Saturday, October 31, 2015
Wisdom Of The Week
12.Trade up on trust even if it means you trade down on competency.
Should you start a company with friends? All things being equal, Reid says yes, because you can move more quickly with trusted friends because you already understand how each other thinks and talks. And moving quickly? That’s critical in the early days of a startup.
But what if all things aren’t equal? If you’re choosing between working with someone who’s a trusted friend and a 7 out of 10 on competence, versus a stranger who’s a 9 out of 10 on competence, who should you pick? Answer: if the trusted friend is a fast learner, pick the trusted friend.
Trade up on trust, even if it means you have to trade down on competency a bit. In other words, choose to work with someone you know who’s a fast learner over someone who’s a bit more qualified who you do not know. Assuming the person you know and trust is in Permanent Beta, he or she can round out their gaps in skills or experience in short order.
I benefitted from Reid’s philosophy on this personally. For some assignments, I was not the most qualified person in the world, or even the most qualified within his own network. But given that we a) completely trust each other, b) I have a good sense of his priorities and values and preferences and he has a good sense of my own priorities and values and preferences, and c) I’m a quick learner, we could move at lightning speed together on projects.
As with so many lessons, I have to continue to re-learn this one. The first time I learned this lesson the hard way at one of my early companies, when we hired someone who looked great on paper in terms of industry accomplishments but who none of us really knew or trusted. The moment we encountered a couple landmines, the lack of trust ruined any hopes at productive group problem solving. The second time I learned this the hard way was at a different company I co-founded, where I traded down on competency too much when bringing on one team member. The trust was all there, and the guy was a fast learner, but the tradeoff down in necessary expertise wasn’t worth it, and the project floundered.
- Ben Casnocha on 10,000 Hours with Reid Hoffman (read the whole thing, its phenomenal)
Should you start a company with friends? All things being equal, Reid says yes, because you can move more quickly with trusted friends because you already understand how each other thinks and talks. And moving quickly? That’s critical in the early days of a startup.
But what if all things aren’t equal? If you’re choosing between working with someone who’s a trusted friend and a 7 out of 10 on competence, versus a stranger who’s a 9 out of 10 on competence, who should you pick? Answer: if the trusted friend is a fast learner, pick the trusted friend.
Trade up on trust, even if it means you have to trade down on competency a bit. In other words, choose to work with someone you know who’s a fast learner over someone who’s a bit more qualified who you do not know. Assuming the person you know and trust is in Permanent Beta, he or she can round out their gaps in skills or experience in short order.
I benefitted from Reid’s philosophy on this personally. For some assignments, I was not the most qualified person in the world, or even the most qualified within his own network. But given that we a) completely trust each other, b) I have a good sense of his priorities and values and preferences and he has a good sense of my own priorities and values and preferences, and c) I’m a quick learner, we could move at lightning speed together on projects.
As with so many lessons, I have to continue to re-learn this one. The first time I learned this lesson the hard way at one of my early companies, when we hired someone who looked great on paper in terms of industry accomplishments but who none of us really knew or trusted. The moment we encountered a couple landmines, the lack of trust ruined any hopes at productive group problem solving. The second time I learned this the hard way was at a different company I co-founded, where I traded down on competency too much when bringing on one team member. The trust was all there, and the guy was a fast learner, but the tradeoff down in necessary expertise wasn’t worth it, and the project floundered.
- Ben Casnocha on 10,000 Hours with Reid Hoffman (read the whole thing, its phenomenal)
Quote of the Day
Once very smart people are paid huge sums of money to exploit the flaws in the financial system, they have the spectacularly destructive incentive to screw the system up further, or to remain silent as they watch it being screwed up by others. The cost, in the end, is a tangled-up financial system. Untangling it requires acts of commercial heroism—and even then the fix might not work. There was simply too much more easy money to be made by elites if the system worked badly than if it worked well. The whole culture had to want to change. “We know how to cure this,” as Brad had put it. “It’s just a matter of whether the patient wants to be treated.
- Michael Lewis, Flash Boys: A Wall Street Revolt
- Michael Lewis, Flash Boys: A Wall Street Revolt
Friday, October 30, 2015
Pedro Domingos’ on “Five Machine Learning Tribes”
Connectionists
By contrast, Domingos said, a group called “connectionists” wants to reverse engineer the brain.
