Thiel isn’t decisive on the topic. Is the definite-optimist view is the dominant approach to candidacy in the grand marketplace of talent today?
Kumon
Kumon franchises are cheap. The branding and rep is good. Tutoring is a very attractive market in general and kumon makes it easier for the teachers. But is it ethical, I wonder? To me it’s ethical if it delivers value to the students. A caveat is that it seemed cruel the kind of mind-numbing maths done by my classmates as a kid who attended Kumon.
A study with very small sample size) says ‘that there may be a significant relationship between participation in the Kumon programme and development in computation skills (p = 0.053), but not with development in mathematical reasoning skills (p = 0.867)’
Going by that alone, it would seem short-term experimental studies don’t explain the small-sample (2 out of 2) size correlation between Kumon attendance as kids and extreme adult success I see in my friends today. A stackexchange-esque Q&A suggests no further effects are suggested by other experimental Kumon literature
Political psychology
If human rights were reframed as entitlements, and laws that protect those contrasted with laws that protect discretions, those two having obvious tensions, I wonder what impact that would have on human rights law reform. Branding is influential.
Effective Altruism politics
Based on Sam Deere, Effective Altruism affiliate and former ALP staffer: tractability of political ideas for conversion to legislation comes down to: novelty, high impact, cost-effectiveness, budgetary considerations, interaction with other policies, popular, ideological and strategic/tactical considerations.
A hereustic for lobbying is to focus on Ministers, not departments, other politicians, yada yada...
Nara
I have heard of an exotic Namibian plant, crowned with thorns that grows in the desert called Nara that bears a fruit which when dried, tastes like chocolate but is highly nutritious. I’m skeptical. It sounds too good to be true. I wonder why it isn’t a common treat around the world now. If the rumours are true, I would love some to grow in the Australian outback, biosecurity permitting.
If you’re employing data scientists in a data rich operating environment in 2016 - yes.
If you’re anyone else...I doubt it.
I’m extremely skeptical about the data science boom. Just because a field is valuable doesn’t mean companies around them are strategically places to capture that value in a market for their owners.
Data science currently operates as:
(1) Big data products—on marketplaces like Amazon Web Services where machine learning algorithms are available via the cloud.
(2) Product—offline data analysis automation tools
(3) Service—manually doing (1) or (2)
Amazon currently dominates (1). Microsoft is a close 2nd. Their delivery is on point. They’ve basically created a platform for people to trade their knowledge about data science as algorithms. Since there are a finite number of algorithms, that can be combinatorially generated and tagged for particular applications, the only real challenge is creating a system for triaging a user’s needs into which particular algorithmic application.
If there is to be an big money to be made in this space by new players, it will be solving that problem.
The issue of algorithm generation for statistical analysis should not be confused with the sophisticated tasks of software and application development. The former requires little creativity, while the latter can utilise immense creativity. I say that as someone who’s forte is data analysis, rather than software.
As for (2), there is likely to be high efficiency in a market between cloud based algorithms and algorithms implemented offline due to extreme low barriers to entry. Basically those first person in with a good method of translating those algorithms offline, surmounting potential legal hazards, and scaling up (no trivial tasks) will make a quick buck. Though, these are problems that I can define. If I can define them, the big names probably already have and are working on solutions for them. You’re out of luck, garage entrepreneurs.
Now (3), the one lay people think of when they think data science, and the one aspiring data scientists entering Kaggle competitions and hopping onto data camp think of. This is a highly commodifiable area, subject to total automation, and outsourcing. There may be some money to be made here as the ubiquity of data rises and you find loyal, computer illiterate clients. However, you’ll be picking up scraps the same way that web design as a profitable avenue for freelancing oddballs to make money worked and continues to work—by essentially ripping people off who don’t know better, when absurdly user friendly DIY web design options are a reality or the guy down the road is a million times better than you, your client just doesn’t know better. Sure, it may be profitable, but ethically it’s questionable.
First principles
Elon Musk cites first principle thinking in physics as a key to identifying neglected market opportunities. Can someone give me an example of how it may work in that application?
Social skills
Simply reframing ″approach anxiety″ to the crude, macho ’bitch butterflies″ has done wonders to dampen the phenomenon. I wonder what if that formula could dampen other anxieties...
Concept learning
I wonder what it is like to have genius level verbal abstract reasoning: e.g. 2SD+for instance as reported by the usual neuropsychological tests. The Wechsler Adult Intelligence Scale (WAIS) ‘Similarities’ subtest measures verbal abstract reasoning or ‘concept learning’ (see concept learning on Wikipedia and the Edutechwiki.. Subjects are asked to say how two seemingly dissimilar items might in fact be similar.
