They don't know that. We have no idea what's required to achieve AI. Now I don't know how long before Ford actually built cars he started saying he's building cars, but if Wikipedia is to be believed, it could not have been more than three-four years. Also, when he started building cars, he pretty much knew what's required to build them. This is not the case for AI.
Yes, we do. Lots of data, lots of training, better algorithms, more understanding of the brain...At this point we still need 10x+ improvements in a lot of areas, but it's pretty clear what we need to do.
If you can process around 100 petabytes per second (1 Google Index of data per second), you could fully simulate a human being, including their brain. We're still a little bit from that, but it's pretty clear we'll get there (barring usual disclaimers about an asteroid, alien invasion, etc).
Source: I work in medical research, doing deep learning, and do research on programming languages and deep learning for program synthesis.
So to build AI all that remains is to understand how it could work.
> but it's pretty clear what we need to do
It isn't (unless by "clear" you mean as clear as in your statement above). I've been following some of the more theoretical papers in the field, and we're barely even at the theory forming stage.
> but it's pretty clear we'll get there.
First of all, I don't doubt we'll get there eventually. Second, I'm not sure simulating a human entirely falls under the category of "artificial". After all, to be useful such a mechanism would need to outperform humans in some way, and we don't even know whether that's possible even in principle using the same mechanism as the brain's.
I read those papers too. And I write code and train models day in and day out. I could get very specific on what needs to be done, but that's what we do at our job. If you're curious, I'd say join the field.
I agree with you in that I don't think for a second anyone can make an accurate prediction of when we will AGI, but I have no doubt that it will be relatively soon, and that OpenAI will likely be one of the leaders, if not the leaders in creating it.
2) Our focus is on helping improve medical and clinical science and cancer tooling first.
3) If we needed AGI to cure cancer, perhaps we'd be working directly on AGI. If anyone thinks this is the case, please let me know, as at the moment I don't think it is.
Exponential process continuing doesn't imply "we're going to get there soon" in any way, shape or form. The desired goal can still be arbitrarily far.
This is absurd. How much data? How much training? What kind of training? How much better do the algorithms need to be? How do you define better? Also we literally don't even know how our brains work, so we don't know how "actual" intelligence works, but you're saying we have a clear road map for simulating it?
Your entire argument distill down to "we just need to do the same things, but better." And even that statement might be wrong! What if standard silicon is fundamentally unsuited for AGI, and we need to overhaul our computing platforms to use more analog electronics like memristors? What if everything we think we know about AI algorithms ends up being a dead end and we've already achieved the asymptote?
I'm not saying AI research is bad. I'm saying it is absolutely unknown by ANYONE what it will take to achieve AI. That's why it's pure research instead of engineering.
Humans don't seem to need anywhere near the same level of data or training that our current models need. That alone is a sign that deep learning may not be enough. The focus on deep learning research has a lot of useful benefits, so I'm not discounting that, but there are a decent amount of smart people who don't believe it's going to lead us to AGI.
Source: I also work in medical research, and am doing deep learning- and I've worked for a company that's focused on AGI, and I've worked with several of the OpenAI researchers.
I find this to be a common misunderstanding. If I show you one Stirch Wrench, and you've never seen one before, you learn instantly and perhaps for the rest of your life you'll know what a Strich Wrench is. The problem is I didn't show you 1 example. You saw perhaps millions of examples (your conscious process filters those out, but in reality think of the slight shaking of your head, the constant pulsing of the light sources around you, etc, to be augmenting that 1 image with many examples). I think humans are indeed training on millions of examples, it's just we are not noticing that.
> That alone is a sign that deep learning may not be enough.
I 100% agree with that. It's going to take improvements in lots of areas, many unexpected, but I think the deep learning approach is the "wings" that will be near the core.
Here's a great article about a paper showing that humans prior knowledge does help with learning new tasks- https://www.technologyreview.com/s/610434/why-humans-learn-f...
However, that doesn't account for how quickly toddlers learn a variety of things with a small amount of information. Even more important, you can also just look at things like AlphaGo- they train on more examples than could be accumulated in a hundred human lifetimes.
For these reasons I don't believe "more data" and "more training" is the answer. We're going to need to do a lot more work figuring out how humans manage recall, how we link together all the data, and I would be surprised if this didn't involve finding out that our brain processes things in ways that are far different than our current deep neural nets. I don't believe incrementalism is going to get us to AGI.
Lots of advances in many fields will lead to something greater than the sum of their parts.
Edit: p.s. I like your comment about toddlers. As a first time father of a 6 month old, its been very intellectually interesting watching her learn, in addition to just being the greatest bundle of joy ever :)
That sounds fascinating. Could you link to some relevant stuff about languages and deep learning for program synthesis? I'd love to read more about this.