SSN was never intended to be a unique ID for things like credit. Its purpose was for social security. I think it's high time the government funded grants to pay our best mathematicians to come up with a new system. Maybe something involving a physical token. Not sure what such a system would look like, but it's obvious SSN for credit and medical purposes is broken. Hell, SSN isn't even guaranteed to be unique!
I could never vote for Hillary because by setting up a private server, she was knowingly trying to skirt around FOIA. This plus all the other backroom DNC deals to ensure her nomination leads me to believe her presidency will just be more backroom deals we'll never know about. I just can't do it. I'll vote 3rd party before I vote Clinton.
Why do your friends stay at IBM? This is an honest question. It seems crazy to me to sit around and wait to get laid off. I couldn't imagine getting up every day and wondering if you'd still have a job at the end of the day for years on end.
I stumbled across his youtube channel awhile back looking for info on etching pcbs. He has a few interesting projects going on. https://www.youtube.com/channe...
I like Sanders. He probably won't win the nomination. I would rather "waste" my vote on a 3rd party in protest than vote for Trump or Clinton. This years election has really opened my eyes as to how "rigged" everything is. I can't even vote in my own primary because I missed the cutoff date weeks ago to switch from independent to democrat. Not that it will even matter because of our state-by-state system which pretty much guarantees the nomination before my state even gets to vote.
When Hostess declared bankruptcy, many people were upset because they grew up eating Twinkies. However, as adults, far fewer people eat them. So of course sales were slow. Instant cameras are the same way. There's a certain nostalgia factor that makes people want to see these things still produced, but on a day-to-day basis people are using digital cameras. Technology and cost will always introduce new products and after a generation of nostalgia the old products disappear forever. I doubt someone born in 2016 will ever care about polaroids.
I tried mint a few years ago when I found the default install of Ubuntu desktop unusable. Could I have customized it to the desktop I wanted? Sure. Or, I could try this new distribution that has a DE that is actually intuitive. If Ubuntu shipped with Cinnamon by default I'd go back to Ubuntu. Ubuntu really shot themselves in the foot a few years ago and I got tired of being a beta tester.
Government to Apple: "Develop the atom bomb. It will only be used just this once and then you can throw away the technology. Also, develop it on your dime."
Head over to NY Times and Washington Post websites and look at the comments. You joke, but many people there are actually saying things like this. I see comments calling for Tim Cook to be charged with treason, saying Apple shouldn't be able to do business in the U.S., etc. The reason shit like this flies in the U.S. is not because of slashdoters, it's people like that who vote congress critters into office.
For most consumers, IoT seems to be 99% rebranded home automation, which has always fallen flat on its face. It reminds me of 3D movies. We see it every few years then people realize it's a gimmick and we go back to business as usual.
And the list goes on. The nest costs 5x-10x more than a low end digital thermostat. I have a sneaking suspicion as with almost all other home automation, upper class people will buy it for the novelty but the rest of the world will keep to their "dumb" devices.
Unfortunately the pi draws a fair amount of current doing nothing. I've used the pi in a solar based project and the pi ended up using more energy than my solenoid. I ended up using an Arduino and a relay to turn the pi off to conserve energy. It also made the project somewhat complicated because you can't just power off the pi, you have to do a proper linux shutdown. You have to coordinate with the AVR to say "OK to kill power now." You're typically looking at about 100mA to 300mA depending the model. The AVR uses less than 1mA in sleep mode.
The ESP8266 seems to be the current competitor for "arduino with effortless network connectivity." They are about 5 dollars and actually available. Just as the pi, it has many limitations that dedicated microcontrollers solved years ago. I've been using a combination of ESP8266 and AVR lately instead of ESP8266 standalone.
The zero is definitely in an awkward spot. It's so-so as a microcontroller replacement (no low power modes, limited number of analog inputs, inputs not 5v tolerant), but the price point makes it otherwise competitive. The ESP8266 is getting high level languages like Lua, micro python, and Basic and priced well. The "low power mode" sucks because it basically just resets the unit and doesn't have interrupt driven wake modes.
I bought a zero and it will probably be my only purchase. It just doesn't seem to do anything special.
