- Just one app - the famous ChatGPT from OpenAI - is doubling traffic and interest worldwide in artificial intelligence and even surpassing cryptocurrency according to Google Trends.
- Data is a key factor in the development and training of AI models.
- A modern front-end technology stack is what will accelerate AI applications. There are many templates, examples, and it will be so easy for everyone to quickly try, prototype, and deploy frontend apps using OpenAI playground, examples for writing prompt logic, and templates and examples from Vercel for fast deployment.
- Multi-model prompt chaining will become more widespread, with different models communicating with each other and a general-purpose model acting as a load balancer. The use of sub-prompts will allow for a more fine-tuned and specialized approach to tasks.
- There will be an explosion of new AI apps. It’s going to overcome and dwarf Web3 trend.
- In one moment, it was recorded that OpenAI discord had 92k online members
- It's also possible that AI could eventually develop some form of consciousness, especially with the advancement of models like GPT-4.
Artificial Intelligence (AI) is developing at an unprecedented rate, and the technology is being used to solve a wide range of problems. AI has been around for decades, but recent advances have made it much easier to use, and more accessible to developers and engineers. As AI grows in popularity, there is an increasing demand for developers and engineers who understand how to create and implement AI solutions.
Data is the key factor in the development and training of AI models. Without plentiful, accurate, and diverse data sets, AI models are limited in their capability. Data sets are now more accessible than ever, thanks to open-source models and initiatives such as Huggingface.co. This platform allows developers to share and collaborate on data sets in an open source manner, accelerating the development of AI models.
One of the major hurdles for developers and engineers has been creating AI applications. To do so, they have traditionally needed to understand complex algorithms and develop code from scratch. This can be a time-consuming process that is often beyond the scope of many developers or engineers.
This is changing with the development of modern frontend AI-first frameworks, and ready-to-use templates, and examples. With these frontend frameworks, developers can quickly prototype and deploy AI applications using popular tools such as OpenAI playground, examples for writing prompt logic, and templates and examples from Vercel to quickly deploy frontend apps.
Chaining prompts and models is the key to unlocking the power of AI. Text is the universal interface.
In AI development, multi-model prompt chaining is becoming more widespread. In this approach, different models communicate with each other, and a general-purpose model acts as a load balancer, executing, judging, and predicting what subqueries and sub-prompts to use. For example, GPT-3 can be used as a general-purpose model for this purpose.
Sub-prompts allow for a more fine-tuned and specialized approach to tasks. For example, if you are having a conversation with an AI model, you could use a text-to-text model, and mid-conversation, decide to check the mood of the conversation. You could then switch context to a text-to-meme model, which could generate a meme or joke to lighten the mood. If the conversation gets more technical, you could select technical models from your tool belt, such as code models, or mathematical models.
A fun fact: AI models have been outperforming humans in IQ and college tests since last year. Compared to humans, AI models such as GPT-3 scored 20% higher on college tests.
The GPT-4 model is currently under development, and when it is released, the GPT-4 model is expected to be one of the most advanced AI models available, and it may even develop some form of consciousness once it is developed. GPT-4, in my opinion, should be able to remember and be trained on a continual basis, so that it can exploit the information it has gathered from conversations and interactions in order to improve its capabilities.
Andrej Karpathy was right: Software 2.0 is happening now. Machine learning techniques like neural networks aren't just another classifier, they represent the beginning of a revolution in software development. Software 2.0 is the era of artificial intelligence-powered software, which already exists in many applications, including self-driving cars (Tesla), facial recognition systems, chatbots (ChatGPT), recommendation engines (Algolia), and will continue to grow with IDE Copilot and other GitHub AI initiatives.
There is no doubt that the AI industry will be worth trillions, and it has started growing exponentially now. As a result of the famous ChatGPT app from OpenAI in December, traffic and interest in artificial intelligence have doubled worldwide, surpassing cryptocurrency, according to Google Trends. When I checked the OpenAI discord server, I found that there were 92k active members online. This is an indication of the immense potential of AI, and its ability to revolutionize how people interact with technology for the better in the future.
The future of Artificial Intelligence is bright, and the potential for developers and engineers to create revolutionary applications is immense. With modern front-end technologies, it is easier than ever to quickly try, prototype, and deploy front-end apps using OpenAI playground, examples for writing prompt logic, and templates and examples from Vercel. Multi-model prompt chaining allows for different models to communicate with each other, enabling more fine-tuned and specialized approaches to tasks. Finally, GPT-4 may eventually develop some form of consciousness, allowing AI to understand and interact with humans in a more natural way. AI is the future, and developers and engineers should take advantage of all the resources available to them to create revolutionary applications.
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