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Neural Networks News
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Predicting Links in Graphs with Graph Neural Networks and DGL.ai
- Link prediction is one of the fundamental tasks in graph analytics, involving the prediction of connections (or links) between nodes using Graph Neural Networks (GNNs). Constructing GNNs is made easier with Deep Graph Library (DGL.ai).
- We learn how to set up a project, preprocess data, build a model, and evaluate it for link prediction on the Twitch Social Network dataset from the Stanford Network Analysis Project (SNAP).
- GraphSAGE is specifically designed for GNNs to obtain node embeddings that capture both the structure and features of each node within the graph. Using GraphSAGE, we set up a three-convolutional-layer model with dropout enabled after each node feature update and a subsequent MLP predictor that outputs a probability.
- To reduce overfitting, we use binary cross-entropy with logits as the loss function and AUC as the metric to evaluate the model.
- We generate predictions for all possible pairs of nodes, allowing us to identify potential new connections and their probabilities.
- By using a relatively small dataset and DGL.ai, we show an effective way to build a link prediction model for graphs. As graphs scale up to millions or billions of nodes and edges, handling them requires more advanced solutions.
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Exploring the Differences: AI, Machine Learning, Deep Learning, and Neural Networks
- AI is the broad field that focuses on creating intelligent machines that can perform tasks that require human-like intelligence.
- Machine learning (ML) is a technique that allows computers to learn from data without being explicitly programmed for each task.
- Deep learning (DL) is a specialized type of machine learning that uses complex structures called neural networks.
- Neural networks are specific models designed to mimic how our brains work.
- Deep learning models excel at complex tasks such as speech recognition and image classification.
- Deep learning has revolutionized the way we analyze large data sets, opening up new possibilities in a variety of fields.
- The more data a deep learning model is given the more accurate it becomes at recognizing patterns and making predictions.
- Traditional neural networks may struggle to handle large data sets, however, deep learning models excel in processing large amounts of data efficiently.
- Training a deep learning model requires a lot of data and resources, while traditional neural networks can be trained faster with smaller datasets.
- AI is the big picture of intelligent machines, while neural networks and deep learning are the smart tools that focus on interpreting and processing data.
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Medium
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The Cycles of AI Winters: A Historical Analysis and Modern Perspective
- AI has gone through periods called 'winters', where research and development have slowed down due to oversize expectations and limitations of technology.
- AI winters are periods marked by cooling off of support for AI technologies.
- The first major AI winter came from oversize expectations about what early AI technologies could achieve.
- A 1966 report, called the ALPAC report, was a principal catalyst for the first AI winter, as it detailed that machine translation was no better than human translation at the time.
- Early AI researchers promised they could solve problems that couldn't be met with the technology of their era.
- The AI winters have led to huge research funding cuts by governments and corporations.
- Research on Natural Language Processing projects, Computer Vision, and General Problem Solving programmes were scaled back considerably in the AI winters.
- The AI winter has affected industries and researchers, who rebranded their work using less ambitious terms.
- Modern deep learning systems today face challenges that echo past concerns in AI development.
- To avoid another AI winter, the AI community has to strike a balance between what it aspires to and what is functionally possible.
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Training the Time Traveler: A Comprehensive Guide to RNNs
- Recurrent Neural Network (RNN) is one of the simplest neural network models for sequence data, including speech and text data.
- RNNs are foundational sequence models in deep learning, ideal for those looking to master handling sequential data such as text, speech, and stock prices.
- We’ll be training a single-layer RNN for a binary classification task.
- Forward propagation is the process of passing the input through a neural network to calculate the output.
- Backpropagation Through Time (BPTT) is the reverse of forward propagation in recurrent neural networks, where gradients of the loss function are calculated and propagated backward through each time step of the input sequence.
- The input could be as simple as a one-hot encoded vector, representing a word.
