{"id":233128,"date":"2023-04-10T12:00:06","date_gmt":"2023-04-10T11:00:06","guid":{"rendered":"https:\/\/www.transcend.org\/tms\/?p=233128"},"modified":"2023-06-20T05:51:49","modified_gmt":"2023-06-20T04:51:49","slug":"the-fear-of-ai-is-overblown-and-heres-why","status":"publish","type":"post","link":"https:\/\/www.transcend.org\/tms\/2023\/04\/the-fear-of-ai-is-overblown-and-heres-why\/","title":{"rendered":"The Fear of AI Is Overblown\u2014and Here\u2019s Why"},"content":{"rendered":"

7 Apr 2023 – <\/em>The unprecedented popularity of ChatGPT has turbocharged the AI hype machine. We are being bombarded daily by news articles announcing humankind\u2019s greatest invention\u2014Artificial Intelligence (AI). AI is \u201cqualitatively different,\u201d \u201ctransformational,\u201d \u201crevolutionary,\u201d \u201cwill change everything,\u201d\u2014they say. OpenAI, the company behind ChatGPT, announced a major upgrade of the technology behind ChatGPT called GPT4. Already, Microsoft researchers<\/a> are claiming that GPT4 shows \u201csparks of Artificial General Intelligence\u201d or human-like intelligence\u2014the Holy grail of AI research. Fantastic claims are made about reaching the point of \u201cAI Singularity<\/a>\u201d of machines equalling and then surpassing human intelligence.<\/p>\n

\"\"<\/a><\/p>\n

The business press talks<\/a> about hundreds of millions of job losses as AI would replace humans in a whole host of professions. Others worry about a sci-fi-like near future where super-intelligent AI goes rogue and destroys or enslaves humankind. Are these predictions grounded in reality, or is this just over-the-board hype that the Tech industry and the VC hype machine are so good at selling?<\/p>\n

The current breed of AI Models are based on things called \u201cNeural Networks.\u201d While the term \u201cneural\u201d conjures up images of an artificial brain simulated using computer chips, the reality of AI is that neural networks are nothing like how the human brain actually works. These so-called neural networks have no similarity with the network of neurons in the brain. This terminology was, however, a major reason for the artificial \u201cneural networks\u201d to become popular and widely adopted despite its serious limitations and flaws.<\/p>\n

\u201cMachine Learning\u201d algorithms currently used are an extension of statistical methods<\/a> that lack theoretical justification for extending them this way. Traditional statistical methods have the virtue of simplicity. It is easy to understand what they do, when and why they work. They come with mathematical assurances that the results of their analysis are meaningful, assuming very specific conditions. Since the real world is complicated, those conditions never hold, and as a result, statistical predictions are seldom accurate. Economists, epidemiologists and statisticians acknowledge this and then use intuition to apply statistics to get approximate guidance for specific purposes in specific contexts. These caveats are often overlooked, leading to the misuse of traditional statistical methods with sometimes catastrophic consequences, as in the 2008 Great Financial Crisis or the LTCM blowup in 1998, which almost brought down the global financial system. Remember Mark Twain\u2019s famous quote, \u201cLies, damned lies and Statistics.\u201d<\/p>\n

Machine learning relies on the complete abandonment of the caution which should be associated with the judicious use of statistical methods. The real world is messy and chaotic and hence impossible to model using traditional statistical methods. So the answer from the world of AI is to drop any pretense at theoretical justification on why and how these AI models, which are many orders of magnitude more complicated than traditional statistical methods, should work. Freedom from these principled constraints makes the AI Model \u201cmore powerful.\u201d They are effectively elaborate and complicated curve-fitting exercises which empirically fit observed data without us understanding the underlying relationships.<\/p>\n

But it\u2019s also true that these AI Models can sometimes do things that no other technology can do at all. Some outputs are astonishing, such as the passages ChatGPT can generate or the images that DALL-E can create. This is fantastic at wowing people and creating hype. The reason they work \u201cso well\u201d is the mind-boggling quantities of training data\u2014enough to cover almost all text and images created by humans. Even with this scale of training data and billions of parameters, the AI Models don\u2019t work spontaneously but require kludgy ad-hoc workarounds to produce desirable results.<\/p>\n

