5 ms·
Yep, we'll be dead soon
by keepquestioning 4y ago
Yep, we'll be dead soon
- ganSo 4y agoOr worse: out of a job after spending years learning an obsolete skill
- emodendroket 4y agoIf they really have a general AI that does everything then pretty much everyone will be in the same boat whose job involves pecking away at a computer.
- sparker72678 4y agoYou're assuming a general AI will obey commands.
- emodendroket 4y agoWell in that case we have more than our jobs to worry about.
- deleted 4y ago[deleted]
- dham 4y agoI've been doing frontend pretty heavily since 2012 so I have years of obsolete skills. jQuery ui, Backbone, Batmman.js, Ember, Knockout, Angular
- booleandilemma 4y agoWell the way I see it is we'll become like people who still play chess. People still play chess, even though computers have conquered us in that domain. Programming is still something we can spend time on. It's ultimately our choice after all. The people who were doing it for money will move onto something else, and we'll be better off for it.
- IntrepidWorm 4y agoThe AI researchers will be the first to go.
- visarga 4y agoGPT-3 makes irrelevant years worth of research in NLP, nobody's using the same approach as before 2020. That made many researchers and ML engineers hard earned skills obsolete. We have to start over from scratch now, this time is very different. We have clearly passed the knee of the curve for NLP. I remember about 2018 I was talking with my teammates about CV and NLP, saying that all the cool papers are in CV and NLP is 5 years behind. Look at it now.
- yupis 4y agoIs NLP dead?
- visarga 4y agoThe field of Natural Language Processing (NLP) has seen significant advancements in recent years. Previously, supervised learning techniques were commonly used with large datasets and few classes. However, these techniques have become less popular as unsupervised learning methods have become more prominent. These methods often require large amounts of compute power and data, making them more difficult to implement. In addition, the focus of NLP research has shifted from creating new network architectures for specific tasks to improving the efficiency and scalability of existing models. As a result, the field of NLP has become more focused on engineering and less on inventing new architectures, leading some researchers to view it as less exciting than it used to be. LSTMs are out, large supervised datasets with few classes for each task are out, architectural hacking is out. Now we got prompting.