Visual Genomes Foundation Starts Visual DNA Project

August 10, 2018

Aug. 10, 2018 — Visual artificial intelligence will take a huge step forward as the Visual Genomes Foundation (VGF) learns and records exabytes of visual DNA features from millions of images. The VGF is open to industry, academia, and government partners.

The key technologies of the VGF are Volume Learning and Visual DNA, described in the brand new book “Synthetic Vision using Volume Learning and Visual DNA” from De Gruyter Press.

Volume Learning and Visual DNA enable a new class of AI applications called LCI (Learning, Cataloging, and Inspection), ideal to open up new Visual AI basic infrastructure market segments. LCI can be used to find known objects, and clearly identify unknown objects.

“Nothing like this has ever been attempted in the history of neuroscience or computer vision”, says Scott Krig, founder of Krig Research and director of the Visual Genomes Foundation. “Nobody has ever created a working model of the entire human visual system – a true synthetic vision system. We have a first generation model working now that will only get better over time. Imagine a new crop of thousands of researchers improving the model, it will advance in huge steps within a few years, just like what happened with the rapid advances in DNNs when thousands of researchers took interest”.

VGF is actively looking for VGF sponsors and partners now, to widen participation. VGF sponsors and partners will have a birds-eye seat to direct the VGF work, and reap the rewards. VGF enables collaboration, commercial spinoffs, and public research.

VGF promotes a new ecosystem of Visual AI applications using a supercomputer cloud infrastructure backbone, connected to embedded devices, drone vehicles, fixed infrastructure cameras, and smart phones.

Krig notes, “The current generation of deep learning, such as DNNs and RNNs, are doing very well, providing super-human capabilities. Volume learning and Visual DNA will not displace deep learning, but rather come along side to solve Visual AI problems not suitable for RNNs and DNNs. DNNs are inference learners – they look for trained, known objects and infer a % match score, but volume learning and VDNA enable additional Visual AI applications.”

DNNs typically learn only one type of feature: gradient feature weights, built up during a tedious forward/backward process which averages together all similar gradients from the training data, losing fine details in the process. DNN gradient feature weights contain no spatial relationships, and are classified as a group to infer image similarity %. DNN inference can be spoofed by prepared, malicious images. Also, DNNs find difficulties processing large images such as 4k or 8k Digital, due to the prohibitive compute workload. DNNs prefer smaller training images to reduce the compute workload, and often downscale all training images to a uniform size of perhaps 600×400 or 300×300 pixels, which is fine for many applications.

However, the VGF synthetic vision system uses Volume Learning to collect massive amounts of visual DNA describing shape, color, texture and icon-like glyph features from any size of image. VDNA describe all pieces of the image.

DNNs collect only one type of feature: gradient edges. But Volume Learning collects 16,000 different types of features as VDNA. Volume Learning decomposes each image scene into thousands of Visual DNA puzzle pieces, organized into strands of visual DNA describing higher-level visual objects. VDNA is sequenced from the images, cataloged in an associative memory, and available to groups of visual learning agents to create LCI applications (Learning, Cataloging, and Inspection).

Volume Learning and Visual DNA cannot solve all visual AI problems, but rather can enable new applications for LCI markets.

VDNA enables a new form of Visual AI – exploratory learning to find both unknown and known objects in unlabeled data. Deep learning is very effective when trained with a large training set of known labeled images. But, VDNA is ideal for finding unknown objects, as well as known objects, and cataloging everything. Labels can be assigned later.

VDNA enables an exploratory learning model, like a visual assistant who can locate both known and unknown objects in a scene, providing positive ID of known objects, visual alerts, visual inventory, and inspection. VDNA also enable time-sequence inspection to find changes in an object, for example weekly medical diagnostics to look for changes in an MRI, CAT or XRAY image. Other examples include scene learning, GIS learning, and general inspection apps.

Synthetic vision models the entire human visual pathway in the brain using a multidimensional volumetric model, inspired by research from the best neuroscience, deep learning, and computer vision. It’s the first model of its kind.

Krig is excited about the potential for the VGF. “The first phase is a cloud-based supercomputer system to do the heavy lifting, that talks to edge devices, like drones and smart phones. The phase 1 goal is to sequence and analyze one million images into their constituent visual DNA features, and create selected LCI apps for commercial use.“

The sky is the limit for new applications, since visual DNA and visual genes open new possibilities beyond current state of the art methods.

Synthetic vision addresses problems deep neural networks (DNNs) do not reach. For example, DNNs usually reduce all images to a uniform size such as 300×300 pixels, losing vast amounts of pixel detail in order to compress the feature set and make the model computable, which is a desirable goal of DNNs. However, volume learning operates on full resolution images, such as 12MP images with 4000 x 3000 pixels from common digital cameras, up to large satellite mosaics of the earth. All pixel details is preserved in the visual feature memory. Also, DNNs are prone to spoofing and false positives, presenting a security and reliability risk. Synthetic vision mitigates spoofing, and may be deployed securely with DNNs.

