I recently made an appointment through an AI secretary to set up a meeting with them; it worked surprisingly well.. They are not hiring a secretary any time soon. Real effect. Also: https://www.theguardian.com/commentisfree/2016/dec/01/stephe...
This AI Boom Will Also Bust
281–290 of 320 posts
Re: This AI Boom Will Also Bust
#282Earlier quoted context omitted.
A lot of revolutionary technologies begin as toys. In the 9th century two brothers created a machine called, "the instrument which plays by itself." It uses a rotating cylinder with "teeth" that triggers keys which were encoded to play music. One can say it's a programmable music machine. Fast forward to the 1700s and artisan engineers in France were creating similar devices that automatically played music. Toys for…
I think your story does more to reinforce the parent post's point. The chain of events you outline spans an entire millennium. Over that time, yes, a lot of technology developed, and eventually we got to computers, and there was even the movement that gives us the term Luddite. But it was still one long chain of gradual, evolutionary developments over the course of a very long expanse of time. At no point was there a…
We never know, that is what the original parent comment was about. Regarding gradual progression, remember - we are now in a highly connected world, where things move at tremendous pace. And a lot more population have access to the latest information. Just like the invention of the wheel, or the invention of steam engines, or the computers, transistors etc, there could be a tipping point, which could happen today as I write or tomorrow or even after a decade. We can only guess. But looking at the speed of things, I would say the probability of it happening very near is very high and is worth the hype.
Re: This AI Boom Will Also Bust
#283Earlier quoted context omitted.
Simple regression has a lot going for it beyond simply being a simpler model. For example, it produces models that are easy to interpret. That is an enormous advantage if you're looking to use data science to help drive strategic decisionmaking. My personal suspicion is, in a market full of people who are using ever more sophisticated algorithms to ratchet up their customer conversion classifiers' F1 scores by .001 p…
I think calling linear regression simple is misleading. While the algorithm is simple, interpreting the results and not falling into one of the many traps is quite difficult!
His comment was relative to ML, not an absolute.
Re: This AI Boom Will Also Bust
#284I recently made an appointment through an AI secretary to set up a meeting with them; it worked surprisingly well.. They are not hiring a secretary any time soon. Real effect. Also: https://www.theguardian.com/commentisfree/2016/dec/01/stephe...
"Ai secretary" in what respect? Natural language processing? Because simply making an entry into a calendar is just a 1980s' level computer programming problem at best, so I think your example needs some clarification/elaboration. Whether something is "AI" also depends on the presence of learning (the stuff you do with Windows voice recognition to set it up is not "learning/teaching" in the AI sense). Most of the things touted as "I" (intelligence) have all the intelligence in the programming effort, but none in the actual program (apart from what was put in).
Re: This AI Boom Will Also Bust
#285It seems the nature of this and VR that they come to boom for a while and then bust, having stagnated. They then wait for the next alignment of underpinning technology, knowledge and culture to emerge again. Last last one I'm aware of was mid-late 1990s, where VRML was gaining traction and new ways of thinking about AI were emerging.
IBM Watson or similar (if I recall IBM was still calling their business AI system Watson back then) seems to be promenant in these two booms and both times the results it gives haven't matches it's marketing hype.
The technology, having been significantly furthered fades into the day to day of computing somewhat until the next boom that drives more short burst innovation and awareness.
Conscious AI and realistic VR is some way off, if we ever see it. Culturally and ethically we are not ready to answer the questions it poses and the cyclical nature gives us more time to digest the latest raft of questions in light of the progress.
Re: This AI Boom Will Also Bust
#286Earlier quoted context omitted.
It is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of sup…
I didn't downvote you, but the TL;DR of the article is that most ML demonstrations to date have been toys and there are no known real-world applications that would justify the "40% of jobs lost!" hype. And you're trying to rebut this by referencing an AI-generated Christmas jingle . I think the author rests their case...
The '40% of jobs lost' figure is reachable without any Singularity-style event. That's what you can get just by applying modern automation techniques to low hanging fruit. Self-driving vehicles, automatic checkouts at supermarkets, fully automated fast food places, robotic warehouse storage/retrieval etc.
If you used to have 10 people working at a supermarket and you now have 6 with self-checkouts, that's your 40% jobs lost (for that supermarket) right there.
Re: This AI Boom Will Also Bust
#287Earlier quoted context omitted.
