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WOSteve brunton is sooo GOATEDDD !!!
#machinelearning #datascience #stem #artificialintelligence
@workiniterations
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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
205.8K
SOPutting robotics to good use 😬
#tech #robotics #electronics #machinelearning #womenintech
@sopi.iscoding
523.1K
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
1.5M
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
1.2M
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
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#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
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
11.1M
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
1.3M
SOAI will not reach human brain intelligence in the way we imagine it. Machines process data; humans create meaning. AI excels at pattern recognition because it is trained on past information, while the human brain constantly generates novel ideas, emotions, intuition, and consciousness without predefined datasets. The brain is not just a processor—it is a self-organizing biological system shaped by evolution, survival instincts, hormones, memory, and subjective experience. AI has compute, but no curiosity. It has speed, but no purpose. It can simulate creativity, but it does not understand what it creates. Human intelligence adapts across unpredictable environments, learns without labeled data, and integrates emotions into decision-making. That’s why comparing FLOPS to neurons is misleading. AI will surpass humans in narrow tasks, but general intelligence, self-awareness, and consciousness remain biological phenomena. The future is not AI replacing humans—it’s AI amplifying human intelligence.
human brain intelligence, artificial intelligence limits, AI vs human brain, consciousness science, neural complexity, cognitive intelligence, biological intelligence, machine learning limits, human creativity, general intelligence
#AIvsHuman
#HumanIntelligence
#ArtificialIntelligence
#Neuroscience
@solyx.ai
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
5.1M
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
8.9M
ENSmart Mechanical Sorting System That Aligns Objects Automatically
In this video, we explain how a clever mechanism aligns randomly placed cylindrical objects and feeds them smoothly into the next stage without human effort.
📌 What you’ll learn in this video:
✔ How the stopper controls object position
✔ How the pusher aligns the cylinder
✔ How smooth feeding into the next stage works
This mechanism is designed to handle cylindrical objects that fall into a chamber in random orientations. A stopper holds the object in place, preventing it from falling directly. Then, a side pusher moves forward, applying force to rotate and align the object into the correct position. Once aligned, the object is released into a funnel and directed into the next stage of the process. This system is widely used in industries for automation, reducing manual effort and increasing efficiency. However, it may face issues like jamming if multiple objects enter at once or if shapes vary.
#Engineering #Automation #MechanicalDesign #Innovation #MachineLearning
@engineering_explainer
✨ Guide de Découverte #Machinelearning
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#Machinelearning est l'une des tendances les plus engageantes sur Instagram en ce moment. Avec plus de 20 million publications dans cette catégorie, des créateurs comme @quantumcurrentai, @engineering_explainer and @explainingthefact mènent la danse avec leur contenu viral. Parcourez ces vidéos populaires anonymement sur Pictame.
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