Formalizing social interactions for robot fleets

The development of functional robots makes the management of fleets of robots of critical importance for robotics and the broader artificial intelligence.  The following study aims at formalizing a broad range of social interactions that can be observed in natural environments, standardizing examples for computer programming of complex interactions between different robots.

Robots of Boston Dynamics
Two famous robots of Boston Dynamics: Atlas and SpotMini

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Computer vision and convolutional neural networks

Computer vision is a key aspect of artificial intelligence that is critical to many applications, from robots movements to self-driving cars and from medical imaging to products recognition in manufacturing plants. This MIT course presents the issues of computer vision and how they are handled with Convolutional Neural Networks together with the latest domains of research and state-of-the-art algorithms architectures.

image classification by a computer

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The Singularity may be nearer than General AI: introducing the Singularity Protocol

A hypothetical architecture of existing machine learning algorithms to automate the self-sustained generation of new scientific and technological advances.

scientific and technological progress graph


While many Artificial Intelligence researchers, companies and engineers are focusing their activities on the creation of Artificial General Intelligence with the hope of one day reaching the Technological Singularity, this paper takes a different approach. It presents a conceptual, speculative yet pragmatic organization of concepts to directly reach exponential scientific and technological progress. This paper is an attempt to design a process that automates the theories generation part of research and development through existing artificial intelligence algorithms and supporting technologies.

Notably relying upon recent developments in the conceptual and technical environment of graph neural networks, as well as upon multiple other technologies, the protocol breaks down the generation of scientific and technological theories in simple steps and propounds existing algorithm candidates to handle each step. With multiple scientific, mathematical and algorithmic references and sources for advanced readers, this paper nevertheless tries to make use of the simplest terms to facilitate the comprehension of a wider audience with minimal background in artificial intelligence.

Even though this paper describes a process that is still purely speculative for now, the Singularity Protocol does present a credible, structured and detailed approach to generate new scientific and technological theories at scale. And though it still needs to go through numerous adaptations, tests and computing challenges, this protocol is exclusively built upon existing technologies and it introduces a plan to gather and structure technical, financial and human resources so as to rapidly develop and implement an organization that could soon lead to the Technological Singularity. Continue reading “The Singularity may be nearer than General AI: introducing the Singularity Protocol”

Conceptual and mathematical summary for machine learning

Machine learning makes use of multiple mathematical formulas and relations to implement the different tasks it can handle. Gathered in the following “cheat sheets” by Afshine and Shervine Amidi, the concepts for supervised and unsupervised learning, deep learning together with machine learning tips and tricks, probabilities, statistics algebra and calculus reminders, are all presented in details with the underlying math.

Gradient descent diagram

Based on the Stanford course on Machine Learning (CS 229), the cheat sheets summarize the important concepts of each branch with simple explanations and diagrams, such as the following table cover underfitting and overfitting.

UnderfittingJust rightOverfitting
Symptoms• High training error
• Training error close to test error
• High bias
• Training error slightly lower than test error• Very low training error
• Training error much lower than test error
• High variance
Regression illustrationIllustrationIllustrationIllustration
Classification illustrationIllustrationIllustrationIllustration
Deep learning illustrationIllustrationIllustrationIllustration
Possible remedies• Complexify model
• Add more features
• Train longer
• Perform regularization
• Get more data

The main machine learning cheat sheets can be found here:

Other mathematics and coding cheat sheets can be found here:

The complete cheat sheets can also be found on Github.

Guide to real Machine Learning applications

This series of articles dives deeper into the actual applications of Machine Learning that are currently in use in many current technological processes and devices.

Amazon Alexa

Through these posts entitled “Machine Learning is Fun!”, Adam Geitgey guides us step by step through the concepts, data, algorithms, code, results and pitfalls of machine learning applications from image, face and speech recognition to language translation and more. It also gathers several different sources for more details on each application and its development.

Image encoding

This series is really dense with detailed code, but it is also explained very clearly, step by step, with detailed illustration. It notably covers the use of a Convolutional Neural Network (including Generative Adversarial Network) and Recurrent Neural Network, together with some of their most prominent applications in daily life. It is a real course not to be missed for any ML developer!

Here is the list of posts with direct links: