#and innovative programs

[guru-kashi-university]

https://gku.ac.in/

Best Private University in Punjab, India

Experience academic excellence at Punjab's premier private university. With state-of-the-art facilities, renowned faculty, and innovative programs, our institution is dedicated to nurturing future leaders. From engineering to management, humanities to sciences, we offer a diverse range of disciplines to suit every passion and career aspiration. Embrace a dynamic learning environment where creativity thrives, ideas flourish, and opportunities abound. Join us on a transformative educational journey and unlock your potential for success in the heart of Punjab, India's educational hub.

[gayvampyr]

any sliver of a chance i had of appreciating AI has been completely decimated by this clownery

[justinspoliticalcorner]

Judd Legum at Popular Information:

In 2024, reliable access to high-speed internet is no longer a luxury; it is a basic necessity. From job applications to managing personal finances and completing school work, internet access is an essential part of daily life. Without an internet connection, individuals are effectively cut off from basic societal activities. 

But the reality is that many people — particularly those living around the poverty line — can not afford internet access. Without internet access, the difficult task of working your way from the American economy's bottom rung becomes virtually impossible.   On November 21, 2021, President Biden signed the bipartisan Infrastructure Investment and Jobs Act. The new law included the Affordable Connectivity Program (ACP), which provided up to $30 per month to individuals or families with income up to 200% of the federal poverty line to help pay for high-speed internet. (For a family of four, the poverty line is currently $31,200.) On Tribal lands, where internet access is generally more expensive, the ACP offers subsidies up to $75 per month.  The concept started during the Trump administration. The last budget enacted by Trump included $3.2 billion to help families afford internet access. The FCC made the money available as a subsidy to low-income individuals and families through a program known as the Emergency Broadband Benefit Program. The legislation signed by Biden extended and formalized the program.  It has been a smashing success.

Today, the ACP is "helping 23 million households – 1 in 6 households across America." The program has particularly benefited "rural communities, veterans, and older Americans where the lack of affordable, reliable high-speed internet contributes to significant economic, health and other disparities." According to an FCC survey, two-thirds of beneficiaries "reported they had inconsistent internet service or no internet service at all prior to ACP." These households report using their high-speed internet to "schedule or attend healthcare appointments (72%), apply for jobs or complete work (48%), do schoolwork (75% for ACP subscribers 18-24 years old)." Tomorrow, the program will abruptly end.  In October 2023, the White House sent a supplemental budget request to Congress, which included $6 billion to extend the program through the end of 2024. There is also a bipartisan bill, the Affordable Connectivity Program Extension Act, which would extend the program with $7 billion in funding. The benefits of the program have shown to be far greater than the costs. An academic study published in February 2024 found that "for every dollar spent on the ACP, the nation’s GDP increases by $3.89." The program will lapse tomorrow because Speaker Mike Johnson (R-LA) refuses to bring either the bill (or the supplemental funding request) to a vote. The Affordable Connectivity Program Extension Act has 225 co-sponsors which means that, if Johnson held a vote, it would pass. 

[...]

The Republican attack on affordable internet

Why will Johnson not even allow a vote to extend the ACP? He is not commenting. But there are hints in the federal budget produced by the Republican Study Committee (RSC). The RSC is the "conservative caucus" of the House GOP, and counts 179 of the 217 Republicans in the House as members. Johnson served as the chair of the RSC in 2019 and 2020. He is currently a member of the group's executive committee.  The RSC's latest budget says it "stands against" the ACP and labels it a "government handout[] that disincentivize[s] prosperity." The RSC claims the program is unnecessary because "80 percent" of beneficiaries had internet access before the program went into effect. For that statistic, the RSC cites a report from a right-wing think tank, the Economic Policy Innovation Center (EPIC), which opposes the ACP. EPIC, in turn, cites an FCC survey to support its contention that 80% of ACP beneficiaries already had internet access. The survey actually found that "over two-thirds of survey respondents (68%) reported they had inconsistent internet service or no internet service at all prior to ACP."

[...] The RSC also falsely claims that funding for the precursor to the ACP, the Emergency Broadband Benefit Program (EBB), "was signed into law at the end of President Biden’s first year in office." This is false. Former President Trump signed the funding into law in December 2020. The RSC's position is not popular. A December 2023 poll found that 79% of voters support "continuing the ACP, including 62% of Republicans, 78% of Independents, and 96% of Democrats."

