#Neural Network Algorithm

[projectchampionz]

DESIGN AND IMPLEMENTATION OF CHATBOT FOR STUDENT INFORMATION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK ALGORITHM

DESIGN AND IMPLEMENTATION OF CHATBOT FOR STUDENT INFORMATION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK ALGORITHM Abstract: Nowadays humans cannot be separated from technology because it has played a great role in human lives. With the development of technology, many things could be easier to do. One of the technologies that can make human lives easier is a chatbot. Chatbot is a digital…

[verilog-official]

hey i need some help

I'm developing a video compression algorithm and I'm trying to figure out how to encode it in a way that actually looks good.

basically, each frame is made up of a grid of 5x8 pixel tiles, each cell being one of 16 tiles. 8 of these tiles can be anything, while the other 8 are hard-coded.

so far, my algorithm simply compares each tile of the input frame to each hard-coded tile, and the 8 tiles that match the least are set to the "custom" tiles, and the other ones that match the hard-coded ones more are set to those hard-coded ones.

this works okay, but doesn't account for if two input frame tiles are the same thing or similar, it would be better to re-use custom tiles (eg, if the whole screen is black — due to the limitations of the screen I'm using, a solid black tile must be a custom tile, but a solid white one can be hard coded).

speed isn't that important, as each frame is only 80x16 pixels at the most, with one bit per pixel, and each tile is 5x8 pixels, for a grid of 16x2 tiles.

TL;DR: I need help writing an algorithm that can arrange 16 tiles into a 16x2 grid, while also determining the best pattern to set 8 of those tiles to, while leaving the other 8 constant.

[ai-innova7ions]

Neturbiz Enterprises - AI Innov7ions

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence. These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware. The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact.

[frank-olivier]

The Mathematical Foundations of Machine Learning

In the world of artificial intelligence, machine learning is a crucial component that enables computers to learn from data and improve their performance over time. However, the math behind machine learning is often shrouded in mystery, even for those who work with it every day. Anil Ananthaswami, author of the book "Why Machines Learn," sheds light on the elegant mathematics that underlies modern AI, and his journey is a fascinating one.

Ananthaswami's interest in machine learning began when he started writing about it as a science journalist. His software engineering background sparked a desire to understand the technology from the ground up, leading him to teach himself coding and build simple machine learning systems. This exploration eventually led him to appreciate the mathematical principles that underlie modern AI. As Ananthaswami notes, "I was amazed by the beauty and elegance of the math behind machine learning."

Ananthaswami highlights the elegance of machine learning mathematics, which goes beyond the commonly known subfields of calculus, linear algebra, probability, and statistics. He points to specific theorems and proofs, such as the 1959 proof related to artificial neural networks, as examples of the beauty and elegance of machine learning mathematics. For instance, the concept of gradient descent, a fundamental algorithm used in machine learning, is a powerful example of how math can be used to optimize model parameters.

Ananthaswami emphasizes the need for a broader understanding of machine learning among non-experts, including science communicators, journalists, policymakers, and users of the technology. He believes that only when we understand the math behind machine learning can we critically evaluate its capabilities and limitations. This is crucial in today's world, where AI is increasingly being used in various applications, from healthcare to finance.

A deeper understanding of machine learning mathematics has significant implications for society. It can help us to evaluate AI systems more effectively, develop more transparent and explainable AI systems, and address AI bias and ensure fairness in decision-making. As Ananthaswami notes, "The math behind machine learning is not just a tool, but a way of thinking that can help us create more intelligent and more human-like machines."

The Elegant Math Behind Machine Learning (Machine Learning Street Talk, November 2024)

Matrices are used to organize and process complex data, such as images, text, and user interactions, making them a cornerstone in applications like Deep Learning (e.g., neural networks), Computer Vision (e.g., image recognition), Natural Language Processing (e.g., language translation), and Recommendation Systems (e.g., personalized suggestions). To leverage matrices effectively, AI relies on key mathematical concepts like Matrix Factorization (for dimension reduction), Eigendecomposition (for stability analysis), Orthogonality (for efficient transformations), and Sparse Matrices (for optimized computation).

