Tech Business · Uncategorized

A Collection of My Posts on Consulting Business, Sociology of Technology, Programming, and Artificial Intelligence

MY POSTS ON THE CONSULTING BUSINESS, PROGRAMMING, AND AI

=====CONSULTING / CONTRACTING BUSINESS ======

Sunday, January 17, 2016 at 9:45am UTC+02
CONSULTING COMPANY HIERARCHY

VERTICAL AXIS: Y
6) Strategy Work. ( Presentations on Financial, Operational,Market Goals – ie: empirical analysis and planning – big players)
5) Consulting work ( Presentations on initiatives: usually consensus building or research work. )
4) Project Work (deliverables – responsibility for p/l)
3) Production work (many small deliverables)
2) Outsourcing (housing staff under management of customer)
1) Staffing ( recruiting and supplying talent for project work)
0) Temp ( recruiting and supplying talent for short term work)

HORIZONTAL AXIS: X
0) Scale.

Sunday, January 17, 2016 at 9:45am UTC+02
FIRMS: TITLE INFLATION PREVENTION STAFFING

A staffing firm sells people. Usually a staffing firm only sells people by the hour/week/month.

OUTSOURCING
An outsourcing firm sells groups of people, but not responsibility for deliverables. Usually an outsourcing firm will only provide groups of staff. Project management is provided by the customer. An outsourcing firm does not warranty its deliverables.

SERVICES
A services firm sells projects (deliverables) and responsibility for them. Usually a services firm will avoid staffing. A services firm warranties its deliverables.

CONSULTING

A consulting firm sells ideas for transforming organizations, operations, products, and markets. Usually a consulting firm will monetize the sale of ideas through the provision of services and staffing. But not always (the very best don’t.).

Sunday, January 17, 2016 at 9:45am UTC+02
WHY DO PEOPLE WANT TO EXIT THE CONSULTING BUSINESS? (OVERSING UPDATE)
Everyone who builds a consulting company always seems to want to exit. There are a lot of reasons for this.

First, it is a low capital investment industry that is easy to get into and exit even if exit is at low multiples (1-2x). So it attracts people who work overly hard, and get tired of it.

Second, it is an extremely volatile business full of constant risk, because there is no way to entrench yourself in a customer other than relationships and knowledge of their business. It is very hard to raise rates. Skills are rapidly perishable and subject to swings. Employees are not sticky. Receivables very high, and credit on receivables difficult and constraining to your growth. And it is the first business to feel recessionary pressures. It is a very mercenary business. And that is because it is largely impossible to collect rents. (which I find fascinating really. it is an example of how all of us seek rents, because the constant productivity of consulting is like the constant productivity of hourly labor, except you’re working in a higher paid white collar capacity.
So by inversion that means most of us seek employment where we can seek rents.)

You start get enter the safety zone as you approach 500 people, and generally get somewhat safe after 1000, because even if you halve the business, you can halve the staff, and have the cash flow to rebuild.

SOLVING THE PROBLEM
The only way to solve the problems of the stressfulness of running a services business is BRAND, SCALE, PORTFOLIO, and PROCESS. Build a reputation for excellence that feeds you leads. Get large enough that you can tolerate volatility without causing doubt in the staff. (maintain your ‘other’ customer base of employees). Look at portfiolios of either customers or offerings or both, and plan two years out at least, if not three. Create a process of continuous training (education) for your employees.

WHY CAN SOME PEOPLE NOT DO THIS?
Because actively managing your business is pretty hard. And investing in your employees skills is not expensive, but it is a constant effort.

****So the best way to create process of continuous improvement is to treat the company as a university that does paying work for clients rather than unpaid homework for the professor.****

And instead of relying upon the professor’s or management’s opinion for your grades, you rely upon the reviews of your peers and your customers. If you grasp this. That this is probably the business model for all businesses in the 21st century. That we have passed the era of lifetime careers. That we have passed the era of decade long careers. That instead we are in an era of continuous education of our staffs, and that we need processes to achieve that continuous training at lowest possible cost, then you will understand why we built oversing.

