Learning to read and write is very hard.
It takes years and years of constant practice to train our brains to seamlessly convert a visual pattern of shapes into an internal auditory stream of speech that we can understand. You might not remember how hard it was to learn to read, but if you have kids in elementary school then you know what I am talking about.
Clearly our brains were not designed for this type of task – it is only though force of will that we coerce them to do it.
Teaching reading and writing is similarly difficult. Our societies have developed a class of people dedicated to accomplishing the task. In the USA, we publicly fund an army of 3 million people whose primary function is to teach our kids to be literate members of society. Each one of these teachers has themselves had years of specialized training just to learn how best to teach these skills. We even require them to be licensed like doctors and lawyers.
Compare this to listening and talking – tasks our brains are clearly well suited for.
Verbal auditory creation and comprehension are a built in feature in human brains. We get them for free with no overhead. When we think to ourselves “I need to stop at the store and get milk”, we naturally do it by talking and listening to an internal verbal monologue. We do not create the visual representation of the letters ‘I–N-E-E-D-T-O-….” on an internal screen.
Almost all children learn to talk and listen without any conscious effort at all. Almost all parents are competent at teaching auditory fluency with no formal training.
So why we spend so much time and money and effort learning and teaching and promoting and testing literacy if it is so damn hard and we are so innately bad at it?
For the past 1,000 years, literacy was a fundamental requirement for participation in the intellectual world.
If you wanted to get ahead in this modern world, you needed to learn to read and write good.
The printed (or carved or pressed) word is an amazing technological achievement. I can read a text from 1,000 years ago and/or 10,000 miles away and precisely receive the knowledge the writer embodied. I can even make a copy of the text to take home with me so others can do the same. A single text can reliably disseminate a vast amount of knowledge to millions of people. Compare this to the telephone-game of auditory knowledge passing where you are lucky if you can a full sentence reliably passed on to a dozen friends over the course of a few seconds.
We are great at auditory speech- but it is a horrible medium for information storage and dissemination.
Until very recently literacy was the only reliable way of storing and sharing information. This is why we spend a huge amount of effort teaching people to read and write. It has allowed us to act as a single massive and long-lasting superintelligent Superorganism. No matter how hard it has been to create and master literacy, it was well worth it.
But times are changing…
First there was the invention of the record player that made it possible to store sound and pass it fatefully forward in time. Then came radio which allowed us to widely disseminate sounds across space. Fast forward to today were I have a machine in my pocket that not only can understand my voice and answer back, but simultaneously has instant access to most of humanity’s accumulated knowledge.
It is not hard to imagine a machine in the near future that is a little bit Siri and and a little bit Watson that can instantly answer any question I might have without me needing to read or write at all. Add in a little OrsonEar and it can also be my personal notebook, diary, secretary, and publishing agent.
About half of the books I want to read are already available in Audiobook form. After a few years practice, I can now listen to a book at 2x speed, which is faster than I can read. It is hard work, but I think my comprehension is higher than when I read and I can use my eyes for other things at the same time. I bet if I had started listening to hyper-fast speech when I was a kid and my brain was still pliable, it would be very natural and easy for me to do it now.
There is currently available technology that can automatically convert written text into spoken text so we could potentially listen to *any* written content.This is still a bit rough, but in the near future this will likely produce better output than even having the author read their own work aloud. Audiobooks could become interactive and self-adapting, automatically explaining words and concepts I don’t know, slowing down when they sense that I am having trouble keeping up, and omitting things that I already know. The experience of a good audiobook will grow to resemble a deep yet facile conversation with an expert rather than a passive and effort-full task.
Anyone who has tried the latest versions of Dragon Dictate knows that we have already passed the milestone where speaking is faster and more accurate than writing or typing for the vast majority of people. This trend will continue and accelerate and it seems likely that typing will soon be as useless a skill as cursive penmanship (which, by the way, is still being taught to my kids!).
Looking forward to into our not-so-distant future, will we have any need for literacy?
In 2030, will someone who knows how to read and write be better off than someone who doesn’t?
More importantly, will someone who knows how to read and write be better off than their equivalent selves if they had done something else with the time and brain cells they would have used up learning to read and write?
What else could our kids learn if they had an extra 3,600 hours of learning time during the phase of their lives when their minds are most open and plastic? And what new abilities might we make room for in their minds if we stopped drafting trillions of neurons into service as poorly performing text-to-speech engines?
