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Google Tensor Processing Units For Chat Conversation Analysis: What You Should Know

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Google Tensor Processing Units which are available via Google Computing Engines are used to accelerate a wide-range of machine learning workloads, including both training and inference. Machine learning (and neural networks in particular) plays a major role in optimizing Google Search, has dramatically improved the quality of Google Translation, makes it easy to find photos through Google Photos. When applied to e-commerce, Machine Learning can be used for chat conversation analysis, outputting insights that assist chat agents in building rapport with customers. Training the underlying machine learning models and running the models once they are trained – a process known as inference – requires a considerable amount of computation. Google built and deployed a family of Tensor Processing Units (TPUs) to support large machine learning computations.

Chat Conversation Analysis

A server rack containing multiple Tensor Processing Units, which are now used to both train AI systems and help them perform real-time tasks

A Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) developed by Google specifically for Machine Learning. In Google Photos, an individual TPU can process over 100 million photos a day. It is also used in RankBrain to provide search results. Furthermore, TPU’s can be used to process a company’s customer service data to generate inights that will assist chat agents with customer care strategies. To promote research in the field, Google has recently announced a cluster of 1,000 Cloud TPUs to researchers in machine learning for free.

The need for Tensor Processing Units emerged roughly six years ago when Google started using computationally expensive Deep Learning models with greater frequency in their products. The computational expense of using these models had everyone at Google worried. In a hypothetical scenario in which customers used Google Voice Search for just three minutes a day, Google would have to run deep neural nets and the speech recognition system for processing the units, meaning Google would have had to double the number of its data centers!

The original TPU was designed to work with Google’s TensorFlow, one of the many open-source software libraries for machine learning for various applications, including chat conversation analysis. Thanks to Google’s advances and its integration of hardware and software, TensorFlow has emerged as one of the leading platforms on which to build AI software. Now, a second version of Google’s TPU system is operational, and is deployed across its Google Compute Engine, a platform other companies and resources can use for computing.

Chat Conversation Analysis

TPU Performance Power

TPUs allow Data Scientists to make predictions quickly, and in turn, enable products that respond in fractions of a second. TPUs are behind every search query; they power accurate vision models that underlie products like Google Image Search, Google Photos and the Google Cloud Vision API; they underpin the groundbreaking quality improvements that Google Translate rolled out last year; and they were instrumental in Google DeepMind's victory over Lee Sedol, the first instance of a computer defeating a world champion in the ancient game of Go.

Google’s TPU underpins the company’s most ambitious and far-reaching technologies. The TPU, which was initially designed for Machine Learning, also forms the basis of AlphaGo Artificial Intelligence System’s predictive and decision-making skills. Google uses the TPU’s computing power to optimize it’s search results. TPU is employed every time a user enters a query into its search engine. Recently, the technology has been applied to machine learning models used to improve Google Translate, Google Photos, and other software to efficiently make use of AI technologies. TPU is essential for Google Products, as well as other Natural Language Processing techniques that facilitate chat conversation analysis. When applied, this technology has far-reaching implications for assisting humans with everyday tasks and decision making, such as to assist chat agents in building rapport with customers.

Learn more about chat conversation analysis from the team at RapportBoost.

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Dr. Michael Housman

About Dr. Michael Housman

Michael has spent his entire career applying state-of-the-art statistical methodologies and econometric techniques to large data-sets in order to drive organizational decision-making and helping companies operate more effectively. Prior to founding RapportBoost.AI, he was the Chief Analytics Officer at Evolv (acquired by Cornerstone OnDemand for $42M in 2015) where he helped architect a machine learning platform capable of mining databases consisting of hundreds of millions of employee records. He was named a 2014 game changer by Workforce magazine for his work. Michael is currently an equity advisor for a half-dozen technology companies based out of the San Francisco bay area: hiQ Labs, Bakround, Interviewed, Performiture, Tenacity, Homebase, and States Title. He was on Tony’s advisory board at Boopsie from 2012 onward. Michael is a noted public speaker and has published his work in a variety of peer-reviewed journals and has had his research profiled by The New York Times, Wall Street Journal, The Economist, and The Atlantic. Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University.

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