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ACM calls for governments and businesses to stop using facial recognition

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An Association for Computing Machinery (ACM) tech policy group today urged lawmakers to immediately suspend use of facial recognition by businesses and governments, citing documented ethnic, racial, and gender bias. In a letter (PDF) released today by the U.S. Technology Policy Committee (USTPC), the group acknowledges the tech is expected to improve in the future but is not yet “sufficiently mature” and is therefore a threat to people’s human and legal rights.

“The consequences of such bias, USTPC notes, frequently can and do extend well beyond inconvenience to profound injury, particularly to the lives, livelihoods and fundamental rights of individuals in specific demographic groups, including some of the most vulnerable populations in our society,” the letter reads.

Organizations studying use of the technology, like the Perpetual Lineup Project from Georgetown University, conclude that broad deployment of the tech will negatively impact the lives of Black people in the United States. Privacy and racial justice advocacy groups like ACLU and the Algorithmic Justice League have supported halts to the use of the facial recognition in the past, but with nearly 100,000 members around the world, ACM is one of the biggest computer science organizations in the world. ACM also hosts large AI annual conferences like Siggraph and the International Conference on Supercomputing (ICS).

The letter also prescribes principles for facial recognition regulation surrounding issues like accuracy, transparency, risk management, and accountability. Recommended principles include:

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  • Disaggregate system error rates based on race, gender, sex, and other appropriate demographics
  • Facial recognition systems must undergo third-party audits and “robust government oversight”
  • People must be notified when facial recognition is in use, and appropriate use cases must be defined before deployment
  • Organizations using facial recognition should be held accountable if or when a facial recognition system causes a person harm

The letter does not call for a permanent ban on facial recognition, but a temporary moratorium until accuracy standards for race and gender performance, as well as laws and regulations, can be put in place. Tests of major facial recognition systems in 2018 and 2019 by the Gender Shades project and then Department of Commerce’s NIST found facial recognition systems exhibited race and gender bias, as well as poor performance on people who do not conform to a single gender identity.

The committee’s statement comes at the end of what’s been a historic month for facial recognition software. Last week, members of Congress from the Senate and House of Representatives introduced legislation that would prohibit federal employees from using facial recognition and cut funding for state and local governments who chose to continue using the technology. Lawmakers on a city, state, and national level considering regulation of facial recognition frequently cite bias as a major motivator to pass legislation against its use. And Amazon, IBM, and Microsoft halted or ended sale of facial recognition for police shortly after the height of Black Lives Matter protests that spread to more than 2,000 cities across the U.S.

Citing race and gender bias and misidentification, the Boston City Council became one of the biggest cities in the U.S. to impose a facial recognition ban. That same day, people learned the story of Detroit resident Robert Williams, who is thought to be the first person falsely arrested and charged with a crime because of faulty facial recognition. Detroit police chief James Craig said Monday that facial recognition software that Detroit uses is inaccurate 96% of the time.

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/J0wFHpYqnZU/

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Microsoft announces new Azure AI capabilities for apps, healthcare, and more

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The latest announcements will help companies enhance their voice-enabled application experiences and provide critical data management across healthcare industries.

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Image: Sompong Rattanakunchon / Getty Images

In the era of digital transformation, more organizations across industries are looking to leverage artificial intelligence (AI) to enhance day-to-day operations. In recent weeks, a number of organizations have tapped AI to help mitigate the spread of the coronavirus. These applications range from using AI systems to monitor social distancing and contact tracing to identifying potential treatments for COVID-19. Earlier today, Microsoft announced a series of updates to the Azure AI system to help with everything from enhanced healthcare data management to leveraging the latest voice-enabled technologies for enhanced customer engagement experiences.

More about artificial intelligence

Text Analytics

In partnership with the Allen Institute of AI and other research groups, Microsoft developed the COVID-19 Open Research Dataset. Utilizing nearly 50,000 scholarly articles, the team created a COVID-19 search engine. This search engine uses Microsoft Cognitive Search and Text Analytics for health to allow researchers to produce new medical insights to combat the spread of the coronavirus.

SEE: Coronavirus: Critical IT policies and tools every business needs (TechRepublic Premium)

As part of the Microsoft announcement, the company unveiled a new Text Analytics for health feature. This new Text Analytics feature will allow healthcare organizations, providers, and researchers to gain insights and correlations from unstructured medical information. This new feature has been trained on a wide spectrum of medical information and is capable of “processing a broad range of data types and tasks, without the need for time-intensive, manual development of custom models to extract insights from the data,” per Microsoft.