Personally, I today would not hesitate to forfeit my ability to read and write if I could instead, say, have a deep intuitive understanding of Hilbert Spaces. Or the ability to imagine a polychronon. Or even just be able contemplate the integration of messier primes. These are all visual spacial tasks that my neocortex might well have been able to master if I had not already committed so much of it learning to recognize various series of letter symbols and the complex and arbitrary rules needed to create them.
Maybe it is time to rethink the supremacy of literacy in education and instead look towards creating future generations of thinkers capable of things we (literally) can not imagine…
I just tried to make a dentist appointment and it sucked. There are never any available slots when I want them, and it is even more frustrating because I know that many of the people reserving those slots are going to end up canceling or not showing up.
This is because all appointment slots costs the same, and in this case the cost is $0. Note that I am talking about the slot and not the actual service you get once you are in the chair. Anytime the costs of something is too low, people will waste it and there will not be enough supply.
The dentist currently crudely compensates for this by overbooking slots, which means I usually have to wait when I show up on time for my appointment
Slots should cost money.
The dentist should figure out how much a slot costs him – that is, how much is does it cost for him to have the office open and all the people and equipment ready in case I do show up for my slot. He should sell the slot for more than is costs him. This way, he is happy if show up or not. Now no-shows become a profit center rather than a hassle.
It is likely that some slots should initially cost more than others. It takes an hour to drive in from New Jersey at 8AM, but only 20 minutes at 10AM, so 8AM slots should be priced high enough to reflect the higher cost of providing them. Then people buying the slots will implicitly tell the dentist if it is worth it for him to sit in traffic on any given day. Some dentists and staff might even be willing (happy?) to work though lunch hour if they were getting paid double. Right now there is no feedback to the dentist about how to efficiently schedule his time to best balance his preferences and those of his patients.
Slots should also be transferable.
Tuesday afternoons are normally very popular and slots then so might cost $200. I buy one for a month from now, but as the date approaches I see that it is going to rain that day. I offer to sell my slot for $100 and take the loss, while someone who lives close to the dentists gets to pick up my choice slot that otherwise would have been to expensive for them.
The dentist might sometimes even want to buy back his own slots in the secondary market. If I have the only sold slot on a Friday afternoon, it might be worth it for the dentist to buy that slot back from me for double what I paid for it so that he can head out to the Hamptons at lunch time.
You can easily imagine websites to facilitate both the initial sales and the secondary market for these slots.Heck, there might even be derivatives markets where you could buy and sell options on slots.
All this would work for restaurant reservations too.
New company doing this!…
There is nothing worse than arriving at your destination only to find the station full. 48 docks… and no where to park. You are stuck with the bike and you can’t do anything else until you find a free dock. The free dock hunt is frustrating and sometimes kills any time saved by taking the bike in the first place.
They do send around fancy CitiBike vans to move bikes around, but the effort is futile.
Having people driving vans full of bikes around NYC is wrong in so many ways.
I’m guessing that keeping one of those vans on the road costs $100k-$200k per year (think about the salaries, gas, insurance, and depreciation to start). I’d also guess that each van can only move 10-100 bikes per hour. There must be a better way.
I always prefer a distributed, market-based solution. Short term…
Simple. The $5 bounty would automatically get applied to your CitiBike account. Do it one every couple of weeks and your annual membership is free. Do it more, and you have a new (fun!) job.
If you don’t have an account, then the trip would just be free. Same checkout procedure as a normal day trip, but when you return the bike to a non-full station you just don’t get charged the normal fee.
First step is a simple software change in the billing system. Quick and straightforward to implement. It could even be done as an offline system that searches for qualifying trips after the fact and then applies a credit to the account. You could start the new policy immediately and bring up the software later, applying the credits and refunds retroactively when the software is ready.
Immediately the number of frustrated people not able to return a bike drops. Huge win for almost no work.
Next step would be to add a…
Alert me when I am within [500 feet] of a full station
… feature to the CitiBike app, just to make it even easier for interested people to collect. In the meantime, if CitiBikeNYC says it is ok to use their data, I’ll happily write a Show Me The (Bike) Money app for people to use until the new CitiBike app is ready.
Next step is to add a bounty for returning bikes to empty stations. This is not as important as the full station bounty (real and mandatory costs of not being able to return a bike are worse than the opportunity costs of not being able to borrow one in the first place), but could still be very effective in keeping bikes well distributed throughout the system.
