The company is clearly on the cutting edge of quantum technology given its recent success with Willow, and it could theoretically monetize the chip in the near future. However, even if that doesn't happen, Alphabet has booming businesses in ad tech and cloud computing that should drive steady ...

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Quantum computing technology became a hot investing topic toward the end of 2024. After Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL) last month unveiled its Willow quantum computing chip, which is significantly less error-prone than earlier iterations of the technology, investors flocked to the ...

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Or did I? Only Google’s new quantum computing chip knows for sure.

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Willow

willow

As a Web3 writer, I tend to shy away from deep tech topics. But when I heard Google’s latest announcement about their new quantum computing chip, Willow, I couldn’t look away. The headline-grabbing claim? A computational problem solved in 5 minutes that would take a classical supercomputer a staggering 10 septillion years to complete. For context, that’s 10,000,000,000,000,000,000,000,000 years — a number so vast that it makes the 13.8 billion-year age of the universe look like a lunch break. Naturally, the buzz was immediate: “Quantum computing is here to crack Bitcoin!” “No encryption is safe!” But hold your horses. Willow’s significance isn’t about rendering our current security systems obsolete overnight. It’s about a new way forward — for computing, for science, and yes, for crypto. Let’s break it down. What Exactly Is Google’s Willow? Google’s Willow chip is a quantum computing breakthrough. It’s not the largest quantum processor out there — it has 105 qubits. But what makes it revolutionary is how it deals with errors. Why Errors Matter in Quantum Computing? In traditional computers, bits represent either a 0 or a 1. But quantum computers use qubits, which can represent both 0 and 1 simultaneously thanks to the principles of superposition and entanglement. This ability lets quantum computers perform multiple calculations at once, potentially solving problems classical computers could never touch. The problem? Qubits are delicate. They’re easily disturbed by their environment, which leads to errors. And the more qubits you add, the more errors you typically get. This challenge has haunted quantum computing researchers for decades. Willow changes the game by demonstrating that as you add more qubits, the number of errors actually decreases. This is huge. It’s a critical step toward building large-scale, reliable quantum computers — the kind that could reshape industries like drug discovery, battery development, and material science. But where does that leave Bitcoin and crypto security? Should Crypto Holders Be Worried? The fear is simple: Quantum computers could theoretically break the cryptographic algorithms that secure Bitcoin and other cryptocurrencies. Bitcoin relies on two key pieces of encryption: 1 - SHA-256 for mining. 2 - ECDSA (Elliptic Curve Digital Signature Algorithm) for transaction signatures. Quantum computers could, in theory, brute-force these algorithms, rendering wallets and transactions vulnerable. If someone could crack ECDSA, they could steal funds by forging signatures. But here’s the thing: Willow isn’t that quantum computer. Not yet. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. So, no, you don’t need to sell your Bitcoin and go off-grid just yet. Why Quantum Computing Still Matters for Crypto Even though Willow can’t break Bitcoin’s encryption today, it’s a wake-up call. It shows that quantum computing isn’t some sci-fi fantasy. It’s evolving rapidly. And each breakthrough — like Willow’s error reduction — brings us closer to a world where quantum computers might pose a real threat to current cryptography. https://x.com/adamscochran/status/1866314726030143606 The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. This isn’t just about Bitcoin. Ethereum, smart contracts, and other blockchain-based systems rely on similar cryptographic protections. Quantum computing could jeopardize the entire Web3 ecosystem if we’re not ready. How Crypto Can Defend Itself Thankfully, the crypto community isn’t sitting idle. There are already discussions around post-quantum cryptography — encryption methods designed to withstand quantum attacks. 1 - Upgrading Bitcoin’s Security Crypto experts like Emin Gün Sirer, co-founder of Ava Labs, have suggested practical steps to protect Bitcoin: - A - Sunsetting Outdated Formats Early Bitcoins, mined by Satoshi Nakamoto, use an old format called P2PK (Pay-to-Public-Key). Unlike modern wallets, this format exposes public keys, making it vulnerable to future quantum decryption. Sirer recommends freezing these coins or setting a sunset date for P2PK transactions. B - Implementing Post-Quantum Algorithms Transitioning Bitcoin to quantum-resistant algorithms is complex but doable. It would likely require a hard fork and widespread adoption of new wallet software. 2 - Ethereum’s Preparedness Ethereum co-founder Vitalik Buterin has already proposed a strategy for Ethereum’s transition to post-quantum security. It would involve a hard fork and user cooperation to upgrade wallets, but it’s a clear and viable plan. Quantum Computing: A Threat or an Opportunity? While the threat to crypto security is real, quantum computing isn’t just about breaking things. It’s about building new possibilities. Imagine a world where quantum computers can: Simulate complex chemical reactions to discover life-saving drugs.