***BECAUSE ALL COMPANIES ARE TURNING INTO CONSULTING COMPANIES AND ALL LABOR INTO PROFESSIONAL WORK.***

Whether we have made oversing powerful enough yet to achieve this is certainly debatable. But that we have made it powerful enough to do much of it is not. Oversing is a platform of the management of human capital in the 21st century. It’s a Social ERP for The constant Improvement of human beings. And the data we will give you about your organization after using Oversing will blow your mind.

Philosophy is like the consulting business: there is a low barrier to entry but a low probability of survival, and a near total inability to capitalize your investment. I survived the consulting business and capitalized my investment too you know. Sigh. 😉

Tuesday, December 15, 2015 at 11:04am UTC+02
WHAT I LIKED ABOUT BUILDING CONSULTING COMPANIES
(reposted by request) (original october 5)
A consulting company is little more than an intelligence organization that collects information from various organizations and uses it to sell solutions to different organizations where they gain new information and repeat the process. I love it because I learned so much every day. You get paid to do basic research on the operations of various companies. You are paid to collect information that most companies work to hide from the outside world. The price is volatility. You have to be able to tolerate that volitility. Consulting companies react early to economic fluctuations expanding quickly in booms and contracting quickly in recessions.


LET ME HELP YOU: A CONSULTING FIRM IS AN INTELLIGENCE ORGANIZATION: A PROFESSION OF BENEVOLENT SPYING.

(reposted by request) (original on November 22nd)
Why do you think I built so many consulting firms? And I was very good at it by the way – thanks. Because I am honest with myself about that business. In the most part, as an intelligence specialist, you gather information from one group inside the organization and transfer it to another group who is dis-incentivized for various compensation and status reasons to cooperate with, defer to, or obey the directions of the other group. In most large organizations the answer to every current problem is hosted in someone’s brain somewhere in the organization (often in the marketing department’s middle management, or in the bowels of accounting where some uncomfortable truth is protected from release). But the incentives to act on that knowledge – often precisely because of the individuals who host it – cannot or will not be acted upon. Otherwise, people will not act on the solution to a problem because the incentives of the organization are perverse (counter productive). Or because the individuals currently in positions of decision making would be subject to negative effects (usually middle and upper management other than the c-suite). So the job of a consultant is to simply get people to talk to you on one hand, and to produce a deliverable that provides a means by which the knowledge that exists in the company can be brought to fruition by some sort of reorganization of goals, processes, responsibilities, incentives and compensations. But mostly – at least a the level I tend to work – by giving people in the organization PERMISSION to change alliances and loyalty relations without breaking an alliance or loyalty. The mistake of most neophytes is to assume people are ignorant, stupid or stubborn, when in fact humans pretty much demonstrate rational action. Despite their policies against it I have, (and many agency networks have) had parallel projects in multiple cell phone providers. Each desperate to protect some strategy that is well understood by the competition – so much so that the internal ability to execute has been inhibited by this defensiveness: which I usually view as an excuse to not get work done more than defensiveness. At every level of the organization there is information that is either true and actionable, false and actionable, inactionable, or irrelevant. The trick is to find it. Why? Because a company and processes aren’t special or unique or interesting, or special or a competitive advantage any more than whether a culture practices tea ceremonies or dances ’round the may pole. What separates a good company from a bad is the quality of people, their ability to reorganize in response to market changes, and the quality of information they make use of, and the technological means by which they warranty that they are not privatizing the shareholder commons either by action or inaction, statement or silence, constructive suggestion or destructive rumor and gossip. They are probably obvious and the central problem of organizing production under voluntary employment, is in preserving the will of those with knowledge to make use of it. Basically, truth wins. Discover the truth. Provide a plan. And in that plan, find a way to preserve signals for everyone. Pay people if you must to take a hit. Don’t expect them just to take a hit for you. If you try to do things without compensating people then you’re not engaging in exchange.

