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You got anything to back that first part up or is this more singularity hyperbole? So far the only use of robots in medicine that I'm aware of has been to assis

You got anything to back that first part up or is this more singularity hyperbole? So far the only use of robots in medicine that I'm aware of has been to assist doctors, such as in delicate surgeries. (Or more mundane stuff like a robot going around handing medicine) AI has huge hurdles to overcome before it can solve real-world problems. Deepmind excelled at Go because it's a board game that works within constraints. (really, you can learn the rules of go in a few minutes) Even self-driving cars depend on optimal conditions and assume an already-built infrastructure. Not only is it FUD to say that AI will usurp us that fast, but it is also insulting to human ingenuity and insight to say that human intelligence can be replaced with a clever algorithm. Don't get me wrong, I'd love for everything to be automated, eliminating human error and inefficiency, launching us into a basic income utopia (ha), but we have to look at the situation realistically and refrain from statements that will hold back the industry. (See things like https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter and references to skynet) The solution is to look at the actual state of the art and to evaluate advances in their proper contexts. Consider this: we still can't make robots that walk like humans (bipedal locomotion) and stay balanced. (There are promising results coming from Boston Dynamics, but AFAIK actual usage has been limited to military stuff like pack dogs)

xbmcuser 10y ago
Not hyperbole I believe that is going to happen and I am not talking about ai or sigunlaraty more about machine learning. Doctors learn about disesases and then with experience start diagnosing them. It's a simple a+b=c now feed the data of millions of patients their symptoms their diagnoses the most effective remedy or cure. Computers will start finding and predicting the problems and diseases faster and more accurately than doctors so we might need more nurses but fewer doctors in the future. An architect designs buildings taking into account building codes and best use of space etc. Feed thousands of blueprints to a deeplearing machine tell all the buliding codes it has to follow. The current climate and what is expected to be in the next few decades it will give you the most efficient building plan that uses the most space and least amount of energy. Feed the machine all the tax law it will find you the best loopholes to avoid taxes A human can't take into account the number of variables a machine will be able to in the next few years and the keep getting more powerful everyear.Google recent announcement of using machine learning to improver server heat efficiency is just the start. Their are thousands of such problems that will be solved with machine and deeplearning and make many human jobs mostly obsolete.
- cooper12 10y ago
  If only it was ever as easy as data in = results out. The quality, accuracy, granularity, accessibility, and existence of data is an important prerequisite. (For healthcare in America, HIPAA will be a big hurdle) Deep learning has been a big advance in how machine learning has been done, but we have to be careful not to oversell it either or assume it is a panacea to all problems in any domain. You can't just say "I'll just use a deep net here" and expect to be done. Getting meaningful results takes training, proper design of networks, filtering, and feedback systems. Lastly there's the issue of what is unspecified. What if an architecture AI designs an optimal building conforming to all proper checks, but is in the end ugly as sin. You spent all that time creating the perfect algorithm that was trained on architecture rules and experience, but either forgot to include or couldn't derive a metric for aesthetics or uniqueness. What if AI doctors become infallible with instant access to contraindications and medical literature, but in the end has no bedside manner and no physical presence to actually convince a patient to listen to them? What if your tax bot was deemed illegal for helping circumvent paying taxes? You might have a solution for all of these, but the fact that problems like these exist/could exit should be a sign that nothing is "a simple a+b=c".

xbmcuser 10y ago

Not hyperbole I believe that is going to happen and I am not talking about ai or sigunlaraty more about machine learning. Doctors learn about disesases and then with experience start diagnosing them. It's a simple a+b=c now feed the data of millions of patients their symptoms their diagnoses the most effective remedy or cure. Computers will start finding and predicting the problems and diseases faster and more accurately than doctors so we might need more nurses but fewer doctors in the future. An architect designs buildings taking into account building codes and best use of space etc. Feed thousands of blueprints to a deeplearing machine tell all the buliding codes it has to follow. The current climate and what is expected to be in the next few decades it will give you the most efficient building plan that uses the most space and least amount of energy. Feed the machine all the tax law it will find you the best loopholes to avoid taxes A human can't take into account the number of variables a machine will be able to in the next few years and the keep getting more powerful everyear.Google recent announcement of using machine learning to improver server heat efficiency is just the start. Their are thousands of such problems that will be solved with machine and deeplearning and make many human jobs mostly obsolete.

cooper12 10y ago
If only it was ever as easy as data in = results out. The quality, accuracy, granularity, accessibility, and existence of data is an important prerequisite. (For healthcare in America, HIPAA will be a big hurdle) Deep learning has been a big advance in how machine learning has been done, but we have to be careful not to oversell it either or assume it is a panacea to all problems in any domain. You can't just say "I'll just use a deep net here" and expect to be done. Getting meaningful results takes training, proper design of networks, filtering, and feedback systems. Lastly there's the issue of what is unspecified. What if an architecture AI designs an optimal building conforming to all proper checks, but is in the end ugly as sin. You spent all that time creating the perfect algorithm that was trained on architecture rules and experience, but either forgot to include or couldn't derive a metric for aesthetics or uniqueness. What if AI doctors become infallible with instant access to contraindications and medical literature, but in the end has no bedside manner and no physical presence to actually convince a patient to listen to them? What if your tax bot was deemed illegal for helping circumvent paying taxes? You might have a solution for all of these, but the fact that problems like these exist/could exit should be a sign that nothing is "a simple a+b=c".