Raw LLM Responses

Alphe Alpha, despite the serious injection of startup capitals, seems more hype and speculative than substantive development. First and foremost, Europe don't have the necessary hardware like Nvidia GPUs (i.e. clusters of ten of thousands H100) that are the foundation of today's generative and other AI models, with ASML chip making machines being the only exception. Beside a critical shortage of semiconductor manufacturers, there is a general lack of talents, not just AI but in all STEM domains in general. Finally, there are just too much bureaucratic red tapes such as the recent passage of the first AI regulation legislation, despite having no AI champions or infrastructure like like dedicated AI chips and acellerators (NPUs, TPUs). This is the same dilemma that the Chinese face at the moment and one which will severely hinder their long term propects, despite having a massive talent pools that create competitive AI applications. But AI apps cannot grow without a parallel growth in AI exponential computing power enabled by products like Nvidia's H100 , GDX cloud and AI services! Think about it. We all know OpenAI ChatGPT and its patron Microsoft with its own CoPilot chatbot, Meta's LaMMa2, Google Gemini and even Chinese Baidu's Ernie. What's Alphe Alpha chatbot or LLM name?!! Likewise, instead of working hard to build a competitive AI model, the AI engineer and many, way too many AI "ethicists" trying to target an often criticized aspect of ChatGPT, its "Anglo-basaed bias," valid point but you still don't have an AI model as sophisticate as that at the end of the day. In other words, more frivilous pondering and bureaucracy. For Europeans who lecture the world about free thinking and freedom of actions, maybe they should try "move fast and break things!" BTW, ChatGPT writes poems in Hindi and historical analysis in dozens of other languages and is the most sophisticated LLM that I have prompted and trained (the latter unofficially as there are too many legal red tapes to overcome:-). With proper training over time, the model becomes more precise and contextual, more ethical.