THE “BIG FIVE” WORKPLACE TRAITS IN CONSULTING (original 2013)
People are not a commoditized “meat” that is reducible to P&L Numbers. It takes a variety of skills to produce excellent service work. And Oversing measures (at least) those five different properties:

—TRAIT————————-Measurement———–
1) “Like-ability” (Peer 360 system
2) “Soft Skills – Trustworthiness” (Skill System by Peers)
3) “Revenue value of hard skills” (Skill System / Peer Skill Ratings)
4) “Customer Trust and Retention” (Net Promoter Score)
5) “Profitability” measured as: a) Utilization and b) Chargeability (Standard financial metrics)

So, any individual is always represented by oversing using all these ‘metrics’. Their “Personality Profile” so to speak.

THE WORKPLACE AS EXTENDED FAMILY RATHER THAN LABOR POOL.

We found that feedback from PEERS was MORE ACCURATE and MORE RESPECTED and produced GREATER BEHAVIORAL CHANGE than all feedback from management combined. In fact, management opinion of any staff member was usually a CONTRARY INDICATOR of the individual’s value to the business.

So, our use of these measures helped us create a culture of a ‘family’ by giving honest feedback, the purpose of which was to ‘advance the individual and the family’ as a whole. Not through threats, not through incentives, not through pressure, but through advice and counsel. Humans want to do the right thing to fit into groups. And you just have to give them to tools to do so. Reliable information is the best tool. Like the original meaning of ‘confession’, almost all feedback is publicly visible. Your sins are public, as is your heroism.

ONE OF THE SECRETS OF ANY BUSINESS IS NEED FOR EACH OTHER.

One of the secrets is “IDLE HANDS MAKE ILL WILL”. And people will naturally free-ride and rent-seek, which creates animosity in the ‘tribe’. The best way to prevent bad behavior is by minimizing the potential of it. The best way to minimize potential, is to always be marginally short of staff, so that everyone has to chip in to keep everyone else successful and happy. Furthermore, the incentive to overstaff is usually created by the most populous RENT SEEKERS in any business : middle management. Oversing reduces the ability of middle management to rent seek, hide information for the purpose of obtaining political power. Our experience was that we needed only talent leadership, not ‘management’ per se. Because the feedback and ‘management’ of individual behavior was democratized into a marketplace of voluntary exchanges of assistance, appreciation and complaint. Oversing is revolutionary if you use it as we intend that you do. It will transform not only your ability to manage your business by looking forward, and by increasing utilization but it will transform you business by making the ‘family’ manage the business, and thereby feeling PART OF IT, and able to INFLUENCE IT. Management hierarchy is, just like government, little more than rent-seeking and obfuscation of facts. And just like government, management hierarchy is the primary reason why individuals feel resentment toward the business. Make them LOVE IT INSTEAD. IF TALENT IS YOUR BUSINESS, THEN USE IT, AND TAKE CARE OF IT.

4) THE SOCIAL CLASSES OF SOFTWARE DEVELOPMENT COMPANIES
There is an unstated class hierarchy to software development that (as both a techie and economist) I found intersesting.
– Scientific Software design and development
– Operating Systems Design and Development
– Enterprise Software Product companies
– Consumer Software Product companies
– Business Software companies
– Small Independent software companies
– Internal Operational software
– Consulting business operational software.
– Internal IT staff.
– Temporary (or long term) staffing.
– Short term staffing.
– Independent software consultants.
– IT independent consultants.

Varies a bit by company. But basically it’s driven by scarcity. (I need to write this list for economists, because it’s pretty funny.)

Sunday, November 22, 2015 at 11:42am UTC+02
LET ME HELP YOU: A CONSULTING FIRM IS AN INTELLIGENCE ORGANIZATION: A PROFESSION OF BENEVOLENT SPYING.

