To hear more audio stories from publishers like The New York Times, download Audm for iPhone or Android. I've never really worried that computers might be gunning for my job. To tell the truth, often, I pray for it. How much better would my life be -- how much better would my editor's life be, to say nothing of the poor readers -- if I could ask an all-knowing machine to suggest the best way to start this column? It would surely beat my usual writing process, which involves clawing at my brain with a rusty pickax in the dim hope that a few flakes of wisdom and insight might, like dandruff, settle on the page.

Nine philosophers explore the various issues and questions raised by the newly released language model, GPT-3, in this edition of Philosophers On, guest edited by Annette Zimmermann. Introduction Annette Zimmermann, guest editor GPT-3, a powerful, 175 billion parameter language model developed recently by OpenAI, has been galvanizing public debate and controversy. As the MIT Technology Review puts it: “OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless”. Parts of the technology community hope (and fear) that GPT-3 could brings us one step closer to the hypothetical future possibility of human-like, highly sophisticated artificial general intelligence (AGI). Meanwhile, others (including OpenAI’s own CEO) have critiqued claims about GPT-3’s ostensible proximity to AGI, arguing that they are vastly overstated. Why the hype? As is turns out, GPT-3 is unlike other natural language processing (NLP) systems, the latter of which often struggle with what comes comparatively easily to humans: performing entirely new language tasks based on a few simple instructions and examples. Instead, NLP systems usually have to be pre-trained on a large corpus of text, and then fine-tuned in order to successfully perform a specific task. GPT-3, by contrast, does not require fine tuning of this kind: it seems to be able to perform a whole range of tasks reasonably well, from producing fiction, poetry, and press releases to functioning code, and from music, jokes, and technical manuals, to “news articles which human evaluators have difficulty distinguishing from articles written by humans”. The Philosophers On series contains group posts on issues of current interest, with the aim being to show what the careful thinking characteristic of philosophers (and occasionally scholars in related fields) can bring to popular ongoing conversations. Contributors present not fully worked out position papers but rather brief thoughts that can serve as prompts for further reflection and discussion. The contributors to this installment of “Philosophers On” are Amanda Askell (Research Scientist, OpenAI), David Chalmers (Professor of Philosophy, New York University), Justin Khoo (Associate Professor of Philosophy, Massachusetts Institute of Technology), Carlos Montemayor (Professor of Philosophy, San Francisco State University), C. Thi Nguyen (Associate Professor of Philosophy, University of Utah), Regina Rini (Canada Research Chair in Philosophy of Moral and Social Cognition, York University), Henry Shevlin (Research Associate, Leverhulme Centre for..

The most exciting new arrival in the world of AI looks, on the surface, disarmingly simple. It's not some subtle game-playing program that can outthink humanity's finest or a mechanically advanced robot that backflips like an Olympian. You start typing and it predicts what comes next. But while this sounds simple, it's an invention that could end up defining the decade to come. The program itself is called GPT-3 and it's the work of San Francisco-based AI lab OpenAI, an outfit that was founded with the ambitious (some say delusional) goal of steering the development of artificial general intelligence or AGI: computer programs that possess all the depth, variety, and flexibility of the human mind. For some observers, GPT-3 -- while very definitely not AGI -- could well be the first step toward creating this sort of intelligence.

So, you've seen some amazing GPT-3 demos on Twitter (if not, where have you been?). This mega machine learning model, created by OpenAI, can write it's own op-eds, poems, articles, and even working code: With GPT-3, I built a layout generator where you just describe any layout you want, and it generates the JSX code for you. GPT3()… the spreadsheet function to rule them all. Impressed with how well it pattern matches from a few examples. The same function looked up state populations, peoples' twitter usernames and employers, and did some math.

