Guest essay by Eric Worrall
Why pre-judging the value of potential stepping stones to a solution, stifles scientific innovation.
Kenneth Stanley, one of the world’s top artificial intelligence researchers, has produced a fascinating presentation, on why focusing on an objective can sabotage innovation.
Consider how you would teach a robot to solve a maze. The obvious thing to do, is to teach the robot to do its best to reach the end of the maze – to reward movements which bring the robot closer to the end of the maze, and punish movements which cause the robot to move away from its objective.
However, researchers quickly discovered that if you give these logical seeming instructions to a robot, something unfortunate happens.
The obsession with finding a solution, and on focusing resources on actions which seem to bring the robot closer to the solution, blinds the robot to the possible paths which actually lead to a solution.
How do you solve this dilemma? Ken’s solution to preventing this kind of hangup is incredibly simple – the way almost every ingenious breakthrough is simple. Instead of punishing the robot if it makes a move which takes it away from its goal, you reward the robot for innovating – for always doing something new. The only caveat is, the robot should try to avoid repeating itself.
Focussing only on trying something new avoids prejudging potential stepping stones to the solution. Until you know what the solution is, there is no reliable way of knowing how valuable a potential stepping stone might be. The only way to solve the maze in my example, is to take a path which leads away from the solution – a path which an entity obsessed with reaching the solution would find very difficult to take.
Or to put it another way, if 3 decades of attempts to build a model which works, has failed to deliver results, maybe its time to stop repeatedly butting your head into the same wall.
This approach to problem solving, focusing on novelty, does not prevent you from monitoring the process, to see whether a solution has been found – solving the problem is the ultimate goal. All novelty search means is that you keep your opinions to yourself – you let the robot do its thing, regardless of how silly any individual move might seem at the time, until it finds the solution on its own terms.
The following is Ken’s presentation “Novelty Search and the Myth of the Objective”. Ken’s presentation covers a lot more than my quick summary – including valuable insights into why scientific advance has stalled in a number of disciplines.
Update – Ken Stanley has published a book, available for pre-order on Amazon, which discusses his ideas in more depth.
Click here to hear an interview with Ken, about his new book.
Insanity: doing the same thing over and over again and expecting different results. – Albert Einstein
I think it is core part of the scientific method. Not the 95% approved scientific method, mind you
Errrr, Francisco – is that not – actually – 97% approved?
Might that sort your micro-problem?
Auto.
It is not uncommon to come across this quote from Einstein.
I wonder how many people here comprehend what it takes to make a scientific breakthrough, to intellectually reach the end of the road and then to formally account for territory previously out of reach to the scores of academic/scientist that could not advance any further. It is the strenuous and most complex endeavor I am aware of. It involves maniacal revisitation and intensive reanalysis of the same set of concerns over very, very extensive periods of time. I would argue that 99% of academics/scientists fail 100% of the time while the remaining 1% make progress 1% of the time.
I’d say that is probably about right.
There is absolutely no reward in science like holding on to your position until unambiguous data permits you to gloatingly look at your friends and colleagues, drop a manuscript on their desks and say, “I told you so.” This is true particularly in field sciences, where a pattern to one person is just noise in the data to another.
Well i have a counter example, the quest for novelty propels the Academy(and politcs) to make even crazy theories and get out of strings of Scientific Method.
The raise of “social” sciences and crazy theories are testimony of that.
The quest for unbridled novelty could also be a testament of unbridled narcissism.
An excellent point. Not all things “novel” are good. But there are tests for merit, and I think the real issue is tossing aside merit for the sole property of novelty.
You miss the point. Unanticipated solutions can only by found by emphasizing novelty, by definition. There are lots of wrong novel solutions, but an infinite number of repeats of the same wrong solutions possible. It’s only the few right novel solutions which are the payoffs.
I agree Alex, and it’s all dressed up as altruism.
Good point, and even the tests for merit are relaxed along the way as seen in IPCC models and prediction accountability.
So in the case of the maze above, just what happened that the ball interpreted as punishment or reward, that led to the solution ??