“The initial VGF research will push the boundaries of computing, demanding petaflops of computer power to challenge the fastest super computers, as well as exabytes of storage”, says Krig.

The visual genomes foundation is inspired by the successful Human Genome Project funded by the USG, which opened the frontiers of human DNA science and genomics, enabling new medical innovations.

Interested sponsors and partners are encouraged to apply to join the VGF.


Source: Visual Genomes Foundation

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Is Amazon’s Plunge into Server Chips a Watershed Moment?

December 11, 2018

For several years now the big cloud providers – Amazon, Microsoft Azure, Google, et al – have been transforming from technology consumers into technology creators in hardware and software. The most recent example bei Read more…

By John Russell

Mellanox Uses Univa to Extend Silicon Design HPC Operation to Azure

December 11, 2018

Call it a corollary to Murphy’s Law: When a system is most in demand, when end users are most dependent on the system performing as required, when it’s crunch time – that’s when the system is most likely to blow up. Or make you wait in line to use it. Read more…

By Doug Black

Clemson’s Cautionary Cryptomining Tale

December 11, 2018

In some ways, the bigger the computer, the more vulnerable it is to cryptomining as Clemson University discovered after cryptominers dug into its Palmetto supercomputer. When a number of nodes on Clemson University’s P Read more…

By Staff

HPE Extreme Performance Solutions

AI Can Be Scary. But Choosing the Wrong Partners Can Be Mortifying!

As you continue to dive deeper into AI, you will discover it is more than just deep learning. AI is an extremely complex set of machine learning, deep learning, reinforcement, and analytics algorithms with varying compute, storage, memory, and communications needs. Read more…

IBM Accelerated Insights

Blurring the Lines Between HPC and AI @ SC18

The dominant topic at SC18 was the convergence of HPC and Artificial Intelligence (AI) with some of the biggest research and enterprise HPC users providing perspectives on how HPC and AI are moving closer together. Read more…

Data West Brings Technology Leaders to SDSC

December 6, 2018

Data and technology enthusiasts from around the world descended upon the San Diego Supercomputing Center (SDSC) for the third annual Data West conference, which is taking place this week on the campus of the University o Read more…

By Alex Woodie

Topology Can Help Us Find Patterns in Weather

December 6, 2018

Topology--–the study of shapes-- seems to be all the rage. You could even say that data has shape, and shape matters. Shapes are comfortable and familiar conc Read more…

By James Reinders

Zettascale by 2035? China Thinks So

December 6, 2018

Exascale machines (of at least a 1 exaflops peak) are anticipated to arrive by around 2020, a few years behind original predictions; and given extreme-scale performance challenges are not getting any easier, it makes sense that researchers are already looking ahead to the next big 1,000x performance goal post: zettascale computing. Read more…

By Tiffany Trader

Robust Quantum Computers Still a Decade Away, Says Nat’l Academies Report

December 5, 2018

The National Academies of Science, Engineering, and Medicine yesterday released a report – Quantum Computing: Progress and Prospects – whose optimism about Read more…

By John Russell

Revisiting the 2008 Exascale Computing Study at SC18

November 29, 2018

A report published a decade ago conveyed the results of a study aimed at determining if it were possible to achieve 1000X the computational power of the the Read more…

By Scott Gibson

AWS Debuts Lustre as a Service, Accelerates Data Transfer

November 28, 2018

From the Amazon re:Invent main stage in Las Vegas today, Amazon Web Services CEO Andy Jassy introduced Amazon FSx for Lustre, citing a growing body of applicati Read more…

By Tiffany Trader

AWS Launches First Arm Cloud Instances

November 28, 2018

AWS, a macrocosm of the emerging high-performance technology landscape, wants to be everywhere you want to be and offer everything you want to use (or at least Read more…

By Doug Black

Move Over Lustre & Spectrum Scale – Here Comes BeeGFS?

November 26, 2018

Is BeeGFS – the parallel file system with European roots – on a path to compete with Lustre and Spectrum Scale worldwide in HPC environments? Frank Herold Read more…

By John Russell

DOE Under Secretary for Science Paul Dabbar Interviewed at SC18

November 21, 2018

During the 30th annual SC conference in Dallas last week, SC18 hosted U.S. Department of Energy Under Secretary for Science Paul M. Dabbar. In attendance Nov. 13-14, Dabbar delivered remarks at the Top500 panel, met with a number of industry stakeholders and toured the show floor. He also met with HPCwire for an interview, where we discussed the role of the DOE in advancing leadership computing. Read more…

By Tiffany Trader

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

Google Releases Machine Learning “What-If” Analysis Tool

September 12, 2018

Training machine learning models has long been time-consuming process. Yesterday, Google released a “What-If Tool” for probing how data point changes affect a model’s prediction. The new tool is being launched as a new feature of the open source TensorBoard web application... Read more…

By John Russell

The Convergence of Big Data and Extreme-Scale HPC

August 31, 2018

As we are heading towards extreme-scale HPC coupled with data intensive analytics like machine learning, the necessary integration of big data and HPC is a curr Read more…

By Rob Farber

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This