đź‘ľ Microsoft is facing a full regulatory probe in the U.K. after the tech giant hired the core team behind Inflection AI, a U.S.-based OpenAI rival Microsoft had previously invested in.
💰 Today’s announcement doesn’t come as a massive surprise, as the CMA revealed in April that it was conducting preliminary enquiries into a triumvirate of AI partnerships. One of those was Microsoft’s recent investment in Mistral AI, a French startup (and double unicorn) working on AI foundation models. It didn’t take long for the CMA to conclude that the investment didn’t qualify for investigation under current merger regulations, given that Microsoft’s stake of less than 1% wouldn’t give the tech giant any meaningful clout in the future direction of the startup.
⚖ With the phase 1 inquiry now under way, the CMA has until September 11 to reach a decision on whether the hiring is tantamount to a “merger,” and if it is, whether it’s likely to damage competition in the United Kingdom. If the CMA decides that it does, it will then proceed the case to a more in-depth “phase 2” probe which can take around six months.
DESPITE DOWNTURN, BRANDS ARE PIVOTING BACK TO THE METAVERSE | [METAPHYSICS]
🏆 On January 18, Gmoney’s lifestyle platform, 9dcc, took the title of “Phygital Brand of the Year” at the 2024 World Economic Forum in Davos.
During the Phygicode Impact Awards ceremony, the entrepreneur was applauded for his groundbreaking work in developing a blockchain-based luxury community and "networked products" — a format of phygital luxury lifestyle items interconnected with blockchain technology through integrated NFC chips.
đź‘š American preppy label Tommy Hilfiger is back exploring the metaverse with its new mobile game, FashionVerse. Co-developed by Hilfiger, Brandible, and game publisher Tilting Point, the playable app uses AI-generated, photorealistic avatars and settings, inviting users to take on the roles of "Stylists" and "Trendsetters." Within the game, pop-ups will present challenges inspired by campaign themes and collection pieces, along with special rewards for players who successfully complete all the challenges.
đź‘ľ MCM, the German fashion label known for its monogrammed leather products, is launching a metaverse platform in partnership with the new virtual world, Caliverse, later this year. The brand teased its digital shop at the Consumer Electronics Show, which will use real-time rendering graphics, 3D live synthesis, and AI technology. The dynamic space will invite consumers to purchase products within a multi-reality environment.
TALA RAISES 5 MILLION POUNDS TO SUPPORT INTERNATIONAL EXPANSION | [FASHION SYSTEM]
đź’¸ British activewear brand Tala, founded by influencer Grace Beverley, has secured five million pounds in a funding round led by London-based venture capital trust Pembroke VCT, one of the early backers of womenswear brand Me+Em.
â„ą Pembroke VCT, founded by Andrew Wolfson, focuses on backing early-stage businesses with innovative founders and previously participated in an investment round in the business in 2021. For this new funding round, the venture capital trust led the round with a 3-million-pound investment alongside existing investors Venrex and Active Partners.
👽 In a statement, Tala said the investment would support the brand’s international expansion, with an initial focus on the US market in response to “significant customer and social media traction,” in the region. Additionally, the funds will be used to explore opportunities for establishing a physical retail presence on the high street and enhance the company’s infrastructure and team capabilities.
🌍 The London-based activewear brand was founded in 2019 to disrupt the fast fashion and activewear industries and has scaled significantly since its last fundraising three years ago, reporting a tenfold increase in its revenue.
This period of expansion has also seen the brand expand into new categories, such as outerwear and shapewear, enter new markets, and establish new channels - all while staying profitable.
AFTER TESLA AND OPENAI, ANDREJ KARPATHY'S STARTUP AIMS TO APPLY AI ASSISTANTS TO EDUCATION | [SINGULARITY]
🦄 Andrej Karpathy, former head of AI at Tesla and researcher at OpenAI, is launching Eureka Labs, an “AI native” education platform. In tech speak, that usually means built from the ground up with AI at its core. And while Eureka Labs’ AI ambitions are lofty, the company is starting with a more traditional approach to teaching.
🔶Eureka Labs envisions AI assistants or personalities that would work with a human teacher to allow “anyone to learn anything,” according to Karpathy, who posted the news on X. Teachers would still design the course material, but they’d be supported by this AI assistant. The startup does not yet appear to have built or tested the efficacy of integrating AI assistants into the classroom. At least one Georgia State University study found that AI teaching assistants helped some students get better grades.
