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Dozens of Kirkland’s Home stores will close as part of the retailers recently announced rebranding efforts. Some existing stores will be converted to Bed Bath & Beyond Home stores as part of the transformation, the company said this week. Kirkland’s will streamline its footprint by closing at least two dozen of its 313 existing Kirkland’s Home stores. The company will launch its first Bed Bath & Beyond Home store in Brentwood, Tennessee, in August 2025, with five stores to follow. Pending the initial market launch, the retailer intends to open approximately 75 additional stores through 2026. The Tennessee-based retailer also plans to open its first physical Overstock store location in Nashville, with about 30 additional stores to open after the initial launch. These plans align with Kirklands broader goal to be a multi-brand retail operator. “By consolidating real estate and leveraging underperforming store closures to reduce excess inventory, we believe we will drive faster inventory turn and maximize return on assets,” the retailer said in a press release. “Following the consolidation, we expect to move forward with approximately 290 of our current store locations as the foundational footprint for Kirkland’s Home, Bed Bath & Beyond Home, and Overstock.” Fast Company contacted the brand to request a list of locations that will close. We will update this story if we receive a reply. Kirkland’s Home rebrand reflects a broader transformation Kirklands corporate name will officially change to The Brand House Collective pending shareholder approval at the company’s next annual meeting on July 24, 2025. Its ticker symbol will also change from “KIRK” to “TBHC,” pending approval next month. Kirkland’s CEO, Amy Sullivan, explained the intention behind the rebrand in the company news release: “We’re aligning our identity with our vision to become a multi-brand merchandising, supply chain and retail operatorand backing it with decisive actions to strengthen our foundation: reducing excess inventory, closing underperforming locations, optimizing real estate assets, and enhancing talent across the organization.” Amy Sullivan will lead as the CEO and chief merchant and creative officer of The Brand House Collective. The company announced the following additions to its corporate team: Chief Operating Officer Jamie Schisler will oversee operations. VP General Merchandising Manager of Bed Bath & Beyond Home Kerri Dlugokinski will lead all merchandising efforts. VP of Supply Chain Courtenay Adolf is responsible for global sourcing, transportation, and distribution centers. The retailer also announced changes to its board of directors. Effective June 24, 2025, appointees Eric Schwartzman, Neely Tamminga, Tamara Ward, and Steve Woodward will serve as board members. In October 2024, Kirkland’s announced a strategic partnership with Beyond, Inc., which owns brands Bed Bath & Beyond, Overstock, and buybuy Baby.
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E-Commerce
Artificial intelligence is evolving at an unprecedented pace, advancing from simple generative tasks to autonomous decision-making through agentic models. Now AI is moving beyond the digital realm into the physical world. This next frontier, known as physical AI, combines advanced algorithms with sensors and actuators, enabling machines to perceive, reason, and act in complex real-world environments. At Hexagon LIVE 2025 in Las Vegas, a physical AI humanoid robot named AEON made its debut. Think Tron meets I, Robot, but powered by next-gen AI and hardware rather than movie magic. Developed by Sweden-based industrial tech giant Hexagon in partnership with Nvidia, AEON is designed for real-world industrial work. It can inspect equipment in cramped industrial corridors, navigate hazardous construction zones, and manage logistics in understaffed warehouses. Spencer Huang, Product Lead for Robotics at Nvidia and the son of CEO Jensen Huang, sees a massive opportunity in humanoids, as their form allows them to perform tasks that are dangerous and demanding for humans. Humanoids are one of the many embodiments of Physical AI. To be able to perform human-like requires powerful brains trained on massive amounts of data, Huang tells Fast Company. During the test phase, the biggest challenge was to ensure these robots can safely perform tasks in complex, dynamic environments. Humanoid robots like AEON aim to help solve an emerging labor crisis. A report from the World Economic Forum shows nearly 50 million manufacturing and logistics jobs remain unfilled globally. In the U.S. alone, the manufacturing sector will need up to 3.8 million new workers by 2033. Hexagon plans to deploy AEON across key industrial applications, including sorting and moving parts, inspecting for defects and compliance, and performing complex tasks such as precision scanning with high-end sensors. What sets AEON apart from other humanoids, including Teslas Optimus Gen 2 and Figure AIs Helix, is its ability to learn. Traditionally, training industrial robots requires months of manual programming and physical testing. AEON skips that. Tasks such as balance, locomotion, and precision manipulation, which usually take five to six months of coding and trial-and-error, took AEON just two to three weeks. The key lies in AEONs ability to generate its own synthetic training data, learn through autonomous simulation and reinforcement, and apply those lessons in the real world. This self-learning AI loop marks a shift in how quickly physical AI systems can adapt to complex environments. Simulation helps solve the robotics data challenge, enabling faster, safer development and testing cycles. It also cuts costs by reducing the need for physical prototypes and hardware, Huang says. A simulation-first approach lays the groundwork for robots to ultimately improve on their own and even generate new scenarios to challenge themselves, all in virtual environments. [Photo: Hexagon/Nvidia] A Platform Shift for Physical Intelligence To build AEON, Hexagon used Nvidias AI supercomputers to train and fine-tune foundation models; the Nvidia Omniverse platform to test and optimize those models in simulation; and IGX Thor robotic computers to run the models on the robot itself. The company also employed Isaac Sim, a robotic simulation tool built on Omniverse, to train AEON on tasks. AEON features 22 multimodal sensors and 12 cameras that enable AI-based spatial awareness, asset scanning, and digital twin creation, without needing retraining for each new environment. AEONs wheel-based locomotion allows it to traverse factories and pivot in all directions at speed, says Arnaud Robert, President of Hexagons Robotics division. It has a battery self-swap mechanism by which it can change its own battery with no downtime, allowing for continuous operations. These