I've noticed Adafruit runs out of the zero within minutes of stock, but the more expensive "kits" may last several hours. I think it really comes down to the 5 dollar price point not being profitable and thus not being a priority. I'm sure at 35 dollars the B+ has at least _some_ wiggle room.
I frankly do not understand gun control in America. Gun control seems to boil down to
1) Getting rid of "assault rifles"
2) Consistent background checks
3) Magazine sizes
That's great and all, but the vast majority of gun violence are handguns. Even more, the.22lr of all things seems to be the deadliest caliber. Whether or not you are for gun control, let's discuss the actual killer: handguns. All this other stuff is just a distraction.
This could have been done with a beaglebone or million other similar boards. A more impressive story would have been if he did this on his home router.
I have some involvement in this field and I can't think of a single time I've seen the term AI in a book or research paper. The only time I ever see anyone use it is in the media or various futurists. Usually people just name their specific subfield or name the types or algorithms in which they specialize. "I specialize in unstable learners" or "I specialize in transfer learning" but I've never heard someone say "I specialize in AI."
I think naming neural networks as they did was probably a very bad idea. The mathematics have very little to do with actual neurons in the brain but the name leads you to believe differently.
Microsoft actually has no other product like R, so I would imagine the real reason they bought R was so they could add an additional layer to their data analysis tools inside SQL server. It's actually a no-brainer. Do a SQL server query, store results in a data frame, run a clustering algorithm against the data to see purchasing habits.
The R ecosystem seems to be the opposite of Microsoft's traditional ecosystems. About the only thing R library designers can agree upon is "dataframe is good." Packages that try to put a consistent front-end on other packages (i.e. caret) definitely helps. However, even something as simple as "does this algorithm want a factor or dummy variables?" may require examining the source code. Other more subtle things like "Does it was the data to be centered and scaled?" may slip by.
I hope Microsoft addresses this. As a researcher, a common task is to compare the performance of many algorithms against a new dataset or a new algorithm that you are developing, and it can be a pain to do in R. Weka is WAY more consistent in handling these sorts of things, but Weka only handles a subset of the tasks people do in R. Something as simple as style-guide docs would go a LONG way.
R is preferred by statisticians. Many statisticians are on the leading front of creating new traditional machine learning algorithms (not the GPU driven or map reduce stuff "hip" companies are dealing with right now). Things like supervised classification tasks and clustering algorithms. This usually means you have access to a researchers implementation of a new algorithm fairly quickly, long before it's in a commercial package. It also means you have to deal with a lot of 1-off code and deciding whether their function wants a row-vector or column-vector.
Python seems to be much more popular with those having a computer science background. There are far fewer machine learning algorithms available in Python. However, if you are going to design a large system, it's generally much more convenient to do in Python. There are Python interfaces to R as well.
Julia is new on the scene and attempts to solve the shortcomings of Python and R (insert xkcd comic here). Performance is good and has interfaces to many languages. I've used it a few times and it's maturing, but it's definitely risky doing any long term project in Julia.
Then there's Java. Weka is a popular machine learning package with a GUI and all of the algorithms available as jar files. Very consistent API and includes pre-processing tools. Weka also has a marketplace for new algorithms. However, many times you just have to write a 1-time script for data cleanup or to compare algorithms, and it's definitely not convenient to do in Java. I haven't seen many pure Java people doing this type of work in the wild. The final implementation may end up in Java, but the initial work seems to almost always be in R and Python.
I think many have abandoned the term AI. There's too much history and it's misleading. Machine learning is more often used, but the phrase I think is most appropriate is statistical learning. It uses past data to predict future results (in the supervised world anyway). Different algorithms have different strengths and weaknesses.. Most serious researchers have also abandoned the idea of trying to strictly model the brain. We probably don't know enough about it to come even close, so in the meantime let's create algorithms that do useful things for us today.
Slashdot Mirror
SSN was never intended to be a unique ID for things like credit. Its purpose was for social security. I think it's high time the government funded grants to pay our best mathematicians to come up with a new system. Maybe something involving a physical token. Not sure what such a system would look like, but it's obvious SSN for credit and medical purposes is broken. Hell, SSN isn't even guaranteed to be unique!
