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…

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…

Introduction

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…

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. …

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…

Introduction

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! 😮 🔥

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.

AI…

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…