Even with all the hacks, the models often develop spurious correlations, i.e., they work for the wrong reasons. For example, it has been reported that many vision models work by exploiting correlations pertaining to image texture, background, angle of the photograph and specific features. These vision AI Models then give bad results in uncontrolled situations. For example, a leopard print sofa would be identified as a leopard; the models don\u2019t work when a tiny amount of fixed pattern noise undetectable by humans is added to the images or the images are rotated, say in the case of a post-accident upside down car. ChatGPT, for all its impressive prose, poetry and essays, is unable to do simple multiplication of two large numbers, which a calculator from the 1970s can do easily.<\/p>\n

The AI Models do not have any level of human-like understanding but are great at mimicry and fooling people into believing they are intelligent by parroting the vast trove of text they have ingested. For this reason, computational linguist Emily Bender called the Large Language Models such as ChatGPT and Google\u2019s BART and BERT \u201cStochastic Parrots\u201d in a 2021 paper<\/a>. Her Google co-authors\u2014Timnit Gebru and Margaret Mitchell\u2014were asked to take their names off the paper. When they refused, they were fired by Google.<\/p>\n

This criticism is not just directed at the current large language models but at the entire paradigm of trying to develop artificial intelligence. We don\u2019t get good at things just by reading about them, that comes from practice, of seeing what works and what doesn\u2019t. This is true even for purely intellectual tasks such as reading and writing. Even for formal disciplines such as Maths, one can\u2019t get good at Maths without practicing it. These AI Models have no purpose of their own. They, therefore, can\u2019t understand meaning or produce meaningful text or images. Many AI critics have argued that real intelligence requires social \u201csituatedness.<\/em>\u201d<\/p>\n

Doing physical things in the real world requires dealing with complexity, non-linearly and chaos. It also involves practice in actually doing those things<\/em>. It is for this reason that progress has been exceedingly slow in Robotics: current Robots can only handle fixed repetitive tasks involving identical rigid objects, such as in an assembly line. Even after years of hype about driverless cars and vast amounts of funding for its research, fully automated driving still doesn\u2019t appear feasible in the near future.<\/p>\n

Current AI development based on detecting statistical correlations using \u201cneural networks,\u201d which are treated as black-boxes, promotes a pseudoscience-based myth of creating intelligence at the cost of developing a scientific understanding of how and why these networks work. Instead, they emphasize spectacles such as creating impressive demos and scoring in standardized tests based on memorized data.<\/p>\n

The only significant commercial use cases of the current versions of AI are advertisements: targeting buyers for social media and video streaming platforms. This does not require the high degree of reliability demanded from other engineering solutions; they just need to be \u201cgood enough.\u201d And bad outputs, such as the propagation of fake news and the creation of hate-filled filter bubbles, largely go unpunished.<\/p>\n

Perhaps a silver lining in all this is, given the bleak prospects of AI singularity, the fear of super-intelligent malicious AIs destroying humankind is overblown. However, that is of little comfort for those at the receiving end of \u201cAI decision systems.\u201d We already have numerous examples of AI decision systems the world over denying people legitimate insurance claims, medical and hospitalization benefits and state welfare benefits.<\/p>\n

AI systems in the United States have been implicated in imprisoning minorities to longer prison terms. There have even been reports of withdrawal of parental rights to minority parents based on spurious statistical correlations, which often boil down to them not having enough money to properly feed and take care of their children. And, of course, on fostering hate speech on social media. As noted linguist Noam Chomsky wrote in a recent article<\/a>, \u201cChatGPT exhibits something like the banality of evil: plagiarism and apathy and obviation.\u201d<\/p>\n

__________________________________________________-<\/p>\n

Bappa Sinha is a veteran technologist interested in the impact of technology on society and politics. <\/em><\/p>\n

This article was produced by <\/em>Globetrotter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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