Welcome to the February edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development.

This month, we spotted among others, articles about AI that can diagnose breast cancer with higher accuracy than experts! Let’s start, as usual, with the comic of the month:

Global AI

1 —Breast Cancer Diagnosis

Interpret screen mammography

A recent evaluation of a AI system for breast cancer screening concludes that it is capable of surpassing human experts in breast cancer prediction.

It is essential to identify breast cancer at earlier stages of the disease when treatment can be more successful. Screening mammography is designed to perform such identification but is complex to analyze and lead to false diagnosis: some healthy patients are diagnosed sick (false positive) and some sick patients are diagnosed healthy (false negative).

This evaluation demonstrated an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives and thus surpassing human experts in breast cancer prediction!

2 —Here is Meena, the Universal Chatbot

On 27th January, a Google brain team introduced Meena, a new open-domain human-like chatbot, meaning Meena talks about any topic and it mimics the human ability to converse freely in natural language.

Meena executes a joke :)

Unlike other state-of-the-art open-domain chatbots (MILABOT, XiaoIce, Gunrock, Mitsuku, and Cleverbot), Meena is an end-to-end Neural Network approach and do not rely on complex frameworks.

Traditionally, chatbot performance is measured through perplexity which measures how accurately the bot anticipates what people will say next. Interestingly, there is no proof that this measure correlates with the chatbot responses being "human-like". To alleviate this issue, the authors proposed a new evaluation metric called Sensibleness and Specificity Average (SSA) relying on humans judging how chatbot responses make sense and are specific. Two things came up :

the best Meena version scores 79% SSA, it outperforms state of the art open-domain chatbots and gets close to human performance (86% SSA)

SSA and perplexity are strongly correlated: the more Meena responses are specific and accurate, the more it is able to predict people's next answers. This is reassuring :)

With these learnings, the authors hope they can get even closer to human capabilities by reducing Meena's perplexity hence increasing SSA performance.

Meena network has 2.6B parameters and was trained over 40B words over 30 days using 2048 TPU cores, impressive!

https://deepai.org/publication/towards-a-human-like-open-domain-chatbot

https://arxiv.org/pdf/2001.09977v1.pdf

3 — Creative AI: The Storytelling of AI Dungeon

AI Dungeon 2 is an AI-generated text adventure game. Unlike the original AI Dungeon that used an AI text generator to build scenes and choices for the player, the recently released AI Dungeon 2 is different in one major way: instead of the set commands and human-written storylines that traditionally limit player freedom, players of AI Dungeon 2 can type whatever they want. The game responds to the player’s text input thanks to a novel adaptation of GPT-2 :

Snapshots of AI Dungeon 2 mobile version

In this blog post, the author tells the story of his adventure as "Henry the Wizard". In a narrative way, he shares his learnings with us, starting from being skeptical at first and enthusiastic in the end.

https://lionbridge.ai/articles/creative-ai-the-storytelling-of-ai-dungeon/

4 —HiPlot: High-dimensional Interactive Plots Made Easy by Facebook

On January 2020, Daniel Haziza, Jérémy Rapin, and Gabriel Synnaeve from Facebook released HiPlot, an interactive tool that allows exploring high dimensional data.

Imagine you collected data from multiple trainings: epoch, dropout, embedding size, learning rate and so on. Hiplot let you explore these dimensions in a simple way, using parallel plots:

HiPlot visualization: parallel plots show dimensions along the "x" axis.

HiPlot is interactive, you can select the data you want to drill down by clicking on it. And it is really simple to install/use, pip install it and give it a try!

https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/

5 —The Arrival of a Train at La Ciotat Station (1895) in Full-HD

"The Arrival of a Train at La Ciotat Station", one of the first movies ever, was produced by Lumière's brothers in 1895. The story goes that when the film was first shown, the audience was so overwhelmed by the moving image of a train coming directly at them that people screamed and ran away! It is probably a cinema myth, though :)

As you can imagine, the movie has aged a little bit. Hopefully, Denis Shiryaev ran a couple of neural-network-based algorithms to improve the situation:

it upscales the input video up to 4K definition, using the GigaPixel AI tool from TopazLab

it increases the FPS using Depth-Aware Video Frame Interpolation (Dain)

Denis Shiryaev says anyone could have done this and the credit should go to the authors of the algorithm that make them public on GitHub. However, it is quite funny to see how fast it got viral on the web, and how it went far beyond the data-scientist community.

Following the publication of the video, DeOldify released a colorized version of this video.

6 — Using ‘Radioactive Data’ to Detect if a Data Set was Used for Training

Another blogpost from Facebook.ai. The authors have developed a new technique to mark the images in a dataset so that researchers can determine whether a particular machine learning model has been trained using those images.

This is helpful to researchers and engineers to keep track of which data was used to train a model so they can better understand how it affects the performance of different neural networks.

Radioactive data used to train a CNN

The term “radioactive” data refer to the use of radioactive markers in medicine that are given to patients before radiography, so as to see a particular organ without harming the patient. Similarly, the "radioactive" marks in the data are harmless meaning they have no impact on the classification accuracy of models but are detectable with high confidence in a neural network.

Blog post: https://ai.facebook.com/blog/using-radioactive-data-to-detect-if-a-data-set-was-used-for-training/

Paper: https://arxiv.org/pdf/2002.00937.pdf

7 — Is Modern Facial Recognition Biased?

This article presents a review of studies about existing solutions for facial recognition. It turns out that many of them have biases such as Asian and African-American faces are falsely identified 10 to 100 times more than Caucasian faces.

In a nutshell, these studies warn against the use of facial recognition systems to make decisions impacting human lives and advocate them to be banned from public places e.g., college campuses, calling for more regulation in 2020.

Read more here: https://lionbridge.ai/articles/is-modern-facial-recognition-biased/

8 — Pandas 1.0.0 Released

First pandas major release in a decade! Pandas is a well-known cornerstone library to whoever need to manipulate data in python. It all started in 2011, and its popularity has been skyrocketing ever since.

No worries, it is not a huge / breaking release says the pandas core team. It is rather a symbolic milestone celebrating the growth of the pandas community.

For this occasion, the core team published this post to share its thoughts about the past decade and the next one.

9 — Machine Learning Co2 Impact

Do you ever wonder about the impact on the environment when you train your algorithms? This online tool lets you compute the Co2 emitted by your training based upon the GPU type and the Cloud provider.

Co2 emitted for training 12 hours RTX2080 on AWS

It also gives you advice such as changing the region of computing to reduce your emission. A good way to empower data scientists :)

https://mlco2.github.io/impact/#compute

10 —Bayesian Product Ranking at Wayfair

Wayfair is an online store for housing furniture proposing more than 14M products to their clients. In this blog post, data scientists at Wayfair share their Bayesian approach to the problem of showing more appealing products to their customers.

Which shower curtains are more appealing for a new customer?

They used the pystan package in Python to implement their solution. They also present other issues they encountered such as the model updating over time (as customer habits change all the time) and an interesting exploitation/exploration tradeoff: on one hand exploit the knowledge they already have to recommend appealing products, on the other hand, try new configurations to gather more data.

https://tech.wayfair.com/data-science/2020/01/bayesian-product-ranking-at-wayfair/?utm_campaign=Data_Elixir&utm_source=Data_Elixir_269