Join one of the largest AI & Continual Learning Italian research groups!

We are looking for expressions of interest from motivated prospective PhD students and Post-Docs to join our Pervasive AI Lab at the University of Pisa & CNR, working on Continual Learning with Deep Architectures and related topics. The lab is one of the larger Italian groups working on AI and Machine Learning, counting more than 40+ people among Professors, Researchers, Post-Docs and PhDs. While the candidates will be free to choose how to better pursue their own research agenda based on their own personal interests, the lab currently favors projects on:

  • Unsupervised/ Self-Supervised/Weekly/Semi-Supervised Continual Learning
  • Continual Sequence Learning
  • Neuroscience-Inspired Continual…

Bringing State-of-the-art Continual Learning to Embedded Devices

Fig.1: iCub Robotic Platforms and the CORe Android Application.

Artificial Intelligence (AI) technologies have the potential to radically transform the way we experience the world and connect with other people by making the objects around us “smarter”: gathering and processing information, making decisions, adapting to changes, and interacting with humans and other objects, rather than being simply hard-programmed in advance to execute very specific functions.

However, most “smart” devices today operate as mere gateways to remote computing and AI infrastructures; this imposes limitations on the usability and response speed, due to the latency of remote communication, and also raises potential privacy concerns.

Indeed, one of the key challenges faced…

Why There Will be no Human-Level Artificial Intelligence Before Understanding Biological Intelligence First.

In the last decade we have witnessed a renewed interest in Artificial Intelligence and revamped hopes for its future developments. This new wave of optimism is appreciable not only from the public debate and the commercial hype but also within the research community itself where in a recent survey more than 90% of AI scientist said Human-level AI will be reached by 2075.

However, it still seems to be rather unclear how we are going to get there. Notably, most AI scientists do not think that “copying the brain” would be a good strategy in the pursuit of Human-level AI…

The new “Agile” in the Machine Learning Era

Fig. 1: The Machine Learning Lifecycle, © copyright [1]

The Agile software development approach, popularized around 2010 by the Manifesto for Agile Software Development, advocates adaptive planning, evolutionary development, early delivery, and continual improvement as key properties in order to provide a rapid and flexible response to increasingly fast changes of the market and its requisites.

As the linear waterfall models, originated in the manufacturing and construction industries, were proved to be unable to provide the competitive edge in the increasingly complex and fast-changing software world, Agile and Scrum models became the standard the facto for software development these days.

But what happens as we move towards Software 2.0

Vincenzo Lomonaco, Keiland Cooper, Natalia Díaz Rodríguez, Timothée Lesort, German I. Parisi, Davide Maltoni, Lorenzo Pellegrini, Giacomo Bartoli, Jidin Dinesh, Manish Agnihotri, Martin Mundt

Fig. 1: ContinualAI official website at

Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “Continual Learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.

ContinualAI is an official non-profit research organization and the largest open community on Continual Learning for AI. …

Towards the concept of Fluid Intelligence

In the last decade we have witnessed a tremendous progress in Artificial Intelligence (AI) due to recent Machine Learning (ML) advances. Deep Learning (DL) models and techniques have enabled a major leap in automation complexity, enabling a large set of new applications involving high-dimensional perceptual data, which were even unthinkable to tackle a couple of decades ago.

Machine Learning, I think for the best, seems to be the forefront runner in the long path towards strong AI.

So, what’s next? What to expect for the future of Machine Learning and AI?

While Medium is not the place to dwell in…

Highlights, Open Issues and Future Directions

Continual Learning Workshop NeurIPS 2018 Official Website


Continual Learning is an exciting topic getting more and more attention lately in the AI community. While the “Continual Learning” workshop was born at NeurIPS in 2016, it did not take place in 2017 leading to its second and most successful edition in 2018, the 7th of December, just a few days ago, with more than 400 attendees and more than 80 submissions! 😮 🔥

Co-written by Vincenzo Lomonaco and Marta Ziosi

“The world’s most valuable resource is no longer oil, but data.” — Copyright © David Parkins, The Economist [1]

The last decade has witnessed tremendous advancements in the context of Artificial Intelligence (AI) to the point that many are framing it not only as a groundbreaking technology but even as “the new electricity” echoing the unique impact its analogue counterpart had and still has on our society.

Despite the great hype and inflated hopes for the imminent future, it is undeniable that recent advances in AI under the name of “Deep Learning” or the more recent rebranding “Differentiable Programming” have radically pushed the boundaries of what’s possible, enabling a rich set of applications which were even unthinkable before.


Brain circuits In Brainbow mice. Neurons randomly choose combinations of red, yellow and cyan fluorescent proteins, so that they each glow a particular color. This provides a way to distinguish neighboring neurons and visualize brain circuits. 2014. HM Dr. Katie Matho.

The last decade has marked a profound change in how we perceive and talk about Artificial Intelligence. The concept of learning, once confined in the corner of AI, has now become so important some people came up with the new term “Machine Intelligence”[1][2][3] as to make clear the fundamental role of Machine Learning in it and further depart form older symbolic approaches.

Recent Deep Learning (DL) techniques have literally swept away previous AI approaches and have shown how beautiful, end-to-end differentiable functions can be learned to solve incredibly complex tasks involving high-level perception abilities.

Yet, since DL techniques have been…

Official Home Page of the CORe50 project.

Hi guys, what’s uuup? :-) I’m Vincenzo Lomonaco, a 2nd year PhD student @ University of Bologna and in this second story on Medium I’d like to cover our latest work on Continual/Lifelong Learning with Deep Architectures.

This work, currently under peer-review, is all about a new dataset and benchmark specifically designed for Continual Learning in the context of Vision, called CORe50.

Below you can find a 5-minutes video presentation (no math, very easy to follow) with the key aspects of our work or you can just skip to the next section!

5-minutes clip on the key concepts of CORe50.

Why CORe50?

Vincenzo Lomonaco

AI & Continual Learning Assistant Professor @ Unipi | Co-Founding President & Lab Director @ | Personal Website:

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store