In 2024, access to the internet is a necessity and not just a luxury, and the Republicans are set to end the Affordable Connectivity Program if no action is taken. The Affordable Connectivity Program (ACP) provided subsidies to low-income people and families to obtain internet access.

[h3ll0-my-n3me-is]

do you think computers are deserving of affection ?

[ indeed I think computers are the most efficient and useful piece of technology to date . ! Of course they aren’t done surprising us yet, the economy is growing delightfully due to these boxes of heaven . ! ! ]

[ well actually it depends on if they do their jobs . . ]

[jrueships]

babygirl please leave the elderly alone

[woolandcoffee]

Semi-regular reminder that I hate the term "artificial intelligence" becoming short hand for "marginally faster data sorting computer program" and I genuinely think a lot of y'all are foolish for taking the marketing bait.

[makers-muse]

CODING FOR KIDS WITH SPECIAL NEEDS

The world of coding can be scary, with obscure symbols and complicated logic. What if we told you it might be a creative and problem-solving playground for children with special needs? Intrigued? Buckle up, because we’re going to discover the thrilling world of adaptive coding strategies!

Here’s how coding transforms into an inclusive playground:

Block-based coding language: Scratch, Blockly, and Codemonkey Jr, these drag-and-drop interfaces make coding more visual and intuitive, which is ideal for children who struggle with traditional text-based coding. Imagine making colorful animations with just a few clicks!

Voice-activated coding: Speech recognition software allows children with physical restrictions to code using voice commands. This enables kids to contribute and express their creativity via code.

Gamification: Gamifying learning makes it more enjoyable and inspiring. RoboGarden, for example, turns coding into a game in which children solve puzzles and accomplish challenges, thus fostering problem-solving abilities in a fun way.

Physical computing: Makey Makey and Sphero robots are two examples of tools that help to connect the digital and physical realms. Coding becomes tangible and interesting for kids when they can program their robots to overcome difficulties or make their own musical instruments.

Multisensory learning: Multisensory learning environments are very beneficial for kids with special needs. Interactive coding exercises that combine visual, tactile, and audio components improve learning and memory. Through the combination of physical building and programming, interactive coding toys such as Botley the Coding Robot or LEGO Education SPIKE Prime offer a hands-on learning experience.

Several initiatives have successfully implemented adaptive learning techniques to teach coding to kids with special needs:

CoderDojo’s Autism Pilot Program: The international network of coding clubs CoderDojo has introduced a trial program, especially for kids with autism. Through the use of customized support, visual aids, and sensory breaks, the program established a warm and inviting atmosphere in which kids could flourish.

Tech Access: TeachAccess promotes accessible and inclusive design in technology-based learning. Their materials and training courses give teachers the skills and resources they need to design coding curricula that are accessible to learners of all skill levels.

Apple’s Everyone Can Code Curriculum: All students can benefit from inclusive and accessible coding instruction thanks to Apple’s curriculum, which includes activities and materials designed specifically for students with disabilities.

Remember: It’s all about finding the correct match! Investigate multiple coding platforms, try out new strategies, and celebrate little wins. There is no one-size-fits-all solution, so customize the learning experience for your child’s individual needs and interests.

Coding is more than just building snazzy websites or mobile apps. It is about developing problem-solving abilities, promoting creativity, and instilling confidence. By embracing adaptive learning methodologies, we can provide access to this exciting world for all children, empowering them to become future coders, inventors, and innovators.

[erp-dubai-2003]

Let's look back & see the effects of past on the future of ERP.

[junk-culture]

👍

[learning-robotics]

Mastering Neural Networks: A Deep Dive into Combining Technologies

How Can Two Trained Neural Networks Be Combined?

Introduction

In the ever-evolving world of artificial intelligence (AI), neural networks have emerged as a cornerstone technology, driving advancements across various fields. But have you ever wondered how combining two trained neural networks can enhance their performance and capabilities? Let’s dive deep into the fascinating world of neural networks and explore how combining them can open new horizons in AI.

Basics of Neural Networks

What is a Neural Network?

Neural networks, inspired by the human brain, consist of interconnected nodes or "neurons" that work together to process and analyze data. These networks can identify patterns, recognize images, understand speech, and even generate human-like text. Think of them as a complex web of connections where each neuron contributes to the overall decision-making process.

How Neural Networks Work

Neural networks function by receiving inputs, processing them through hidden layers, and producing outputs. They learn from data by adjusting the weights of connections between neurons, thus improving their ability to predict or classify new data. Imagine a neural network as a black box that continuously refines its understanding based on the information it processes.

