Market Trends & Best Practices from Early Adopters

I was recently interviewed by #TresconTechReview ahead of a talk that I gave at the World AI Show — Qatar, 2020, and this is the transcript of the interview (with some edits). In this article, I’m going through some market trends and observations on the latest developments in the world of AI and Analytics. Hope you enjoy the reading. Feel free to comment, especially if you see things differently or simply have a different perspective!

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Photo by Tim Graf on Unsplash

1. Thank you for taking out time to chat with us. To begin with, how do you see the landscape of AI and data analytics developing globally? …


The role of data analytics in building a stronger, more resilient society

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Preparing for the “never-normal”

In my previous article, Facing down the storm, I explored how communities and organizations have turned to data and analytics to cope with the COVID-19 crisis, and how the world of data has mobilized through crowd-sourcing initiatives, academic institutions, and tech companies to fight the pandemic. In this article, I focus on the longer-term perspective, the need for greater resilience, and how data analytics will help organizations to better prepare for the next crisis and to recover faster.

In times of uncertainties, everything is in flux, and a new normal succeeds to the previous normal. Everything is temporary and normality disappears. In this “never-normal” world, organizations need to keep delivering value to their shareholders, employees and customers. They also need to ensure they can maintain business continuity through crisis of different natures. But what crisis? What is the next threat that we need to plan for? Financial crashes, nationalist and climate wars, pandemics, terrorist attacks on vital infrastructures, natural disasters? …


A data-driven response to the COVID-19 crisis

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In any crisis, we have an opportunity to learn from it and emerge stronger. What is the COVID-19 crisis telling us about ourselves, our ability to cope with unpredictable events, the resilience of our economy, communities, and society at large? In many ways, we were not ready for a full-blown pandemic, as Bill Gates pointed out in a TED Talk… 5 years ago. We knew it was a matter of time before something like this happened. …


Inside AI

Are we creating monsters?

As artificial intelligence (AI) and machine learning (ML) applications weave into more and more aspects of our lives, voices are rising to express concerns about the ethical implications, the potential discrimination fueled by algorithmic bias (“Algorithms, the Illusion of Neutrality”), and the lack of transparency and explainability of black box models (“X-AI, Black Boxes and Crystal Balls”).

We are building systems that are beyond our intellectual ability to comprehend. Who can seriously pretend that they understand the hundreds of millions of lines of code used in a self-driving car?

AI is rapidly evolving towards more autonomy and human-like cognitive activities such as natural language processing and computer vision. Algorithms need less and less supervision to function. In some cases, they are even starting to rewrite bits of their own code. Those “generic algorithms” evolve, just as organisms do naturally. No wonder that some academic research labs are now looking for ways to understand algorithms by treating them like animals in the wild, observing their behaviors in the world. …


Inside AI

On our road to trusted AI, I discussed in my previous blog the question of bias, how it travels from humans to machines, how it is amplified by AI applications, the impacts in the real world, for individuals and for businesses, and the importance to proactively tackle this problem. Today, I’ll address the issue of explainability and transparency of the so-called “black box” models.

Choosing between Explainability and Accuracy?

To trust, one must understand. This is true of relationships between human beings. It is also true when it comes to adopting systems that augment human capabilities to generate insight and make decisions. It’s a partnership between humans and machines. …


Inside AI

From humans to algorithms

Bias is a fundamental human characteristic. We are all biased, by our very nature, and every day we make countless decisions based on our gut feelings. We all have preconceived ideas, prejudices, and opinions. And that is fine, as long as we recognize it and take responsibility for it.

The fundamental promise of AI, besides the dramatic increase of data processing power and business efficiency, is to help reduce the conscious or unconscious bias of human decisions. At the end of the day, this is what we expect from algorithms, isn’t it? Objectivity, mathematical detachment rather than fuzzy emotions, fact-based rather than instinctive decisions. …


Inside AI

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Artificial Intelligence (AI) is re-shaping the world we live in. There is no lack of hype to fuel the enthusiasm, and to be fair, the promise is inviting.

We are told that AI holds promise for humanity in general. We will be able to better diagnose, cure and even predict illnesses. We will be able to produce and distribute enough resources to feed a fast-growing population. We will be able to improve living conditions in our cities, and even mitigate the effects of climate change.

It also holds promise for businesses. It will help them to radically reduce costs and improve operational efficiency with automatic decision-making in a variety of domains such as supply chain optimization, demand forecasting, predictive maintenance, fraud avoidance, risk reduction, cybersecurity, and robotics. A world of new opportunities will open up, and businesses will reinvent customer engagement with new levels of personalization, natural language processing and behaviour analysis. …

About

Olivier Penel

Passionate about data-driven innovation (AI, IoT, Analytics…), how it creates value and how it relates to bigger questions such as privacy and ethics

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