Have you fallen down the ChatGPT rabbit hole yet? Interacting with the latest technology illuminates the fact that we are on the cusp of another artificial intelligence (AI) revolution. It’s no longer a matter of “if” but “when.” In fact, we may already be in the midst of it, and AI technology is infiltrating the Web3 space. So let’s take a look at what all the fuss is about! In the spirit of this article, ChatGPT was used to assist in its creation.
What is ChatGPT, and how did we get here?
ChatGPT is a large language model developed by OpenAI, which is considered to be an example of the latest developments in AI technology. It is a neural network-based model that has been trained on a massive amount of text data, allowing it to generate human-like text in a wide range of contexts. In other words, you can ask ChatGPT to write a story, generate a form, share a recipe, or even create computer code based on the parameters you enter. Impressively, the response is almost instantaneous, and the use-case possibilities are virtually endless.
The evolution of AI technology that led to the development of ChatGPT can be traced back to the early days of AI research in the 1950s, when this new field of study was named. In the beginning, AI focused on developing specific, narrow tasks such as playing chess or solving mathematical equations. This approach is known as “good old-fashioned AI” (GOFAI). However, these systems were not very flexible and could not adapt to new situations.
In the 1980s and 1990s, AI began to focus on developing more general and flexible systems, such as expert systems and decision-support systems. These systems used rule-based systems and knowledge representation techniques to perform specific tasks. They were able to reason and make decisions based on a set of predefined rules. Some projects you likely have heard of from this period include DeepBlue, which beat the reigning world chess champion, and the first toy robot, Furby.
With the invention of machine learning in the 2000s, AI began to shift towards developing systems that could learn from data and improve over time. Machine learning is a method that allows computers to learn from data without being explicitly programmed. This allowed AI systems to become more versatile and adaptable to new situations. Watson, the Jeopardy champion, Siri, and Amazon’s Alexa are a result of these AI advancements.
In recent years, advances in deep learning have led to the development of highly advanced and versatile AI systems, such as ChatGPT. Deep learning is a subset of machine learning that uses neural networks to model complex patterns in data. This allows AI systems to learn and improve through the use of large data sets and neural networks, making them more powerful and able to perform a wider range of tasks. Of note is that ChatGPT’s data set only goes through 2021. Therefore if you asked it to share about an event that occurred in 2022 or beyond, it would be unable to provide factual information.
AI Use-Cases
There are a wide variety of use cases for the latest AI technology, not only in Web3 across several industries. Here are some examples, but keep in mind that this is not an exhaustive list.
Image/video creation and analysis: AI can be used to create images and analyze visual data, such as recognizing objects in images or detecting faces in videos.
Natural language processing: AI can be used to understand and generate human language, such as for speech recognition and language translation.
Robotics: AI can be used to control and coordinate robots, making them more autonomous and capable of performing complex tasks.
Healthcare: AI is being used to assist with diagnosing medical conditions, developing personalized treatment plans, and analyzing medical data to improve patient outcomes.
Finance: AI is being used to detect fraudulent transactions, assess credit risk, and make investment decisions.
Autonomous vehicles: AI is being used to develop autonomous vehicles that can drive themselves without human input.
Retail and E-commerce: AI is being used to recommend products, optimize pricing, and forecast demand.
Cybersecurity: AI is being used to detect and respond to cyber threats in real time.
Agriculture: AI is being used to optimize crop yields, reduce water usage, and detect pests and diseases.
Energy: AI is being used to optimize energy consumption and production and to predict and prevent equipment failures.
What AI tools are available to consumers?
The AI toolbox is growing rapidly! Here are some neat options to check out:
ChatGPT: Generates human-like text in a wide range of contexts. Created by OpenAI.
Chat Sonic: A conversational AI chatbot that provides up-to-date factual information using Google’s knowledge graph for the latest information on events and topics occurring at that moment.
Jasper: An AI Content Generator which offers a handy Chrome extension for direct workflow integration.
GitHub Co-Pilot: GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor.
Point-E: An AI that generates 3D models. Created by OpenAI.
DALL·E 2: A new AI system that can create realistic images and art from a description in natural language. Created by OpenAI.
Stable Diffusion: An easy-to-use interface for creating images using the recently released Stable Diffusion image generation model. An open-source alternative to DALL·E 2.
ImgCreator.AI: An AI image generation tool. ImgCreator.ai is best suited for creating illustrations, anime, and concept design images. You can also provide an image to imgcreator.ai to edit any erased part of this image using a text description, just like text-driven photoshop!
Issues with using AI — ethical and otherwise
Wow, this all sounds amazing and exciting, but what are some potential pitfalls and issues that arise with the use of such technology?
Bias: AI systems can perpetuate and even amplify existing biases in the data they are trained on, leading to unfair and discriminatory outcomes.
Lack of Explainability & Fact Checking: Many AI systems, particularly those based on deep learning, are considered “black boxes” because it is difficult or impossible to understand how they arrived at a particular decision or prediction. AI confidently provides inaccurate/false information so it appears factual to the layman.
Privacy: As AI systems collect and process large amounts of data, there is a risk that this data could be misused or mishandled, potentially violating individuals’ privacy rights. Many Web3 implications will likely arise in this area.
Security: AI systems can be vulnerable to hacking or other forms of malicious attacks, which could compromise sensitive information or disrupt critical services.
IP/Copywrite Complications: Who will have legal rights over AI derivatives? No opt-out option exists for creators/owners to keep their content out of the data sets utilized (atleast for now). This debate has already begun in the Web3 space, especially around AI-generated art.
What’s coming next?
We know that ChatGPT is set to release a new version in 2023 (rumors of March are swirling online). Some speculations as to what may be coming down the pike for the AI technology space as a whole include the use of continuously trained models becoming widespread, meaning the AI will constantly be learning up-to-date information. The integration of AI technology will likely become widespread in daily workflows across an incredible array of industries, and use cases will continue to grow. Will we soon have a bunch of WALL-Es rolling around the planet to help clean up our human mess? Only time can tell.
Like Web3, this space is moving incredibly fast, too fast to figure out how the above issues will be resolved before further advancements are made. The AI train has left the station, and there is no stopping it now! We’d love to hear your thoughts on AI technology. Can you tell what portions of this article were written by ChatGPT? Drop us a line on Twitter to let us know what you think!
Resources:
This article was created with the assistance of ChatGPT
https://www.g2.com/articles/history-of-artificial-intelligence
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