Afleveringen
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In this episode we discuss home automation system design and present an example using Apple’s Home App and a couple of Leviton dimmer switches.
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This project was initially envisioned as a screenplay that I've adapted into a podcast. It revisits the Space Shuttle Challenger disaster though the eyes of Richard Feynman.
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Zijn er afleveringen die ontbreken?
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To the regular listeners, apologies for the hiatus. Last year was both professionally and personally epic which demanded attention to other pursuits.
Now that things are more settled, I am drawn back to this outlet to share with a slightly augmented mission / format.
Aside from a personal diary providing an output for my curiosity, I’d like ‘Robot Breakfast’ to encompass greater breadth.
As I experiment with formats, I ask for both your patience and feedback on what’s working or not.
So with that… the next episode is a creative exploit. A story of one of my earliest memories and a case study in engineering ethics as a young engineer. The story of the Challenger Space Shuttle and Richard Feynman’s role. Hope you enjoy.
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Our Responsibility as Scientists
We are at the very beginning of time for the human race. It is not unreasonable that we grapple with problems. There are tens of thousands of years in the future. Our responsibility is to do what we can, learn what we can, improve the solutions and pass them on. It is our responsibility to leave the men of the future a free hand. In the impetuous youth of humanity, we can make grave errors that can stunt our growth for a long time. This we will do if we say we have the answers now, so young and ignorant; if we suppress all discussion, all criticism, saying, "This is it, boys, man is saved!" and thus doom man for a long time to the chains of authority, confined to the limits of our present imagination. It has been done so many times before.
It is our responsibility as scientists, knowing the great progress and great value of a satisfactory philosophy of ignorance, the great progress that is the fruit of freedom of thought, to proclaim the value of this freedom, to teach how doubt is not to be feared but welcomed and discussed, and to demand this freedom as our duty to all coming generations.
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The reason I love this lecture by Feynman is that it’s just so timeless. I first found it about 15 years ago and it pretty much changed my life. Ever since then I've tried to keep an open mind, and know that it's okay to be ignorant, and that it’s okay to have fear.
And that the true thing is not to be without fear, but to be courageous. To work and function through the fear. The other thing I took from this was that democracy itself was birthed out of the 18th century scientific enlightenment. In other words it was born out of a philosophy of ignorance.
And, to think that the speech was given in 1955. Just shows the merit of how timeless these thoughts and ideas are.
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Education, for Good & Evil
Once some thought that the possibilities people had were not developed because most of these people were ignorant. With education universal, could all men be Voltaires? Bad can be taught at least as efficiently as good. Education is a strong force, but for either good or evil.
Communications between nations must promote understanding: So went another dream. But the machines of communication can be channeled or choked. What is communicated can be truth or lie. Communication is a strong force also, but for either good or bad.
The applied scientists should free men of material problems at least. Medicine controls diseases. And the record here seems all to the good. Yet there are men patiently working to create great plagues and poisons. They are to be used in warfare tomorrow.
Nearly everybody dislikes war. Our dream today is peace. In peace, man can develop best the enormous possibilities he seems to have. But maybe future men will find that peace, too, can be good and bad. Perhaps peaceful men will drink out of boredom. Then perhaps drink will become the great problem which seems to keep man from getting all he thinks he should out of his abilities.
Clearly, peace is a great force, as is sobriety, as are material power, communication, education, honesty, and the ideals of many dreamers.
We have more of these forces to control than did the ancients. And maybe we are doing a little better than most of them could do. But what we ought to be able to do seems gigantic compared with our confused accomplishments.
Why is this? Why can't we conquer ourselves?
Because we find that even great forces and abilities do not seem to carry with them clear instructions on how to use them. As an example, the great accumulation of understanding as to how the physical world behaves only convinces one that this behavior seems to have a kind of meaninglessness. The sciences do not directly teach good or bad.
Through all ages men have tried to fathom the meaning of life. They have realized that if some direction or meaning could be given to our actions, great human forces would be unleashed. So, very many answers must have been given to the question of the meaning of it all. But they have been of all different sorts, and the proponents of one answer have looked with horror at the actions of the believers of another. Horror, because from a disagreeing point of view all the great potentialities of the race are being channeled into a false and confining blind alley. In fact, it is from the history of the enormous monstrosities created by false belief that philosophers have realized the apparently infinite and wondrous capacities of human beings. The dream is to find the open channel.
What, then, is the meaning of it all? What can we say to dispel the mystery of experience?
If we take everything into account, not only what the ancients knew, but all of what we know today that they didn't know, then I think that we must frankly admit that we do not know.
