The research team behind Meta AI's Detectron project has recently been awarded the PAMI Mark Everingham Prize for contributions to the computer vision community. We first open-sourced the Detectron codebase five years ago as a collection of state-of-the-art algorithms for tasks such as object detection and segmentation. It has since evolved and advanced in important ways thanks to the contributions of both the open source community and many researchers here at Meta. In 2019, we released a ground-up rewrite of the codebase entirely in PyTorch to make it faster, more modular, more flexible, and easier to use in both research-first and production-oriented projects. Earlier this year, we released Detectron2Go, a state-of-the-art extension for training and deploying efficient object detection models on mobile devices and hardware, as well as significantly improved baselines based on the recently published state-of-the-art results produced by other experts in the field. Several members of the Detectron team sat down to discuss the project's origins, advances, and future.

Drones are remarkable things, nonetheless. The other day, a representative of the U.S. National Forest Service was reporting on their use for re-seeding incinerated forests in California, speeding the process at a vastly reduced cost. It occurred to me, though, that the same dark cloud/silver lining thinking applied. Imagine all the out-of-work foresters heading for the unemployment line in the shade of a flock of drones. It occurred to me, as well, that this replacement of "repetitive task" workers with artificial intelligence (AI) technology was fully expected. The first wave of AI pushed millions of such laborers out of the labor force.

This week, on the new-media platform Feral File, artist Refik Anadol presents Unsupervised, an exhibition of works created by training an artificial intelligence model with the public metadata of The Museum of Modern Art's collection. Spanning more than 200 years of art, from paintings to photography to cars to video games, the Museum's collection represents a unique data set for an artist who has worked with many different public archives. The AI-based abstract images and shapes in Unsupervised are interpretations of the Museum's wide-ranging collection, weighted toward the exhibition of new artworks at MoMA this fall. Starting with the exhibition opening on November 18, new artworks will be revealed and released over three days. Each work will be made available to collectors as nonfungible tokens, or NFTs. MoMA curators Paola Antonelli and Michelle Kuo sat down with Anadol and Casey Reas, the artist-founder of Feral File, to talk about the ecology of mobile images, art in the age of mechanical learning, and the question: What if a machine tried to create "modern art"? This conversation has been edited for length and clarity. Paola Antonelli: Refik, how did you start thinking of your Machine Hallucinations series, of which Unsupervised is a part? Refik Anadol: Five years ago, I was very fortunate to be one of the artists in residence at the Google Artists and Machine Intelligence program. This was the moment of DeepDream's development, the very first time we were witnessing AI algorithms making an impact on the art and technology communities.

Zest AI, a leader in software for credit underwriting, today announced it has been selected as an honoree in Fast Company's inaugural 2021 Next Big Things in Tech Awards. The awards recognize the companies and technologies that promise to redefine their industries and create a positive impact for consumers, businesses and society at large in the next five years. Specifically, Zest AI was recognized for its Fairness Kit, a set of software applications created in response to demand from banks and credit unions looking to reduce bias from consumer lending. Zest's patented machine learning-based solution automatically optimizes credit underwriting models for both accuracy and fairness, creating real options for the first time for lenders that want to close the racial approval rate gap in consumer credit. "We are honored to have our fair lending efforts recognized by Fast Company for this award," said Mike de Vere, CEO of Zest AI. "Traditionally, lenders have had to make trade-offs between accuracy in risk prediction and minimizing disparate impact. Through our technology, these lenders can make more accurate decisions and say yes to more people who might have struggled to get affordable credit. This award further endorses our mission of making fair and transparent credit available to everyone."

Welcome to The TechCrunch Exchange, a weekly startups-and-markets newsletter. It's inspired by the daily TechCrunch column where it gets its name. If you aren't in the United States, it's a little hard to explain. In short, certain deficiencies in our policing and judicial systems flared brightly as the week came to a close. So, today's Exchange newsletter will be shorter than intended. Hug the people you love, and everyone else.

