Daniel and Chris get you Fully-Connected with open source software for artificial intelligence.
In addition to defining what open source is, they discuss where to find open source tools and data, and how you can contribute back to the open source AI community.
Daniel and Chris get you Fully-Connected with open source software for artificial intelligence.
A lot of effort is put into the training of AI models, but, for those of us that actually want to run AI models in production, performance and scaling quickly become blockers. Nikita from MemSQL joins us to talk about how people are integrating ML/AI inference at scale into existing SQL-based workflows. He also touches on how model features and raw files can be managed and integrated with distributed databases.
This full connected has it all: news, updates on AI/ML tooling, discussions about AI workflow, and learning resources. Chris and Daniel breakdown the various roles to be played in AI development including scoping out a solution, finding AI value, experimentation, and more technical engineering tasks. They also point out some good resources for exploring bias in your data/model and monitoring for fairness.
Daniel and Chris go beyond the current state of the art in deep learning to explore the next evolutions in artificial intelligence. From Yoshua Bengio’s NeurIPS keynote, which urges us forward towards System 2 deep learning, to DARPA’s vision of a 3rd Wave of AI, Chris and Daniel investigate the incremental steps between today’s AI and possible future manifestations of artificial general intelligence (AGI).
For years now I’ve been asking AI/ML experts when these powerful-yet-complicated tools will become available to average developers like you and me. It’s happening! Just look at how high-level this text generation code sample is:
import openai prompt = """snipped for brevity's sake""" response = openai.Completion.create(model="davinci", prompt=prompt, stop="\n", temperature=0.9, max_tokens=100)
They’re oftening all kinds of language tasks: semantic search, summarization, sentiment analysis, content generation, translation, and more. The API is still in beta and there’s a waitlist, but this is exciting news, nonetheless.
Neuropod is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.
This looks nice because you can make your inference code framework agnostic and easily switch between frameworks if necessary. Currently supports TensorFlow, PyTorch, TorchScript, and Keras.
The CEO of Darwin AI, Sheldon Fernandez, joins Daniel to discuss generative synthesis and its connection to explainability. You might have heard of AutoML and meta-learning. Well, generative synthesis tackles similar problems from a different angle and results in compact, explainable networks. This episode is fascinating and very timely.
Acme is a library of reinforcement learning (RL) agents and agent building blocks. Acme strives to expose simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. The design of Acme also attempts to provide multiple points of entry to the RL problem at differing levels of complexity.
On the heels of NVIDIA’s latest announcements, Daniel and Chris explore how the new NVIDIA Ampere architecture evolves the high-performance computing (HPC) landscape for artificial intelligence. After investigating the new specifications of the NVIDIA A100 Tensor Core GPU, Chris and Daniel turn their attention to the data center with the NVIDIA DGX A100, and then finish their journey at “the edge” with the NVIDIA EGX A100 and the NVIDIA Jetson Xavier NX.
Chandler McCann tells Daniel and Chris about how DataRobot engaged in a project to develop sustainable water solutions with the Global Water Challenge (GWC). They analyzed over 500,000 data points to predict future water point breaks. This enabled African governments to make data-driven decisions related to budgeting, preventative maintenance, and policy in order to promote and protect people’s access to safe water for drinking and washing. From this effort sprang DataRobot’s larger AI for Good initiative.
Learn how a CNN model transforms different images into class predictions with all of the intermediate steps along the way. It’s interactive, so you can select individual neurons and inspect the details.
Daniel and Chris get you Fully-Connected with AI questions from listeners and online forums:
- What do you think is the next big thing?
- What are CNNs?
- How does one start developing an AI-enabled business solution?
- What tools do you use every day?
- What will AI replace?
- And more…
Daniel and Chris have a fascinating discussion with Anna Goldie and Azalia Mirhoseini from Google Brain about the use of reinforcement learning for chip floor planning - or placement - in which many new designs are generated, and then evaluated, to find an optimal component layout. Anna and Azalia also describe the use of graph convolutional neural networks in their approach.