design choices separate it from what we have seen on the market. For initial training, human demonstrations are collected using teleoperation tools such as the Apple Vision Pro, which streams natural hand movements into a simulated environment in Isaac Lab. These authentic human actions serve as the foundation for all subsequent data generation. Synthetic motion generation takes these demonstrations and creates a large number of new motion trajectories, says Huang. This multiplies the available training data, enabling robots to learn from a much wider range of scenarios than manual collection alone. Hexagon is also exploring the use of Nvidias Isaac GR00T N1.5 open foundation model to enhance AEONs reasoning capabilities, and GR00T-Mimic to generate larger volumes of synthetic motion data from just a few human demonstrations. Unlike many humanoids still in the R&D phase, AEON is headed for production. It is already set to pilot in real-world environments with German automotive company Schaeffler and Swiss aircraft manufacturer Pilatus, taking on tasks ranging from part inspection to reality capture. AEON can do a reality capture of an average factory in an hour, and this could be done multiple times a day, says Robert. AEONs awareness and spatial intelligence algorithms give us a significant edge. If the humanoid is moving quickly across a factory floor and detects a person 10 feet away, it will automatically slow down or adjust its trajectory to avoid getting too close. A New Playbook for Robotics AEON suggests that physical AI is catching up fast. Could its simulation-first, agentic approach be the blueprint for a new class of physical AI systems? Nearly every, if not all, humanoid company is exploring and embracing simulation to bootstrap their development and alleviate the bottlenecks and constraints of capturing real-world data, Huang explains. Not every company has the time or the resources to ely on human demonstrations only. Simulation will continue to play a critical role in robotics. Robert adds that growing labor shortages and the need for uninterrupted operations are pushing companies beyond automation toward fully autonomous solutions. In todays landscape, industry leaders increasingly view a cost-effective humanoid as not just an advantage, but a necessity, says Robert. Our vision is to build an autonomous future, and AEON is our flagship product in executing that strategy. We expect to introduce several variants in the years ahead, all in pursuit of that vision.
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E-Commerce
Music streaming service Deezer said Friday that it will start flagging albums with AI-generated songs, part of its fight against streaming fraudsters.Deezer, based in Paris, is grappling with a surge in music on its platform created using artificial intelligence tools it says are being wielded to earn royalties fraudulently.The app will display an on-screen label warning about “AI-generated content” and notify listeners that some tracks on an album were created with song generators.Deezer is a small player in music streaming, which is dominated by Spotify, Amazon and Apple, but the company said AI-generated music is an “industry-wide issue.” It’s committed to “safeguarding the rights of artists and songwriters at a time where copyright law is being put into question in favor of training AI models,” CEO Alexis Lanternier said in a press release.Deezer’s move underscores the disruption caused by generative AI systems, which are trained on the contents of the internet including text, images and audio available online. AI companies are facing a slew of lawsuits challenging their practice of scraping the web for such training data without paying for it.According to an AI song detection tool that Deezer rolled out this year, 18% of songs uploaded to its platform each day, or about 20,000 tracks, are now completely AI generated. Just three months earlier, that number was 10%, Lanternier said in a recent interview.AI has many benefits but it also “creates a lot of questions” for the music industry, Lanternier told the Associated Press. Using AI to make music is fine as long as there’s an artist behind it but the problem arises when anyone, or even a bot, can use it to make music, he said.Music fraudsters “create tons of songs. They upload, they try to get on playlists or recommendations, and as a result they gather royalties,” he said.Musicians can’t upload music directly to Deezer or rival platforms like Spotify or Apple Music. Music labels or digital distribution platforms can do it for artists they have contracts with, while anyone else can use a “self service” distribution company.Fully AI-generated music still accounts for only about 0.5% of total streams on Deezer. But the company said it’s “evident” that fraud is “the primary purpose” for these songs because it suspects that as many as seven in 10 listens of an AI song are done by streaming “farms” or bots, instead of humans.Any AI songs used for “stream manipulation” will be cut off from royalty payments, Deezer said.AI has been a hot topic in the music industry, with debates swirling around its creative possibilities as well as concerns about its legality.Two of the most popular AI song generators, Suno and Udio, are being sued by record companies for copyright infringement, and face allegations they exploited recorded works of artists from Chuck Berry to Mariah Carey.Gema, a German royalty-collection group, is suing Suno in a similar case filed in Munich, accusing the service of generating songs that are “confusingly similar” to original versions by artists it represents, including “Forever Young” by Alphaville, “Daddy Cool” by Boney M and Lou Bega’s “Mambo No. 5.”Major record labels are reportedly negotiating with Suno and Udio for compensation, according to news reports earlier this month.To detect songs for tagging, Lanternier says Deezer uses the same generators used to create songs to analyze their output.“We identify patterns because the song creates such a complex signal. There is lots of information in the song,” Lanternier said.The AI music generators seem to be unable to produce songs without subtle but recognizable patterns, which change constantly.“So you have to update your tool every day,” Lanternier said. “So we keep generating songs to learn, to teach our algorithm. So we’re fighting AI with AI.”Fraudsters can earn big money through streaming. Lanternier pointed to a criminal case last year in the U.S., which authorities said was the first ever involving artificially inflated music streaming. Prosecutors charged a man with wire fraud conspiracy, accusing him of generating hundreds of thousands of AI songs and using bots to automatically stream them billions of times, earning at least $10 million. Kelvin Chan, AP Business Writer
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