No. Image and audio recognition in many specific cases are now solved problems. This is substantial progress.
This AI Boom Will Also Bust
251–260 of 320 posts
Re: This AI Boom Will Also Bust
#252I have a hard time understanding why even technical people use the term "AI" today. Its use should be limited to sensational media and cheesy sci-fi. It's roughly equivalent to saying "computery thingamabob". I would call a pocket calculator an AI too. Why not? It carries out certain mental tasks better than our brains do.
Worth noting that that first AI program could run on a not-so-modern pocket calculator these days:
https://en.wikipedia.org/wiki/Logic_Theorist
The fact that AI is now a meaningless term is mostly a testament to the success of AI.
Re: This AI Boom Will Also Bust
#253I 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…
Agree generally. Except being unimpressed unless performance is achieved on sub Google scale hardware. Today's Google supermachine is tomorrow's raspberry pie. No need to artificially constrain our bounds. There is, after all, the inevitability of Moores law.
Moore's law has been dead for a while now. Most of the chip in your phone is powered off because otherwise it would burn up. Highly recommend watching this video: https://www.youtube.com/watch?v=_9mzmvhwMqw
Re: This AI Boom Will Also Bust
#254Earlier quoted context omitted.
This was a really nice breakdown, thanks. You mentioned that that Deep learning was a method of choice for AI researched because Deep it has unlocked a lot of new application. I have a question - is it also a "method of choice" for researchers because its not well understood yet why Deep Learning actually works?
Good question. Just one anecdotal data point here ... So take it for what it's worth. I'm a grad student focusing on reinforcement learning but have a lot of interaction with many deep learning folk. I'd have to say that they seem mostly motivated to learn how to make deep learning even more powerful and how to apply it. Not so much solving what's going on inside the box.
We do understand in general, just not every neuron and input weight in particular, because they are too many for limited human working memory to hold at once.
An automated labyrinth could be solved by hand up to a size, but if it becomes larger, it would become "impossible to understand". We'd need to rely on computers to find the route for us.
It's not computer magic, just an ability to hold more data at once. We're limited to 7-8 things - try to remember a string of more digits and see if you can - it's hard, we just have that kind of limitation. So any computer algorithm that can't be broken down into 7-8 understandable parts is hard to grasp.
Of course we can make ML tools synthesize the preferred input of any neuron. That does help a little.
Re: This AI Boom Will Also Bust
#255Earlier quoted context omitted.
So is image recognition, but I think the main contention is that solving Go required little additional cleverness to what has already existed. The ideas of self-play, Monte Carto methods, and neural networks are not new and not novel for the problem. Not that it's so trivial -- I'm sure it took a while to work out the exact architecture and details, but what of that work actually teaches you something?
The combination of the methods was certainly novel as far as I am aware. Also, it was not clear ahead of time that it would actually succeed in defeating the best humans. Demonstrating that it could be done is a grear achievement. There is also the symbolic value. It was a "coming of age" event to a certain degree. I believe go was the last classic board game that researchers had been longing to conquer. I haven't lo…
By that token, IBM's wins at chess and Jeopardy also deserve to be seen as "coming of age" events. And while the wins certainly showcased IBM's engineering prowess, I'm unconvinced Deep Blue or Watson moved the AI needle in a meaningful way. If they had, others would have followed in their technical footsteps. But AFAIK, no one has. I believe this lack of high tech repercussion will be true of AlphaGo too. Novel AI for game play just doesn't transfer outside the game.
Re: This AI Boom Will Also Bust
#256Earlier quoted context omitted.
It's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.
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 read your consciousness post and will reply here because that thread is already locked. I think the problem of consciousness comes from its definition. The word consciousness is too large, encloses too many aspects of cognition, religion and philosophy to be useful. It's a suitcase word, reifying various meanings into one name.
What we need instead of "consciousness" is a set of concepts:
- "perception" - means representing inputs in such a way as to be able to adapt to the environment to achieve goals (I place qualia here)
- "judgement" - the ability to select the best action for the current state
- "reward" - comes from time to time, and is used as a signal in behavior learning
These three concepts : perception, judgement and reward are simpler, clearer and less ambiguous. They have implementations in AI, not human level yet, but getting there. These concepts make the problem concrete instead of bringing up 2000 years of attempts to "get consciousness" based off armchair philosophising.
A more interesting question than "what is consciousness?" would be: what are the reward signals that train the human brain. We do Reinforcement Learning in the brain, but the reward signal is complex, made of multiple channels, which are evolutionarily optimized for our survival. It's the engine of drive that puts us in motion. We need to reverse engineer that in order to replicate human consciousness in silico.
Consciousness is a replicator, a system that is concerned with survival and maintaining balance in the face of perturbations and entropy. What it does is to make us go find food, make babies and protect from danger - essential actions without which the human species would disappear, and consciousness with it. Or, in other words, it maximizes its rewards over time, reward being having food, shelter, company, access to learning and a few more. But it's just limited number of reward types, and they are much simpler to understand than consciousness itself.
So consciousness is just that thing that maintains itself instead of becoming disorganized and dissipating out like most physical processes that lack a self stabilizing, self replicating dynamics.
Consciousness is a balancing system for the colony of cells that just want to pass their genes into the next generation.
Re: This AI Boom Will Also Bust
#257When 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.…
You're ignoring form factor for optimized technology.
Yes, a quality TOF system would be great. However good luck convincing consumers to adopt hardware with lidar on it. The Tango is having enough trouble on it's own and it does pretty well for consumer systems with IR.
Besides that you can't do FTDT with laser systems AFAIK. You need something to capture unseen places, such as ultrasonics/HF - which I guess you could argue fall into TOF but I haven't seen that work done.
In the end my money (literally!) is on the opposite if your approach, namely building better RGB systems because there are already a trillion cameras deployed that we can extract from.
Re: This AI Boom Will Also Bust
#258I have a hard time understanding why even technical people use the term "AI" today. Its use should be limited to sensational media and cheesy sci-fi. It's roughly equivalent to saying "computery thingamabob". I would call a pocket calculator an AI too. Why not? It carries out certain mental tasks better than our brains do.
I think "AI" does still have meaning, in that the goal of AGI hasn't gone away. Yes, any goal that's tangential to AGI probably shouldn't be called AI. But as long as the constituent tasks needed by AI (vision, learning, speech in/out, etc) continue to improve rapidly, especially due to advances in a single technology like DL, it's inevitable and IMO appropriate that the umbrella moniker used to describe DL and its impacts remains "AI".
Re: This AI Boom Will Also Bust
#259What is "new this time" is that computers/machines can see and hear and even speak, whether we call it AI or "Deep Learning" or "Machine learning". So there is going to be more impact from Vision/Voice based applications rather than data analytics/predictions. Eg. Self Driving Cars, Medical Diagnosis, Video Analytics, Autonomous machines/Robotics, Voiced based interactions. All of these combined could be as disruptive as Internet itself.
Re: This AI Boom Will Also Bust
#260I'm sorry but I'm not buying it.
ML companies are already tackling tasks which have major cost implications:
https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...
http://med.stanford.edu/news/all-news/2016/08/computers-trou...
Those are just the two I had off the top of my head. We apply ML tasks for object/scene classification and they blow away humans. Not only that we're already structuring a GAN for "procedural" 3D model generation - in theory this will decimate the manual 3D reconstruction process.