It was programmed not to repeat known errors, so it didn’t get “stuck”.
well in the first video, the ball never did try making a right turn instead of a U-turn.
Rewarding novelty may work with a the shown puzzle where the number of options is small but suppose instead it was a puzzle with say, 50 binary decision points. Rewarding novelty is in effect biasing toward a brute force search. With large search spaces, it is no more the answer either.
This.
Actually, DAV, rewarding novelty, where there is a large number of possibilities bias towards no solution in our lifetime, if you will. the solution might be as simple as the maze – 4 right turns and 3 lefts, but would taking 4 right turns in a row be “novel” or would it be repetitive? Would the robot choose to follow that first right turn with a second, or would it, out of novelty triy to turn left? then, confronted with no forward movement, would it have turned left a second time since that would be novel at that point and proceeded back out of the maze? Rewarding novelty might very well never solve the problem.
Ken covers this issue in his presentation. If you are attempting a truly deep search, you have to give up on achieving a pre-determined objective, and just keep an eye out for something “interesting”. Being open to any “interesting” discovery, rather than ignoring outcomes which don’t fit your pre-determined criteria, helps tilt the odds back in your favour.
Ken’s point is that for most real world searches, there is no set of criteria you can apply to determine the “best” decision at any particular point in the search, because you don’t have the information you need to make that determination – you don’t yet know how to reach the destination.
Ken doesn’t claim his technique is a cure all – there are some classes of problems where other algorithms work better. For example, if you wanted to solve the travelling salesman problem (shortest path linking a set of cities), you would apply an evolutionary algorithm. But in a real sense the travelling salesman problem has already been “solved” – you might need a new solution to a particular instance of the problem, but solving any instance of the problem is just a matter of applying a well known algorithm. The classes of problem Ken is talking about are problems where the solution is totally unknown, where you are taking a step out into the unknown, to try to find a totally new solution.
Well in patent law, novelty, while necessary, is not sufficient. It must not be obvious to a person of ORDINARY skill in the art.
In other words, you don’t get a patent for doing nothing more than applying the basic ordinary skills of your art, even if you never performed that exact task before. (nor if nobody else did it before).
On the other hand, in copyright law, you can repeat the same “one from column A, one from column B, …. ” process over and over again, and ALL of your resulting garbage, is copyrighted.
Joseph Haydn wrote 104 symphonies (at least), and most of them were junk when he wrote them, and still are. Likewise, Mozart wrote 41 (at least) and at least three of them are noteworthy.
But none of those ancients turned out garbage at the rate that modern “rock” bands do. And yes, the moderns do occasionally do something good.
I have never used the same design for something, ever again; always a new design; but hardly a patentable design.
So novelty is of limited value.
Real insight maybe.
Of course the solution to a maze is to follow the same wall, and if you find yourself back at a point you have previously visited, switch to the opposite wall and follow that. Note that the simple maze in the diagram can be solved by following the left wall, producing the illustrated solution – or the right one, which produces a shorter solution, avoiding the blind alleys. Now the correct algorithm is a real advance, and is rather more sophisticated than some instruction not to repeat mistakes. However, there is no way of knowing whether it will be quicker to start following the left or the right wall. Finding the optimal solution entails exploring both alternatives. That of itself is a paradigm that climate science would do well to adopt.
Until the maze has more than “one central island” … And if those multiple center islands interlock with one another (imagine several “C” shapes keyed into each other) so if you “switch walls” within a key or keyhole, you find yourself still “on” the same central island but on the mating wall of the other central island keyhole.
You’d need a spraypaint can to mark each wall as you “leave” a wall.
Could the quest for unbridled novelty also be a luxury? I’m busy viewing the video and obviously, dohh, by definition, novelty is the most important factor in groundbreaking research. (Slaps himself upside the head a few times.)
It can’t hurt to try something new and see what your morning coffee tastes like with just a drop of jalapeno oil. I don’t think everyone has the time and patience to try a new way every time you brush your teeth. But a good lover will combine experience of what works with a constant curiosity to know what else works too.