When an average person talks to someone at just 30 points of IQ less than average (IQ 70 - the cut off point for intellectual disability), that experience may be comparable to a genius (130) talking to an average person. When it comes to particular subscales that relate to ‘’understand’’ such as concept learning, this may not in fact match up with IQ. It’s conceivable there may be savants with incredible high concept formation with incredibly low IQ’s. This presents an additional layer of complexity to a hypothetical interaction between a low IQ concept savant and high IQ person concept lay-person that I can’t even simulate, mentally. So, I’m opening it to the floor for a fun thought experiment.
If you’re employing data scientists in a data rich operating environment in 2016 - yes.
The big reason for the rise of “data science” is that all operating environments are now, or will soon become, data rich.
An example: I have a friend who is a chemical engineer by training and works for E-Ink. His mandate is to improve the efficiency of the chemical manufacturing plants that produce the material. This work involves a small amount of actual chemistry, and a large amount of statistical analysis of the vast trove of sensor readings and measurements produced by the plant’s operation.
Elon Musk cites first principle thinking in physics as a key to identifying neglected market opportunities. Can someone give me an example of how it may work in that application?
Recently moridinamael wrote about diswashers:
As a pampered modern person, the worst part of my life is washing dishes. (Or, rinsing dishes and loading the dish washer.) How long before I can buy a robot to automate this for me?
If you reason from first principles then there’s nothing stopping a device in which you input a pile of disher and that afterwards sorts them into the cupboard from existing.
Especially with the recent advances in machine vision and google opensourcing Tensor flow.
Another nonautomated kitchen task is cutting vegetables. There no good reason why a robot shouldn’t cut vegetables as well as humans.
You could just have a two-dishwasher system where the dishwasher takes the place of the cupboard.
It seems like a robot that automated the task of moving clean dishes into a cupboard would be an idea where the potential benefits, if any, are too small to currently justify the major development effort that would be required. Maybe in the future when AI becomes far more widespread and ‘easy’ to develop.
I think that there are people who don’t like to deal with washing dishes even through they have a dishwasher. I don’t think the task is trival in a sense that people wouldn’t be willing to invest money into a device that fixes the issue.
Apart from that a redesigned device that builds on smart sensors and nanotech filters could also operate with a lot less water.
GE’s design of a kitchen of the future with a smart sink that can automatically wash dishes is also interesting.
If I look into my kitchen the most recent invention is the microwave.
A few health conscious people I know have nanotech water filters for the water in their sink but apart from that the kitchen is mostly didn’t change.
I think that it would be possible to build something better by investing the kind of money that went into Tesla and SpaceX.
I would expect that in a decade we see a lot more sensors in the average kitchen then today.
The orbital-systems shower is a good example how nanotech plus sensors can produce a shower that performs better than the old shower.
I think my dream system would be something I can pile all the dirty dishes into which melts them down, separates out the food into something that goes into the trash (this doesn’t have to be automated) and then reconstitutes the dishes.
Recently moridinamael wrote about diswashers: As a pampered modern person, the worst part of my life is washing dishes. (Or, rinsing dishes and loading the dish washer.) How long before I can buy a robot to automate this for me?
the only real challenge is creating a system for triaging a user’s needs into which particular algorithmic application.
Yes, but the full solution to this is basically AI-complete.
There may be some money to be made here as the ubiquity of data rises and you find loyal, computer illiterate clients. However, you’ll be picking up scraps the same way that web design as a profitable avenue for freelancing oddballs to make money worked and continues to work—by essentially ripping people off who don’t know better, when absurdly user friendly DIY web design options are a reality or the guy down the road is a million times better than you, your client just doesn’t know better. Sure, it may be profitable, but ethically it’s questionable.
Do you feel the same way about all within-firm IT services? (Including stuff like internal web design in ‘IT.’)
This presents an additional layer of complexity to a hypothetical interaction between a low IQ concept savant and high IQ person concept lay-person that I can’t even simulate, mentally
I think that various debates about postmoderism could be of that nature. Postmodernists often have a high amount of concepts.
If you hire a good webdesiner for doing your website the designer has experience in creating websites and ordering information. A good designer can do a better job.
In the same sense a person who doesn’t understand what concepts like sensitivity and specificity mean won’t be able to use data analysis tools well.
As for (2), there is likely to be high efficiency in a market between cloud based algorithms and algorithms implemented offline due to extreme low barriers to entry. Basically those first person in with a good method of translating those algorithms offline, surmounting potential legal hazards, and scaling up (no trivial tasks) will make a quick buck. Though, these are problems that I can define. If I can define them, the big names probably already have and are working on solutions for them. You’re out of luck, garage entrepreneurs.
People are snapping up data scientists in preparation for the move from Data science as a product to data science as a commodity. Historically, big companies have been awful at making this transition, but once they realize their mistake, eager to make up lost time.. An entrepreneur who looks to be bought up buy one of these market laggards could to really well.
