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Deep Learning News
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Study Shows Advances in High-Order Neural Networks for Industrial Applications
- A recent study has demonstrated advancements in high-order neural networks for industrial applications.
- The study focuses on quadratic neural networks, which restrict the polynomial function to the second order to ensure stable training.
- Various versions of quadratic neurons are discussed, with the approach proposed by Fan et al. being considered as a general version.
- The researchers from multiple institutions collaborated on the study, providing valuable insights and findings for industrial applications.
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Researchers Develop Advanced Methods for Fault Diagnosis Using Blind Deconvolution
- Researchers have developed advanced methods for fault diagnosis using blind deconvolution.
- Blind deconvolution is considered an ill-posed problem in the absence of prior information.
- Kurtosis is utilized as an optimization objective function in blind deconvolution.
- Several optimization methods have been developed for blind deconvolution.
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AI and Signal Processing Unite to Diagnose Machine Faults Faster
- Researchers have combined artificial intelligence (AI) with signal processing to diagnose faults in machines faster and more effectively. The classifier-guided blind deconvolution (ClassBD) approach co-optimizes blind deconvolution-based signal feature extraction and deep learning-based fault classification. The system uses two filters: one for time-domain quadratic convolutional filters (QCNN) to extract periodic impulses and another in the frequency domain. The ClassBD framework aims to seamlessly integrate BD with deep learning classifiers via co-optimisation of model parameters. The method was tested on three datasets, and results show ClassBD outperforms other methods in noisy conditions, providing better interpretability.
- BD has been a successful approach used to extract bearing fault-specific features from vibration signals under strong background noise. However, one of the major challenges is integration with fault-diagnosing classifiers due to differing learning objectives. When combined, classifiers and BD share separate optimization spaces. Integration has the potential to cause BD issues such as enhancing the cyclic impulses of the fault signal while reducing differences between fault severities. The system developed aims to use classified information to instruct BD to extract features necessary to distinguish classes amid strong noise.
- The ClassBD system includes two neural network modules: one time-domain QCNN module and another of linear filters for signals in the frequency domain. ClassBD aims to integrate BD and deep learning classifiers. This is achieved by employing a deep learning classifier to teach BD filters. The fault labels provide useful information in guiding the BD to distinguish features. ClassBD is the first method to diagnose bearing faults under heavy noise while providing good interpretability.
- The quadratic neural filter enhances the filter's capacity to extract periodic impulses in the time domain. Meanwhile, the linear neural filter offers the ability to filter signals in the frequency domain and improves BD performance. The entire ClassBD system has plug-and-play capability and can be used as a module in the first layer of deep learning classifier, while physics-informed loss and uncertainty-aware weighing loss strategy are used to optimize both classifiers and BD filters.
- The research team conducted computational experiments on three datasets, two public and one private. The ClassBD system was shown to outperform other methods in noisy conditions on all datasets, providing more accurate results and better interpretability.
- In conclusion, combining AI and signal processing allows fault diagnosis in machinery faster and more effectively, providing better interpretability with high accuracy and efficiency. It is an essential step in ensuring the reliable operations of rotating machinery.
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Medium
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Building AI systems is fun.
- Building production-grade AI requires a combination of engineering knowledge and practical AI.
- It’s easy to get overwhelmed by the velocity of new developments in the AI ecosystem.
- To avoid becoming less effective, pick one or two core domains to focus on deeply.
- Create focused “no-noise” slots, plan learning in sprints, and implement structured breaks.
- Understand the core principles of classical algorithms and foundational NLP methods.
- Grasp the principles of Convolutional Neural Networks and deep learning frameworks.
- Understand the principles of generative models like GANs, VAEs, and large language models.
- Incremental updates can be exciting but rarely a radical leap.
- Focus on underlying mechanisms, evidence of actual impact, and cut through the noise.
- Critical thinking saves you from chasing every buzzword-laden release.
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TechCrunch
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A popular technique to make AI more efficient has drawbacks
- Quantization, a widely-used technique to make AI models more efficient, has limits and the industry may soon be approaching them. Quantization lowers the number of bits needed to represent information, but researchers have found that quantized models perform worse than the original unquantized versions if trained over a long period on lots of data, which spells bad news for AI firms training very large models. Scaling up models eventually provides diminishing returns and data curation and filtering may have an impact on efficacy.
- Labs are reluctant to train models on smaller data sets and so researchers suggest that training models in low precision can make them more robust. The optimal balance has yet to be discovered, but low quantization precision will result in a noticeable step down in quality, unless the original model is incredibly large in terms of parameter count. There are no shortcuts and bit precision matters, according to the researchers, who believe that future innovations will focus on architectures designed for low-precision training.
