![]() The books affected were reported to include between "dozens" and "hundreds" of books containing LGBTQIA+ themes, often labeling them as "adult material" when similar books containing heterosexual characters remain at the top of the sales ranking. Among those that yielded no results were "lgbtq", "pride", and "queer". For instance, Amazon's book store experienced a "cataloging error" in which 57,310 books lost their "sales ranking" a number used to help books show up quicker in the book suggestions algorithm. And, no training data is without bias, not even the ones generated through automation. How can AI be biased?Īny algorithm is only as good as its training data. So essentially much like human beings, this machine could now “learn” from its experiences. His basic idea was this: instead of telling the computer the exact steps required to solve a problem, instead, show it examples of the problem to solve, and let it figure out how to solve it itself. Computers, as any programmer will tell you, are giant morons, not giant brains”. In his classic 1962 essay Artificial Intelligence: A Frontier of Automation Arthur Samuel, an IBM researcher wrote, “Programming for computations is, at best, a difficult task, not primarily because of any inherent complexity in the computer itself, but rather, because of the need to spell out every minute step of the process in the most exasperating detail. To understand this better, let’s back up a bit - how exactly do machine learning models work? Even more dubious is that the exact cause of death cannot be ascertained- owing to the complexity of machine learning models, their reasoning is not always straightforwardly interpretable to humans, creating a black-box effect. This doesn’t necessarily mean their AI became sentient and developed homicidal tendencies - only that certain crucial safety flaws and algorithmic biases were overlooked in its design. Uber did not face any charges citing that “there was no basis for criminal liability”. We may not have to wait another 9 years though this has already happened at a time when confidence in autonomous vehicles was at an all-time high - In May 2018 a self-driving Uber hit Elaine Herzberg, a pedestrian in Arizona, resulting in her death. ![]() Based in 2031, the show revolves around a self-driving taxi being hacked and ordered to kill an anonymous human victim. India is gradually catching on to the potential harms posed by AI - as is evident from the Indian absurdist sci-fi TV series featuring Radhika Apte and Jackie Shroff, “OK Computer”. While AI has transformed convenience across several industries, it is not without its shortcomings - the digital economy has a significant impact on labour, identity, and human rights. When we stream through Netflix or Prime or Hotstar, AI is being used to create teasers, highlights, recaps, and trailers for shows that can boost viewership - no more sifting through hours of information to decide what we want to watch.īe it route optimisation in Google Maps, recommended news feeds in Instagram or AI chatbots in banking/healthcare/e-commerce portals, we can now receive a quickly accessible, highly personalised interface wherever we go. When we book a cab from Ola or Uber, the algorithm can forecast demand and alert nearby cabs, therefore decreasing the expected time of arrival. When we order food from Swiggy or Zomato, there is a backend algorithm that returns restaurants and dishes in response to a query using fuzzy text matching and geo-location filtering. The era of smart technologyĪI has made our lives incredibly fast and efficient in a lot more ways than we may realise. Today’s debate is no longer a question of if and how AI will participate in society - it is a complex discussion about how we should employ this collection of technologies to run our communities, businesses and governments in effective yet human-centred ways. ![]() Fitbits, GPS, images, credit card purchases have all ensured that every human being on the planet today is a living, breathing data warehouse. ![]() So why all the interest and investment now? The simple answer is, data. The methods popular today for building recommendation engines, spam classifiers or traffic predictions are not fundamentally different from the algorithms invented decades ago. When people talk about AI today, they’re mostly talking about machine learning: a sub-field of computer science that dates back to at least the 1950s. Understanding what AI can do and how it fits into your strategy is the beginning, not the end, of that process”. “As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive. ![]()
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