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| Fetch.ai 
| #FET
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| FET Price: | $1.45 | | Volume: | $0.3 B | | All Time High: | $3.47 | | Market Cap: | $3.7 B |
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| Circulating Supply: | 2,578,264,490 |
| Exchanges: | 46
| | Total Supply: | 2,719,493,896 |
| Markets: | 69
| | Max Supply: | — |
| Pairs: | 66
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The price of #FET today is $1.45 USD.
The lowest FET price for this period was $0, the highest was $1.45, and the exact current price of one FET crypto coin is $1.44774.
The all-time high FET coin price was $3.47.
Use our custom price calculator to see the hypothetical price of FET with market cap of ETH or other crypto coins. |
The code for Fetch.ai is #FET.
Fetch.ai is 5.6 years old. |
The current market capitalization for Fetch.ai is $3,732,654,527.
Fetch.ai is ranked #27 out of all coins, by market cap (and other factors). |
There is a very large volume of trading today on #FET.
Today's 24-hour trading volume across all exchanges for Fetch.ai is $252,970,092. |
| The circulating supply of FET is 2,578,264,490 coins, which is 95% of the total coin supply. |
FET is well integrated with many pairings with other cryptocurrencies and is listed on at least 46 crypto exchanges.
View #FET trading pairs and crypto exchanges that currently support #FET purchase. |
Few-Shot Learning: Helping AI Learn from Little
Few-shot learning is emerging as a captivating and challenging domain In the fast-evolving realm of artificial intelligence. This concept is focused on teaching AI to make accurate classifications or predictions from a very limited dataset, which heralds a significant shift from traditional machine learning methods. Imagine teaching a computer to differentiate between two unfamiliar animals, such as Armadillos and Pangolins, using only a handful of images. This is the essence of few-shot learning — a task simple for humans but notably complex for computers! — The Game of Few-Shot Learning - To understand few-shot learning, consider a scenario where you’re shown four images: two contain Armadillos and two contain Pangolins. Without prior knowledge of these animals, it’s possible to spot differences by observing their ears or scale sizes. Now, if you are presented with a new image (the ‘query’), you could likely identify whether it depicts an Armadillo or a Pangolin. Humans excel at this kind of task, learning from very few examples. However, the challenge is to enable computers to do the same with limited data. In traditional machine learning, vast amounts of data are required, particularly in deep neural network training. But in few-shot learning the objective is to make accurate predictions from a sparse dataset. This approach fundamentally differs from standard supervised learning. Instead of training a... 
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The Paperclip Maximizer Fallacy
Welcome to the future. AI is everywhere: controlling our cars, managing our homes, and more. Yet, even with such progress, we still face tough questions about AI ethics. One thought experiment that has gained notoriety in discussions about ethics is the Paperclip Maximizer, first posed by philosopher Nick Bostrom. It has quickly grown into a cautionary tale that embodies both the promises and dangers of AI, echoing from academic symposiums to Silicon Valley tech labs. Why should we care about a hypothetical AI whose only goal is to manufacture paperclips? — Understanding the Paperclip Maximizer - Imagine a superintelligent AI system programmed with a seemingly simple objective: to maximize the production of paperclips. The AI is so efficient that it begins transforming all available materials into paperclips. Sounds good so far! But then comes the twist: the AI is so obsessed with its objective that it starts converting everything into paperclips: houses, cars, and even humans. Eventually, the entire planet becomes a haven of paperclips. While it may be a compelling narrative for late-night philosophizing or an episode of Black Mirror, how grounded is it in scientific or logical feasibility? The heart of the issue is that AI, in its relentless pursuit of a programmed objective, might disregard any unintended but catastrophic consequences. It’s not that the AI has malevolent intentions, it’s that it doesn’t have in...
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What Really Caused the AI Boom
The path to artificial intelligence is one for the history books. A labyrinth of innovations has collectively breathed life into the AI we know today. While the intricate dance between hardware, algorithms, and data has been ongoing for years, the rhythm has recently intensified, propelling AI into the limelight. But what were the factors that led to it? — The Era of Gigantic Neural Networks - Let’s rewind a bit. Neural networks before 2012 were like quaint little villages — small and manageable, sporting just a few thousand neurons. The post-2012 era, however, saw them grow into bustling metropolises: large, deep, and complex. This growth in size and depth wasn’t merely for show. Larger models demonstrated improved performance across a multitude of tasks, hinting at the potential of scaling towards Artificial General Intelligence (AGI) and possibly — even Superintelligent AI. — GPUs: Gaming Chips Turn AI Powerhouses - Interestingly, the video game industry can claim credit for the growth of AI. Graphic Processing Units (GPUs), initially developed to satisfy gamers’ demands for better graphics, soon became AI researchers’ workhorses. While the architectural skeleton of GPUs remained relatively consistent, the scale, speed, and efficiency ballooned. Modern GPUs, like the V100, can churn out computations at a staggering rate, rivaling the supercomputers from just a decade ago. When companies u...
