The Difficulties Presented by High-Dimensional Data

A central use of the PolyChord tool is solving problems presented by high-dimensional data. High-dimensional data is present for many businesses in today’s world of BigData, where data has been gathered and there are many many variables. It is analysed in an attempt to create data models and formulas. If an accurate model is produced then the model can be used to make accurate future predictions. However, creating a model to fit high-dimensional data has been, until now, a notoriously difficult task.

This article by Sunil Sapra discusses the use of high-dimensional data and where it occurs in common commercial scenarios, and is a great explanation of the difficulties that come with fitting a model to high-dimensional data.

Here at PolyChord, we have been able to overcome a majority of these difficulties by using advanced Bayesian Data Science techniques. Unlike other tools, PolyChord takes an orthogonal approach using nested sampling to navigate thousands of dimensions and compute without compromises or approximation.

****Disclaimer: Dr. Sapra is not associated with the PolyChord organisation and has kindly given us permission to use his well written paper****

An Inside Look into a Food Supply Chain Workshop

Two weeks ago, PolyChord attended the UK Canada Food Supply Chain Collaboration Workshop delivered by Innovate UK and 100% Open in partnership with the High Commission of Canada. This two-day event was a great opportunity to introduce, initiate, and encourage technological collaboration within the food sectors. The workshop created an environment fostering the exchange of ideas and a wonderful team environment.

The three-minute presentations given by each attending member was an excellent way to show the diversity and qualifications of all attending the workshop. Attendees were then split up by table and asked to present a Post-It board of the key issues or challenges experienced within different sectors of the food supply chain industry. For example, problems of nutrition, quality, sustainability and regulation were all addressed.

The workshop took on a blue sky thinking approach, where innovation is encouraged and there are truly no wrong answers. Tables were then asked to brainstorm potential solutions or opportunities while utilising the specialized strengths of each attendee. PolyChord, therefore, demonstrated the use of artificial intelligence in solving food industry-relatedd problems. Tables then moved Post-Its from onto a roadmap marking the challenges and opportunities over a timeline of less than two years to greater than five years.   

The next day attendees placed themselves into their own teams of two and were asked to fill out a pitch template. The teams were asked to identify a major problem, how to solve the problem, draw out a business model, differentiate the innovation from competition, and then identify what is needed from the audience. Filling out this pitch template was a great way to develop a framework for Innovate UK and NRC joint funding competition entitled: UK and Canada: enhancing industrial productivity. This funding competition provides a phenomenal opportunity for innovative research and collaboration.

PolyChord was honoured to have the chance to meet key members and specialists of countless different food sectors, from farm production to consumer goods companies. We are particularly thankful to have met from those in the NRC, the STFC, University of Guelph, University of Portsmouth, Liquid Vision, and Unilever. We look forward to bettering our acquaintance and potential future collaboration. PolyChord would also like to thank Innovate UK for enabling us to attend this prestigious event, we are extremely grateful.  

5 AI Podcasts for AI Newcomers

AI is becoming heavily integrated into everyday society, however, learning about AI can initially be difficult or confusing. Here are some helpful podcasts that you don’t need a PhD to listen to:

Linear Digressions

Hosted By: Katie Malone and Ben Jaffe

Why We Recommend It: This Podcast is extremely listener-friendly. Episodes average about 20 minutes a-piece and make advanced concepts easy to understand.

Click HERE to listen

Talking Machines

Hosted By: Neil Lawrence and Katherine Gorman

Why We Recommend It: This Podcast’s episode lengths vary and is still understandable for those not well-versed in the AI language. Talking Machines also features interviews with AI professionals, and takes questions from their viewers.

Click HERE to listen

Eye On A.I.

Hosted By: Craig S. Smith

Why We Recommend It: Episodes are on average between 20-30 minutes and feature interviews with fascinating AI innovators. These interviews help listeners to understand how AI advancements affect the world on a global scale.

Click HERE to listen

This Week in Machine Learning & AI

Hosted By: Sam Charrington

Why We Recommend It: While this Podcast’s episodes average about 50 minutes, it provides frequent updates on the newest innovations of the AI field. TWiML&AI also features exclusive and thought-provoking interviews with experts in the AI and Machine Learning community.

Click HERE to listen

Data Skeptic

Hosted By: Kyle Polich (mini-episodes are hosted by Linh Da Tran)

Why We Recommend It: This Podcast’s episodes range from about 20-45 minutes. Data Skeptic covers basic AI and Data Science concepts and how they relate to the world. Polich starts out with basic explanations and goes more in-depth as the Podcast continues.

Click HERE to listen

Those are just a few of our favorite podcasts about machine learning, big data, and artificial intelligence. If you have a favorite AI podcast not featured on this list let us know!

*Disclaimer: None of the above podcasts are affiliated with PolyChord and the views expressed by the hosts and guests are entirely their own*

3 Ted Talks that Prove You Shouldn’t Be Afraid of AI

How AI Can Enhance Our Memory, Work, and Social Lives. Tom Gruber.

Tom Gruber, cofounder and head designer of the company that created Siri, discusses the incredible possibilities AI presents. He discusses how great the benefits could be for humanity if AI continues to advance. How it could help the disabled, the mentally incapacitated, and the sick. You will walk away from this video feeling so much better about the possibilities of the future.

How AI Could Compose a Personalized Soundtrack to Your Life. Pierre Barreau.

Pierre Barreau, young entrepreneur, CEO, and co-founder of AIVA, explains and demonstrates how his company is combining AI and music. Watch as Barreau explains how AI can enhance human creativity and plays the beautiful music technology has created.

How AI Can Save Our Humanity. Kai-Fu Lee.

Kai-Fu Lee, PhD, previous head of Apple R&D, and now technology executive. Lee addresses some of the fears people have of AI and how AI can actually help humanity in the long run.

*Disclaimer: None of the above speakers nor the Ted Conference are affiliated with PolyChord*

Confused About Neural Networks?

Though Artificial Intelligence is ingrained in everyday life, the concepts behind AI are quite complex and you may feel as though you need a PhD to understand it all. Not to fear, there are many videos to help you understand some of the basic concepts involved in AI.

One of our favorite explanations is by Dr. Mike Pound from the YouTube Channel Computerphile. Click here to watch “Neural Network that Changes Everything

Here, Dr. Pound gives a brief introduction to neural networks using the concept of house pricing. He then explains how he utilizes the neural networks within his profession of image analysis and how neural networks are used for facial recognition.

***Disclaimer: Dr. Mike Pound and the Computerphile channel are NOT associated with PolyChord***

Neural Network types, our capabilities

PolyNet can train all of these  types of neural network. This stage one neural network trainer is mostly limited by the number of connections (right now it can manage  ~O(1000s). The actual architecture doesn’t matter – all shown below will work with PolyNet-  so much as its scale of connectivity. Many neural networks in use right now fall well within these capabilities. As we develop further iterations, numbers of connections will expand.
(This excellent graphic was made by Fjodor van Veen at the asimov