Form Recognizer

Currently, unstructured medical data is stored in forms comprised of objects, tables, and other ordering components. To effectively gain insights from this unstructured data, people historically have had to manually label or code each of these document types. To assist with this arduous process, Microsoft also announced a generally available Form Recognizer tool enabling individuals more expeditiously extract this data in an accurate and efficient way.

“Our Cognitive Document Processing (CDP) offer enables clients to process and classify unstructured documents and extract data with high accuracy resulting in reduced operating costs and processing time. CDP leverages the powerful cognitive and tagging capabilities of the Form Recognizer to extract effortlessly, keyless paired data and other relevant information from scanned/digital unstructured documents, further reducing the overall process time,” said Mark Oost, chief technology officer at Sogeti.

SEE: Hiring Kit: Computer Research Scientist (TechRepublic Premium)

Custom Commands

Microsoft also announced a Custom Commands feature designed to assist with voice-enabled applications and integration. Overall, the feature merges the Azure’s Speech to Text, Text to Speech, and Language Understanding allowing customers to quickly add their voice capabilities to their apps “with a low-code authoring experience.” Custom Commands uses Speech in Cognitive Services capabilities and is now generally available. Microsoft also announced that its Neural Text to Speech would be offering language support with “15 new natural-sounding voices based on state-of-the-art neural speech synthesis models.”

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Source: https://www.techrepublic.com/article/microsoft-announces-new-azure-ai-capabilities-for-apps-healthcare-and-more/#ftag=RSS56d97e7

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Giving your content a voice with the Newscaster speaking style from Amazon Polly

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Audio content consumption has grown exponentially in the past few years. Statista reports that podcast ad revenue will exceed a billion dollars in 2021. For the publishing industry and content providers, providing audio as an alternative option to reading could improve engagement with users and be an incremental revenue stream. Given the shift in customer trends to audio consumption, Amazon Polly launched a new speaking style focusing on the publishing industry: the Newscaster speaking style. This post discusses how the Newscaster voice was built and how you can use the Newscaster voice with your content in a few simple steps.

Building the Newscaster style voice

Until recently, Amazon Polly voices were built such that the speaking style of the voice remained the same, no matter the use case. In the real world, however, speakers change their speaking style based on the situation at hand, from using a conversational style around friends to using upbeat and engaging speech when telling stories. To make voices as lifelike as possible, Amazon Polly has built two speaking style voices: Conversational and Newscaster. Newscaster style, available in US English for Matthew and Joanna, and US Spanish for Lupe, gives content a voice with the persona of a news anchor. Have a listen to the following samples:

With the successful implementation of Neural Text-to-Speech (NTTS), text synthesis no longer relies on a concatenative approach, which mainly consisted of finding the best chunks of recordings to generate synthesized speech. The concatenative approach played audio that was an exact copy of the recordings stored for that voice. NTTS, on the other hand, relies on two end-to-end models that predict waveforms, which results in smoother speech with no joins. NTTS outputs waveforms by learning from training data, which enables seamless transitions between all the sounds and allows us to focus on the rhythm and intonation of the voice to match the existing voice timbre and quality for Newscaster speaking style.

Remixd, a leading audio technology partner for premium publishers, helps publishers and media owners give their editorial content a voice using Amazon Polly. Christopher Rooke, CEO of Remixd, says, “Consumer demand for audio has exploded, and content owners recognize that the delivery of journalism must adapt to meet this moment. Using Amazon Polly’s Newscaster voice, Remixd is helping news providers innovate and keep up with demand to serve the growing customer appetite for audio. Remixd and Amazon Polly make it easy for publishers to remain relevant as content consumption preferences shift.”

Remixd uses Amazon Polly to provide audio content production efficiencies at scale, which makes it easy for publishers to instantly enable audio for new and existing editorial content in real time without needing to invest in costly human voice talent, narration, and pre- and post-production overhead. Rooke adds, “When working with news content, where information is time-sensitive and perishable, the voice quality, and the ability to process large volumes of content and publish the audio version in just a few seconds, is critical to service our customer base.” The following screenshot shows Remixd’s audio player live on one of their customer’s website Daily Caller.

“At the Daily Caller, it’s a priority that our content is accessible and convenient for visitors to consume in whichever format they prefer,” says Chad Brady, Director of Operations of the Daily Caller. “This includes audio, which can be time-consuming and costly to produce. Using Remixd, coupled with Amazon Polly’s high-quality newscaster voice, Daily Caller editorial articles are made instantly listenable, enabling us to scale production and distribution, and delight our audience with a best-in-class audio experience both on and off-site.”

