Is ML truly ready for real-world deployment?

Over the last years, state-of-the-art Deep Learning models are highly effective in solving many complex real-world problems — boosting progress in computer vision and natural language processing and impacting nearly every industry. However, these models are vulnerable to well-designed input samples called adversarial examples.

With the advance in AI systems, most companies are focusing on increasing AI performance rather than quality and safety. To this day researchers are exploring new approaches to address Robustness, Safety, and Security in AI.

Adversarial examples have attracted significant attention in machine learning, but the reasons for their…

Key Words: Symbolic AI, Deep Learning, Neuro-Symbolic AI


Artificial Intelligence( AI ) has been witnessing a monumental growth in bridging the gap between the capabilities of humans and machines.

Building thinking machines have been a human obsession for decades. Despite, the current advances in AI and ML, concerns about trust, safety, interpretability, and accountability of AI were raised by influential thinkers. In this article, we will discover the next wave of AI systems by introducing the Neuro-Symbolic AI approach.

Why Deep Learning is not enough?

In recent years Artificial Intelligence, specifically Deep Learning has gained popularity thanks to the availability of computational power and the…

Jouini Ghada

Data Scientist Passionate about Tech in General and Artificial Intelligence in particular.

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