The Applications of Matrices - What I wish my teachers told me way earlier (Zach Star, October 2019)

Transformers are a type of neural network architecture introduced in 2017 by Vaswani et al. in the paper “Attention Is All You Need”. They revolutionized the field of NLP by outperforming traditional recurrent neural network (RNN) and convolutional neural network (CNN) architectures in sequence-to-sequence tasks. The primary innovation of transformers is the self-attention mechanism, which allows the model to weigh the importance of different words in the input data irrespective of their positions in the sentence. This is particularly useful for capturing long-range dependencies in text, which was a challenge for RNNs due to vanishing gradients. Transformers have become the standard for machine translation tasks, offering state-of-the-art results in translating between languages. They are used for both abstractive and extractive summarization, generating concise summaries of long documents. Transformers help in understanding the context of questions and identifying relevant answers from a given text. By analyzing the context and nuances of language, transformers can accurately determine the sentiment behind text. While initially designed for sequential data, variants of transformers (e.g., Vision Transformers, ViT) have been successfully applied to image recognition tasks, treating images as sequences of patches. Transformers are used to improve the accuracy of speech-to-text systems by better modeling the sequential nature of audio data. The self-attention mechanism can be beneficial for understanding patterns in time series data, leading to more accurate forecasts.

Attention is all you need (Umar Hamil, May 2023)

Geometric deep learning is a subfield of deep learning that focuses on the study of geometric structures and their representation in data. This field has gained significant attention in recent years.

Michael Bronstein: Geometric Deep Learning (MLSS Kraków, December 2023)

Traditional Geometric Deep Learning, while powerful, often relies on the assumption of smooth geometric structures. However, real-world data frequently resides in non-manifold spaces where such assumptions are violated. Topology, with its focus on the preservation of proximity and connectivity, offers a more robust framework for analyzing these complex spaces. The inherent robustness of topological properties against noise further solidifies the rationale for integrating topology into deep learning paradigms.

Cristian Bodnar: Topological Message Passing (Michael Bronstein, August 2022)

Sunday, November 3, 2024

[ai-revolution]

Artificial Intelligence Revolutionizes the Music World: The Case of "Neural Notes Revolution"

Artificial intelligence (AI) is rapidly transforming our world, permeating sectors from healthcare to industry, education to transportation. This technology, which aims to replicate and surpass human cognitive abilities, promises to revolutionize the way we live and work.

The applications of AI are numerous and ever-expanding: from medical diagnosis to autonomous driving, data analysis to content creation. A particularly intriguing field is music, where AI is demonstrating remarkable potential.

Recently, there has been much discussion about AI-based music generation platforms like "Suno" and "Udio," accused of violating numerous artists' copyrights to train their algorithms. These controversies highlight the complex ethical and legal issues that AI raises in the artistic field.

In this context, the Italian project "Neural Notes Revolution" emerges, demonstrating how, with the aid of AI programs, the study of algorithms suitable for targeted generation of musical styles, voices, song structures, and with adequate post-processing, it's possible to produce musical pieces of any genre and style, in any language, in relatively short timeframes.

The project also leverages other generative AI platforms such as OpenAI's ChatGPT (Microsoft group, of which Elon Musk was a co-founder), Anthropic's Claude AI, and Google's Gemini. These technologies allow for the generation of texts, both original and based on precise or imaginative prompts, in numerous languages, even using expressions typical of specific localities and dialects.

However, "Neural Notes Revolution" still faces some challenges. The results provided by ChatBOTs require careful verification, and in the music field, generation platforms have significant limitations. In particular, "Suno" and "Udio" lack a precise and rigorous syntax that allows for accurate results. Often, the outcomes are even opposite to those desired, forcing a trial-and-error approach. One of the major limitations is the near-total impossibility of having clear style changes within the same song.

Expected future developments include the ability to modify produced songs in a targeted manner. It would be useful to have separate files for the vocal part, the musical backing, and the lyrics in subtitle format. Moreover, there's hope to be able to modify individual parts of text or music, and above all, to have a correct and rigorously respected syntax for the song structure and use of styles.

The use of these platforms raises several issues. On one hand, they offer new creative possibilities and democratize music production. On the other, they raise concerns about copyright, artistic authenticity, and the future of work in the music industry.

In conclusion, while giving space to creativity, we are still far from competing with the styles, voices, and tones of artists of all time. However, in defense of the "new artists" of the AI era, it must be recognized that creativity and skill are still necessary to produce musical pieces of a certain depth. This is particularly relevant in a modern musical landscape that often offers music devoid of artistic and cultural significance. AI in music thus represents both a challenge and an opportunity, requiring a balance between technological innovation and preservation of human artistic expression.

