As my colleagues and I attend 3 major data and technology industry events in New York City this coming week – ML Ops, Finovate, and the granddaddy of them all, Strata Data Conference – it is interesting to note how far we have come in a few short decades. It may be hard to imagine today, but there was a time not too very long ago when data analysts, with a few notable exceptions, were relegated to the hidden recesses of most corporations. Better to toil away in the bowels than to be shown the light of day. For many decades, even as information technology (IT) emerged as a critical business function, data was viewed more as something that firms filed away in vaults for the mandatory seven years to comply with regulators, and not a business asset that could be mined to unlock critical business insights. Data was perceived as the purview of those who were sometimes derisively referred to as data geeks or "propeller heads". This was long before Silicon Valley, or Wall Street, embraced the term "geek".

Long View is proud to announce that the company has been recognized as a leader in sustainability by its key partners Microsoft and Cisco -- having been named a Sustainability Changemaker by Microsoft Canada and winner of Cisco's Digital Sustainability Challenge 2022. Microsoft recognized Long View as a Sustainability Changemaker in 2022 for its heavy investment in creating a government machine learning solution that will protect oceans from overfishing and communities from the harmful impact of severe flooding events. They did this by building repeatable, scalable, cross-industry solutions for the modernization of business-critical systems and deploying state-of-the-art artificial intelligence platforms for better business and sustainability decisions. Cisco has awarded the company for its work over the past year in partnership with Sensible Building Science to offer clients innovative solutions to automate ventilation, heating and cooling in commercial buildings to zones where occupants are located. This has been proven to provide 5-10% in carbon reductions which, if scaled across a market of 5.5 million commercial buildings, would result in a carbon reduction of 3 to 6 million tonnes.

Welcome to The Long View--where we peruse the news of the week and strip it to the essentials. Let's work out what really matters. Unless you've been living under a rock for the past week, you'll have seen something about Stable Diffusion. Like DALL-E and Midjourney, you give it a textual "prompt" and it generates amazing images (or sometimes utter garbage). Mark Hachman calls it'The new killer app' "Fine-tune your algorithmic art" AI art is fascinating.

Looking ahead, I'm excited about this month's launch of our new hardware and software platform called Microsoft Azure Percept. It's an entire platform that aims to simplify the ways customers can enable AI capabilities at the edge. It seamlessly integrates with other Azure services, including Azure Machine Learning, Azure Live Video Analytics, and Azure Cognitive Services, to help provide detailed insight. The platform also provides detailed vision and audio insights in real time.

As the coronavirus continues to wreak havoc on businesses around the world, it's clear that many companies are accelerating the push for AI tools that can keep their employees safer and more connected--while also keeping their customers happier and more satisfied. It's essential, however, that in the rush for AI adoption, business leaders take the time to consider the human factor in AI adoption. After all, humans are the ones who must be willing to use the technology if it's going to be successful. Let's roll back for a moment. Even without the coronavirus emergency, there are two potential "problem areas" in any AI rollout.

At Azavea, our mission is to create advanced geospatial technology and research for civic and social impact. That mission has led us to some interesting places - we've worked with the World Bank to try to reduce traffic accidents globally, created open source tools for applying machine learning to satellite imagery, and even testified in court based on our research into gerrymandering and how to solve it. Aiming for civic and social impact in our work is so fundamental to our constitution as a company that we've written it into our charter. And as a Certified B Corporation, we participate in bi-annual audits on everything from our carbon footprint, to employee compensation, to our involvement in our local Philadelphia community. Suffice to say, in order to pursue these rather lofty ideals we find ourselves tending to take the long view when weighing business decisions... the really long view.

It may be hard to imagine today, but there was a time not too very long ago when data analysts, with a few notable exceptions, were relegated to the hidden recesses of most corporations. Better to toil away in the bowels than to be shown the light of day. For many decades, even as information technology (IT) emerged as a critical business function, data was viewed more as something that firms filed away in vaults for the mandatory seven years to comply with regulators, and not a business asset that could be mined to unlock critical business insights. Data was perceived as the purview of those who were sometimes derisively referred to as data geeks or "propeller heads". This was long before Silicon Valley, or Wall Street, embraced the term "geek".

As my colleagues and I attend 3 major data and technology industry events in New York City this coming week – ML Ops, Finovate, and the granddaddy of them all, Strata Data Conference – it is interesting to note how far we have come in a few short decades. It may be hard to imagine today, but there was a time not too very long ago when data analysts, with a few notable exceptions, were relegated to the hidden recesses of most corporations. Better to toil away in the bowels than to be shown the light of day. For many decades, even as information technology (IT) emerged as a critical business function, data was viewed more as something that firms filed away in vaults for the mandatory seven years to comply with regulators, and not a business asset that could be mined to unlock critical business insights. Data was perceived as the purview of those who were sometimes derisively referred to as data geeks or "propeller heads". This was long before Silicon Valley, or Wall Street, embraced the term "geek".

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!