Imagine an innovation in pipes. If this innovation were general, something that made all kinds of pipes cheaper to build and maintain, the total benefits could be large, perhaps even comparable to the total amount we spend on pipes today. (Or even much larger.) And if most of the value of pipe use were in many small uses, then that is where most of these economic gains would be found.
When I read this article, I understand it to be talking about the things that most businesses will be able to get value out of now and in the near future.
This is a great list of things that deep learning may one day be able to help with, but I don't think it's a list of things that most businesses can use right now - have you ever interacted with a DL based help desk? Have you listened to the quality of the music composed by DL? We're a long way out from these being acceptable.
Just because you can list 14 preliminary attempts which produce OK demos, doesn't mean there is widespread application.
And exceed mankind? A lot of these don't - are you trying to tell me that the compositions by Emily exceed Beethoven? The Beatles? I don't think so, and I don't think you'll find many people in agreement if you do.
P.S, Cucumber sorting? There's a lot of demand for that! 😂
"Autonomous vehicles will also completely eliminate "choke points" caused by such things as accidents, road construction, and bridges. It will be huge...even in NYC, the public transit capital of the U.S."
Again, will never happen, maybe in 150 years, if Manhattan isn't under water.
Many cases of ML can be replaced by linear regression, but if so, you probably aren't doing the right analysis. People ask me if I'm a data scientist. Sure, but that's not the focus. I'm a complexity scientist first. I completely agree with some of the posts that mention that there are only certain use cases where you can get away with just prediction. Many more you have to figure out how the system works or you will make the wrong decisions.
Ned Sharpless: We did an analysis of 1,000 patients, where the humans meeting in the Molecular Tumor Board-- doing the best that they could do, had made recommendations. So not at all a hypothetical exercise. These are real-world patients where we really conveyed information that could guide care. In 99 percent of those cases, Watson found the same the humans recommended. That was encouraging.
Charlie Rose: Did it encourage your confidence in Watson?
Ned Sharpless: Yeah, it was-- it was nice to see that-- well, it was also-- it encouraged my confidence in the humans, you know. Yeah. You know--
Charlie Rose: Yeah.
Ned Sharpless: But, the probably more exciting part about it is in 30 percent of patients Watson found something new. And so that’s 300-plus people where Watson identified a treatment that a well-meaning, hard-working group of physicians hadn’t found.
Charlie Rose: Because?
Ned Sharpless: The trial had opened two weeks earlier, a paper had come out in some journal no one had seen -- you know, a new therapy had become approved—
Long commute times does not equate to inefficiency. There are other outside factors affecting the commute time like distance. For the subway cars you go faster, they must accelerate faster, requiring more energy usage, which increases costs; or longer distances between stops, which reduces utility. NYC has a good blend of express and regular trains. I love London and it's tube and train system, but express tube lines would be fabulous (I mean utterly brilliant!) to use, versus stopping at virtually station.
There are also economical and social effects from that scenario. It's true that driving is the leading cause of death in Western countries. If we factor that large number back into the population, with the added population growth that comes from it, more economic productivity or options will be required to support the increased number of people.
I clearly want to see people live longer, disease eradicated, minimal accidental and avoidable deaths. I also think of the implications to all strata of society, how they will all be supported, and their true quality of life.
We can never escape the law of unintended consequences.
Manual transmissions don't compare well too the psychology of feeling in control. Why do people fear flying more than driving when driving is exponentially more dangerous? That same feeling of being in control of one's destiny, even though it's much deadlier. Humans become accustomed to things around them and fear the unknown.
Driverless personal vehicles will take a very long time to achieve an 80% adoption rate, as in multiple generations. Driverless freight trucks and taxis are a different story.
Railroads are a combination of both low and high tech. While the quality and cost associated with laying track may have improved, having sufficient engines to pull freight cars is cost prohibitive to lay track and bring freight all the way to its destination. Rail tracks are a single purpose medium, unlike roads, which can be used by many types of wheeled vehicles.
Due to this, the hub methodology is used instead, and freight moves across shared mediums to their destinations, and either unloaded at a commercial establishment, or redistributed into smaller vehicles for delivery to individuals.
The same additional learnings mentioned by a previous component also apply to drones for home delivery.
I still believe that the ripple effect from the minimization of human freight truck drivers will have a much larger impact than many imagine, more so than previous historical disruptions. The pace of discovery, implementation and impact has been steadily increasing, not remaining the same. The application of the discovery in adjacent areas usually has the biggest impact. The same economic principle applies here too.
Thank you so much for raising this! I 100% agree. The "this time is different" mentality in the current AI wave will be proved wrong - this AI boom will also bust, and another AI winter will come. Here's my take: Everyone is infatuated with the ability to predict, but most people overestimate the number of domains where prediction can be translated directly into an action with a positive ROI. You don't just need to predict, you need to act in such a way as to influence the outcome. Usually that requires understanding of cause and effect. Guess what? Now you're doing real science, not data science. Linear model with interpretable coefficients on cleaned up data sounds good all of a sudden! But okay, I'm going overboard to make a point. There is amazing progress being made in lots of areas and tons of great new applications coming. So I think the next AI winter may be more of a California winter, where lots of things continue to thrive and grow. I'm just saying the hype has outpaced the reality.
