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Welcome Dear Readers to the new “AI Focus” newsletter, 7th issue. We hope to brighten up your mood a bit and give you useful insights! Grab your go-to snack, brew a cup of your favorite beverage, and let's jump into it!

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Here’s a quick overview:

1. OpenAI forms safety committee

2. Crazy Google search results

3. China's latest AI chatbot is trained on President Xi Jinping's political ideology

4. What is a Neural Network? Simple Guide

5. Top 5 Writing Tools

OpenAI forms safety committee

OpenAI has announced the formation of a new safety and security committee and the commencement of training for a new artificial intelligence model to replace GPT-4, the foundation of its ChatGPT chatbot. The committee, which includes CEO Sam Altman and Chairman Bret Taylor, along with other board members and technical experts, will advise the board on crucial safety and security decisions.

This move comes amidst ongoing debates about AI safety, highlighted by a recent resignation of a researcher critical of OpenAI’s focus on product development over safety. The committee’s initial task is to assess and enhance OpenAI's safety protocols, with their recommendations to be made public after 90 days.

Speed-up LLM Development with Gretel and Predibase

In this on-demand workshop, Gretel.ai Chief Scientist, Yev Meyer, Ph.D., and Predibase DevRel Engineer, Alex Sherstinsky, will teach you how to leverage Gretel Navigator, a generative AI system designed to create, edit, and augment tabular data using natural language or SQL prompts, and Predibase, the developer platform for fine-tuning and serving LLMS, to quickly and cost-effectively train high-performing LLMs. Access the workshop here. 

In this workshop, you'll learn how to:

• Generate synthetic training data with Gretel Navigator
• Design and iterate on synthetic data for your specific needs
• Leverage Predibase to fine-tune and serve LLMs using state-of-the-art techniques
• Speed up innovation and reduce AI development costs

Watch workshop on-demand

New Google’s AI search giving people crazy answers

Google's experimental "AI Overviews" tool has generated some bizarre and incorrect responses. Users searching for how to make cheese stick to pizza better were told to use "non-toxic glue," and geologists were said to recommend eating one rock per day.

These responses, seemingly based on Reddit comments or satirical articles, have been mocked on social media. Google stated these were isolated incidents and that the tool generally works well, providing high-quality information for most queries.

The company is refining its systems and addressing policy violations. This isn't Google's first issue with AI products; it previously paused its chatbot Gemini due to criticism and faced challenges with its predecessor.

China's latest AI chatbot is trained on President Xi Jinping's political ideology

China has developed a new AI chatbot trained on President Xi Jinping's ideology, "Xi Jinping Thought," as part of efforts to integrate his principles into technology. The chatbot, created by the Cyberspace Administration of China (CAC), was trained on seven databases, including one dedicated to Xi's doctrine. This initiative aligns with broader strategies to promote Xi's ideas, already taught in schools and accessible through the "Study Xi Strong Nation" app. 

The AI model is intended for internal use, focusing on cybersecurity and IT applications, generating reports, summarizing information, and providing translations. However, it remains unclear if the chatbot will be available to the public. This development underscores China's ambitions to lead in AI by 2030, despite facing competition from U.S. firms like OpenAI. Chinese AI models, developed by companies like Alibaba and Baidu, are subject to strict censorship and avoid politically sensitive topics.

What is a Neural Network? Simple Guide

Neural networks are a fundamental concept in artificial intelligence (AI) that mimic the way the human brain processes information. This guide will help you understand what neural networks are, how they work, and introduce you to some basic tools used to create them.

What is a Neural Network?

A neural network is a system of algorithms designed to recognize patterns. It interprets sensory data through a kind of machine perception, labeling, and clustering of raw input. The key components of a neural network are:

1. Neurons: These are the basic units of a neural network. Inspired by the human brain, each neuron receives input, processes it, and passes on the result to the next layer of neurons.

2. Layers:

   - Input Layer: This is where the network receives data. For example, in image recognition, the input layer would receive the pixel values of an image.

   - Hidden Layers: These are layers where computations are performed. The term "hidden" simply means that they are not directly observable in the input or output. There can be multiple hidden layers in a network.

   - Output Layer: This layer produces the final result of the network's computations, such as identifying an object in a picture or predicting a price.

3. Activation Functions: These are mathematical functions that determine the output of a neuron. They introduce non-linearity into the model, allowing the network to learn from the errors and make more accurate predictions.

How Do Neural Networks Work?

Neural networks work by passing data through the input layer, through one or more hidden layers, and finally through the output layer. Here’s a simplified explanation:

1. Data Input: Data is fed into the input layer. For instance, an image's pixel data could be the input.

2. Processing in Hidden Layers: Each neuron in the hidden layers receives input, processes it using a weighted sum and an activation function, and passes the result to the next layer. The weights are adjustable parameters that the network uses to learn from the data.

3. Output Generation: The final layer produces an output based on the processed information from the hidden layers. For example, in an image recognition task, the output layer might indicate the probabilities of different categories (like cat, dog, car, etc.).

4. Learning Process: Neural networks learn through a process called training, where they adjust the weights based on the error in their predictions. This is done using algorithms like backpropagation, which helps minimize the error by adjusting the weights iteratively.

Introduction to Keras and TensorFlow

While understanding the theoretical aspects of neural networks is important, creating and training them is where the magic happens. Two popular tools for building neural networks are Keras and TensorFlow.

- TensorFlow: Developed by Google, TensorFlow is a powerful open-source platform for machine learning. It provides a comprehensive ecosystem for building and deploying machine learning models.

- Keras: Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. It’s user-friendly, modular, and easy to extend, making it an excellent choice for beginners and professionals alike.

Practical Applications

Neural networks are used in a variety of applications, including:

- Image Recognition: Identifying objects within images, used in technologies like facial recognition and medical imaging.

- Speech Recognition: Converting spoken language into text, used in virtual assistants like Siri and Alexa.

- Language Translation: Automatically translating text from one language to another, used in services like Google Translate.

- Game Playing: Learning strategies to play and win games, such as in AlphaGo, which defeated human champions in the game of Go.

Neural networks are a cornerstone of modern AI, enabling machines to perform tasks that typically require human intelligence. By simulating how the human brain processes information, neural networks can recognize patterns, make decisions, and improve through learning.

Top 5 Writing Tools

1️⃣ Academic Help

A platform offering a variety of writing tools and resources to assist with academic writing.

2️⃣ Deceptioner

A writing tool designed to enhance creativity and improve the quality of written content.

3️⃣ Quick Creator

A tool for generating content quickly and efficiently with minimal effort.

4️⃣ Jenni AI

An AI-powered writing assistant that helps create and improve various types of documents.

5️⃣ AISEO

A platform providing AI-driven SEO and writing tools to optimize and enhance content.

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Missed prior reads … don’t fret, with GenAI nothing is old hat. Grab a beverage and slip into the archives: Scarlett Johansson's OpenAI Dispute, Apple's Next Move, and AI in Cancer Research |AI Talks, Global Engagement, and Future Legislation: This Week's Insight |Apple's Next Wave of Innovations and More | Weekly Dive into Future Tech | Insights & Updates: Navigating the AI Landscape | Roses are red, violets are blue, NEW newsletter is here for YOU!