A naukri.com initiative
ML News
Medium
20h
178
Comparing DiPhyx and dxflow with Databricks, Google Colab, and SageMaker
- DiPhyx is a platform for scientific computing workflows, emphasizing reproducibility and collaboration.
- dxflow focuses on managing and optimizing scientific computing workflows across different environments.
- Databricks is a unified analytics platform with data processing capabilities.
- Google Colab and SageMaker are also popular platforms in the data science and machine learning space.
Read Full Article
10 Likes
Medium
21h
55
Image Credit: Medium
Why Apache Spark Outperforms Hadoop MapReduce for Iterative Algorithms
- Apache Spark outperforms Hadoop MapReduce for iterative algorithms in big data processing.
- MapReduce involves significant read and write operations to disk, which can be slow and inefficient for iterative algorithms.
- Spark leverages in-memory computing, avoiding disk I/O bottleneck and providing faster processing for iterative algorithms.
- Spark's in-memory computing capability makes it a game-changer for efficient processing of big data with iterative algorithms.
Read Full Article
3 Likes
Medium
22h
223
Image Credit: Medium
Introduction To Robotics
- Robotics involves the design, construction and operations of robots.
- Key components of robotics include sensors, actuators, control systems, power supply, end effectors, and software.
- The history of robotics dates back to the 1920s, and it has evolved significantly over the years.
- Robotics is a field that combines engineering, science, and technology to create machines that enhance human capabilities and improve efficiency in various industries.
Read Full Article
13 Likes
Medium
23h
325
Image Credit: Medium
Gated Exponential Linear Unit
- The Gated Exponential Linear Unit (GELU) is a sophisticated activation function used in neural networks.
- GELU combines linear and non-linear operations, along with a gating mechanism, to enhance learning capabilities.
- It introduces non-linearity and allows the network to learn complex relationships between inputs and outputs.
- GELU is a powerful activation function with smooth nature and adaptive filtering capabilities, contributing to its success in deep learning applications.
Read Full Article
19 Likes
Marktechpost
1d
44
This AI Paper Introduces Rational Transfer Function: Advancing Sequence Modeling with FFT Techniques
- Researchers have introduced the Rational Transfer Function (RTF) approach for efficient sequence modeling.
- RTF leverages transfer functions and Fast Fourier Transform (FFT) techniques.
- The RTF approach eliminates the need for memory-intensive state-space representations.
- RTF demonstrated improved training speed and accuracy across various benchmarks and tasks.
Read Full Article
2 Likes
Marktechpost
1d
138
Enhancing Graph Classification with Edge-Node Attention-based Differentiable Pooling and Multi-Distance Graph Neural Networks GNNs
- Researchers have developed a new hierarchical pooling method for GNNs called Edge-Node Attention-based Differentiable Pooling (ENADPool).
- ENADPool uses hard clustering and attention mechanisms to compress node features and edge strengths, improving graph classification performance.
- They also introduce a Multi-distance GNN (MD-GNN) model to reduce over-smoothing and enhance graph representation.
- ENADPool and MD-GNN outperform other graph deep learning methods in benchmark datasets.
Read Full Article
8 Likes
Medium
1d
255
Image Credit: Medium
In an era where technology evolves at breakneck speed, few innovations have captured our collective…
- High implementation costs: Developing and implementing AI systems can be expensive.
- Job displacement: Automation through AI can lead to the displacement of jobs.
- Bias and fairness: AI systems can unintentionally perpetuate biases if trained on biased datasets.
- Lack of transparency: Many AI models, especially neural networks, are often considered "black boxes".
Read Full Article
15 Likes
Medium
1d
263
Image Credit: Medium
OpenAI Confirms ChatGPT Live Stream Event — The AI Community Is Excited!
- OpenAI has confirmed a live stream event that has generated excitement in the AI community.
- Rumors of a ChatGPT-powered search engine or open-source language model have been dispelled.
- The event will showcase new work that OpenAI believes people will love.
- The AI community eagerly awaits the live demo and new releases from the event.
Read Full Article
15 Likes
Medium
1d
56
Image Credit: Medium
Can KAN(Kolmogorov-Arnold Networks) replace MLPs?
- Choosing the right neural network architecture is crucial in machine learning.
- MLPs (Multi-Layer Perceptrons) have been widely used for their simplicity and adaptability.
- KAN (Kolmogorov-Arnold Networks) introduce new features, but MLPs remain unmatched in terms of flexibility.
