Why Generative AI is trending these days?

1. Technological Advancements: Recent breakthroughs in AI architectures, such as GPT-4 and other large language models, have significantly improved the capabilities of generative AI, making it more powerful and versatile.

2. Wide Range of Applications: Generative AI can be used for diverse tasks, including content creation (text, images, music), code generation, chatbots, personalized recommendations, and more, which broadens its appeal across various industries.

3. Increased Accessibility: Cloud services and AI platforms have made advanced AI tools more accessible to developers, businesses, and even hobbyists, lowering the barrier to entry.

4. Business Value: Companies are recognizing the potential for generative AI to drive innovation, improve efficiency, and create new products and services, leading to increased investment and adoption.

5. Enhanced User Experience: Generative AI can provide highly personalized and engaging user experiences, which is highly valued in areas like marketing, customer service, and entertainment.

6. Media and Public Interest: The impressive capabilities of generative AI, such as creating human-like text and realistic images, capture public imagination and media attention, contributing to its trendiness.

7. Open Source and Community Efforts: The open-source movement and collaborative research communities have accelerated the development and dissemination of generative AI technologies.

Scientists use generative AI to answer complex questions in physics

Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.

Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.

Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.


Source-Link: MIT

Generative AI and LLMs for Dummies
David Baum, 2024

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Inside Generative AI
Rick Spair, 2024

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

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How Coders Can Survive—and Thrive—in a ChatGPT World

Artificial intelligence, particularly generative AI powered by large language models (LLMs), could upend many coders’ livelihoods. But some experts argue that AI won’t replace human programmers—not immediately, at least.

“You will have to worry about people who are using AI replacing you,” says Tanishq Mathew Abraham, a recent Ph.D. in biomedical engineering at the University of California, Davis and the CEO of medical AI research center MedARC.

Here are some tips and techniques for coders to survive and thrive in a generative AI world.

Stick to Basics and Best Practices
While the myriad AI-based coding assistants could help with code completion and code generation, the fundamentals of programming remain: the ability to read and reason about your own and others’ code, and understanding how the code you write fits into a larger system.

Find the Tool That Fits Your Needs
Finding the right AI-based tool is essential. Each tool has its own ways to interact with it, and there are different ways to incorporate each tool into your development workflow—whether that’s automating the creation of unit tests, generating test data, or writing documentation.

Clear and Precise Conversations Are Crucial
When using AI coding assistants, be detailed about what you need and view it as an iterative process. Abraham proposes writing a comment that explains the code you want so the assistant can generate relevant suggestions that meet your requirements.

Be Critical and Understand the Risks
Software engineers should be critical of the outputs of large language models, as they tend to hallucinate and produce inaccurate or incorrect code. “It’s easy to get stuck in a debugging rabbit hole when blindly using AI-generated code, and subtle bugs can be difficult to spot,” Vaithilingam says.

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Nvidia delays next gen AI chip as investors issue ‘bubble’ warning

Nvidia highly anticipated “Blackwell” B-200 artificial intelligence chip will reportedly be delayed, sending the near-term future of the entire AI industry into a state of uncertainty.

Tech news outlet The Information claims that a Microsoft employee and at least two other people familiar with the situation have stated that the new chip’s launch date has been pushed back by at least three months due to a design flaw.

While Nvidia hadn’t given a public launch date, CEO Jensen Huang recently announced that the company would begin sending engineering samples “this week” on July 31 at the SIGGRAPH event in Denver, Colorado.

Source-Link : MSN

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