Tag Archives: ai

AI-Powered Conservation

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Artificial Intelligence has rapidly transformed from a technological novelty into a driving force across multiple sectors, from healthcare and finance to education and environmental conservation. As organizations worldwide harness AI’s analytical capabilities to solve complex problems, one promising application has emerged in wildlife protection and environmental monitoring.

Microsoft’s AI for Good Lab illustrates the potential of applying AI to projects that range from monitoring endangered species to modeling Earth’s natural systems and enhancing disaster response and preparedness. Since 2018, the lab has launched over 200 projects worldwide, combining artificial intelligence with initiatives focusing on sustainability, humanitarian action, and health.

An example of their innovative approach is the development of SPARROW (Solar-Powered Acoustic and Remote Recording Observation Watch), a new tool in biodiversity surveillance technology. This sophisticated system employs solar-powered devices equipped with energy-efficient AI chips. The devices are capable of operating autonomously for years while transmitting data via low-Earth orbit satellites.

Implementing these systems on a larger scale will require overcoming obstacles such as weather interference, equipment durability in harsh environments, and the complex task of filtering out background noise in dense forest environments. However, the potential of this technology has already been demonstrated through initial projects.

In a pioneering study, biologist Jenna Lawson deployed 350 audio monitors throughout Costa Rica’s Osa Peninsula to track Geoffroy’s spider monkeys. These primates are sensitive to environmental changes and difficult to track on the ground. Using SPARROW’s AI systems, Lawson collected and analyzed vast amounts of recorded data. Published in March, her findings revealed that the monkeys avoided areas near roads and plantations, highlighting the need to rethink and redesign conservation efforts like the wildlife corridors that bisect the region’s protected reserves.

Microsoft’s commitment to global conservation continues to expand. Plans to deploy SPARROW devices across all continents by late 2025 are underway. The collected data will be open-sourced, making it accessible to researchers worldwide while protecting sensitive location information from potential misuse. This initiative is a step forward in understanding and addressing the causes of the extinction risks faced by 28% of plant and animal species. As this technology continues to evolve, it offers a promising blueprint for how AI and conservation can work together to safeguard Earth’s biodiversity.

MTV VMAs 2024: Shoppable Live TV Takes Center Stage

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The 2024 MTV Video Music Awards (VMAs) transformed the viewing experience with a growing  partnership between Paramount Global and Shopsense AI. This collaboration enabled real-time shopping of outfits and designer looks featured during the awards show, and marked a significant shift in content monetization for legacy media companies.

Shopsense’s AI-powered lens allowed viewers to snap photos of outfits during the show, browse similar items suggested by their product recognition algorithm, and make purchases directly from their phones. This seamless shopping experience was designed to enhance engagement and for users to “go through that shopping journey without pausing the content” according to Shopsense’s co-founder and president Bryan Quinn in an interview with CNBC.

In the age of streaming, as traditional TV advertising revenues decline, media companies like Paramount are looking for innovative solutions to boost profits. This new live shopping feature is expected to drive consumer engagement and conversion rates during high-profile events like the VMAs. As the AI continues to improve, it is predicted that this trend will proliferate with other media giants like Disney exploring similar shoppable ad formats.

Retailers including Macy’s, Nordstrom, and Urban Outfitters are leveraging this partnership to capture consumer interest at the moment they’re inspired by what they see on TV. The approach capitalizes on impulse buying, offering curated collections and lookalikes at various price points. As AI continues to transform the advertising and retail sectors, live shopping represents a growing trend that blends entertainment and commerce. This partnership could shape the future of shopping by turning live television into an interactive retail experience.

Accounting Industry Gears Up for AI

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Artificial intelligence isn’t exactly a new trend – people have been dreaming about robots and automated labor since ancient times, and the phrase “AI” was actually coined as far back as 1956, at a conference at Dartmouth College in New Hampshire.

In practical terms, too, artificial intelligence has been part of our world for more than a decade, since IBMs Watson computer competed on TV’s Jeopardy! program (and defeated two ex-champions) in 2011. Since then, machine learning has come to play a role in virtually every area of modern life, from shopping to entertainment to investing and more.

By nature, however, the accounting sector is a conservative one, and accounting firms have been slow to adopt developments that rely on AI.

That trend appears to be changing. Patrick Morrell, Chief Revenue Officer at accounting software startup Anduin, says the emergence of practical AI solutions and the economic challenges of the coronavirus pandemic have forced the industry to rethink its relationship with machine learning.

“Firm leaders still may be saying: ‘But I have partners and clients married to their pen and paper. How do I bridge the gap between the old way and the new?’

“The most effective way to build AI expertise among your workforce and create a sustainable firm of the future is to focus on AI solutions that are tailored to the accounting industry and employ “mutual learning” to solve problems and support your staff,” Morrell writes in Accounting Today.

Morrell says the industry’s use of AI is actually better described as “mutual learning,” a combination of algorithm-generated information which is then reviewed and implemented (or rejected) by human analysts.

“In systems built with mutual learning, AI tools can recommend data-driven actions in response to specific problems, and then humans can either approve those moves or edit and course-correct those actions as necessary. In either case, humans learn how data can shape decision-making. The AI itself then learns from those approvals or course-corrections, applying the human input to improve the AI’s models and algorithms. This, in turn, allows the AI to make better-informed recommendations in the future that will ideally require less human input—over time, the tool and the human continually increase their individual knowledge and collaborative effectiveness, creating ongoing and compounding value together,” he adds.