Optimize battery designs to extend electric vehicle range.


Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Quantum computing could lead to more secure blockchains, faster consensus algorithms, and decentralized systems that are truly unbreakable. The Real Takeaway Google’s Willow isn’t here to kill Bitcoin. It’s here to remind us that technology evolves, and we need to evolve with it. If you’re in Web3, now’s the time to: Stay Informed: Follow quantum computing advancements. They matter more than ever.
Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption.
Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? What are your thoughts on the quantum threat to crypto? Are we prepared, or is it time to hit the panic button? As a Web3 writer, I tend to shy away from deep tech topics. But when I heard Google’s latest announcement about their new quantum computing chip, Willow, I couldn’t look away. The headline-grabbing claim? A computational problem solved in 5 minutes that would take a classical supercomputer a staggering 10 septillion years to complete. 10 septillion years For context, that’s 10,000,000,000,000,000,000,000,000 years — a number so vast that it makes the 13.8 billion-year age of the universe look like a lunch break. Naturally, the buzz was immediate: “Quantum computing is here to crack Bitcoin!” “No encryption is safe!” But hold your horses. Willow’s significance isn’t about rendering our current security systems obsolete overnight. It’s about a new way forward — for computing, for science, and yes, for crypto. Let’s break it down. What Exactly Is Google’s Willow? Google’s Willow chip is a quantum computing breakthrough. It’s not the largest quantum processor out there — it has 105 qubits. But what makes it revolutionary is how it deals with errors. Why Errors Matter in Quantum Computing? In traditional computers, bits represent either a 0 or a 1. But quantum computers use qubits, which can represent both 0 and 1 simultaneously thanks to the principles of superposition and entanglement. This ability lets quantum computers perform multiple calculations at once, potentially solving problems classical computers could never touch. The problem? Qubits are delicate. They’re easily disturbed by their environment, which leads to errors. And the more qubits you add, the more errors you typically get. This challenge has haunted quantum computing researchers for decades. Willow changes the game by demonstrating that as you add more qubits, the number of errors actually decreases. This is huge. It’s a critical step toward building large-scale, reliable quantum computers — the kind that could reshape industries like drug discovery, battery development, and material science. But where does that leave Bitcoin and crypto security? Should Crypto Holders Be Worried? The fear is simple: Quantum computers could theoretically break the cryptographic algorithms that secure Bitcoin and other cryptocurrencies. Bitcoin relies on two key pieces of encryption: 1 - SHA-256 for mining. 2 - ECDSA (Elliptic Curve Digital Signature Algorithm) for transaction signatures. Quantum computers could, in theory, brute-force these algorithms, rendering wallets and transactions vulnerable. If someone could crack ECDSA, they could steal funds by forging signatures. But here’s the thing: Willow isn’t that quantum computer. Not yet. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. So, no, you don’t need to sell your Bitcoin and go off-grid just yet. Why Quantum Computing Still Matters for Crypto Even though Willow can’t break Bitcoin’s encryption today, it’s a wake-up call. It shows that quantum computing isn’t some sci-fi fantasy. It’s evolving rapidly. And each breakthrough — like Willow’s error reduction — brings us closer to a world where quantum computers might pose a real threat to current cryptography. https://x.com/adamscochran/status/1866314726030143606 https://x.com/adamscochran/status/1866314726030143606 The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. This isn’t just about Bitcoin. Ethereum, smart contracts, and other blockchain-based systems rely on similar cryptographic protections. Quantum computing could jeopardize the entire Web3 ecosystem if we’re not ready. How Crypto Can Defend Itself Thankfully, the crypto community isn’t sitting idle. There are already discussions around post-quantum cryptography — encryption methods designed to withstand quantum attacks. 1 - Upgrading Bitcoin’s Security Crypto experts like Emin Gün Sirer, co-founder of Ava Labs, have suggested practical steps to protect Bitcoin: - A - Sunsetting Outdated Formats A - Sunsetting Outdated Formats Early Bitcoins, mined by Satoshi Nakamoto, use an old format called P2PK (Pay-to-Public-Key). Unlike modern wallets, this format exposes public keys, making it vulnerable to future quantum decryption. Sirer recommends freezing these coins or setting a sunset date for P2PK transactions. B - Implementing Post-Quantum Algorithms B - Implementing Post-Quantum Algorithms Transitioning Bitcoin to quantum-resistant algorithms is complex but doable. It would likely require a hard fork and widespread adoption of new wallet software. 2 - Ethereum’s Preparedness Ethereum co-founder Vitalik Buterin has already proposed a strategy for Ethereum’s transition to post-quantum security. It would involve a hard fork and user cooperation to upgrade wallets, but it’s a clear and viable plan. Quantum Computing: A Threat or an Opportunity? While the threat to crypto security is real, quantum computing isn’t just about breaking things. It’s about building new possibilities. Imagine a world where quantum computers can: Simulate complex chemical reactions to discover life-saving drugs. Optimize battery designs to extend electric vehicle range. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Simulate complex chemical reactions to discover life-saving drugs. Simulate complex chemical reactions to discover life-saving drugs. Optimize battery designs to extend electric vehicle range. Optimize battery designs to extend electric vehicle range. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Quantum computing could lead to more secure blockchains, faster consensus algorithms, and decentralized systems that are truly unbreakable. The Real Takeaway Google’s Willow isn’t here to kill Bitcoin. It’s here to remind us that technology evolves, and we need to evolve with it. If you’re in Web3, now’s the time to: Stay Informed: Follow quantum computing advancements. They matter more than ever. Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption. Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? Stay Informed: Follow quantum computing advancements. They matter more than ever. Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption. Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? What are your thoughts on the quantum threat to crypto? Are we prepared, or is it time to hit the panic button?

