Feb 28, 2018 · newsletter

Comparing human and agent performance: DeepMind releases PsychLab

Google’s DeepMind released PsychLab this week, which has been developed internally and released to the public as part of DeepMind’s efforts to apply decades of research in cognitive science/neuroscience to advance the state of the art in machine learning and artificial intelligence. Many modern machine learning models have taken inspiration from principles derived from decades of research in cognitive science/neuroscience. This announcement, along with the accompanying paper, provide an open-source playground for testing how agents (built using LSTM deep learning alrogirthms) perform when compared to humans on a slew of cognitive tasks that are fairly well-understood and widely used to study human perception.

The research findings point out some potentially non-obvious ways in which the models used to build artificial agents are missing some fundamental aspects of how primate (human and non-human) vision and cognition operate. For instance, one experiment that tested psychophysical thresholds of an agent found that visual acuity was affected by the size of the images presented. This led researchers to build a secondary model that loosely approximated the fovea (the center of the retina at the back of the eye, where sight is most acute) to improve performance. The need for this secondary model was only made clear by comparing performance of the agent to human performance and relating the difference back to human physical anatomy.

Interestingly, the agent failed to produce many well-known effects in humans. The pattern of differences between humans and the agent isn’t complete enough yet to make any big theoretical claims, but it appears that the agent has some deficits in integrating information over time, yet is spared the deficit typically seen in humans when asked to search for an object composed of the conjunction of two features (e.g., orientation and color). As these patterns continue to emerge, they will inform how new models are developed and will more clearly delineate the fundamental differences between how agents and humans perform cognitive tasks.

Figure from the PsychLab paper

Most of the machine learning models in production today, as opposed those used for more pure research, are aimed at automating tasks typically performed by humans or augmenting already-existent human capabilities. Currently, many practitioners make tuning decisions to increase the efficiency of machine learning models, but may be inadvertently making trade-offs that affect how well their models actually reliably replicate or augment human abilities. This collaboration between machine learning and cognitive science/neuroscience research, as it evolves, will bring to light new potential approaches to decrease that error.

This type of open-source release allows practitioners to test their models on a myriad of cognitive tasks. This will greatly increase the speed at which machine learning models will change based on cognitive science/neuroscience. This burgeoning era gives us a moment to pause, however, and think critically about what a particular business might want to gain by using machine learning. It’s clear that we’re not yet near a generalized Artificial Intelligence (and these experiments reinforce that idea). As machine learning algorithms borrow more and more from cognitive science/neuroscience, we can think of these models in different ways. We can think of them as evolving along the same trajectory as humans (akin to studying infant brains and how they change and evolve throughout a human’s life); we can look at these models as something related to human cognition, but fundamentally separate (akin to the research on non-human primates); or we can think of these models as performing something entirely their own - with no tie to human evolution or development.

As this field continues to develop, and we begin to employ machine learning in every facet of business, it’s important to establish and maintain a goal for these tools, perhaps with these distinctions in mind.

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Feb 28, 2018 · newsletter
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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. 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. 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. 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. 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. 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. more

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