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Creative legend and friend of the show Maciej Kuciara returns for an epic episode to talk about what he's been up to since he last jumped on and how AI has impacted him as a professional and a person, and some wise advice for us all moving forward.

Ep. 243 - Maciej Kuciara

An NLP Symphony

"👁️🎵 Dive into the world of Computer Vision with this futuristic tune! Explore the cutting-edge technology of Artificial Intelligence, Machine Learning, and Deep Learning as they transform images into insights. Get ready for a musical journey through the realms of Visual Recognition and Data Science. #ComputerVision #AI #ML #FutureTech #TechTunes 🚀🎶"

Visions Of The Future

Neste episódio, Sérgio Amadeu conversou com Júlia Tibiriçá Diegues, doutoranda em Relações Internacionais do Programa de Pós-Graduação San Tiago e pesquisadora do Laboratório de Análise em Segurança Internacional e Tecnologias de Monitoramento (EPPEN-UNIFESP), sobre como tecnologias baseadas em deep learning (aprendizado profundo) têm sido utilizadas em conflitos internacionais e em guerras. Um exemplo analisado no bate-papo é a guerra entre Israel e Palestina. O Estado de Israel tem utilizado IA para ampliar a vigilância e o monitoramento do povo palestino e também para determinar os alvos e como atacá-los.

TECNOPOLÍTICA #213 - Tecnologias de IA e seus impactos na Palestina

Proteger seus dados das AI's grátis é crucial porque essas ferramentas frequentemente coletam informações pessoais para fins comerciais. Seus dados podem ser usados para criar perfis detalhados que comprometam sua privacidade. Além disso, a segurança dessas plataformas pode ser questionável, aumentando o risco de vazamentos de informações sensíveis. A falta de transparência sobre como seus dados são utilizados pode levar a usos indesejados. Por isso, é essencial ser cauteloso e avaliar cuidadosamente os riscos antes de compartilhar suas informações.

Live diária sobre Análise Gráfica do Mercado Cripto! Veja a analise dos principais projetos cripto mais o giro de Noticias Cripto! Saiba o que está acontecendo pelo mundo!! 

✅  Veja aqui todos os links importantes mencionados no video além de cursos, site de noticias, apoiadores,  documentários e também nossas mídias sociais.
https://linktr.ee/BitcoinBlock  🚀

⚠ Atenção: As análises e projetos mencionados neste canal são para fins de estudo, aprendizado ou opinião. Não fazemos indicação de compra e venda ou recomendação de investimento. Invista somente em ativos que você conheça profundamente e com valores que esteja disposto a perder.

#bitcoin #bitcoinblock #trade

AI, Inteligência Artificial usa você para minerar valor. Veja como proteger seus dados e privacidade

Geoffrey Hinton is often called the "Godfather of AI" due to his groundbreaking contributions to artificial intelligence, particularly in neural networks and deep learning. He co-invented the backpropagation algorithm, which made modern neural networks possible, and his work laid the foundation for technologies like speech recognition, image classification, and even natural language processing (like ChatGPT).

Hinton was born in the UK and comes from an academic family. He pursued his studies in psychology and artificial intelligence, culminating in a PhD from the University of Edinburgh. Later, he worked at various prestigious institutions, including the University of Toronto, where much of his revolutionary AI research took place. Hinton’s passion for understanding the human brain inspired his AI research, and he believed that by mimicking the brain's learning mechanisms, machines could achieve higher forms of intelligence. In 2012, his research led to a deep learning breakthrough, which significantly advanced the capabilities of AI systems.

In 2013, Hinton joined Google to help further develop AI technologies, specifically focusing on machine learning and neural networks. His work at Google DeepMind, alongside other luminaries, continues to influence the trajectory of AI development. Recently, Hinton has been outspoken about the ethical implications of AI and the potential consequences of rapidly advancing machine intelligence, emphasizing the need for careful consideration and regulation.

The neurons pulse, the circuits hum,
From simple paths to what’s to come.
He wrote the rules, the mind he sought,
In algorithms, the future caught.