Does Google’s Willow Quantum Chip Put Bitcoin’s Cryptography at Risk? 

গুগলের উইলো কোয়ান্টাম চিপ কি বিটকয়েনের ক্রিপ্টোগ্রাফিকে ঝুঁকিতে ফেলেছে? 

Sætter Googles Willow Quantum Chip Bitcoins kryptografi i fare? 

Το Willow Quantum Chip της Google θέτει σε κίνδυνο την κρυπτογραφία του Bitcoin; 

¿El chip cuántico Willow de Google pone en riesgo la criptografía de Bitcoin?

Apakah Chip Kuantum Willow Milik Google Membahayakan Kriptografi Bitcoin?

האם השבב הקוונטי של ווילו של גוגל מעמיד את הקריפטוגרפיה של ביטקוין בסיכון? 

Google の Willow Quantum チップはビットコインの暗号化を危険にさらすのか?

Vai Google Willow Quantum Chip apdraud Bitcoin kriptogrāfiju? 

Google's Willow Quantum Chip Bitcoin's Cryptography අවදානමට ලක් කරයිද? 

A e vë në rrezik çipi kuantik Willow i Google kriptografinë e Bitcoin? 

Оё Chip Quantum Willow Google криптографияи Bitcoin-ро зери хатар мегузорад? 

Google-ning Willow kvant chipi Bitcoin kriptografiyasini xavf ostiga qo'yadimi? 

Google, Microsoft, and IBM Are Fighting for Quantum Supremacy—Here’s Who’s Winning

