GPT-4 Launches Today: The Rise of Generative AI from Neural Networks to DeepMind and OpenAI

GPT-4 launch illustrated with Stable Diffusion (CC BY-SA 4.0)
GPT-4 launch illustrated with Stable Diffusion (CC BY-SA 4.0)

With today’s launch of OpenAI’s GPT-4, the next generation of its Large Language Model (LLM), generative AI has entered a new era. This latest model is more advanced and multimodal, meaning GPT-4 can understand and generate responses based on image input as well as traditional text input (see GPT-4 launch livestream).

Generative AI has rapidly gained popularity and awareness in the last few months, making it crucial for businesses to evaluate and implement strategies across a wide range of industries, including e-commerce and healthcare. By automating tasks and creating personalized experiences for users, companies can increase efficiency and productivity in various areas of value creation. Despite being in development for decades, it’s high time for businesses to apply generative AI to their workflows and reap its benefits.

Before you dive into GPT-4, let’s take a quick look back at the evolution of generative AI…

The history of generative AI begins in the late 1970s and early 1980s when researchers began developing neural networks that mimicked the structure of the human brain. The idea behind this technology was to assemble a set of neurons that could pass information from one to another with some basic logic, and together the network of neurons could perform complicated tasks. While minimal advances were made in the field, it remained largely dormant until 2010, when Google pioneered deep neural networks that added more data, hardware, and computing resources.

In 2011, Apple launched Siri, the first mass-market speech recognition application. In 2012, Google used the technology to identify cats in YouTube videos, finally reviving the field of neural networks and AI. Both Google and NVIDIA invested heavily in specialized hardware to support neural networks. In 2014, Google acquired DeepMind, which built neural networks for gaming. DeepMind built AlphaGo, which went on to defeat all the top Go players, a pivotal moment because it was one of the first industrial applications of generative AI, which uses computers to generate human-like candidate moves.

In 2015, OpenAI was founded to democratize AI and was established as a non-profit organization. In 2019, OpenAI released GPT-2, a large-scale language model capable of producing human-like text. However, GPT-2 sparked controversy because it could produce fake news and disinformation, raising concerns about the ethics of generative AI.

In 2021, OpenAI launched DALL-E, a neural network that can create original, realistic images and art from textual description. It can combine concepts, attributes, and styles in novel ways. A year later, Midjourney was launched by the independent research lab Midjourney. Also in 2022, Stable Diffusion, an open-source machine learning model developed by LMU Munich, was released that can generate images from text, modify images based on text, or fill in details in low-resolution or low-detail images.

OpenAI launched ChatGPT in November 2022 as a fine-tuned version of the GPT-3.5 model. It was developed with a focus on enhancing the model’s ability to process natural language queries and generate relevant responses. The result is an AI-powered chatbot that can engage in meaningful conversations with users, providing information and assistance in real-time. One of the key advantages of ChatGPT is its ability to handle complex queries and provide accurate responses. The model has been trained on a vast corpus of data, allowing it to understand the nuances of natural language and provide contextually relevant responses.

Today’s launch of GPT-4 marks a significant milestone in the evolution of generative AI!

This latest model, GPT-4, is capable of answering user queries via text and image input. The multimodal model demonstrates remarkable human-level performance on various professional and academic benchmarks, indicating the potential for widespread adoption and use. One of the most significant features of GPT-4 is its ability to understand and process image inputs, providing users with a more interactive and engaging experience. Users can now receive responses in the form of text output based on image inputs, which is a massive step forward in the evolution of AI.

Bing has already integrated GPT-4 and offers both chat and compose modes for users to interact with the model. With the integration of GPT-4, Bing has significantly enhanced its capabilities to provide users with more accurate and personalized search results, making it easier for them to find what they are looking for.

The disruptive potential of generative AI is enormous, particularly in the retail industry. The technology can create personalized product recommendations and content, and even generate leads, saving sales teams time and increasing productivity. However, the ethical implications of generative AI cannot be ignored, particularly in the creation of disinformation and fake news.

To sum up, generative AI is here to stay, and companies must evaluate and implement strategies swiftly. As generative AI technology advances, so do the ethical concerns surrounding its use. Therefore, it is critical for companies to proceed with caution and consider the potential consequences of implementing generative AI into their operations.

Are you already using generative AI for a more productive workflow?

What improvement do you expect from GPT-4 in this regard? I look forward to reading your ideas in the comments to this LinkedIn post:

Authenticity in Photography: Samsung’s Moon Shots Controversy and the Ethics of Synthetic Media

Side-by-side comparison of the original capture and the synthesized version
Side-by-side comparison of the original capture and the synthesized version: Generative AI technology adds texture and details on moon shots, blurring the line between real and synthesized images.

Generative AI has made waves around the world with its ability to create images, videos, and music that are indistinguishable from human-made content. But what happens when this technology is applied to photography, and the images we capture on our devices are no longer entirely real?

While Samsung claims that no overlays or texture effects are applied, a recent Reddit post suggests otherwise. The post provides evidence that Samsung’s moon shots are “fake” and that the camera actually uses AI/ML to recover/add the texture of the moon to the images.

The use of AI in photography is not new, as many devices already use machine learning to improve image quality. But the use of generative AI to create entirely new images raises ethical questions about the authenticity of the content we capture and share – especially when the photographer is unaware that their images are being augmented with synthesized content.

What do you think about the use of generative AI in photography? Is it okay for a phone to use this technology to synthesize a photo, or is it crossing a line?