But in admitting this, we have probably found the open channel.
This is not a new idea; this is the idea of the age of reason. This is the philosophy that guided the men who made the democracy that we live under. The idea that no one really knew how to run a government led to the idea that we should arrange a system by which new ideas could be developed, tried out, tossed out, more new ideas brought in; a trial and error system. This method was a result of the fact that science was already showing itself to be a successful venture at the end of the 18th century. Even then it was clear to socially minded people that the openness of the possibilities was an opportunity, and that doubt and discussion were essential to progress into the unknown. If we want to solve a problem that we have never solved before, we must leave the door to the unknown ajar.
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The Remarkable Idea
When we read about this in the newspaper, it says, "The scientist says that this discovery may have importance in the cure of cancer." The paper is only interested in the use of the idea, not the idea itself. Hardly anyone can understand the importance of the idea, it is so remarkable. Except that, possibly, some children catch on. And when a child catches on to an idea like that, we have a scientist. These ideas do filter down (in spite of all the conversation about TV replacing thinking), and lots of kids get the spirit -- and when they have the spirit you have a scientist. It's too late for them to get the spirit when they are in our universities, so we must attempt to explain these ideas to children.
I would now like to turn to a third value that science has. It is a little more indirect, but not much. The scientist has a lot of experience with ignorance and doubt and uncertainty, and this experience is of very great importance, I think. When a scientist doesn't know the answer to a problem, he is ignorant. When he has a hunch as to what the result is, he is uncertain. And when he is pretty darn sure of what the result is going to be, he is in some doubt. We have found it of paramount importance that in order to progress we must recognize the ignorance and leave room for doubt. Scientific knowledge is a body of statements of varying degrees of certainty -- some most unsure, some nearly sure, none absolutely certain.
Now, we scientists are used to this, and we take it for granted that it is perfectly consistent to be unsure -- that it is possible to live and not know. But I don't know whether everyone realizes that this is true. Our freedom to doubt was born of a struggle against authority in the early days of science. It was a very deep and strong struggle. Permit us to question -- to doubt, that's all -- not to be sure. And I think it is important that we do not forget the importance of this struggle and thus perhaps lose what we have gained. Here lies a responsibility to society.
We are all sad when we think of the wondrous potentialities human beings seem to have, as contrasted with their small accomplishments. Again and again people have thought that we could do much better. They of the past saw in the nightmare of their times a dream for the future. We, of their future, see that their dreams, in certain ways surpassed, have in many ways remained dreams. The hopes for the future today are, in good share, those of yesterday.
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The Grand Adventure
The same thrill, the same awe and mystery, come again and again when we look at any problem deeply enough. With more knowledge comes deeper, more wonderful mystery, luring one on to penetrate deeper still. Never concerned that the answer may prove disappointing, but with pleasure and confidence we turn over each new stone to find unimagined strangeness leading on to more wonderful questions and mysteries -- certainly a grand adventure!
It is true that few unscientific people have this particular type of religious experience. Our poets do not write about it; our artists do not try to portray this remarkable thing. I don't know why. Is nobody inspired by our present picture of the universe? The value of science remains unsung by singers, so you are reduced to hearing -- not a song or a poem, but an evening lecture about it. This is not yet a scientific age.
Perhaps one of the reasons is that you have to know how to read the music. For instance, the scientific article says, perhaps, something like this: "The radioactive phosphorus content of the cerebrum of the rat decreases to one-half in a period of two weeks." Now, what does that mean?
It means that phosphorus that is in the brain of a rat (and also in mine, and yours) is not the same phosphorus as it was two weeks ago, but that all of the atoms that are in the brain are being replaced, and the ones that were there before have gone away.
So what is this mind, what are these atoms with consciousness? Last week's potatoes! That is what now can remember what was going on in my mind a year ago -- a mind which has long ago been replaced.
This is what it means when one discovers how long it takes for the atoms of the brain to be replaced by other atoms, to note that the thing which I call my individuality is only a pattern or dance. The atoms come into my brain, dance a dance, then go out; always new atoms but always doing the same dance, remembering what the dance was yesterday.
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They following text is from a public meeting held by the national Academy of Sciences, Caltech November 2, 3 and 4, 1955.
The Value of Science
by: Richard P. Feynman
From time to time people suggest to me that scientists ought to give more consideration to social problems – especially that they should be more re- sponsible in considering the impact of science on society. It seems to be generally believed that if the scientists would only look at these very dif- ficult social problems and not spend so much time fooling with less vital scientific ones, great success would come of it.