The new NTTS technology enables newscaster voices to be more expressive. However, although the expressiveness vastly increases how natural the voice sounds, it also makes the model more susceptible to discrepancies. NTTS technology learns to model intonation patterns for a given punctuation mark based on data it was provided. Because the intonation patterns are much more extreme for style voices, good annotation of the training data is essential. The Amazon Polly team trained the model with an initial small set of newscaster recordings in addition to the existing recordings from the speakers. Having more data leads to more robust models, but to build a model in a cost- and time-efficient manner, the Amazon Polly team worked on concepts such as multi-speaker models, which allow you to use existing resources instead of needing more recordings from the same speaker.

Evaluations have shown that our newscaster voice is preferred over the neutral speaking style for voicing news content. The following histogram shows results for the Joanna Newscaster voice when compared to other voices for the news use case.

Using Newscaster style to voice your audio content

To use the Newscaster style with Python, complete the following steps (this solution requires Python 3):

  1. Set up and activate your virtual environment with the following code:
    $ python3 -m virtualenv ./venv
    $ . ./venv/bin/activate

  2. Install the requirements with the following code:
    $ pip install boto3 click

  3. In your preferred text editor, create a file say_as_newscaster.py. See the following code:
    import boto3
    import click
    import sys polly_c = boto3.client('polly') @click.command()
    @click.argument('voice')
    @click.argument('text')
    def main(voice, text): if voice not in ['Joanna', 'Matthew', ‘Lupe’]: print('Only Joanna, Matthew and Lupe support the newscaster style') sys.exit(1) response = polly_c.synthesize_speech( VoiceId=voice, Engine='neural', OutputFormat='mp3', TextType='ssml', Text = f'<speak><amazon:domain name="news">{text}></amazon:domain></speak>') f = open('newscaster.mp3', 'wb') f.write(response['AudioStream'].read()) f.close() if __name__ == '__main__': main()

  4. Run the script passing the name and text you want to say:
    $ python ./say_as_newscaster.py Joanna "Synthesizing the newsperson style is innovative and unprecedented. And it brings great excitement in the media world and beyond."

This generates newscaster.mp3, which you can play in your favorite media player.

Summary

This post walked you through the Newscaster style and how to use it in Amazon Polly. The Matthew, Joanna, and Lupe Newscaster voices are used by customers such as The Globe and Mail, Gannetts’ USA Today, DailyCaller and many others.

To learn more about using the Newscaster style in Amazon Polly, see Using the Newscaster Style. For the full list of voices that Amazon Polly offers, see Voices in Amazon Polly.


About the Authors

Joppe Pelzer is a Language Engineer working on text-to-speech for English and building style voices. With bachelor’s degrees in linguistics and Scandinavian languages, she graduated from Edinburgh University with an MSc in Speech and Language Processing in 2018. During her masters she focused on the text-to-speech front end, building and expanding upon multilingual G2P models, and has gained experience with NLP, Speech recognition and Deep Learning. Outside of work, she likes to draw, play games, and spend time in nature.

Ariadna Sanchez is a Research Scientist investigating the application of DL/ML technologies in the area of text-to-speech. After completing a bachelor’s in Audiovisual Systems Engineering, she received her MSc in Speech and Language Processing from University of Edinburgh in 2018. She has previously worked as an intern in NLP and TTS. During her time at University, she focused on TTS and signal processing, especially in the dysarthria field. She has experience in Signal Processing, Deep Learning, NLP, Speech and Image Processing. In her free time, Ariadna likes playing the violin, reading books and playing games.

Source: https://aws.amazon.com/blogs/machine-learning/giving-your-content-a-voice-with-the-newscaster-speaking-style-from-amazon-polly/

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The NVIDIA Data Science Interview

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NVIDIA Corporation is an American technology giant based in Santa Clara, California. NVIDIA designs graphics processing units (GPUs) for the expanding gaming and professional markets, as well as system on chip units (SoCs) for the mobile computing and automotive market. Data scientists at NVIDIA leverage the most advanced data science technology in their products that ultimately get used in professional visualization, data centers, artificial intelligence, virtual reality, and self-driving cars.

Aside being a data-driven company, NVIDIA also helps data scientists across a wide range of industry (medicine, finance, operations etc.) make better use of their data and enhanced performance. With products like RAPIDS (an NVIDIA open source end-to-end GPU accelerated data science library that runs on Nvidia GPUs) and DGX line of GPUs, Nvidia has provided data scientists, machine learning/deep learning scientists and developers with the compute power they need to get their works done.