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From Attention Economy to Intention Economy
Have you ever experienced information overload? For the last few decades, our digital interactions have been heavily influenced by the concept of the Attention Economy, where companies essentially compete to grab our focus amidst the online noise. However, a new paradigm is emerging: the Intention Economy. In this emerging model, our interactions may well be shaped by the need to understand and cater to our actual intentions or needs. AI, especially in the form of AI agents, is playing a pivotal role as the main driver for this transformative shift. — Understanding the Attention Economy - At the dawn of the 21st century, the popularization of the Internet and digital media platforms led to a surge in content creation. It quickly became apparent that human attention was the primary currency and in the 1970’s the term ‘Attention Economy’ was coined by political scientist Herbert Alexander Simon to represent this new marketplace. However, this attention-centric model has its pitfalls. With data creation becoming faster than ever, the focus shifted from the quality of content to mere quantity. Recent data supports the claim that a significant portion of global content was generated in just the last two years! As a result, consumers often find themselves inundated with vast amounts of irrelevant, low-quality information. making it challenging to discern quality information from the noise. — Transition to the Inte...
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Unpacking Bimodal Neural Networks
AI is a space where patterns and rhythms that imitate human cognition keep emerging. Enter the domain of Bimodal Neural Networks, where AI doesn’t just see or hear — it does both simultaneously, merging these senses to offer a richer understanding of the world. You can think about it like this: It is now possible to train AI to appreciate an orchestra, both the visual spectacle of the performance and the auditory magic of the music. — Dual Channels, Harmonized Understanding - People naturally take in info from many senses, always mixing different signals to get a full picture. In the same way, Bimodal Neural Networks use two kinds of info — usually pictures and sounds — and work with them together. This isn’t just about doing two things, it’s about getting a mixed view, kind of like how seeing and hearing help us understand what’s happening on a busy road. — Why Bimodal? - Single modality networks focusing on either vision or sound have made remarkable strides in recent years. Image recognition algorithms can discern objects in a cluttered photo, while voice recognition systems transcribe or respond to our spoken words with increasing accuracy. But real life isn’t just about looking or listening. A video of a music show, for example, isn’t complete if a tool only sees the players or hears the songs. Bimodal networks capture the richness of such experiences, offering a 360-degree pers...
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Decoding AI Consciousness: A Dive into the Mind of the Machine
In the ever-evolving world of tech, a big question stands out for many: Can we make AI feel like us? This question isn’t about whether AI can beat a human in a game of chess or write a story. It is about whether it could someday experience the world as living beings do. As AI evolves and becomes more intricate, the debate about its consciousness has gained momentum. While AI might convincingly mimic human conversation, could it ever truly feel? — The Challenge of Consciousness in AI - For us, being aware means understanding what’s happening around us and knowing who we are. But for machines, this idea isn’t as straightforward. A groundbreaking paper from NYU shines a light on this very topic, suggesting a structured approach to evaluate AI’s potential consciousness. There are two major takeaways from this report: Scientific Applicability to AI: Contrary to some opinions, consciousness isn’t just a nebulous concept. It can be scientifically studied. More importantly, findings from such research could be relevant when assessing AI consciousness., The Theory-Heavy Approach: While it’s tempting to judge consciousness based on behavior, it might be misleading for AI. Machines can mimic behavior without truly understanding or feeling it. So, researchers advocate a deeper approach, examining whether AI systems exhibit functions that scientific theories link to consciousness., — Indicators of Potential Con...