Types of Neural Networks

From simple feedforward networks to complex convolutional and recurrent networks, neural networks come in various forms, each designed for specific tasks. Feedforward networks are great for straightforward tasks, while convolutional neural networks (CNNs) excel in image recognition, and recurrent neural networks (RNNs) are ideal for sequential data like text or speech.

Why Combine Neural Networks?

Advantages of Combining Neural Networks

Combining neural networks can significantly enhance their performance, accuracy, and generalization capabilities. By leveraging the strengths of different networks, we can create a more robust and versatile model. Think of it as assembling a team where each member brings unique skills to tackle complex problems.

Applications in Real-World Scenarios

In real-world applications, combining neural networks can lead to breakthroughs in fields like healthcare, finance, and autonomous systems. For example, in medical diagnostics, combining networks can improve the accuracy of disease detection, while in finance, it can enhance the prediction of stock market trends.

Methods of Combining Neural Networks

Ensemble Learning

Ensemble learning involves training multiple neural networks and combining their predictions to improve accuracy. This approach reduces the risk of overfitting and enhances the model's generalization capabilities.

Bagging

Bagging, or Bootstrap Aggregating, trains multiple versions of a model on different subsets of the data and combines their predictions. This method is simple yet effective in reducing variance and improving model stability.

Boosting

Boosting focuses on training sequential models, where each model attempts to correct the errors of its predecessor. This iterative process leads to a powerful combined model that performs well even on difficult tasks.

Stacking

Stacking involves training multiple models and using a "meta-learner" to combine their outputs. This technique leverages the strengths of different models, resulting in superior overall performance.

Transfer Learning

Transfer learning is a method where a pre-trained neural network is fine-tuned on a new task. This approach is particularly useful when data is scarce, allowing us to leverage the knowledge acquired from previous tasks.

Concept of Transfer Learning

In transfer learning, a model trained on a large dataset is adapted to a smaller, related task. For instance, a model trained on millions of images can be fine-tuned to recognize specific objects in a new dataset.

How to Implement Transfer Learning

To implement transfer learning, we start with a pretrained model, freeze some layers to retain their knowledge, and fine-tune the remaining layers on the new task. This method saves time and computational resources while achieving impressive results.

Advantages of Transfer Learning

Transfer learning enables quicker training times and improved performance, especially when dealing with limited data. It’s like standing on the shoulders of giants, leveraging the vast knowledge accumulated from previous tasks.

Neural Network Fusion

Neural network fusion involves merging multiple networks into a single, unified model. This method combines the strengths of different architectures to create a more powerful and versatile network.

Definition of Neural Network Fusion

Neural network fusion integrates different networks at various stages, such as combining their outputs or merging their internal layers. This approach can enhance the model's ability to handle diverse tasks and data types.

Types of Neural Network Fusion

There are several types of neural network fusion, including early fusion, where networks are combined at the input level, and late fusion, where their outputs are merged. Each type has its own advantages depending on the task at hand.

Implementing Fusion Techniques

To implement neural network fusion, we can combine the outputs of different networks using techniques like averaging, weighted voting, or more sophisticated methods like learning a fusion model. The choice of technique depends on the specific requirements of the task.

Cascade Network

Cascade networks involve feeding the output of one neural network as input to another. This approach creates a layered structure where each network focuses on different aspects of the task.

What is a Cascade Network?

A cascade network is a hierarchical structure where multiple networks are connected in series. Each network refines the outputs of the previous one, leading to progressively better performance.

Advantages and Applications of Cascade Networks

Cascade networks are particularly useful in complex tasks where different stages of processing are required. For example, in image processing, a cascade network can progressively enhance image quality, leading to more accurate recognition.

Practical Examples

Image Recognition

In image recognition, combining CNNs with ensemble methods can improve accuracy and robustness. For instance, a network trained on general image data can be combined with a network fine-tuned for specific object recognition, leading to superior performance.

Natural Language Processing

In natural language processing (NLP), combining RNNs with transfer learning can enhance the understanding of text. A pre-trained language model can be fine-tuned for specific tasks like sentiment analysis or text generation, resulting in more accurate and nuanced outputs.

Predictive Analytics

In predictive analytics, combining different types of networks can improve the accuracy of predictions. For example, a network trained on historical data can be combined with a network that analyzes real-time data, leading to more accurate forecasts.