It seems to me that we do think about these problems from time to time, but we don’t put a full-time effort into them – the reasons being that we know we don’t have any magic formula for solving social problems, that social problems are very much harder than scientific ones, and that we usually don’t get anywhere when we do think about them.
I believe that a scientist looking at nonscientific problems is just as dumb as the next guy – and when he talks about a nonscientific matter, he sounds as naive as anyone untrained in the matter. Since the question of the value of science is not a scientific subject, this talk is dedicated to proving my point – by example.
The first way in which science is of value is familiar to everyone. It is that scientific knowledge enables us to do all kinds of things and to make all kinds of things. Of course if we make good things, it is not only to the credit of science; it is also to the credit of the moral choice which led us to good work. Scientific knowledge is an enabling power to do either good or bad – but it does not carry instructions on how to use it. Such power has evident value – even though the power may be negated by what one does with it.
I learned a way of expressing this common human problem on a trip to Honolulu. In a Buddhist temple there, the man in charge explained a little bit about the Buddhist religion for tourists, and then ended his talk by telling them he had something to say to them that they would never forget – and I have never forgotten it. It was a proverb of the Buddhist religion:
To every man is given the key to the gates of heaven; the same key opens the gates of hell.
What then, is the value of the key to heaven? It is true that if we lack clear instructions that enable us to determine which is the gate to heaven and which the gate to hell, the key may be a dangerous object to use.
But the key obviously has value: how can we enter heaven without it?
Instructions would be of no value without the key. So it is evident that, in spite of the fact that it could produce enormous horror in the world, science is of value because it can produce something.
Another value of science is the fun called intellectual enjoyment which some people get from reading and learning and thinking about it, and which others get from working in it. This is an important point, one which is not considered enough by those who tell us it is our social responsibility to reflect on the impact of science on society
Is this mere personal enjoyment of value to society as a whole? No! But it is also a responsibility to consider the aim of society itself. Is it to arrange matters so that people can enjoy things? If so, then the enjoyment of science is as important as anything else.
But I would like not to underestimate the value of the world view which is the result of scientific effort. We have been led to imagine all sorts of things infinitely more marvelous than the imaginings of poets and dreamers of the past. It shows that the imagination of nature is far, far greater than the imagination of man. For instance, how much more remarkable it is for us all to be stuck – half of us upside down – by a mysterious attraction to a spinning ball that has been swinging in space for billions of years than to be carried on the back of an elephant supported on a tortoise swimming in a bottomless sea.
I have thought about these things so many times alone that I hope you will excuse me if I remind you of this type of thought that I am sure many of you have had, which no one could ever have had in the past because people then didn’t have the information we have about the world today.
For instance, I stand at the seashore, alone, and start to think.
There are the rushing waves mountains of molecules
each stupidly minding its own business
trillions apart
yet forming white surf in unison.
Ages on ages before any eyes could see year after year
thunderously pounding the shore as now. For whom, for what?
On a dead planet
with no life to entertain.
Never at rest
tortured by energy
wasted prodigiously by the sun
poured into space.
A mite makes the sea roar.
Deep in the sea
all molecules repeat
the patterns of one another
till complex new ones are formed.
They make others like themselves
and a new dance starts.
Growing in size and complexity
living things
masses of atoms
DNA, protein
dancing a pattern ever more intricate. Out of the cradle
onto dry land
here it is
standing:
atoms with consciousness;
matter with curiosity.
Stands at the sea,
wonders at wondering: I
a universe of atoms
an atom in the universe.
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I’m in the middle of a move and reducing all the crap I’ve managed to hoard over the years. While tackling a stack, I came across a document I received from a lifer coworker which contained gems.
This unofficial doc contained rules for navigating a successful career in engineering leadership though it also applies for any institutional leadership position. Here’s the document unedited in its entirety.
