11–20 of 77 posts
It seems they missed mentioning Ray Kurzweil, the AI master who's a Director of Engineering at Google.
"The singularity is near: When humans transcend biology" "The age of spiritual machines: When computers exceed human intelligence"
I'd argue that Stephen Boyd is more of a master of AI than Kurzweil.
lol. This is the grand payoff?
"They’ve also found that the models tend to become more accurate the more data they consume. That may be the next big goal for Google: building AI models that are based on billions of data points, not just millions. " I'm not versed in machine learning, but it looks to me that any model whose output quality is dependent on the quantity of data it ingests is deeply flawed. There's no doubt a bigger number of samples w…
Until you have reached a very large subset of all available information, more data allows you to make better predictions. Period. That is as true for machine learning as it is of the human brain. You often want your models to also perform well when you have fewer data points. Those are two separate - if in effect related - design goals.
I knew this was a Wired article without even looking at the URL.
I remember on my pre-broadband days here in Brazil subscribing to Doctor Dobbs Journal to the tune of 25 USD per issue and being happy. Each issue filled with little gems that would advance my knowledge a lot... these days its all those glossy covers with photoshop covers and over the top headlines.
:-(
>About a year later, Google had reduced Android’s voice recognition error rate by an astounding 25 percent. lol. This is the grand payoff?
"They’ve also found that the models tend to become more accurate the more data they consume. That may be the next big goal for Google: building AI models that are based on billions of data points, not just millions. " I'm not versed in machine learning, but it looks to me that any model whose output quality is dependent on the quantity of data it ingests is deeply flawed. There's no doubt a bigger number of samples w…
Until you have reached a very large subset of all available information, more data allows you to make better predictions. Period. That is as true for machine learning as it is of the human brain. You often want your models to also perform well when you have fewer data points. Those are two separate - if in effect related - design goals.
"They’ve also found that the models tend to become more accurate the more data they consume. That may be the next big goal for Google: building AI models that are based on billions of data points, not just millions. " I'm not versed in machine learning, but it looks to me that any model whose output quality is dependent on the quantity of data it ingests is deeply flawed. There's no doubt a bigger number of samples w…
https://static.googleusercontent.com/media/research.google.c...
I knew this was a Wired article without even looking at the URL.
me too... argh, I hate those headlines. Its a proof of how much tech and digital culture journalism has fallen. I remember on my pre-broadband days here in Brazil subscribing to Doctor Dobbs Journal to the tune of 25 USD per issue and being happy. Each issue filled with little gems that would advance my knowledge a lot... these days its all those glossy covers with photoshop covers and over the top headlines. :-(
"They’ve also found that the models tend to become more accurate the more data they consume. That may be the next big goal for Google: building AI models that are based on billions of data points, not just millions. " I'm not versed in machine learning, but it looks to me that any model whose output quality is dependent on the quantity of data it ingests is deeply flawed. There's no doubt a bigger number of samples w…
Statistics developed as a science because of the need to overcome the weakness of large samples being expensive. Machine learning has taken off as a direct result of the field's ability to take advantage of and get serious performance gains from the massive amounts of data being generated and leveraged recently.
Here is the best summation I can reference, and I can tell you from personal experience it is very true:
"The accuracy & nature of answers you get on large data sets can be completely different from what you see on small samples. Big data provides a competitive advantage. For the web data sets you describe, it turns out that having 10x the amount of data allows you to automatically discover patterns that would be impossible with smaller samples (think Signal to Noise). The deeper into demographic slices you want to dive, the more data you will need to get the same accuracy."
http://www.quora.com/Big-Data/Why-the-current-obsession-with...
"They’ve also found that the models tend to become more accurate the more data they consume. That may be the next big goal for Google: building AI models that are based on billions of data points, not just millions. " I'm not versed in machine learning, but it looks to me that any model whose output quality is dependent on the quantity of data it ingests is deeply flawed. There's no doubt a bigger number of samples w…
It's rapidly becoming apparent that some algorithms (eg Deep Learning related models) work much better at scale than on small amounts of data. It doesn't make sense to discount these better algorithms because they don't work as well as other models when tested against less data.
It is also apparent that these models require significantly more computing power to perform well than other models. That doesn't make them less worthy, just a cost people must consider.
It turns out that intelligence is hard..