Join the conversation on LinkedIn:

Storytelling with Data: A Data Visualization Guide for Business Professionals

Storytelling with Data: A Data Visualization Guide for Business Professionals
Storytelling with Data: A Data Visualization Guide for Business Professionals

We know that data can be a powerful tool for decision-making, but it can also be overwhelming and difficult to communicate to others in a meaningful way. This is where Storytelling with Data by Cole Nussbaumer Knaflic comes in. I chose this book for our #datamustread book club because it offers a practical guide to creating compelling data stories that resonate with your audience. Today, as we celebrate International Women’s Day, I’m excited to share this book written by a talented female author.

The Power of Storytelling with Data

In this book, we’ll explore the importance of storytelling in data visualization and how it can make your data more impactful. Cole emphasizes the need to understand your audience and context before creating a data story. Using real-world examples, she will show you how to go beyond the traditional tools to create a narrative that is engaging, informative, and compelling.

Data Visualization Best Practices

Storytelling with Data covers the key principles of effective data visualization. From choosing the right type of graph to eliminating clutter, Cole provides practical tips and tools for making your data visually appealing and easy to understand. She also shows how to use design concepts to make your data more engaging.

The Art of Data Storytelling

We’ll also learn how to craft a data story that resonates with your audience. Cole explains how to use data to create a compelling narrative that leads your audience to the key insights. By harnessing the power of storytelling, you can create visual stories that stick with your audience.

Throughout the book, Cole uses numerous real-world examples to illustrate her points. From the good to the bad, she provides insightful commentary on a variety of data stories. She also shares anecdotes and personal experiences that demonstrate the power of data storytelling.

Order Storytelling with Data Today

Storytelling with Data is essential reading for anyone who wants to improve their data visualization skills. By following the lessons in this book, you can turn your data into powerful visual stories that resonate with your audience. If you’re looking to make your data more compelling and actionable, I highly recommend this book. You can order it using this link, which supports both me and the author. If you want to practice more, consider reading one of the other books written by the same author: Storytelling with Data: Let’s Practice!

The Big Book of Dashboards: A Comprehensive Guide to Building Effective Business Dashboards

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios
The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios

As a data enthusiast, you know that creating an effective dashboard is not just about presenting data, it’s about communicating insights and making data-driven decisions. That’s why I’ve chosen The Big Book of Dashboards for our #datamustread book club. Written by Andy Cotgreave, Steve Wexler, and Jeffrey Shaffer, this book is a comprehensive reference that provides real-world solutions for building effective business dashboards.

Let’s dive into what makes this book so valuable for data enthusiasts.

Real-world solutions for building effective business dashboards

The Big Book of Dashboards covers dozens of examples that address different industries and departments, such as healthcare, transportation, finance, human resources, marketing, customer service, and sports, among others. It also covers different platforms, including print, desktop, tablet, smartphone, and conference room displays. With this book as your guide, you’ll be able to match great dashboards with real-world business scenarios.

Practical and effective visualization examples

The book is organized based on scenarios and offers practical and effective visualization examples. You’ll find an entire section of the book devoted to addressing many practical and psychological factors you will encounter in your work. The expert authors have combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They bring uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world.

Tools, guidance, and models to produce great dashboards

The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage. A well-designed dashboard can point out risks, opportunities, and more, but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. This book will help you avoid those challenges and produce dashboards that truly make a difference.

Examples of modern, effective designs

What sets The Big Book of Dashboards apart from its peers is that it contains a number of examples of modern, effective designs. The examples are of good visualizations, rather than bad, and this focus means that the reader is exposed to a vast range of quality content, rather than endless examples of what “not to do.”

Order The Big Book of Dashboards Today

Once you’ve read The Big Book of Dashboards, you’ll want to keep it nearby as a guide for your data visualization projects. The book provides 20+ high-quality dashboards based on all kinds of data from different industries, with detailed explanations of why certain chart types, layouts, and colors were used, why they work well, why other choices were not good enough, and what could be changed to make the dashboard even better.

Order your copy of The Big Book of Dashboards today with this link that supports both me and the authors. Also, comment on the LinkedIn post below for a chance to enter our book giveaway. Happy reading!

9 Key Elements of a Successful Data Strategy for Business Growth

Get the Competitive Edge with Decisively Digital - The Ultimate Guide to Data Strategy
Get the Competitive Edge with Decisively Digital – The Ultimate Guide to Data Strategy

Data is a valuable asset that can give businesses a competitive edge and drive growth in today’s digital age. But without a clear and well-defined data strategy, companies risk missing out on the benefits that data provides. To help your business succeed in the digital world, here’s an overview of nine essential elements of a comprehensive data strategy.

    1. Goals and Objectives: Define specific goals and objectives that the company wants to achieve through its data efforts, such as improving customer experiences or optimizing business processes.
    2. Data Sources: Identify the most valuable data types and determine where they will come from, such as internal transaction or customer data and external market research.
    3. Data Management and Storage: Outline how data will be collected, organized, and stored consistently, accurately, and compliantly, with data management tools and technologies.
    4. Data Analysis and Reporting: Define how data will be analyzed and used to inform business decisions, with data visualization tools, dashboards, and reporting systems.
    5. Data Governance: Establish clear roles and responsibilities for data management, guidelines for data use and access, and ensure ethical and regulatory compliance.
    6. Data-driven Culture: Foster a data-driven culture by providing training and resources for data-driven decision making.
    7. Data Security and Privacy: Ensure data is collected, stored, and used securely and in compliance with privacy regulations.
    8. Data Integration and Interoperability: Define how data will be integrated and shared across systems and platforms.
    9. Data Quality and Accuracy: Ensure data is accurate and up-to-date, with processes for data cleansing and enrichment.

A data strategy is a must-have tool for any company that wants to fully realize the benefits of its data. It provides a clear roadmap for data collection, management, and analysis and helps organizations make better use of their data, drive growth, and succeed in today’s digital world. Get more insights and in-depth information by reading the book Decisively Digital (on Amazon).