Manifold Blog

Manifold Blog

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


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

How to Get Started with Machine Learning — For Executives and Engineers

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

The machine learning (ML) space can seem intimidating — where do you start, and how does it all connect? At Manifold, we've compiled a list of resources borne of our expertise that we think will help you form a strong ML foundation. We hope to continue updating and improving this list, and may reshare it out periodically in the hopes that those 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


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

Preparing Your Data for Predictive Analytics

Posted by Sourav Dey on Nov 16, 2018 2:17:23 PM

By Kyle Seaman, Head of Product at Sentenai, and Sourav Dey, Co-Founder and CTO at Manifold

Predictive analytics is an undeniably valuable technology, with research indicating its market size could top $12 billion USD by 2022. Across a range of industries, businesses, and applications, using historical data to predict future outcomes can lead to greater operational efficiency in a variety of ways. Predictive analytics can enable organizations to streamline their operational processes, optimize their demand forecasting, drastically reduce downtime, and better understand their customers’ propensity to buy.

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Topics: Data science

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