Why Jensen Huang is Completely Wrong About Quantum Computing

Imagine attempting to understand consciousness by entangling the human brain with a quantum computer. The futility of this is not just the baseless science, but what it does not rule out. Why would consciousness be a quantum process in the brain? What are the possible other candidates for conscious experiences in the brain that this quantum option has definitively ruled out? And with advances in cellular and molecular neuroscience, why is there no clear quantum observation, or microtubule medications? Microtubules are said to be involved in anesthesia, but what else has an effect during anesthesia aside from microtubules? Why are microtubules not implicated in general functions, aside from anesthesia? There are several drugs whose mechanisms are known to retain neurotransmitters in the synaptic cleft. There are several mental disorders that are known to involve the abundance or the lack of certain neurotransmitters. Neurotransmitters [dominating chemical signals] are always involved in every explained function of the brain, and so are electrical signals [or ions]. If chemical signals can be induced or inhibited with an effect on conscious experience, how is quantum entanglement or superposition connected to chemical signals? If electrical signals are in transport, how do they relate to quantum qubits? How does any consciousness theory strike out electrical and chemical signals responsibility for consciousness? How does quantum consciousness explain how stimulants work, how sedatives work, or how psychedelics work, SSRIs, or several other brain medications? Quantum consciousness should explain why electrical and chemical signals should be ruled out, then present its influence on everything else before it should even cross a theoretical threshold, en route to [possible] future experiments. Electrical and chemical signals, for example, exceed neurons as the final stops for how the brain works. Electrical and chemical signals have several formations or configurations for which functions are defined. As electrical signals travel in sets across clusters of neurons, they, conceptually, strike to access configurations of chemical signals that specify functions. This means that a specific taste, sight, smell, and so forth are configurations that are accessed by interactions [or strikes] of [sets of] electrical signals on [sets of] chemical signals. The distinct [possible] configurations for chemical signals allow electrical signals to know where to go, to avoid striking into the wrong configuration, so to speak. So, electrical signals, in relay, for the regulation of breathing go to a set where they fit differently from the ones that go for vision or for the regulation of digestion and so forth, conceptually. Neuroscience is at the point where all that has been observed to be mechanized by electrical and chemical signals need an overall explanation or a postulation for how they do. For example, it is possible to theorize that the human mind is the collection of all the electrical and chemical signals, with their interactions and features, in sets, in clusters of neurons, across the central and peripheral nervous systems. It is by their interactions that they set up functions. It is their features that grade the limits or extent of those functions. How does quantum consciousness define the human mind? Is there a relationship between consciousness and the mind? If so, what is this relationship, and how does quantum delineate the mind from the brain, within the cranium? Are microtubules the mind? How do they configure a smell, in contrast to a sight? How do they shape memory, differently from how they define feelings? What is subjectivity and what is experience? Is an experience possible without subjectivity? What is in the class of subjectivity that is different from experience? What answers has quantum consciousness ever presented to these questions in ways that are at least promising? Subjectivity can be theorized to be a grader or qualifier for the interactions of electrical and chemical signals in sets. It is theorized that subjectivity is a grader that is more common among sets of signals, than control or intent, which is not in every set of signals. Attention is also a grader, as well as awareness. These graders are different from functions like memory, feelings, emotions, and regulations, which are experiences. Graders, like subjectivity, attention, and others place how those functions are experienced. Quantum consciousness is not progress for neuroscience, for mental health, for natural intelligence, against mental disorders, or for consciousness. The misplaced experimental plan would be a waste of research resources. There is a recent article in IFLScience, Scientists Want To Entangle Human Brains With Quantum Computers To Learn About Consciousness, stating that, "Let’s say we have 'N' qubits in our brain and 'M' qubits in an external quantum computer, with the letters referring to a certain number of qubits. If a person could entangle their brain with this quantum computer, they could create an expanded quantum superposition involving 'N+M' qubits. If we now tickle this expanded superposition to make it collapse, then this should be reported by the person participating in this experiment as a richer experience. That’s because in their normal conscious experience, they typically need 'N' bits to describe the experience, but now they need 'N+M' bits to describe it. I call this the 'expansion protocol', as it would allow us to expand consciousness in space, time and complexity. In an experimentum crucis, one would establish a physical link between a human brain and a quantum computer that would enable coherent interactions and mediate entanglement. If our conjecture is accurate, this should enable richer conscious experiences of the combined system, requiring more descriptive bits than the experiences the human reports without the link. Before the systems are coupled, their respective states exist in separate state spaces, known as Hilbert spaces, of dimension N and M, respectively. After they are made to interact, the wave function describing the combined system |𝜓𝐶𝑦𝑏𝑜𝑟𝑔〉 resides in an 𝑁×𝑀 -dimensional Hilbert space," the team writes. "We conjecture that a superposition forming in this higher dimensional state space would be experienced by the subject as a richer experience as compared to a superposition state forming in the lower N-dimensional Hilbert space describing the isolated brain of the subject." There is a recent blog by Google, Meet Willow, our state-of-the-art quantum chip, stating that, "Willow has state-of-the-art performance across a number of metrics, enabling two major achievements. The first is that Willow can reduce errors exponentially as we scale up using more qubits. This cracks a key challenge in quantum error correction that the field has pursued for almost 30 years. Second, Willow performed a standard benchmark computation in under five minutes that would take one of today’s fastest supercomputers 10 septillion (that is, 1025) years — a number that vastly exceeds the age of the Universe. The Willow chip is a major step on a journey that began over 10 years ago. As part of Google Research, our team has charted a long-term roadmap, and Willow moves us significantly along that path towards commercially relevant applications. We tested ever-larger arrays of physical qubits, scaling up from a grid of 3x3 encoded qubits, to a grid of 5x5, to a grid of 7x7 — and each time, using our latest advances in quantum error correction, we were able to cut the error rate in half. In other words, we achieved an exponential reduction in the error rate. This historic accomplishment is known in the field as “below threshold” — being able to drive errors down while scaling up the number of qubits. You must demonstrate being below threshold to show real progress on error correction, and this has been an outstanding challenge since quantum error correction was introduced by Peter Shor in 1995. There are other scientific “firsts” involved in this result as well. For example, it’s also one of the first compelling examples of real-time error correction on a superconducting quantum system — crucial for any useful computation, because if you can’t correct errors fast enough, they ruin your computation before it’s done. And it’s a "beyond breakeven" demonstration, where our arrays of qubits have longer lifetimes than the individual physical qubits do, an unfakable sign that error correction is improving the system overall." Imagine attempting to understand consciousness by entangling the human brain with a quantum