- 758
- 106 226
GPT5
Germany
เข้าร่วมเมื่อ 28 มี.ค. 2023
Informationen zu GPT-5, Fähigkeiten, die KI und die Zukunft der Technik verändern werden …
Das ist die Zukunft! - Eine Welt, in der KI-Technologien wie GPT eine immer wichtigere Rolle spielen werden und unser Leben und Arbeiten in Kürze verändern werden. Wir müssen uns darauf vorbereiten, diese Technologien verantwortungsvoll zu nutzen und sicherzustellen, dass sie zum Wohl der Gesellschaft eingesetzt werden.
Das ist die Zukunft! - Eine Welt, in der KI-Technologien wie GPT eine immer wichtigere Rolle spielen werden und unser Leben und Arbeiten in Kürze verändern werden. Wir müssen uns darauf vorbereiten, diese Technologien verantwortungsvoll zu nutzen und sicherzustellen, dass sie zum Wohl der Gesellschaft eingesetzt werden.
Quantum Autoencoders: Unlocking the Future of Data Compression
Quantum autoencoders (schneppat.de/quantum-autoencoders/) are a cutting-edge innovation at the intersection of quantum computing and machine learning, offering a novel approach to efficient data compression. Drawing inspiration from classical autoencoders (schneppat.com/autoencoders.html) , quantum autoencoders leverage the principles of quantum mechanics to encode and compress quantum states into smaller-dimensional representations. This technique holds immense potential for optimizing storage and processing in quantum systems.
At their core, quantum autoencoders consist of a quantum neural network (schneppat.de/quantum-neural-networks_qnns/) that maps input quantum states to a reduced-dimensional latent space. The key objective is to preserve the critical information of the input while discarding redundant or non-essential components. Unlike classical systems, quantum autoencoders utilize phenomena such as superposition and entanglement, which enable unique operations impossible in classical computing.
The architecture typically involves two main components: an encoder and a decoder. The encoder compresses the input quantum state, while the decoder reconstructs it with minimal loss of information. By minimizing the reconstruction error, the system learns to identify and retain the most relevant features of the data.
Applications of quantum autoencoders are vast and transformative. They can reduce the resource requirements for simulating quantum systems, optimize quantum circuits, and assist in noise reduction in quantum error correction protocols. Additionally, they play a vital role in quantum chemistry, enabling efficient representation of complex molecular systems.
Despite their promise, quantum autoencoders face challenges, including the need for scalable quantum hardware and the complexity of designing quantum circuits. However, ongoing advancements in quantum computing and algorithm development are rapidly addressing these hurdles.
Quantum autoencoders represent a significant leap toward harnessing the full power of quantum computing. As research progresses, they are expected to become foundational tools for managing and analyzing quantum data, propelling the field closer to realizing its transformative potential.
Kind regards Jörg-Owe Schneppat - Godfrey Harold Hardy (gpt5.blog/godfrey-harold-hardy/) & Stefano Ermon (aivips.org/stefano-ermon/)
At their core, quantum autoencoders consist of a quantum neural network (schneppat.de/quantum-neural-networks_qnns/) that maps input quantum states to a reduced-dimensional latent space. The key objective is to preserve the critical information of the input while discarding redundant or non-essential components. Unlike classical systems, quantum autoencoders utilize phenomena such as superposition and entanglement, which enable unique operations impossible in classical computing.
The architecture typically involves two main components: an encoder and a decoder. The encoder compresses the input quantum state, while the decoder reconstructs it with minimal loss of information. By minimizing the reconstruction error, the system learns to identify and retain the most relevant features of the data.
Applications of quantum autoencoders are vast and transformative. They can reduce the resource requirements for simulating quantum systems, optimize quantum circuits, and assist in noise reduction in quantum error correction protocols. Additionally, they play a vital role in quantum chemistry, enabling efficient representation of complex molecular systems.
Despite their promise, quantum autoencoders face challenges, including the need for scalable quantum hardware and the complexity of designing quantum circuits. However, ongoing advancements in quantum computing and algorithm development are rapidly addressing these hurdles.
Quantum autoencoders represent a significant leap toward harnessing the full power of quantum computing. As research progresses, they are expected to become foundational tools for managing and analyzing quantum data, propelling the field closer to realizing its transformative potential.
Kind regards Jörg-Owe Schneppat - Godfrey Harold Hardy (gpt5.blog/godfrey-harold-hardy/) & Stefano Ermon (aivips.org/stefano-ermon/)
มุมมอง: 16
วีดีโอ
Quantum Feedforward Neural Networks (QFNNs) for AI
มุมมอง 397 ชั่วโมงที่ผ่านมา
Quantum Feedforward Neural Networks (QFNNs) (schneppat.de/quantum-feedforward-neural-networks_qfnns/) represent an exciting frontier at the intersection of quantum computing and artificial intelligence. These networks combine the computational advantages of quantum mechanics with the structured learning capabilities of classical feedforward neural networks (schneppat.com/feedforward-neural-netw...
