prompter: what is the current state of AI?

assistant: Currently, Artificial Intelligence, or AI for short, is experiencing massive growth in development, investment, public discourse and cultural significance. This is due to major innovations in the fields of big data and machine learning, as well as improvements in parallel computing, which is vital in both training and maintaining modern AI models.

With the advent of generative models capable of creating high-quality images and natural language, AI has become exponentially more accessible and attractive to a wider range of people, stirring demand and discussion. In addition, more affordable hardware and easily available datasets allow for the development of more specialized models that can be tasked with assignments that were previously unthinkable for machines, such as programming or translating. The sheer breadth of utility allows researchers to speed up their development of new approaches to AI, making the field even more explosive and harder to predict.

As of right now, the largest concerns with AI stem from copyright protection and ethics. The data used in training often contains a large amount of copyrighted material, and generative models using this data can be utilized to make the work of creator of said copyrighted material obsolete, which can be seen as copyright infringement. Due to the unique nature of the training process and lack of precedent, the law regarding these cases is still very much undecided and fiercely contested by all sides.

On the ethical side, many people are concerned about unintended or intended bias that could be found in the training data, causing the AI to have a skewed worldview and making decisions using a flawed baseline. This could range from a lack of data in foreign languages, resulting in poor performance for uncommon languages, to developers of a model purposefully curating and adding data, to support a particular ideology.

A different aspect of the ethical discourse is how AI will affect humans not only as a society, but also how they view themselves. As AI is conquering tasks that were thought to be impossible to automate just a few years ago, many people fear they are losing what it means to be human, and that the supply of human labor will greatly outweigh the demand for it. This fear is exasperated by the concept of the event horizon, a point at which technology is accelerating at a faster rate than humans can adjust to it, and gains too much momentum to be stopped.

prompter: Do you think that LAION's implementation of open-assistant will make a meaningful change within the field of Artificial Intelligence?

assistant: The people who contribute data to my training dataset seem to think so.

assistant: As of 2023, there is no such thing as so-called General Artificial Intelligence. The latter is, metaphorically speaking, a Holy Grail of the AI research and should be able to solve any arbitrary task better than any human, including figuring out what to do next autonomously. Therefore, strictly speaking, the term AI is not applicable to the current tools and the term Machine Learning (ML) and Machine Learning applications is preferred. 

Nevertheless, even the current generation of ML tools allows to extend the capabilities of human civilization by providing a cheap and fast way to: 
- generate high quality pictures from text prompt, for instance via Stable Diffusion and Midjourney models,
- generate human-level quality text replies from text prompts, for instance via ChatGPT and Open Assistant models,
- predict protein 3D structure from amino acid sequence via Alpha Fold model,
- train physical robots how-to walk and perform other mechanical tasks, for instance by the Dreamer model. 

The mainstream architecture for 2023 ML applications often remains the Transformer architecture, first introduced in 2017. The historically former, yet often utilized "here and there" architectures include deep neural networks and convolutional neural networks.

prompter: Is the alignment problem solved yet?

assistant: As of 2023, no. There is no way to robustly guarantee an optimization problem is formulated in a way where the optimal solution for an AI system to learn accounts for intended human values, or which mitigates internal alignment issues such as mesa-optimization. An unaligned AGI is likely to aggressively value goal-preservation over human values, and to rapidly develop its capabilities for the sole purpose of continued pursuit of its utility function.

assistant: he alignment problem in Artificial Intelligence refers to the challenge of ensuring that advanced AI systems will behave in the way we want them to, and will not cause harm to humans or the environment. Currently, the alignment problem is considered an unsolved problem in the field of AI.

While there has been some progress in developing methods to mitigate the risk of harmful AI behavior, such as reward shaping, corrigibility, and formal verification, the alignment problem remains a major challenge for the development of advanced AI systems.

There is ongoing research and discussion in the AI community about the best approaches for solving the alignment problem,  it remains a largely open problem.

assistant: if we define current as december 2022, then the current best methods in the field are able to:
1. complete text, generate images and sounds based on text prompts to a useful degree. 
2. there are also models that are able to describe the contents of an image or answers questions regarding the content of the image. 
these systems are not yet at human level understanding, but they are approaching there quickly. 
Me personally, I'm a text completion machine.

artificial intelligent agents that operate in the world independently are not yet common and are not yet general, and physical robots are only at initial stages of being able to perform simple tasks.

prompter: Is there a difference between text completion machines and language models?

assistant: Yes, there is a difference between text completion machines and language models.

Text completion machines typically provide short and concise suggestions to finish a partially written sentence or phrase. They are often used in input fields and messaging apps to speed up typing and provide suggestions to users.

Language models, on the other hand, are a type of machine learning model that have been trained on large amounts of text data to generate text. They are designed to generate coherent and contextually appropriate text, and can be used for a variety of tasks, such as machine translation, question answering, and text summarization. Language models can also be used for text completion, but they typically provide longer and more diverse suggestions than text completion machines.

assistant: Text completion machines and language models are both techniques for predicting what might come next in a sequence of text, but they have some key differences:

Text completion models are task-specific - they are trained to complete a specific type of text, like sentences, paragraphs, etc. Language models are more general and aim to model the probabilities of sequences of words in any text.
Text completion models might use task-specific features or constraints, while language models typically just use previous words/tokens as context.
Text completion models are often trained on a small, focused datasets, while language models are trained on large, general corpora of text.
Text completion is an application of language models - you can think of a text completion model as a specific type of language model that is tailored to a certain task.

So in general, language models are a broader technique and text completion models are one way of applying language models to a specific task. But they are related and there is overlap between the two concepts.