Manifold Blog

Manifold Blog

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

Machine Learning Background and Training Resources

Posted by Martin Davy on Jan 10, 2019 7:00:00 AM

Before I started at Manifold, I knew a little about the machine learning (ML) space, but wanted a better grounding in it. I asked CEO Vinay Seth Mohta for some more information, and found the resources he shared tremendously helpful. My research turned up some additional resources of my own, as well.

The following resource compilation includes those items, as well as a few added by others on the Manifold team. We hope to continue updating and improving this list, and may reshare it out periodically in the hopes that others who embark on this journey can have an even smoother and more fulfilling experience.

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Topics: Machine learning

Exploration vs. Exploitation in Reinforcement Learning

Posted by Rajendra Koppula on Jan 8, 2019 7:00:00 AM

Introduction

The last five years have seen many new developments in reinforcement learning (RL), a very interesting sub-field of machine learning (ML). Publication of "Deep Q-Networks" from DeepMind, in particular, ushered in a new era. As RL comes into its own, it's becoming clear that a key concept in all RL algorithms is the tradeoff between exploration and exploitation. In this post, we will simulate a problem called the "multi-armed bandit" in order to understand the details of this tradeoff. 

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Topics: Machine learning

3 Ways Artificial Intelligence Could Boost the Success of Your Business

Posted by Vivek Mohta on Jan 4, 2019 7:00:00 AM

As the artificial intelligence field continues to grow, businesses across the country have found that techniques are coming out of the research lab and into the applied realm to benefit their operations.

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Topics: AI at the edge, Computer vision, Data engineering

2018 Proved that Computer Vision is the Most Powerful Manifestation of AI

Posted by Vivek Mohta on Dec 25, 2018 7:00:00 AM

You probably use computer vision every day and don’t even think about it. Enjoy checking out the latest Snapchat filters? That’s computer vision. Unlock your iPhone with your face? That’s computer vision, too. Use your phone to deposit your latest paycheck and get some cash in your bank account? Well, that’s also computer vision.

Computer vision as we know it is at a tipping point. Thanks to industry-wide development efforts and advances in deep learning algorithms and graphics processors, we’re doing things that were unimaginable just a decade ago.

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Topics: Computer vision

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