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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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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

The New Stack Icon The New Stack

How I built an on-premises AI training testbed with Kubernetes and Kubeflow

This is part 4 in a cool series on The New Stack exploring the Kubeflow machine learning platform.

I recently built a four-node bare metal Kubernetes cluster comprising CPU and GPU hosts for all my AI experiments. Though it makes economic sense to leverage the public cloud for provisioning the infrastructure, I invested a fortune in the AI testbed that’s within my line of sight.

The author shares many insights into the choices he made while building this dream setup.

How I built an on-premises AI training testbed with Kubernetes and Kubeflow

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.

Python github.com

`whereami` uses WiFi signals & ML to locate you (within 2-10 meters)

If you’re adventurous and you want to learn to distinguish between couch #1 and couch #2 (i.e. 2 meters apart), it is the most robust when you switch locations and train in turn. E.g. first in Spot A, then in Spot B then start again with A. Doing this in spot A, then spot B and then immediately using “predict” will yield spot B as an answer usually. No worries, the effect of this temporal overfitting disappears over time. And, in fact, this is only a real concern for the very short distances. Just take a sample after some time in both locations and it should become very robust.

The linked project was “almost entirely copied” from the find project, which was written in Go. It then went on to inspire whereami.js. I bet you can guess what that is.

HackerNoon Icon HackerNoon

Why ML in production is (still) broken and ways we can fix it

Hamza Tahir on HackerNoon:

By now, chances are you’ve read the famous paper about hidden technical debt by Sculley et al. from 2015. As a field, we have accepted that the actual share of Machine Learning is only a fraction of the work going into successful ML projects. The resulting complexity, especially in the transition to “live” environments, lead to large amounts of failed ML projects never reaching production.

Productionizing ML workflows has been a trending topic on Practical AI lately…

Why ML in production is (still) broken and ways we can fix it

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. ;-)

Elixir thinkingelixir.com

ML is coming to Elixir by way of José Valim's "Project Nx"

Elixir creator José Valim stopped by the Thinking Elixir podcast to reveal what he’s been working on for the past 3 months: Numerical Elixir!

This is an exciting development that brings Elixir into areas it hasn’t been used before. We also talk about what this means for Elixir and the community going forward. A must listen!

Queue up this episode and/or stay tuned for an upcoming episode of The Changelog where we’ll sit down with José after his LambdaDays demo to unpack things even more.

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).

Machine Learning huyenchip.com

The MLOps tooling landscape in early 2021 (284 tools)

Chip Huyen:

While looking for these MLOps tools, I discovered some interesting points about the MLOps landscape:

  1. Increasing focus on deployment
  2. The Bay Area is still the epicenter of machine learning, but not the only hub
  3. MLOps infrastructures in the US and China are diverging
  4. More interests in machine learning production from academia

If MLOps is new to you, Practical AI did a deep dive on the topic that will help you sort it out. Or if you’d prefer a shallow dive… just watch this.

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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