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Artificial Intelligence

Multiverse raises $27M for quantum software targeting LLM leviathans


We’re still years away from seeing physical quantum computers break into the market with any scale and reliability, but don’t give up on deep tech. The market for high-level quantum computer science — which applies quantum principles to manage complex computations in areas like finance and artificial intelligence — appears to be quickening its pace.

In the latest development, a startup out of San Sebastian, Spain, called Multiverse Computing is announcing that it has raised €25 million (or $27 million) in an equity funding round led by Columbus Venture Partners. The funding, which values the startup at €100 million ($108 million), will be used in two main areas. The startup plans to continue building out its existing business working with startups in verticals like manufacturing and finance; and it wants to forge new efforts to work more closely with AI companies building and operating large language models.

In both cases, the pitch is the same, CEO Enrique Lizaso-Olmos: “optimization.”

In other words, as computing becomes more advanced, it can be more costly and in some cases too complex to execute consistently. Multiverse’s pitch is that its software platform Singularity — designed to apply across a wide range of industries like finance, manufacturing, energy, cybersecurity and defense — can be used to run and optimize complicated modelling and predictive applications more efficiently.

In AI, the focus is more squarely on applying the platform to compress Large Language Models, with a new product called CompactifAI honing in on the calculations that are constantly being made when building and querying LLMs, to cut out more noise and speed up the work (and thus reliability) when producing results.

The company claims that it can compress LLMs “with quantum-inspired tensor networks” by more than 80% with the software, while still producing accurate results. If true, that could have large implications for how companies buy and use processors, addressing one of the big bottlenecks in the industry to date.

Lizaso-Olmos cuts a polymath figure, starting out his career more than 30 years ago by first qualifying as a medical doctor, then taking a second degree in mathematics, and then a third in computer engineering with a Phd that somewhat tied these things together, a PhD in biostatistics. He then picked up an MBA, he said. Over the course of all that he picked up like-minded thinkers and friends, and some of them — namely Roman Orus and Samuel Mugel — were interested in the concept of quantum software and were already making names for themselves through academic work around the subject.

“Multiverse started in a WhatsApp group,” he jokes. The year was 2017, and for the thought experiment, a few of them thought it “would be fun” to write a scientific paper about what you could do with quantum in finance.

The paper ended up getting accepted for a conference taking place at the university in Toronto, so they went along to that. Arriving, Lizaso-Olmos saw that the paper was getting shared around and discussed and suddenly it looked like people might use it as inspiration for enterprising ambitions. That was when Lizaso-Olmos’s MBA-radar kicked in and he pulled his two friends together for a serious IRL chat.

And that is how they, along with Alfonso Rubio, started Multiverse Computing.

That initial exploration of quantum and financial technology that was the subject of that paper became the company’s first commercial application, and where it picked up its first traction. Since then it has widened out into other sectors and counts Moody’s Analytics, Bosch, BASF, Iberdrola, Credit Agricole and BBVA among its customers, and Lizaso-Olmos says that together, industrial and energy clients, who like the greener aspects of more efficient computing, today actually account for more of the company’s business than finance.

Alongside Columbus, previous backer Quantonation Ventures also participated alongside new backers like the European Innovation Council Fund, Redstone QAI Quantum Fund, and Indi Partners.

“Multiverse’s exceptional team will soon apply their unparalleled capability to deliver quantum and quantum-inspired software solutions also within the life sciences and biotechnology markets, where Columbus Venture Partners will help to identify unmet market needs and high-profile industrial partners,” Javier Garcia, a partner at Columbus Venture Partners, in a statement.

While the pitch to verticals seems to have connected with customers, what remains to be seen is how its ambition to go one level higher, to target deep tech and AI companies themselves, might play out for Multiverse. Others competing in the same space include the Alphabet spinout Sandbox AQ, Quantum Motion, and Classiq.



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by Team SNFYI

Facebook is testing a new feature that invites some users—mainly in the US and Canada—to let Meta AI access parts of their phone’s camera roll. This opt-in “cloud processing” option uploads recent photos and videos to Meta’s servers so the AI can offer personalized suggestions, such as creating collages, highlight reels, or themed memories like birthdays and graduations. It can also generate AI-based edits or restyles of those images. Meta says this is optional and assures users that the uploaded media won’t be used for advertising. However, to enable this, people must agree to let Meta analyze faces, objects, and metadata like time and location. Currently, the company claims these photos won’t be used to train its AI models—but they haven’t completely ruled that out for the future. Typically, only the last 30 days of photos get uploaded, though special or older images might stay on Meta’s servers longer for specific features. Users have the option to disable the feature anytime, which prompts Meta to delete the stored media after 30 days. Privacy experts are concerned that this expands Meta’s reach into private, unpublished images and could eventually feed future AI training. Unlike Google Photos, which explicitly states that user photos won’t train its AI, Meta hasn’t made that commitment yet. For now, this is still a test run for a limited group of people, but it highlights the tension between AI-powered personalization and the need to protect personal data.

