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Could Generative AI pose an existential threat to us?

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  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 existential threats, they high
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Data Scientist's Self Defeating Prophecy

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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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Cartoon Corner

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    Defining success on your own terms Stay in your rabbit hole
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Delude of Accuracy

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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 defaulters fo
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When not to use Artificial Intelligence

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  Artificial intelligence is at the peak of the Gartner hype cycle[ 1 ]. It can pressure teams to take up unreasonable projects. One needs to perform his/her due diligence before embarking on a machine learning use case. Alternative solutions can work out to be cheaper and more efficient.   Projects with no timely action A Machine Learning(ML) project may be brilliant in itself. But unless the business can act upon the results, the model is of no use. Not only should we be able to take action we should also be able to take this action promptly. Here are a couple of examples. Employee attrition : Consider a team working on a model to forecast employee turnover. The data science team goes to great lengths to collect data from several corners, including social media, browsing habits, and so on. They develop a robust black-box model using this hard-won data to determine whether an employee would quit in the next 30 days. When the HR gets these results, he/she does not know what to do. Unle
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Virus: Discussing macroscopic possibilities

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Our world view is limited by our own senses and our size and scale. I am throwing out a possibility of studying viruses through another lens.  Many people believe that COVID-19 is the mother earth’s fight against human infection [ 0 ] . It is quite possible that viruses are earth’s antibodies trying to keep the earth healthy. Viruses are known to keep the balance and diversity of microbes in the oceans.   [1]   Viruses have been eliminating species that run wild since the beginning of life on earth. They are a major driver in human evolution [   2 ][   3 ] . Recent studies have shown that viruses could have been the reason we have life on earth. [   4 ][   5 ] So, there is more to viruses than it is commonly known. In this blog, I would like to discuss the possibility of studying viruses through another lens. Allow me to take you through some seemingly unrelated illustrations to make my point.   Every mammal on this planet instinctively develops a natural equilibrium with the surroundi
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