The issues of segregation were *real* issues, not some made-up "I can't handle basic adult life" tom-foolery.
Trying to conflate SJWs and their crying because a wrong-think speaker is invited to a debate with actual liberals trying to reduce the very real consequences for racial segregation is a very SJW thing to do though, so kudos on the gymnastics.
I was about to mod this down, but perhaps explaining all the things that are wrong with this is a better way to go.
You are correct in that 100% coverage would be needed to kill off the virus. HIV is notorious for repopulating a system if even as much as a single virion is alive. What is completely off is that 6 SNPs would protect against antibody recognition when there are 3 of them, all broadly neutralizing. The antibodies can very well have overlapping epitopes for different patterns resulting in recognition regardless of any variation, as has already been shown to be the case by TFA. Ignoring the entire purpose of the broadly neutralizing antibodies is necessary for your analysis to seem accurate.
Next, the matters of genomics. If the antibodies recognize any specific phenotype in the viral episome, and that phenotype is conserved, it is quite tricky for the virus to produce an immunity without altering a core protein of itself. Usually, that would not be a problem for a virus such as HIV as there is lots and lots of variation possible within a protein without changing its function, but when dealing with broadly neutralizing antibodies the change must be significant or it will still get caught in a broad recognition. Hence, it is not enough to change a nucleotide or two, that change must result in a significantly different phenotype which means the expressed protein would function differently (or not at all). That is why these antibodies have already been proven to work efficiently.
In short: no, your interpretation does not represent how it works at all.
That is like comparing buttsex to different quantum mechanics models. They aren't comparable in any reasonable sense, and they do completely different things with different purposes.
According to Tom's Hardware, this story is a misunderstanding, and does not represent the actual words of the presentation.
From TH:
Some media outlets are reporting that GlobalFoundries is working with Tesla on AI technology for its cars. This erroneous report stems from a comment GloFlo's CEO Sanjay Jha made on stage on Wednesday at the fab's annual get-together in San Jose....
But what Jha actually said—which we can confirm because we were present to hear it firsthand—was that GlobalFoundries is trying to attract companies as business models change:
"As we develop these new technologies, we are also seeing a big shift in the business model and the foundry business. What is happening is that system companies like Google, like Amazon, like Tesla, like Microsoft, are coming directly to foundries. They are working directly with IP companies and system development companies because they want to control the hardware and software."
Global Foundries is not saying that it's working with Tesla--but that's not to say that AMD isn't working with Tesla. Jim Keller, formerly the chief architect for AMD's microprocessors, is now VP of autopilot hardware at Tesla. Last year, AMD lost what Tesla CEO Elon Musk called a tight race against Nvidia for the auto company's GPU/AI business. Since that time, AMD has continued to show strength across multiple sectors. The CNBC report said that its sources tied AMD and Tesla together, but neither AMD or Tesla will comment on the situation. The report indicated that Tesla was on a mission to develop its own chip for autonomous cars in order to be more vertically integrated, but that Tesla was potentially relying on building that "on top of AMD intellectual property." That particular wording certainly paints a dotted line to GlobalFoundries.
I get the feeling he's ranting his way up to outright saying "Only Google (Brain) counts as a *real* AI company, everyone else is a faker, and they have cooties too".
Either that or he's giving an open invitation to start a flamewar about who is and isn't a *real* AI engineer.
Perhaps the difference is found in what Ratner considers a success and what Bergquist considers a success? If you need a $2 billion revenue to consider a movie successful, then it probably correlates quite well to the Rotten Tomatoes score.
So the South Korean government is willing to do anything to show that they're trustworthy except actually doing trustworthy stuff? If they actually had some transparency and you know, trustworthiness, this wouldn't be necessary.
Why not start with a little transparency at the top? Isn't that the corruption mess that started this whole failure?
Yes, that is why I specifically recommended something like rule-based learning, a classification technique built on 1) a ruleset that specifies an outcome given a specific set of observations and 2) a set of metarules that specify how unclear cases should be handled and any upper/lower bounds on certainty/quality of data etc required. With such a system you can pinpoint exactly which rules were used for every decision made, and how altering the base metarules would affect the given decisions. It also allows you to set your own safeties however you want (you could choose to only recommend surgeries if the success outcome is given a greater than 95% probability etc)
Unfortunately for us, the accuracy of rule-based models tends to be slightly lower than the current fads of deep learning, even though it allows for transparency and sub-classifications, something deep learning methods will never achieve.
