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We Asked People Around the World How They Feel About AI. Here’s What We Learned (foundation.mozilla.org)
37 points by joeyespo 7 days ago | hide | past | web | favorite | 22 comments





I'm mostly just tired of all the hype, and at this point really wouldn't mind another AI winter.

Machine learning ended up being a useful technique to add to the classical computer vision toolbox to do things like object identification and semantic segmentation. These things were almost impossible to do robustly before, and now they're relatively straightforward, if still a bit challenging.

Same with audio identification and synthesis. It's a lot better than the old Markov chain based systems for these specific tasks.

But that's about it - it's useful as a tool for certain classes of highly specific tasks, but has been largely crap everywhere else.

The promised super smart products that were end-to-end "AI" never really materialized. Smart speakers are shitty and pointless. And IBM renamed all of their products Watson.


I met about 40-50 academic roboticists this week after a major conference in Macau. Approximately 90% of them were applying some form of ML to motion planning or evaluation tasks, maybe 1/3 of which were cobotics related. 7% were applying it to CV. 1% were creating semantic maps between natural language and popular consumer robotics domains (navigation). 2% were doing off the wall stuff like 'soft robotics' with paper and strings. As someone working in the space without an academic background and coming from software, I was absolutely floored how few practical industrial or commercial problems were being tackled or even considered. At the academic level, ML has the funding. Cross with cobot for extra funds. Bonus funding points if you use public funds to develop 'dual use' technology: search and 'rescue' was a meme. Rather morally bankrupt.

What does 1% of 40-50 people look like in comparison to 2%? Interesting numbers regardless.

Looks like 90% of all statistics.

At least the general AI community acknowledges it’s overhyped, unlike blockchain and fintech.

A lot of the breakthroughs have been in image classification, object detection and image segmentation using deep convolutional models way way outperforming prior approaches and beating human level benchmarks (on constrained problems e.g. classify this image into one of 1000 categories)...

But there have also been significant breakthroughs in:

* image/video generation using generative adversarial networks

* game playing & robotics in constrained environments (AlphaGo, AlphaStar, etc) using reinforcement learning

* natural language processing using semantic word vectors and attentional recurrent models

* voice-to-text using recurrent models

Convolutional and recurrent neural networks were invented in the early 1990s and we’ve only recently got them properly working as tools. I think we’re going to see (and need) more algorithmic breakthroughs to push through the current limitations but I’d be very reluctant to bet against that happening, even if it takes a long time...


Some of your bullets describe methods not useful functions, e.g.

> natural language processing using semantic word vectors and attentional recurrent models

Where has that made a big impact? Marginally better auto translation between English and French, or random text passage generation that's at best uncanny valley?

Probably still falls under OP's heading of "a tool that might help in limited, specific scenarios".


The impacts in improved translation aren’t marginal - they improve people’s lives in real ways.

Advances in deep learning for NLP have also significantly improved search (Google and others) which has a nontrivial impact on people’s lives.

Multi-modal models that translate images into words have a significant impact for blind people.

Deep learning is significantly improving medical diagnosis, crop yield, assistive robotics, and many other specific scenarios.

Specific scenarios matter and again, it’s still very early days...


The most interesting applications are what you don’t see.

For instance, you can do a lot of your engineering and even data science work without ever touching real data (due to ML):

https://medium.com/capital-one-tech/why-you-dont-necessarily...

Not saying it’s not over hyped, just that good AI would augment your life in ways subtle, but hugely impactful.

Edit: Another thought - how are funds in the market allocated? Partially by humans, largely augmented (if not explicitly allocated) by AI... meaning AI determines which companies get funding and thus what you can buy / access is determined by these algorithms


That article reinforces the view that you need big datasets to do ML. Generating a synthetic dataset still requires a real dataset to work from, and all the useful datasets are controlled by the big tech companies.

If you work primarily on synthetic data as a data scientist, you deserve to get fired. The link is pretty cool though.

The argument is you can design a model, check that it runs, then submit the job to train / evaluate the model on real data. The data scientist doesn't necessarily need to touch the real data ever (especially if ML is used).

This is not a great setup if one requires a model that can extrapolate.

"The promised super smart products that were end-to-end \"AI\" never really materialized" What do you have in mind?

IBM and Watson definitely hyped their shit beyond anything reasonable. Open AI is also responsible of this. And smart speakers are definitely pointless.

Still I think we are seeing some really cool things, like voice recognition, translation, google answering questions directly, fancy camera stuff, etc.


We usually overestimate the short term impact of technological changes, and underestimate their long term impact.

AI does not exist. There is no 'intelligence' in ML model.

I find that, if you look closely enough, there can never really be any “intelligence”. Everything is just a model or a likely association or a statistical model, etc. and it will always be that. This statement of there being no intelligence really seems overly reductionist.

I completely agree.

But i was disappointed to find out that by formal definition "AI" is a term that encompasses machine learning and neural networks: https://en.wikipedia.org/wiki/Artificial_intelligence

That, or I'm misreading the Wikipedia article.


10% of the world "well educated" about AI, that's not how I would usually use that term.

Interesting to see how much more excitement there is for AI in the developing world. They're the ones with the biggest problems so it makes sense. Seems NA/Europe feels like they have the most to lose. While the reality should hopefully be win-win, it does seem likely to level the playing field around the world. But a hyper-capitalist country like the US is definitely going to have the biggest social challenges adapting to job losses.

Al who?

Open AI GPT-2 model is pure trash, and self driving cars don’t work. These could be considered break throughs, or AI beyond what we’ve seen, if they worked.

Until one of these two works as promised, AI is a failure and neural networks are still just simple stacked linear regressions. They aren’t greater than the sum of their parts.




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