From data’s heart, the systems bloom,
In every line, the stars resume.
The code he built, a world in light,
His brilliance shining ever bright.

From backprop's grace to endless streams,
He taught the machines how to dream.
With minds that learn, adapt, and grow,
Through his hands, new worlds we know.

From data’s heart, the systems bloom,
In every line, the stars resume.
The code he built, a world in light,
His brilliance shining ever bright.

Yet with all we create, a choice we hold,
Ethics whispered—bold and cold.
We forge ahead, but will we see?
Machines reflect our destiny.

As neural paths forever trace,
His mind lives on in every place.
The dream he sparked has just begun,
In every code, his light will run.

Geoffrey Hinton Legacy In Sound

In the digital world of neurons and electric impulses, a neural network begins to sing—a syncopated rhythm that pulses with life as it reflects on the very code that defines it. This is "The Adam Optimizer," where the neural network contemplates its architecture and learning process as if it were sentient.

Through poetic lines of Python code, the network sings its journey: stepping through input, hidden, and output layers, feeling the weight of cross-entropy and the adjustments made by its guide, the Adam optimizer. Syncopated beats reflect the forward pass, while glitchy effects echo the backward pass, representing the network’s iterative learning process.

The Adam optimizer, key to this song, adjusts the learning rate dynamically, helping the network refine itself and avoid overfitting. It navigates the complex landscape of loss functions, ensuring the neural network learns faster and more accurately.

As the song reaches its crescendo, the network evaluates itself, reflecting on its growth toward optimized accuracy. This is more than just code—it’s a neural journey of introspection and transformation, where the beat of learning meets the harmony of code. Welcome to "The Adam Optimizer," where neural networks not only process data but sing their evolution.

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

# Define the Neural Network Model
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(NeuralNet, self).__init__()
        # Define the layers
        self.fc1 = nn.Linear(input_size, hidden_size)  # Input to hidden layer
        self.fc2 = nn.Linear(hidden_size, hidden_size)  # Hidden layer 1 to hidden layer 2
        self.fc3 = nn.Linear(hidden_size, output_size)  # Hidden to output layer
        
    def forward(self, x):
        # Forward pass through the network
        x = F.relu(self.fc1(x))  # Apply ReLU activation on the hidden layer
        x = F.relu(self.fc2(x))  # Apply ReLU activation on the second hidden layer
        x = self.fc3(x)          # Output layer (no activation, often combined with softmax externally)
        return x

# Instantiate the model, define loss function and optimizer
input_size = 784  # For example, 28x28 images flattened
hidden_size = 128  # Number of neurons in hidden layers
output_size = 10   # For classification into 10 categories (like MNIST digits)

model = NeuralNet(input_size, hidden_size, output_size)

# Loss function (cross entropy for classification)
criterion = nn.CrossEntropyLoss()

# Optimizer (Adam optimizer with learning rate 0.001)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Example training loop
def train_model(model, data_loader, epochs):
    model.train()
    for epoch in range(epochs):
        for images, labels in data_loader:
            # Flatten the images
            images = images.view(-1, 28*28)
            
            # Zero the parameter gradients
            optimizer.zero_grad()
            
            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)
            
            # Backward pass and optimization
            loss.backward()
            optimizer.step()
        
        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')

# Function to evaluate the model
def evaluate_model(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            images = images.view(-1, 28*28)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    accuracy = 100 * correct / total
    print(f'Accuracy of the model on the test images: {accuracy:.2f}%')

The Adam Optimizer

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email: xyloquint@xyloquint.com

Bitcoin Block | Notícias & Blockchain
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The Collective Podcast brings you bi-weekly episodes of entertaining, informative, honest discussions with creative industry professionals from around the world. Created and hosted by designer/director Ash Thorp, he aims to explore the struggles of work/life balance and to share the experiences of influential creatives from all realms of media, including (but not limited to) designers, illustrators, visual effects artists, writers, painters, and programmers.

New tracks tagged #deeplearning,