A glimpse into what the future of computing may look like.

Discussing the pervasiveness of computers would be, without doubt, unnecessary. In the era of the Internet of Things (did it ever really arrive?) the mantra is everything computes. The unprecedented evolution of processors and computers in all of their forms, from desktop all-in-ones to Raspberry Pi-like microcontrollers, was somehow predicted by the Moore law. But in recent times, this law is approaching its physical limit. The length of silicon circuits is approaching the point were the quantum laws start interfering with the behavior of electrons.

New fields of computer science and computer engineering are starting to emerge. In the tech…

On the role of curiosity in humans and artificial intelligence agents.

You can find a GitHub repository with the code to reproduce the experiments at the end of the article.

From a survival point of view, the main biological needs that drive animals and humans are not particularly different. Humans and animals need to eat and drink in order to survive, take shelter, and they feel an impulse to reproduce in order to keep the species alive. But, as it is evident, the behavior of humans and animals differ completely. Why is that?

The evolution of the human brain has created areas that are not present or particularly developed in animals…

Giving a drone the ability to autonomously follow you using deep learning-based computer vision techniques like object detection and depth prediction.

The self flying Skydio Drone — all Image rights belong to Skydio

Drones are becoming increasingly popular to their versatility and amazing imaging technology; From delivery to photography, a lot can be done with these flying devices. They are dexterous in the air and can be piloted with a remote controller, and can reach great heights and distances. All these features made drones a great device for photographers and video-makers. Many drones come with an attached camera, such as an action camera, that allows the drone to shoot pictures and videos from incredible perspectives.

Visualizing and decoding brain activity with neural networks.

You can find all the code of this article in this online Colaboratory Notebook, and you can run it directly on your browser. Here’s the GitHub repo.

Follow me on Twitter for updates on my work and more: https://twitter.com/normandipalo

The nervous system is an incredibly complex structure. Across your whole body, more than a hundred thousand kilometers of nerves wire every part of it with your spinal cord and brain. This “grid” transmits electrical impulses that control every movement. And every one of those commands starts from your brain, an even more amazing structure of neurons that communicate with electrical…

Reverse engineering iPhone X’s new unlocking mechanism.

You can find here all the Python code.

Follow me on Twitter for updates on my work and more: https://twitter.com/normandipalo

One of the most discussed features of the new iPhone X is the new unlocking method, the successor of TouchID: FaceID.
Having created a bezel-less phone, Apple had to develop a new method to unlock the phone in a easy and fast way. While some competitors continued using a fingerprint sensor, placed in a different position, Apple decided to innovate and revolutionize the way we unlock a phone: by simply looking at it. Thanks to an advanced (and remarkably small)…

Mixing, creating and searching aesthetically coherent designs with neural networks.

One of the most discussed aspects of artificial intelligence and computer science its whether machines can be creative or not. This discussion is as old as the first computer, but in recent times amazing results from Generative Adversarial Networks and similar architectures really made the discussion bigger.
Being a creative and a technologist, I recently focused on a similar, but intrinsically different topic: can AI augment human creativity? Can a designer enhance its inspiration using machine intelligence?

In the beginning, there was Paint. One of the firsts, if not the first, software instruments to help creatives be creative. Then, people…

How the paradigm is shifting from programming to teaching machines.

Soon we won’t program computers. We’ll train them like dogs.

This is the title of a quite recent Wired article. What they expose there is how, since machines are getting smarter thanks to AI and machine learning, they will understand our language and communication methods, and not vice versa.

As big as a statement as it could seem, recent progress in AI and ML tends to suggest that this could indeed be the future: computers are able to understand “human friendly” data, such as pictures, voice, language, and produce meaningful results, being trained on vast amounts of data.

Using real muscles to walk on a human-like model.

The code for this post can be found in this GitHub repository.

One of the 2017 NIPS Challenges is «Learning to Run»: as the name suggests, the task is to design and develop a learning algorithm that is capable of controlling a bio-mechanical model of the human body to make it walk. The actuators, differently from most robotic cases, are legs muscles, 9 for each leg. The authors of the challenge modified the OpenSIM Environment to adapt it to a reinforcement learning setting, thus adding a reward signal.

A step-by-step guide using small and efficient neural networks and a bit of magic.

Neural networks, and particularly deep learning research, have obtained many breakthroughs recently in the field of computer vision and other important fields in computer science. Among many different application, one technology that is currently on the rising is self-driving cars. Everybody has heard of them, all the major company seem to invest heavily on this new-millenium gold rush. AI-powered cars that can take you anywhere while you spend your time, well, not driving. In this post I will show you how to train a neural network to steer autonomously using only images of the road ahead. You can find all…

Tackling the bottlenecks of the physical world.

This is the second part of a series of posts. You can find here the Intro and Part 1, about Evolutionary Algorithms. You can find the code for the described algorithms and experiments in this GitHub repository.

In the recent years, Reinforcement Learning has had a reinassence. Various breakthroughs and remarkable results have gained the attention of the whole scientific community, and even of the pop culture: from AlphaGo to DQN applied to Atari, to the very recent OpenAI DOTA 2 bot.

New algorithms and architectures have been released at an ashtoning speed, beating state-of-the-art results and solving new tasks…

Norman Di Palo

deep learning x robots. twitter: @normandipalo

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