Slater’s Laws for Engineers and Team Leaders
Rule 1: The purpose of business is to generate profit for the shareholders
* Jobs are a necessary byproduct of making money
* Products are the necessary byproduct of the chosen method of making money
* Profit is in everyone's (employee, company, & customer) interest
* The definition of quality is meeting the customer's expectation for the product every time at the agreed upon price and at the agreed upon time
Rule 2: Cost is one of the many technical variables a great engineer controls
* A job that is a financial disaster but a technical success is a failure
* A job that is a financial success but a technical disaster is a success
* Find paths that let you have both technical and financial success
* Losing money is a failure for both your customer and your stockholders. Neither is happy when you close your doors
* Do what is necessary (alternate paths, alternate parts, etc.) to maintain schedule. It's the least expensive choice in the long run
Rule 3: You create your own job security
* You do not have job security by hoarding information - eventually management will realize it is not to their advantage to have an irreplaceable employee
* "Learn from your superiors, teach your subordinates and all three of you will move up" - Andrew Carnegie
* If you can't show annually how you personally produced 10x your salary in revenue, you're paid too much and will probably be replaced
* The company has no obligation to you beyond paying for services rendered
* If the management can find a way to replace what you do more effectively they will - it's their job - find a way to do your job better so they can't do it better without you
* Learn more every year. It's your responsibility to avoid skill obsolescence
* H.R. represents only the company, not the employees
* You are an expendable asset. Make sure they don't expend your value before you are ready to leave
Rule 4: Understand the uses of the products you design
* Systems Engineers may pick specifications without knowing the cost
* Write specifications for the items you really need to make your system work
* Don't specify anything you don't require for your system to work as un-needed specifications drive cost and schedule unnecessarily
* Do not try to use a function you did not specify because you think it's implied. The designer will probably have chosen to leave it out
* Work with the designers early to adjust specifications to make the optimal use of the available technology without adding unnecessary cost
* Specify exactly what you need - no more no less.
* It's not a specification if it can be changed. It's a wish list. Wishlists cost more to implement than specifications
* Never put blinders on. You may have an idea that makes the unit less expensive or more reliable if you make minor changes
Rule 5: Take personal ownership of everything you do
* Your reputation is on the line each time your sign your name
Rule 6: Treat every design as if your life depends on it because someone's life probably does
Rule 7: Know when it's good enough.
* A great engineer knows when to stop improving the design because it meets its intended usage
Rule 8: Know the purpose and operation of every component in the design
* Don't make changes unless you KNOW the purpose of each part
* Don't assume you understand how an alternate part will function
Rule 9: Identify risks early and address them
* If you think something might be a problem, it probably already is one
* Have a fallback plan in place so you don't cause a schedule delay
* Execute the fallback plan with enough time to keep it viable until the primary path works
* Sole source parts put you at the mercy of your supplier - try to design with parts with multiple sources
* Prototype and test exhaustively any circuit you have not used before
Rule 10: Be flexible
* Bad things happen
* Plan your work and work your plan
* No plan survives the start of the task
* Work to be ahead of schedule at all times so you have margin when bad things happen
Rule 11: Do your homework
* Try to think of the questions others will ask and have answers before they ask
Rule 12: Anticipate your leaderships needs
* Try to have what they need done before they ask
Rule 13: Don't be afraid to make a mistake
* The only people that don't make mistakes are those that don’t do anything
* Your failures are faster paths to knowledge than your successes
* Sometimes, you have to make a determination without all the data
* This is your best engineering judgment. It may turn out to be wrong later. If you get it right more than you get it wrong, you've done well
Rule 14: Everyone climbs the ladder
* Everyone climbs the ladder of success. It doesn't define you
* Everyone falls. Your falls don't define you
* How you respond after a fall defines you. If you pick yourself up, learn from your mistake, dust yourself off, and start trying again, you will be a success
* Statistically, 1 in 4 new business starts will succeed for 15 years
* Statistically, on average, an entrepreneur will fail twice before having a business succeed
* Morale of the story. Winners don't quit. They modify their plan and go forward
Rule 15: Always learn from your mistakes
* There is plenty of room for new mistakes so try not to make the same one twice
Rule 16: Ask your leadership for help but always bring a suggested solution unless you are truly stumped
* Always keep your leadership informed
Rule 17: Ask for help before it is offered by your leadership
* Always keep your leadership informed
Rule 18: Never reject help if offered by your leadership
* Everyone needs help now and then
* If they offer, they think you need it and fighting it will only make you look naïve and is career limiting
* If you are told you need help, accept it gracefully. To fight it is career limiting or ending
* Did I mention Always keep your leadership informed?
Rule 19: Once you determine your requirements, start identifying the best way to test
* Do this before you start the design
* Design in the tests - provide access as part of the design
* Think about ways to make the test shorter and easier
* Think about structural tests - computers will repeat the program the same way every time it runs. Test that the computer runs, not that the program gives you the same result on each successive unit
Rule 20: Design with production and test in mind
* Design is sexy but production pays the bills
* If you want the opportunity to do more design,, make your designs so others can easily support them. Otherwise you'll be doing a lot of production support and won't get to design
* Cost effective designs are the best designs - see Rule 1
Rule 21: Keep Einstein's simplicity definition in mind at all times
* Paraphrased - "Keep your solutions as simple as possible but no simpler"
* Elegance is simplicity, Simplicity is Elegance
* Overkill is the best answer if it is the simplest answer to the problem
* A complex solution to a problem is not elegant if a simpler solution is available
Rule 22: Keep Einstein's definition of insanity in mind at all times
* Doing the same thing repeatedly and expecting different results is the definition of insanity
Rule 23A: Keep your leadership informed
* It is never a good idea to let others tell your leadership how things are going. Let them know the facts first so someone else can't spin a story
Rule 23B: Keep large meetings short
* A large meeting has never solved any problem
* Most attendees are not participants
* Select your attendees carefully
* Always have an agenda
* Always have a purpose for the meeting - status, brainstorming, organizing a group, etc.