computer. The futility of this is not just the baseless science, but what it does not rule out. Why would consciousness be a quantum process in the brain? What are the possible other candidates for conscious experiences in the brain that this quantum option has definitively ruled out? And with advances in cellular and molecular neuroscience, why is there no clear quantum observation, or microtubule medications? Microtubules are said to be involved in anesthesia, but what else has an effect during anesthesia aside from microtubules? Why are microtubules not implicated in general functions, aside from anesthesia? There are several drugs whose mechanisms are known to retain neurotransmitters in the synaptic cleft. There are several mental disorders that are known to involve the abundance or the lack of certain neurotransmitters. Neurotransmitters [dominating chemical signals] are always involved in every explained function of the brain, and so are electrical signals [or ions]. If chemical signals can be induced or inhibited with an effect on conscious experience, how is quantum entanglement or superposition connected to chemical signals? If electrical signals are in transport, how do they relate to quantum qubits? How does any consciousness theory strike out electrical and chemical signals responsibility for consciousness? How does quantum consciousness explain how stimulants work, how sedatives work , or how psychedelics work, SSRIs, or several other brain medications? how stimulants work, how sedatives work Quantum consciousness should explain why electrical and chemical signals should be ruled out, then present its influence on everything else before it should even cross a theoretical threshold, en route to [possible] future experiments. Electrical and chemical signals, for example, exceed neurons as the final stops for how the brain works. Electrical and chemical signals have several formations or configurations for which functions are defined. As electrical signals travel in sets across clusters of neurons, they, conceptually, strike to access configurations of chemical signals that specify functions. This means that a specific taste, sight, smell, and so forth are configurations that are accessed by interactions [or strikes] of [sets of] electrical signals on [sets of] chemical signals. The distinct [possible] configurations for chemical signals allow electrical signals to know where to go, to avoid striking into the wrong configuration, so to speak. So, electrical signals, in relay, for the regulation of breathing go to a set where they fit differently from the ones that go for vision or for the regulation of digestion and so forth, conceptually. Neuroscience is at the point where all that has been observed to be mechanized by electrical and chemical signals need an overall explanation or a postulation for how they do. For example, it is possible to theorize that the human mind is the collection of all the electrical and chemical signals, with their interactions and features, in sets, in clusters of neurons, across the central and peripheral nervous systems. It is by their interactions that they set up functions. It is their features that grade the limits or extent of those functions. How does quantum consciousness define the human mind? Is there a relationship between consciousness and the mind? If so, what is this relationship, and how does quantum delineate the mind from the brain, within the cranium? relationship between consciousness and the mind? Are microtubules the mind? How do they configure a smell, in contrast to a sight? How do they shape memory, differently from how they define feelings? What is subjectivity and what is experience? Is an experience possible without subjectivity? What is in the class of subjectivity that is different from experience? What answers has quantum consciousness ever presented to these questions in ways that are at least promising? Subjectivity can be theorized to be a grader or qualifier for the interactions of electrical and chemical signals in sets. It is theorized that subjectivity is a grader that is more common among sets of signals, than control or intent, which is not in every set of signals. Attention is also a grader, as well as awareness. These graders are different from functions like memory, feelings, emotions, and regulations, which are experiences. Graders, like subjectivity, attention, and others place how those functions are experienced. Quantum consciousness is not progress for neuroscience, for mental health, for natural intelligence, against mental disorders, or for consciousness. The misplaced experimental plan would be a waste of research resources. There is a recent article in IFLScience , Scientists Want To Entangle Human Brains With Quantum Computers To Learn About Consciousness , stating that, IFLScience Scientists Want To Entangle Human Brains With Quantum Computers To Learn About Consciousness "Let’s say we have 'N' qubits in our brain and 'M' qubits in an external quantum computer, with the letters referring to a certain number of qubits. If a person could entangle their brain with this quantum computer, they could create an expanded quantum superposition involving 'N+M' qubits. If we now tickle this expanded superposition to make it collapse, then this should be reported by the person participating in this experiment as a richer experience. That’s because in their normal conscious experience, they typically need 'N' bits to describe the experience, but now they need 'N+M' bits to describe it. I call this the 'expansion protocol', as it would allow us to expand consciousness in space, time and complexity. In an experimentum crucis, one would establish a physical link between a human brain and a quantum computer that would enable coherent interactions and mediate entanglement. If our conjecture is accurate, this should enable richer conscious experiences of the combined system, requiring more descriptive bits than the experiences the human reports without the link. Before the systems are coupled, their respective states exist in separate state spaces, known as Hilbert spaces, of dimension N and M, respectively. After they are made to interact, the wave function describing the combined system |𝜓𝐶𝑦𝑏𝑜𝑟𝑔〉 resides in an 𝑁×𝑀 -dimensional Hilbert space," the team writes. "We conjecture that a superposition forming in this higher dimensional state space would be experienced by the subject as a richer experience as compared to a superposition state forming in the lower N-dimensional Hilbert space describing the isolated brain of the subject." There is a recent blog by Google , Meet Willow, our state-of-the-art quantum chip , stating that, Google Meet Willow, our state-of-the-art quantum chip "Willow has state-of-the-art performance across a number of metrics, enabling two major achievements. The first is that Willow can reduce errors exponentially as we scale up using more qubits. This cracks a key challenge in quantum error correction that the field has pursued for almost 30 years. Second, Willow performed a standard benchmark computation in under five minutes that would take one of today’s fastest supercomputers 10 septillion (that is, 1025) years — a number that vastly exceeds the age of the Universe. The Willow chip is a major step on a journey that began over 10 years ago. As part of Google Research, our team has charted a long-term roadmap, and Willow moves us significantly along that path towards commercially relevant applications. We tested ever-larger arrays of physical qubits, scaling up from a grid of 3x3 encoded qubits, to a grid of 5x5, to a grid of 7x7 — and each time, using our latest advances in quantum error correction, we were able to cut the error rate in half. In other words, we achieved an exponential reduction in the error rate. This historic accomplishment is known in the field as “below threshold” — being able to drive errors down while scaling up the number of qubits. You must demonstrate being below threshold to show real progress on error correction, and this has been an outstanding challenge since quantum error correction was introduced by Peter Shor in 1995. There are other scientific “firsts” involved in this result as well. For example, it’s also one of the first compelling examples of real-time error correction on a superconducting quantum system — crucial for any useful computation, because if you can’t correct errors fast enough, they ruin your computation before it’s done. And it’s a "beyond breakeven" demonstration, where our arrays of qubits have longer lifetimes than the individual physical qubits do, an unfakable sign that error correction is improving the system overall."