The impact of AI is always presented as such an absolute, e.g.'it's going to take all jobs' in a sector. The reality is that it doesn't have to be that effective to wreak havoc. In terms of supply and demand, even if it only 20% of jobs in a certain industry or demographic were 'taken', that would significantly impact salaries. If there was no alternative, I am sure most people would accept a 10%, 20%, 30% decrease in remuneration to stay employed, to be more attractive then the next person competing in a lowered demand scenario. In a similar vein, even if it only increases productivity by 20%, that is still significant and industry/government/armed forces/education/logistics/et al will be compelled to utilise it.
So, no, I don't think it's going to bust. And I don't think it is has to be that effective to have profound implications for society. And for the sake of our children, we need to wake up to what it really means and regulate it.
"But isn't the main reason that self-driving cars are feasible now and not twenty years ago is that prediction tech is better?"
There have massive improvements in many hardware aspects, as well software:
1) The cost of 10 gigabyte hard drive 20 years ago was about $2000, and a 10 gigabyte flash drive would have cost far more than the price of a car. That's if a 10 gigabyte flash drive was available 20 years ago...which it wasn't. Now a 10 gigabyte flash drive is a couple of dollars.
2) The cost of a 12 megapixel camera 20 years ago was about $15,000. Now it's less than $100.
3) The price per gigaflop of computing power 20 years ago was about $40,000. Now it's less than 40 cents.
When I read this article, I understand it to be talking about the things that most businesses will be able to get value out of now and in the near future.
This is a great list of things that deep learning may one day be able to help with, but I don't think it's a list of things that most businesses can use right now - have you ever interacted with a DL based help desk? Have you listened to the quality of the music composed by DL? We're a long way out from these being acceptable.
Just because you can list 14 preliminary attempts which produce OK demos, doesn't mean there is widespread application.
And exceed mankind? A lot of these don't - are you trying to tell me that the compositions by Emily exceed Beethoven? The Beatles? I don't think so, and I don't think you'll find many people in agreement if you do.
P.S, Cucumber sorting? There's a lot of demand for that! 😂
"Autonomous vehicles will also completely eliminate "choke points" caused by such things as accidents, road construction, and bridges. It will be huge...even in NYC, the public transit capital of the U.S."
Again, will never happen, maybe in 150 years, if Manhattan isn't under water.
Hunny, my friends and I are not the ones driving the cars. I can't help it if the bridge and tunnel folks like driving into town.
You are profoundly out of touch. Do you live in New York? Have you tried using public transit for a year?
Do yourself a favor and do it and get back to me. You clearly do not live in a public-transit city.
Many cases of ML can be replaced by linear regression, but if so, you probably aren't doing the right analysis. People ask me if I'm a data scientist. Sure, but that's not the focus. I'm a complexity scientist first. I completely agree with some of the posts that mention that there are only certain use cases where you can get away with just prediction. Many more you have to figure out how the system works or you will make the wrong decisions.
"Do you have some examples of what Watson found that we missed? Not being snarky, I'm genuinely interested and curious."
http://www.cbsnews.com/news...
Ned Sharpless: We did an analysis of 1,000 patients, where the humans meeting in the Molecular Tumor Board-- doing the best that they could do, had made recommendations. So not at all a hypothetical exercise. These are real-world patients where we really conveyed information that could guide care. In 99 percent of those cases, Watson found the same the humans recommended. That was encouraging.
Charlie Rose: Did it encourage your confidence in Watson?
Ned Sharpless: Yeah, it was-- it was nice to see that-- well, it was also-- it encouraged my confidence in the humans, you know. Yeah. You know--
Charlie Rose: Yeah.
Ned Sharpless: But, the probably more exciting part about it is in 30 percent of patients Watson found something new. And so that’s 300-plus people where Watson identified a treatment that a well-meaning, hard-working group of physicians hadn’t found.
Charlie Rose: Because?
Ned Sharpless: The trial had opened two weeks earlier, a paper had come out in some journal no one had seen -- you know, a new therapy had become approved—
Long commute times does not equate to inefficiency. There are other outside factors affecting the commute time like distance. For the subway cars you go faster, they must accelerate faster, requiring more energy usage, which increases costs; or longer distances between stops, which reduces utility. NYC has a good blend of express and regular trains. I love London and it's tube and train system, but express tube lines would be fabulous (I mean utterly brilliant!) to use, versus stopping at virtually station.
I don't know if that last word was a typo, autocorrect, or written on purpose, but it's hilarious!