I could never vote for Hillary because by setting up a private server, she was knowingly trying to skirt around FOIA. This plus all the other backroom DNC deals to ensure her nomination leads me to believe her presidency will just be more backroom deals we'll never know about. I just can't do it. I'll vote 3rd party before I vote Clinton.
Why do your friends stay at IBM? This is an honest question. It seems crazy to me to sit around and wait to get laid off. I couldn't imagine getting up every day and wondering if you'd still have a job at the end of the day for years on end.
I stumbled across his youtube channel awhile back looking for info on etching pcbs. He has a few interesting projects going on. https://www.youtube.com/channe...
I like Sanders. He probably won't win the nomination. I would rather "waste" my vote on a 3rd party in protest than vote for Trump or Clinton. This years election has really opened my eyes as to how "rigged" everything is. I can't even vote in my own primary because I missed the cutoff date weeks ago to switch from independent to democrat. Not that it will even matter because of our state-by-state system which pretty much guarantees the nomination before my state even gets to vote.
When Hostess declared bankruptcy, many people were upset because they grew up eating Twinkies. However, as adults, far fewer people eat them. So of course sales were slow. Instant cameras are the same way. There's a certain nostalgia factor that makes people want to see these things still produced, but on a day-to-day basis people are using digital cameras. Technology and cost will always introduce new products and after a generation of nostalgia the old products disappear forever. I doubt someone born in 2016 will ever care about polaroids.
I tried mint a few years ago when I found the default install of Ubuntu desktop unusable. Could I have customized it to the desktop I wanted? Sure. Or, I could try this new distribution that has a DE that is actually intuitive. If Ubuntu shipped with Cinnamon by default I'd go back to Ubuntu. Ubuntu really shot themselves in the foot a few years ago and I got tired of being a beta tester.
Can't put the shit back in the horse
Government to Apple: "Develop the atom bomb. It will only be used just this once and then you can throw away the technology. Also, develop it on your dime."
Head over to NY Times and Washington Post websites and look at the comments. You joke, but many people there are actually saying things like this. I see comments calling for Tim Cook to be charged with treason, saying Apple shouldn't be able to do business in the U.S., etc. The reason shit like this flies in the U.S. is not because of slashdoters, it's people like that who vote congress critters into office.
I didn't even notice. The coffee maker is literally just a Mr. Coffee with wifi! My $20 coffee maker has a built in timer which I already don't use.
For most consumers, IoT seems to be 99% rebranded home automation, which has always fallen flat on its face. It reminds me of 3D movies. We see it every few years then people realize it's a gimmick and we go back to business as usual.
I think what will kill iot is that it's just frankly too expensive. A perfect example is the Belkin WeMo line of iot enabled products.
* 150 dollars for a slow cooker
* 150 dollars for a coffee maker
* 200 dollars for a humidifier
* 40 dollars for a plugin relay switch
And the list goes on. The nest costs 5x-10x more than a low end digital thermostat. I have a sneaking suspicion as with almost all other home automation, upper class people will buy it for the novelty but the rest of the world will keep to their "dumb" devices.
Unfortunately the pi draws a fair amount of current doing nothing. I've used the pi in a solar based project and the pi ended up using more energy than my solenoid. I ended up using an Arduino and a relay to turn the pi off to conserve energy. It also made the project somewhat complicated because you can't just power off the pi, you have to do a proper linux shutdown. You have to coordinate with the AVR to say "OK to kill power now." You're typically looking at about 100mA to 300mA depending the model. The AVR uses less than 1mA in sleep mode.
The pi actually has no analog inputs (I thought it had one).
The ESP8266 seems to be the current competitor for "arduino with effortless network connectivity." They are about 5 dollars and actually available. Just as the pi, it has many limitations that dedicated microcontrollers solved years ago. I've been using a combination of ESP8266 and AVR lately instead of ESP8266 standalone.
