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Tag: Large Language Models

  • Large Language Models: A Double-Edged Sword with Bias Concerns Rising
    Artificial Intelligence

    Large Language Models: A Double-Edged Sword with Bias Concerns Rising

    by Quantum NewsDecember 18, 2024
  • AI Explains Predictions in Plain Language Now Available
    Artificial Intelligence

    AI Explains Predictions in Plain Language Now Available

    by Quantum NewsDecember 13, 2024
  • AI Models Accurately Analyze Sentiment in Green Finance Reports
    Artificial Intelligence

    AI Models Accurately Analyze Sentiment in Green Finance Reports

    by Quantum NewsDecember 11, 2024
  • Revolutionizing Student Success Prediction with Large Language Models
    Artificial Intelligence

    Revolutionizing Student Success Prediction with Large Language Models

    by Quantum NewsDecember 5, 2024
  • Optimizing Enterprise Storage for Large Language Model Deployments
    Artificial Intelligence

    Optimizing Enterprise Storage for Large Language Model Deployments

    by Quantum NewsNovember 28, 2024
  • Design Knowledge Boosts Accuracy in Large Language Models
    Artificial Intelligence

    Design Knowledge Boosts Accuracy in Large Language Models

    by Quantum NewsNovember 26, 2024
  • How Procedural Content Generation Boosts Player Engagement
    Artificial Intelligence

    How Procedural Content Generation Boosts Player Engagement

    by Quantum NewsNovember 20, 2024
  • AI-Powered Democracy: Can Large Language Models Enhance Civic Engagement?
    Artificial Intelligence

    AI-Powered Democracy: Can Large Language Models Enhance Civic Engagement?

    by Quantum NewsNovember 20, 2024
  • Unlocking Sensor Systems' Full Potential with Large Language Models
    Artificial Intelligence

    Unlocking Sensor Systems’ Full Potential with Large Language Models

    by Quantum NewsNovember 17, 2024
  • Revolutionizing Education: The Rise of Large Language Models
    Artificial Intelligence

    Revolutionizing Education: The Rise of Large Language Models

    by Quantum NewsNovember 7, 2024
  • AI Outperforms Humans in Strategic Card Games, Study Finds
    Artificial Intelligence

    AI Outperforms Humans in Strategic Card Games, Study Finds

    by Quantum NewsNovember 7, 2024
  • Teachers Play Key Role in Shaping LLM-Supported Learning Environments
    Artificial Intelligence

    Teachers Play Key Role in Shaping LLM-Supported Learning Environments

    by Quantum NewsNovember 7, 2024
  • AI + Manufacturing. A Comparison of Large Language Models (LLMs)
    Artificial Intelligence

    AI + Manufacturing. A Comparison of Large Language Models (LLMs)

    by Quantum NewsNovember 7, 2024
  • Customized AI Models Boost Mobile Intelligence by 12.7% and Reduce Memory Use
    Artificial Intelligence

    Customized AI Models Boost Mobile Intelligence by 12.7% and Reduce Memory Use

    by Quantum NewsNovember 7, 2024
  • Generative AI Revolution: Transforming Computing and Beyond
    Artificial Intelligence

    Generative AI Revolution: Transforming Computing and Beyond

    by Quantum NewsNovember 4, 2024
  • AI Tools Streamline Hospital Reporting, Enhance Healthcare Delivery
    Artificial Intelligence

    AI Tools Streamline Hospital Reporting, Enhance Healthcare Delivery

    by Quantum NewsOctober 21, 2024
  • Large Language Models Surpass Humans in Cybersecurity Knowledge
    Artificial Intelligence

    Large Language Models Surpass Humans in Cybersecurity Knowledge

    by Quantum NewsOctober 20, 2024
  • Large Language Models Vulnerable to Hacking: Experts Weigh In
    Artificial Intelligence, Quantum Security