by Team SNFYI

News Update Bymridul     |    March 14, 2024 Meesho, an online shopping platform based in Bengaluru, has announced its largest Employee Stock Ownership Plan (ESOP) buyback pool to date, totaling Rs 200 crore. This buyback initiative extends to both current and former employees, providing wealth creation opportunities for approximately 1,700 individuals. Ashish Kumar Singh, Meesho’s Chief Human Resources Officer, emphasized the company’s commitment to rewarding its teams, stating, “At Meesho, our employees are the driving force behind our success.” Singh further highlighted the company’s dedication to providing opportunities for wealth creation despite prevailing macroeconomic conditions. This marks the fourth wealth generation opportunity at Meesho, with the size of the buyback program increasing each year. In previous years, Meesho conducted buybacks worth over Rs 8.2 crore in February 2020, Rs 41.4 crore in November 2020, and Rs 45.5 crore in October 2021. Meesho’s profitability journey began in July 2023, making it the first horizontal Indian e-commerce company to achieve profitability. Despite turning profitable, Meesho continues to maintain positive cash flow and focuses on enhancing efficiencies across various cost items. The company’s revenue from operations for FY 2022-23 witnessed a remarkable growth of 77% over the previous year, amounting to Rs 5,735 crore. This growth can be attributed to Meesho’s leadership position as the most downloaded shopping app in India in both 2022 and 2023, increased transaction frequency among existing customers, and a diversified category mix. Additionally, Meesho’s focus on improving monetization through value-added seller services contributed to its revenue growth. Meesho also disclosed its audited performance for the first half of FY 2023-24, reporting consolidated revenues from operations of Rs 3,521 crore, marking a 37% year-over-year increase. The company achieved profitability in Q2 FY24, with a significant reduction in losses compared to the previous year. Furthermore, Meesho recorded impressive app download numbers, reaching 145 million downloads in India in 2023 and surpassing 500 million downloads in H1 FY 2023-24. Follow Startup Story Source link

by Team SNFYI

You might’ve heard of Grok, X’s answer to OpenAI’s ChatGPT. It’s a chatbot, and, in that sense, behaves as as you’d expect — answering questions about current events, pop culture and so on. But unlike other chatbots, Grok has “a bit of wit,” as X owner Elon Musk puts it, and “a rebellious streak.” Long story short, Grok is willing to speak to topics that are usually off limits to other chatbots, like polarizing political theories and conspiracies. And it’ll use less-than-polite language while doing so — for example, responding to the question “When is it appropriate to listen to Christmas music?” with “Whenever the hell you want.” But Grok’s ostensible biggest selling point is its ability to access real-time X data — an ability no other chatbots have, thanks to X’s decision to gatekeep that data. Ask it “What’s happening in AI today?” and Grok will piece together a response from very recent headlines, while ChatGPT, by contrast, will provide only vague answers that reflect the limits of its training data (and filters on its web access). Earlier this week, Musk pledged that he would open source Grok, without revealing precisely what that meant. So, you’re probably wondering: How does Grok work? What can it do? And how can I access it? You’ve come to the right place. We’ve put together this handy guide to help explain all things Grok. We’ll keep it up to date as Grok changes and evolves. How does Grok work? Grok is the invention of xAI, Elon Musk’s AI startup — a startup reportedly in the process of raising billions in venture capital. (Developing AI’s expensive.) Underpinning Grok is a generative AI model called Grok-1, developed over the course of months on a cluster of “tens of thousands” of GPUs (according to an xAI blog post). To train it, xAI sourced data both from the web (dated up to Q3 2023) and feedback from human assistants that xAI refers to as “AI tutors.” On popular benchmarks, Grok-1 is about as capable as Meta’s open source Llama 2 chatbot model and surpasses OpenAI’s GPT-3.5, xAI claims. Image Credits: xAI Human-guided feedback, or reinforcement learning from human feedback (RLHF), is the way most AI-powered chatbots are fine-tuned these days. RLHF involves training a generative model, then gathering additional information to train a “reward” model and fine-tuning the generative model with the reward model via reinforcement learning. RLHF is quite good at “teaching” models to follow instructions — but not perfect. Like other models, Grok is prone to hallucinating, sometimes offering misinformation and false timelines when asked about news. And these can be severe — like wrongly claiming that the Israel–Palestine conflict reached a ceasefire when it hadn’t. For questions that stretch beyond its knowledge base, Grok leverages “real-time access” to info on X (and from Tesla, according to Bloomberg). And, similar to ChatGPT, the model has internet browsing capabilities, enabling it to search the web for up-to-date information about topics. Musk has promised improvements with the …