In the last few years, the hype around artificial intelligence has been increasing (again). Most of it is due to companies like [OpenAI][1], [Google][2], [DeepMind][3] (Google subsidiary), [Meta][4], and others producing truly groundbreaking research and innovative showcases in the field. From machines winning complex games like [Go][5] and [Dota 2][6]to a variety of content generation techniques that produce text, images, audio, and now video, these technologies will have an impact on our future. It feels like we have experienced this hype towards AI in the past, but it never really materialized into anything relevant to our lives. From IBM’s Watson attempts to revolutionize healthcare to the prophecies of self-driving cars, we have been told about how AI will improve our society, yet there always seems to be something preventing us from getting there. On one side, technology might not be there yet for some of those advanced problems, in another, humans tend to be skeptical of machines taking over some of our areas of expertise (Skynet didn’t help here). However, this time it feels different. Firstly, use cases are way less ambitious than in the past and have concrete practical (and fun) applications; secondly, research in the last 5-10 years had some of the major leaps ever in the machine and deep learning fields. [Generative Adversarial Networks (GANs)][7], [Diffusion Models][8], and [Transformer Models][9] are good examples of such breakthroughs. Thirdly, this time around the required technology and processing power are here to enable us to run and train these massive networks. It is estimated that OpenAI spent around $10M to $20M to train its GPT-3 text-to-text model. Cost should be higher with models dealing with images. Where Are We and How We Got Here? So, where are we right now?

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