- While KAN networks offer the potential for updating intermediate activation layers, they can be rewritten as MLPs with a similar parameter count.
Read Full Article
3 Likes
Medium
1d
303
Image Credit: Medium
Why the AI Community Is Excited About the Kolmogorov-Arnold Network (KAN)
- The AI community is excited about the Kolmogorov-Arnold Network (KAN) due to its potential applications and fresh approach.
- KANs have the potential to replace MLP (Multi-Layer Perceptron) in both theory and application.
- However, there are challenges in understanding the full capabilities and limitations of KANs and adapting existing frameworks and hardware to support them.
- Despite the challenges, researchers and practitioners are intrigued by the improved efficiency, interpretability, and wider range of applications promised by KANs.
Read Full Article
18 Likes
Medium
1d
299
Image Credit: Medium
Understanding Generative AI: Revolutionizing Creativity and Innovation
- Generative AI refers to algorithms that can generate new content by learning patterns from existing data.
- Generative AI models are built using deep learning techniques like GANs and VAEs.
- Generative AI is transforming fields like art, music, writing, and gaming by enabling new forms of creativity.
- However, it also raises ethical concerns such as intellectual property rights, misinformation, and biases.
Read Full Article
18 Likes
Scitechdaily
1d
356
Image Credit: Scitechdaily
Predicting Chaos With AI: The New Frontier in Autonomous Control
- Advanced machine learning algorithms have shown potential in efficiently controlling complex systems, promising significant improvements in autonomous technology and digital infrastructure.
- Recent research highlights the development of advanced machine learning algorithms capable of controlling complex systems efficiently.
- Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.
- Researchers found that using machine learning tools to create a digital twin, or a virtual copy, of an electronic circuit that exhibits chaotic behavior successfully predicted and controlled the system.
- Advanced devices like self-driving cars and aircraft often rely on machine learning-based controllers.
- The new algorithm offers significant improvements in power consumption and computational demands as compared to traditional machine learning-based controllers, which is critical in scenarios like self-driving vehicles where milliseconds can make a difference between life and death.
- Compact enough to fit on an inexpensive computer chip, the team's digital twin built to optimize a controller's efficiency and performance resulted in a reduction of power consumption. It achieves this by using machine learning approach called reservoir computing.
- Researchers directed their model to complete complex control tasks and revealed that their approach achieved a higher accuracy at the tasks than its linear counterpart and is significantly less computationally complex than a previous machine learning-based controller.
- The algorithm was recently made available to scientists, which not only inspires potential advances in engineering but also provide an important economic and environmental incentive for creating more power-friendly algorithms.
- Future work will likely be steered toward training the model to explore other applications like quantum information processing.
Read Full Article
21 Likes
Medium
1d
322
Image Credit: Medium
The Authenticity of Information in AI Apps
- AI apps are widespread in various sectors, but not all data is accurate.
- Verify information from multiple sources before relying solely on AI apps.
- Approach AI apps with a critical mindset and seek guidance from experts when needed.
- The rise of AI apps can lead to job insecurity in professions that rely on human expertise.
Read Full Article
19 Likes
Medium
1d
303
Latest updates on Dimensionality reduction part2(Machine Learning 2024)
- A supervised dimensionality reduction method called Gradient Boosting Mapping (GBMAP) is proposed to find a good set of features or distance measures in supervised learning.
- GBMAP uses the outputs of weak learners to define the embedding, which provides better features for the learning task.
- The embedding coordinates automatically ignore irrelevant directions and can be used to find a principled distance measure between points.
- GBMAP is fast and performs well in regression and classification tasks compared to state-of-the-art methods.
Read Full Article
18 Likes
Medium
1d
334
Latest updates on Dimensionality reduction part1(Machine Learning 2024)
- Intracellular protein patterns regulate vital cellular functions by coupling protein dynamics on the cell membrane to dynamics in the cytosol.
- Recent studies have shown that modeling cytosolic dynamics without considering concentration gradients normal to the membrane may overlook crucial aspects of pattern formation.
- A generic framework has been developed to project cytosolic dynamics onto the lower-dimensional surface, accounting for cytosolic concentration gradients in both static and evolving geometries.
- This framework utilizes a small number of dominant characteristic concentration profiles, similar to basis transformations of finite element methods, to approximate the cytosolic dynamics.
Read Full Article
20 Likes
For uninterrupted reading, download the app