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Manceps

OUR LATEST ARTICLES

Could A.I. ‘un-redact’ the Mueller Report?

Could A.I. ‘un-redact’ the Mueller Report?

Last June, our friends over at the Columbian covered our quest to un-redact the Mueller report. Here's the first couple paragraphs:
Attorney General William Barr significantly redacted former special counsel Robert Mueller’s 448-page report, including information about ongoing grand jury trials.

The redactions were no doubt a source of frustration for millions of Americans eager to learn the full scope of Mueller’s findings after investigating alleged links between Russia and President Trump’s election. And a public statement from Mueller himself earlier this week offered no revelations beyond what was already in the report. So unless Barr has a sudden and drastic change of opinion, there’s no way for the public to know what’s behind those blacked-out sections.

Or is there?

That was the premise of a meetup event hosted this week by Camas-based technology company Manceps, which brought together about two dozen programmers and software engineers for a high-concept challenge: Could an artificial intelligence program be trained to fill in the blanks and “un-redact” the Mueller report?

Read the Full Story Here…

10.02.2017

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Our Most Recent Articles

DevFest West Coast 2020

DevFest West Coast 2020

Video: Machine Learning Engineering with Tensorflow Extended

Video: Machine Learning Engineering with Tensorflow Extended

Video: How to Build a Reproducible ML Pipeline

Video: How to Build a Reproducible ML Pipeline

Video: ML adventures with AutoML and TFHub

Video: ML adventures with AutoML and TFHub

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DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

Video: How to Build a Reproducible ML Pipeline

Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.

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OUR HEADQUARTERS
Headquartered in the heart of Portland, Oregon, our satellite offices span North America, Europe, the Middle East, and Africa.

(503) 922-1164

Our address is
US Custom House
220 NW 8th Ave
Portland, OR 97209

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