Once we find a solution that seems to be the best, like the optimum route to work and back every day, the search for novelty could become a luxury to keep us from getting bored.
That is not a “counter example.” It simply is the way things really work. The robot in the original article has to explore the “solution space” available to find an actual solution. Some of those are “crazy,” some are not. From above we can tell that. from the robot’s eye view, the information does not exist until the space has been explored. Once that space has been mapped, if the robot has adequate storage, it will not explore that space again.
The problem with the social sciences is that, like climate, ecology and economics (which is in truth a mere subset of ecology), the social sciences deal with extraordinarily complex phenomena and worse, most of those phenomena are “robots” composed of collections of individuals exploring the available “space” for solutions to problems. The solutions can be either evolutionary or adaptive and they may be optimal, barely work, or lead the social entity involved down the path to extinction. The problems may be real, perceived, or so absurd that almost anyone outside the community can see that the problem is a delusion.
An excellent example of a delusional solution could be the Shaker communities of the 19th century. Shakers did not reproduce. They relied on recruitment instead with what some would regard as predictable results. Whatever the “problem” was that the Shakers were attempting to solve, their solution was a non-violable one that lead to community extinction. Some Shakers remained in the community the community to death, while others left community to lead other kinds of lives. All we have left now of Shaker society are some fine furniture forms.
Well even today, we have communities that do not reproduce, but do recruit. And they are lauded as examples to the rest of us to revere.
go figure.
commieBob April 2, 2015 at 9:05 am
Robert, I have 36 years in the oil and gas industry. I’ll tell you that the principle of doing the same thing over and over again is deeply embedded in industry “leaders”. There is a social disincentive for trying something new. If you fail at what worked before or what the others are doing, you get sympathy: obviously there were other factors beyond your control that did you in, so here, have some more money. If you fail three times doing the same thing, well, you’re an idiot for not making a reasonable thing work. If you try something new and it fails, you were an idiot for trying. If it works, well, you clearly identified something new in the environment that others hadn’t picked up on yet – your approach isn’t new, the environment is. You are not smart, just luckily observant.
I recall struggling with new projects and budgets during the late 80’s, during one of the downturns in the industry, in which we had to bring forth ways to succeed in the “new” situation. All our new ideas were shot down. I concluded that the real directive was “to do exactly what we have always done, but faster, cheaper and more profitably”.
The try-try-again-without-change appears to be our modus operadi as a species. Speculative action is viewed highly suspiciously. Which is why skeptics are demonized. We all “know” what should be done, so those who think or act otherwise are bringing us to disaster.
Doug,
I think a lot of what you write about is to do with the types of personalities that percolate to the top of organisations and how they tend to act to not let a new generation show them up as being the ‘old guard’. A lot of the innovators just leave.
Just talking with one of my sons this evening he is in that boat with bosses who employed him expressly to develop a new portion of their business but then will not resource him to do so rather spending development money on the parts that they established before he came along. Go figure.
Not so much the novelty driver as the ‘my’ novelty, ‘I invented it’, ‘its my baby’, stay away from my funding mentality.
When I worked for other people, in the olden days, we ( the people who made the stuff actually happen) would shake our heads and have group hugs at the dopey management calls, the doing completely dud deals with their buddies etc. We called them ‘sparkling executive decisions’. The guy who invented that term was an Ulsterman with that wonderful accent and it drips with the sarcasm that only an Irishman can properly articulate.
Since you’ve been in the O&G business that long, I’m sure you’re well aware of how the entire industry treated George Mitchell as though he was completely insane for his belief that the oil shales could be incredibly productive, if he could just figure out how to frac them right.
Yep, that idea was just bug-eyed crazy, it was. Nobody wanted anything to do with it for 20 years while Mitchell worked on it, alone.