Terrible for what purpose? Humans seem pretty good at throwing things to each other and catching them. I'm very bad at coming up with a good numeric estimate of linear size. As a fencer, I could never tell you how many inches between me and my opponent, how long his or my arms are, how tall he is, etc. I could definitely tell you which parts of our bodies are within reach of each other's arm extension, fleche, lunge,…
Terrible for the purpose of proving a general-purpose stereo machine vision system is practical. The distance at which a stereo vision system can capture precise depths depends on the distance between eyes, and the eyes' angular resolution. Human depth perception works well for things within about 10m, but when you get out to 20-40m humans get a lot less info from stereo vision. When you get to that distance, humans…
It depends what you mean by "practical", though. If you mean "practical for use as a dense 3D reconstruction technique" then sure, it's pretty bad. If you mean "usable under an incredible range of lighting conditions and situations" then I'd say it's practical.
Edit: IMO binocular vision is probably more to do with redundancy than depth perception. If you damage or lose an eye, you can still operate at near full capacity. Losing vision 'in the wild' is a death sentence.
Re: This AI Boom Will Also Bust
#288Earlier quoted context omitted.
Stereo vision is obviously highly effective in biology as it has independently evolved a great many times. Time-of-flight may be poised for a renaissance, but it scales badly and is active, not passive. Stereo vision, and its big brother light fields, are far more general and are certainly not going to "die out".
> Stereo vision is obviously highly effective in biology as it has independently evolved a great many times This argument sounds like a second cousin of the "chemicals are bad, but if it is natural it is good" argument. Just because it evolved in nature doesn't mean it's optimal, or it's the best system under massively different constraints. And who knows what evolution would have thrown up after a few more billion y…
I think the point he was making is that it is effective in different environmental challenges and constraints. Not that it's optimal. So it might still be useful for building into a robot that's supposed to explore a environment(think space and planets for example). It may get relegated to academia, if we find other optimal solutions for our robots doing our boring chores though.
Re: This AI Boom Will Also Bust
#289When I was at Watson this is the first thing I told every customer: before you start with AI are you already doing the more mundane data science on your structured data? If not, you shouldn't go right away for the shiny object. This said I still believe the article is mistaken in its evaluation of potential impact (and its fuzzy metaphore of pipes). Unstructured or semi-structured or dirty data is much more prevalent…
And before you do mundane data science on your structured data, you should figure out if there is a better way to get cleaner raw data, more data, as well as more accurate data. For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision. It's useful for now but not something to build a business around. Better time-of-flight depth cameras will be here soon enough.…
> For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision
Maybe for regular "cameras", but parallax still gives the most accurate results for 3D reconstruction from satellite images, where it is a very lively area of research. The resolution, and surface coverage, of radar satellites is far worse than what you can obtain by stereo matching optical images taken by the powerful telescopes on satellites. I guess in other fields like microscopy it makes a lot of sense also. Not all imaging happens indoor on commodity cameras!
Re: This AI Boom Will Also Bust
#290I understand that most people working with deep learning wouldn't want this type of thinking to spread amongst the public, and I surely don't want it either. But you have to be totally unaware of reality to think that DL is the definitive tool for AI. Most impressive results in DL in the past 2 years happended like this: >deepmind steals people from the top ML research teams in univerisites around the world >these pe…
> I'll be impressed when they solve Pacman on a Raspberry Pi I think this is the thing people don't quite get when they buy into the hype. These systems are extremely inefficient. Requiring terrabytes if not petabytes of data and basically a powerplant next to a data center to power the whole thing. The work is valuable and pushing the boundary on what the hardware can do is great but so far all these things lack any…
It's not all hype. We are using deep learning to solve real issues in our research, things that might not get done as well due to the representational learning aspect and our relatively small manpower. It's a definite advance when it comes to improving state-of-the-art and will have impact across all sorts of things, particularly small groups who have problems but don't have hundreds of people and ten years to figure it out.
The powerplant criticism is more true of the training phase and much less true of using the resulting networks on a larger scale. The good thing about the "hype" is that more resources are being directed into the field so they'll be more efficient processing platforms (e.g., ASIC or FPGA) and better delineation of what's really needed and if there are possible shortcuts (e.g., ReLU).
The black box problem will prevent its use in some systems, but even in some areas of medicine, it will be fine because medical AI must be used in conjunction with the final decision-maker, much like how Watson is being positioned. A deep learning system that detects anomalies in patient imaging with very high precision will be useful even if it can't explain why it thought it was an anomaly. It's quality control for the radiologist, etc.