Generative AI, a growing and prominent segment of artificial intelligence, refers to systems capable of producing content autonomously, ranging from text, images and music, to even code. Unlike traditional AI systems, which are primarily deterministic and perform tasks based on explicit rules or supervised learning, generative AI models are designed to create new data that mirrors the characteristics of their training data. This capability has profound implications, transforming industries by automating creative processes, enhancing human creativity, and opening new avenues for innovation.
According to Fortune Business Insights, the global generative AI market was valued at approximately US$ 43.87 billion in 2023. This market is projected to increase from US$ 67.18 billion in 2024 to US$ 967.65 billion by 2032. This dominance can be attributed to several factors, including the presence of key technology companies, significant investments in AI research and development, and a robust ecosystem that fosters innovation and collaboration. As businesses across various sectors increasingly adopt generative AI solutions to enhance their operations, the market is poised for unprecedented growth and transformation in the coming years.
Large language models (LLMs) are closely associated with generative AI and specifically focused on text generation and comprehension. LLMs such as OpenAI’s GPT-4 and Google’s PaLM are built on massive datasets encompassing a wide range of human knowledge. These models are trained to understand and generate human language with a high degree of coherence and fluency, making them instrumental in applications ranging from conversational agents to automated content creation.
This story is from the November 2024 edition of Open Source For You.
Start your 7-day Magzter GOLD free trial to access thousands of curated premium stories, and 9,000+ magazines and newspapers.
Already a subscriber ? Sign In
This story is from the November 2024 edition of Open Source For You.
Start your 7-day Magzter GOLD free trial to access thousands of curated premium stories, and 9,000+ magazines and newspapers.
Already a subscriber? Sign In
Amazon Bedrock: A Boon for the Financial Services Industry
Amazon Bedrock is a fully managed service that provides access to foundation models from top AI providers, enabling organisations to build and scale generative AI applications. It is specifically designed to bring AI solutions to the financial sector. Let's explore all that it can do...
Quantum-Safe VPNs: The Future of Secure Communication
As quantum computing continues to advance, it poses a significant threat to traditional cryptographic algorithms that secure our digital communications. Virtual private networks (VPNs), which rely heavily on encryption, are particularly vulnerable. Quantum-safe VPNs utilise post-quantum cryptographic algorithms to protect against quantum attacks.
Popular Open Source Toolkits for Quantum Machine Learning
Quantum machine learning is becoming increasingly popular due to its ability to solve the complex problems of the AI age. Here are a few open source libraries and frameworks that help with quantum computations.
Quantum Computing: Harnessing Open Source for Innovation and Accessibility
We explore how open source initiatives are shaping the future of quantum computing, making it more accessible and driving innovation through collaboration.
How Quantum Computing Differs from Classical Computing
Despite being in its infancy, quantum computing has numerous potential applications in modelling, cybersecurity, AI/ML, and other fields. But how do quantum and classical computing compare with each other? Let's find out...
From Bits to Qubits: The Growth Story of Quantum Computing
Quantum computing may still be in the early stages of evolution, but its potential impact on everyday life is significant. We delve into the key concepts behind it, the reasons for its rapid growth, and how global advancements are shaping its future.
Pytket: A Comprehensive Guide to Quantum Circuit Design
Pytket stands out as a powerful toolkit in the realm of quantum computing, offering a suite of features that cater to both researchers and industry practitioners. Its key strengths include optimisation, platform-agnostic support, flexible quantum circuit design and hybrid algorithm support. These features make Pytket a versatile tool for various quantum computing applications, from machine learning and cryptography to optimisation problems in industrial settings.
Cirq: The Open Source Framework for Programming Quantum Computers
Explore the key features, capabilities, and impact of Cirq, an open source quantum computing framework developed by Google, on the quantum programming landscape.
The Role of Open Source in Accelerating Quantum AI
Here's an overview of how open source frameworks are being utilised to build quantum machine learning models, including quantum neural networks and quantum kernel methods. The challenges and future directions in the quantum AI landscape are also discussed.
Quantum Machine Learning: An Overview
Quantum machine learning (QML) is a burgeoning field at the intersection of quantum computing and artificial intelligence. In recent years, the integration of quantum mechanics with machine learning algorithms has sparked substantial interest among researchers and technologists alike. Here's a quick look at the essentials of creating quantum algorithms for AI models, their practical use cases on open source platforms, and best practices for implementing these advanced algorithms.
