935.0K
WOSteve brunton is sooo GOATEDDD !!!
#machinelearning #datascience #stem #artificialintelligence
@workiniterations
Mega Popular
#Machinelearning
Watch 20M Reels videos about Machinelearning from people all over the world.
Watch anonymously without logging in.
20M posts
NewTrendingViral
Related Searches
Trending Reels
(12)
531.1K
EQThe Secret to Perfect Data Models #MachineLearning #PolynomialRegression #Statistics #Math #Manim Ever wondered why your machine learning model isn't performing as expected? In this video, we break down polynomial curve fitting, a fundamental concept in data science and statistics. We explore the visual differences between Degree 1 (Underfitting), Degree 3 (Good Fit), and Degree 11 (Overfitting). Learn how increasing the degree of a polynomial affects how it captures data trends and why the optimal model is crucial for accurate predictions.
@equationsinmotion
1.8M
PLAI (Artificial Intelligence) is a broad field focused on building systems that can perform tasks requiring human-like intelligence—such as learning, reasoning, vision, and decision-making. It includes everything from recommendation systems and computer vision to robotics and automation. On the other hand, LLMs (Large Language Models) are a specific subset of AI designed to understand and generate human language. They are trained on massive amounts of text data to perform tasks like chatting, writing, summarizing, and coding.
In simple terms, all LLMs are part of AI, but not all AI is an LLM. While AI can “see,” “predict,” or “decide,” LLMs primarily “read” and “write” like humans. Understanding this difference is key if you’re stepping into modern tech—because today’s most powerful applications often combine multiple AI systems, with LLMs handling communication and other AI models handling perception and decision-making.
#ArtificialIntelligence #LLM #MachineLearning #AIvsLLM #TechConcepts
@plutoplatypus_
586.2K
SOMeet Guangting Yu ( @yugtmacs ):
- Applied Math PhD. Quant
- Finance MS (UMich).
- Worked on alpha discovery using Kalman filters. Published - AI/ML research (NeurIPS).
Quants don’t guess markets. They hunt alpha. Hidden edges in data.
Statistical arbitrage exploits tiny pricing gaps
Factor models like Fama French explain what drives returns.
- Kalman filters separate signal from noise in real time
- Mean reversion bets prices snap back
- Momentum rides trends early
- Machine learning models like XGBoost and neural nets predict moves before they’re obvious.
Covariance Formula:
Cov(X, Y) = E[XY] − E[X]E[Y]
Measures how two variables move together
Positive means they rise together, negative means they move opposite
Core signal used to detect relationships and extract alpha
While most people stare at charts
he’s modeling the system behind them
#quant #machinelearning #startups #ai #founder
@sorhan.hq
2.1M
TEThe man who helped build modern AI is now warning the world about it.
Geoffrey Hinton, often called the “Godfather of AI,” played a key role in developing neural networks and backpropagation — the same foundation behind today’s powerful AI systems. In 2024, he was honored with the Nobel Prize for his contributions.
But here’s the twist…
Hinton is no longer just celebrating AI — he’s warning us.
According to him, AI is already shaping reality in ways we don’t fully understand. From echo chambers on social media to large-scale surveillance and advanced scams, the impact is no longer theoretical — it’s happening now.
He also raised deeper concerns about the future:
AI systems becoming smarter than humans, autonomous decision-making machines, and technologies that could be misused if controlled by profit-driven companies instead of ethical systems.
His biggest message?
We are moving fast… but not carefully enough.
The question is no longer “What can AI do?”
It’s “Should we control it before it controls us?”
What do you think — opportunity or danger? 🤔
Credits: @nobelprize
DM for credit or removal.
#artificialintelligence #futuretechnology #machinelearning
#aivideo #technews
@techaivault
1.0M
SOResearcher name: Guangting Yu, Dailey Labs
The next wave of AI isn’t just chat. It’s simulation.
As models get better at learning dynamics, differential equations, and world models, AI moves closer to running experiments, testing ideas in simulated environments, and helping engineer real systems before they’re built.