There are also economical and social effects from that scenario. It's true that driving is the leading cause of death in Western countries. If we factor that large number back into the population, with the added population growth that comes from it, more economic productivity or options will be required to support the increased number of people.
I clearly want to see people live longer, disease eradicated, minimal accidental and avoidable deaths. I also think of the implications to all strata of society, how they will all be supported, and their true quality of life.
We can never escape the law of unintended consequences.
Manual transmissions don't compare well too the psychology of feeling in control. Why do people fear flying more than driving when driving is exponentially more dangerous? That same feeling of being in control of one's destiny, even though it's much deadlier. Humans become accustomed to things around them and fear the unknown.
Driverless personal vehicles will take a very long time to achieve an 80% adoption rate, as in multiple generations. Driverless freight trucks and taxis are a different story.
Railroads are a combination of both low and high tech. While the quality and cost associated with laying track may have improved, having sufficient engines to pull freight cars is cost prohibitive to lay track and bring freight all the way to its destination. Rail tracks are a single purpose medium, unlike roads, which can be used by many types of wheeled vehicles.
Due to this, the hub methodology is used instead, and freight moves across shared mediums to their destinations, and either unloaded at a commercial establishment, or redistributed into smaller vehicles for delivery to individuals.
The same additional learnings mentioned by a previous component also apply to drones for home delivery.
I still believe that the ripple effect from the minimization of human freight truck drivers will have a much larger impact than many imagine, more so than previous historical disruptions. The pace of discovery, implementation and impact has been steadily increasing, not remaining the same. The application of the discovery in adjacent areas usually has the biggest impact. The same economic principle applies here too.
Do you have some examples of what Watson found that we missed? Not being snarky, I'm genuinely interested and curious.
Thank you so much for raising this! I 100% agree. The "this time is different" mentality in the current AI wave will be proved wrong - this AI boom will also bust, and another AI winter will come. Here's my take: Everyone is infatuated with the ability to predict, but most people overestimate the number of domains where prediction can be translated directly into an action with a positive ROI. You don't just need to predict, you need to act in such a way as to influence the outcome. Usually that requires understanding of cause and effect. Guess what? Now you're doing real science, not data science. Linear model with interpretable coefficients on cleaned up data sounds good all of a sudden! But okay, I'm going overboard to make a point. There is amazing progress being made in lots of areas and tons of great new applications coming. So I think the next AI winter may be more of a California winter, where lots of things continue to thrive and grow. I'm just saying the hype has outpaced the reality.
"And for the sake of our children, we need to wake up to what it really means and regulate it.""Regulate" AI? What would that involve?
The impact of AI is always presented as such an absolute, e.g.'it's going to take all jobs' in a sector. The reality is that it doesn't have to be that effective to wreak havoc. In terms of supply and demand, even if it only 20% of jobs in a certain industry or demographic were 'taken', that would significantly impact salaries. If there was no alternative, I am sure most people would accept a 10%, 20%, 30% decrease in remuneration to stay employed, to be more attractive then the next person competing in a lowered demand scenario. In a similar vein, even if it only increases productivity by 20%, that is still significant and industry/government/armed forces/education/logistics/et al will be compelled to utilise it.
So, no, I don't think it's going to bust. And I don't think it is has to be that effective to have profound implications for society. And for the sake of our children, we need to wake up to what it really means and regulate it.
"But isn't the main reason that self-driving cars are feasible now and not twenty years ago is that prediction tech is better?"
There have massive improvements in many hardware aspects, as well software:
1) The cost of 10 gigabyte hard drive 20 years ago was about $2000, and a 10 gigabyte flash drive would have cost far more than the price of a car. That's if a 10 gigabyte flash drive was available 20 years ago...which it wasn't. Now a 10 gigabyte flash drive is a couple of dollars.
2) The cost of a 12 megapixel camera 20 years ago was about $15,000. Now it's less than $100.
3) The price per gigaflop of computing power 20 years ago was about $40,000. Now it's less than 40 cents.
http://i.imgur.com/FqobtB7.jpg
It appears the article's writer had not any internet access for quite a while.
Here are a sequence cognitive fields/tasks, where sophisticated neural models EXCEED mankind:
1) Language translation (eg: Skype 50+ languages)2) Legal-conflict-resolution (eg: 'Watson')3) Self-driving (eg: 'OTTO-Self Driving' )5) Disease diagnosis (eg: 'Watson')6) Medicinal drug prescription (eg: 'Watson')7) Visual Product Sorting (eg: 'Amazon Corrigon' )8) Help Desk Assistance ('eg: Digital Genius)9) Mechanical Cucumber Sorting (eg: 'Makoto's Cucumber Sorter')10) Financial Analysis (eg: 'SigFig')11) E-Discovery Law (eg: ' Social Science Research Network.')12) Anesthesiology (eg: 'SedaSys')13) Music composition (eg: 'Emily')14) Go (eg: 'Alpha Go')
...