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🤖🔎 Battling Data Downtime: How Monte Carlo is transforming data observability with AI After helping invent the ‘dataobs’ category, Monte Carlo is working to bring it into the AI era. In more ways than one: •Data influx: As more companies want to put their data to work in genAI...

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This Week in Startups

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This Week in Startups2 years ago

Thank you to our partner @Kalshi! Kalshi is the largest regulated predictions market—now lets you trade on US elections. TWIST listeners: Visit to see live odds, trade, and get $20 when you deposit $100.

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Tips Travels Monaco , Fairmont Monte Carlo is good choice for stay luxury hotel best rooftop pool in the heart of Monaco, with a sparkling view of the Mediterranean. To fully embrace the charm of Monaco, the heated outdoor pool at the Fairmont Monte Carlo is a true gem perched on the hotel’s rooftop. Swim a few laps at the top of Monte Carlo, unwind on one of the comfortable loungers, and enjoy a menu of light, flavorful dishes while admiring the sea. From April to September, the pool transforms into the vibrant setting of Nikki Beach Monte Carlo, offering a unique experience in the Principality. 📍Fairmont Monte Carlo 📍Monte Carlo 📍Monaco 🇲🇨 #Fairmont #Luxuryhotel #luxurylife #montecarlo #Monaco #Travel #Travellife
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Tips Travels Monaco , Fairmont Monte Carlo is good choice for stay luxury hotel best rooftop pool in the heart of Monaco, with a sparkling view of the Mediterranean. To fully embrace the charm of Monaco, the heated outdoor pool at the Fairmont Monte Carlo is a true gem perched on the hotel’s rooftop. Swim a few laps at the top of Monte Carlo, unwind on one of the comfortable loungers, and enjoy a menu of light, flavorful dishes while admiring the sea. From April to September, the pool transforms into the vibrant setting of Nikki Beach Monte Carlo, offering a unique experience in the Principality. 📍Fairmont Monte Carlo 📍Monte Carlo 📍Monaco 🇲🇨 #Fairmont #Luxuryhotel #luxurylife #montecarlo #Monaco #Travel #Travellife

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Soak it in Lorenzo 🥹🇮🇹 Lorenzo Musetti is just the THIRD Italian Monte-Carlo finalist in the Open Era Rolex Monte-Carlo Masters | #RolexMonteCarloMasters
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Soak it in Lorenzo 🥹🇮🇹 Lorenzo Musetti is just the THIRD Italian Monte-Carlo finalist in the Open Era Rolex Monte-Carlo Masters | #RolexMonteCarloMasters

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one thing i find interesting about Grass is the growing importance of data infrastructure as AI scales AI labs need massive amounts of data, but accessing public web data is becoming increasingly difficult Grass uses a distributed network of internet connections to help collect that data and make it available to AI companies i believe access to high-quality data will become an increasingly important competitive advantage in AI, which makes this an interesting area to follow
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HOME HISTORY! 🇲🇨🤩🇫🇷 Arneodo is the 1st Monegasque player EVER to win a title in Monte-Carlo Manuel Guinard | Rolex Monte-Carlo Masters | #RolexMonteCarloMasters
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AI data centers are projected to use more energy than the country of Japan. Trump’s solution is beautiful: Force AI companies to build their own power. Meanwhile, Elon applies to launch one *million* satellites to put AI data centers in space.
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AI data centers are projected to use more energy than the country of Japan. Trump’s solution is beautiful: Force AI companies to build their own power. Meanwhile, Elon applies to launch one *million* satellites to put AI data centers in space.

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MONTE CARLO RECAP 🏆 The wins are stacking up for Sinner, the points gap is growing, and even Zverev is looking across the court trying to figure it out. Who has the best shot to disrupt this run before the next major? Watch the full Monte Carlo recap NOW! Link in bio 🔗

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Basically they are collecting human behaviour and motion. In next few years, all the data will be transferred to train robots. Similar process followed with Machine learning - AI. India is a big market. Data is everything now, it will be used to train an AI.
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Quite a way to start the week in Monte-Carlo.

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Is the future of AI $NEAR … ? OpenAI is racing toward a $1T IPO, quickly becoming a centralized beast of epic proportions. But what happens to your data in that world?? As chronear explains, “We can't let centralized actors determine how they store or how they use user data. It should be the users who actually control the custody or use of their data." NEAR AI is building a truly open AI, open source, where users control their data and store their data locally. What AI do you use daily? And would you switch if a better decentralized option existed?
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We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:

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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

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Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."
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Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

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92,663 views • 9 months ago

🎉I’m excited to announce our $18M Series A, with participation from Venrex, The General Partnership ("TheGP") , Virtue , Caffeinated Capital , Array Ventures, GreatPoint Ventures , LiveRamp, Haystack, Also Capital, Life Extension Ventures , Circle & Co, and WS Investments. The first generation of AI was built on public, human-curated data. The next will be built on real-world data. The datasets carrying real human behavior are what now separate one AI system from another. AI companies want them. The companies that hold them want to put them to work. The barrier has always been risk. We solved that risk in healthcare, one of the most regulated data environments there is. Today we bring it to the rest of the economy.
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🎉I’m excited to announce our $18M Series A, with participation from Venrex, The General Partnership ("TheGP") , Virtue , Caffeinated Capital , Array Ventures, GreatPoint Ventures , LiveRamp, Haystack, Also Capital, Life Extension Ventures , Circle & Co, and WS Investments. The first generation of AI was built on public, human-curated data. The next will be built on real-world data. The datasets carrying real human behavior are what now separate one AI system from another. AI companies want them. The companies that hold them want to put them to work. The barrier has always been risk. We solved that risk in healthcare, one of the most regulated data environments there is. Today we bring it to the rest of the economy.

Shubh Sinha

109,610 views • 4 months ago

What does Google want with Spirit Airlines data? It turns out, it’s likely to help Google train AI on how employees work. Teaching AI agents to do white-collar work may require data that is largely private, buried in corporate emails and chats. Reporter Billy Perrigo investigates what that means for the labor market for AI, Explained by Humans. Read more here:
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What does Google want with Spirit Airlines data? It turns out, it’s likely to help Google train AI on how employees work. Teaching AI agents to do white-collar work may require data that is largely private, buried in corporate emails and chats. Reporter Billy Perrigo investigates what that means for the labor market for AI, Explained by Humans. Read more here:

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60,308 views • 2 months ago