    Large Language Models Vulnerable to Hacking: Experts Weigh In

    by Quantum NewsOctober 17, 2024
  • Revolutionizing Recommendation Systems: Hybrid Framework Boosts User Engagement
    Artificial Intelligence

    Revolutionizing Recommendation Systems: Hybrid Framework Boosts User Engagement

    by Quantum NewsOctober 16, 2024
  • What is an LLM?
    Artificial Intelligence

    What is an LLM?

    by Quantum NewsOctober 14, 2024
  • Unlocking Accurate Recommendations with LLMs in Cross-Domain Systems
    Artificial Intelligence

    Fairness Matters: LLMs’ Impact on Group Recommendation Outcomes

    by Quantum NewsOctober 8, 2024
  • Large Language Models Unlock Efficient Similarity Identification Across Diverse Domains
    Artificial Intelligence

    Large Language Models Unlock Efficient Similarity Identification Across Diverse Domains

    by Quantum NewsOctober 7, 2024
  • CityUHK Unleashes AI Power on Collaborative Innovation Network Platform
    Artificial Intelligence

    CityUHK Unleashes AI Power on Collaborative Innovation Network Platform

    by Quantum NewsOctober 2, 2024
  • Revolutionizing Speech Recognition: Large Language Models' Breakthroughs
    Artificial Intelligence

    Speech Recognition: Large Language Models’ Breakthroughs

    by Quantum NewsSeptember 30, 2024
  • Revolutionizing AI: New Framework Boosts Large Language Model Truthfulness
    Artificial Intelligence

    Revolutionizing AI: New Framework Boosts Large Language Model Truthfulness

    by Quantum NewsSeptember 28, 2024
  • Riding in Level Four AVs Feels Safe and Comfortable Study
    Artificial Intelligence

    Riding in Level Four AVs Feels Safe and Comfortable Study

    by Quantum NewsSeptember 17, 2024
  • LLMs Revolutionize Virtual Assistants with Intelligent Process Automation
    Artificial Intelligence

    LLMs Revolutionize Virtual Assistants with Intelligent Process Automation

    by Quantum NewsAugust 28, 2024
  • Fine-Tuning Large Language Models in Federated Learning Settings: A Comprehensive Solution
    Quantum Computing

    Fine-Tuning Large Language Models in Federated Learning Settings: A Comprehensive Solution

    by Quantum NewsAugust 26, 2024
  • Large Language Models Show Promise in Diagnosing Rare Diseases
    Artificial Intelligence

    Large Language Models Show Promise in Diagnosing Rare Diseases

    by Quantum NewsAugust 26, 2024
  • LLMs Bridge Gap Between Spanish and English in Financial Transactions
    Artificial Intelligence

    LLMs Bridge Gap Between Spanish and English in Financial Transactions

    by Quantum NewsAugust 26, 2024
  • Revolutionizing AI-Generated Content: Power of Retrieval-Augmented Generation Uncovered
    Artificial Intelligence

    Revolutionizing AI-Generated Content: Power of Retrieval-Augmented Generation Uncovered

    by Quantum NewsAugust 26, 2024
  • How AI Transformers Work and LLMs?
    Technology News

    How AI Transformers Work and LLMs?

    by Ivy DelaneyAugust 24, 2024
  • AI Pipeline Unlocks Medical Data with 90% Accuracy
    Artificial Intelligence

    AI Pipeline Unlocks Medical Data with 90% Accuracy

    by Quantum NewsAugust 24, 2024
  • How Large Language Models's Work?
    Artificial Intelligence

    How Large Language Models’s Work?

    by Quantum NewsAugust 22, 2024
  • How LLM's Work?
    Technology News

    How LLM’s Work?

    by Quantum NewsAugust 22, 2024
  • MIT Researchers Harness Language Models to Detect Equipment Failures
    Artificial Intelligence

    MIT Researchers Harness Language Models to Detect Equipment Failures

    by Quantum NewsAugust 14, 2024
  • AMD Acquires Silo AI to Boost Artificial Intelligence Capabilities
    Artificial Intelligence

