My experience with AI

Natam

Transcript

1. MY EXPERIENCE WITH AI The concepts presented in this presentation are intended to be simple and easy to understand
2. Disregarding the academic experience, I always researched for libraries on artificial intelligence, watson, google cloud api, etc ... How I meet AI
3. I will resist giving this emerging discipline a name, but if the acronym “AI” continues to be used as placeholder nomenclature going forward, let’s be aware of the very real limitations of this placeholder. Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead The Revolution Hasn’t Happened Yet
4. First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence We can put AI in three categories: super, general and narrow. General AI would have all of the characteristics of human intelligence, including the capacities mentioned above. Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI. Super AI is something that do better than a human, like chess AI. AI
5. Machine learning is what makes an algorithm evolve, without it your AI will always give the same results. - Supervised - Unsupervised - Reinforcement ML
6. If you want to apply machine learning and AI techniques to a problem, you don’t necessarily need an in-depth understanding of the math to get a good result. Libraries such as TensorFlow and PyTorch allow someone with a bit of Python experience to build state of the art models whilst the math is taken care of behind the scenes. If you’re looking to get deep into machine learning and AI research, through means of a PhD program or something similar, having an in-depth knowledge of the math is paramount. MATH
7. MY USE CASE Conversation is the more complex AI actually
8. Thank You Questions?