In the midst of the COVID-19 pandemic, Daniel and Chris have a timely conversation with Lucy Lu Wang of the Allen Institute for Artificial Intelligence about COVID-19 Open Research Dataset (CORD-19). She relates how CORD-19 was created and organized, and how researchers around the world are currently using the data to answer important COVID-19 questions that will help the world through this ongoing crisis.
A fun little project that uses a neural network to map your facial movements onto an avatar of your choice. You have to watch the demo to get the full effect.
If you say… “Hey, computer, play me some music” and then it starts playing you some music, there’s a number of things that have to have happened for that to come true.
Meta-Blocks is a modular toolbox for research, experimentation, and reproducible benchmarking of learning-to-learn algorithms. The toolbox provides flexible APIs for working with MetaDatasets, TaskDistributions, and MetaLearners (see the figure below). The APIs make it easy to implement a variety of meta-learning algorithms, run them on well-established and emerging benchmarks, and add your own meta-learning problems to the suite and benchmark algorithms on them.
This repo is still under “heavy construction” (a.k.a. unstable) so downloader beware, but it’s worth a star/bookmark for later use.
AI legend Stuart Russell, the Berkeley professor who leads the Center for Human-Compatible AI, joins Chris to share his insights into the future of artificial intelligence. Stuart is the author of Human Compatible, and the upcoming 4th edition of his perennial classic Artificial Intelligence: A Modern Approach, which is widely regarded as the standard text on AI. After exposing the shortcomings inherent in deep learning, Stuart goes on to propose a new practitioner approach to creating AI that avoids harmful unintended consequences, and offers a path forward towards a future in which humans can safely rely of provably beneficial AI.
So many AI developers are coming up with creative, useful COVID-19 applications during this time of crisis. Among those are Timo from Deepset-AI and Tony from Intel. They are working on a question answering system for pandemic-related questions called COVID-QA. In this episode, they describe the system, related annotation of the CORD-19 data set, and ways that you can contribute!
Here is my python source code for training an agent to play Tetris. It could be seen as a very basic example of Reinforcement Learning’s application.
Demo on YouTube.
Daniel Wilson and Rob Fletcher of ESRI hang with Chris and Daniel to chat about how AI powered modern geographic information systems (GIS) and location intelligence. They illuminate the various models used for GIS, spatial analysis, remote sensing, real-time visualization, and 3D analytics. You don’t want to miss the part about their work for the DoD’s Joint AI Center in humanitarian assistance / disaster relief.
Have you ever posted an image on the public internet and thought, “What if someone used this for something?” Thomas Smith did and what he discovered about Clearview AI is disturbing…
Someone really has been monitoring nearly everything you post to the public internet. And they genuinely are doing “something” with it.
The someone is Clearview AI. And the something is this: building a detailed profile about you from the photos you post online, making it searchable using only your face, and then selling it to government agencies and police departments who use it to help track you, identify your face in a crowd, and investigate you — even if you’ve been accused of no crime.
I realize that this sounds like a bunch of conspiracy theory baloney. But it’s not. Clearview AI’s tech is very real, and it’s already in use.
How do I know? Because Clearview has a profile on me. And today I got my hands on it.
Practical AI is a weekly podcast that’s marking artificial intelligence practical, productive, and accessible to everyone. If world of AI affects your daily life, this show is for you.
From the practitioner wanting to keep up with the latest tools & trends…
(clip from episode #68)
To the AI curious trying to understand the concepts at play and their implications on our lives…
(clip from episode #39)
Expert hosts Chris Benson and Daniel Whitenack are here to keep you fully-connected with the world of machine learning and data science.
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I used multilingual unsupervised methods (MUSE) to train cross-lingual word embeddings for over 500 languages. I then used these embeddings to extract components of the phrase “wash your hands” from existing target language documents. This resulted in translations of “wash your hands” in 510 languages not currently supported in any public translation platform.
Catherine Breslin of Cobalt joins Daniel and Chris to do a deep dive on speech recognition. She also discusses how the technology is integrated into virtual assistants (like Alexa) and is used in other non-assistant contexts (like transcription and captioning). Along the way, she teaches us how to assemble a lexicon, acoustic model, and language model to bring speech recognition to life.