<s>[INST] what is the current state of AI? [/INST] 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.</s>\ <s>[INST] Is the alignment problem solved yet? [/INST] 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.</s>