    AMD Acquires Silo AI to Boost Artificial Intelligence Capabilities

    by Quantum NewsAugust 13, 2024
  • AI Helps Develop Safer Antibiotics to Combat Resistant Bacteria
    Artificial Intelligence

    AI Helps Develop Safer Antibiotics to Combat Resistant Bacteria

    by Quantum NewsJuly 31, 2024
  • ChatGPT Shows Promise in Detecting Deepfake Images Despite Limitations
    Artificial Intelligence

    ChatGPT Shows Promise in Detecting Deepfake Images Despite Limitations

    by Quantum NewsJune 30, 2024
  • UC Davis Team Enhances Error Detection in C Programs with LLM-Integrated Analysis
    Artificial Intelligence

    UC Davis Team Enhances Error Detection in C Programs with LLM-Integrated Analysis

    by Quantum NewsJune 25, 2024
  • Revolutionising Language Models: MatMul-Free Method Achieves High Performance with 61% Less Memory Usage
    Artificial Intelligence

    Revolutionising Language Models: MatMul-Free Method Achieves High Performance with 61% Less Memory Usage

    by Quantum NewsJune 9, 2024
  • LLMs, An Introduction to the AI Technology of 2024
    Artificial Intelligence

    LLMs, An Introduction to the AI Technology of 2024

    by Kyrlynn DMay 27, 2024
  • AI Models Like ChatGPT Could Revolutionize Systematic Reviews in Healthcare, Study Suggests
    Artificial Intelligence

    AI Models Like ChatGPT Could Revolutionize Systematic Reviews in Healthcare, Study Suggests

    by Quantum NewsMay 23, 2024
  • Zapata AI and Tech Mahindra Modernise Telecom with Quantum-Based Generative AI
    Quantum Computing Business News

    Zapata AI and Tech Mahindra Modernise Telecom with Quantum-Based Generative AI

    by Paul JamesMay 14, 2024
  • Musk's AI Model Grok Enhances Research in Aesthetic Plastic Surgery, Outperforms Rivals
    Artificial Intelligence

    Musk’s AI Model Grok Enhances Research in Aesthetic Plastic Surgery, Outperforms Rivals

    by Quantum NewsApril 26, 2024
  • Microsoft Unveils Phi-3: Powerful, Cost-Effective Small Language Models for AI Accessibility
    Artificial Intelligence

    Microsoft Unveils Phi-3: Powerful, Cost-Effective Small Language Models for AI Accessibility

    by Quantum NewsApril 24, 2024
  • Federated Learning Tackles Data Accessibility in Open-Source AI Software Engineering
    Artificial Intelligence

    Federated Learning Tackles Data Accessibility in Open-Source AI Software Engineering

    by Physics NewsApril 14, 2024
  • IBM and Spain Unite to Develop World's Leading Spanish Language AI Model
    Artificial Intelligence

    IBM and Spain Unite to Develop World’s Leading Spanish Language AI Model

    by Quantum NewsApril 12, 2024
  • Quantum Computing Enhances AI Language Processing, Promises Improved Reliability
    Artificial Intelligence, Quantum Algorithms

    Quantum Computing Enhances AI Language Processing, Promises Improved Reliability

    by Quantum NewsApril 4, 2024
  • Quantum Computing Meets AI: Researchers Explore Quantum Implementation of ChatGPT
    Artificial Intelligence

    Quantum Computing Meets AI: Researchers Explore Quantum Implementation of ChatGPT

    by Quantum NewsMarch 19, 2024
  • AI2 and AMD Unveil OLMo: A 70 Billion Parameter Open Language Model for Scientific Discovery
    Artificial Intelligence

    AI2 and AMD Unveil OLMo: A 70 Billion Parameter Open Language Model for Scientific Discovery

    by Quantum NewsFebruary 22, 2024
  • Embracing Deeptech's Full Potential Amid Global Instability in 2024
    Deep Tech, Disruptive Technology

