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Neural Networks News
Medium
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The Multi-Armed Bandit Problem
- The concept of the Multi-Armed Bandit (MAB) problem revolves around decision-making under uncertainty.
- In the MAB framework, the decision-maker has limited or no information about the rewards associated with each action.
- The challenge is to balance exploration and exploitation to maximize cumulative rewards over time.
- Various algorithms have been developed to address the MAB problem, offering efficient solutions in real-world applications.
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Medium
4h
68
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Everything You Need to Know About Lllama 3 8B & 70B
- Meta AI has released the third iteration of its Llama series, Llama 3. Llama 3 is a game-changer in the world of AI, with an exceptional contextual understanding that effortlessly handles complex tasks. Refined post-training processes reduce false refusal rates, improve response alignment, and enhance the model's ability to generate more accurate responses with more relevant interactions.
- Llama 3 uses 128K tokens that effectively encode language, substantially improving its performance. The model doubles its predecessor's capacity, with an 8K context length and trained sequences of 8,192 tokens, using a mask to prevent self-attention from crossing document boundaries.
- With over 5% of the training data consisting of high-quality non-English content covering 30 languages, Llama 3 is well-prepared for multilingual use cases. The 8-billion and 70-billion parameter models of Llama 3 have demonstrated impressive performance in their scale.
- Meta's Llama 3 is now available as a highly intelligent and accessible AI assistant across Meta's ecosystem, including Facebook, Instagram, WhatsApp, Messenger, and the web. The AI assistant enables users to ask questions, learn, create, and connect with the things that matter to them more efficiently. Meta also plans to test multimodal Meta AI on their Ray-Ban Meta smart glasses.
- Meta has updated its Responsible Use Guide (RUG) to ensure responsible use and prevent misuse by bad actors as Meta AI puts GPT-4-level power into users' hands. Advanced trust and safety tools such as Llama Guard 2 are used to ensure appropriate model usage, while Code Shield and Cybersec Eval 2 address AI safety concerns, including prompt injection vulnerabilities.
- As the Llama series continues to evolve and expand, it will be fascinating to see how these models are applied and what new possibilities they unlock in the realm of artificial intelligence. The impact of these models goes beyond improving Meta's products and includes the potential to drive significant progress in crucial fields like science and healthcare.
- Meta has heavily invested in developing cutting-edge AI models that they are responsibly open-sourcing. The company seeks to provide better, safer products, accelerate innovation, and foster a healthier market by making the world's most advanced AI accessible to everyone.
- Meta continues to train even more powerful models, with over 400 billion parameters currently in training. Llama 3's high-quality dataset sets the stage for remarkable advancements in AI technology, as data plays a crucial role in model training.
- To ensure its AI technology leads to better products and advancements that could benefit humanity, Meta empowers users and developers to explore and build innovative products and experiences by releasing Meta AI as a free-to-use, ChatGPT-like assistant.
- With continuous updates and refinements to safety measures, Meta AI states it will maintain its integrity, stay ahead of potential threats and maintain user trust as their models become smarter.
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Medium
13h
32
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Fine-Tuning Precision: The Science of Neural Network Quantization
- Quantization involves the conversion of continuous numerical values, such as those found in the parameters and activations of neural networks, into discrete representations.
- Quantization helps mapping a broad range of real numbers onto a smaller set of discrete values.
- Neural networks often comprise millions to billions of parameters, making them computationally expensive to train, deploy, and execute, particularly on resource constrained devices.
- Quantizing neural network parameters, we can dramatically reduce their memory requirements and computational overhead associated with these models.
- Quantization can be classified into two main types: uniform and non-uniform. Uniform quantization involves dividing the input space into evenly spaced intervals, while non-uniform quantization allows for more flexible mappings.
- Quantization can target different levels including weights, activations, or the entire network.
- Post-Training Quantization (PTQ) quantizes the neural network after it has been trained, while Quantization-Aware Training (QAT) integrates quantization into the training process itself.
- Quantization-aware training tends to yield better results in terms of accuracy retention as it simulates the effects of quantization during training, allowing better adaption of the model to constraints.
- Quantization represents a critical advancement in the field of artificial intelligence, enabling the widespread adoption of AI in diverse real-world applications, driving innovation and progress in the field.