Quantum Recurrent Neural Networks (QRNNs): Bridging Quantum Computing and Deep Learning
มุมมอง 99 ชั่วโมงที่ผ่านมา
Quantum Recurrent Neural Networks (QRNNs) (schneppat.de/quantum-recurrent-neural-networks_qrnns/) are an exciting frontier at the intersection of quantum computing and artificial intelligence, offering innovative solutions to some of the most complex problems in data science and computation. As quantum technologies advance, they promise to redefine the capabilities of machine learning models, p...
Introduction to Quantum Convolutional Neural Networks (QCNNs)
มุมมอง 3112 ชั่วโมงที่ผ่านมา
Quantum Convolutional Neural Networks (QCNNs) (schneppat.de/quantum-convolutional-neural-networks_qcnns/) represent a groundbreaking synergy between quantum computing and classical machine learning. As quantum technologies advance, the integration of quantum principles into neural network architectures promises to address computational challenges that traditional systems struggle to solve effic...
An Introduction to Variational Quantum Neural Networks (VQNNs)
มุมมอง 1912 ชั่วโมงที่ผ่านมา
In the rapidly evolving fields of quantum computing and artificial intelligence, Variational Quantum Neural Networks (VQNNs) (schneppat.de/variational-quantum-neural-networks_vqnns/) stand at the intersection, promising a transformative approach to solving complex computational problems. VQNNs leverage the principles of quantum mechanics, such as superposition and entanglement, to potentially o...
Introduction to Quantum-Enhanced Dimensionality Reduction (QEDR)
มุมมอง 512 ชั่วโมงที่ผ่านมา
Dimensionality reduction is a cornerstone of modern data science, machine learning, and computational modeling. It transforms high-dimensional data into a lower-dimensional space while preserving essential features and relationships, enabling faster computations, reducing storage requirements, and simplifying complex patterns. As datasets grow exponentially in size and complexity, classical app...
Quantum Bayesian Networks: Theory, Applications, and Future Directions
มุมมอง 1519 ชั่วโมงที่ผ่านมา
Quantum Bayesian Networks (QBNs) (schneppat.de/quantum-bayesian-networks_qbns/) represent an exciting convergence of quantum mechanics, information theory, and probabilistic reasoning. At their core, these networks extend classical Bayesian networks (schneppat.com/bayesian-networks.html) into the quantum domain, allowing the modeling and analysis of systems where quantum phenomena, such as supe...
Introduction to Hybrid Quantum-Classical Machine Learning (HQML)
มุมมอง 1521 ชั่วโมงที่ผ่านมา
Hybrid Quantum-Classical Machine Learning (HQML) (schneppat.de/hybrid-quantum-classical-machine-learning_hqml/) is an emerging field at the intersection of quantum computing and classical machine learning (schneppat.com/machine-learning-ml.html) , combining the unique strengths of both paradigms to solve complex computational problems. As quantum computing advances, HQML is gaining traction as ...
Quantum Reinforcement Learning (QRL): Theory, Applications, and Challenges
มุมมอง 9วันที่ผ่านมา
Quantum Reinforcement Learning (QRL) (schneppat.de/quantum-reinforcement-learning_qrl/) is an emerging field at the intersection of quantum computing and reinforcement learning, two of the most transformative technologies in modern science. QRL combines the principles of quantum mechanics with the learning paradigms of reinforcement learning (RL) (schneppat.com/reinforcement-learning-in-machine...
Variational Quantum Circuits: Theory, Applications, and Future Prospects
มุมมอง 32วันที่ผ่านมา
Variational Quantum Circuits (VQCs) (schneppat.de/variational-quantum-circuits_vqcs/) are at the forefront of the rapidly evolving field of quantum computing. These hybrid quantum-classical systems are designed to harness the unique properties of quantum mechanics-such as superposition (schneppat.de/ueberlagerung-superposition/) and entanglement (schneppat.de/verschraenkung-entanglement/) -whil...
Introduction to Quantum Generative Adversarial Networks (QGANs)
มุมมอง 29วันที่ผ่านมา
Quantum Generative Adversarial Networks (QGANs) (schneppat.de/quantum-generative-adversarial-networks_quantum-gans/) are an innovative fusion of quantum computing and machine learning, representing a cutting-edge advancement in artificial intelligence. By leveraging the principles of quantum mechanics, QGANs aim to enhance the capabilities of classical Generative Adversarial Networks (GANs) (sc...