This very ambitious approach involves actually creating artificial neurons and connecting them in a neural network. Domingos calls this approach “deep learning” and shows how companies like Google are applying it to areas like vision and image processing, machine translation and experimental neural networks like Google's Cat Network that helps the computer to recognize cat images.
Taking the example of a cat image network, Domingos talks about how neurons work on a weighted value of inputs, and how binary results can be enhanced into a “continuous value” with methods like back propagation. All of this leads the computer to be able to learn more about a given set of information criteria – in this case, about what is and is not a cat, to be able to more correctly label random sets of images.
The Evolutionaries
Another radically different approach, says Domingos, involves looking at evolution as a phenomenon.
“Evolution made your brain and everything else,” says Domingos, articulating the idea and philosophy behind the evolutionary mindset. “So it must be a good thing.”
In essence, Domingos says, evolutionaries are applying the idea of genomes and DNA in the evolutionary process to data structures. The survival and offspring of units in an evolutionary model are the performance data. An algorithm for an evolutionary learning project would mimic those processes in key ways.
Domingos likens it to farmers and what they do with selective breeding, but notes that because the process is being applied to specific technologies, the model is a bit different. However, using the example of robotic selection, he goes into detail about a process of “robot evolution”, and how researchers can start with random assemblies and 3D print the best performing models.
“You wind up with surprisingly smart and robust robots,” says Domingos. “You can learn surprisingly powerful things this way.”
The Bayesians
The Bayesians, Domingos says, deal in uncertainty and solutions. Their master algorithm solution is called probabilistic inference.
Domingos explains that researchers can take a hypothesis and apply a type of “a priori” thinking, believing that there will be some outcomes that are more likely. They then update a hypothesis as they see more data.
“After some iteration of this,” Domingos says. “Some hypotheses become more likely than others.”
Domingos talks about strategies for efficient computing that support this process. He mentions vision learning applied to spam filtering, which is a key way to stop spammers from clogging up user inboxes. As another sort of scientific process, the probabilistic models do bring a certain concrete result to Machine Learning
The Analogizers
The fifth tribe of Machine Learning philosophers, Domingos says, is made up of analogizers, or pioneers in the field of matching particular bits of data to each other. Although it sounds simple and rudimentary, Domingos says it's really at the heart of a lot of outcomes that are extremely effective for some kinds of Machine Learning. He cites one of the leading proponents of this method, Douglas Hofstadter, in saying that “all intelligence is nothing but analogy.”
The master algorithm here, he says, is the “nearest neighbor” principle. Nearest neighbor outcomes can give results that are similar to neural network models. Domingos gives the example of two country models with defined city locations, but with undefined borders. Through the application of the analogy principles, the computer generates a likely border. Domingos calls this “generalizing from similarity” and suggests that it has economic ramifications for technology. One example, he says, is the movie advice technologies that supply movie ratings based on known data sets, where users get recommendations based off of what others have watched previously.
“It's a very nice type of similarity-based learning.” Domingos says, adding another example of how real results can boost profits for companies: one third of Amazon sales, he says, are based on recommendations.
Tribes Come Together
In closing, Domingos talks about how all five of these tribes have something key to offer and how the best Machine Learning technologies combine all five angles. In addition, he says, some new ideas are also needed to further refine Machine Learning into something that would give us the future outcomes we’ve anticipated for a long time, including things like cancer cures, home robots, and worldwide neural networks.
“This is only the beginning,” Domingos says. “There's much more that remains to be done.”
Indeed, these Machine Learning technologies are rapidly advancing toward future results that will change the ways that we view our interactions with computers and digital technologies. Some of that future depends on the work of these five “tribes” and how they can push the boundaries of what’s possible with Artificial Intelligence.
- More Here
By contrast, Domingos said, a group called “connectionists” wants to reverse engineer the brain.
This very ambitious approach involves actually creating artificial neurons and connecting them in a neural network. Domingos calls this approach “deep learning” and shows how companies like Google are applying it to areas like vision and image processing, machine translation and experimental neural networks like Google's Cat Network that helps the computer to recognize cat images.
