've built a simple AI that I can talk to on my phone and computer, that can control my home, including lights, temperature, appliances, music and security, that learns my tastes and patterns, that can learn new words and concepts, and that can even entertain Max. It uses several artificial intelligence techniques, including natural language processing, speech recognition, face recognition, and reinforcement learning, written in Python, PHP and Objective C. In this note, I'll explain what I built and what I learned along the way. In some ways, this challenge was easier than I expected.


15 examples of artificial intelligence in marketing

#artificialintelligence

Here are a whole bunch of case studies and use cases, as a complete primer for AI in our industry. Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription. Uniting information from diverse datasets is a common use of AI. Under Armour is one of the many companies to have worked with IBM's Watson.


15 examples of artificial intelligence in marketing

#artificialintelligence

Artificial intelligence (see the Wikipedia definition), specifically machine learning, is an increasingly integral part of many industries, including marketing. Here are a whole bunch of case studies and use cases, as a complete primer for AI in our industry. Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription. Uniting information from diverse datasets is a common use of AI.