    Embracing Deeptech’s Full Potential Amid Global Instability in 2024

    by Quantum NewsFebruary 19, 2024
  • PRESTO Framework Maps Multiverse of Machine Learning Models, Enhancing Reliability and Robustness
    Machine Learning

    PRESTO Framework Maps Multiverse of Machine Learning Models, Enhancing Reliability and Robustness

    by Ivy DelaneyFebruary 7, 2024
  • Quantum Leap in Privacy: Quantum Neural Networks Utilize PATE for Secure Machine Learning
    Machine Learning

    Quantum Leap in Privacy: Quantum Neural Networks Utilize PATE for Secure Machine Learning

    by Quantum NewsFebruary 6, 2024
  • China Approves Over 40 AI Models in Six Months, Aiming to Rival U.S. in AI Development
    Artificial Intelligence

    China Approves Over 40 AI Models in Six Months, Aiming to Rival U.S. in AI Development

    by Quantum NewsJanuary 31, 2024
  • How We Achieved Our 2024 Performance Target of #AQ 35 January 25, 2024 by IonQ Staff Today, we announced that IonQ Forte, our flagship, commercially available quantum computer, successfully passed the #AQ 35 benchmark suite. #AQ 35 was IonQ’s technical target for 2024 and we are excited to have achieved it a year ahead of schedule. This blog post will dive into the technical progress made since we achieved #AQ 29 seven months early in June 2023. IonQ Performance Roadmap IonQ’s goal is to build quantum computers that can deliver value to our customers by successfully executing the applications they care about. As such, our roadmap is based on achieving higher and higher performance on an application based benchmark that is representative of the most promising, commercial algorithmic approaches. When we shared our performance roadmap for the first time, no such application based benchmark existed - and so IonQ built on the work of the largest quantum industry consortium, the QED-C - to develop a benchmark called Algorithmic Qubits (#AQ). Since then, we have been laser focused on optimizing across the entire quantum computing stack to attain the ambitious targets laid out in our roadmap, knowing that the higher the #AQ a system offers, the more commercial value we can deliver to our partners and customers. IonQ Forte, which was made commercially available for the first time at #AQ 29, brings us one step closer to the era of enterprise-grade quantum computing - where quantum computers can offer value to enterprises investing in quantum application development. IonQ hardware and software roadmap IonQ hardware and software roadmap IonQ Forte, an Innovative Approach to Trapped Ion Quantum Computing When we announced Forte in 2022, one of its key differentiating features was software reconfigurability. By this, we meant that we can dynamically reconfigure elements in Forte instead of requiring fabrication in other architectures. For example, qubit count can be tuned via a flexible surface ion trap, and ions at different locations can be addressed dynamically via Forte’s acousto-optic deflectors (AODs), which enable the precise steering of control lasers. As such, Forte was designed from the beginning to be able to push the boundaries of performance through a combination of hardware and software upgrades. First with #AQ 29, and now with #AQ 35, IonQ is positioning Forte as a system that is capable of running wider circuits while maintaining high gate fidelity and all-to-all qubit connectivity. At IonQ, when we think about performance of a quantum computer, we think in terms of the end-to-end customer experience. We want to make sure that applications are optimized for our hardware, that the hardware delivers our expected high performance, and that appropriate error mitigation is applied to provide maximum value. This means that performance naturally breaks into three different factors: Application optimization: First, we make sure that the application of interest is implemented using a best-in-class algorithm for the problem a customer is trying to solve, and that it is compiled as efficiently as possible for our unique machine hardware. This factor is all about making sure that the application is expressed as efficiently as possible. For example, if we can use a classical computer to efficiently reduce the number of two qubit gates that are performed to do an equivalent quantum circuit, this can lead to dramatically better performance. Another factor that makes a huge difference here is the connectivity of the quantum processor: quantum computers in which more qubits can be coupled together support more efficient application compilation than those with more limited connectivities. Hardware optimization: Once the application is as efficient as we know how to make it, we need to run it on quantum hardware. This factor is all about getting as much raw performance from the hardware as possible, and is frequently characterized in terms of the number of physical qubits and single- and two-qubit gate fidelities. Gate fidelities aren’t the whole story though: we also care about unintentional qubit interactions (crosstalk), time drift of the calibration state of the machine, and decoherence times. At IonQ, we employ advanced techniques in the design and operation of our systems to improve the hardware performance. For example, we use diagnostic characterization to figure out where the problems lie, and fix them via combinations of hardware improvements, pulse-level control optimization, and gate-level techniques like dynamical decoupling. Error mitigation: Now that we’ve done as much as we can optimizing the application and hardware, we still have one step left to deliver the most customer value. Error mitigation is a broad term referring to data processing techniques that can boost the effective performance of noisy quantum circuit operations. Some error mitigation