Taking the example of a cat image network, Domingos talks about how neurons work on a weighted value of inputs, and how binary results can be enhanced into a “continuous value” with methods like back propagation. All of this leads the computer to be able to learn more about a given set of information criteria – in this case, about what is and is not a cat, to be able to more correctly label random sets of images.
The Evolutionaries
Another radically different approach, says Domingos, involves looking at evolution as a phenomenon.
“Evolution made your brain and everything else,” says Domingos, articulating the idea and philosophy behind the evolutionary mindset. “So it must be a good thing.”
In essence, Domingos says, evolutionaries are applying the idea of genomes and DNA in the evolutionary process to data structures. The survival and offspring of units in an evolutionary model are the performance data. An algorithm for an evolutionary learning project would mimic those processes in key ways.
Domingos likens it to farmers and what they do with selective breeding, but notes that because the process is being applied to specific technologies, the model is a bit different. However, using the example of robotic selection, he goes into detail about a process of “robot evolution”, and how researchers can start with random assemblies and 3D print the best performing models.
“You wind up with surprisingly smart and robust robots,” says Domingos. “You can learn surprisingly powerful things this way.”
The Bayesians
The Bayesians, Domingos says, deal in uncertainty and solutions. Their master algorithm solution is called probabilistic inference.
Domingos explains that researchers can take a hypothesis and apply a type of “a priori” thinking, believing that there will be some outcomes that are more likely. They then update a hypothesis as they see more data.
“After some iteration of this,” Domingos says. “Some hypotheses become more likely than others.”
Domingos talks about strategies for efficient computing that support this process. He mentions vision learning applied to spam filtering, which is a key way to stop spammers from clogging up user inboxes. As another sort of scientific process, the probabilistic models do bring a certain concrete result to Machine Learning
The Analogizers
The fifth tribe of Machine Learning philosophers, Domingos says, is made up of analogizers, or pioneers in the field of matching particular bits of data to each other. Although it sounds simple and rudimentary, Domingos says it's really at the heart of a lot of outcomes that are extremely effective for some kinds of Machine Learning. He cites one of the leading proponents of this method, Douglas Hofstadter, in saying that “all intelligence is nothing but analogy.”
The master algorithm here, he says, is the “nearest neighbor” principle. Nearest neighbor outcomes can give results that are similar to neural network models. Domingos gives the example of two country models with defined city locations, but with undefined borders. Through the application of the analogy principles, the computer generates a likely border. Domingos calls this “generalizing from similarity” and suggests that it has economic ramifications for technology. One example, he says, is the movie advice technologies that supply movie ratings based on known data sets, where users get recommendations based off of what others have watched previously.
“It's a very nice type of similarity-based learning.” Domingos says, adding another example of how real results can boost profits for companies: one third of Amazon sales, he says, are based on recommendations.
Tribes Come Together
In closing, Domingos talks about how all five of these tribes have something key to offer and how the best Machine Learning technologies combine all five angles. In addition, he says, some new ideas are also needed to further refine Machine Learning into something that would give us the future outcomes we’ve anticipated for a long time, including things like cancer cures, home robots, and worldwide neural networks.
“This is only the beginning,” Domingos says. “There's much more that remains to be done.”
Indeed, these Machine Learning technologies are rapidly advancing toward future results that will change the ways that we view our interactions with computers and digital technologies. Some of that future depends on the work of these five “tribes” and how they can push the boundaries of what’s possible with Artificial Intelligence.
- More Here
Thursday, October 29, 2015
RankBrain - Google's New ML Based Search Algorithm
RankBrain is one of the “hundreds” of signals that go into an algorithm that determines what results appear on a Google search page and where they are ranked, Corrado said. In the few months it has been deployed, RankBrain has become the third-most important signal contributing to the result of a search query, he said.
“I was surprised,” Corrado said. “I would describe this as having gone better than we would have expected.”
The addition of RankBrain to search is part of a half-decade-long push by Google into AI, as the company seeks to embed the technology into every aspect of its business. “Machine learning is a core transformative way by which we are rethinking everything we are doing,” said Google’s Chief Executive Officer Sundar Pichai on the company’s earnings call last week.