Doug, very interesting what you said. I was talking to a girl yesterday who has good job, but has been head-hunted by another organisation, who interviewed her, she doesn’t know whether to take the job (if it is offered to her), or not. When I asked her why rock the boat, if she is happy where she is, she told me, quite logically, in my view, that she can take risks now because she doesn’t have children or a mortgage and is still living with her parents who would support her if it didn’t work out. This sounds fairly obvious, but it wasn’t to a 59 year old in the tail end of a career, getting to my age and “rocking the boat” is not necessarily a good idea,
From your spelling Oi would guess you are American, in the UK we have endless rules and regulations which stifle initiative, which can lead to the end of a career. The robot analogy is a good one because the young (who have relatively,nothing to lose) will by nature, take more risks than I would, because I have a lot to lose if it goes belly up. This is why young people are more employable than the middle-aged, because we behave like frustrated, penalised, robots!
I think you are interjecting risk/reward into the discussion, as it needs to be. If the owner of the robot loses nothing each time it makes a wrong turn, then there is no problem letting it try until it succeeds. However, if there was a hefty price to pay for any wrong decision would the owner of the robot stand by and watch continued failure?
Tom, did you miss the bit where punishing failure failed?
re: Brian H, April 2, 2015 at 9:13 pm
Punishment of failure fails if the punishment means nothing to the entity being punished. That would include the entity being too stupid to understand how the punishment will effect it.
“The following is Ken’s presentation “Novelty Search and the Myth of the Objective”. Ken’s presentation covers a lot more than my quick summary – including valuable insights into why scientific advance has stalled in a number of disciplines……”
Such as in the area of nuclear power and nuclear technology thanks to all the anti-nuke propaganda being spewed out on a regular basis.
In routing Integrated Circuits and Printed Circuit boards design tools have struggled with this for a long time, they use a procedure called simulated annealing to find a better overall route by allowing a less than ideal path to be selected.
http://en.wikipedia.org/wiki/Simulated_annealing
Serendipitously, it turns out that butting up against that wall pays really well.
Pay wall?
In some fields of science, assumptions and subequent inferences are taught as facts, and anyone who points this out is treated with suspicion or contempt. Promising areas of research are forbidden, not because they mightn’t have value but because the facts they could reveal are not acceptable to the people who control the funding. They just don’t advance certain political agendas and so are forbidden.
Remember Eisenhower’s warning about the military-industrial complex? I wager you don’t know the second half of the warning in which he explains that the marriage of government and academia was what was largly responsible for the dangerous situation.
These people are obsessed with money because it’s power, so they’ve endeavored to control all of it, and the little bit of private money they cannot control galls them. Like Mr Potter in the movie, “It’s a Wonderful Life,” there are still a few people not under his control, and it galled him no end.
Well, Third Stooge, most people might not be familiar with Eisenhower’s second admonition concerning the marriage of science and government – but I’m sure most of the regulars here are.
Tomb,
You insulted me and then spoke on behalf of everyone else, while ignoring my premise.
I don’t know what better describes our current politics and governance. And here is the second warning which has also come to be.
It is interesting Larry, that while Eisenhower’s 1st warning is part of the cultural lexicon, his second warning is not and difficult to find. I only found it by going directly to the speech. Thanks for pointing it out!
Eisenhower’s warnings are more accurate than all of the models combined. That we have ignored his warnings is much more problematic than any various changes to the climate since one is an internal issue corrosive to the nations core and the other an external factor which humanity has been amazingly adept at addressing since it’s inception.
Reasonable clear minds believe in the scientific method only because it is proven to be the best method for discovering the truth or solving a problem. Better to only believe in something when there is evidence and strong argument. Today money and power have conflated the entity with the principle. Ideologues, self-righteous, elites, may proclaim their faith in “science” as and when it suits their ideological or financial needs, however religion is the only place for faith.
Larry, the point Eisenhower makes is not simply that a marriage of government and academia will be responsible for the “dangerous situation.” What he says is much more complex and nuanced:
The prospect of domination of the nation’s scholars by Federal employment, project allocations, and the power of money is ever present and is gravely to be regarded.
Yet, in holding scientific research and discovery in respect, as we should, we must also be alert to the equal and opposite danger that public policy could itself become the captive of a scientific-technological elite.
