Equitus AI

@BaptisteVicini Classic ETL is dead. While Scale AI leads in human-labeled visual data, KGNN takes a different path: fully automated semantic labeling for unstructured text. KGNN vs Scale AI → equitus.ai/compare/kgnn-v…

Think of Scale AI as the oil refineries of artificial intelligence. Raw data is worthless. Refined, labeled data powers everything. While competitors build cars, Wang owns the roads they drive on. That's asymmetric leverage at unprecedented scale:

The most powerful man in AI isn't Musk, Altman, or Amodei. It's a 28-year-old billionaire who controls the data feeding every major AI system. OpenAI, Meta, and the Pentagon depend on his infrastructure. Here’s why every tech CEO is racing to catch him:

@amir While Scale AI leads in human-labeled visual data, KGNN takes a different path: fully automated semantic labeling for unstructured text. KGNN vs Scale AI → equitus.ai/compare/kgnn-v…

👀 New: Pretty incredible situation happening with Meta Platforms and Scale AI as its ceo Alex Wang could end up in a top role at Meta.

@decruz @ruima right... while Scale AI leads in human-labeled visual data, KGNN takes a different path: fully automated semantic labeling for unstructured text. KGNN vs Scale AI → equitus.ai/compare/kgnn-v…

@ruima isn't his "AI" just a bunch of outsourced workers?

😂 😂 this FT quote on Scale AI cofounder Alexandr Wang

@nathanbenaich While Meta moves on Scale AI, some startups are taking a different route. 💡 KGNN offers a fully automated, no human-in-the-loop approach to data labeling, AI-ready, scalable, and still at a smart entry point for investors. 👉 equitus.ai/compare/kgnn-v… #AI #KGNN #ScaleAI #LLM

so basically meta is buying scale ai, but below a threshold to trigger reviews etc

@ns123abc 💡Have a look at our alternative: KGNN, fully automated, no human-in-the-loop, built for scale and affordability. equitus.ai/compare/kgnn-v… #AI #DataLabeling #KGNN #ScaleAI #LLM

🚨NEWS: Google, Microsoft, OpenAI and xAI planning to step back from Scale AI

@opinion @DaveLeeBBG Big move by Meta. While Scale AI leads in human-labeled visual data, KGNN takes a different path: fully automated semantic labeling for unstructured text. KGNN vs Scale AI → equitus.ai/compare/kgnn-v… #AI #DataLabeling #KGNN #ScaleAI #LLM

Mark Zuckerberg just dropped $14.3 billion to hire the 28-year-old creator of Scale AI for Meta’s artificial intelligence crack team. @DaveLeeBBG explains why Big Tech is betting big bucks on AI talent 🎥

@BaptisteVicini Alexandr Wang nailed the data bottleneck for visual AI. At Equitus, we took another route, automating semantic labeling for unstructured text, on-prem and at scale. 📊 KGNN vs Scale AI: 👉 equitus.ai/compare/kgnn-v… #AI #DataLabeling #RAG #LLM #KnowledgeGraph

Meet Alexandr Wang, founder of Scale AI. While others chase flashy algorithms, Wang built the data supply chain. Major AI models need labeled training data to learn. But his real breakthrough was just beginning:

@JensHonack Impressive growth from Scale AI, tackling visual data with human-in-the-loop labeling. At Equitus AI, we take a different path: automated semantic labeling for unstructured text. 📊 KGNN vs Scale AI: 👉 equitus.ai/compare/kgnn-v… #AI #DataLabeling #RAG #LLM #KnowledgeGraph

Today, Scale AI powers the world's leading AI systems by providing essential data labeling. His company works with OpenAI, Google, Meta, and the Department of Defense. In 2024, Scale AI reached a $13.8 billion valuation. But he didn't just build a company.

The US is at war with China. And it's not only because of tariffs. There is a bigger one happening right now. And this guy is bringing it to attention. Here's what he found:🧵

𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗚𝗲𝗻𝗔𝗜 𝗼𝗻 𝗜𝗕𝗠 𝗣𝗼𝘄𝗲𝗿 𝘄𝗶𝘁𝗵 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝘁𝗿𝗮𝗰𝗲𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘀𝗽𝗲𝗲𝗱. 𝗝𝗼𝗶𝗻 𝘂𝘀 𝗳𝗼𝗿 𝗮𝗻 𝗲𝘅𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 of AI. 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿: ibm.co/43vdKZx

𝟲𝟳% 𝗼𝗳 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗿𝗮𝘀𝗵. 𝗧𝗵𝗲 𝗯𝗿𝘂𝘁𝗮𝗹 𝘁𝗿𝘂𝘁𝗵? AI isn’t failing because of algorithms. It’s drowning in 𝗯𝗿𝗼𝗸𝗲𝗻 𝗱𝗮𝘁𝗮 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀. 𝗔𝗜 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝗴𝗼𝗼𝗱 𝗮𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗼𝗻.

𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗩𝗶𝗱𝗲𝗼 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: 𝗘𝗾𝘂𝗶𝘁𝘂𝘀 𝗩𝗶𝗱𝗲𝗼 𝗦𝗲𝗻𝘁𝗶𝗻𝗲𝗹 (𝗘𝗩𝗦) 🎥 In your current surveillance network, the real challenge isn’t just capturing video.

🚀 Join us at IBM 𝗧𝗲𝗰𝗵𝗫𝗰𝗵𝗮𝗻𝗴𝗲! We are excited to contribute to the conference and demonstrate why Equitus is a trusted partner in driving digital transformation.

Equitus is reshaping the way we Integrate Data. Unlike conventional ETL, KGNN (Knowledge Graph Neural Network) minimizes the need for complex data pipelines and opens the door to Faster, more Efficient Data Workflows.

"𝗧𝗵𝗲 𝗹𝗶𝗺𝗶𝘁𝘀 𝗼𝗳 𝗺𝘆 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗹𝗶𝗺𝗶𝘁𝘀 𝗼𝗳 𝗺𝘆 𝘄𝗼𝗿𝗹𝗱." * If your system can't Define it, Explain it, Correlate it, and put it into Context, does it really exist? Or maybe the Data is here but it's just noise.

𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀 𝗢𝗳𝗳𝗶𝗰𝗶𝗮𝗹𝗹𝘆 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲𝗱 𝗮𝘀 𝗮 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗧𝗼𝗼𝗹 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Prioritizing data quality and governance is essential as demands grow.

Data is king, and Data is everywhere—on-premises, in the cloud, and at the edge. A whopping 87% of organizations expect their applications to be spread across even more locations in the next two years, according to a 2023 study by Enterprise Strategy Group.

The Smooth Talker vs. The Deep Thinker LLMs excel at generating human-like text, making them the smooth talkers of the AI world. But when it comes to deep, logical reasoning, Knowledge Graphs are the real experts, connecting information with precision.

Verticalized AI models are crucial for enterprise adoption, as generic models often fall short in meeting sector-specific needs. Adapting AI to industry-specific challenges leads to more relevant and effective solutions.

Are you Connecting the Dots or Drawing Pink Unicorns? 🦄 Connecting the dots the wrong way can lead to seeing things that aren’t really there... When biases or incomplete data guide your analysis, you might end up with conclusions that are more fantasy than fact.

94% of IT leaders have spearheaded a #cloud repatriation project in the past three years. 🔒 Key drivers for repatriation: 🛡️ Unexpected security issues (41%) 💸 Higher-than-expected costs (29%) ⚡ Performance issues 🔄 Compatibility problems #Technology #Innovation #AI