- The goal is to compute the gradient of loss function w.r.t all the learnable parameters of the neural network, i-e: weights and biases.
- The M input examples can be incorporated into matrices for efficient computation.
- In the vectorized approach all the input examples will be fed into the RNN simultaneously, however, the items of the sequences will be processed sequentially.
- We’ll benefit from a temporary variable P, to store our intermediate results.
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Medium
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Neural Style Transfer in Python
- Neural Style Transfer (NST) is a process that uses CNNs to blend the content and style of two images.
- VGG-19, a 19-layer deep CNN, is commonly used in NST. Its pre-trained model parameters are frozen.
- Activation features of the content and style images are extracted using specific layers of the VGG-19 model.
- Gram matrix, capturing relationships between feature maps, is used to represent style elements like color and texture.
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Neuromorphic Computing: A Brain-Inspired Revolution in Processing
- Neuromorphic computing is a paradigm shift in processing inspired by the human brain.
- It utilizes artificial neurons, synapses, and spiking neural networks (SNNs) for efficient and adaptive processing.
- SNNs encode information in the timing of spikes, making them suitable for time-series data processing.
- Neuromorphic computing has potential applications in healthcare, aviation, and other fields.
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The Anatomy of a Neuron
- An artificial neuron could be constructed as a physical analogue device in which the input weights were rheostats and with the sigmoid function as a circuit made up of transistors, resistors, capacitors, and perhaps even inductors.
- The precision and accuracy of physical rheostats are insufficient for most applications, so artificial neural networks are usually implemented as digital simulations.
- The complete neuron comprises the weighted inputs summation function and the sigmoid transfer function.
- The simulated neuron function can be used to build a complete multi-layer perceptron.
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Retired Augmented Generation
- Retrieval augmented generation (RAG) is a hybrid approach in AI language models that combines generative and retrieval-based methods.
- RAG combines the ability of generative models to create coherent text with the precision of retrieval systems that access a vast database of pre-existing knowledge.
- Unlike standalone language models, RAG models can query external sources in real-time to enhance the accuracy and contextuality of their responses.
- RAG is proving to be a pivotal development in areas such as content generation, customer service, and research.
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Medium
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The Dawn of Artificial Intelligence: From Ancient Dreams to Mathematical Foundations
- Human desire to replicate artificial life seems across civilizations.
- Ramon Llull’s mechanical reasoning system used rotating wheels with logical propositions.
- George Boole’s revolutionary work proposed that human reasoning could be reduced to algebraic operations.
- Gottlob Frege’s “Begriffsschrift” introduced predicate logic, providing a formal system for expressing complex logical relationships.
- Turing’s concept of computability was revolutionary.
- Turing’s famous 1950 paper proposed what we now call the Turing Test.
- As Claude Shannon did in his work information itself was quantified.
- McCulloch and Pitts proffered the first convincing demonstration that neural networks could perform logical operations.
- Their work suggests that intelligence may actually be more like a spectrum built around a binary human versus machine divide.
- The debate continues: or can we actually replicate human thought, or are we actually creating something completely new but equally valuable?
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From Brain to Neural Networks: Geoffrey Hinton and the Nobel Shaking Up Science
- Geoffrey Hinton's Nobel Prize in Physics underscores a fascinating intersection between physics and AI.
- Hinton’s models draw heavily on principles of statistical physics to improve learning efficiency in neural networks.
- The prize now acknowledges AI as a field that bridges traditional sciences, highlighting an interdisciplinary future where physics, AI, and other scientific fields may collaboratively tackle complex global challenges.
- Hinton’s work has unwittingly revived the age-old debate: is AI more at home in the realms of computer science or physics?
- At its core, Hinton’s pioneering neural networks rely on principles borrowed from statistical mechanics.
- Hinton’s foundational techniques include backpropagation and Boltzmann machines, which turned raw computational power into genuine learning ability.
- Hinton's concerns touch on the ultimate question: What happens when machines get too smart for their own good — or ours?