Challenges and Solutions

Technical Challenges

Combining neural networks can be technically challenging, requiring careful tuning and integration. Ensuring compatibility between different networks and avoiding overfitting are critical considerations.

Data Challenges

Data-related challenges include ensuring the availability of diverse and high-quality data for training. Managing data complexity and avoiding biases are essential for achieving accurate and reliable results.

Possible Solutions

To overcome these challenges, it’s crucial to adopt a systematic approach to model integration, including careful preprocessing of data and rigorous validation of models. Utilizing advanced tools and frameworks can also facilitate the process.

Tools and Frameworks

Popular Tools for Combining Neural Networks

Tools like TensorFlow, PyTorch, and Keras provide extensive support for combining neural networks. These platforms offer a wide range of functionalities and ease of use, making them ideal for both beginners and experts.

Frameworks to Use

Frameworks like Scikit-learn, Apache MXNet, and Microsoft Cognitive Toolkit offer specialized support for ensemble learning, transfer learning, and neural network fusion. These frameworks provide robust tools for developing and deploying combined neural network models.

Future of Combining Neural Networks

Emerging Trends

Emerging trends in combining neural networks include the use of advanced ensemble techniques, the integration of neural networks with other AI models, and the development of more sophisticated fusion methods.

Potential Developments

Future developments may include the creation of more powerful and efficient neural network architectures, enhanced transfer learning techniques, and the integration of neural networks with other technologies like quantum computing.

Case Studies

Successful Examples in Industry

In healthcare, combining neural networks has led to significant improvements in disease diagnosis and treatment recommendations. For example, combining CNNs with RNNs has enhanced the accuracy of medical image analysis and patient monitoring.

Lessons Learned from Case Studies

Key lessons from successful case studies include the importance of data quality, the need for careful model tuning, and the benefits of leveraging diverse neural network architectures to address complex problems.

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Best Practices

Strategies for Effective Combination

Effective strategies for combining neural networks include using ensemble methods to enhance performance, leveraging transfer learning to save time and resources, and adopting a systematic approach to model integration.

Avoiding Common Pitfalls

Common pitfalls to avoid include overfitting, ignoring data quality, and underestimating the complexity of model integration. By being aware of these challenges, we can develop more robust and effective combined neural network models.

Conclusion

Combining two trained neural networks can significantly enhance their capabilities, leading to more accurate and versatile AI models. Whether through ensemble learning, transfer learning, or neural network fusion, the potential benefits are immense. By adopting the right strategies and tools, we can unlock new possibilities in AI and drive advancements across various fields.

FAQs

What is the easiest method to combine neural networks?

The easiest method is ensemble learning, where multiple models are combined to improve performance and accuracy.

Can different types of neural networks be combined?

Yes, different types of neural networks, such as CNNs and RNNs, can be combined to leverage their unique strengths.

What are the typical challenges in combining neural networks?

Challenges include technical integration, data quality, and avoiding overfitting. Careful planning and validation are essential.

How does combining neural networks enhance performance?

Combining neural networks enhances performance by leveraging diverse models, reducing errors, and improving generalization.

Is combining neural networks beneficial for small datasets?

Yes, combining neural networks can be beneficial for small datasets, especially when using techniques like transfer learning to leverage knowledge from larger datasets.

[grey-space-computing]

Deliver personalized user experiences with machine learning in your app. Understand your users better and give them exactly what they need. 🔗Learn more: https://greyspacecomputing.com/custom-mobile-application-development-services/  📧 Visit: https://greyspacecomputing.com/portfolio

[usadvlottery]

US Visa Policies Embrace Innovation: In a dynamic global landscape, the United States is adapting its visa policies to embrace innovation. With a focus on attracting top talent, fostering entrepreneurship, and encouraging technological advancement, these policies aim to facilitate the entry of skilled professionals, researchers, and entrepreneurs who contribute to America's economic growth and technological leadership. Through streamlined processes, expanded visa categories, and initiatives such as the Startup Visa Program, the US is welcoming innovators from around the world, fostering collaboration, and driving innovation forward."

[shansmithac]

Explore the complete guide to web and mobile apps: advantages, challenges, comparisons, pros, and cons. Stay informed with the latest insights and best practices.

[marandsviet]

(via "Artificial Intelligence is Within Us" Classic T-Shirt for Sale by MarandSviet)

[wickedhawtwexler]

writing is so funny. when i first started writing this story in 2016, i included a character who was basically a self-insert character. she is now the lesbian biologist version of walter white