🔊Despite stating that Eureka Labs aims to build AI teaching assistants, Karpathy also noted that the new venture’s first product will be an AI course,LLM101n, an undergraduate-level class that will help students train their very own AI. This mini-me will be like a smaller version of the AI teaching assistant Eureka Labs hopes to build and scale, according to Karpathy. The AI pioneer wrote on X, and onEureka’s bare-bones new website, that the course materials will be available online, and that the startup will run both digital and physical cohorts of people going through the materials together.
đź’µThroughout his career at Tesla and OpenAI, Karpathy has continued to be an educator. He currently leads an online course called Neural Networks: Zero to Hero that helps students learn to build neural networks from scratch in code. Karpathy also has a YouTube channel where he semi-regularly posts lectures on LLMs and AI.
ANALYSING AI WITH DAINA BURNES OF BOLD METRICS | [SINGULARITY]
🌵What’s your working definition of AI? Does it differ from the public understanding, which is currently dominated by large language models and generative text-to-image models? And how does that definition manifest itself in your solution(s)?
🕸 Much of the recent popularity in consumer accessible AI applications is based on the context of natural language, text, or image-based prompts. AI also holds the power to analyze vast amounts of multi-dimensional quantitative data to extract information and predictive insights that hold the potential to drive actionable outcomes. In the context of Bold Metrics, AI manifests itself in our size and fit solutions by accurately creating a shopper’s digital twin – represented with more than 50 body measurements of that shopper – and providing garment-specific size recommendations tailored to that individual’s body shape and fit preferences. Our AI-powered system considers a multitude of factors, including body measurements, garment specifications, and continuous machine learning on purchases, and returns behaviors to deliver personalized sizing guidance that enhances the shopping experience for consumers and drives tangible results for our clients. By harnessing AI’s capabilities toward a practical application in the apparel industry, we’re revolutionizing the way people shop for clothes and helping brands gain access to digital twin body measurement data at scale.
🌵Beyond the technologies themselves, the current AI era has also opened up a wider willingness to challenge long-standing and traditional ways of working in almost every area of fashion. When we consider the tools the industry uses for sizing and fit – both in-house and consumer-facing – there definitely seems to be an opportunity for disruption. How are you deploying generative AI, and in concert with what other technology, to encourage creators and consumers to rethink fit and size?
🕸 Historically, size charts have been the traditional approach to assisting consumers with sizing, and while they offer an entry point for fit guidance, they are quite limited in utility as consumers typically do not know their detailed body measurements. Sizing tools that ask about one’s size in other brands, require taking full body photos or using measuring tapes risk detracting from the experience, and present hurdles in the purchase process. Bold Metrics generative AI platform has overcome these limitations to provide a more personalized, accurate, adoptable, and adaptable sizing experience, leading to improved shopper confidence and reduced returns.
🌵As we have seen throughout this report, another key use case for AI is extracting new insights and new value from both novel and pre-existing datasets. For direct-to-consumer brands and retailers, body and sizing data fits into both those categories: organizations already hold historic and aggregated information, but tools like Bold Metrics are also opening up new data streams and new links between datapoints. How do you see AI changing the data that’s available to brands and retailers, and changing what they are able to do with that information?
🕸 Generative AI is bringing entirely new data sets to brands and retailers. Behind the scenes, anytime a consumer engages our solutions and responds to the survey prompts to receive a size recommendation, that consumer’s digital twin body measurement data is being captured and related to their purchase and return behavior. This AI body capture creates the same detail of body shape and measurement data as though their customers had stepped into a highly accurate 3D body scanner, but without the cumbersome scan. This 3D digital twin data is a game-changer for the apparel industry. Never before have brands and retailers had a practical way to collect this data at scale, short of installing physical scanners in stores (a solution that is neither scalable nor practical) the industry as a whole has been relatively blinded to their customer’s real body measurement data and how it relates to their fit coverage across their demographics. The ability to break down customers’ body measurements by product and purchase/returns can completely change the product design process, allowing for a data-driven quantification of their fits and size gradation systems. This has the power to greatly impact overall return rates with better-fitting products that enter the market in addition to a better capture of the overall market with more apt fits for diverse body shapes and sizes.
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