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Emotion Simulation in AI: A New Frontier in Human-Machine Synergy
Technology is often mischaracterized as cold and impersonal. Yet there is a growing trend in AI that could be a game-changer for AI’s image: Emotion Simulation. Let’s dive into what this means for both consumers and the tech industry. — Humanizing AI: The Why and the How - The idea of machines mimicking human traits isn’t entirely foreign. So let’s stretch our imagination a bit further: What if AI didn’t just mimic us but could also resonate with our emotions? Imagine a healthcare bot that could detect a patient’s anxiety and respond with empathy or a virtual assistant in your car that could sense your frustration in traffic and play your favorite calming tune. Achieving this level of connection isn’t science fiction — it’s becoming a reality through facial recognition, voice analysis, sentiment analysis, and monitoring physiological signals. By simulating emotions, AI can move beyond mere functionality to provide more personalized and empathetic interactions. — The Real-World Applications - This isn’t just about making our devices more friendly. Emotion simulation in AI has practical applications that could revolutionize industries: Customer Service: AI chatbots can provide more compassionate support transforming the customer experience., Mental Health: Virtual therapists who can assist human professionals in providing continuous care., Entertainment: Video games and virtual reality exper...
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Behind the Curtain: Explainable AI
Artificial Intelligence, once confined to the echelons of high-brow science fiction, now flows beneath the surface of our everyday. From the smartphones in our pockets to the digital assistants that orchestrate our smart homes, the AI age is upon us. Yet, with its extraordinary advancements comes an ever-looming shadow: How do these intricate systems make their decisions? In a bid to lay bare the mysteries of AI, a new vanguard in tech is emerging — Explainable AI (XAI). Simply put, XAI is an approach within AI that aims to make its decision-making processes clear and understandable to humans. Let’s dive into this movement, seeking to understand why clarity in AI is not just an intellectual pursuit but an urgent necessity. — A World Hungry for Transparency - Imagine an AI system designed to screen job applications, which continually sidelines candidates from a particular region. The algorithm may be efficient, but without understanding its decision-making process, it’s impossible to identify inherent biases. This scenario, unfortunately, isn’t a fictional one. At its essence, XAI endeavors to untangle the vast web of computations and decisions within AI models, enabling humans to interpret and understand their rationale. In fields such as medicine, finance, and even the judiciary, where AI is rapidly gaining ground, this clarity can be the difference between life-altering decisions and uninformed choices. Pio...
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Neurosymbolic AI Agents: Where Classical Meets Cutting-Edge
In the rapidly evolving realm of artificial intelligence, there’s a blend of old and new taking center stage: Neurosymbolic AI. For those on the cutting edge of AI innovation, this isn’t just another buzzword. It’s the nexus where classical AI meets modern neural networks, promising a leap in AI capabilities. — Understanding Neurosymbolic AI - To understand the significance of neurosymbolic AI, we need to go back in time. Classical AI, or symbolic AI, was all about predefined rules and symbols. This system used logic and structured knowledge representation to draw conclusions. While it excelled in certain tasks, it was rigid and lacked adaptability. Enter neural networks, the darlings of modern AI. These systems learn from vast amounts of data, allowing them to perform complex tasks like image recognition and language translation with astonishing accuracy. But here’s the catch: they often act as black boxes, with their decision-making processes being somewhat elusive. Neurosymbolic AI seeks to combine the best of both worlds. It fuses symbolic reasoning’s structured, logical nature with the learning capabilities of neural networks. Combining the technology with AI agents can yield amazing results. Agents can learn, reason, and generalize from limited data, while also providing transparent decision-making processes. — Why Neurosymbolic AI Agents Matter - One major advantage of neurosymbolic AI is its abi...
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The Rise of Sparse Learning
In the noise of ever-evolving narratives surrounding the AI landscape one might be forgiven for overlooking the term ‘sparse learning.’ Let’s tune into the concept that’s promising to streamline and redefine AI’s future. — From Dense Complexity to Sparse Efficiency - Picture the bustling streets of New York during peak hours. Every lane is occupied, traffic is dense, and whilst things are moving: everything seems packed and congested. Traditional deep learning models resemble this traffic scenario, with millions, sometimes billions, of parameters. While undeniably powerful these models can become excessively complex, resource-draining, and occasionally redundant. Enter sparse learning, which gets to work optimizing these neural pathways, ensuring a smoother, less congested flow of information. In technical parlance it’s about ensuring that only the most essential connections in a neural network are active and the rest are turned off or ‘sparse.’ — Why the Hype Around Sparse? - sparse learning isn’t just about creating sleek models. It addresses some pressing challenges in AI: Computational Efficiency: Sparse models require fewer resources. Think about the potential energy savings, particularly when scaling operations., Enhanced Generalization: Sparse structures tend to generalize better to unseen data, often improving the model’s adaptability., Model Interpretability: Fewer active parameters ...
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