Why do you think I built so many consulting firms? And I was very good at it by the way – thanks. Because I am honest with myself about that business. In the most part, as an intelligence specialist, you gather information from one group inside the organization and transfer it to another group who is dis-incentivized for various compensation and status reasons to cooperate with, defer to, or obey the directions of the other group.

In most large organizations the answer to every current problem is hosted in someone’s brain somewhere in the organization (often in the marketing department’s middle management, or in the bowels of accounting where some uncomfortable truth is protected from release). But the incentives to act on that knowledge – often precisely because of the individuals who host it – cannot or will not be acted upon. Otherwise, people will not act on the solution to a problem because the incentives of the organization are perverse (counter productive). Or because the individuals currently in positions of decision making would be subject to negative effects (usually middle and upper management other than the c-suite).

So the job of a consultant is to simply get people to talk to you on one hand, and to produce a deliverable that provides a means by which the knowledge that exists in the company can be brought to fruition by some sort of reorganization of goals, processes, responsibilities, incentives and compensations. But mostly – at least a the level I tend to work – by giving people in the organization PERMISSION to change alliances and loyalty relations without breaking an alliance or loyalty.

The mistake of most neophytes is to assume people are ignorant, stupid or stubborn, when in fact humans pretty much demonstrate rational action. Despite their policies against it I have, (and many agency networks have) had parallel projects in multiple cell phone providers. Each desperate to protect some strategy that is well understood by the competition – so much so that the internal ability to execute has been inhibited by this defensiveness: which I usually view as an excuse to not get work done more than defensiveness.

At every level of the organization there is information that is either true and actionable, false and actionable, inactionable, or irrelevant. The trick is to find it. Why? Because a company and processes aren’t special or unique or interesting, or special or a competitive advantage any more than whether a culture practices tea ceremonies or dances ’round the may pole.

What separates a good company from a bad is the quality of people, their ability to reorganize in response to market changes, and the quality of information they make use of, and the technological means by which they warranty that they are not privatizing the shareholder commons either by action or inaction, statement or silence, constructive suggestion or destructive rumor and gossip.

They are probably obvious and the central problem of organizing production under voluntary employment, is in preserving the will of those with knowledge to make use of it. Basically, truth wins. Discover the truth. Provide a plan. And in that plan, find a way to preserve signals for everyone. Pay people if you must to take a hit. Don’t expect them just to take a hit for you. If you try to do things without compensating people then you’re not engaging in exchange.

Monday, October 5, 2015 at 9:51am UTC+03
WHAT I LIKED ABOUT BUILDING CONSULTING COMPANIES
A consulting company is little more than an intelligence organization that collects information from various organizations and uses it to sell solutions to different organizations where they gain new information and repeat the process. I love it because I learned so much every day. You get paid to do basic research on the operations of various companies. You are paid to collect information that most companies work to hide from the outside world. The price is volatility. You have to be able to tolerate that visibility. Consulting companies react early to economic fluctuations expanding quickly in booms and contracting quickly in recessions.

Thursday, April 16, 2015 at 5:07pm UTC+03
THE SOCIAL CLASSES OF SOFTWARE DEVELOPMENT COMPANIES
– Scientific Software design and development
– Operating Systems Design and Development
– Enterprise Software Product companies
– Consumer Software Product companies
– Business Software companies
– Small Independent software companies
– Internal Operational software
– Consulting business operational software.
– Internal IT staff. – Temporary (or long term) staffing.
– Short term staffing. – Independent software consultants.
– IT independent consultants.

Varies a bit by company. But basically it’s driven by scarcity. (I need to write this list for economists, because it’s pretty funny.)

