A six-month AI retrospective, and where things go from here
Back in August, I wrote about emerging trends in the AI sector, and made some predictions regarding the changes, both creative and disruptive, that AI would usher in. In the months since, much of it has played out almost exactly as I described. Many fields have already experienced significant disruption, with no sign of that trend slowing down. Meanwhile, existing AI models are proving to have new, hidden capabilities on a near daily basis, and the open-source nature of much of the research is contributing to ever-greater cycles of AI acceleration.
So, where do we stand today?
Six months ago, I wrote:
Digital artists, photographers, writers, and creatives of all stripes will be affected by the adoption of AI tools like GPT-3, DALL•E, and Stable Diffusion. Not only will these tools introduce all sorts of new competition into already-crowded fields, they so skew the balance of time vs quality in terms of output that it will be nearly impossible for artists to compete with them on their own. Widespread adoption of these tools among creative professionals might come more from competitive necessity than anything else.
The surprise open-source release of Stable Diffusion in late August proved to be an absolute game-changer, and resulted in an influx of attention and a level of exploration, experimentation, and excitement that the field of AI has never seen before.
New capabilities are being discovered on a daily basis across a variety of fields, from generative music to video to virtual reality, and small startups are at the advantage right now, though Google and Microsoft have at last awoken from their slumber and are laser-focused on consumer-facing AI.
On prompt engineering, I said:
The field of prompt engineering might be relatively new, but it will be increasingly important with regards to these co-creative AI tools, and we’ll likely see some specialization and focus on prompt optimization within models like DALL•E and Stable Diffusion as a new generation of creative professionals learns how best to use these new tools. Until then, there will likely be increased demand for competent prompt engineers to do the necessary work required for early-stage exploration of this emerging field.
Prompt Engineers are the biggest winners in this market, commanding impressive salaries in what is otherwise a bearish tech market.
I predicted that writers and programmers would see medium-term impacts, many of which are playing out with the recent layoffs in both silicon valley as well in print media.
Professional writers will benefit greatly from AI tools, which can help act as a side-by-side editor and co-author, able to expand on topics, consolidate larger blocks of text into tl;dr’s, and even generate text at different reading levels. Likely impacts will be seen at large editorial shops, where fewer staff writers and editors may be needed, as well as with ghostwriters, who will soon find a solid ally in AI when it comes to slogging through the boring parts of the writing process, able to concentrate on the intent of the piece while the AI can act as fact-checker or fact-fetcher, able to cross-reference concepts and ideas across a wide range of sources and data sets.
This flipside of this trend is that these same AI tools that are obviating some programmers and writers are empowering others, and a whole new field of augmented creative professionals is emerging in the wake of disruption. The long-tail effects of this transformation cannot be overstated, and will ultimately prove to be the type of generational shift that will change the face of tech business forever.
What the future holds
Back in August, I wrote:
With their ability to generate realistic looking images of people, places, vehicles, and weather, AI art tools will facilitate the creation of disinformation(intentionally misleading content shared with an intent to cause harm) on a massive, automated scale, which will inevitably lead to misinformation (misleading content shared without an intent to cause harm) when it is shared on social media by those who don’t know any better.
There is a critical and immediate need to raise awareness about the types of content that these machine learning models can generate, as far too many people already believe everything they read online; which will only get worse going forward as these AI tools gain marketshare and become general use products.
Consider the following New York Times Headline and you’ll understand my concern regarding the generative capabilities of these AI models:
Deepfakes will continue to be problematic, and current watermarking techniques are easily defeated. AI-detection algorithms are worse than useless at this time, their results being treated as absolute truth while the limitations and failure modes of these AI models are often swept under the rug in the face of breathless marketing copy promising far more than they can currently deliver on.
Still to come: Artist lawsuits, copyright fights, theory of mind and consciousness, censorship, and AI ethics.
Over the next four weeks I will take a deeper look into the changes, good, bad, and weird, that we can expect as AI continues to usher in an immense amount of technological upheaval in a very short timeframe.
Buckle up, things are only going to move faster from here.
It’s going to be a wild ride.
Nicholas Ptacek is a veteran writer and technologist, with close to 20 years of experience in the cybersecurity industry building award-winning computer security software. Their work has been featured extensively in print and news media, including Not Boring, CNNMoney, Macworld, and MacDirectory magazine, along with numerous press appearances in publications including The Information and Vice.
Nicholas has been documenting the AI landscape while exploring the capabilities and artistic output of generative AI models including ChatGPT, GPT-3, Stable Diffusion, and DALL-E. You can follow these AI experiments on Twitter at: @nptacek
This is essay 1of 4 for TNS Creators’ 3rd Cohort