- The performance of quantized models is influenced by how models are trained and the precision of data types. Most models today are trained at 16-bit or half-precision before being post-train quantized to 8-bit precision. Low precision is seen as desirable for inference costs, but it has its limitations.
- Contrary to popular belief, AI model inferencing is often more expensive in aggregate than model training. Google spent an estimated $191m training one of its Gemini models. But if the company used the model to generate 50-word answers to half of all Google Search queries, it would spend around $6bn a year.
- Quantizing models with fewer bits representing their parameters are less demanding mathematically and computationally. But quantization may have more trade-offs than previously assumed.
- The industry must move away from scaling up models and training on massive datasets because there are limitations that cannot be overcome.
- In the future, architectures that deliberately aim to make low-precision training stable will be important and low-precision training will be useful in some scenarios.
- AI models are not fully understood, and known shortcuts that work in many kinds of computation do not necessarily work in AI. Researchers of AI models believe that low quantization precision will result in a noticeable step down in quality, unless the original model is incredibly large in terms of parameter count.
- Kumar and his colleagues' study was at a small scale, and they plan to test it with more models in the future. But he believes that there is no free lunch when it comes to reducing inference costs.
- Efforts will be put into meticulous data curation and filtering so that only the highest quality data is put into smaller models. Kumar concludes that reducing bit precision is not sustainable and has its limitations.
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Hackernoon
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Become a Problem Solving Machine With 'AI Thinking'—No Neural Implants Necessary!
- AI and ML have become essential tools across industries to automate repetitive tasks, predict trends, and help us make more informed decisions. When starting AI and ML, clearly define the problem and ensure you have relevant data. Match specific needs with suitable AI techniques, and leverage existing tools like AutoML to simplify your work. Training and testing are critical for creating a model that is robust and accurate in real-world scenarios. Developing an AI solution is an iterative process rather than a one-time effort. Finally, it is crucial to recognize that not every situation requires the application of AI.
- Starting with a simple, practical understanding of your problem will help you clarify how AI could assist. By focusing on the desired outcomes for your support ticket management, you’ll better understand whether AI is the right fit and what kind of model might be needed. Data is the fuel that powers AI. The more relevant, clean data you have, the better the AI can perform. For support ticket management, this data might come from ticket logs, customer emails, chat transcripts, or feedback forms.
- Once you’ve clearly defined the problem and gathered relevant data, it’s time to identify the best AI approach for the task. Common approaches include prediction tasks, classification tasks, pattern recognition and automation and decision making among others. By leveraging these AI techniques, you can streamline processes, improve ticket handling accuracy, and gain insights that help anticipate and address customer needs. You don’t have to build AI models from scratch! Many robust AI and ML tools are available to help you get started without needing extensive expertise.
- Training and testing are central to creating a reliable AI model. This process ensures that your model not only learns from past data but also generalizes well to new, unseen data. Developing an AI solution is an iterative process rather than a one-time effort. By continuously experimenting, learning from failures, and making incremental improvements, you can enhance the effectiveness of your AI system.
- While AI offers powerful solutions for many complex problems, it’s crucial to recognize that not every situation requires its application. Sometimes, traditional programming or rule-based systems may be more effective and efficient. By carefully assessing whether AI is necessary for your tasks, you can avoid over-engineering solutions.
- In conclusion, adopting an AI mindset involves understanding your problem, identifying available data, and leveraging the right tools for effective solutions—without needing to master complex algorithms. By focusing on problem-solving, iterating your approach, and knowing when traditional methods are more suitable, you’ll be prepared to enhance processes and improve outcomes.
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Medium
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What is GenAI?
- Generative AI is a subset of artificial intelligence that can produce original content like text, audio, images, video, or software code as a response to a user’s request or prompt.
- To create a foundation model that can support multiple gen AI applications, generative AI involves three phases - training, tuning, and retuning.
- Various methods can be used for tuning the generative AI like fine-tuning or reinforcement learning with human feedback (RLHF).
- Generative AI models rely on various large pre-trained machine learning models like foundation models (FMs) and large language models (LLMs) that are trained to develop deep patterns and relationships in data.
- Generative AI produces various types of content like text, images, video, audio, software code, and design and art.
- Generative AI offers several benefits like dynamic personalization, improved decision-making, constant availability, etc.
- Generative AI also has some limitations like security concerns, cost, limited creativity, etc.
- It possesses a black box problem, and enhancing interpretability and transparency is necessary to gain trust and adoption.
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Medium
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Can I learn AI without coding?
- You can learn AI concepts and theory without coding through courses, books, and videos.
- No-code AI tools allow building AI models without coding using drag-and-drop interfaces.
- Many AI-powered everyday tools are designed for users with no coding experience.