The Data Science Role at NVIDIA

The roles of a data scientist at NVIDIA varies across specific teams, products, and features. Generally data scientist functions and roles at NVIDIA may span across a wide scope of data science concepts but are primarily focused on machine learning and deep learning. This means having an in-depth understanding of developing solutions on cloud computing clusters while also deploying ML and DL models at scale.

Required Skills

  • MS/PhD in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields.
  • 3 years plus (8+ years for senior-level) work or research experience with Python, C++ software development.
  • Extensive experience with Machine Learning/Deep Learning algorithms with frameworks such as TensorFlow, PyTorch, XGboost, Scikit-learn, or Spark.
  • Proficient in the use of any of the following: C, Python, Scala, SQL, Java, or C++ programming languages.
  • Ability to build ETL pipelines in a cloud environment.
  • Ability to cohesively work with multiple levels and teams across organizations (Engineering, Product, Sales and Marketing team).
  • Effective verbal/written communication, and technical presentation skills.

What kind of data science role?

NVIDIA has no dedicated data science department, but there are a wide variety of data science teams each having its own unique process. There is a data science team working on data centers, a data team on RAPIDS, and an AI-driven auto team and software development team. Data scientist roles at NVIDIA may differ slightly and in some cases overlap.

Depending on the teams, the functions of a data scientist may stretch across being a deep learning engineer to primarily working as a research scientist focused on computer vision.

Basic responsibilities include:

  • Develop and demonstrate solutions based on NVIDIA’s state-of-the-art ML/DL, data science software and hardware technologies to customers.
  • Perform in-depth analysis and optimization to ensure the best performance on GPU architecture systems.
  • Applying deep learning solutions to areas such as object detection, segmentation, video understanding, sequence prediction, adaptive computing, memory networks, reduced precision training and inference, graph compilers, reinforcement learning, search, distributed and federated training, and more.
  • Collaborate with key industry partner/customer developers to provide ML solutions applied to their products and technologies.
  • Partner with Engineering, Product and Sales teams to secure design wins at customers.
  • Work closely with customer’s data science, ML/DL developers and IT teams.

The NVIDIA Interview Process

Like most tech companies, the data science hiring process at NVIDIA starts with an initial phone screen with recruiting, and then is followed by a technical phone screen with a team manager. After finishing through the technical phone screen you then proceed to the onsite interview comprised of 7 one-on-one interviews (each lasting between 30 to 60 minutes) with a hiring manager, team members, and a product manager.

Initial Screen

This is a resume-based phone interview with HR or a hiring manager. This interview is exploratory in nature and requires a run-down of your resume and relevant past projects to determine if you are a good fit for the position/team.

Technical Screen

After the initial phone screen, a technical interview with a data scientist will be scheduled. This interview is between 45 and 60 minutes long, and it involves questions around a real-life NVIDIA problem. Expect to explain your machine learning experience in-depth and talk about how you might design a ML or DL system and scale the process.

Onsite Interview

The onsite interview is the last interview stage in the NVIDIA data scientist hiring process. The onsite interview process for a data scientist at NVIDIA comprises of 7 interview rounds, either one-on-one or with a small panel of interviewers, consisting of team members, a team manager, and a product manager, with each interview rounds lasting between 45 and 60 minutes.

This interview is a combination of various data science concepts including data analytics, software engineering, machine learning, and NVIDIA’s core culture and values. For the technical questions, candidates are expected to perform coding exercise on a whiteboard or on a laptop provided by NVIDIA.

Questions in this interview span across advanced statistical concepts to deep learning implementation and design. For the technical aspect, remember to practice coding in compiler languages as well as Python, and also practice questions on machine learning algorithms and coding in Tensorflow, Keras, or other deep learning frameworks.

Last Tips

  • It really helps to have prior experience in GPU development. This means GPGPU programming and design practices or working for a potential competitor such as Intel, AMD, etc… in some sort of data science capacity.
  • NVIDIA has been breaking the technology barrier with their GPU and chip development. This generally done by hiring researchers or people out of academia which established records of thought leadership in a technical area or industry segment. This also means they are willing to pay an extraordinary sum in total compensation to do so as well.

NVIDIA Data Science Interview Questions

  • Given a time series dataset, how would you detect an anomaly?
  • What’s the difference between a True Positive and a False Positive?
  • Implement gradient descent in Tensorflow.
  • Design a recommendation engine from end to end from a dataset to deployment in production.
  • Write down the equation for linear regression.
  • Explain how a decision tree works under the hood.

This article was originally published on Interview Query Blog and re-published to TOPBOTS with permission from the author.

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Source: https://www.topbots.com/the-nvidia-data-science-interview/?utm_source=rss&utm_medium=rss&utm_campaign=the-nvidia-data-science-interview

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