How about spending some time and money figuring out how to make a human-interpretable learning system? You know, reinvent rule-based classification, reducts, and Rough Set theory stuff. That's something that will actually advance the field instead of another Alexa or Siri for easier ad-targeting,
That way, when Skynet finally decides to assassine Kanye, at least we can go to the databanks and see exactly how it reasoned.
And at foreign hospitals, physicians complained its advice is biased toward American patients and methods of care.
Are you seriously telling me that they sold a multi-million dollar machine and didn't even include a goddamn machine learning step to adapt to local variations? Aren't the IBM guys supposed to be experts? Or at least guys that know how to pick up a fucking phone and dial an expert?
This is the kind of rookie mistake I see in my undergrads... I sure hope I'm reading this wrong, because it sounds like people might die from maltreatment over this.
At its heart, Watson for Oncology uses the cloud-based supercomputer to digest massive amounts of data — from doctor’s notes to medical studies to clinical guidelines. But its treatment recommendations are not based on its own insights from these data. Instead, they are based exclusively on training by human overseers, who laboriously feed Watson information about how patients with specific characteristics should be treated.
Ahh I guess I was wrong. There is no machine learning at all yet.
In the case of Watson for Oncology, those human operators are a couple dozen physicians at a single, though highly respected, U.S. hospital: Memorial Sloan Kettering Cancer Center in New York. Doctors there are empowered to input their own recommendations into Watson, even when the evidence supporting those recommendations is thin.
But hey, looks like the dying part could be correct. I only hope those doctors know what the variations across the world requires, because they will be giving recommendations both for japanese highschool girls and African village elders without even knowing it, and I don't think those groups have the same contextual issues.
TFS calls this a teacher. She is not. She's Facebook. Her kids aren't the customers, they are the product. She's selling brand indoctrination to young children and charging the companies for it.
What the hell does a child learn by using Twitter? To be a worse person? To avoid having a self-developed opinion? To jump on the harassment campaign because it's fun when it's not coming your way? The joys of death threats? To always share everything all the time and never read a book or introspect? Instagram? That service that causes the most depression in its users? Yeah, that's a great tool for kids. Nothing says well-developed like hiding all the pictures of your life that aren't perfect. Nothing teaches you self-respect like living for "likes". Should we really teach kids to be emotionally dependent prostitutes?
This isn't a teacher, this is the incarnation of greed above humanity and technology replacing instead of supporting mental growth.
It's not necessarily the issue of P=NP, though such a solution absolutely would resolve this issue.
The "problem" here is that there is no cost function. This is an Ariadne's Thread dilemma in that you can only verify your solution once you have placed as many queens as your placement will allow. You cannot place a single queen on an n x n board and then conclude that you are one step closer to a solution. There is no way to subdivide this problem, hence no way to solve it in a "sufficiently" fast manner (i.e. a greedy algorithm/DP or similar). You are constrained to exhaustive/B&B/randomized approaches, guaranteeing a upper bound of O(SUCK).
It does not say fat == good. The study says that it's better to be on the upper normalized end (quintile 5) when compared to quintile 1 of the fat intake distribution. The study does in no way advocate switching to an all-fat diet to live forever, nor does it suggest that you should eat at McDonalds every day (though TFS seems to do this). It should also be noted that this has been known for a long time. Fat is a slow energy source and has always been considered healthy when consumed within reason. The quick energy sources (such as refined sugars and other carbs) have also been considered health risks in larger doses for a long time.
The study is also facing a major hurdle that I do not see controlled for anywhere - 18 countries are included (from NA an EU). This means that different fat diets can mean different things (i.e. a high fat intake in the US is not necessarily the same proxy as a high fat intake in Norway, nor are the needs necessarily the same in those countries). It would be much better to check these things in regional studies (though it can be damned hard to get the numbers high enough for any kind of power), and to keep cultural backgrounds separate (as those would likely affect the diet).