The zero is definitely in an awkward spot. It's so-so as a microcontroller replacement (no low power modes, limited number of analog inputs, inputs not 5v tolerant), but the price point makes it otherwise competitive. The ESP8266 is getting high level languages like Lua, micro python, and Basic and priced well. The "low power mode" sucks because it basically just resets the unit and doesn't have interrupt driven wake modes.
I bought a zero and it will probably be my only purchase. It just doesn't seem to do anything special.
I've noticed Adafruit runs out of the zero within minutes of stock, but the more expensive "kits" may last several hours. I think it really comes down to the 5 dollar price point not being profitable and thus not being a priority. I'm sure at 35 dollars the B+ has at least _some_ wiggle room.
I frankly do not understand gun control in America. Gun control seems to boil down to
.22lr of all things seems to be the deadliest caliber. Whether or not you are for gun control, let's discuss the actual killer: handguns. All this other stuff is just a distraction.
1) Getting rid of "assault rifles"
2) Consistent background checks
3) Magazine sizes
That's great and all, but the vast majority of gun violence are handguns. Even more, the
This could have been done with a beaglebone or million other similar boards. A more impressive story would have been if he did this on his home router.
I have some involvement in this field and I can't think of a single time I've seen the term AI in a book or research paper. The only time I ever see anyone use it is in the media or various futurists. Usually people just name their specific subfield or name the types or algorithms in which they specialize. "I specialize in unstable learners" or "I specialize in transfer learning" but I've never heard someone say "I specialize in AI."
I think naming neural networks as they did was probably a very bad idea. The mathematics have very little to do with actual neurons in the brain but the name leads you to believe differently.
Microsoft actually has no other product like R, so I would imagine the real reason they bought R was so they could add an additional layer to their data analysis tools inside SQL server. It's actually a no-brainer. Do a SQL server query, store results in a data frame, run a clustering algorithm against the data to see purchasing habits.
The R ecosystem seems to be the opposite of Microsoft's traditional ecosystems. About the only thing R library designers can agree upon is "dataframe is good." Packages that try to put a consistent front-end on other packages (i.e. caret) definitely helps. However, even something as simple as "does this algorithm want a factor or dummy variables?" may require examining the source code. Other more subtle things like "Does it was the data to be centered and scaled?" may slip by.
I hope Microsoft addresses this. As a researcher, a common task is to compare the performance of many algorithms against a new dataset or a new algorithm that you are developing, and it can be a pain to do in R. Weka is WAY more consistent in handling these sorts of things, but Weka only handles a subset of the tasks people do in R. Something as simple as style-guide docs would go a LONG way.
R is preferred by statisticians. Many statisticians are on the leading front of creating new traditional machine learning algorithms (not the GPU driven or map reduce stuff "hip" companies are dealing with right now). Things like supervised classification tasks and clustering algorithms. This usually means you have access to a researchers implementation of a new algorithm fairly quickly, long before it's in a commercial package. It also means you have to deal with a lot of 1-off code and deciding whether their function wants a row-vector or column-vector.
Python seems to be much more popular with those having a computer science background. There are far fewer machine learning algorithms available in Python. However, if you are going to design a large system, it's generally much more convenient to do in Python. There are Python interfaces to R as well.
Julia is new on the scene and attempts to solve the shortcomings of Python and R (insert xkcd comic here). Performance is good and has interfaces to many languages. I've used it a few times and it's maturing, but it's definitely risky doing any long term project in Julia.
Then there's Java. Weka is a popular machine learning package with a GUI and all of the algorithms available as jar files. Very consistent API and includes pre-processing tools. Weka also has a marketplace for new algorithms. However, many times you just have to write a 1-time script for data cleanup or to compare algorithms, and it's definitely not convenient to do in Java. I haven't seen many pure Java people doing this type of work in the wild. The final implementation may end up in Java, but the initial work seems to almost always be in R and Python.
I think many have abandoned the term AI. There's too much history and it's misleading. Machine learning is more often used, but the phrase I think is most appropriate is statistical learning. It uses past data to predict future results (in the supervised world anyway). Different algorithms have different strengths and weaknesses.. Most serious researchers have also abandoned the idea of trying to strictly model the brain. We probably don't know enough about it to come even close, so in the meantime let's create algorithms that do useful things for us today.