techniques such as zero noise extrapolation, scale exponentially in the number of experimental samples. Others, such as sharpening (also known as plurality voting) do not require additional samples, but can degrade performance of some circuits. While error mitigation does not scale indefinitely, it can extend the reach of today’s quantum processors considerably, and as stated before, we want to be able to provide our customers maximum value. Improvements along any of these three factors can move the needle on realized customer value and IonQ’s strategy for attaining our #AQ targets has always been anchored in improvements across all three factors. Understanding what factor is driving increases can help our customers better understand how #AQ improvements will translate to a specific application. Application based benchmarking is unique in its ability to reward improvements across the entire quantum computing stack - which is exactly how our customers will experience our quantum computers. IonQ #AQ scaling strategy relies on optimization across the entire quantum computing stack. IonQ #AQ scaling strategy relies on optimization across the entire quantum computing stack. Pushing IonQ Forte From #AQ 29 to #AQ 35 In the case of our #AQ 35 announcement, two critical factors contributed: increased qubits (hardware optimization) and more efficient compilation (application optimization). Let’s dive into each of these now. IonQ Forte underwent two main hardware optimizations to improve performance and scale. First, the qubit count was increased from 30 to 36. Thanks to optimizations made to the configurable AOD, these additional qubits were added without impacting gate fidelities or connectivity. This is a huge win, since the computational state space of the original 30 qubits has dimension about 1 billion, while 36 qubits has dimension over 68 billion. Second, new detection optics were designed and installed to accurately image and measure the longer qubit chain. But hardware enhancements are only one part of the story: we also boosted the performance of our compiler. In our #AQ 29 results, we were depth-limited by two circuit families in the #AQ repository: Monte Carlo (MC) and Amplitude Estimation (AE) circuits. To pass #AQ 35, we need to pass the MC and AE benchmark circuits on 7 qubits, which in their Qiskit specification require 982 and 868 two-qubit gates, respectively. The compiler used to pass #AQ 29 optimized these circuits to reduce the MC gates by 46% and the AE gates by 32%. More recently, by applying novel compilation strategies that efficiently search for repeated small-qubit blocks within a circuit, the #AQ 35 compiler reduced the two-qubit gate counts for MC by 97% to just 26 gates and by 95% to 36 gates for AE. It’s important to note that, even after extensive compiler optimization, we still need to run deep circuits. In particular, our AQ #35 volume is bounded by Phase Estimation on 35 qubits (243 two-qubit gates), Hamiltonian Simulation on 35 qubits (306 two-qubit gates), and Quantum Fourier Transform on 26 qubits (335 two-qubit gates). Forte’s hardware and software optimizations increased the system’s #AQ from 29 to 35. Forte’s hardware and software optimizations increased the system’s #AQ from 29 to 35. The #AQ 35 compiler is highly effective on the MC and AE circuits and we expect that additional circuits run by our customers will benefit as well from this compilation innovation. While we do not intend to suggest that all applications will scale through the #AQ 35 compiler alone, these results are proof that detailed application optimization can be beneficial and are worth continued investment. We are excited to announce that these specific optimizations will soon be turned on for IonQ’s default cloud compiler, so customers can take advantage of these performance gains with no additional cost or effort. The Path Forward and #AQ 64 #AQ was launched with a vision of evolution, ensuring the benchmark remains a proxy for customer value. #AQ was launched with a vision of evolution, ensuring the benchmark remains a proxy for customer value. We believe application-based benchmarks are the best way to understand how a system will perform against actual problems, and therefore we think they are the most important, and useful, way to benchmark systems. To ensure we are putting real world customer problems at the center of our research and development and product strategy, we will continue to invest in application-based benchmarks. We plan to release a technical manuscript describing more of the details of our #AQ 35 achievement in the next couple of months. While we continue to work in an open and collaborative way to help our customers benchmark system performance, our eyes are fixed on the next target on our hardware roadmap: #AQ 64. We believe #AQ 64 will be a turning point for the industry, where circuits that are large enough and complex enough to create commercial value are able to be successfully executed on IonQ hardware. The attainment of #AQ 64 will be dependent on hardware optimizations driven by our transition to Barium qubits and the deployment of reconfigurable multi-core quantum architectures enabled by new IonQ trap technology. In addition, software optimization and error mitigation will continue to improve algorithmic results as we work to unlock even more commercial value for our customers. While we are excited about reaching our technical target for 2024 a year ahead of schedule, we are committed to remaining ambitious in our hardware, software and application development for the rest of 2024. We hope you will join us on our journey by getting access to IonQ Forte to solve your problems, subscribing to our email list, or exploring job opportunities.
    Artificial Intelligence

