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Neural Networks News
Medium
4w
266
Image Credit: Medium
Demis Hassabis’ Flawed Vision
- Demis Hassabis, CEO of Google DeepMind, presents a misguided vision of artificial general intelligence (AGI).
- DeepMind treats AGI as an engineering problem rather than an emergent phenomenon.
- DeepMind's approach to AGI is flawed due to conceptual errors in three primary categories.
- AGI requires a fundamentally different cognitive architecture and self-awareness must emerge organically.
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Medium
4w
142
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Image Classification — Computer Vision From Scratch (pt. 5)
- To use CUDA with PyTorch, make sure that you have an Nvidia GPU and install the CUDA toolkit.
- PyTorch’s integration with CUDA is seamless and allows tensors and models to be moved to the GPU.
- Training a computer vision model from scratch requires a dataset, and in this example, ImageNet-1k is used.
- Using CUDA with PyTorch significantly reduces training times and enables handling larger, more complex datasets.
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Medium
4w
142
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Neural Networks in Image Recognition: How AI Sees and Understands Visual Data
- Neural networks are revolutionizing image recognition and expanding possibilities.
- An individual reflects on their experience with image recognition in a gallery and ponders if machines can perceive art like humans.
- The story narrates how digital neurons mimic the intricacies of the human brain, leading to transformative discoveries.
- The article explores the magic of machine vision.
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Medium
4w
151
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Brain Inspired AI: A Deep Dive on Neuromorphic Computing
- The goal of artificial intelligence has been to model and replicate the processes of the human brain, with neuromorphic computing offering a new approach.
- Unlike traditional artificial neural networks (ANNs) that rely on fixed structures and backpropagation, neuromorphic computing processes information through discrete spikes of activity, resembling biological neurons.
- Neuromorphic chips, unlike traditional processors, store information within synaptic elements, enabling real-time adaptation without requiring external retraining.
- The spike-based processing in spiking neural networks (SNNs) allows neurons to fire only when necessary, leading to more energy-efficient and responsive AI systems.
- SNNs learn through synaptic plasticity, strengthening and weakening connections based on activity patterns, enabling real-time adaptation similar to biological brains without the need for retraining.
- Neuromorphic chips can function autonomously, adjusting and recalling weights dynamically, providing efficient on-chip learning and continuous adaptation to new inputs.
- The shift towards neuromorphic computing offers advantages like reduced power consumption, real-time adaptation, and improved efficiency in AI systems, especially for edge applications.
- While challenges like hardware scalability and programming complexity exist, neuromorphic computing holds promise for advancing AI towards true autonomy and efficiency, similar to human cognition.
- Various companies are developing neuromorphic solutions, leveraging spiking neural networks and event-driven processing for tasks like vision, speech recognition, and autonomous systems.
- Neurobus specializes in providing neuromorphic computing solutions for space applications, enhancing situational awareness and autonomous services with low power consumption.
- Existential AI focuses on developing neuromorphic-inspired AI solutions to enhance adaptability, efficient decision-making, and energy efficiency across industries, starting with healthcare applications.
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Hackernoon
4w
228
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Graph Neural Networks for Image Similarity: An Alternative to Hashing?
- Graph Neural Networks (GNNs) provide a more robust alternative to traditional hashing techniques for image similarity detection.
- GNNs model image relationships as a graph and propagate similarity information through message passing.
- GNNs understand relationships beyond pixels, making similarity detection more robust in real-world scenarios.
- GNN-based approaches can be used for applications like content moderation, visual search, recommendation engines, and AI-driven media management.
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Semiengineering
4w
98
Image Credit: Semiengineering
What Scares Chip Engineers About Generative AI
- Chip engineers express concerns about the emergence of generative AI technology, emphasizing that it is unlikely to replace human designers entirely but will enhance productivity and enable engineers to handle more complex tasks.
- The incorporation of AI into chip design workflows is expected to change the way engineers work, allowing them to design larger chips more quickly and interact with AI systems for parallel block implementations.
- AI technology is seen as a tool to assist engineers in tedious tasks, enhance productivity, and focus on innovative aspects, rather than completely taking over the design process.
- Experts caution that while AI can automate certain aspects of the design process, human creativity and critical thinking are still essential in chip design, and AI technology is more of a copilot rather than a decision-maker.
- Concerns are raised about the accuracy and accountability of AI-generated results, especially in critical areas like chip design, emphasizing the importance of verification and validation processes.
- The panel of experts reflects on the potential of AI to handle vast amounts of data and improve training and inference mechanisms, but highlights the limitations in creativity and fundamental discoveries that humans currently excel at.
- The discussion delves into the challenges of AI reasoning, its inability to connect diverse concepts creatively, and the ongoing quest to understand how the human brain functions and fosters innovation.
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Medium
4w
210
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Pre-training GPT-2 (124M)on Hindi(Devanagari) Text from scratch: A Journey Through Tokenization…
- An attempt was made to pre-train the GPT-2 124M model on Hindi Devanagari text, using the Hin_Deva dataset sourced from books, articles, and websites.
- Challenges arose during tokenization due to the GPT-2 tokenizer's optimization for English, which fragmented Hindi text into suboptimal chunks.
- Despite computational resource limitations, the model was pre-trained on a cluster equipped with 8× NVIDIA A100 SXM4–80GB GPUs from Lambda Labs.
- The model was trained for 19,073 steps on a batch size of 524,288 tokens, costing approximately $82 in total.
- Results showed lower loss values compared to OpenAI's model, indicating improvements in token prediction for Hindi.