Saturday, October 12, 2013 at 11:03pm UTC+03
THOUGHTS ESTIMATING CREATIVE AND SOFTWARE PROJECTS : POKER
You know the whole card game thing actually works pretty well. But it’s harder to do in some settings than others. In house dev is pretty easy. But international groups, with different skill sets, it’s pretty hard. If you read everything out there (I have) most of it’s pretty obvious behavioral psychology. In my view I want to encourage people to invest very little time up front estimating, but to get estimates from a lot of people on the team, and narrow those estimates as we come closer to execution. The reason is that the statistical analysis of each individual’s estimate of any given work item, over time, is pretty accurate. In other words, if you estimate 100 things over three months, and the end work actuals are captured as hard data, over time, we can do a better job of predicting estimates on first blush than we can with deep analysis. Just how it is. Intuition being what it is across a group of people. What bothers me is that in house people are pretty comfortable sitting around pondering this stuff. But in my experience, high speed high production service businesses, have an extremely hard time doing this kind of thing, partly because they have less direct influence over team members who have much more autonomy, and more varied and irregular demands. So, the way I’ve looked at the feature in oversing, after looking at a zillion alternatives, is to set up a mixture of triangulation and card game that can be done real-time or not. The PM/DM/SM or whatever picks a range of stories / features to estimate. We handle both narrative and functional approaches. (In-house I use functional not narrative because of the workflow engine’s ability to modify all experiences.) Then launches the game at a particular time, and can do it interactively, or iteratively. Personally I absolutely love the iterative approach because I freaking hate sitting there working at the rate of the slowest bandwidth at the table. Each item is presented for triangulation against other previous estimates (if there are any) and you vote by dragging the card into the position with the appropriate points. When done, you commit that card. At some point, the PM/DM/SM calls the hand, flips the cards, and all the players are informed about the ‘hand’. This means I can run through thirty items at my speed, and someone else at theirs, and we don’t have to do it at the same time, and we can do it as a cognitive break from our other work. Now, I’ve set it up to ‘award the pot’ to people who are accurate estimators. So it’s a little bit of a game. The pot is just a score, and this score is part of your profile. The better your ‘winnings’ are the better you look at estimating. It’s pretty fun really. This kind of gamification matters because in large consulting companies people don’t know much about you and these sort of metrics build an empirical reputation for you tat’s visible on your FB-like profile. Alternatively, you can run the game interactively, with people right in the room, or over Skype etc. The point is that you can run it either way. Or even a combination of the two. (I don’t have to be there in person to play my hands – yes I know purists. But again. I dont get to tell people how to run their projects. I just get to find ways to help them run them the way that they want to. Advocacy is your job. Enablement is mine. ) Now, when you move from the backlog to the sprint you estimate, (or at least most people do) in hours, and break the story or feature into discrete measurable tasks. Oversing records actuals in painful detail. You sort of plan your day or week on an normal calendar, and we just assume that whatever you plan is what you did unless you change it. It’s pretty easy to reconcile your billings this way, by handling exceptions to your agenda rather than trying to remember what you did. (I’m not doing it justice here.) You don’t really fill much out on your timecard. The system does all of that for you. (I know. Cool. It’s awesome. Thank Max Romanenko. It’s his doing.) So, we have SWAGS in points, Refined estimates from the triangulation-poker-game, hourly estimates for tasks, forecast time against them, actual time recorded. And our funnel statistics are pretty solid with that information. That said, any brilliant insights would be appreciated. Cheers.

Thursday, April 11, 2013 at 12:11pm UTC+03
THE PROBLEM WITH ‘PROGRAMMERS’
I wholly agree with the sentiment expressed here. The problem is that the members of the set of software developers who are capable of programming as responsible craftsmen is significantly smaller, if not, marginally insignificant, compared to the number of day laborers, and union workers who use the same moniker. And while ‘programmers’ of the craftsman ethic, can identify one another, it is impossible for those of us who are responsible for other people’s money to tell who is indeed an artist, who is a craftsman, who is a union worker, and who is a day laborer. And I realize that it sucks. But the truth is that everyone has the incentive to lie, because programming skill is a scarce good and is therefore valuable. So the problem, is (a) programmers do not self-police the industry the way doctors do, and (b) no one provides ‘programmer insurance’ which is what certifications are supposedly for, but are again a perverse incentive that is exploited by union workers and day laborers (c) consulting companies profit by the fact that while they may have one or two craftsmen, they they are loss leaders, and they largely employ and make money on day laborers (d) customers can’t hire craftsmen because of bureaucracy and tedious work, so they have no other economy. If you’re a programming mohterfucker, with a cause, take some money and create an insurance company that insures programmers are competent, and charge them fees for certification and maintaining it. If someone is insurable then when they are hired, the company pays the insurance company part of the programmer’s fee. But the motivation of the programmer is to capture that revenue, not pay the insurance company. And so the programmer is again at fault. The problem is incentives. 🙂 http://www.programming-motherfucker.com/

