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:

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 the April edition here. Have fun!

Not so long ago, Andrej Karpathy famously tweeted: ‚ÄúGradient descent can write code better than you. I‚Äôm sorry.‚ÄĚ What he was trying to say is that¬†neural networks, which use Gradient descent optimization technique, will soon be able not just to write code, but to¬†write code better than us¬†‚Äď software developers. Stay relevant in the rising AI industry an learn all you need to know about deep learning¬†here!

Watson’s Creator Wants to Teach AI a New Trick: Common Sense

David Ferrucci, the¬†man who built IBM‚Äôs¬†Jeopardy!-playing machine, Watson, is explaining a children‚Äôs story to his new creation. His asks seemingly simple yet hard question¬†‚ÄúCan we ever get machines to actually understand what they read?‚ÄĚ. It is the question that starts most of the AGI discussions.¬†

Read the complete article here.

Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

A few-shot classification is the classification method in which the training set contains classes that are completely different from those that will be available at test time. In this particular case, the training process builds a model that can classify new classes with only several examples. With Meta-Dataset, a new dataset created by Google, you can create this kind of models.

Read the complete article here.

AI advances to better detect hate speech

Guys from Facebook AI released this article in which they explain how they fight against hate speech on their network. This is one of the complex and important tasks that they need to do. For this, they use various  AI techniques that are listed in this article. Now, these techniques proactively detect 88.8 percent of the hate speech content that they remove.

Read the complete article here.

AI systems are worse at diagnosing disease when training data is skewed by sex

Removing bias from data is one of the most important things we should consider when we are building AI systems. This is especially the case if we create it for some kind of healthcare system. In this article, the authors discuss the dangers of not having diverse data in your dataset and the effects of such a mistake. 

Read the complete article here.

The Big Bad NLP Database

The title says it all. This is a site that contains a huge number of NLP datasets. It is like a playground for engineers who are building this type of system. Have fun peeps, have fun!

Read the complete article here.

40 Years on, PAC-MAN Recreated with AI by NVIDIA Researchers

Do you like Pac-Man? What a stupid question, of course you like Pac-Man. Engineers from NVIDIA liked it so much that they rebuild it, but only using the AI. They used their Generative Adversarial network GameGAN that was trained on 50,000 episodes of the game. Beyond cool!

Read the complete article here.

Recommendation systems 2

Artificial Intelligence Consumes a Startling Amount of Power ‚Äď MIT System Reduces the Carbon Footprint

Training AI systems require a lot of processing power. A lot of processing power equals a lot of energy spent, which in turn is bad for the environment. This is why cool kids from MIT developed a new automated AI system with improved computational efficiency and a much smaller carbon footprint.

Read the complete article here.

Old tools, new tricks: Improving the computational notebook experience for data scientists

In this article, a team from Microsoft discusses problems of Computational notebooks and gives suggestions on how to improve. The problems that are discussed are gathered from semi-structured interviews, followed by a survey with over 150 data scientists. 

Read the complete article here.

Pose Animator

This awesome project is creating cartoon “deep fakes”, ie.¬†2D vector illustration and animates its containing curves in real-time based on the recognition result from PoseNet and FaceMesh. It borrows the idea of skeleton-based animation from computer graphics and applies it to vector characters.

Read the complete article here.

TensorFlow 2.2.0 Released

TensorFlow 2.2.0 is released four months after 2.1.0. In this article, you can find the list of all features and changes that are available in the new subversion.

Read the complete article here.

OpenAI debuts gigantic GPT-3 language model with 175 billion parameters

Yes, it is a 175 billion parameter, 700Gb NLP model coming from OpenAI. It achieves awesome results even when it is not pre-trained. 

Read the complete article here.

Facebook AI, AWS partner to release new PyTorch libraries

Interesting news from Facebook and AWS.¬†They have partnered to develop new libraries targeted at large-scale elastic and fault-tolerant model training and high-performance PyTorch model deployment. We can’t wait to see the results. How about you?

Read the complete article here.

COVID-19 Research Explorer

Google‚Äės AI team has released a new tool to help researchers traverse through a trove of coronavirus papers, journals, and articles. The¬†COVID-19 research explorer tool¬†is a¬†semantic search interface that sits on top of the¬†COVID-19 Open Research Dataset¬†(CORD-19).¬†

Read the complete article here.

TFRT: A new TensorFlow runtime

Probably the biggest complaint about TensorFlow is its speed. That is why along with 2.2.0 Google presented TFRT Рa new runtime that will replace the existing TensorFlow runtime. It is responsible for the efficient execution of kernels on targeted hardware.

Read the complete article here.

About positive psychology and ethics in AI

In a very interesting interview Creative Director and Co-Founder of the Nature 2.0 DAO Sovereign Nature Initiative Florian Schmitt and AI Ethicist John C. Havens talk about the importance of well-being and how AI can shape the future,

Read the complete article here.

Rubik's Code

Rubik's Code

Building Smart Apps

Rubik‚Äôs Code¬†is a boutique data science and software service company with more than 10 years of experience in Machine Learning, Artificial Intelligence & Software development. Check out the¬†services¬†we provide. Eager to learn how to build¬†Deep Learning¬†systems using Tensorflow 2 and Python? Get our ‚ÄėDeep Learning for Programmers‚Äė ebook¬†here! Read our blog posts¬†here.
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