Elon Musk cites first principle thinking in physics as a key to identifying neglected market opportunities. Can someone give me an example of how it may work in that application?
The classic Musk example is taking the cost of raw materials to make a spaceship—he saw that they were many orders of magnitude the actual cost of a spaceship, so he figured there were probably efficiency problems that people simply hadn’t solved.
Thoughts this week:
Career stategy
Thiel isn’t decisive on the topic. Is the definite-optimist view is the dominant approach to candidacy in the grand marketplace of talent today?
Kumon
Kumon franchises are cheap. The branding and rep is good. Tutoring is a very attractive market in general and kumon makes it easier for the teachers. But is it ethical, I wonder? To me it’s ethical if it delivers value to the students. A caveat is that it seemed cruel the kind of mind-numbing maths done by my classmates as a kid who attended Kumon.
A study with very small sample size) says ‘that there may be a significant relationship between participation in the Kumon programme and development in computation skills (p = 0.053), but not with development in mathematical reasoning skills (p = 0.867)’
Going by that alone, it would seem short-term experimental studies don’t explain the small-sample (2 out of 2) size correlation between Kumon attendance as kids and extreme adult success I see in my friends today. A stackexchange-esque Q&A suggests no further effects are suggested by other experimental Kumon literature
Political psychology
If human rights were reframed as entitlements, and laws that protect those contrasted with laws that protect discretions, those two having obvious tensions, I wonder what impact that would have on human rights law reform. Branding is influential.
Effective Altruism politics
Based on Sam Deere, Effective Altruism affiliate and former ALP staffer: tractability of political ideas for conversion to legislation comes down to: novelty, high impact, cost-effectiveness, budgetary considerations, interaction with other policies, popular, ideological and strategic/tactical considerations.
A hereustic for lobbying is to focus on Ministers, not departments, other politicians, yada yada...
Nara
I have heard of an exotic Namibian plant, crowned with thorns that grows in the desert called Nara that bears a fruit which when dried, tastes like chocolate but is highly nutritious. I’m skeptical. It sounds too good to be true. I wonder why it isn’t a common treat around the world now. If the rumours are true, I would love some to grow in the Australian outback, biosecurity permitting.
Is data science a profitable industry?
Anyway, it’s super easy to transmit machine learning how-to online, it’s the most popular class at Stanford. Can I get a ‘market efficiency’? It’s not long till we automate the basic tasks since machine learning is an empirical field too, so it can be subject to it’s own learning in polynomial time. I reckon people neglect this (and thus, the market efficiency is quickly gained) because there is little public education to self taught programmers about tihs kind of thing, outside of boring lectures
If you’re a data science training provider—yes.
If you’re employing data scientists in a data rich operating environment in 2016 - yes.
If you’re anyone else...I doubt it.
I’m extremely skeptical about the data science boom. Just because a field is valuable doesn’t mean companies around them are strategically places to capture that value in a market for their owners.
Data science currently operates as:
(1) Big data products—on marketplaces like Amazon Web Services where machine learning algorithms are available via the cloud.
(2) Product—offline data analysis automation tools
(3) Service—manually doing (1) or (2)
Amazon currently dominates (1). Microsoft is a close 2nd. Their delivery is on point. They’ve basically created a platform for people to trade their knowledge about data science as algorithms. Since there are a finite number of algorithms, that can be combinatorially generated and tagged for particular applications, the only real challenge is creating a system for triaging a user’s needs into which particular algorithmic application.
If there is to be an big money to be made in this space by new players, it will be solving that problem.
The issue of algorithm generation for statistical analysis should not be confused with the sophisticated tasks of software and application development. The former requires little creativity, while the latter can utilise immense creativity. I say that as someone who’s forte is data analysis, rather than software.
As for (2), there is likely to be high efficiency in a market between cloud based algorithms and algorithms implemented offline due to extreme low barriers to entry. Basically those first person in with a good method of translating those algorithms offline, surmounting potential legal hazards, and scaling up (no trivial tasks) will make a quick buck. Though, these are problems that I can define. If I can define them, the big names probably already have and are working on solutions for them. You’re out of luck, garage entrepreneurs.
Now (3), the one lay people think of when they think data science, and the one aspiring data scientists entering Kaggle competitions and hopping onto data camp think of. This is a highly commodifiable area, subject to total automation, and outsourcing. There may be some money to be made here as the ubiquity of data rises and you find loyal, computer illiterate clients. However, you’ll be picking up scraps the same way that web design as a profitable avenue for freelancing oddballs to make money worked and continues to work—by essentially ripping people off who don’t know better, when absurdly user friendly DIY web design options are a reality or the guy down the road is a million times better than you, your client just doesn’t know better. Sure, it may be profitable, but ethically it’s questionable.