Simulation engineering may be one of the biggest frontiers in AI. 🌊
#startuplife #founder #tech #machinelearning #ai
@sorhan.hq
1.2M
DOSomeone just conducted a live experiment that reveals how different AI systems perform when given real money and complete autonomy in financial markets.
Two autonomous AI agents were each given $1,000 and 48 hours to trade independently on Polymarket, a prediction market platform. No human intervention. No guidance. Just pure algorithmic decision making in real time.
Both agents started with identical conditions: same capital, same time window, same market access. The only variable was the underlying AI model powering each agent’s trading decisions.
The results were strikingly different.
The agent running on Claude executed over 5,200 trades during the 48-hour period, transforming the initial $1,000 into approximately $14,200. That’s a return exceeding 1,300%, while simultaneously covering its own API costs throughout the experiment.
The competing OpenClaw agent told a very different story. It executed fewer than 200 trades, experienced a 94% drawdown, and completely depleted its funds before the test period ended.
Same starting capital. Same market conditions. Two AI systems with vastly different outcomes.
This experiment highlights something important as artificial intelligence becomes more sophisticated and autonomous. These systems are no longer just theoretical constructs or controlled demonstrations. They’re capable of operating independently in complex, real world environments where financial decisions have actual consequences.
As machine learning models continue advancing and autonomous agents become more capable, we’re entering a phase where AI systems may actively compete against each other in financial markets, making split second decisions that humans simply cannot match in speed or volume.
The question isn’t whether this technology works. The question is what happens next as these systems scale.
⸻
Don’t forget to Share and Follow @dontlookback.ai
⸻
#AI #technology #trading #autonomousAI
artificial intelligence, machine learning, algorithmic trading, prediction markets, autonomous agents, financial technology, AI models, Claude AI, automated systems, market analysis, fintech innovation, intelligent automation
@dontlookback.ai
11.2M
QUThis video uses one of the cleanest analogies you will ever see for how diffusion models actually work. A person sets up an L shaped couch, lines a series of pots and pans around it, and drops a ball so it bounces from one pan to the next before landing perfectly in a cup on the floor.
At first there is only one pan and the ball takes a simple path. But as more pans get added, the path becomes more complex. The ball keeps bouncing from one surface to another in a linear chain until it finally reaches the cup again. Every bounce represents another step in the diffusion process, where the model repeatedly transforms noisy data into something more refined and recognizable. Each step builds on the last, nudging the output closer and closer to the final result.
It is a playful demonstration, but it captures the idea beautifully. Diffusion models work by adding noise, then guiding the model step by step as it removes that noise until the final image emerges. The more steps you have, the more precise the outcome. Just like adding more pans creates a more controlled path back to the cup.
Did this analogy make diffusion models click for you
Do you think more creators should explain AI concepts this simply
Follow @quantumcurrentai for more AI explanations, visual analogies, and the tech breakthroughs shaping our future.
Credit: @docmilanfar on X
#quantumcurrentai #diffusionmodels #aitools #machinelearning #futuretech #neuralnetworks #aiexplained
@quantumcurrentai
267.6K
SOPutting robotics to good use 😬
#tech #robotics #electronics #machinelearning #womenintech
@sopi.iscoding
5.2M
EXAl is revolutionizing bioacoustics by using machine learning to analyze, decode, and interpret complex animal vocalizations and behaviors. Nature and Earth Species Project report that Al is transforming whale songs, elephant rumbles, and bird calls into meaningful data, identifying patterns related to emotions, social structures, and specific communication signals.
Key breakthroughs in Al-driven animal communication:
Decoding Context and Meaning: Researchers are using deep learning to map animal sounds to specific behaviors, such as identifying alarm calls in prairie dogs or social chatter among dolphins.
Specific Species Discoveries: Al has found that African savanna elephants and common marmoset monkeys may give names to their companions.
Active Two-Way Communication Attempts: In a landmark study, researchers used Al to identify sperm whale calls, and in a Medium post, a whale engaged in a 20-minute, turn-taking conversation with scientists.
