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What GPT-3 Means for Non-Technical Professionals

Photo by Kevin Ku on Unsplash

A Caveat on Hype

As is the case with any new advanced technology, GPT-3 isn’t perfect. It breaks a bunch of times, it needs a human to guide it often, and it sometimes spits out gibberish.

3 Example Use Cases

(1) Turning legalese into plain English: I trained GPT-3 how turn legal text into simple English without writing any code. You can see the example from my tweet below.

Source: Jay Alammar’s intro to neural networks

Benefits for Non-Technical Professionals

Clearly the potential for GPT-3 is ubiquitous but we can broadly group the benefits in these buckets:

  • Gains in Productivity — You can use GPT-3 as an AI productivity assistant to automate repetitive tasks. I’ve already trained it on a few simple emails I send regularly and all I have to do is feed it prompts which it uses to generate a kind email reply. The archaic way of doing this historically was to hire an assistant who would draft documents for you to review, edit, sign, and send. Now an AI can do this for you.
  • Creativity Boost — If you get creative block, tell GPT-3 what general structure you want for a piece of writing and it will generate random (but potentially cohesive) sentences that might lead to a new thought. Don’t like them? Then tell it to try again and it will auto-generate more sentences that could help get you out of a jam.
  • Democratized Access to Technical Understanding — GPT-3 can be used as an education tool to accelerate learning. As I show with the legalese translator, it can help remove barriers to technical understanding by translating industry lingo into plain language, which is key when you are trying to learn something new.

Challenges to Consider

As is the case with any technology, there will be many challenges with its use. To name but a few:

  • Malicious Use Cases — GPT-3 can be used at scale for sophisticated phishing scams, propaganda, and fraud. Society will need to be vigilant by default when it comes to all digital content going forward. We will also need to rethink what it means to author something. Copyright laws, academic reviews, and attribution are going to get real tricky.
  • Biased AI Models — GPT-3 is trained on existing human records and they are full of bias and stereotypes across qualities such as race, gender, and religion. Until such biases are sufficiently mitigated for, AI outputs have to be challenged and considered in the context of the kind of society we would like to live in.
  • Cost and Access — GPT-3 reportedly cost $12m to train and so far it’s in a closed invite-only beta. To benefit society more widely, the cost of training similar models will need to come down substantially so that the technology is more accessible and evenly distributed to foster innovation.
Article by Peter H Lewis in 1994 (source)



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