* Assign topics to a smaller group to be addressed outside of the meeting and a report back time and date.
* Be prompt - it's disrespectful to the other attendees to be waiting for you
* Try keep the meeting to the scheduled time. Everyone is busy
* DO NOT use a computer or cell phone during a meeting. You are not paying attention to the topic and are being disrespectful of the others in attendance
Rule 24A: Communications is the key to everything
* Always keep your leadership informed
* The most effective communications are face to face the participants can read body language, voice inflections, see pictures, and written data, and ask clarifying questions
* The next most effective communications are by telephone, you only lose body language
* The next most effective communications are by letter, memo or email - You lose both body language, verbal inflection, and clarifying questions but you maintain detailed written information
* Email should be used to confirm verbal discussions and create records
* Email can also be used to document a recipients lack of responses
* Never argue via email. Come to a consensus (preferred) or agree to disagree then document via email
* When you reply to email, reply to those that have a need for the data. Reply all is of limited value and wastes a lot of other people's time
* The least effective form of communications is texting
* Texting should only be used to set up one of the other forms of communication or to ask a very simple question that may need an immediate answer
Rule 24B: Know the basics cold
* Understand the meaning behind the equations
* Be able to visualize the interactions
* Fight against your own tendency to assume things
* Most problems can be reduced to the basics. Ohm's law, Maxwell's equations, Shannon's sampling theorem, Nyquist equations, Schroedingers Equation, Eulers identity, Pythagorean equation, etc.
* Work to simplify problems until the basics apply
Rule 25: Defining the problem is 90% of any task
* Any engineer can solve problems and equations
* Great Engineers can define the problems to be solved
Rule 26: Don't assume anything
* Ask the basic questions even if everyone "Knows" that question doesn't apply
* Most of the time, everyone else will assume they know the things that can be omitted without checking. They're usually wrong
* Avoid the tendency to jump to a solution until you understand the problem. If you have a solution before you understand the problem, you will miss key elements and get a chance to solve the problem again because you will not have solved it the first time
Rule 27: Always think first
* Your most effective and useful tool is inside your head (your brain). Use to it first
* Simplify problems in your head first
* Reach for other tools like simulators, Matlab, software, calculators etc. after you have used your best tool
Rule 28: No one and nothing is perfectly useless, you can always use them as a bad example.
* You can learn from everyone whether you think they did something right or wrong
* Take advantage of your learning opportunities
Rule 29: Never turn down an opportunity to take a seminar or course even if you think you already know the topic well
* You might be surprised
* Take advantage of your learning opportunities
Rule 30: Discuss problems with colleagues
* You will need to better understand the problem to explain it
* Explaining a problem is a true test of your understanding
* While you're explaining you might gain insight to the issue
* They might ask clarifying questions that will cause you to receive those rare flashes of brilliance we all get
Rule 31: Keep your perspective
* 100 years from now, no problem you're solving will be remembered
* Family / Religion must come first Work comes second
* Entertainment comes last. Respect that in others and honor it yourself
* Care about your fellow employees. When you ask how they're doing, be interested in their answer
* Remember the story of the task master who drove his people hard. The employees disliked him greatly. They were on a program that was behind schedule and over budget. Yet, when one of his employees wives got terminally ill and the employee ran out of sick and vacation time, the task master went against HR and instead of firing the employee, he provided an overhead number and assigned the employee to the hospital for the last two weeks of the wife's life. The employee told his co-workers and the co-workers rallied around the task master working uncompensated overtime for weeks. They brought the program back on schedule and budget. The employees respected the task master and they would follow him anywhere and do what was needed for him
* Don't expect anything from others you won't or haven't done yourself
Rule 32: Problem solving is best done with rigor
* You may get lucky and jump to the right conclusion sometimes and that will be very fast. However, if you jump to the wrong conclusion, you can skip the most important pieces of information. If you skip the important pieces, you may never come back and get them
* There is nothing wrong with a working hypothesis as long as you don't become emotionally invested in the hypothesis
* Analysis paralysis is a far worse problem than trying something based on the available data and learning something new. Just make sure of what you learn and try top fill in the unknowns in the problem
* Control of variables is critical to successful problem solving. Make sure you only change one thing in any experiment so you can determine the impact of that variable
* If you change multiple variables and you get lucky and find the solution to your problem, you will regret not understanding which variable was the cause when something else comes along and causes the problem again
Rule 33: There is only one stupid question
* It is: "Is the question I'd like to ask stupid and will it make me look stupid?" The real answer to the stupid question is: It doesn't matter, ask anyway.
