Orchestrate all the Things

Economic theory is known to be constrained by a number of inefficiencies in its modeling. Salesforce researchers claim AI can help address that, leading to more robust economic policies.

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As Ben Lorica will readily admit, at the risk of dating himself, he belongs to the first generation of data scientists. In addition to having served as Chief Data Scientist for the likes of Databricks and O'Reilly, Lorica advises and works with a number of venture capitals, startups and enterprises, conducts surveys, and chairs some of the top data and AI events in the world. That gives him a unique vantage point to identify developments in this space.

Having worked in academia teaching applied mathematics and statistics for years, at some point Lorica realized that he wanted his work to have more practical implications. At that point the term "data science" was not yet coined, and Lorica's exit strategy was to become a quant. Fast forwarding to today, Lorica still has friends in the venture capital world.

That includes Intel Capital's Assaf Araki, with whom Lorica co-authored two recent posts on data management and AI trends. We caught up with Lorica to discuss those, as well as new areas for growth and the trouble with unicorns and what to do about it.

What's a good enough weather prediction? That's a question most people probably don't give much thought to, as the answer seems obvious -- an accurate one. But then again, most people are not CTOs at DTN.

Lars Ewe is, and his answer may be different than most people's. With 180 meteorologists on staff providing weather predictions worldwide, DTN​ is the largest weather company you've probably never heard of.

Weather forecast, too, is all about data and models these days. But balancing accuracy and viability is a fine act, especially at global scale and when the stakes are high.

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Everything counts in large amounts. You don't have to be Google, or to build large AI models, to benefit from writing efficient code. But how do you measure that?

It's complicated, but that does not mean that people are not trying. That's what Abhishek Gupta and the Green Software Foundation (GSF) are relentlessly working on.

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What does sustainability actually mean for organizations? Can it be measured, and if yes, how? Obvious questions with less than obvious answers, even for sustainability and ESG professionals like James Phare.

Phare shares his experience and assessment of sustainability and its relationship with the ESG space, its current state and trajectory, and how data and analytics can help.

Award-winning AI research - check. Startup and enterprise experience - check. Venture capital and Mark Benioff backing - check. Is that enough for Richard Socher's you.com to take on Google?

Here is why and how he aims to do that.

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Decision intelligence is one of those terms that sound vaguely familiar, even if you've never come across it before. Like many category-defining terms, it can mean different things to different people. This is a feature category-defining terms either have by design, or acquire through extensive use.

In October 2021, Gartner identified DI as a 2022 Top Trend. A number of vendors have identified with that category, and Aera Technology is among them, claiming to have been doing DI before it was called DI.

Today, Aera is announcing new capabilities for its Aera Decision Cloud at the Gartner Supply Chain Symposium/Xpo™ 2022. Aera founder and CTO Shariq Mansoor weighed in on DI, Aera's offering, and how it is relevant for supply chains and beyond.

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It sounds like a contradiction in terms, but disaster and disruption management is a thing. Disaster and disruption is precisely what ensues when catastrophic natural events occur, and unfortunately, the trajectory the world is on seems to be exacerbating the issue. In 2021 alone, the US experienced 15+ weather/climate disaster events with damages exceeding $1 billion.

Previously, we have explored various aspects of the ways data science and machine learning intertwine with natural events - from weather prediction, to the impact of climate change on extreme phenomena and measuring the impact of disaster relief. AiDash, however, is aiming at something different: helping utility and energy companies as well as governments and cities manage the impact of natural disasters, including storms and wildfires.

We connected with AiDash co-founder and CEO Abhishek Singh to learn more about its mission and approach, as well its newly released Disaster and Disruption Management System (DDMS)

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Is deep learning really going to be able to do everything? Opinions on the potential of this opinion to prove true vary. Geoffrey Hinton, awarded for pioneering deep learning, is not entirely unbiased in so opining. However others, including Hinton's deep learning collaborator Yoshua Bengio, are looking to infuse deep learning with elements of a domain still under the radar: operations research.

Machine learning and its deep learning variety are practically household names by now. There is lots of hype around deep learning, as well as a growing number of applications. However, as applications of deep learning are proliferating, its limitations are also becoming better understood. Presumably that's the reason why Bengio turned his attention to operations research.

In 2020, Bengio and his collaborators surveyed recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. They advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so.

To this day, however, there is no publicly visible operations research renaissance to speak of, and commercial applications remain few compared to machine learning. Nikolaj van Omme and Funartech want to change that.

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Data quality is part of data intelligence. It's a topic that lot of people are concerned about, and it makes engagement and adoption around data intelligence solutions better. With many data quality solutions with different approaches available in the market, customers need to be able to choose the one that works best for them.

Plus, if you are someone like Alation, a vendor whose core business is not data quality -- if you can't beat them, join them. As Alation CEO and co-founder Satyen Sangani shared, that was the thinking behind today's announcement of the Alation Open Data Quality Initiative (ODQI) for the modern data stack.

As Alation notes, the program provides customers with the freedom of choice and flexibility when choosing the best data quality and data observability vendors to fit the needs of their modern, data-driven organizations. We caught up with Sangani to discuss the ODQI and where it fits in the broader data intelligence landscape, as well as Alation's strategy and evolution.

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