    Northwestern University Team Boosts AI Language Understanding with BERT Integration

    by SchrödingerJanuary 26, 2024
  • Johns Hopkins Researchers Tackle Errors in Robot Programming with Large Language Models
    Technology News

    Johns Hopkins Researchers Tackle Errors in Robot Programming with Large Language Models

    by Quantum NewsJanuary 25, 2024
  • Alibaba Cloud Develops AutoPCF: A Revolutionary Framework for Automated Carbon Footprint Estimation
    Artificial Intelligence

    Alibaba Cloud Develops AutoPCF: A Revolutionary Framework for Automated Carbon Footprint Estimation

    by Quantum NewsJanuary 25, 2024
  • Carnegie Mellon Team Explores Integration of AI Subdisciplines for Intelligent Behavior
    Artificial Intelligence

    Carnegie Mellon Team Explores Integration of AI Subdisciplines for Intelligent Behavior

    by Quantum NewsJanuary 25, 2024
  • Multiverse Computing Shortlisted for 'Future Unicorn' Status Among Europe's Top Tech Scale-ups
    Quantum Funding

    Multiverse Computing Shortlisted for ‘Future Unicorn’ Status Among Europe’s Top Tech Scale-ups

    by Quantum NewsJanuary 19, 2024
  • Qrypt Joins NVIDIA Inception to Fortify AI Data Security with Quantum-Secure Encryption
    Quantum Computing Business News, Quantum Security

    Qrypt Joins NVIDIA Inception to Fortify AI Data Security with Quantum-Secure Encryption

    by The Quantum MechanicJanuary 11, 2024
  • NVIDIA's BioNeMo Boosts AI-Driven Drug Discovery on Amazon Web Services
    Artificial Intelligence

    NVIDIA’s BioNeMo Boosts AI-Driven Drug Discovery on Amazon Web Services

    by The Quantum MechanicJanuary 3, 2024
  • Deep Mind Pioneer's use of AI and LLMs to make Discoveries in Mathematics with FunSearch
    Artificial Intelligence

    Deep Mind Pioneers use AI and LLMs to make Discoveries in Mathematics with FunSearch

    by SchrödingerDecember 15, 2023

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