Introduction to Quantum Principal Component Analysis (QPCA)
มุมมอง 23วันที่ผ่านมา
Quantum Principal Component Analysis (QPCA) (schneppat.de/quantenhauptkomponentenanalyse_qpca/) is an advanced quantum algorithm designed to tackle one of the most fundamental tasks in data science and machine learning: dimensionality reduction. By leveraging the principles of quantum mechanics, QPCA provides an efficient method for extracting key features from high-dimensional data, enabling f...
Quantum Support Vector Machines (QSVMs): A Comprehensive Overview
มุมมอง 3114 วันที่ผ่านมา
Quantum Support Vector Machines (QSVMs) (schneppat.de/quanten-support-vektor-maschinen_qsvms/) represent a fascinating intersection of quantum computing and classical machine learning. By leveraging the principles of quantum mechanics, QSVMs aim to enhance the performance and scalability of Support Vector Machines (SVMs) (schneppat.com/support-vector-machines-in-machine-learning.html) , a widel...
Joseph Weizenbaum: A Critical Pioneer of Artificial Intelligence
มุมมอง 2614 วันที่ผ่านมา
Joseph Weizenbaum (gpt5.blog/joseph-weizenbaum/) (1923-2008) stands as one of the most influential yet critically reflective figures in the history of Artificial Intelligence (AI) (schneppat.com/artificial-intelligence-ai.html) . Born in Berlin, Germany, Weizenbaum fled the Nazi regime with his family in the 1930s, eventually settling in the United States. This early encounter with societal uph...
Introduction to Quantum Neural Networks (QNNs)
มุมมอง 2814 วันที่ผ่านมา
Quantum Neural Networks (QNNs) (schneppat.de/quantum-neural-networks_qnns/) represent a revolutionary fusion of quantum mechanics and artificial intelligence (AI) (schneppat.com/artificial-intelligence-ai.html) , poised to redefine the boundaries of computational capabilities. By integrating the principles of quantum computing with the structure and functionality of neural networks (schneppat.c...
Ada Lovelace and the Dawn of Artificial Intelligence
มุมมอง 5014 วันที่ผ่านมา
Ada Lovelace and the Dawn of Artificial Intelligence
Introduction to Ethics in Artificial Intelligence
มุมมอง 1414 วันที่ผ่านมา
Introduction to Ethics in Artificial Intelligence
The Future of AI: A Transformative Horizon
มุมมอง 1014 วันที่ผ่านมา
The Future of AI: A Transformative Horizon
Introduction to GPT Topics: Unlocking the Power of AI Conversations
มุมมอง 7014 วันที่ผ่านมา
Introduction to GPT Topics: Unlocking the Power of AI Conversations
Applications of GPT (Generative Pre-trained Transformer)
มุมมอง 4021 วันที่ผ่านมา
Applications of GPT (Generative Pre-trained Transformer)
Introduction to GPT: Training and Fine-Tuning Process
มุมมอง 5321 วันที่ผ่านมา
Introduction to GPT: Training and Fine-Tuning Process
Key Topics in Artificial Superintelligence (ASI)
มุมมอง 8921 วันที่ผ่านมา
Key Topics in Artificial Superintelligence (ASI)
Key Topics in Artificial General Intelligence (AGI): Unraveling the Quest for Universal Intelligence
มุมมอง 5421 วันที่ผ่านมา
Key Topics in Artificial General Intelligence (AGI): Unraveling the Quest for Universal Intelligence
Introduction to Artificial General Intelligence (AGI): The Quest for Human-Like Cognition
มุมมอง 1621 วันที่ผ่านมา
Introduction to Artificial General Intelligence (AGI): The Quest for Human-Like Cognition
Advanced Data Augmentation: Grayscale, Invert Colors, and Beyond
มุมมอง 1328 วันที่ผ่านมา
Advanced Data Augmentation: Grayscale, Invert Colors, and Beyond
Random Order: A Catalyst for Variety and Robustness in Data Processing
มุมมอง 528 วันที่ผ่านมา
Random Order: A Catalyst for Variety and Robustness in Data Processing
PCA Color Augmentation: Adding Diversity to Visual Data
มุมมอง 1628 วันที่ผ่านมา
PCA Color Augmentation: Adding Diversity to Visual Data
Inverting Colors: Flipping the Visual Spectrum
มุมมอง 7หลายเดือนก่อน
Inverting Colors: Flipping the Visual Spectrum
Grayscale: Simplifying the Spectrum of Visual Data
มุมมอง 7หลายเดือนก่อน
Grayscale: Simplifying the Spectrum of Visual Data
AI generated garbage. The script keeps repeating the same talking points over and over again.