That said, it's a good story and it certainly casts the dietary effects on lifestyle in a different light (it's unlikely that the reality is completely opposite of this study even though there definitely are flaws). I would however be very skeptical of the bad fats results since that one has previously shown to have a potent non-linear relationship to health (up to a certain point it's fine, after that it quickly tapers off into *calamity* territory). Or, as stated in the study
The uncertainty regarding the effect of saturated fatty acids on clinical outcomes in part might be due to the fact that most observational cohort studies have been done in high-income countries where saturated fatty acid intake is within a limited range (about 7–15% of energy)
Other than that, very interesting. And remember, it's energy in - energy out that matters.
For once it might be Beyonce and Justin Bieber worrying about their jobs getting outsourced instead of programmers and service personnel. I like this new plot twist.
You have given a great example for both how this law should look and how it should not look. You say that one should specifically avoid words such as faggot and nig nog. The latter is an excellent word to avoid, and I would have no problem with putting that into a specific law ("One may not the use the word nig nog specifically"). The former shows the problem. Faggot can mean many things. In old English it means a bundle of sticks, or the person that gathers the bundle of sticks. In British it can mean a cigarette butt. In large communities on the internet it means, I don't know really but something like "person" or "you". It can also be a reference to a homosexual, either used in a denigratory manner towards homosexuals, or used in a non-denigratory manner between homosexuals.
So when you prosecute someone, how do you determine which context was used? Perhaps it's crystal clear, but perhaps it's not. How do you stop someone from using this law outside of its intended context? How do you stop it from being used for political or ideological agendas?
Slashdot Mirror
I think you are making a dangerous mistake here. It is not 'blacks' that want segregation, it is the SJW crowd.
You are a very angry man. I hope you get the help you need. And I hope you don't tweet your controversial ideas every night.
The issues of segregation were *real* issues, not some made-up "I can't handle basic adult life" tom-foolery.
Trying to conflate SJWs and their crying because a wrong-think speaker is invited to a debate with actual liberals trying to reduce the very real consequences for racial segregation is a very SJW thing to do though, so kudos on the gymnastics.
I was about to mod this down, but perhaps explaining all the things that are wrong with this is a better way to go.
You are correct in that 100% coverage would be needed to kill off the virus. HIV is notorious for repopulating a system if even as much as a single virion is alive. What is completely off is that 6 SNPs would protect against antibody recognition when there are 3 of them, all broadly neutralizing. The antibodies can very well have overlapping epitopes for different patterns resulting in recognition regardless of any variation, as has already been shown to be the case by TFA. Ignoring the entire purpose of the broadly neutralizing antibodies is necessary for your analysis to seem accurate.
Next, the matters of genomics. If the antibodies recognize any specific phenotype in the viral episome, and that phenotype is conserved, it is quite tricky for the virus to produce an immunity without altering a core protein of itself. Usually, that would not be a problem for a virus such as HIV as there is lots and lots of variation possible within a protein without changing its function, but when dealing with broadly neutralizing antibodies the change must be significant or it will still get caught in a broad recognition. Hence, it is not enough to change a nucleotide or two, that change must result in a significantly different phenotype which means the expressed protein would function differently (or not at all). That is why these antibodies have already been proven to work efficiently.
In short: no, your interpretation does not represent how it works at all.
That is like comparing buttsex to different quantum mechanics models. They aren't comparable in any reasonable sense, and they do completely different things with different purposes.
According to Tom's Hardware, this story is a misunderstanding, and does not represent the actual words of the presentation.
From TH:
Some media outlets are reporting that GlobalFoundries is working with Tesla on AI technology for its cars. This erroneous report stems from a comment GloFlo's CEO Sanjay Jha made on stage on Wednesday at the fab's annual get-together in San Jose. ...
But what Jha actually said—which we can confirm because we were present to hear it firsthand—was that GlobalFoundries is trying to attract companies as business models change:
"As we develop these new technologies, we are also seeing a big shift in the business model and the foundry business. What is happening is that system companies like Google, like Amazon, like Tesla, like Microsoft, are coming directly to foundries. They are working directly with IP companies and system development companies because they want to control the hardware and software."
