Top 15 AI Articles You Should Read This Month – July 2020

Usually, every month we write an article about the best and most promising AI research papers from that month. You can check them out here:

• January
• February
• March
• April
• May
• June
• July

In addition to that, we list fifteen AI articles we have found amazing that month. This collection of articles should give you an overview of what happened that month in the AI industry both from technical, business and from an ethical perspective. You can check out previous months here:

• March
• April
• May
• June

Have fun!

Awesome GPT-3 – collection of demos and articles about the OpenAI GPT-3 API

GPT-3 has been in the center of attention for the past couple of months. Innovations that use this tool are simply amazing. This GitHub repository collects all the cool articles about GPT-3.

Intel community releases framework for ethically using artificial intelligence

AI systems are evolving very quickly. That is why The U.S. intelligence community released artificial intelligence principles and an ethics framework. This document is meant to outline the intelligence community’s broad values and guidance for the ethical development of AI.

AutoML-Zero: Evolving Code that Learns

In a recent paper, Google researchers propose an AutoML-Zero, as a part of their plan to democratize Machine Learning with ML. This approach automatically searches for machine learning algorithms from scratch. AutoML-Zero uses only basic mathematical operations as building blocks and applies evolutionary methods to automatically find the code for complete ML algorithms.

The Panopticon Is Already Here

Xi Jinping is using AI to enhance his government’s totalitarian control. He’s exporting this technology to regimes around the world. So, this article asks us “What should China learn from 1984?”.

TrojanNet – a simple yet effective attack on machine learning models

If the previous article got you scared, have no worries, this one is proposing a solution to it 🙂 Since majority of the industry is using transfer learning for their products, malicious actors can use the same fine-tuning process to insert hidden triggers in a deep learning model by training it with poisoned data.

TensorFlow Object Detection API Officially Supports TF2: Google

TF Object Detection API is finally compatible with TensorFlow 2. Now you can utilize all fancy features of TF2 like Eager-mode binaries, COCO pre-trained weights and evaluations, etc.

Unesco launches global consultation on AI ethics

UNESCO has launched a global online consultation on the ethics of artificial intelligence (AI), which will be used by the organisation’s international group of AI experts to help draft a framework governing how the technology is applied globally.

Machines Can Learn Unsupervised ‘At Speed of Light’ After AI Breakthrough, Scientists say

Photon based hardware has been in research for years. Researchers from George Washington University in the US discovered that using photons within neural network (tensor) processing units (TPUs) could create more powerful and power-efficient AI.

Tesla ‘very close’ to level 5 autonomous driving technology, Musk says

Is this truth or another Elon’s marketing move, it remains to be seen.

This GitHub Dev is Writing a Telenovela with Deep Learning

This is such a fun and insane idea, we just had to put it in here. After all “telenovelas are hell” 🙂

Beyond the AI hype cycle: Trust and the future of AI

This is a really good overview of where is the AI industry at the moment.

RL Unplugged: Benchmarks for Offline Reinforcement Learning

If you are working with Reinforcement Learning, you will probably find this quite useful. RL Unplugged is suite of benchmarks for offline reinforcement learning. 

Artist Uses AI to Generate Realistic Faces of Subjects From World’s Most Iconic Paintings

Denis Shiryaev  used neural networks to generate realistic faces of artistic subjects like Mona Lisa, the Roman goddess Venus, and the pair from the American Gothic painting. 

NVIDIA Breaks 16 AI Performance Records in Latest MLPerf Benchmarks

Training GANs – From Theory to Practice

We love GANs! However, we know how hard it is to put these models from theory to practice. This article is a good guide for that!