That is, he fears a positive feed back between government funding and academia. Where funding federal money dominates academia and in turn academia comes to influence policy far beyond what is wise. He also draws the conclusion that the hazard is a direct result of the establishment of the post-Korea military-industry-research cycle, caused by the growing Cold War.
“Or to put it another way, if 3 decades of attempts to build a model which works, has failed to deliver results, maybe its time to stop repeatedly butting your head into the same wall.”
Heck yes! Perhaps it is time to question the very basis of the current paradigm that James Hansen came up with involving CO2. Just maybe CO2 don’t warm the planet after all.
I invent for a living and I can tell you that Ken’s approach is sound principle. All of my break troughs have been mistakes or throwing out assumptions and repeating previous steps that did not work with the assumptions in place. It took me several years to change to a format similar to Ken’s example and quit making assumptions based on the objective.
His presentation points to the fact that I am likely to be retired in the near future. The current window is likely to be small to finish my current projects. I suspect we humans will obsolete ourselves possibly within my lifetime.
Or to put it another way, if 3 decades of attempts to build a model which works, has failed to deliver results, maybe its time to stop repeatedly butting your head into the same wall.
The trouble is they’re not looking for a model that ‘works’, they’re looking for one that proves their theory correct.
In a NUTSHELL
Well said!
Or, of course -whisper it – they seek a model that covers salary and, perhaps bonuses, and pension contributions, until retirement.
I do wonder (a little) at how little emphasis is based on paper authors’ proximity to retirement – versus the likely timeline for fairly assessing their magnum opus . . . . . .
Should I go back to watching the daily rise and fall of the price of marine plywood?
Auto
May I suggest an edit?
“The trouble is they’re not looking for a model that ‘works’, they’re looking for one that appears to prove their theory correct or is at least good enough for a headline.
😎
Nah, just one that keeps the alarmist position in place and the money rolling in. It’s tough having to move on to another field when it’s about all they have ever known. Big pay cuts are no fun.
I currently do not have a conclusion on what is likely driving our climates from a natural or an anthropogenic perspective. I do see many possible paths but nothing concrete.
Many here have made their conclusions and often interject them in the comment section of WUWT threads to remind us of their views and that we are wrong to not consider their conclusions as the basis for skepticism.
The weight of the evidence suggests it is not CO2-based. There are periods such as the Mediaeval, Roman and Minoan Warm Periods that had far less CO2 and were warmer than today. The Ordovician period had 11 times the CO2 and an ice age. It hasn’t warmed for 17 years despite rising CO2 and there is no tropospheric hotspot basically disproving the central positive feedback tenet of their hypothesis.
CO2 rise trails temperature by 800 years.
These basic points do not deny the GHG nature of CO2 and we do not even almost fully understand climate but CO2 is not a major climate driver, if climate sensitivity was high the feedback amplification of CO2 would create a runaway Greenhouse.
It never has.
Water is obviously the key. The radiative and convective actions of water in our atmosphere, in particular the cumulonimbic radiative subsidence at the emission level are significant counterbalancing negative feedbacks. The warmer it gets the more the atmospheric layers expand and expose heat to the emission level and radiate more heat away (one mechanism). Low level clouds also provide a cooling albedo effect.
Venus didn’t have this water mechanism as it lacks the liquid iron dynamo effect at it’s core like the Earth to give it a protective electromagnetic field, thus the hydrogen and oxygen was swept away or photo-disassociated by the solar wind while the heavier CO2 remained in the atmosphere leading to a runaway Greenhouse.
When made to think about it, Atmospheric Physicists tend to say that all gases in an atmosphere could be termed greenhouse gases. But the effect is caused by Mass, not internal radiation,
This looks like a repackaging of the classic “exploration” vs “exploitation” balance issue in machine learning. Just exploration reduces to random search, a futile effort in a big enough search space. Just exploitation gets stuck in local optima. The right balance is problem dependent and algorithm dependent.