[jcmarchi]

From Recurrent Networks to GPT-4: Measuring Algorithmic Progress in Language Models - Technology Org

New Post has been published on https://thedigitalinsider.com/from-recurrent-networks-to-gpt-4-measuring-algorithmic-progress-in-language-models-technology-org/

From Recurrent Networks to GPT-4: Measuring Algorithmic Progress in Language Models - Technology Org

In 2012, the best language models were small recurrent networks that struggled to form coherent sentences. Fast forward to today, and large language models like GPT-4 outperform most students on the SAT. How has this rapid progress been possible? 

Image credit: MIT CSAIL

In a new paper, researchers from Epoch, MIT FutureTech, and Northeastern University set out to shed light on this question. Their research breaks down the drivers of progress in language models into two factors: scaling up the amount of compute used to train language models, and algorithmic innovations. In doing so, they perform the most extensive analysis of algorithmic progress in language models to date.

Their findings show that due to algorithmic improvements, the compute required to train a language model to a certain level of performance has been halving roughly every 8 months. “This result is crucial for understanding both historical and future progress in language models,” says Anson Ho, one of the two lead authors of the paper. “While scaling compute has been crucial, it’s only part of the puzzle. To get the full picture you need to consider algorithmic progress as well.”

The paper’s methodology is inspired by “neural scaling laws”: mathematical relationships that predict language model performance given certain quantities of compute, training data, or language model parameters. By compiling a dataset of over 200 language models since 2012, the authors fit a modified neural scaling law that accounts for algorithmic improvements over time. 

Based on this fitted model, the authors do a performance attribution analysis, finding that scaling compute has been more important than algorithmic innovations for improved performance in language modeling. In fact, they find that the relative importance of algorithmic improvements has decreased over time. “This doesn’t necessarily imply that algorithmic innovations have been slowing down,” says Tamay Besiroglu, who also co-led the paper.

“Our preferred explanation is that algorithmic progress has remained at a roughly constant rate, but compute has been scaled up substantially, making the former seem relatively less important.” The authors’ calculations support this framing, where they find an acceleration in compute growth, but no evidence of a speedup or slowdown in algorithmic improvements.

By modifying the model slightly, they also quantified the significance of a key innovation in the history of machine learning: the Transformer, which has become the dominant language model architecture since its introduction in 2017. The authors find that the efficiency gains offered by the Transformer correspond to almost two years of algorithmic progress in the field, underscoring the significance of its invention.

While extensive, the study has several limitations. “One recurring issue we had was the lack of quality data, which can make the model hard to fit,” says Ho. “Our approach also doesn’t measure algorithmic progress on downstream tasks like coding and math problems, which language models can be tuned to perform.”

Despite these shortcomings, their work is a major step forward in understanding the drivers of progress in AI. Their results help shed light about how future developments in AI might play out, with important implications for AI policy. “This work, led by Anson and Tamay, has important implications for the democratization of AI,” said Neil Thompson, a coauthor and Director of MIT FutureTech. “These efficiency improvements mean that each year levels of AI performance that were out of reach become accessible to more users.”

“LLMs have been improving at a breakneck pace in recent years. This paper presents the most thorough analysis to date of the relative contributions of hardware and algorithmic innovations to the progress in LLM performance,” says Open Philanthropy Research Fellow Lukas Finnveden, who was not involved in the paper.

“This is a question that I care about a great deal, since it directly informs what pace of further progress we should expect in the future, which will help society prepare for these advancements. The authors fit a number of statistical models to a large dataset of historical LLM evaluations and use extensive cross-validation to select a model with strong predictive performance. They also provide a good sense of how the results would vary under different reasonable assumptions, by doing many robustness checks. Overall, the results suggest that increases in compute have been and will keep being responsible for the majority of LLM progress as long as compute budgets keep rising by ≥4x per year. However, algorithmic progress is significant and could make up the majority of progress if the pace of increasing investments slows down.”

Written by Rachel Gordon

Source: Massachusetts Institute of Technology

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[la-principessa-nuova]

A fun way to think about this, for people who don’t know what an algorithm really is, is that algorithm is basically a fancy word for recipe. It’s a set of instructions to take a set of inputs (ingredients) and produce a desired output.