* Make all questions about the data (i.e. what data do we need?, What data do we have?, What is the data telling us?)
Rule 34: Use analogies that everyone understands as a good frame of reference for explaining problems and concepts
* The simpler the better. If you make your analogies simple enough that any person can understand them, you will probably be understood
* Developing the analogies helps you better understand the problem
* Explaining a problem to someone else helps you better understand the problem too
Rule 35: Let your results speak for you
* Politics are never a good long term career answer
* Live by politics, die by politics
* It is impossible to argue with success. Facts and data trump all other arguments even political arguments
Rule 36: Never take credit for someone else's work
* Give credit to those that actually did the work
* Only take credit for those things you did
* Over the long term, it will become apparent who was the driving force behind the successes
* Even if you are the one driving a team's success, give the team the credit. They will respect you for your humility and will work with you again
Rule 37: Do not make arguments personal
* Know that some people will take losses personally
* Try to always allow others to save face
* Do not gloat over wins in arguments or issues. Just move on
* Once an argument is over, win or lose, move on
RULE38: THE FIRST STEP OF GETTING OUT OF A HOLE IS SToP DIGGING!
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Gaming & cinema production has converged…..
Which promises to further democratize and even the playing field for those willing to learn. Today it’s possible to take a gaming computer and build 3D works that approach triple A titles and Hollywood films. Up until this point I’ve spent a lot of time on this substack dealing with theory and science, but starting today we’ll get serious. Specifically, how to go about building the next generation of content in a 3D environment for cinema, gaming, VR and AR. This is where the theoretical gets practical.
So with that, here’s the plan
* Learn creator tools to enter the metaverse as a creator:
* Simultaneous 3D asset creation for immersive, Cinema & Games
* Collaboration workflows for large teams of creators
* Learn home-based virtual production: Unreal engine with green screen & motion capture suit
* Learn to use & build AI tools to aid in creative process
* Agents for realistic NPCs
* Productivity tools
* Develop spatial decentralized apps
The goal is to learn the steps and develop a workflow that supports collaboration with the largest amount of artists which is paramount for the coming future. To this end, I have designed a workstation based on unreal and blender since they are the most stringent.
Computer stats
Gaming Chassis: Thermaltake AH T600 Full Tower E-ATX Gaming Case
CPU: AMD RyzenTM Threadripper 3970X 3.7GHz [4.5GHz Turbo] 32Core 64Threads 144MB L3 Cache 280W Processor
Motherboard: MSI TRX40 PRO WIFI ATX with WiFi 6, RGB, Dual LAN, 4 PCIe x16, 1 PCIe x1, 8 SATA3, 2 M.2 SATA,PCIe
RAM System Memory: 64GB (16GBx4) DDR4 3200MHz Quad Channel Memory (Performance Memory by Major Brands)
Video Card: GeForce RTXTM 3090 24GB GDDR6X (Ampere)
Video Capture Card: Elgato 4K
Power Supply: 1,000 Watts
Primary Hard Drive: 1TB MSI SPATIUM M370 PCIe NVMe SSD + 3TB SATA III Hard Drive Combo (Combo Drive)
Secondary Hard Drive: 2TB Samsung 870 QVO-Series SATA-III 6 Gb per second SSD - Read Write: Up to 560,530 MB per second
External Storage: 3 Samsung T7 2TB SSDs, 2 Sandisk Extreme Pro V2 4 TB NVMe™ SSD at 2000 MB per second
Monitor: Samsung 49" Odyssey G9 Gaming Monitor
Why this config?
The main drivers were speed and realtime raytracing/rendering. See puget systems website on building a system for Unreal for more information.