Global Foundries is not saying that it's working with Tesla--but that's not to say that AMD isn't working with Tesla. Jim Keller, formerly the chief architect for AMD's microprocessors, is now VP of autopilot hardware at Tesla.
Last year, AMD lost what Tesla CEO Elon Musk called a tight race against Nvidia for the auto company's GPU/AI business. Since that time, AMD has continued to show strength across multiple sectors.
The CNBC report said that its sources tied AMD and Tesla together, but neither AMD or Tesla will comment on the situation. The report indicated that Tesla was on a mission to develop its own chip for autonomous cars in order to be more vertically integrated, but that Tesla was potentially relying on building that "on top of AMD intellectual property." That particular wording certainly paints a dotted line to GlobalFoundries.
Full story at http://www.tomshardware.com/ne...
I get the feeling he's ranting his way up to outright saying "Only Google (Brain) counts as a *real* AI company, everyone else is a faker, and they have cooties too".
Either that or he's giving an open invitation to start a flamewar about who is and isn't a *real* AI engineer.
This sounds like the least interesting crime show I have ever heard of, and I will not watch the dramatization even if Tom Hanks plays the lead.
Are they Trump hotels? Cause that could certainly explain the 180.
That's not true. Sometimes you pay for the product but you are still the product.
Perhaps the difference is found in what Ratner considers a success and what Bergquist considers a success?
If you need a $2 billion revenue to consider a movie successful, then it probably correlates quite well to the Rotten Tomatoes score.
So the South Korean government is willing to do anything to show that they're trustworthy except actually doing trustworthy stuff? If they actually had some transparency and you know, trustworthiness, this wouldn't be necessary.
Why not start with a little transparency at the top? Isn't that the corruption mess that started this whole failure?
Yes, that is why I specifically recommended something like rule-based learning, a classification technique built on 1) a ruleset that specifies an outcome given a specific set of observations and 2) a set of metarules that specify how unclear cases should be handled and any upper/lower bounds on certainty/quality of data etc required.
With such a system you can pinpoint exactly which rules were used for every decision made, and how altering the base metarules would affect the given decisions. It also allows you to set your own safeties however you want (you could choose to only recommend surgeries if the success outcome is given a greater than 95% probability etc)
Unfortunately for us, the accuracy of rule-based models tends to be slightly lower than the current fads of deep learning, even though it allows for transparency and sub-classifications, something deep learning methods will never achieve.
How about spending some time and money figuring out how to make a human-interpretable learning system? You know, reinvent rule-based classification, reducts, and Rough Set theory stuff. That's something that will actually advance the field instead of another Alexa or Siri for easier ad-targeting,
That way, when Skynet finally decides to assassine Kanye, at least we can go to the databanks and see exactly how it reasoned.
Wait, you did the pun? I thought I did the pun.
Deal. Find me a single bottle that says it uses sisomso.
And at foreign hospitals, physicians complained its advice is biased toward American patients and methods of care.
Are you seriously telling me that they sold a multi-million dollar machine and didn't even include a goddamn machine learning step to adapt to local variations? Aren't the IBM guys supposed to be experts? Or at least guys that know how to pick up a fucking phone and dial an expert?
This is the kind of rookie mistake I see in my undergrads...
I sure hope I'm reading this wrong, because it sounds like people might die from maltreatment over this.
At its heart, Watson for Oncology uses the cloud-based supercomputer to digest massive amounts of data — from doctor’s notes to medical studies to clinical guidelines. But its treatment recommendations are not based on its own insights from these data. Instead, they are based exclusively on training by human overseers, who laboriously feed Watson information about how patients with specific characteristics should be treated.
Ahh I guess I was wrong. There is no machine learning at all yet.
In the case of Watson for Oncology, those human operators are a couple dozen physicians at a single, though highly respected, U.S. hospital: Memorial Sloan Kettering Cancer Center in New York. Doctors there are empowered to input their own recommendations into Watson, even when the evidence supporting those recommendations is thin.
But hey, looks like the dying part could be correct. I only hope those doctors know what the variations across the world requires, because they will be giving recommendations both for japanese highschool girls and African village elders without even knowing it, and I don't think those groups have the same contextual issues.
