Why do so many users, including Bubble users, use OpenAI, even though more models are available in the Groq Cloud, Groq is faster and cheaper, and there’s even a free plan available? And more models are being added all the time?
OpenAI was essentially first to market with an easy to use API and is now widely established on bubble via streaming plugins
Groq has also an easy to use API and plugins and templates for Bubble are available.
Groq sucks if you hit the limits of the free plan. They have a ‘pay as you go’ developer plan which is just a teaser for enterprise sales. Groq have no intention of releasing a plan for the every day developer anytime soon, it is all just a funnel to get enterprise clients.
If you can work with the low rate limits for free then it is great. If you can’t then it won’t work for you. The middle plan on Groq simply doesn’t exist.
You can get something like this for free on the free plan in 20 seconds. I can’t see any limits.
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The Complete History of Artificial Intelligence: From Ancient Dreams to Modern Reality
Artificial intelligence (AI) has been a topic of human fascination for centuries, with ancient Greeks mythologizing about robots and artificial beings. However, it wasn’t until the mid-20th century that AI began to take shape as a field of research and development. In this article, we’ll delve into the complete history of AI, highlighting the key milestones, breakthroughs, and the most important protagonists who have contributed to its evolution.
Ancient Origins (3000 BCE - 1950 CE)
The concept of artificial intelligence dates back to ancient Greece, where myths told of robots and artificial beings created to serve human-like purposes. The Greek myth of Pygmalion, who fell in love with his own creation, Galatea, is a prime example. Similarly, in ancient China, the Lie Zi text (circa 300 BCE) describes a mechanical robot that could perform tasks autonomously.
Fast-forwarding to the 19th and 20th centuries, writers like Jules Verne and Isaac Asimov explored the idea of AI in science fiction. Their works, such as “Twenty Thousand Leagues Under the Sea” and “I, Robot,” respectively, sparked the imagination of scientists and engineers, laying the groundwork for the development of AI.
The Dartmouth Summer Research Project (1956)
The modern era of AI began in 1956, when computer scientist John McCarthy coined the term “Artificial Intelligence” at the Dartmouth Summer Research Project. This gathering of prominent mathematicians, computer scientists, and cognitive scientists marked the beginning of AI research as we know it today.
The Founding Fathers of AI
- Alan Turing (1912-1954): A British mathematician, computer scientist, and logician, Turing is widely considered the father of computer science and AI. His 1950 paper, “Computing Machinery and Intelligence,” proposed the Turing Test, a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
- Marvin Minsky (1927-2016) and Seymour Papert (1929-2016): This duo’s 1969 book, “Perceptrons,” introduced the concept of multi-layer neural networks and laid the foundation for modern neural networks.
- John McCarthy (1927-2011): An American computer scientist and cognitive scientist, McCarthy is credited with coining the term “Artificial Intelligence” and developing the Lisp programming language, which became a standard for AI research.
Rule-Based Expert Systems (1956-1980s)
The first AI program, Logical Theorist, was developed in 1956 by Allen Newell and Herbert Simon. This rule-based system was designed to simulate human problem-solving abilities. The 1960s and 1970s saw the rise of expert systems, which mimicked human decision-making processes using sets of rules and logical deductions.
Machine Learning and Knowledge Representation (1980s-1990s)
The 1980s witnessed a shift towards machine learning, with the introduction of algorithms like decision trees and neural networks. This period also saw the development of knowledge representation techniques, such as semantic networks and frames, which enabled machines to reason about the world.
AI Winter (1980s-1990s)
Despite the progress made, AI research faced significant funding cuts and a decline in interest during the 1980s and 1990s, dubbed the “AI Winter.” This was largely due to the failure of expert systems to deliver on their promises and the limitations of machine learning algorithms at the time.
Resurgence and Modern Breakthroughs (2000s-present)
The 21st century has seen a resurgence of interest in AI, driven by advances in computing power, data storage, and machine learning algorithms.
- Deep Learning: The development of deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has enabled AI systems to achieve state-of-the-art performance in image and speech recognition, natural language processing, and other areas.
- Big Data and Cloud Computing: The availability of large datasets and cloud computing resources has facilitated the training of complex AI models and enabled the deployment of AI systems in various industries.
- Robotics and Embodiment: Advances in robotics have led to the development of autonomous vehicles, drones, and humanoid robots, which are capable of interacting with their environment and performing complex tasks.
Modern Protagonists
- Andrew Ng (1976-present): A pioneer in deep learning, Ng co-founded Coursera and Google Brain, and has made significant contributions to the development of AI algorithms and applications.
- Demis Hassabis (1976-present), Shane Legg (1976-present), and Mustafa Suleyman (1984-present): The co-founders of DeepMind, a leading AI research organization acquired by Alphabet Inc. in 2014, have developed cutting-edge AI algorithms and applied them to various domains, including healthcare and energy.
- Fei-Fei Li (1976-present): A computer scientist and director of the Stanford Artificial Intelligence Lab (SAIL), Li has made significant contributions to AI research, particularly in the areas of image recognition and natural language processing.
The Future of AI
As AI continues to evolve, we can expect to see significant advancements in areas like:
- Explainability and Transparency: Developing AI systems that can provide insights into their decision-making processes and are more transparent in their operations.
- Human-AI Collaboration: Creating AI systems that can seamlessly collaborate with humans, leveraging their strengths and compensating for their weaknesses.
- AI for Social Good: Applying AI to address pressing global challenges, such as climate change, healthcare, and education.
The history of AI is a testament to human ingenuity and the power of collaboration. As we move forward, it is essential to ensure that AI is developed and deployed in a responsible and ethical manner, prioritizing the well-being of humanity and the planet.
In conclusion, the complete history of AI is a rich tapestry of ideas, innovations, and individuals who have contributed to its evolution. As we continue to push the boundaries of AI research and development, we must remember the pioneers who paved the way and strive to create a future where AI benefits all of humanity.
Groq works great on a BYOK model or for individual use. I am talking about if you put it into an app and have many users, you will reach the limits fast.
Hence the need to implement soft retries, which I have implemented in my Groq streaming plugin to minimize application errors