The world is changing and so is the way it is measured. For decades, policymakers and the private sector have relied on data released by official statistical institutions to assess the state of the economy.

Sir David Hand gave a brilliant plenary talk and set the stage for a great panel discussion by cautioning us to remember that thinking is required and to be aware of all the dark data out there -- the data that we don't see, but that we need to take into account. Dark Data: Why What You Don't Know Matters is his latest book (see a blog post about it; if you haven't read it, you can get a sample excerpt). The panelists included Cameron Willden, statistician at W.L. Gore, who supports engineers and scientists across many different product lines; Sam Gardner, founder of Wildstats Consulting, with more than 30 years of experience doing statistical problem solving for government and industry; and JMP's Jason Wiggins, a 20-year US Synthetic veteran with expertise in process optimization, measurement systems analysis and predictive modeling/data mining. We ran out of time before we could answer all the questions from the livestream audience, but our panelists have kindly agreed to provide answers to many of them, further sharing the wisdom from their collective experiences. The questions are grouped by topic -- there were so many, we are doing two posts.

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In the past few years, more data has been produced than in the millennia of human history before. This data represents a gold mine in terms of commercial value and also important reference material for policy makers. But much of this value will stay untapped -- or, worse, be misinterpreted -- as long as the tools necessary for processing the staggering amount of information remain unavailable. In this article, we'll look at how machine learning can give us insight into patterns in this sea of big data and extract key pieces of information hidden in it. The core of machine learning consists of self-learning algorithms that evolve by continuously improving at their assigned task.

As humans, we love contrast. It is no wonder that experts, while defining the impact of Artificial Intelligence (AI) on the future of humankind, are at two ends of the spectrum. One is a happy scenario of human beings and artificially intelligent machines coexisting in perfect harmony. Another is an Orwellian dystopia of AI dominance over human intelligence and civilization. While there may be disagreements about the future, everyone agrees on the impact and growing ubiquity of AI.

Murat Sonmez, managing director of the World Economic Forum, explains how big data and machine learning can help find a vaccine for COVID-19. Dan Patterson, senior producer for CNET and CBS News, spoke with Murat Sonmez, managing director of the World Economic Forum, how big data and machine learning can help find a vaccine for COVID-19. The following is an edited transcript of their conversation. Murat Sonmez: The World Economic Forum is a 50-year-old international organization, focused on public-private cooperation, bringing together business, international organizations, governments, academia, and civil society, to understand how we can shape a better future for the world and create action groups to that effect. In terms of the data piece, we launched an initiative called the Center for the Fourth Industrial Revolution to really look at how we can leverage emerging technologies, to accelerate these solutions.

A physician-turned-entrepreneur raised in Kashmir is now part of a team using big data and machine learning to help detect useful patterns in the tsunami of public health data generated world-wide by the COVID-19 crisis and do what he can for those back home. Junaid Nabi, a public health researcher at Brigham and Women's Hospital and Harvard Medical School in Boston, says his experiences with the health system in the developing world drives his current work. "Growing up in Kashmir, a society marred with social, economic, and healthcare disparities, I was exposed to the inherent inequities in my community at an early age," he said, "During the final years of my training, I had an opportunity to work with some non-profit organizations, especially the rescue teams during the Savar building collapse in Dhaka, Bangladesh." "This is when I noticed that clinical medicine does not answer all the questions clinical work asks." Nabi, who is also an Aspen New Voices Fellow, is now working with colleagues at Harvard Medical School and Harvard School of Public Health to develop digital tools that harness big data and machine learning to rapidly evaluate patterns in the data pouring in from clinical research. "I believe machine learning has an important role in COVID-19," he said.

The area burned by wildfires each year across the Western United States has increased by more than 300 percent over the past three decades, and much of this increase is due to human-caused warming. Warmer air holds more moisture, and the thirsty air sucks this from plants, trees, and soil, leaving forest vegetation and ground debris drier and easier to ignite. Future climate change, accompanied by warming temperatures and increased aridity, is expected to continue this trend, and will likely exacerbate and intensify wildfires in areas where fuel is abundant. Park Williams, a Lamont-Doherty Earth Observatory associate research professor and a 2016 Center for Climate and Life Fellow, studies climatology, drought, and wildfires. He has received a $641,000 grant from the Zegar Family Foundation that he'll use to advance understanding of the past and future behavior of wildfires. His goal is to create a tool to help scientists understand why wildfires in Western U.S. states have changed over the last century, ways they may evolve in the future, and how humans can most effectively respond to them.

Before going to the world of integration, machine learning, etc., I would like to discuss with all of you about a scenario many of you might experience when you live in a mega city. I lived in the London suburbs for almost 2 years (and it's a city quite close to my heart too), so let me use London as this story's background. When I moved to London, one question which came to my mind was whether I should buy a car or not. The public transport system in London is quite dense and amazing (Oh!!! I just love the amazing London Underground and I miss it in Toronto).

Data aren't anything more than information that is stored on a computer. There's nothing particularly interesting about data besides the fact that we have a lot of it. Many corporations and media would have you believe that the combination of data and algorithms begets AI, or even that algorithms themselves are AI in some cases. This is not the case. Machine learning pioneer and philosopher Judea Pearl put it best: "Data are dumb."