If you want a glimpse of the future, check out how developers are using gpt-3. This natural language processor was trained on parameters ten times greater than its most sophisticated rival and can be used to answer questions and write astoundingly well. Creative professionals everywhere, from top coders to professional writers marvel at what gpt-3 can produce even now – in its relative infancy. Yesterday, New York Times tech columnist Farhad Manjoo wrote that the short glimpse the general public has taken of gpt-3 "is at once amazing, spooky, humbling, and more than a little terrifying. GPT-3 is capable of generating entirely original, coherent, and sometimes even factual prose. And not just prose -- it can write poetry, dialogue, memes, computer code, and who knows what else." Manjoo speculated on whether a similar but more advanced AI might replace him someday.

Elon Musk is sounding the alarm that there is a strong possibility that humans will be overtaken by artificial intelligence within the next five years. The billionaire engineer, who co-founded the artificial intelligence research lab OpenAI in 2015 and was an early investor in DeepMind, has often warned in recent years about the species-ending threat posed by advanced AI. "My assessment about why AI is overlooked by very smart people is that very smart people do not think a computer can ever be as smart as they are. And this is hubris and obviously false," Musk told The New York Times. Musk added that the invaluable experience of working with different types of AI at Tesla has given him the confidence to say "that we're headed toward a situation where AI is vastly smarter than humans, and I think that time frame is less than five years from now. But that doesn't mean that everything goes to hell in five years. It just means that things get unstable or weird."

Back on October 12, 2019, the world witnessed a previously unimaginable accomplishment- the first sub-two-hour marathon was run in an incredible time of 1:59:40 by Kenyan native Eliud Kipchoge. He would later say in regards to the amazing achievement that he "expected more people all over the world to run under 2 hours after today" [1]. While Kipchoge set new records in long distance running, across the world a team of natural language processing (NLP) experts at OpenAI, the Elon Musk-backed AI firm, published a new transformer-based language model with 1.5 billion parameters that achieved previously unthinkable performance in nearly every language task it faced [2]. The main takeaway from the paper by many experts was that bigger is better-the intelligence of transformer models can dramatically increase with the scale of parameters. In March of 2020, this theory gained support with OpenAI's release of version three of the model or GPT-3 which encapsulates a staggering 175 billion parameters and achieved even more remarkable performance than version 2, despite sharing, quite literally, the same architecture [3].

As someone with a neuroscience background, it's interesting to see the parallels between the parameter size & number of neurons in the recent GPT-3 unveiling versus the cortical regions of the brain responsible for language processing. Estimates put cortical neurons at 25B, and there is somewhere on the order of 7K connections between each neuron (further pruned over time), putting the total number of cortical parameters at somewhere close to 175T. Let's say language processing areas are 1/5th of this, the rest being dedicated to vision, higher order processing, executive function, etc. This puts the language generating portion of the human brain at maybe 35T parameters - yet we're capable of producing such fantastic results with GPT-3's 175B, a number which is 1/200th of the size. Of course, the two are not directly equatable - GPT-3, for example, has'read' several million books' worth of content at this point, whereas the average human has read just a few dozen by the time they're able and language-producing, but it's still interesting to reason about.

So far in 2020's epic battle of nature vs. man vs. technology, man is not winning any "Best in Show" contests. So, needless to say, when a colleague sent me an article about the GPT-3 language generator released last week by the nonprofit artificial intelligence research company OpenAI, I was intrigued. The article is a blog post authored by Argentinian software engineer Manuel Araoz in which he describes his experiments with GPT-3 on the bitcointalk.org When he plugged some sample text into the model -- a few sentences from an existing forum post, for example -- GPT-3 generated an original body of text which mimicked the sentence structure, grammar and other subtleties of the sample… and with incredible results. GPT-3's predicted sentences read like they were written by a human.

Sometimes it's tempting to think of every technological advancement as the brave first step on new shores, a fresh chance to shape the future rationally. In reality, every new tool enters the same old world with its same unresolved issues. In a moment where society is collectively reckoning with just how deep the roots of racism reach, a new paper from researchers at DeepMind -- the AI lab and sister company to Google -- and the University of Oxford presents a vision to "decolonize" artificial intelligence. The aim is to keep society's ugly prejudices from being reproduced and amplified by today's powerful machine learning systems. The paper, published this month in the journal Philosophy & Technology, has at heart the idea that you have to understand historical context to understand why technology can be biased.