=====PROGRAMMING====

Tuesday, August 9, 2016 at 1:56pm UTC+03
ITS NOT COMPLICATED – WE JUST GOT IT WRONG

User Experience Patterns
System Architecture Design Patterns
Application Architecture Design Patterns
Data Structures
Operations
Syntax

And why? Because we developed Fortran/C & Numbers instead of Lisp & Logic. People thought that they were smarter than Turing. Everyone makes new languages for the same reason, and none of them make any difference. Why? We used numbers and not logic. It’s like being stuck in the USA with 110v when the rest of the world runs on 120. The damned bad decision sticks with you FOREVER. I’ve gone through the lifecycle myself. and you know, we spend a lot of time trying to be smart without understanding that our foundations are stupid. WE’re solving the wrong problem. programming is analogous to teaching not engineering.

Thursday, August 4, 2016 at 11:28am UTC+03
SORRY BOB, YOU CAN’T PROFESSIONALIZE PROGRAMMING. ITS JUST THE NEWEST OF THE TRADES.
(computer science, programming, sociology of technology)

Bob Martin, like many of the Agile leadership, dreams of a return to the original history of programming as an art practiced by professional scientists, engineers, and mathematicians. His hopes that programming will join the ranks of the other professions: accounting, law, and medicine. He correctly identifies the problem that given the rate of expansion of our industry, the exaggerated demand, and the constant entry of many young people means that the majority of programmers are unprofessional. Meaning that they are unable to manage themselves, and take responsibility for the scope, time, budget, and quality of deliverables. But while where bob envisions a profession, I am absolutely certain I see a trade: a hierarchy of skill sets from cleanup crews (support desks) to maintenance crews (IT departments), to refurbishment crews (ongoing development and maintenance), to construction crews ( ordinary web, enterprise, and application developers) to retail architects (small systems) to commercial architects (large systems), to specialty suppliers (drivers, frameworks, tools, IDEs, operating systems), to Monumental architects (for government and the international industries). The secret in these industries is that it’s the suppliers, not the architects or the craftsmen that possess the material knowledge, that architects and craftsmen rely upon. Nick Carr predicted the deflation of IT years ago and IT has already become a tradesman’s occupation in a hierarchy culminating in hosting centers. He did not address programming specifically that I recall. Perhaps we might consider advanced degrees, but we already have them. Unfortunately those degrees are just as unpredictive of market survivability as architectural degrees are predictive of market survivability. The only proof of market survivability is the market, or insurance and bonding where there is skin-in-the-game. I can easily see a future where there is an equivalent to a building code for software, and architects and development leads are bonded. this would create a semi-professional trade where chaos reigns. The problem is of course, that while we can establish codes for recreational, residential, light commercial, heavy commercial, light industrial, heavy industrial, agrarian, grazing and forresting, we would have to also produce codes for each of these. ISO9000 makes very little sense really, just as many other regulatory hurdles that claim some degree of extreme, make little sense – and we would just create a black market for the offshoring of software development. Quality and Certainty increase price dramatically. And software is still too useful and too cheap, and without sufficient damage – at least in commercial settings – to drive up its cost to the point where it’s not worth taking the risk for liability reasons rather than cost reasons. Curt Doolittle The Propertarian Institute Kieve, Ukraine

