ONLINE CONFERENCE - oneAPI DevSummit for AI 2022 - 12 July 2022

 

Are you a researcher, data scientist, or developer looking to build AI applications and seamlessly scale them from edge to cloud?

Join us for a day of discovery with renowned industry experts who will demystify the latest technologies, tools, trends, and techniques.

• Drop-in optimizations across popular frameworks and libraries for deep learning, machine learning, and data analytics—TensorFlow, PyTorch, scikit-learn, and more.
• Intel AI tools for end-to-end development—data preparation, training, inference, deployment, and scaling
• A hands-on workshop on dinosaur hunting (yes, you read that correctly)
• Opportunities to attend tech talks and panel discussions with tech experts from Google, Accenture, RedHat, JD.COM, Aible and more.

Expand your view and vision across the AI technology spectrum to get started on your development journey or supercharge your existing one.

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Google Research: Looking Back at 2020, and Forward to 2021 by Jeff Dean via @googleai

Google has a massive impact on the tools, applications and research that help steer the data science community. As with prior years, this retrospective by Jeff Dean is amazing in its scope. Includes useful summaries, screenshots, videos and linked references throughout.

I found this really interesting and showed a number of areas that I was not aware of the scale of Google involvement. The look forward was a great place to learn of areas that are going to come at some point and worth planning for as it will be there and possible soon.

Why You Should Get Google’s New Machine Learning Certificate by Frederik Bussler via @kdnuggets

 Google is offering a new ML Engineer certificate, geared towards professionals who want to display their competency in topics like distributed model training and scaling to production. Is it worth it?

This was really interesting and definitely made the case as to when you should or should not take the certification exam. I like the idea that you might be better off getting lots of experience and publish some results to get known for your skill and knowledge in machine learning instead.

Google has released a tool to spot faked and doctored images by Karen Hao via @techreview

Google has released a tool to spot faked and doctored images.

Very much needed - I really hope it works and is adopted widely.

Google just published 25 million free datasets by @tjwaterman99 via @TDataScience

What you need to know about the largest data repository in the world.

Useful for practice if nothing else.

Google Research: Looking Back at 2019, and Forward to 2020 and Beyond by @JeffDean via @googleai

Google has a massive impact on the tools, applications and research that help steer the data science community. As with prior years, this retrospective by Jeff Dean is amazing in its scope. Includes useful summaries, screenshots, videos and linked references throughout.

This is just golden and everyone really needs to read this. You get some many ideas and learn a great deal from this too.

Google denies it’s using private health data for AI research by Gerrit De Vynck via @infomgmt

Google’s deal with Ascension has been under scrutiny since the Wall Street Journal reported on Monday the company was collecting identifiable data on millions of patients and using it to build new products.

Interesting. I'm sure there is a data privacy issue there, although how would you know they have been using your data??

This New Google Technique Help Us Understand How Neural Networks are Thinking by @jrdothoughts via @TDataScience

Interpretability remains one of the biggest challenges of modern deep learning applications. The recent advancements in computation models and deep learning research have enabled the creation of highly sophisticated models that can include thousands of hidden layers and tens of millions of neurons.

I found this fascinating and it is worth a read as well as a bookmark.

Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson by Olexander Kolisnykov via @topbots

The article will guide you through the best MLaaS platforms on the market and lists some infrastructural decisions to be made and some important considerations to keep in mind when choosing an MLaaS platform.

This has so much detail and is very very useful. This is worth a bookmark and if the platform allows applause of full cudos to the author Olexander.

The Google Cloud Developer’s Cheat Sheet by/via @gregsramblings

Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes)

A great resource via his Github library.

Google AI Blog:Predicting the Generalisation Gap in Deep Neural Networks by Yiding Jiang via @googleai

Here’s a description of a new technique that uses margin distributions to better predict a DNN’s generalization gap.

Seems a great idea to use what the Google AI team have made available in their Github is a great idea and should not be ignored. Links to a lot of sources are given thought the article.

Recent Acquisitions in the Business Intelligence field together with a longer view list

Within the past week, we’ve seen the acquisitions of the two biggest players in the modern BI landscape, Looker and Tableau. And if you broaden your view to the entire analytics tech stack, it’s bigger: here are the major acquisitions over the past year:

• Stitch. Acquired by Talend on 11/7/2018 for $60m.
• Alooma. Acquired by Google on 2/19/19 for an undisclosed amount.
• Periscope. Acquired by Sisense on 5/14/19 for an undisclosed amount.
• Looker. Acquired by Google on 6/6/19 for $2.6b.
• Tableau. Acquired by Salesforce on 6/10/19 for $15.7b.

Wow - who is next?? It's obviously big business.

Google’s Duplex Uses A.I. to Mimic Humans (Sometimes) by Brian X. Chen and Cade Metz via @nytimestech

In the free Google Duplex service, bots call restaurants and make reservations. These bots are pretty impressive, except for when the bot is actually a person.

This is really interesting and definitely the way things will develop going forward.

Google's People + AI Guidebook by/via @GoogleAI

This Guidebook is really useful and interesting to read. I think if everyone used it then it would provide a great starting point.

Something to bookmark and print out for a great starting point on standards and guidelines for use in your own organisation if you decide to start developing any AI for your own use.

Rules of Machine Learning: Best Practices for ML Engineering by/via @googledevs

This document, patterned after the Google C++ Style Guide, provides Google’s best practices for machine learning. ”If you have taken a class in machine learning or built or worked on a machine­-learned model, then you have the necessary background to read this document.”

This is VERY useful and definitely worth a bookmark. 

Google has launched a new end-to-end AI platform by @fredericl via @TechCrunch

Google announced the beta launch of the company’s AI Platform. The platform brings together a variety of existing and new products that allow you to build a full data pipeline to pull in data, label it (with the help of a new built-in labelling service), and then use either existing classification, object recognition, or entity extraction models, or existing tools like AutoML or the Cloud Machine Learning Engine to train and deploy custom models.

This should make it far easier for companies to dip their foot into the use of ML and AI areas with minimum investment.

How Silicon Valley Puts the ‘Con’ in Consent by/via @nytopinion

If no one reads the terms and conditions, how can they continue to be the legal backbone of the internet?

A very interesting take on all the terms and conditions that we know we should read as much as they all assume you won't.

Firm Led by Google Veterans Uses A.I. to ‘Nudge’ Workers Toward Happiness by Daisuke Wakabayashi via @nytimes

Three former Google employees are pitching an AI system designed to increase employee satisfaction at work.

A great use of AI and ML that will hopefully provide the right nudges in order to make people do the right thing to motivate their staff.

How to build your own AlphaZero AI using Python and Keras by David Foster via @Medium

This tutorial shows you how to build a replica of the AlphaZero methodology to play the game Connect 4—and how to adapt the code for other games.

This looks really good and is worth following and trying.

Machine learning — Is the emperor wearing clothes? by Cassie Kozyrkov via @Medium

Cassie Kozyrkov, chief decision intelligence engineer at Google, offers a "behind-the-scenes look at how machine learning works."

This was really interesting and made me think about everything in a bit more detail.