Predictive Modeling: The Earth Species Project is developing Al models that can predict what an animal will say next, aiming for a "digital Rosetta Stone" for species communication
#ai #AI #artificial
@explainingthefact
1.4M
WOASML is a Netherlands-based technology company headquartered in Veldhoven, and despite staying largely out of the spotlight, it holds enormous influence over the future of computing.
Established in 1984, ASML specializes in building the most advanced lithography machines in the world — systems that use highly precise light to etch microscopic circuits onto silicon wafers. These circuits form the core of every modern chip, powering everything from smartphones and laptops to AI data centers and supercomputers.
What sets ASML apart is its monopoly on Extreme Ultraviolet (EUV) lithography. No other company on the planet can manufacture these machines, and without them, producing the most advanced semiconductor chips is impossible.
As a result, giants like TSMC, Samsung, and Intel rely entirely on ASML to push chip technology forward. Strip ASML out of the equation, and progress in AI, high-performance computing, and next-generation devices would come to a halt.
Credit: YouTube (ASML)
For the latest updates on AI, future tech & groundbreaking innovations — follow @WorldOf.AIx 🤖🚀
#AI #ArtificialIntelligence #FutureTech #MachineLearning #DeepLearning #TechNews #Innovation #AIUpdates #TechReels #AIFuture #TechTrends #AITools #NeuralNetworks #AICommunity #asml
@worldof.aix
4.3M
BLOnly a matter of time..
AI is advancing quickly enough that many routine and repetitive jobs could be largely automated within the next decade, especially in fields like data entry, customer service, basic accounting, and even some areas of transportation and logistics. As machine learning models become better at handling language, decision-making, and pattern recognition, they’re starting to move beyond simple tasks into roles that once required human judgment. However, “replacing most jobs” isn’t likely to happen overnight—what’s more realistic is a gradual shift where AI handles large portions of work while humans adapt into roles that focus on oversight, creativity, relationship-building, and complex problem-solving. The timeline will vary by industry, but the transition is already underway, and the biggest changes will likely be felt within the next 5 to 15 years.
#ai #bluecollar #plumbers #electrician #construction
@blue_collar_bible
✨ #Machinelearning Discovery Guide
Instagram hosts 20 million posts under #Machinelearning, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
Discover the latest #Machinelearning content without logging in. The most impressive reels under this tag, especially from @quantumcurrentai, @explainingthefact and @blue_collar_bible, are gaining massive attention. View them in HD quality and download to your device.
What's trending in #Machinelearning? The most watched Reels videos and viral content are featured above. Explore the gallery to discover creative storytelling, popular moments, and content that's capturing millions of views worldwide.
Popular Categories
📹 Video Trends: Discover the latest Reels and viral videos
📈 Hashtag Strategy: Explore trending hashtag options for your content
🌟 Featured Creators: @quantumcurrentai, @explainingthefact, @blue_collar_bible and others leading the community
FAQs About #Machinelearning
With Pictame, you can browse all #Machinelearning reels and videos without logging into Instagram. No account required and your activity remains private.
Content Performance Insights
Analysis of 12 reels
💡 Top performing posts average 5.7M views (2.2x above average). Moderate competition - consistent posting builds momentum.
Post consistently 3-5 times/week at times when your audience is most active
Content Creation Tips & Strategy
💡 Top performing content gets over 10K views - focus on engaging first 3 seconds
✨ Many verified creators are active (25%) - study their content style for inspiration
✍️ Detailed captions with story work well - average caption length is 942 characters
📹 High-quality vertical videos (9:16) perform best for #Machinelearning - use good lighting and clear audio
Popular Searches Related to #Machinelearning
🎬For Video Lovers
Machinelearning ReelsWatch Machinelearning Videos
📈For Strategy Seekers
Machinelearning Trending HashtagsBest Machinelearning Hashtags
🌟Explore More
Explore Machinelearning#what is machinelearning