Here is one novelty, intensely disliked and opposed by some who ‘presume to know’ all unknowns.
http://www.vukcevic.talktalk.net/Back1.gif
It was devised when only 3 previous ( 2 + and one – ) peaks where known. Since then one negative peak hit the target, soon we will know if the positive one is going to do it too. If it does it will be even more fervently opposed by the ‘settled’ science, despite it being based on precise and well defined orbital numbers.
I could speculate how it works, so could many readers of this blog how it could or it couldn’t, but to be truthful I don’t think anyone knows either way.
All of nature follows various rules one of which is the wave rule. When you make a wave, each wave is weaker than the previous one unless more energy is fed into the system.
The sun follows this very much. As it moves about the Milky Way galactic system, it changes its energy levels based on gravity situations relative to other star groups within the spiral arm (Perseus Arm in the present case).
Our local star is somewhat unstable.
The same kind of limited sample problem exists with cycles and peaks in the AMO. It could be modeled much like the solar case. The larger problem is side stepping the limitations of the AMO cycle count and variance of amplitude to diminish it in models and predictions. The cycle influence does not go away it is just over-generalized as an average cycle which it is not. The science and policy worlds lose in that case.
I wonder what it would look like in this cycle problem——-
http://www.climate4you.com/images/AMO%20GlobalAnnualIndexSince1856%20With11yearRunningAverage.gif
North Atlantic has two more cyclical events but running on a different time scale
http://www.vukcevic.talktalk.net/EAS.gif
Resourceguy
I just realise there is one more oscillation in the north Atlantic, beside the two shown in the graph above, and that is the geomagnetic Y (east) component, again running on yet another time scale.
See my post further below at April 2, 2015 at 12:09 pm.
North Atlantic is throbbing with all kind of oscillations.
You devised this formula in 2003? No tweaks? Honest?
Mr. Westhaver
Tweaks are: 152 required to set amplitude, year 1943.5 is required to set time reference, cos2pi/3 to set initial phase, numbers are from astronomy ( largest planet’s orbit 11.862 and synodic period 19.859, the names get me into trouble).
I hardly knew anything about sunspots. In 2003, my daughter (now Oxford postgraduate, working for well known and by far the largest world company in its field) was showing me her geography homework. I noticed sunspot cycles graph, my electronics background told me: that is a rectified amplitude modulated signal. I ‘de-rectified’ it and did simple assessment of cosA + cosB, and left it. Few days later I realised that approximate numbers 23.5 and 20 are actually 2 x 11.862 and 19.859.
See also my comment further down at April 2, 2015 at 12:09 pm.
Well I like it, but…. it may be right for the wrong reasons 🙂
I would like it even more than you do, if I can convincingly show why it happens to be so.
It is good to start with being right, the reasons can be disentangle at some future time
Any advance is strangled at birth by starting with being wrong
The graph you show is not based on the link you give to the source. Honesty has to be earned and you get a fail here.
I used to work for Analog Precision on computer board prototypes way back in the very early 1970’s.
Designing a good ‘electronic information path’ was a highly ARTISTIC skill requiring a sense of ‘this is a good thing’ based on knowledge of how Nature evolves various solutions to say, moving water through a root system.
These computer components evolved just like Nature but in reverse: from ‘higher level plants’ to ‘bacteria types’ to ‘viruses’. Smaller and more independently enclosed.
Most computer creators back then were all ‘antisocial/crazy/outside the box/hippie type/pot smokers’. Seriously. The old guard hated us.
The second example is the obvious one to me, but then I’m left-handed.
While I applaud the presentation, I just don’t find anything new in it.
Bloke down the pub April 2, 2015 at 9:58 am
The trouble is they’re not looking for a model that ‘works’, they’re looking for one that proves their theory correct.”
Exactly correct. And obvious.
Eric Worrall,
I enjoy your contributions to WUWT. I often read them. You use the term Novelty. I have also proposed a similar notion but I refer to it as “Wonder”
The Scientific Method is a great means to grind to the details. It is useless to the inspiration necessary to have the initial spark. You say novelty, I say wonder.
