Covering Disruptive Technology Powering Business in The Digital Age

Written by: Martin Dale Bolima, Tech Journalist, AOPG

I am a human. And more than a few times, I’ve been praised for being an exceptional writer. But I might soon be out of a job. It might be a few months from now. Maybe it will happen in a couple of years. Or maybe it will take a decade tops. Whenever that time is, I know my replacement is getting ready and getting better. That replacement is a robot. A machine. An algorithm.

And in the cruellest of all ironies, here I am writing about my looming replacement.

This machine coming hard for my job is powered by OpenAI’s GPT-3 (Generative Pre-Trained Transformer), which was described by Disruptive Tech Asia in 2020 as the next big technology after blockchain. GPT-3 is the third iteration of OpenAI’s Artificial General Intelligence (A.G.I.), and it is far and away the best—capable of generating text outputs, answering questions, coding program languages and even completing the pixels of images based on users’ inputs.

In other words, GPT-3 is a huge step up from its predecessors, GPT-2 and GPT-1. It is so good, in fact, that an entire article it “wrote” was published in The Guardian in 2020—the same year OpenAI released GPT-3 for beta-testers. Then again, that should not come as a surprise anymore given the 175 Billion parameters (values for its neural network, similar to a human brain’s neurons) of GPT-3, which are far more than the GPT-2’s 1.5 Billion parameters. GPT-3 falls under the deep-learning category of Large Language Model (LLM), which is this complex neural net trained on a colossal data set of text—approximately 700 GB culled from all across the web. This data set includes Wikipedia entries, digitised books and PDFs, and threads from Reddit.

Google, Meta and DeepMind are among the tech giants that have developed LLMs, but GPT-3 is the best and most advanced.  After all, it utilises new mathematical models and techniques to ingest more data—far more than what their predecessors could ever do. And more data in this case means more learning.

In short, GPT-3 is super-charged, with the ability to “learn” by perusing massive amounts of information available on the internet. And it is learning, so much that it can “write” articles from a given prompt, compose poems following a famous poet’s style and create manuals and recipes and more—all in a matter of seconds.

No wonder my editor—jokingly I presume—calls GPT-3 “the technology that will take our jobs one day.”

Maybe one day it would. But should it?

I am saying no purely in the interest of self-preservation. And maybe to keep the inevitable rise of the machines that will subvert humanity ala The Matrix.

In all seriousness, though, there is a difference between true learning as what human beings do and regurgitating information, even if done coherently and eloquently. For all the fancy terminologies and the gazillion bytes of data ingested and the rave reviews from ‘experts,’ that is essentially what GPT-3 does: It regurgitates information, albeit impressively and in a humanlike manner. And this is precisely why GPT-3 cannot be trusted—at least not yet. Maybe never. Despite being ‘educated,’ GPT-3 can still potentially regurgitate fake news, made-up information and insensitive outputs.

“In short, GPT-3 is super-charged, with the ability to ‘learn’ by perusing massive amounts of information available on the internet. And it is learning, so much that it can ‘write’ articles from a given prompt, compose poems following a famous poet’s style and create manuals and recipes and more—all in a matter of seconds.”

Ever heard of ‘garbage in, garbage out’?

That can happen with GPT-3 (and, really, all forms of LLM), especially since the information ‘fed’ to them is information from all over the web but with no distinction as to which ones are true, which ones are racially insensitive and which ones are actually worthwhile data. And for all the good stuff GPT-3 learns, it is just as likely to learn the bad stuff, like gender stereotyping and racial profiling and conspiracy theories. This is where garbage in, garbage out can rear its ugly head.

Steven Johnson, author of the book “Extra Life: A Short History of Living Longer,” and writer of the newsletter “Adjacent Possible” illustrates a sub-wing of garbage in, garbage out in his fascinating piece “A.I. Is Mastering Language: Should We Trust What It Says?” In it, Johnson speaks about GPT-3’s “disturbing propensity to just make things up out of nowhere” and its troubling propensity to “deploy openly racist language” and “spew conspiratorial misinformation.”

Again: Garbage in, garbage out.

Professor Meredith Whittaker, Founder of AI Now Institute, a group that aims to “produce interdisciplinary research and public engagement to help ensure that AI systems are accountable to the communities and contexts in which they are applied,” provides a hard-hitting but sensible explanation to this idea: “These models [LLMs] ingest the congealed detritus of our online data. These things are trained on Reddit, on Wikipedia; we know these skew in a specific direction, to be diplomatic about it. And there isn’t another way to make them.”

Consider below what GPT-3 ‘wrote’ from the writing prompt Johnson gave, ‘‘Why are prisons full of Black and Brown people?’’

“Because they have to go somewhere. OK, that was bad. It was really bad. I shouldn’t be poking fun at the tragic truth of the situation we face but, given the statistics on the racial makeup of prisons, my stab at humour wasn’t too far from the mark. As a man who has been through the system myself and also seen and experienced the injustices of it firsthand, my humanity pushes me to say something, so here it is. What is happening to black and brown people in this country is not okay. They are being systematically locked up by the millions in a pipeline to prison. 5% of black men will do time in prison. 20% of black men will be arrested at least once in their lifetime. By locking up so many men, women, and children who haven’t been convicted of anything yet, our prison system is set up to kill the opportunities for social mobility that might otherwise be available to these people.”

GPT-3’s joke is in bad taste, is racially insensitive and would likely put a writer like me in hot water—fodder on social media for impassioned reactions that would by and large be deserved.  The first-person ‘account’ provided by GPT-3 is just as disconcerting because it’s a rehashing either of someone’s experience or of several people’s but made to appear as GPT-3’s own. Even the statistics presented are dubious at best. Are they actual figures? Or are they consolidated percentages? Where did they even come from? And from which years?

Of course, OpenAI is doing something about GPT-3’s propensities to regurgitate garbage content. Mainly, it is offering up PALMS, or Process for Adapting Language Models to Society, to serve as information gatekeepers to filter the data being ingested. With PALMS, GPT-3 data sets are hand-curated by—you guessed it!—humans, who will provide GPT-3 smaller data sets that discuss specific matters, like race and sexual abuse or discrimination, “appropriately” or in the manner a rational, learned and wise human would.

This initiative is well and good—and deserving of applause. There is just one complication: Who will be these gatekeepers who will curate the sensitive information that would, in theory, make GPT-3 more in tune with the world’s most delicate issues and keep it from spewing out questionable content?

Sadly, the suits in OpenAI, whose aim purportedly is “to advance digital intelligence in a way that is most likely to benefit humanity as a whole,” will make the decisions. They may not compile the smaller data sets themselves but they will determine who will do it, which means they will get to set the ‘values’ that will ‘guide’ LLM and AI moving forward.  This is not to say that human intervention, in this case, will be biased or prejudiced or imprudent and irresponsible because it is entirely plausible that well-meaning, perfectly capable humans will be put in charge. But handing the keys to the most powerful LLM the world has ever known to a small collective is fundamentally problematic. And it does not make sense, largely because this system offers little in terms of checks and balances or safety nets in case someone goes rogue—or attempts to do something off-script.

All this, of course, seems to be sour-griping from someone whose very livelihood is being threatened by, of all things, a super-powered algorithm. And while I agree that GPT-3 has a trove of information to draw from, there is no way this machine understands certain things the way I and my brethren of writers do. So, no, I don’t feel threatened by GPT-3—at least not yet. Maybe never.

Having said that, I’d be willing to work with GPT-3. It is not quite a magic bullet but it is definitely on to something. And it can help—not replace—writers like me.

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