First principles
Elon Musk cites first principle thinking in physics as a key to identifying neglected market opportunities. Can someone give me an example of how it may work in that application?
Social skills
Simply reframing ″approach anxiety″ to the crude, macho ’bitch butterflies″ has done wonders to dampen the phenomenon. I wonder what if that formula could dampen other anxieties...
Concept learning
I wonder what it is like to have genius level verbal abstract reasoning: e.g. 2SD+for instance as reported by the usual neuropsychological tests. The Wechsler Adult Intelligence Scale (WAIS) ‘Similarities’ subtest measures verbal abstract reasoning or ‘concept learning’ (see concept learning on Wikipedia and the Edutechwiki.. Subjects are asked to say how two seemingly dissimilar items might in fact be similar.
When an average person talks to someone at just 30 points of IQ less than average (IQ 70 - the cut off point for intellectual disability), that experience may be comparable to a genius (130) talking to an average person. When it comes to particular subscales that relate to ‘’understand’’ such as concept learning, this may not in fact match up with IQ. It’s conceivable there may be savants with incredible high concept formation with incredibly low IQ’s. This presents an additional layer of complexity to a hypothetical interaction between a low IQ concept savant and high IQ person concept lay-person that I can’t even simulate, mentally. So, I’m opening it to the floor for a fun thought experiment.
The big reason for the rise of “data science” is that all operating environments are now, or will soon become, data rich.
An example: I have a friend who is a chemical engineer by training and works for E-Ink. His mandate is to improve the efficiency of the chemical manufacturing plants that produce the material. This work involves a small amount of actual chemistry, and a large amount of statistical analysis of the vast trove of sensor readings and measurements produced by the plant’s operation.
Recently moridinamael wrote about diswashers:
As a pampered modern person, the worst part of my life is washing dishes. (Or, rinsing dishes and loading the dish washer.) How long before I can buy a robot to automate this for me?If you reason from first principles then there’s nothing stopping a device in which you input a pile of disher and that afterwards sorts them into the cupboard from existing. Especially with the recent advances in machine vision and google opensourcing Tensor flow.
Another nonautomated kitchen task is cutting vegetables. There no good reason why a robot shouldn’t cut vegetables as well as humans.
http://www.robot-coupe.com/en-exp/catalogue/vegetable-preparation-machines,3/
You could just have a two-dishwasher system where the dishwasher takes the place of the cupboard.
It seems like a robot that automated the task of moving clean dishes into a cupboard would be an idea where the potential benefits, if any, are too small to currently justify the major development effort that would be required. Maybe in the future when AI becomes far more widespread and ‘easy’ to develop.
I think that there are people who don’t like to deal with washing dishes even through they have a dishwasher. I don’t think the task is trival in a sense that people wouldn’t be willing to invest money into a device that fixes the issue.
Apart from that a redesigned device that builds on smart sensors and nanotech filters could also operate with a lot less water.
GE’s design of a kitchen of the future with a smart sink that can automatically wash dishes is also interesting.
If I look into my kitchen the most recent invention is the microwave.
A few health conscious people I know have nanotech water filters for the water in their sink but apart from that the kitchen is mostly didn’t change.
I think that it would be possible to build something better by investing the kind of money that went into Tesla and SpaceX.
I would expect that in a decade we see a lot more sensors in the average kitchen then today.
The orbital-systems shower is a good example how nanotech plus sensors can produce a shower that performs better than the old shower.
I think my dream system would be something I can pile all the dirty dishes into which melts them down, separates out the food into something that goes into the trash (this doesn’t have to be automated) and then reconstitutes the dishes.
Something like a Star Trek transporter for tableware?
Imagine what it was like before the dishwasher.
/goes off to watch Downton Abbey :-P
Yes, but the full solution to this is basically AI-complete.
Do you feel the same way about all within-firm IT services? (Including stuff like internal web design in ‘IT.’)
I think that various debates about postmoderism could be of that nature. Postmodernists often have a high amount of concepts.
If you hire a good webdesiner for doing your website the designer has experience in creating websites and ordering information. A good designer can do a better job.
In the same sense a person who doesn’t understand what concepts like sensitivity and specificity mean won’t be able to use data analysis tools well.
People are snapping up data scientists in preparation for the move from Data science as a product to data science as a commodity. Historically, big companies have been awful at making this transition, but once they realize their mistake, eager to make up lost time.. An entrepreneur who looks to be bought up buy one of these market laggards could to really well.
The classic Musk example is taking the cost of raw materials to make a spaceship—he saw that they were many orders of magnitude the actual cost of a spaceship, so he figured there were probably efficiency problems that people simply hadn’t solved.