Manifold Blog

Manifold Blog

Manifold Welcomes Andy Song to the Team

Posted by Manifold Team on Feb 28, 2019 9:38:00 AM

Andy Song has recently joined Manifold as General Counsel. In this capacity, he will manage all of the company's legal affairs, including corporate governance, compliance, privacy and security, employment, intellectual property rights, licensing, and other contracts.

Prior to Manifold, Andy was Senior Counsel at Apple, where he advised on intellectual property, acquisitions, licensing, and other complex commercial transactions. He managed a large, global docket of litigation for Apple. He began his career at the law firm Munger, Tolles & Olson after clerking for the Honorable Raymond C. Fisher of the U.S. Court of Appeals for the Ninth Circuit. At Manifold, he looks forward to working on strategic client and partner deals as the company grows.

“It's an exciting time to be working in the AI field,” he says, “Someday, it may not be considered a field at all because it will be used in every industry. The variety of AI applications will present novel legal and public policy questions, and I'm excited to help Manifold be a thought leader in this space.”

When he's not working, Andy can be found playing chess and other board games with his kids, and trying out new places to eat.

Andy earned his BA in economics from Yale University, and his JD from Harvard Law School.

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Topics: News

We Need to Build Interactive Computer Vision Systems

Posted by Ajay Mishra on Feb 28, 2019 6:00:00 AM

You hear strong proclamations about how AI is taking over the world. And then, you read about how sophisticated AI models are easily fooled by small perturbations in input (see the figure below).

Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

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Topics: Computer vision

How to Quickly Build a Gesture Recognition System

Posted by Rajendra Koppula on Feb 14, 2019 7:00:00 AM

Gesture recognition is a key part of the future of design, and is poised to become the next inflection point in how we interact with devices.

Gesture-based interactions are already prevalent in AR and VR devices; for example, here are some available interactions from Microsoft HoloLens. But gestures have the potential to make a far-reaching impact beyond these specialized uses cases: imagine interacting with everyday objects and machines with just a motion of your hand, instead of pushing buttons or turning knobs. This future may not be as far-off as it seems. The principal driver behind the progress in this space is state-of-the-art computer vision technology that enables machines to recognize human gestures.

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Topics: Data science, Computer vision

Efficient Data Engineering

Posted by Jakov Kucan on Feb 7, 2019 7:32:34 AM

A typical data engineering problem, often referred to as extract, transform and load (ETL), consists of the following:

  1. take data in one place (extract)
  2. change its form (transform)
  3. move it to a new place, in this new form (load)

This process gets interesting when data volumes are large, and you have to consider performance. Long turnaround time (e.g., a run taking several hours or days) makes the typical serially iterative software engineering approach inefficient. In this article, we offer some tips on re-structuring the software engineering process and leveraging the cloud to make iteration more efficient.

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Topics: Data engineering

Using Dask in Machine Learning: Best Practices

Posted by Jason Carpenter on Jan 31, 2019 6:00:00 AM

Introduction

The Python ecosystem offers a number of incredibly useful open source tools for data scientists and machine learning (ML) practitioners. One such tool is Dask, available from Anaconda. At Manifold, we have used Dask extensively to build scalable ML pipelines.

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Topics: Data science, Data engineering, Machine learning

Before Optimizing Industrial Equipment with AI, Optimize Your Data

Posted by Sourav Dey on Jan 21, 2019 7:00:00 AM
These days, just about everything is "smart," from IoT toasters to internet-connected toilet paper dispensers. The existence of such devices points to the increasing availability of resources that enable more important pursuits. The related costs are decreasing, meaning it's possible to collect vast amounts of data from sensors attached to expensive equipment like oil and gas rigs, earth-moving tools, and factory machinery.
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Topics: AI at the edge

Incremental Synchronization: Replicating Actions vs. State

Posted by Jakov Kucan on Jan 15, 2019 7:00:00 AM
Whenever searching for an optimal solution to a problem, one is faced with design decisions on the appropriate architecture and approach. This post discusses one such problem, in order to highlight key decision points: data synchronization from one store to the other. We contrast two approaches and pose questions that can help inform the design decisions. The approaches we look at are: replicating source actions (insert, update, delete) at the destination data store, and replicating the state of the source store in the destination store.
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Topics: Data engineering

Manifold Welcomes Meghan Blanchette to the Team

Posted by Manifold Team on Jan 14, 2019 6:00:00 AM

Meghan Blanchette has recently joined Manifold as Director of Communications. In this capacity, she will be creating and executing a communications plan that helps colleagues be as successful as possible during sales conversations, as well as building content relationships with outside companies.

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Topics: News

Manifold Welcomes Michael Stefferson to the Team

Posted by Manifold Team on Jan 14, 2019 6:00:00 AM

Dr. Michael Stefferson has recently joined Manifold as a Machine Learning Engineer. He will be building end-to-end machine learning pipelines for Manifold's customers — helping to improve, restructure, or design novel solutions for data-driven problems. Michael has extensive experience in numerical analysis, data science, and software engineering.

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Topics: News

Manifold Welcomes Dragos Velicanu to the Team

Posted by Manifold Team on Jan 14, 2019 6:00:00 AM

Dragos Velicanu has recently joined Manifold as a Data Engineer. In this capacity, he will be building production-level ETL pipelines, and designing and implementing new systems that can scale with large amounts of data. Dragos has a strong background in particle physics research, analysis, software engineering, and data processing workflows.

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Topics: News

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