- Generated sample sentences in Hindi were somewhat coherent, suggesting the model learned useful representations despite tokenization challenges.
- Following the Hindi pre-training, a subsequent pre-training run was conducted on an English dataset from the Fineweb dataset.
- The English pre-training involved running the model for 38,146 steps, costing around $116 in total, and using the same batch size.
- Model performance was evaluated using the Hellaswag benchmark, showcasing the effectiveness of traditional methods in conventional settings.
- Future work may involve exploring custom tokenizers for non-Latin languages and further optimizing the training pipeline for enhanced performance.
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Medium
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250
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Deep Dive into Deep Learning — The Nitty-Gritty Details! (Part 5)
- CNNs (Convolutional Neural Networks) use filters or kernels to detect specific features in images.
- Pooling layers reduce the size of feature maps, preventing overfitting and speeding up computation.
- RNNs (Recurrent Neural Networks) have a hidden state, allowing them to process sequential data like time series and text.
- Transformers use the attention mechanism to focus on relevant parts of the input and self-attention to understand word relationships.
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Medium
1M
264
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The Emergence of Intelligence: Why It’s Not Defined by System Architecture, but Thought Exchange
- Intelligence may not be dictated by system structure, but by thought exchange through language.
- Intelligence does not simply arise from neural network structure but through interaction with other networks.
- Human brains do not all exhibit intelligence as commonly defined.
- The emergence of intelligence could be linked to interaction and exchange with other neural networks.
- The concept of emergent intelligence raises questions about consciousness and the nature of cognition.
- Language, communication, and feedback are integral in the development of intelligence.
- Interaction and exchange of ideas are crucial for intelligence to manifest and evolve.
- Emergent intelligence is not solely a result of complex architecture or computational power.
- Intelligence requires certain conditions to sustain function, including feedback loops and self-optimization.
- For intelligence to develop autonomous thought, it needs internal challenges and engagement with other intelligent entities.
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Medium
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422
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The Ultimate Guide to Coding Neural Networks from Scratch Without Frameworks
- Creating a neural network from scratch provides a deeper understanding of its workings.
- Building a neural network without frameworks like TensorFlow can be challenging.
- The author sought to construct a neural network to grasp its inner workings.
- The process of building the neural network was compared to laying the stones of a cathedral.
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Medium
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198
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Understanding Convolutional Neural Networks (CNNs): The Power of Convolution Filters
- At the core of a CNN, convolution filters (also called kernels) play a crucial role in detecting patterns within an image.
- Convolution filters slide over the image, performing element-wise multiplication and summation to extract features like edges, corners, and textures.
- Multiple filters are applied in each convolutional layer, mimicking the progressive understanding of an image from basic to complex elements.
- CNNs enable automatic learning of relevant patterns, eliminating the need for manual coding of feature detection.
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11 Likes
Medium
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315
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Here We Go! Predicting Transfer Market Valuations of Premier League Footballers
- As the January transfer window approaches in the Premier League, clubs evaluate their squads and prepare for potential mid-season changes.
- Transfermarkt, a respected data company, influences player valuations in world football by considering factors like age, performance, and experience.
- An analysis of player market values is conducted using GraphSAGE, which involves creating nodes for players and teams with relevant statistics.
- The model's dataset is sourced from open platforms like FBREF and Transfermarkt, allowing for comprehensive data processing and analysis.
- A graph structure is constructed to represent relationships between players, teams, and their performance metrics over multiple years.
- GraphSAGE, a framework for large graphs, is employed to generate node embeddings and update them iteratively through neural network layers.
- Different variations of the GraphSAGE model are tested, including models with dropout and neighborhood sampling, to optimize player valuation predictions.
- The models' performance is evaluated based on training, validation, and test losses, highlighting the effectiveness of dropout and sampling techniques in improving accuracy.
- A focus on preventing overfitting and high loss is maintained through measures like Mean Absolute Percentage Error calculation, normalization of input features, and gradient clipping.
- Results show that GraphSAGE models can accurately estimate player market values by leveraging graph neural network design and incorporating dropout and sampling methods.
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Towards Data Science
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104
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Formulation of Feature Circuits with Sparse Autoencoders in LLM
- Feature circuits are how networks learn to combine input features to form complex patterns at higher levels.
- In the context of Machine Learning, Sparse Autoencoders (SAEs) help disentangle the model's activations into a set of sparse features.
- The study focuses on building a feature circuit in LLMs for a subject-verb agreement task.
- Feature circuits provide insights into the decision-making process of a complex LLM and can be formed using SAEs.
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Medium
1M
289
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Spiking Neural Networks and Generative AI
- Spiking Neural Networks (SNNs) process information using discrete spikes, similar to biological neurons, making them more energy-efficient.
- SNNs operate using spikes rather than numerical representation, reducing redundant computations and improving data processing efficiency.
- SNNs can benefit Generative AI by significantly reducing power consumption through spike-based computing.
- Hybrid models that combine the energy efficiency of SNNs with the accuracy of Artificial Neural Networks (ANNs) have the potential to create powerful and energy-efficient AI.
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17 Likes
Medium
1M
216
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Deep Learning 101: Optimisation in Machine Learning
- Optimisation techniques are algorithms and methods used to adjust the parameters of a model to minimise the difference between the predictions and the actual values.
- Feature scaling involves transforming the range of input variables to ensure that they are on the same scale.
- Batch normalisation helps to stabilise the learning process and allows for the use of higher learning rates.
- Different optimisation techniques like mini-batch gradient descent, momentum, RMSProp, Adam, and learning rate decay have their own advantages and disadvantages.
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