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A Lifelong Journey
I’ve been working on this article for many years under different names:
* Database cinema ~ 2008
* Automatic editing ~ 2016
* Emergent Narrative Film Structure ~ 2016
* Cinematic AI ~ 2017
* Simulation Cinema ~ 2020
* Unified Narrative Theory ~ 2021
Thirteen years and I have submitted to the fact that I will continue writing about this for the foreseeable future. The verbiage will adapt as science progresses but the theoretical aims remain the same. How to tackle viewer agency while maintaining “story” with emotional levels comparable to cinema. I am more optimistic than ever about Metaverse “storytelling.” The reasons are threefold:
* The pandemic has normalized “virtual life”
* Immersive hardware is everywhere thanks to the Quest 2 (Oculus-Facebook) and Apple’s rumored entrance, global internet penetration (5G/Starlink) & 3D creation tools (Unreal, Blender) and workflow standardization (Omniverse)
* Media companies are normalizing virtual production workflows (Mandalorian) and casual non-traditional interactive shows (Bandersnatch, Rival Peak, Artificial). Not to mention the proliferation of Metaverse apps where players are creating a virtual world and monetizing their virtual creations (Axie Infinity, Roblox, Horizons)
All of this points to the virtual future we’ve been predicting for decades through fiction like Ready Player One, The Matrix and Snowcrash to name a few. This is no longer an intellectual conversation, it is happening now, and will only gain steam. This presents an unprecedented opportunity to create narratives that are virtually unbounded by time, space or embodiment. The Unified Narrative Theory is an approach toward building a framework for these new types of non-linear narratives, where users can choose to be passive or active protagonists. The challenge for creators is how to approach narrative design with multiple user types and non-linearities. The Unified Narrative Theory is an approach which aims to achieve three things.
* A numerical mechanism for measuring the effectiveness of a story experience in real-time
* A non-linear system that adapts based on narrative feedback loops
* A system that scales and manages narratives across collaborative & non collaborative users (multi-user protagonist based narratives)
Let's take these one at a time.
1. An objective mechanism for measuring the effectiveness of story experience in real-time
A first principled approach, Art vs. Trash
Art is defined as any work that inspires yet also quenches a curiosity to satisfaction. Which is just a way of saying that art is complete in and of itself. The problem is that what is art to some is trash to others. And the difference is in the mental work the viewer does while interpreting the work. This work is often overlooked as there has never been a way to do anything useful with it until now.
From our very first moments we are flooded with stories. Some are encoded in song, others are parental dreams we are programmed with. Lullabies are the very first stories we hear before we’re able to comprehend language itself. It turns out the frequency response of lullabies is normalized across language in the industrialized world. Which means that we can take a baby from one continent to another and regardless of language, the way humans satiate a crying baby have strikingly similar frequency responses (pitch/tempo). If we look at the nervous system responses of satiated vs. crying babies we see a relaxation of heart rate, electrodermal activity, pupil dilation and eye gaze. The same holds true for stressed vs. relaxed adults under the influence of a narrative.
The first and fourth episodes of this podcast laid some of the ground work for a measurement system. In the first episode Dr. Picard discussed emotion AI, the ability of a computer to determine nervous system responses correlated to self-reported words for emotions. These correlations are mapped to the valence and arousal emotional model below.
The takeaway is that we can directly measure arousal and valence (albeit less reliably) with electronic circuits. So now if we define the aims of narratives, (or artists intentions) in terms of arousal and valence we can objectively measure the success of the narrative with continuous tests. This is particularly useful for interactive media where the viewer has agency.
Valence and arousal model for narratives
In episode 4 of the podcast I sat down with Paul Gulino and Dr. Connie Shears the authors of the science of screenwriting book. Their work presents an objective discussion around narrative model techniques and maps them into their scientific counterparts. Most of the historical literature in narrative theory focuses on rules obtained over the past 2,000 years which I’ve outlined below for Western narratives. But Gulino and Spears correctly suggest something deeper at work beyond the “rules” largely discovered by trial and error and that furthermore that artists should focus on effects over rules. The conclusion is that each of us brings our stuff into the work as viewers, which greatly influences our interpretation over the auteur’s intention. Any unified narrative methodology must take this into account if it has any chance. We require both a translation of the narrative into valence, arousal, and a feedback loop to verify its success.
2. A non-linear system that adapts based on narrative feedback loops
First let’s review narrative theory! Presented below are the most popular rules we have today for traditional narratives.
Our goal throughout the narrative is for the viewers to empathize with our protagonist by adopting their hopes and fears. We can tell if we’re successful by reinterpreting our narrative into emotions that vary over time. Below is an example where emotions are outlined for each sequence in a story. Though there is no way to measure an emotion directly what we can do is measure the relative nervous system modulations among viewers to gain approximations. Because nervous systems are measured with electrical circuits we can measure the power of an emotion as a vector sum along two dimensions. Taking the RSS of valence and arousal we can define a Content Engagement Power (CEP) value to represent the emotional engagement of any user at a given point in time.