So far, RankBrain is living up to its AI hype. Google search engineers, who spend their days crafting the algorithms that underpin the search software, were asked to eyeball some pages and guess which they thought Google’s search engine technology would rank on top. While the humans guessed correctly 70 percent of the time, RankBrain had an 80 percent success rate.
- More Here
“I was surprised,” Corrado said. “I would describe this as having gone better than we would have expected.”
The addition of RankBrain to search is part of a half-decade-long push by Google into AI, as the company seeks to embed the technology into every aspect of its business. “Machine learning is a core transformative way by which we are rethinking everything we are doing,” said Google’s Chief Executive Officer Sundar Pichai on the company’s earnings call last week.
So far, RankBrain is living up to its AI hype. Google search engineers, who spend their days crafting the algorithms that underpin the search software, were asked to eyeball some pages and guess which they thought Google’s search engine technology would rank on top. While the humans guessed correctly 70 percent of the time, RankBrain had an 80 percent success rate.
- More Here
Quote of the Day
If you want to test cosmetics, why do it on some poor animal who hasn't done anything? They should use prisoners who have been convicted of murder or rape instead. So, rather than seeing if perfume irritates a bunny rabbit's eyes, they should throw it in Charles Manson's eyes and ask him if it hurts.
- Ellen DeGeneres, My Point...And I Do Have One
- Ellen DeGeneres, My Point...And I Do Have One
Wednesday, October 28, 2015
Quote of the Day
The talent for self-justification is surely the finest flower of human evolution, the greatest achievement of the human brain. When it comes to justifying actions, every human being acquires the intelligence of an Einstein, the imagination of a Shakespeare, and the subtlety of a Jesuit.
- Michael Foley, The Age Of Absurdity: Why Modern Life Makes It Hard To Be Happy
- Michael Foley, The Age Of Absurdity: Why Modern Life Makes It Hard To Be Happy
Tuesday, October 27, 2015
Quote of the Day
I beg you, to have patience with everything unresolved in your heart and to try to love the questions themselves as if they were locked rooms or books written in a very foreign language. Don’t search for the answers, which could not be given to you now, because you would not be able to live them. And the point is to live everything. Live the questions now. Perhaps then, someday far in the future, you will gradually, without even noticing it, live your way into the answer.
- Rainer Maria Rilke, Letters to a Young Poet
- Rainer Maria Rilke, Letters to a Young Poet
Monday, October 26, 2015
What do Children Know of Their Own Mortality?
One of the most remarkable studies, and perhaps, one of the most remarkable studies in the whole of palliative care, was completed by the anthropologist Myra Bluebond-Langner and was published as the book The Private Worlds of Dying Children.
Bluebond-Langner spent the mid 1970’s in an American child cancer ward and began to look at what the children knew about their own terminal prognosis, how this knowledge affected social interactions, and how social interactions were conducted to manage public awareness of this knowledge.
Her findings were nothing short of stunning: although adults, parents, and medical professionals, regularly talked in a way to deliberately obscure knowledge of the child’s forthcoming death, children often knew they were dying. But despite knowing they were dying, children often talked in a way to avoid revealing their awareness of this fact to the adults around them.
Bluebond-Langner describes how this mutual pretence allowed everyone to support each other through their typical roles and interactions despite knowing that they were redundant. Adults could ask children what they wanted for Christmas, knowing that they would never see it. Children could discuss what they wanted to be when they grew up, knowing that they would never get the chance. Those same conversations, through which compassion flows in everyday life, could continue.
- More Here
Bluebond-Langner spent the mid 1970’s in an American child cancer ward and began to look at what the children knew about their own terminal prognosis, how this knowledge affected social interactions, and how social interactions were conducted to manage public awareness of this knowledge.
Her findings were nothing short of stunning: although adults, parents, and medical professionals, regularly talked in a way to deliberately obscure knowledge of the child’s forthcoming death, children often knew they were dying. But despite knowing they were dying, children often talked in a way to avoid revealing their awareness of this fact to the adults around them.
Bluebond-Langner describes how this mutual pretence allowed everyone to support each other through their typical roles and interactions despite knowing that they were redundant. Adults could ask children what they wanted for Christmas, knowing that they would never see it. Children could discuss what they wanted to be when they grew up, knowing that they would never get the chance. Those same conversations, through which compassion flows in everyday life, could continue.
- More Here
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