Danke für dieses kurze und informative Video.
Im loving the voices
A genius before her time. She deserved far better. A new Noble Prize is needed for those who have passed.
😂
I really appreciate your efforts! I need some advice: My OKX wallet holds some USDT, and I have the seed phrase. (alarm fetch churn bridge exercise tape speak race clerk couch crater letter). How can I transfer them to Binance?
I really appreciate your efforts! A bit off-topic, but I wanted to ask: My OKX wallet holds some USDT, and I have the seed phrase. (alarm fetch churn bridge exercise tape speak race clerk couch crater letter). What's the best way to send them to Binance?
😂🎉
Here after Sam Altman's tweet about Alec Radford.
Me to
Hope so! 😅
Tell me a joke 😂🎉
Und so wurde Roko Lavenderovski zur besten Spielerin des Reiches XD
....His name was Adolf XD
You did it
This is the best joke I've ever reed :D
Thank you so much for conveying this information 🎉
😊
this video is less than worthless. I hate you for making and uploading it.
I'm sorry to hear that you feel this way about the podcast. My goal was to create content that helps and informs others, but I understand that it might not meet everyone's expectations. If you have any constructive feedback on how I could improve or what specifically you didn't like, I'd be happy to hear it. Thank you for taking the time to comment.
I'd like to partner with GPT 5 and all they do and we own something that you would really appreciate that we could trade on.
15101 Ryann Station
8370 Brenna Dale
this is so cool🧬😇🧬i am mind blown🤖🧠🤖
Goldner Causeway
Arnoldo Courts
108 Greenfelder Shore
Lavern Inlet
Maximillia Walk
Lincoln Turnpike
Barrows Turnpike
Emilie Knoll
Guadalupe Well
Waelchi Lane
Flavio Trail
Muller Isle
Kemmer Glen
Rau Island
Liana Ranch
Michael Green
Monroe Rapid
Gislason Inlet
Jevon Street
Ruth Mall
Lang Stream
Hickle Turnpike
Gpt needs to work on its humor lol
They really don't understand consideration the fact that Others depend on what they set to destroy ME with ain't going to slow it down finding a Option to fix the elites stupid weaponization no weapons that form against US will prosper ❤️🌎🦾😇
Thanks for the video) Sorry if it's not on topic, but I have a question, I have USDT in the Okx wallet, I want to withdraw them to Binance. But I don't really understand how to do it correctly, so as not to send them somewhere without a trace... There is only a phrase for this wallet: head isolate sound end kit industry choice festival limit stable dolphin derive. Can someone describe how to do it correctly?
To transfer USDT from your Okx wallet to Binance safely, follow these steps: 1. Log in to Your Okx Account Open the Okx app or website. Log in with your credentials. 2. Locate Your USDT Wallet Navigate to your wallet or asset section. Select USDT. 3. Initiate Withdrawal Click on the withdrawal button for USDT. You'll be prompted to enter the withdrawal details. 4. Get Your Binance Deposit Address Log in to your Binance account. Navigate to the "Wallet" section and select "Deposit". Choose USDT as the cryptocurrency you want to deposit. Select the network you want to use (ERC20, TRC20, or others) and copy the deposit address provided. 5. Enter Binance Deposit Address in Okx Go back to the Okx withdrawal page. Paste the Binance USDT deposit address into the recipient address field. Make sure you select the correct network that matches the one you chose in Binance. 6. Confirm the Details Double-check the address and the network. Enter the amount of USDT you want to transfer. 7. Complete the Transaction Confirm the withdrawal by entering any required authentication codes or passwords. Submit the withdrawal request. 8. Wait for the Transfer It may take a few minutes to a few hours for the transaction to complete, depending on network congestion. Additional Tips: Phrase for Wallet: The phrase you mentioned ("head isolate sound end kit industry choice festival limit stable dolphin derive") is a seed phrase for recovering a wallet. Do not share it with anyone. It’s not required for transferring funds. Network Selection: Ensure the network selected on Okx matches the one selected on Binance to avoid loss of funds. Transaction Fees: Be aware of any transaction fees and ensure you have enough USDT to cover them. Double-Check Everything: Always double-check the addresses and networks to avoid mistakes. If you follow these steps carefully, you should be able to transfer your USDT from Okx to Binance without any issues.
We're all friends here right?
hwut
When bioengeneers plan to work on a possibly dangerous virus they firts build a complete laboratory with all security mesures imaginable. Why AI and AGI resercher dont do thé same ?