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  Learn about neural networks intutively Here is the spread sheet used in the video. feel free to download it / copy it and try it yourself. https://docs.google.com/spreadsheets/d/1IqUMzU2WBqUSoYJX0jvBmOdWiss_WLA_Yn_DiwhTklo/edit?usp=sharing Your support helps me create more valuable content, share fresh ideas, and keep this blog thriving. Every contribution, big or small, makes a huge difference. Thank you for supporting my work and being part of this journey! https://buy.stripe.com/00wdRb9gNdHL4o1gZMaR200

Sometime in mid 2023 Sir Geoffry Hilton announced his resignation from Google to be able to speak freely about existential dangers from AI. For those of us who may not know, Geoffry Hilton is the fatherly figure for Neural Networks. After the first AI winter, when enthusiasm and investment in AI had faded, He continued his research on neural networks, with support from his colleagues at the University of Toronto. In 2018 he and Yoshoa Bengio received the Turing Award for their work. The Turing Award is the equivalent of the Nobel prize for a computer scientist. He also said, “It is not inconceivable that AI could wipe out humanity”. He started a petition to stop AI development and In mid-2023 a group of very he along with other prominent individuals like Bill Gates and Sam Altman signed this statement of AI risk. All of this suggests that we should take their fears seriously. Dr. Andrew NG called for open discussions and I found most arguments favoured anthropomorphized projections o...

  In recent days, prominent figures in the field of AI, like Geoffrey Hinton, Sam Altman, etc., have sparked discussions about the potential dangers of generative AI.  There is something eerie about chat GPT-like bots. One can think of many dangers with the current AI tech, but none of them seem existential.  Andrew NG  called for the LinkedIn community to  list the existential risks of AI . While it is challenging to pinpoint the exact reasons behind these concerns, there is an undeniable discomfort associated with the latest advancements in AI technology. This article is an attempt to evaluate fears surrounding generative AI, examining its impact on various aspects of our lives and contemplating whether it poses a terminal threat to the human species. 1. The Misdemeanor Bots: When interacting with chat GPT-like bots, one cannot help but think of less dangerous spam emails or encountering age-inappropriate content. Although these experiences might not be existe...

Jonah, a biblical prophet, was tasked with delivering Gods prophecy to the people of Ninevah, an ancient Mesopotamian city. He was sent out to Ninevah to warn the city's inhabitants of divine wrath to come as a result of their sins. After an eventful journey, John reaches Ninevah and warns the citizens of the impending doom. Citizens of Ninevah repent their actions and pray every day. Jonah waits outside the city for God to come and punish its citizens. However, God does not show up because the citizen changed their ways for the better. Because the prophecy revealed early and people intervened, it did not come true. Such prophesies are called self-defeating prophecies. In such cases, determining whether the prophecy would have come true is difficult, and the foreteller's credibility may be called into question. Modern-day digital soothsayers or data scientists face this challenge routinely. We are frequently expected to predict an undesirable outcome and prevent it from occurri...

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  Accuracy is one of the metrics used to evaluate a predictive model. This word is also an every-day English word. How this word is interpreted/communicated in meetings between business users and ML engineers can have significant implications. While there are many other metrics such as precision, recall, F1 score, and so on[ 1 ], most business users relate to accuracy. Many a time, this metric, accuracy can be misleading. Decisions based on an on-the-surface evaluation of any single model metric can result in losses.   Let me clarify what I mean. A bank wants to predict who is likely to default on a loan and decide if it should disburse the loan or not. Now, what is an acceptable accuracy for the predictive model? Can I use a model that has 20% accuracy? Is 90% good enough to put my models in production? Well, it depends. Is 95 % accuracy good enough? Accuracy is the number of records that are correctly classified. Let us say, we have 95% accuracy. The bank sees about two defa...