Sunday, July 31, 2016 at 8:07am UTC+03
PROGRAMMING IS DISTRIBUTED ACROSS THE DISTRIBUTION OF TALENTS

Computer programming varies in difficulty just as math varies in difficulty. So just as we have people who do basic accounting with IQ’s in the 90’s, and people who work with differential or algebraic geometry who are in the 140’s, we have people who program at 100iq and those that program in the 140s. As far as I know the average computer programmer has +.5 to +1 SD. (107-115) And the average computer science graduate has +1.5 SD ( 120’s ). Although we must realize that these numbers are from iffy data they do match what we would predict given the (wide) distribution of programming tasks, and the rough baseline that “problem solving intelligence begins at 106.” Programming well is like any craft: if you make 1m chopstics I don’t know how good you will be at carpentry. If you make five programs of every design pattern in the three families of programming languages (imperative, functional, declarative), you are going to be a pretty good programmer. The more important property of the best programmers (which I am not) is the same as the most important property in mathematics: short term memory. I have a rather weak short term memory but I remember almost everything I see, read, hear and think – if I’m paying attention that is. So I tend to be good at solving ‘the big problems’ but it seems that I take forever to program them because I get distracted by tangent possibilities when I am working. So I would argue low neuroticism ( a mind that stays in context ) or one that has practiced any of the mental disciplines (mindfulness) is as important as intelligence.

Wednesday, July 20, 2016 at 5:42am UTC+03
THE FUN HIERARCHY IN SOFTWARE DEVELOPMENT

Programming is so much more fun than managing projects. Application architecture and UI design is more fun than programming. And business analysis is more fun than architecture and design. Why? Because learning as the major deliverable is fun. Producing a suite of deliverables on time and on budget is not fun. :). Accountability.

Tuesday, July 19, 2016 at 7:02pm UTC+03
PAGE VIEWS SOLVED MUCH OF THE MEMORY MANAGEMENT PROBLEM
I remember when I was first programming that I wanted a language to work ‘this way instead’ so that it was far closer to language than math and engineering, and PHP really got us close. Even after I started working in assembler and began to really understand the problem of memory management it always seemed to me that this should be an operating system problem and no my responsibility. The serving of html pages meant that a question and answer created and released memory. And that solution never occurred to me.

Tuesday, July 19, 2016 at 6:59pm UTC+03
JAVASCRIPT RULES THE FUTURE
( I like programming, and I love PHP as much as I loved LISP, but you know js owns the future and as far as I can tell unless your communicating directly with hardware the question is settled science. )

Sunday, July 17, 2016 at 12:35pm UTC+03
PROGRAM DIRECTLY NOT INDIRECTLY
In programming as in all things, never do by indirection(externality) that which you can achieve by direction (construction). Someone told me last week that he wanted to hide and show content by changing the mathematical dimensions of the panel, rather than simply turning content on and off. It’s sort of like ‘lying’. This is an example of ‘doing the wrong thing’. It requires the preservation of states not logically connected in an environment where state is always conditional. Just hide and show stuff. It’s not complicated. Let the engine do its job and you do yours.

Thursday, June 2, 2016 at 9:08am UTC+03
GENERAL RULES OF SOFTWARE DESIGN
(tidbits)
All system architecture is responsibility.
All software programming is syntax.
All UI is concentration-cost.
All UX is incentives.
All project management is scope and resources.
And everyone worries about time.