Next step would to be bring some dynamic pricing into the system so that the bounty is high for really valuable moves (apparently downtown Brooklyn to Chelsea on a Sunday night) and low for not so valuable ones (Cliff Street to Fulton Street ever). Update the app with a new option to…
Show me all bike rewards more than [$25] ranked by [nearest to me]
Start using demand prediction to get ahead of the curve and move bikes before stations are empty/full. A predictive/reactive self-balancing system without heavy fixed costs like vans and bike wranglers, and a new class of professional bike riders. Please?
London Citibike could use some marketizing too…
A combination of hardware and software that would keep track of all your belongs inside your home. If you wanted to find (say) your scissors, you would just search for them on the system and it would tell you where they were last seen.
There have been a spate of systems that try to solve this problem by attaching a piece of hardware to each object to be tracked, but this would be impractical and expensive for more than a few objects.
OrsonObject would be pervasive and, once set up, would automatically keep track of everything by tracking objects by their appearance. To set up the system, all you would need to do is install a bunch of tiny cameras around your home such that they had a pretty good view of almost everywhere. This is not expensive or as much of a hassle as it sounds- there are systems like VueZone with tiny wireless cameras that you stick onto the wall with double sided tape.
I am not a big fan of the VueZone system specifically, but it shows that this technology is nearly ready. Since the OrsonObjects system doesn’t need to stream video, it would be possible to make the cameras even smaller and cheaper.
The cameras would automatically take pictures whenever anything happened in front of them- typically you moving stuff. These images would get uploaded to cloud-based servers in the background. These servers would use continuous recognition to find and any objects and track their movement throughout the space.
This is hard, but possible (or almost possible). People have made a lot of progress in automated object recognizers lately. Since the processing is all done off-line, you could bring to bear huge, cheap computational resources to do the recognition and tracking.
The user interface to the system would look a lot like Piccassa’s face view, showing a list of all recognized objects. You could sort the list by things like object size, most recently moved, or most often moved. You’d click on the object you are looking for and it would bring up the last time that object was moved – directing you to the object’s current location.
These system could be extended to recognize objects that are often used together and group them logically into sets. It could keep track of most used objects and predict when you might need an object based on what other objects you are currently gathering (he has the coffee grinder…. and the sugar… which usually means that next he will need the filters also- which I last saw in the top drawer) and suggest their location to you when it seams like you are looking.
Looking ahead into the future, the system could be expanded to also manage the location of objects for you. You’d have a piece of furniture that looked like a large bureau. It would have a large, easily accessible shelf top in front. Anytime you were done using an object, you would drop it on the shelf and the system would automatically store it inside. Objects would be stored much very efficiently than you could in normal drawers and cabinets since the system could put every object where it fit best. Objects would be retrieved in seconds any time you needed them. This would save users both time and space.
I recently saw Stephen Wolfram give a great talk where he mentioned that Alpha correctly answers more than 90% of all questions. My guess is that this number is artificially high because people have learned what sorts of questions Alpha is good at and don’t bother asking it questions that they know it can’t answer.
I personally get most frustrated when I know Alpha knows an answer, but I can not trick it into telling me. For example…
Here, Alpha’s answer is totally hopeless even though I know it knows all the necessary facts. I just wish that I would tell Alpha how to answer a question like this, both so that it would give me the answer I want but also so that it would be then be able to answer this type of question if anyone else asked it.
How can you expand the realm of questions that Alpha can correctly answer?
Unfortunately, as productive and brilliant as the Wolfram employes are, they will never have to resources to do everything. Compounding that, they do not have an empirical way to prioritize their efforts. What work should you do next? They should consider measures like…
- how hard is the work? Will it take a summer intern one hour, or a team of PhD’s a month?
- how much does the work expand the number of answerable questions? Will the work make a whole new class of questions answerable, or just make it possible to use a different form to an already answerable question?
- how valuable are the questions the work would make answerable? The ability to answer “How many fingers am I holding up?” may not have as much value as being able to answer “What is the structure of a protein that is able to selectively block the absorption of carbohydrates?”
Wolfram is in a pretty good position to estimate how much effort it might to to add some functionality to the system, but the other two are much harder and they are critical. There is no way currently for Wolfram to be able to expend the correct amount of effort in the correct places to improve the system with maximal efficiency. They just don’t have either the resources or the information they would need.
Markets are the answer
Markets are great at solving problems like this. They can prioritize problems based on which solutions would provide the most total value, and then provide the incentives to for people to actually create these solutions. They even encourage solutions to come from people in a position to most efficiently supply them.