- Ongoing research aims to mitigate the accuracy loss associated with quantization and strike a balance between precision and efficiency.
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Medium
20h
14
Explainable AI: Why is It the Talk of the Town Right Now?
- Explainable AI (XAI) aims to shed light on the inner workings of AI models, enabling users to understand and trust the output and outcomes generated by machine learning models.
- Machine learning algorithms can become black boxes, leading to a lack of transparency and making the reliability, fairness, and credibility of AI systems questionable.
- XAI offers comprehensible justifications for the decisions AI systems make, promoting credibility, accountability, and acceptance.
- Explanations from XAI can enable us to understand and address biases, detect and mitigate errors, provide audit-ready explanations, and empower humans to optimize models effectively.
- Lack of understandability is one of the primary issues facing XAI researchers, along with difficulties in achieving balance between performance and clarity.
- Explanations must be tailored to user-specific needs, promoting a broader understanding of AI’s inner workings.
- Future XAI developments may involve ‘XAI-by-Design,’ which embeds explication techniques directly into AI model architectures, making models more inherently transparent.
- Counterfactual explanations will provide insights into the causal structure underlying the model’s decisions, allowing XAI systems to not only answer ‘what’ but also ‘why.’
- Regulations requiring explainability for certain AI applications will help drive the development of effective XAI techniques, and integrating AI with human expertise is a promising area for the future.
- Kanerika is a leading technology consulting firm specializing in AI, Machine Learning, and Generative AI, with expertise in delivering high-quality, value-driven AI solutions.
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Medium
1d
64
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The Future of the NFL: Predicting Plays Using Artificial Neural Networks
- Using neural networks, a team of researchers sought to accurately predict NFL plays, which could help defensive coaches in selecting an optimal defense strategy that could thwart the opponents, as well as bring a premature end to dominant teams like the Patriots.
- The team based their model on NFL data sourced from the nflfastR, which contains over 350 variables and play-by-play data stretching back to 1999.
- The model, which used Long Short-Term Memory (LSTM) algorithms, managed 69.5% accuracy when applied only to the Patriots between 2012 and 2020.
- However, the team found that the correlation between certain features and play-type was insufficient, with several correlated parameters occurring only after the play.
- The team improved the model's accuracy by 4% by adding more data from all NFL teams from all available years and utilizing more features.
- The model could not guarantee perfect accuracy in predicting play-calling, but could be leveraged as a helpful tool in decision-making by coaches.
- Using more data was found to be more important than targeting specific coaches, as highly unpredictable coaches like Belichick could hinder model accuracy.
- Models like these could bring new rule considerations to the game, forcing the NFL to decide on how to handle extreme advantages that accurate models could procure for teams.
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Medium
1d
309
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Activation Functions in NN
- The activation function called 'No Activation' simply outputs the given value without any processing.
- The Sigmoid activation function is used for binary classification tasks, providing outputs in the range of [0,1].
- The Tanh activation function is used for cases where three output cases are required: negative, neutral, and positive, with a range of [-1,1].
- The Rectified Linear Unit (ReLU) activation function returns the input value for positive inputs, and 0 for negative inputs, making it popular in neural networks.
- The Leaky ReLU activation function is an improved version of ReLU, allowing small non-zero outputs for negative inputs.
- The Softmax activation function is used for multiclass classification, producing a probability distribution over multiple classes.
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Medium
1d
166
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Remote Working And AI Tools: The 2024 Landscape
- Remote work has given rise to digital nomads who leverage technology to work while traveling.
- Remote entrepreneurship allows businesses to tap into a global talent pool and save on overhead costs.
- Challenges include communication and collaboration difficulties and the need for proper mental health care.
- Advancements in technology like VR, AR, and AI tools are shaping the future of remote work and entrepreneurship.
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Medium
2d
303
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Simple Explanation of Kolmogorov-Arnold Networks (KANs)
- Kolmogorov-Arnold Networks (KANs) are neural networks that feature learnable activation functions on the edges of the network.
- KANs do not use traditional linear weights and offer enhanced interpretability and visualization compared to MLPs.
- Empirically, KANs outperform MLPs in various tasks and require fewer parameters.
