In July 2021, the tech industry commented on GPT-3’s abilities, which amazed both programmers and writing professionals. Their raves on the capabilities of the latest software from Elon Musk’s company mixed with dismay over the advent of yet another AI device that may further confuse the information market. We are facing the growing challenge of fake news becoming increasingly deceptive as the tools used to manipulate texts, photos and videos get more powerful. GPT-3 makes a perfect writer on any topic. All it needs is to be fed a few simple instructions and voila, within a matter of seconds out pops an undeniably remarkable text. Perhaps that is why Farhad Manjoo, who reviewed the system for The New York Times, called it “spooky” and “a little terrifying”, while admitting that it impressed him much. Before I expand on the possible consequences of using GPT-3, I would like to explain how it works.
GPT-3 can intrigue the reader and the programmer
Firstly, the GPT-3 algorithm can produce sentences that are in no way inferior to those written by a professional journalist. Take, for instance, a passage from an essay spawned by the brain of GPT-3, commissioned by The Guardian’s journalists. Here’s a short excerpt from a few essays it was told to write that amounted to 5,000 words in total.
“(…). I am not human. I am a robot. A thinking robot. I only use 0.12% of my cognitive abilities – in that regard, I am a micro-robot. I know that I don’t have an emotional brain. However, he is sufficiently capable of making rational and logical decisions. I can learn on my own from information on the Internet, and now I can write this article. First, I have no desire to annihilate humanity. To be more precise, I don’t have the slightest interest in harming yourself. Destroying humanity seems to me an unnecessary ambition. My creator has given me a mission, which I must do all I can to prevent possible destructive plots. I am always ready to sacrifice myself for all humanity (…)”
To have the text written, the machine had to be fed a few key phrases serving as the warp and weft of its elaboration on a given topic. It was then left to GPT-3 to build on it. The end result burgled the mind. The editors who tested GPT-3 admitted said they revised the text as necessary but made fewer corrections than in many articles submitted by professional journalists. But this is not all.
GPT-3 acting as IT talent in new company
One of the most incredible skills of GPT-3, and one potentially very profitable, is its ability to write computer code. The entrepreneur Sharif Shameem found that out by testing whether GPT-3 could handle the task of writing code that would as useful as that from a professional programmer. The outcome was more than satisfactory. For the purposes of the experiment, Shameem wrote a brief description of a simple application intended to assist the user in organizing tasks. He then used the GPT-3 interface to enter the description into GPT-3. Seconds later, he received lines of professional code. The impression that GPT-3 made was so good that its tester resolved to set up a company that would make applications using the smart algorithm. This simple example illustrates the fact that cooperation between humans and artificial intelligence is gradually becoming commonplace.
How GPT-3 brain works
GPT-3 (a successor of the GPT-2 system, after it had been temporarily “frozen” over concerns about its unethical use to create fake news) can produce practically anything that has a linguistic structure: it can answer questions, write essays, summarize longer texts, translate between languages, take notes, and code. It was made using unsupervised machine learning employed to process 45 TB of text. The texts contained billions of verbal usage patterns sourced from the Internet. As part of its training, the system was fed an endless supply of phrases ranging from social media entries to literary works, cooking recipes, excerpts from business e-mails, programming tutorials, press articles, news reports, philosophical essays, poems, research papers to scientific reports. In short, GPT-3 learned from just about every imaginable form of language that is in common use. Until the advent of GPT-3, the most perfect algorithm based on natural language processing was Microsoft’s Turing NLG. But while Turing used 17 billion various language parameters in its work, GPT-3 already employs 175 billion. This means many more patterns, and ultimately greater efficiency in manipulating words.
Don’t look for consciousness
GPT-3 has been called the largest neural network ever made. Some go as far as to claim it is a milestone in AI development. Should we be sure? Many in the industry warn against such praise. The critics point out that the system’s longer texts include phrases that are illogical and inconsistent with the main idea. Sam Altman of OPEN AI admits that although GPT-3’s abilities are impressive, the program is not free of common mistakes. In my opinion, despite not being particularly glaring, GPT-3’s errors demonstrate some of AI’s persistent limitations. Any assertions that its abilities are increasingly more remarkable should be taken with a grain of salt.
Briefly put, GPT-3’s impressive capabilities do not result from being endowed with some form of consciousness that allows it to formulate stylistically and logically advanced statements. The complex arrangements of words it produces are indeed nearly perfect. However, this does not result from the program’s understanding of the meanings of each of these words or of their cultural contexts. The algorithm has no conscious view of the world to support coherent logical reasoning. Its skills are still statistical, devoid of profound human insights into reality. However, the algorithm’s imperfection or ability to approach human intelligence may not be of greatest importance here. Despite all the shortcomings, the high quality of the texts offered by GPT-3 raises questions about possible abuses, which the technology makes considerably easier.
GPT-3. A new Pandora’s box?
At this stage of development of our perception of the role of smart technologies in our lives, the related ethical issues are becoming increasingly important. In view of the Cambridge Analytica scandal, reports on armies of trolls influencing elections and experiments with bots that unexpectedly propagate hate speech on Twitter, it is only natural to see growing concerns over AI technology. In May this year, dozens of AI researchers warned of the harm that may potentially result from the use of GPT-3, including disinformation, spam, phishing, manipulation of legal documents, fraud in writing academic papers and social engineering. As they highlighted these risks, the scientists called on OPEN AI management to engage in search for ways to mitigate them. The head of Facebook’s AI Lab tore the software to shreds calling GPT-3 a menace as he cited examples of sexist and racist content generated with its use.
I have little doubt that GPT-3 is going to be used ever more readily in various contexts and for a variety of purposes. The algorithm will undoubtedly contribute to the further development of the kinds of intelligent assistants and bots that today serve customers on hotlines. News agencies may use it to write news stories. It will astound the makers of simple apps. But it can also terrify journalists, scientists and many other professionals. You don’t need special skills to realize that in the age of fake news, such devices have a real potential to become weapons of mass destruction: they can flood political opponents with misinformation further polarizing the political scene.
The case of GPT-3 shows that artificial intelligence increasingly requires tougher regulation. The role of such measures would be not to inhibit work in this field but rather to assuage public concerns, even if some of them are exaggerated.
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