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ការសាកល្បងជម្រៅនៃការយល់ចិត្ត AI: គោលពិន្ទុ Q3 2024

AI empātijas dziļuma pārbaude: 2024. gada 3. ceturkšņa etaloni

Тестирање на длабочините на емпатијата со вештачка интелигенција: репери од кварталот 3 2024 година

Тестирование глубины эмпатии ИИ: контрольные показатели третьего квартала 2024 года

AI empatiyasining chuqurligini sinab ko'rish: 2024 yil 3-chorak ko'rsatkichlari

OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work, a quick overview of my testing methodology should be helpful. Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario. I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product.
When I buy a new appliance, I do not read the instruction manual very thoroughly.
When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had.
I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer.


Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores.


Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too! OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work , a quick overview of my testing methodology should be helpful. Q1 benchmark work Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario . Testing the Extents of AI Empathy: A Nightmare Scenario I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. untrained and unprompted trained It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had. I am curious about life on other planets. I often forget the precise details of conversations I've had. I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too!

OpenAI o1 - Questionable Empathy

OpenAI o1 - Empatia Questoinable

OpenAI o1 - tvivlsom empati

OpenAI o1 - Empatía cuestionable

OpenAI o1 - Empathie questionnable

OpenAI o1 - 疑問の余地のない共感

OpenAI o1 - ការយល់ចិត្តដែលអាចសួរបាន។

OpenAI o1 — apšaubāma empātija

OpenAI o1 - эргэлзээтэй өрөвдөх сэтгэл

OpenAI o1 - Kwelobohloko ye e Botšišwago

OpenAI o1 - Ntwela-vusiwana lowu Vutisaka

OpenAI o1 - сумнівна емпатія

OpenAI o1 - shubhali hamdardlik

Empathy in AI: Evaluating Large Language Models for Emotional Understanding

এআই-তে সহানুভূতি: আবেগগত বোঝাপড়ার জন্য বড় ভাষার মডেলের মূল্যায়ন

Empathie in der KI: Bewertung großer Sprachmodelle zum emotionalen Verständnis

Empatía en la IA: evaluación de modelos de lenguaje grandes para la comprensión emocional