- Courses and learning platforms like Coursera, edX, and Udemy offer AI courses that require little or no coding.
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Medium
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What Is an AI Generalist? Growth and Scope in a Transforming World
- An AI generalist is someone or something that possesses a diverse skill set across areas like machine learning, natural language processing, and computer vision.
- AI generalists can handle a wide variety of tasks and are capable of adapting, pivoting, and solving problems from multiple angles.
- The demand for AI generalists is growing rapidly as they are able to bridge diverse fields and transform roles in various industries.
- AI generalists contribute to innovation by combining knowledge, creativity, and adaptability to tackle complex challenges and reshape industries.
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Medium
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Launch Your Digital eBook Store Effortlessly
- EBBuddy is a tool that allows anyone to start their online eBook store in minutes.
- EBBuddy comes preloaded with over 10,000 eBooks across popular genres, eliminating the need for sourcing products.
- Users have reported earning additional income ranging from $300 to $5,000 with EBBuddy.
- EBBuddy offers a one-time payment promotion for early adopters, providing unlimited access to all features and 30 reseller licenses.
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Medium
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What Is Deep Learning? Breaking It Down for Beginners
- Deep learning is a subset of AI focused on algorithms called neural networks, which are inspired by the structure and function of the human brain.
- It enables machines to learn from large datasets and improve performance on tasks like prediction and classification.
- Deep learning doesn’t require extensive manual feature engineering, as it automatically identifies patterns in data through layered computations.
- Practical applications of deep learning include speech recognition, image classification, and self-driving cars.
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Medium
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Applications and working of Vision Language Models (VLMs)
- Vision Language Models (VLMs) have a wide range of applications including generating captions for images, Question Answering (VQA), Zero-Shot and Few-Shot Image Classification, and document analysis.
- VLMs can also support text-based image search, object recognition, image segmentation, chatbots, automated data labeling, and the processing of videos.
- VLMs can be trained using pre-training and supervised fine-tuning (SFT). At different stages of training, the weights of some VLM components are unfrozen and updated, while keeping others frozen, for effective learning. The training includes alignment with human preferences.
- VLMs are characterized by Vision Encoder, Text Encoder, and Decoder Language Model components, which work together to extract information from images and text, and fuse them to generate text output.
- Fusion mechanisms like Cross-Attention and attention based mechanisms are used to combine the visual embeddings and text embeddings.
- The type of training data and the training objective can change in different stages of training, and synthetic data is also used commonly in VLM training for better results.
- The VLM training data is in the form of image-text pairs, interleaved image-text documents, image-instruct-answer triplets, and even pdfs.
- Most VLMs are built upon Transformer models. The main idea in architecture of VLMs is extracting visual features and textual features, and then combining that information and utilizing it during text generation through LLM.
- VLMs are well suited for Vision-Language Navigation (VLN), Multimodal Machine Translation, and Text to Image Generation
- Parameter Efficient Fine-Tuning (PEFT) techniques like LoRA are commonly used during the LLM training. Different self-supervised learning based pre-training objectives are used in pre-training.
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Medium
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Cold War
- The Cold War was a period of geopolitical tension between the United States and the Soviet Union from 1947 to 1991.
- It was characterized by ideological conflicts, proxy wars, and nuclear arms race.
- The two superpowers, along with their allies, formed the Western Bloc and the Eastern Bloc.
- The Cold War ended with the collapse of the Soviet Union in 1991.
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Medium
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Boost Your Income with Revolutionary Multi-AI Content Creation
- The Multi-AI Model App is a revolutionary tool for content creation.
- It combines over 75 advanced AI models to produce unique and engaging content.
- Users have reported earning $653 per day using this app.
- The app is designed for both beginners and experienced marketers.
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Medium
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Understanding Generative Adversarial Networks (GANs)
- The generator network of GANs transforms random noise into meaningful data through a series of convolutional layers.
- The discriminator network distinguishes between real and generated samples and provides binary classification output.
- The theoretical foundation of GANs relies on probability theory, game theory, and information theory.
- Training process of GANs is based on a minimax optimization problem that involves updating the generator and discriminator networks.
- Several GANs architectures like StyleGAN, CycleGAN, and Self-Attention GAN have been developed to provide better control, stability, and flexibility.
- Mode collapse, vanishing gradients, and poor convergence are some of the major challenges faced during GANs training.
- GANs have been used in various domains including image and video processing, generating synthetic datasets, identifying outliers, and data-intensive fields.
- The future of GANs includes higher-quality and multimodal outputs, controllable generation, and AI-generated creativity and art.
- GANs also pose ethical concerns such as deepfakes, privacy and data security, intellectual property, fair use of synthetic data, and bias in AI models.
- Regulation and responsible use of GANs are necessary for the development and growth of the technology.
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