Blog

Aug 14, 2015 · post

Why Now? Some Preconditions for Technology Innovations

We like to hold fast to the myth of the individual creative genius as the source of the world’s most impactful scientific revolutions or disruptive innovations. But it’s consoling to recall how Isaac Newton consoled his rival Robert Hooke: “If I’ve seen further than others, it was by standing on the shoulders of giants.”

image
This is French how painter Nicolas Poussin represents Cedalion providing sight to the blind Orion, a mythological pair associated with each generation’s progress over its predecessors.

Fast Forward Labs clients frequently ask why the algorithms we explore in our reports have all of the sudden become so important. While the answers to these questions are extremely complex, we devote part of each report to explaining the history of a given technique and suggesting what changes had to take place to render each innovation possible and practical. 

Take deep learning. A basic form of the neural nets that underly deep learning algorithms have been around since the 1950s. In 1958, Frank Rosenblatt told the New York Times that his Perceptron would be the beginning of computers that could walk, talk, see, write, reproduce themselves, and be conscious of existence. As fifty years have passed without generating sentient machines - and in spite of the fear of the so-called “intelligence explosion” that dominated discussions at Effective Altruism - we know these statements were exaggerated given the state of technology at that time. But three recent developments have caused resurgent interest in and applicability of neural nets.

First, there’s been strong progress in our theoretical understanding of the networks themselves. Second, GPU (Graphical Processing Unit) computation has become affordable. GPUs were primarily developed for video gaming and similar applications, but are also optimized for exactly the kind of processing that neural networks require. Finally, large image and video datasets are now available. This, more than anything, has motivated and enabled significant progress for both research and industry applications. 

In a recent interview with Inverse, Fast Forward Research Analyst Jessica Graves similarly described how the shifts in the economics of data storage have made big data so important. She explains:

“Storage capabilities leaped over time. If you think in terms of the web, there’s a negligible hardware cost difference per user between storing data about 10 thousand users or 10 million users. I just read a piece about the development of tracking cookies in the ‘90s which made it easier to get a better sense of unique visitor information on websites. It’s cheaper than ever to store information about what consumers are doing on all parts of the web, down to how many seconds a user spent hovering over a candid celebrity photo.”

image

What’s your take? Why is deep learning just now gaining traction? And what material changes do we hope will occur to render the next big shift in AI possible?

Read more

Newer
Aug 19, 2015 · post
Older
Aug 7, 2015 · post

Latest posts

Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series.
...read more
Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck.
...read more
Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers.
...read more
Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance.
...read more
Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books.
...read more
Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team.
...read more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)

Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Notebook

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!
https://colab.research.google.com/github/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.