When a site produces an algorithm to recommend content, the input comes from the information they have about you, other users, and the content they are ranking, and the output is an ordered list of top picks for you. It basically takes in everything and then the person writing the algorithm chooses which inputs to actually use.

Imagine that like as the entire grocery store is available to be used, so the person writing the recipe lists out the specific ones used within the steps of the recipe as ingredients to use.

Then they write all the steps in a way that another cook could understand, maybe with helpful notes (comments) along the way describing why they did certain things. And in the end they have a recipe that someone else could follow, make informed changes to, explain the reasoning for decisions, etc.

That’s a traditional algorithm. Sorting by a single field like kudos is the simplest form of this, like a recipe for toast.

Ingredients:

Bread, 1 slice

Instructions:

Put the bread in the toaster.

Pull down the lever.

Wait until it pops up.

Enjoy your toast!

Is that a recipe? Yes, clearly.

Now let’s consider what the equivalent of ML-based recommendation systems (the key differentiator of what people often refer to as “the algorithm”) is:

Ingredients:

The entire grocery store

Instructions:

Put the grocery store into THE MACHINE

THE MACHINE should be set to 0.135, 0.765, 0.474, 0.8833… (this list continues for hundreds of entries)

Consume your personalized Feed™️

Is that a recipe? Technically yes.

“Ao3 needs an algorithm” no it doesn’t, part of the ao3 experience is scrolling through pages of cursed content looking for the one fic you want to read until you get distracted by a summary so cursed that it completely derails your entire search

[ice-shoes]

Not targeted at anyone in particular and I'm saying this as someone who is extremely anti-ChatGPT, but I am begging people to be specific when they talk about AI, when it's not clear what the context is. Saying AI is like saying science, obviously there are some commonalities but the term can literally cover so many things.

[niggadiffusion]

Beats, Bytes & The Future Sound: AI Meets Electronic Music

Electronic music has always been about pushing boundaries, breaking rules, and bending sound into new dimensions. Now, artificial intelligence is stepping into the booth, reshaping how beats are built, melodies emerge, and tracks come to life. This isn’t about robots replacing producers—it’s about a new creative partnership, where human intuition meets machine-driven possibilities.The Evolution…

[manmishra]

Quantum Computing and Artificial Intelligence: The Future of Technology

Discover how quantum computing is revolutionizing artificial intelligence. Learn about Quantum AI, its applications, quantum algorithms, and how it can accelerate AGI development. Explore the future of AI powered by quantum computing.

Quantum computing and artificial intelligence (AI) are two of the most revolutionary technological advancements in modern times. AI has already made significant progress using classical computers, but its potential is hindered by the computational limits of traditional computing systems. Quantum computing, with its immense processing power, is expected to drive AI into new frontiers, enabling…

[msn-technology]

Top 9 AI Tools for Data Analytics in 2025

In 2025, the landscape of data analytics is rapidly evolving, thanks to the integration of artificial intelligence (AI). AI-powered tools are transforming how businesses analyze data, uncover insights, and make data-driven decisions. Here are the top nine AI tools for data analytics that are making a significant impact: 1. ChatGPT by OpenAI ChatGPT is a powerful AI language model developed by…

[aifrontiers]

The Building Blocks of AI - Algorithms, Data, and Neural Networks

💡 Lesson 2 is live!

We’re diving deeper into the core building blocks of AI—algorithms, data, and neural networks. 🧠 In this lesson, you’ll learn how these elements power AI systems, from simple models to advanced neural networks that mimic the human brain.

📺 Watch now: AI Fundamentals: Lesson 2 - Mastering Algorithms, Data, and Neural Networks

Don’t miss this important step in mastering AI!

[ai-innova7ions]

The Magic of AI!

Minimax AI - Text to Video Generator (For now it is Free to Use!).

The Text to Video is amazingly simple to use and from what I've seen so far it is unlimited.

There are several other Videos to explore and use their video prompts to generate your very own videos. - Pretty Awesome!

Are you absolutely certain that you want to miss out on the AI Trend?

[innova7ions]

Discover the Power of Fliki AI for Creators!