TFS calls this a teacher. She is not. She's Facebook. Her kids aren't the customers, they are the product. She's selling brand indoctrination to young children and charging the companies for it.
What the hell does a child learn by using Twitter? To be a worse person? To avoid having a self-developed opinion? To jump on the harassment campaign because it's fun when it's not coming your way? The joys of death threats? To always share everything all the time and never read a book or introspect?
Instagram? That service that causes the most depression in its users? Yeah, that's a great tool for kids. Nothing says well-developed like hiding all the pictures of your life that aren't perfect. Nothing teaches you self-respect like living for "likes". Should we really teach kids to be emotionally dependent prostitutes?
This isn't a teacher, this is the incarnation of greed above humanity and technology replacing instead of supporting mental growth.
I assume that it's already available to most, if not all, ad agencies a price.
It's not necessarily the issue of P=NP, though such a solution absolutely would resolve this issue.
The "problem" here is that there is no cost function. This is an Ariadne's Thread dilemma in that you can only verify your solution once you have placed as many queens as your placement will allow. You cannot place a single queen on an n x n board and then conclude that you are one step closer to a solution. There is no way to subdivide this problem, hence no way to solve it in a "sufficiently" fast manner (i.e. a greedy algorithm/DP or similar). You are constrained to exhaustive/B&B/randomized approaches, guaranteeing a upper bound of O(SUCK).
Yes it would. The Queen's Puzzle is an NP-complete problem, hence a solution to it would solve every other NP-complete problem.
It does not say fat == good. The study says that it's better to be on the upper normalized end (quintile 5) when compared to quintile 1 of the fat intake distribution. The study does in no way advocate switching to an all-fat diet to live forever, nor does it suggest that you should eat at McDonalds every day (though TFS seems to do this). It should also be noted that this has been known for a long time. Fat is a slow energy source and has always been considered healthy when consumed within reason. The quick energy sources (such as refined sugars and other carbs) have also been considered health risks in larger doses for a long time.
The study is also facing a major hurdle that I do not see controlled for anywhere - 18 countries are included (from NA an EU). This means that different fat diets can mean different things (i.e. a high fat intake in the US is not necessarily the same proxy as a high fat intake in Norway, nor are the needs necessarily the same in those countries). It would be much better to check these things in regional studies (though it can be damned hard to get the numbers high enough for any kind of power), and to keep cultural backgrounds separate (as those would likely affect the diet).
That said, it's a good story and it certainly casts the dietary effects on lifestyle in a different light (it's unlikely that the reality is completely opposite of this study even though there definitely are flaws). I would however be very skeptical of the bad fats results since that one has previously shown to have a potent non-linear relationship to health (up to a certain point it's fine, after that it quickly tapers off into *calamity* territory). Or, as stated in the study
The uncertainty regarding the effect of saturated fatty acids on clinical outcomes in part might be due to the fact that most observational cohort studies have been done in high-income countries where saturated fatty acid intake is within a limited range (about 7–15% of energy)
Other than that, very interesting.
And remember, it's energy in - energy out that matters.
For those of us that have not actually built a kernel, is 36 seconds astonishingly fast?
I did a little checking and here's what I found. It's faster than 37 seconds, but not as fast as 35 seconds.
For once it might be Beyonce and Justin Bieber worrying about their jobs getting outsourced instead of programmers and service personnel. I like this new plot twist.
"Riling up" was in response to GP.
You have given a great example for both how this law should look and how it should not look.
You say that one should specifically avoid words such as faggot and nig nog. The latter is an excellent word to avoid, and I would have no problem with putting that into a specific law ("One may not the use the word nig nog specifically"). The former shows the problem.
Faggot can mean many things. In old English it means a bundle of sticks, or the person that gathers the bundle of sticks. In British it can mean a cigarette butt. In large communities on the internet it means, I don't know really but something like "person" or "you". It can also be a reference to a homosexual, either used in a denigratory manner towards homosexuals, or used in a non-denigratory manner between homosexuals.
So when you prosecute someone, how do you determine which context was used? Perhaps it's crystal clear, but perhaps it's not. How do you stop someone from using this law outside of its intended context? How do you stop it from being used for political or ideological agendas?