Public policies that supply public goods, especially those involve collaboration by limiting individual liberty, always give rise to controversies over governance legitimacy. Multi-Agent Reinforcement Learning (MARL) methods are appropriate for supporting the legitimacy of the public policies that supply public goods at the cost of individual interests. Among these policies, the inter-regional collaborative pandemic control is a prominent example, which has become much more important for an increasingly inter-connected world facing a global pandemic like COVID-19. Different patterns of collaborative strategies have been observed among different systems of regions, yet it lacks an analytical process to reason for the legitimacy of those strategies. In this paper, we use the inter-regional collaboration for pandemic control as an example to demonstrate the necessity of MARL in reasoning, and thereby legitimizing policies enforcing such inter-regional collaboration. Experimental results in an exemplary environment show that our MARL approach is able to demonstrate the effectiveness and necessity of restrictions on individual liberty for collaborative supply of public goods. Different optimal policies are learned by our MARL agents under different collaboration levels, which change in an interpretable pattern of collaboration that helps to balance the losses suffered by regions of different types, and consequently promotes the overall welfare. Meanwhile, policies learned with higher collaboration levels yield higher global rewards, which illustrates the benefit of, and thus provides a novel justification for the legitimacy of, promoting inter-regional collaboration. Therefore, our method shows the capability of MARL in computationally modeling and supporting the theory of calculus of consent, developed by Nobel Prize winner J. M. Buchanan.

AI leads to'great return on investment' in dealing with pandemic The UAE approached the COVID-19 pandemic "as a scientist," said Omar Al Olama, the UAE's Minister of Artificial Intelligence, Digital Economy and Remote Work Applications. Al Olama was appointed by the UAE as the first artificial intelligence (AI) minister in the world in 2017, when he was just 27 years old. That year, his ministry launched a strategy "to become one of the world leaders in AI by 2031." The COVID-19 pandemic, it turns out, may have accelerated the UAE's applications of AI to governance and public health, and to establishing the Emirates as a world leader in AI, as Al Olama, now 31, explained in an exclusive Zoom interview with Al-Monitor on Nov. 18. Al Olama describes a policy response to the pandemic by the UAE that has been data- and analytics-driven and characterized by openness to different ideas, nimbleness in response to changing events, and willingness to accept calculated risks. "We actually were very open to many different solutions, and many different theories out there," he said. "And we worked with everyone, from the East and the West, to try to find the right solutions that can be deployed in the UAE to make us go back to living a relatively normal life. Not the normal life that we're used to. People still need to wear masks. There's still a lot of focus on the general community's safety, but AI played a big role in getting us to this point."

Hit play on the player above to hear the podcast and follow along with the transcript below.This transcript was automatically generated, and then edited for clarity in its current form. There may be some differences between the audio and the text. Welcome back to Talking Tech. This month marks a huge milestone for Microsoft and its Xbox video game console. Twenty-years ago, this month, Microsoft launched the original Xbox.

The real estate industry has started leveraging the power of artificial intelligence and machine learning to provide efficient services to property managers, landlords, tenants, and others. AI has helped to build a dedicated platform for real estate like property management software and many more for automating a complicated processes. This cutting-edge technology helps to build a strong relationship between landlords and tenants. Here is an exclusive interview with Andrews Moses, Co-Founder and CEO, Tenantcube, where he elaborates how the company integrates AI/ML into real estate to offer a property management software for efficient service with a team of real estate expertise. Tenantcube offers next-generation property management software and is designed to be an end-to-end platform fulfilling the needs of landlords, property managers, and tenants. The software drastically improves the entire experience of renting and saves time and money spent on traditional methods of rental property management.

A few years ago, at the Princeton Club in Manhattan, I chanced on a memorable chat with the Harvard psychologist Steven Pinker. His spouse, the philosopher Rebecca Goldstein, with whom he was tagging along, had been invited onto a panel to discuss the conflict between religion and science and Einstein's so-called "God letter," which was being auctioned at Christie's. Pinker had recently published Enlightenment Now: The Case for Reason, Science, Humanism, and Progress. I was eager to pepper him with questions, mainly on religion, rationality, and evolutionary psychology. I remember I wanted Pinker's take on something Harvey Whitehouse, one of the founders of the cognitive science of religion, told me in an interview--that my own little enlightenment, of becoming an atheist in college, was probably mostly a product of merely changing my social milieu. I wasn't so much moved by rational arguments against the ethics and existence of God but by being distanced from my old life and meeting new, non-religious friends. I recall Pinker almost pouncing on that argument, defending reason's power to change our minds. He noted that people especially high in "intellectance," a personality trait now more commonly called "openness to experience," tend to be more curious, intelligent, and willing to entertain new ideas. I still think that Pinker's way of seeing things made more sense of my experience in those heady days. I really was, for the first time, trying my best to think things through, and it was exhilarating. We talked until the event staff shelved the wine, and parted ways at a chilly midtown intersection.