Fliki AI is a cutting-edge tool revolutionizing the AI industry by offering a seamless platform for generating high-quality content efficiently. Important Note: This video contains Affiliate links, where if someone navigates to them, there is a possibility that a commission will be paid to me by the affiliate.

Fliki AI:

With the increasing demand for AI-generated content, Fliki AI stands out as a game-changer in the field. Generating AI content efficiently is crucial for businesses and content creators looking to streamline their workflow and produce engaging articles quickly.

Fliki AI simplifies the content creation process, saving time and resources while maintaining top-notch quality. By using Fliki AI, you can unlock a plethora of benefits such as improved productivity, enhanced creativity, and access to a wide range of article ideas tailored to your specific needs. Stay tuned to discover how Fliki AI can elevate your content creation!

#aicontentgeneration #artificialintelligencerevolution

[jcmarchi]

The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

New Post has been published on https://thedigitalinsider.com/the-way-the-brain-learns-is-different-from-the-way-that-artificial-intelligence-systems-learn-technology-org/

The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

Researchers from the MRC Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning.

This new insight may guide further research on learning in brain networks and may inspire faster and more robust learning algorithms in artificial intelligence.

Study shows that the way the brain learns is different from the way that artificial intelligence systems learn. Image credit: Pixabay

The essence of learning is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In artificial intelligence, this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.

However, the biological brain is superior to current machine learning systems. For example, we can learn new information by just seeing it once, while artificial systems need to be trained hundreds of times with the same pieces of information to learn them.

Furthermore, we can learn new information while maintaining the knowledge we already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.

These observations motivated the researchers to identify the fundamental principle employed by the brain during learning. They looked at some existing sets of mathematical equations describing changes in the behaviour of neurons and in the synaptic connections between them.

They analysed and simulated these information-processing models and found that they employ a fundamentally different learning principle from that used by artificial neural networks.

In artificial neural networks, an external algorithm tries to modify synaptic connections in order to reduce error, whereas the researchers propose that the human brain first settles the activity of neurons into an optimal balanced configuration before adjusting synaptic connections.

The researchers posit that this is in fact an efficient feature of the way that human brains learn. This is because it reduces interference by preserving existing knowledge, which in turn speeds up learning.

Writing in Nature Neuroscience, the researchers describe this new learning principle, which they have termed ‘prospective configuration’. They demonstrated in computer simulations that models employing this prospective configuration can learn faster and more effectively than artificial neural networks in tasks that are typically faced by animals and humans in nature.

The authors use the real-life example of a bear fishing for salmon. The bear can see the river and it has learnt that if it can also hear the river and smell the salmon it is likely to catch one. But one day, the bear arrives at the river with a damaged ear, so it can’t hear it.

In an artificial neural network information processing model, this lack of hearing would also result in a lack of smell (because while learning there is no sound, backpropagation would change multiple connections including those between neurons encoding the river and the salmon) and the bear would conclude that there is no salmon, and go hungry.

But in the animal brain, the lack of sound does not interfere with the knowledge that there is still the smell of the salmon, therefore the salmon is still likely to be there for catching.

The researchers developed a mathematical theory showing that letting neurons settle into a prospective configuration reduces interference between information during learning. They demonstrated that prospective configuration explains neural activity and behaviour in multiple learning experiments better than artificial neural networks.

Lead researcher Professor Rafal Bogacz of MRC Brain Network Dynamics Unit and Oxford’s Nuffield Department of Clinical Neurosciences says: ‘There is currently a big gap between abstract models performing prospective configuration, and our detailed knowledge of anatomy of brain networks. Future research by our group aims to bridge the gap between abstract models and real brains, and understand how the algorithm of prospective configuration is implemented in anatomically identified cortical networks.’

The first author of the study Dr Yuhang Song adds: ‘In the case of machine learning, the simulation of prospective configuration on existing computers is slow, because they operate in fundamentally different ways from the biological brain. A new type of computer or dedicated brain-inspired hardware needs to be developed, that will be able to implement prospective configuration rapidly and with little energy use.’

Source: University of Oxford

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[neturbizenterprises]

Neturbiz Enterprises YouTube Channel - AI - Innovations

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence.

These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware.

The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact. The AI industry presents numerous opportunities for affiliate marketers to promote cutting-edge tools and services. Each Platform may offer different commission rates.

#artificial intelligence #generative ai