In any event I agree that novelty is essential. A foolish consistency is the hobgoblin of small minds.
Great article. Keep writing.
Thanks Paul 🙂
Here are two of my favorite science quotes:
“The essential point in science is not a complicated mathematical formalism or a ritualized experimentation. Rather the heart of science is a kind of shrewd honesty that springs from really wanting to know what the hell is going on!” Saul-Paul Sirag
“The world is full of magical things patiently waiting for our wits to grow sharper.” – Eden Phillpotts
Love it!
Encourage everyone to watch the video… can open your eyes to how it could be extrapolated to every scientific field.
Maybe someone(s) here can educate me –
How does one punish or reward a robot?
algorithmically
As Richard Russo wrote – the real competition in an English department
is for the role of “straight man”. Good one.
You punish or reward the owner of the robot.
For robots both the punishments and rewards are identical, load in more advanced program.
I confess to ignorance about mazes. But if I had to write a program to exit a maze my first approximation would be “choose right or left, then follow that wall to the exit.” For those who may have studied that problem, are there algorithms that in fact are more reliable and/or faster? In particular is there an algorithm using innovative (random?) trials that works better?
Yes, that would work. But the idea is to test AI – to test the ability of the robot to discover its own solution.
You need a small modification: if you find yourself retracing the same wall, switch to the other one. Obvious if you consider a central island in the maze, which you might follow round and round if you didn’t switch.
Eric,
I have several patents that were produced by assuming novelty inherent in system analysis. As an exercise I would often abandon conventions and take a reverse approaches or perpendicular postures to the problem at hand. I can’t tell you the number off times some old fart told me something to the effect that “the science is settled”. Some people are so limited in their mental wiring that their imaginations fail to consider a different meal time. Novelty causes a great deal of work and a huge number of failures but every so often a NOVEL solution is availed… and I make money! 🙂
Novelty precedes science. Science is the reliable and necessary boring part that explains everything in a cogent manner.
The benefits of novelty seems to cross into every field. The crucial key to novelty search seems to be to switch off the internal censor, to let ideas percolate without shooting them down before they have time to develop. In business it is call brainstorming, in writing and art it is called creativity, in science it is called innovation.
Well summarized. I love your stuff. Keep writing.
The best way to search for novelty in science through theoretical thought is to understand the nature of the critical assumptions made in any field of interest as well as understanding how key parameters interact to create outcomes.
Then you have to be prepared to question whether any of the assumptions may be invalid in certain situations and, if so, what the consequences of that would be.
However, in many cases, the drive for innovation is a totally unexpected experimental observation which begs an obvious question: why?
I was supervising an MD student years ago and we were trying to create novel transgenic mice targeting expressing of a particular protein to specific tissues. During the breeding, we started to notice that we simply weren’t generating male homozygotes, which of course is a prerequisite to create an effective breeding colony.
I wasn’t still around to be involved in working out what was going on, but we certainly weren’t starting out from the point of asking ‘what does this protein do in that tissue?’, since the relevant tissue was far away from where we were actually trying to target expression to.
Noticing unexpected things is a very useful skill to have if you want to innovate…….
Dr. Stanley’s description of the inadvertent collaborations on the picbreeder site reminds me of Matt Ridley’s talk “When Ideas Have Sex”:
http://www.ted.com/talks/matt_ridley_when_ideas_have_sex?language=en#t-100193
… or, when the foundation gets laid …
The problem with climate models is dogma. The scientists abandoned the systematic understanding of natural climate drivers decades ago to join the CO2 warming bandwagon. Greenhouse gas forcing became the major part of every model. Today, it is clear that such thinking is flawed, but unfortunately the understanding of natural climate drivers is stuck where it got to decades ago.
Not sure what “program” they used for the ‘bot, but it wasn’t “follow the right side”. (following the left side is just “wrong” 😉 )
Yup: https://www.daniweb.com/software-development/c/code/463066/right-hand-rule-maze-solver
End first, then we will show you how we got it!