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Marketing campaign to Gather and Share Machine Studying Use Circumstances

Posted by Hee Jung, Developer Relations Group Supervisor / Soonson Kwon, Developer Relations Program Supervisor

Github, extra to learn

* This challenge is supported by Google Influence Fund.

Election Watch: Making use of ML in Analyzing Elections Discourse and Citizen Participation in Nigeria

By Victor Dibia, ML Google Developer Knowledgeable (USA)

This challenge explores using GCP instruments in ingesting, storing and analyzing knowledge on citizen participation and election discourse in Nigeria. It started on the premise that the proliferation of social media interactions offers an attention-grabbing lens to review human conduct, and ask essential questions on election discourse in Nigeria in addition to interrogate social/demographic questions.

It’s based mostly on knowledge collected from twitter between September 2018 to March 2019 (tweets geotagged to Nigeria and tweets containing election associated key phrases). General, the information set accommodates 25.2 million tweets and retweets, 12.6 million unique tweets, 8.6 million geotagged tweets and three.6 million tweets labeled (utilizing an ML mannequin) as political.

By analyzing election discourse, we are able to be taught a couple of essential issues together with – points that drive election discourse, how social media was utilized by candidates, and the way participation was distributed throughout geographic areas within the nation. Lastly, in a rustic like Nigeria the place up to date demographics knowledge is missing (e.g., on group constructions, wealth distribution and so on), this challenge reveals how social media can be utilized as a surrogate to deduce relative statistics (e.g., existence of diaspora communities based mostly on election dialogue and wealth distribution based mostly on gadget kind utilization throughout the nation).

Information for the challenge was collected utilizing python scripts that wrote tweets from the Twitter streaming api (matching sure standards) to BigQuery. BigQuery queries had been then used to generate combination datasets used for visualizations/evaluation and coaching machine studying fashions (political textual content classification fashions to label political textual content and multi class classification fashions to label basic discourse). The fashions had been constructed utilizing Tensorflow 2.0 and skilled on Colab notebooks powered by GCP GPU compute VMs.

References: Election Watch web site, ML fashions descriptions one, two

Bioacoustic Sound Detector (To establish hen calls in soundscapes)

By Usha Rengaraju, TFUG Organizer (India)

(Hen picture is taken by Krisztian Toth @unsplash)

“Visionary Perspective Plan (2020-2030) for the conservation of avian variety, their ecosystems, habitats and landscapes within the nation” proposed by the Indian authorities to assist in the conservation of birds and their habitats impressed me to take up this challenge.

Extinction of hen species is an growing world concern because it has a huge effect on meals chains. Bioacoustic monitoring can present a passive, low labor, and cost-effective technique for finding out endangered hen populations. Current advances in machine studying have made it doable to routinely establish hen songs for widespread species with ample coaching knowledge. This innovation makes it simpler for researchers and conservation practitioners to precisely survey inhabitants tendencies and so they’ll be capable to usually and extra successfully consider threats and modify their conservation actions.

This challenge is an implementation of a Bioacoustic monitor utilizing Masked Autoencoders in TensorFlow and Cloud TPUs. The challenge might be offered as a browser based mostly software utilizing Flask. The deep studying prototype can course of steady audio knowledge after which acoustically acknowledge the species.

The objective of the challenge once I began was to construct a fundamental prototype for monitoring of uncommon hen species in India. In future I wish to broaden the challenge to observe different endangered species as effectively.

References: Kaggle Pocket book, Colab Pocket book, Github, the dataset and extra to learn

Persona Labs’ Digital Personas

By Martin Andrews and Sam Witteveen, ML Google Developer Specialists (Singapore)

Over the past 3 years, Crimson Dragon AI (an organization co-founded by Martin and Sam) has been growing real-time digital “Personas”. The important thing concept is to allow customers to work together with life-like Personas in a format just like a Zoom name : Chatting with them and seeing them reply in actual time, simply as a human would. Naturally, every Persona might be tailor-made to duties required (by adjusting the looks, voice, and ‘motivation’ of the dialog system behind the scenes and their corresponding backend APIs).

The elements required to make the Personas work successfully embody dynamic face fashions, expression era fashions, Textual content-to-Speech (TTS), dialog backend(s) and Speech Recognition (ASR). A lot of this was constructed on GCP, with GPU VMs working the (many) Deep Studying fashions and mixing the outputs into dynamic WebRTC video that streams to customers by way of a browser front-end.

A lot of the earlier years’ work focussed on making the Personas’ faces behave in a life-like manner, whereas ensuring that the general latency (i.e. the time between the Persona listening to the person asking a query, to their lips beginning the response) is saved low, and the rendering of particular person photos matches the 25 frames-per-second video charge required. As you may think, there have been many Deep Studying modeling challenges, coupled with exhausting engineering points to beat.

By way of backend applied sciences, Google Cloud GPUs had been used to coach the Deep Studying fashions (constructed utilizing TensorFlow/TFLite, PyTorch/ONNX & extra lately JAX/Flax), and the real-time serving is completed by Nvidia T4 GPU-enabled VMs, launched as required. Google ASR is presently used as a streaming backend for speech recognition, and Google’s WaveNet TTS is used when multilingual TTS is required. The system additionally makes use of Google’s serverless stack with CloudRun and Cloud Features being utilized in a few of the dialog backends.

Go to the Persona’s web site (linked beneath) and you may see movies that exhibit a number of elements : What the Personas appear to be; their Multilingual functionality; potential purposes; and so on. Nevertheless, the movies can’t actually exhibit what the interactivity ‘looks like’. For that, it’s finest to get a reside demo from Sam and Martin – and see what real-time Deep Studying mannequin era seems to be like!

Reference: The Persona Labs web site



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