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Practical AI Practical AI #133

25 years of speech technology innovation

To say that Jeff Adams is a trailblazer when it comes to speech technology is an understatement. Along with many other notable accomplishments, his team at Amazon developed the Echo, Dash, and Fire TV changing our perception of how we could interact with devices in our home. Jeff now leads Cobalt Speech and Language, and he was kind enough to join us for a discussion about human computer interaction, multimodal AI tasks, the history of language modeling, and AI for social good.

The Changelog The Changelog #439

Elixir meets machine learning

This week Elixir creator José Valim joins Jerod and Practical AI’s Daniel Whitenack to discuss Numerical Elixir, his new project that’s bringing Elixir into the world of machine learning. We discuss why José chose this as his next direction, the team’s layered approach, influences and collaborators on this effort, and their awesome collaborative notebook project that’s built on Phoenix LiveView.

Practical AI Practical AI #132

Generating "hunches" using smart home data 🏠

Smart home data is complicated. There are all kinds of devices, and they are in many different combinations, geographies, configurations, etc. This complicated data situation is further exacerbated during a pandemic when time series data seems to be filled with anomalies. Evan Welbourne joins us to discuss how Amazon is synthesizing this disparate data into functionality for the next generation of smart homes. He discusses the challenges of working with smart home technology, and he describes how they developed their latest feature called “hunches.”

AI (Artificial Intelligence) exxactcorp.com

Disentangling AI, machine learning, and deep learning

This article starts with a concise description of the relationship and differences of these 3 commonly used industry terms. Then it digs into the history.

Deep learning is a subset of machine learning, which in turn is a subset of artificial intelligence, but the origins of these names arose from an interesting history. In addition, there are fascinating technical characteristics that can differentiate deep learning from other types of machine learning…essential working knowledge for anyone with ML, DL, or AI in their skillset.

Disentangling AI, machine learning, and deep learning

Practical AI Practical AI #127

Women in Data Science (WiDS)

Chris has the privilege of talking with Stanford Professor Margot Gerritsen, who co-leads the Women in Data Science (WiDS) Worldwide Initiative. This is a conversation that everyone should listen to. Professor Gerritsen’s profound insights into how we can all help the women in our lives succeed - in data science and in life - is a ‘must listen’ episode for everyone, regardless of gender.

Python github.com

A PyTorch-based speech toolkit

SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, multi-microphone signal processing and many others.

Currently in beta.

Practical AI Practical AI #124

Green AI 🌲

Empirical analysis from Roy Schwartz (Hebrew University of Jerusalem) and Jesse Dodge (AI2) suggests the AI research community has paid relatively little attention to computational efficiency. A focus on accuracy rather than efficiency increases the carbon footprint of AI research and increases research inequality. In this episode, Jesse and Roy advocate for increased research activity in Green AI (AI research that is more environmentally friendly and inclusive). They highlight success stories and help us understand the practicalities of making our workflows more efficient.

Tooling github.com

Search inside YouTube videos using natural language

Use OpenAI’s CLIP neural network to search inside YouTube videos. You can try it by running the notebook on Google Colab.

The README has a bunch of examples of things you might search for and the results you’d get back. (“The Transamerica Pyramid”, anyone?)

The author also has another related project where you can search Unsplash in like manner.

Practical AI Practical AI #122

The AI doc will see you now

Elad Walach of Aidoc joins Chris to talk about the use of AI for medical imaging interpretation. Starting with the world’s largest annotated training data set of medical images, Aidoc is the radiologist’s best friend, helping the doctor to interpret imagery faster, more accurately, and improving the imaging workflow along the way. Elad’s vision for the transformative future of AI in medicine clearly soothes Chris’s concern about managing his aging body in the years to come. ;-)

Ines Montani github.com

Introducing spaCy 3.0

You may recall spaCy from this episode of Practical AI with its creators. If not, now’s a great time to introduce yourself to the project. 3.0 looks like a fantastic new release of the wildly popular NLP library. The list of new and improved things is too long for me to reproduce here, so go check it out for yourself.

There’s also three YouTube videos accompanying the release. That’s evidence of just how much effort and polish went in to this.

Machine Learning marksaroufim.substack.com

Machine Learning: The Great Stagnation

This piece by Mark Saroufim on the state of ML starts pretty salty:

Graduate Student Descent is one of the most reliable ways of getting state of the art performance in Machine Learning today and it’s also a fully parallelizable over as many graduate students or employees your lab has. Armed with Graduate Student Descent you are more likely to get published or promoted than if you took on uncertain projects.

and:

BERT engineer is now a full time job. Qualifications include:

  • Some bash scripting
  • Deep knowledge of pip (starting a new environment is the suckier version of practicing scales)
  • Waiting for new HuggingFace models to be released
  • Watching Yannic Kilcher’s new Transformer paper the day it comes out
  • Repeating what Yannic said at your team reading group

It’s kind of like Dev-ops but you get paid more.

But if you survive through (or maybe even enjoy) the lamentations and ranting, you’ll find some hope and optimism around specific projects that the author believes are pushing the industry through its Great Stagnation.

I learned a few things. Maybe you will too.

Practical AI Practical AI #119

Accelerating ML innovation at MLCommons

MLCommons launched in December 2020 as an open engineering consortium that seeks to accelerate machine learning innovation and broaden access to this critical technology for the public good. David Kanter, the executive director of MLCommons, joins us to discuss the launch and the ambitions of the organization.

In particular we discuss the three pillars of the organization: Benchmarks and Metrics (e.g. MLPerf), Datasets and Models (e.g. People’s Speech), and Best Practices (e.g. MLCube).

Practical AI Practical AI #118

The $1 trillion dollar ML model 💵

American Express is running what is perhaps the largest commercial ML model in the world; a model that automates over 8 billion decisions, ingests data from over $1T in transactions, and generates decisions in mere milliseconds or less globally. Madhurima Khandelwal, head of AMEX AI Labs, joins us for a fascinating discussion about scaling research and building robust and ethical AI-driven financial applications.

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