Russian Translation - Response to ChatGPT
Today's work in artificial intelligence is amazing. We've taught computers to beat the most advanced players in the most complex games. We've taught them to drive cars and create photo-realistic videos and images of people. They can re-create works of fine-art and emulate the best writers. Yet I know that many businesses still need people to, e.g., read PDF documents about an office building and write down the sizes of the leasable units contained therein. If artificial intelligence can do all that, why can't it read a PDF document and transform it into a machine-readable format? Today's artificial intelligence algorithms can recreate playable versions of Pacman just from playing games against itself 1. So why can't I get a computer to translate my colleague's financial spreadsheet into the format my SAP software wants?
Despite two decades of advancements in artificial intelligence, it feels that the majority of office work consists of menial mental tasks. We should expect that artificial intelligence would automate this work in much the same way that past machines automated physical labor. Indeed, many writers have been sounding the alarms about the coming job losses. 2 Though I believe that artificial intelligence would make both existing jobs more interesting and create more jobs in yet-unthought-of fields. In practice, however, I still see many people doing jobs that computers should be able to do but just cannot today. Why is that?
I think part of the problem may be in the way we interact with computers. Computers are based on an architecture that requires explicit, precise instructions on how to manipulate data. Even with voice-controlled virtual assistants on our smartphones, we still interact with them by giving them explicit, precise (albeit higher-level) instructions. Artificial intelligence algorithms likely can infer many of those instructions implicitly. Perhaps we are awaiting a second information revolution - maybe using Excel for modern business tasks is like writing software in machine code when higher-level programming languages are available. This might be true, but I think we face two more immediate problems: a lack of data and a lack of awareness.
Today's artificial intelligence is powered by data. And the bulk of today's data comes from the internet - text, images, videos, and our interactions with them. If a group of software engineers wanted to create a model that could, for example, identify the make and model of a car in a picture, they could start with a pre-trained model from other researchers that detects objects in pictures and then "top it up" by training on a smaller set of examples that just includes cars. This is called transfer learning. But there is no existing "document-understanding" model that we could adapt to our specific business processes via transfer learning. The excel spreadsheets, marketing brochures, legal contracts, and other documents that make up the business world are hidden in email inboxes and other silos within various companies. No group of researchers can train a "document-understanding" model simply because they don't have access to the relevant documents or appropriate training labels for them.
What's more, artificial research teams lack an awareness of the specific business processes and tasks that could be automated in the first place. Researchers would need to develop an intuition of the business processes involved. We haven't seen this happen in too many areas. The big successes have happened where the problem is easily understood and has many publicly-available examples (machine translation), where there is a promise of a massive ROI (self-driving cars), or where a large company arbitrarily decides to throw enough resources at the problem until they can crack it (AlphaGo).
This means, however, that we can expect artificial intelligence to succeed in automating business processes when 1) researchers are able to focus on a specific problem, and 2) they are able to accumulate enough data to train a workable model. (Another criterion for success is that should aim to empower the people involved in the process, not replace them, but that is for a different discussion.) And where they succeed, people who work in those industries can expect to be spending more of their time doing interesting, creative work and less time doing dull, time-consuming tasks. A great example of this is Proda 4, a London-based startup that can automatically standardize commercial real estate data. Startups or initiatives that promise more general solutions are likely to run into difficulties.
The upside of this is that there is a near-endless stream of business opportunities with significant moats around them. Each business process is a chance for automation, and therefore an opportunity for a business to save an entire industry much time and money and pocket some of the profits. If one can understand the AI research well enough, understand the business process thoroughly enough, and the gather enough data to train a model, then one can build a profitable company out of it.
Thank you to Gabriella Abraham and Jagna Feierabend for reading earlier drafts of this essay.