Empathie dans l'IA : évaluation de grands modèles linguistiques pour la compréhension émotionnelle

एआई में सहानुभूति: भावनात्मक समझ के लिए बड़े भाषा मॉडल का मूल्यांकन

AIにおける共感：感情理解のための大規模言語モデルの評価

AI의 공감: 정서적 이해를 위한 대규모 언어 모델 평가

Empatia em IA: avaliando grandes modelos de linguagem para compreensão emocional

Эмпатия в ИИ: оценка больших языковых моделей для эмоционального понимания

Yapay Zekada Empati: Duygusal Anlayış İçin Geniş Dil Modellerinin Değerlendirilmesi

Sự đồng cảm trong AI: Đánh giá các mô hình ngôn ngữ lớn để hiểu cảm xúc

人工智能中的同理心：评估大型语言模型以实现情感理解

Testing the Extents of AI Empathy: A Nightmare Scenario

এআই সহানুভূতির পরিধি পরীক্ষা করা: একটি দুঃস্বপ্নের দৃশ্য

Testen des Ausmaßes der KI-Empathie: Ein Albtraumszenario

Probando el alcance de la empatía con la IA: un escenario de pesadilla

Tester l’étendue de l’empathie de l’IA : un scénario de cauchemar

एआई सहानुभूति के विस्तार का परीक्षण: एक दुःस्वप्न परिदृश्य

AI 共感の程度をテストする: 悪夢のシナリオ

AI 공감의 범위 테스트: 악몽 시나리오

Testando a extensão da empatia com IA: um cenário de pesadelo

Проверка степени эмпатии ИИ: кошмарный сценарий

Yapay Zeka Empatisinin Kapsamlarını Test Etmek: Bir Kabus Senaryosu

Kiểm tra mức độ đồng cảm của AI: Kịch bản ác mộng

测试人工智能同理心的程度：噩梦场景

Designed for developers familiar with Azure and generative AI, the guide walks you through the process of harnessing the power of the DALL-E model for image generation. Introduction The Microsoft team has recently launched an Open AI challenge, in which a developer can learn how to build Azure AI solutions and apps. Prerequisite Experience working with Azure and Azure portals. An understanding of generative AI. Experience in one high-level programming language like C# or Python Steps to Create Open AI Service on Azure with “Dall-E” model deployed. Day 1 — Azure Open AI Challenge Getting Started Considering Azure Open AI Service is running on the Azure portal and the DALL-E model is deployed successfully. Step 1: Create a Console Application To test image generation, create a console application in Visual Studio or Visual Studio Code. dotnet new console Step 2: Read the Configuration Read the configuration from the appsettings.json file IConfigurationBuilder builder = new ConfigurationBuilder().AddJsonFile("appsettings.json");
 IConfigurationRoot configuration = builder.Build();
 string? aoaiEndpoint = configuration["AzureOAIEndpoint"] ?? "";
 string? aoaiKey = configuration["AzureOAIKey"] ?? ""; Step 3: Generate Console Prompt Get the user prompt on the console ready for image generation Console.Clear();
 Console.WriteLine("Enter a prompt to request an image:");
 string prompt = Console.ReadLine() ?? ""; Step 4: Call the DALL-E model using HttpClient Finally, Call the model using the HTTP client, and retrieve the blob URL from the API response as shown below. using (var client = new HttpClient())
 {
 var contentType = new MediaTypeWithQualityHeaderValue("application/json");
 var api = "openai/deployments/test-dall-e-3/images/generations?api-version=2024-02-15-preview";
 client.BaseAddress = new Uri(aoaiEndpoint);
 client.DefaultRequestHeaders.Accept.Add(contentType);
 client.DefaultRequestHeaders.Add("api-key", aoaiKey);
 var data = new
 {
 prompt = prompt,
 n = 1,
 size = "1024x1024"
 };
 
 var jsonData = JsonSerializer.Serialize(data);
 var contentData = new StringContent(jsonData, Encoding.UTF8, "application/json");
 var response = await client.PostAsync(api, contentData);
 