There is no way to ensure this will happen for a passive audience (no feedback loop) which is what makes the film industry inherently risky and thus celebrity driven. However, if we were to measure neurological feedback at various times in the story, then we can adjust our tactics in real-time to recover CEP thus ensuring the success of our narrative. In addition, this tactic reduces risk and increases the entertainment value of the work. In this scenario, we’re dynamically adjusting the experience based on viewers’ real-time feedback. Viewers are therefore on an agency spectrum and can move from passive to active interaction with immersive tech. In either case our narrative feedback system adapts via story element tests.
Viewer agency, measurement, and narrative feedback loops are what enables this approach toward a universal narrative theory. As we learn more about how our nervous system interacts with media, our ability to implement this methodology will get better over time. We are only scratching the surface with our crude current day approach.
AI Agents
Our story element test scheme also employs AI agents which adapt the narrative in realtime. These agents dynamically change environments, scenes, characters and dialog to name a few variables. The system changes these elements to converge each user along a planned arc within the story world. Our story element test schemes are interactive agents driven by Artificial Intelligence within the narrative domain. Work in this arena has shown impressive results with over 85% accuracy within the narrative domain. Newer open source technologies including GPT-3 have drastically lowered the cost of entry for technologies which can converse as humans do. GPT-3 can also be used as a web service further simplifying ease of use within custom built software or gaming engine IDE’s. The future is bright!
3. A system that scales and manages narratives across collaborative & non collaborative users (multi-user protagonist based narratives)
The goal here is management of narrative amongst multiple user-protagonists which is akin to a game master in D&D. This adds an additional layer of complexity because intergroup dynamics influence the nervous system modulations we’re able to read.
For example, consider you’re watching a show with an extroverted friend who can’t keep from outbursting during the show. On a subconscious level, these outbursts can influence your experience. Now if you were to watch the show a second time alone, you’d likely to have a much different experience. This is sometimes referred to as the dominance effect on emotions, and presents another dimension that must be considered in designing such a system.
The Metaverse is often described as a virtual environment full of games and other user generated content where people can interact with one another. What’s often not mentioned is what storytelling will become in this new medium. Once interactivity is introduced, media is often reduced to gamification rewards. What Unified Narrative Theory attempts to achieve is a framework where the rewards are narrative through hyper personalization. The goal is to achieve something very different than traditional games where the conceits are gameplay and the narratives are overwhelmingly linear. Through the use of narrative feedback, AI, and collaborative narrative processing, we can achieve a new type of narrative without the “on the rails” feeling normally associated with interactive media. The goal is a new type of media whereby the use of interactivity is designed to personalize narrative rewards in lieu of gameplay & singular narratives.
This is not a replacement for traditional stories, but rather a new tool to exploit the exciting power and complexity of multidimensional non-linear storytelling. Just as an artist chooses his medium, there’s a new generation of creators ready to tackle this complexity of reality bending media.
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In this episode I sit down with Science of Screenwriting authors Paul Joseph Gulino & Dr. Connie Shears to discuss their work, the truth of screenwriting strategy and the importance of Effects over Rules.
▬ Contents of this episode ▬▬▬▬▬▬▬▬▬▬
00:00:00 - Intro
00:02:26 - Bordell & Film Theory
00:06:35 - Screenwriting Sequence Structure
00:16:45 - Why Combine Science & Screenwriting
00:20:00 - Mental Schemas
00:27:20 - Film Industrial Complex
00:31:35 - Pandemics, Social Media & Metaphorical Messaging
00:58:06 - This Hologram is Hungry!
01:05:50 - Why wait 10 pages? Rules vs. Effects
01:09:16 - What's Next?
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A few thoughts on the podcast, why I created it and what I hope people get out of it.
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Thoughts on “Belief in Science”.
For a working definition I refer to physicist David Deutsch’s ideas presented in his book and also in his TED talk on good explanations.
From there I’ll introduce a few personal tenets and wrap it up in two minutes and thirty seconds.
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In this episode I had the honor of hosting Dr. Rosalind Picard. Inventor, professor and engineer who has literally wrote the book on Emotion AI. In addition to creating the field, she is the cofounder of two Emotion AI companies (Affectiva & Empatica) and also a professor at the M.I.T media lab. Join us as we discuss her work, robots, cognition and tech aided mindfulness.
https://mitpress.mit.edu/books/affective-computing
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