Person re-identification (re-ID) can be viewed as an image retrieval problem. The emergence of this task can be attributed to 1) the increasing demand of public safety and 2) the widespread large camera networks in theme parks, university campuses and streets, etc. Both causes make it extremely expensive to rely solely on brute-force human labor to accurately and efficiently spot a person-of-interest or to track a person across cameras.

Based on PyTorch.

Chris caught up with Jennifer Marsman, Principal Engineer on the AI for Earth team at Microsoft, right before her speech at Applied Machine Learning Days 2019 in Lausanne, Switzerland. She relayed how the team came into being, what they do, and some of the good deeds they have done for Mother Earth. They are giving away $50 million (US) in grants over five years! It was another excellent example of AI for good!

Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community.

If you’re anything like us, your New Year’s resolutions probably included an AI section, so this week we explore some of the learning resources available for artificial intelligence and deep learning. Where you go with it depends upon what you want to achieve, so we discuss academic versus industry career paths, and try to set you on the Practical AI path that will help you level up.

Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community.

This week we look back at 2018 - from the GDPR and the Cambridge Analytica scandal, to advances in natural language processing and new open source tools. Then we offer our predications for what we expect in the year ahead, touching on just about everything in the world of AI.

Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community.

This week we discuss BERT, a new method of pre-training language representations from Google for natural language processing (NLP) tasks. Then we tackle Facebook’s Horizon, the first open source reinforcement learning platform for large-scale products and services. We also address synthetic data, and suggest a few learning resources.

Daniel and I talked about this AI painting on Practical AI #18 a few weeks ago. At the time the painting was expected to yield $7,000 - $10,000 US.

Since then, Christie’s held an auction, and the Portrait of Edmond Belamy sold for an incredible $432,500 US.

Himani Agrawal joins Daniel and Chris to talk about how she got into data science and artificial intelligence, and offers advice to others getting into these fields. She goes on to describe the role of artificial intelligence and machine learning within AT&T and telecom in general.

Finish him!

NVIDIA Chief Scientist Bill Dally joins Daniel Whitenack and Chris Benson for an in-depth conversation about ‘everything AI’ at NVIDIA. As the leader of NVIDIA Research, Bill schools us on GPUs, and then goes on to address everything from AI-enabled robots and self-driving vehicles, to new AI research innovations in algorithm development and model architectures. This episode is so packed with information, you may want to listen to it multiple times.

The purpose of this project is to provide a shortcut to developers and researchers for finding useful resources about Deep Learning for Natural Language Processing.

Organized by papers, datasets, courses, books, blogs, tutorials and more…

PyTorch is a flexible deep learning framework that allows automatic differentiation through dynamic neural networks (i.e., networks that utilise dynamic control flow like if statements and while loops). It supports GPU acceleration, distributed training, various optimisations, and plenty more neat features. These are some notes on how I think about using PyTorch, and don’t encompass all parts of the library or every best practice, but may be helpful to others.

How do you you teach a neural network to code? One screenshot with matching HTML at a time. 😂

Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software. The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code.

Currently, the largest barrier to automating front-end development is computing power.