- KANs are suitable for scientific research and have applications in discovering new mathematical and physical laws.
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Medium
3d
353
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Kolmogorov-Arnold Networks: The Fresh Approach Shaking Up AI
- Kolmogorov-Arnold Networks (KANs) are shaking up AI by reimagining activation functions within neural networks.
- Unlike Multi-Layer Perceptrons (MLPs), KANs use flexible, learnable univariate functions as weights and activation components.
- This innovative approach allows KANs to fluidly adapt information flow as they are trained.
- KANs have the potential to tackle complex tasks in more capable and intuitive ways compared to traditional models.
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Medium
3d
322
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What are Convolutional Neural Networks (CNN)? The Art of Computer Vision For Beginners
- Convolutional Neural Network or CNN is a type of neural network used for computer vision tasks.
- CNNs have unique architecture that allows them to process images efficiently.
- The main building blocks of CNNs are the convolutional layer, pooling layer, and flatten layer.
- CNNs are able to detect features and objects by using multiple layers of convolutional filters.
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Medium
4d
64
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Discover the secret of predicting cryptocurrency prices using neural networks!
- Neural networks like Recurrent Neural Networks (RNN), Convulsive Neural Networks (CNN), Convolutional Recurrent Neural Networks (CRNN), and Generative Adversarial Neural Networks (GAN) can be used to predict cryptocurrency prices.
- These neural networks can analyze time series data, including cryptocurrency price data, to identify patterns and make informed investment decisions.
- Using neural networks, traders and investors can analyze large amounts of data and uncover hidden patterns in the cryptocurrency market.
- While neural networks can provide valuable insights, it's important to note that market risks and unforeseen events can still impact cryptocurrency prices.
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Medium
5d
312
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Feature importance in Neural Network
- The importance of features in a neural network is determined by how much the output changes when a feature is changed by 1 unit.
- The process of calculating feature importance is similar to how parameters are adjusted in a neural network using partial derivatives and the chain rule.
- To compute feature importance, all possible routes from the input to the output are considered, and the weights and derivatives of activation functions along these routes are multiplied.
- The resulting multiplications for each input are summed to determine the overall feature importance.
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Analyticsinsight
6d
811
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Neural Networks Power Explosive Growth in $7.9 Trillion AI Sector
- The global economy of Neural Networks may be improved by 7.9 trillion dollars per year through generative AI.
- Major players in the generative AI sector include Tesla, Accenture, Palantir Technologies, ServiceNow, and the Future of Artificial Intelligence Corp.
- Scope AI Corporation is widening its problem space and appointing a new CEO to ensure sustainable growth.
- Tesla focuses on artificial intelligence with their High-Self Drive software, Accenture adopts a 'Human by Design' approach, Palantir pioneers AI-enabled solutions, and ServiceNow provides AI-powered workflow solutions.
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Medium
6d
396
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A Simplified Explanation Of The New Kolmogorov-Arnold Network (KAN) from MIT
- The Kolmogorov-Arnold Network (KAN) is a new architecture from MIT that promises to revolutionize neural networks.
- KAN redefines the role of activation functions by incorporating univariate functions that act as both weights and activation functions.
- This innovative approach allows for activation at edges and modular non-linearity, potentially enhancing learning dynamics and input influence on outputs.
- KAN has the potential to enable networks that are fundamentally more capable of handling complex tasks.
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Semiengineering
7d
333
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Research Bits: April 30
- Researchers from the Max Planck Institute for the Science of Light and Massachusetts Institute of Technology have developed reconfigurable recurrent operators based on sound waves for photonic machine learning. The method allows optical neural networks to be programmable on a pulse-by-pulse basis without complicated structures and transducers.
- Scientists at the University of Florida have built a 3D ferroelectric-gate fin nanomechanical resonator that enables spectral processors to integrate different frequencies on a monolithic chip for wireless communications. The processors deliver enhanced performance and have indefinite scalability.
- Researchers from MIT and MIT-IBM Watson AI Lab have developed an on-device digital in-memory compute machine learning accelerator that is resistant to side-channel and bus-probing attacks. The accelerator splits data into random pieces to combat side-channel attacks and utilizes encryption and physically unclonable functions to prevent bus-probing attacks.
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