- Hinton’s ethical stance signals a new era in AI development, one that insists on a balanced approach between innovation and regulation.
- Hinton’s Nobel Prize in Physics sends a profound message to the scientific community: AI research isn’t just about creating clever algorithms.
- Interdisciplinary research could soon become the standard, allowing AI to act as a bridge between the “traditional” and “experimental” sciences.
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Pyimagesearch
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NeRFs Explained: Goodbye Photogrammetry?
- Neural Radiance Fields (NeRFs) removes many of the geometric concepts needed in 3D Reconstruction, particularly in Photogrammetry.
- NeRFs estimate the light, density and color of every point in the air using 3 blocks: input, neural net and rendering.
- Block A: In NeRFs, we capture the scene from multiple viewpoints and generate rays for every pixel of each image.
- Block B: A simple multi-layer perceptron Neural Network in NeRFs regresses the color and density of every point of every ray.
- Block C: To accurately render a 3D scene via volumetric rendering, NeRFs first removes points belonging to the 'air', and then for every point of the ray, learns whether it hits an object, how dense it is, and what’s its color.
- Since its first introduction, there have been multiple versions of NeRFs, from Tiny-NeRFs, to KiloNeRFs and others, making it faster and better resolution.
- Neural Sparse Voxel Fields use voxel-ray rendering instead of regular light ray, making NeRFs 10x faster than before.
- KiloNeRF uses thousands of mini-MLPs, rather than calling one MLP a million times making it 2,500x faster than NeRFs while keeping the same resolution.
- NeRFs have a lot of compute, so it's an offline approach, mainly used for photographing an object and spending around 30+ minutes on its reconstruction.
- NeRFs are pretty effective for generating 3D reconstruction using Deep Learning, but the process can be made faster and easier with newer algorithms such as Gaussian Splatting.
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Medium
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Neural Machine Translation: Unveiling the Encoder-Decoder Architecture
- Neural Machine Translation employs deep learning techniques, utilizing extensive datasets of translated sentences to train models capable of translating between various languages.
- The Encoder-Decoder structure is a traditional and well-established version of NMT, consisting of two recurrent neural networks (RNN) that work together to form a translation model.
- The encoder processes the input sequence to generate a set of context vectors, which are then used by the decoder to produce an output sequence.
- The incorporation of attention mechanisms in the encoder-decoder architecture enables the model to focus on specific parts of the input for better translation accuracy, especially in longer sentences.
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My Machine Learning Journey: Perfect Roadmap for Beginners
- Learning through practical challenges and building projects is an effective approach for mastering machine learning.
- For ML engineering roles, deep math expertise may not always be necessary; focusing on engineering skills is valuable.
- Python is essential, and understanding core algorithms and supervised learning algorithms is recommended.
- Deploying projects, using frameworks like AWS or Azure, and clarifying career goals are essential steps to excel in ML engineering.
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Medium
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Is AI Just Math?
- AI is not just math, it is an oversimplification.
- Neural networks, a key aspect of AI, rely on mathematical techniques like statistical analysis and probability.
- However, neural networks are not solely based on statistical mathematics and do not explicitly model probability distributions.
- Neural networks can be viewed as graphs, but they do not heavily rely on graph theory or graphical modeling.
- Mathematical optimization techniques play a role in deep learning but are not central to the representation or inference aspects of AI.
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Medium
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Human Cognition vs. AI: How Our Minds and Machines Process Creativity Differently
- Human cognition and AI, specifically large language models like ChatGPT, differ in processing creative tasks.
- Humans are constrained by attentional limits and rely on sequential processing, while AI can process multiple ideas simultaneously through parallel computation.
- AI's parallel processing allows for efficient generation of diverse creative outputs, enhancing efficiency in creative industries.
- However, human creativity adds depth, emotion, and contextual understanding that AI currently lacks.
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