 // Get the revised prompt and image URL from the response
 var stringResponse = await response.Content.ReadAsStringAsync();
 JsonNode contentNode = JsonNode.Parse(stringResponse)!;
 JsonNode dataCollectionNode = contentNode!["data"];
 JsonNode dataNode = dataCollectionNode[0]!;
 JsonNode revisedPrompt = dataNode!["revised_prompt"];
 JsonNode url = dataNode!["url"];
 Console.WriteLine(revisedPrompt.ToJsonString());
 Console.WriteLine(url.ToJsonString().Replace(@"\u0026", "&"));
 
 } Test Run 1 Now, add the following prompt to the console window. aliens in Punjabi attire The console application will generate the following response and the user has to open the Blob URL in any browser. "A fascinating scene showing a group of extraterrestrial creatures donning traditional Punjabi attire. The aliens each have their own unique physical characteristics, yet their outfits are distinctively Punjabi. Imagine elaborate turbans, vibrant salwar kameez, and sparkling bangles. To put an interesting spin on the scene, some of the aliens are participating in traditional Punjabi activities like performing bhangra and carrying a jug of lassi. The background is a typical Punjabi village with interesting architectural features that seem to blend into an alien landscape."
 "<URL will be here>" Output Test Run 2 Now, add the following prompt to the console window. a cartoon character playing circket in the football field The console application will generate the following response and the user has to open the Blob URL in any browser. "A cartoon character with spiky blue hair and emerald green eyes, wearing a traditional cricket uniform consisting of a white shirt, trousers, and a red cricket cap. This character is striking a cricket ball with a willow bat in the middle of a vast, well-maintained football field. The field is vibrant green and has white line markings, with two goal posts at each end. The sky above is a cheerful cerulean with a few fluffy cumulus clouds scattered about."
 "<URL will be here>" Output Complete Code Please find below the complete code in the Program.cs file. using System.Net.Http.Headers;
 using System.Text.Json.Nodes;
 using System.Text.Json;
 using System.Text;
 using Microsoft.Extensions.Configuration;
 
 try
 {
 // Get config settings from AppSettings
 IConfigurationBuilder builder = new ConfigurationBuilder().AddJsonFile("appsettings.json");
 IConfigurationRoot configuration = builder.Build();
 string? aoaiEndpoint = configuration["AzureOAIEndpoint"] ?? "";
 string? aoaiKey = configuration["AzureOAIKey"] ?? "";
 
 // Get prompt for image to be generated
 Console.Clear();
 Console.WriteLine("Enter a prompt to request an image:");
 string prompt = Console.ReadLine() ?? "";
 
 // Call the DALL-E model
 using (var client = new HttpClient())
 {
 var contentType = new MediaTypeWithQualityHeaderValue("application/json");
 var api = "openai/deployments/test-dall-e-3/images/generations?api-version=2024-02-15-preview";
 client.BaseAddress = new Uri(aoaiEndpoint);
 client.DefaultRequestHeaders.Accept.Add(contentType);
 client.DefaultRequestHeaders.Add("api-key", aoaiKey);
 var data = new
 {
 prompt = prompt,
 n = 1,
 size = "1024x1024"
 };
 
 var jsonData = JsonSerializer.Serialize(data);
 var contentData = new StringContent(jsonData, Encoding.UTF8, "application/json");
 var response = await client.PostAsync(api, contentData);
 
 // Get the revised prompt and image URL from the response
 var stringResponse = await response.Content.ReadAsStringAsync();
 JsonNode contentNode = JsonNode.Parse(stringResponse)!;
 JsonNode dataCollectionNode = contentNode!["data"];
 JsonNode dataNode = dataCollectionNode[0]!;
 JsonNode revisedPrompt = dataNode!["revised_prompt"];
 JsonNode url = dataNode!["url"];
 Console.WriteLine(revisedPrompt.ToJsonString());
 Console.WriteLine(url.ToJsonString().Replace(@"\u0026", "&"));
 
 }
 }
 catch (Exception ex)
 {
 Console.WriteLine(ex.Message);
 } Complete Code on GitHub Make sure to give it a star on GitHub and provide feedback on how to improve the tool further..!! AzureOpenAI/samples/Azure.OpenAI.ImageGenerationExamples at main · ssukhpinder/AzureOpenAI C# Programming🚀 Thank you for being a part of the C# community! Before you leave: If you’ve made it this far, please show your appreciation with a clap and follow the author! 👏️️ Follow us: | | | | | X LinkedIn Dev.to Hashnode Newsletter Tumblr Visit our other platforms: | | | | GitHub Instagram Tiktok Quora Daily.dev Also published here

Taking the Azure Open AI Challenge: Image Generation - Day 2

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