For markets to work, each question needs to have a time-value associated with it. This quantifies how important and urgent the question is.
Some questions might not be very valuable to any one person, but they are a little valuable to lots and lots of people over and over again. There might be 100 million people willing to pay $0.05 every day for the answer to “What is the weather?”
Some questions might be extremely valuable to one person, one time. There might be a forgetful user willing to pay $100,000 for the answer to “What is a password that would match the following hash?”
And everything in between.
Every answer also needs a time-value. This quantifies how difficult and time consuming generating the answer is.
Q: “What is fastest way to get to New York City from Heathrow Terminal 1?” Time-value: $50 for an answer in the next 60 seconds, otherwise $0.
Q: “What are the prime factors of this 512 digit number?” Time-value: $250 for an answer today, $249 tomorrow, $248 the next day, etc…
Once you capture the time-value of questions and answers, the more valuable a questions is the more likely it will be answered, and answered quickly. Just as importantly, questions that are not worth the effort will not get answered.
Note that questions need not come before their answers. I should be able to enter an answer into the system at any time along with a price, and my answer will sit there until someone asks the matching question- assuming that the price they are willing to pay for the answer is less than or equal to my price for the answer.
I should also be able to offer answers to a whole class of questions, not just individual ones. I might offer to answer any question in the form of “What is the interest rate for a ___ year loan?” where the blank can be any number from 1 to 30.
I further should be able to offer answers that are compiled by combining the answers from sub-questions that I don’t necessarily know how to answer. I might offer to answer the question “Which has a lower interest rate between a 10 year or 30 year loan?” by myself asking the two questions “What is the interest rate for a 10 year loan?” and “What is the interest rate for a 30 year loan?”and then answering based on the answers I got. This would only be worth doing if the value of the question I was answering was more than the cost of the sub-questions (plus hopefully a little extra for me to keep for my effort of putting them together).
How would this work on Alpha?
This market approach could be practically implemented on Alpha by adding a few features.
First you need to be able to time-value questions. This could be as simple as adding a button on the Alpha results page that basically says “This is not the answer I was looking for, but I would be willing to pay”…
This immediately gives Wolfram a metric to prioritize on. They could also add a “Most Wanted Questions” gallery to the site so users could see and vote for the most popular and valuable open questions.
Next they could create a way to supply external answers to open questions. This could start as simply a “I know the answer!” button added to the open questions gallery. Users could click on the button and enter an answer.
So far this is a lot like uClue, but different. On uClue, once a question is answered- it is over. There is no incentive to answer questions that might have a low value but are very popular so lots of people want the answer. Also, there is no way to systematically answer questions- you have to wait for a specific question to be asked and then answer it.
With Alpha there is the possibility to answer a whole class of questions rather than just a single instance of a single question. This would require way to to submit “Answer Filters” into the Alpha system. The “Answer Filters” would likely come in the form of Mathamatica programs (Alpha uses Mathamatica internally) and would take the form of “I can answer questions that that a certain form, and I am willing to do so for these prices.”. Note that a Mathamatica program has full access to the internet, so an answer filter could potentially connect to an external web service to get any external data needed and not be constrained to the (amazingly rich) resources available in Mathamatica or Alpha. Wolfram almost certainly already has a of doing this internally, so it would be a matter of opening it up and adding some functionality.
Now every time Alpha gets a question, it can offer it to any and all of the Answer Filters in the system that claim to be able to solve it. Since people only pay for answers that they deem to be correct, the system can sort the matching Answer Filters with the cheapest and most likely to be right answer is listed first. This very similar to the way Google sorts AdWords ads – the top ad is not the one with the highest bid nor it is the one most likely to be clicked, but instead an optimization of both of those factors.
As more people offered to pay for answers, more people would create “Answer filters” able to provide them- especially for valuable and popular questions. As more Answer Filters are added to the system, the Alpha system would become more and more capable and attract more users. Growth is no longer limited by Wolfram’s resources – the growing userbase is now helping guide Alpha to learn exponentially more and more in exactly the places where it will be most useful.
There is friction with trying to actually get money from people asking questions, especially for questions with relatively low prices but that are still valuable because they get asked very often. In these cases, advertizing revenue might be a way to compensate the answer filter creators. Alternately, Wolfram Corp. could just pay “answer bounties” themselves out of a pot, similarly to the way Amazon makes free books available with their Kindle Selects program. This strategy is based on the idea that the long-term value of having a vibrant system with lots of answer filters is worth shelling out some cash now.