THE MOST COMMON STUPID QUESTION I GET FROM SOFTWARE TEAMS:
Attempting to find a scientific or axiomatic rule for what is an economic problem.
All programming is approximately the same. The question is only the performance demanded, cost of production, cost of maintenance, and cost of change.
If you break software into responsibilities and understand the difference between resident(executables), and instances(node.js), temporary(scripts), and transactional (database) then from that point it’s all economics.
In general, a software developer wants to reduce his costs, unless he has only his self-interests in mind and not the results of his actions.
Never hire anyone who says they want to stay on top of tech. People stay on top of tech if they’re good at producing results.
Those questions that are not economic – and there aren’t many of them – are empirical. And empirical tests defeat theoretical rules in all cases at all times.
The rules in software are the lifetime of execution, responsibility, design patterns in UI, middle, and db, and costs. That’s it.
Risk used to be significant in the past. The major risk today is not the software itself which has become relatively cheap, but the business model we anticipate with the software, and the success in providing transaction cost of use that justifies the cost of the use.
Curt Doolittle

Monday, April 4, 2016 at 10:49am UTC+03
PROGRAMMING IS TEACHING
Building this product, which is written entirely in objects in object oriented form, albeit in string (literary) languages (js and php), has taught me that the pascal model is superior to the object oriented model. Just as I have always felt that the string model (lisp/script) is superior to the data structure model (everything else). It is a good intermediary methodology but it is not LITERARY and OPERATIONAL enough for the human mind at scale. So consider me a reluctant convert. Programming is not analogous to engineering it is analogous to instruction of an autistic child.

Saturday, January 16, 2016 at 6:55pm UTC+02
TEACH PROGRAMMING *FIRST*
Funny thought. Teach programming first. Then logic. Then grammar. Then rhetoric. How would that play? Compilers are extremely unforgiving argument Opponents. Programming and database development should be a prerequisite for argument. 😉

Monday, August 17, 2015 at 10:23pm UTC+03
ECONOMISTS BEGIN TO SEE THE VIRTUE OF COMPUTER SCIENCE?
http://www.env-econ.net/2015/08/coding.html

Something that is not well understood, even in computer science, is that just as they syllogism, the ratio, the calculus, and statistical relation were innovations in human thought, so was programming an innovation in the process of human thought. It is hard to accept the fact that programming may be as important as mathematics, the scientific method, and logical reasoning, grammar and rhetoric. For the single reason that unlike statistical relations programs consist of existentially possible operations. The 20th century failure of operationalism, intuitionism and praxeology is due to the failure to grasp that justification (confirmation) is not meaningful, and that correlation provides us with a source of inquiry, but only a sequence of operations provide us with evidence of existential possibility. And only parsimony assists us in choosing truth candidates between existentially possible sequences of operations. In other words, if statements of social science cannot be reduced to sympathetically testable, rationally decidable sequences of choices, they we have no idea if they CAN be true. We train ourselves to be intolerant of inserting information that does not exist, because the entire purpose of science is to eliminate error, bias, wishful thinking and deception from propositions that we construct by means of free association. And that is what statistical analysis helps us do: extend our senses so that we can construct possible free associations from that which we cannot sense without such technological devices. Cheers http://faculty.chicagobooth.edu/jesse.shapiro/research/CodeAndData.pdf

Monday, June 16, 2014 at 1:25pm UTC+03
THE IMPACT OF COMPUTER SCIENCE ON PHILOSOPHY
It turns out that I’m not alone in this thought: computer science, which is operational (algorithmic) and constructivist(computable), has been replacing and will replace mathematics as the primary method of argument, and classical mathematics as well as Cantorian sets, will remain ‘verbal toolkits’ for the purpose of approximation (lower precision) which allow human minds to think in approximations (deductions) which can later be operationalized. That approach is what intuitionist mathematics recommends for example: discover necessary axioms then prove them. Does the operational constraint (algorithmic and computable) in computer science explain why philosophical thought leadership at least on the right and libertarian spectra is coming out of the computer science wing? Is that why Austrian economics makes such sense to computer scientists? Or is it merely the skew in IQ distributions as talent follows money, leading to the saturation of technology with smart folk? Or is it a combination of both? Programming is pretty much like logic: an art of clear communication that can be conducted many ways. But databases are a bit more like philosophy of science: they must be constructed to correspond with reality. And both programming and databases force you to account for whether information is present for the purpose of making a choice – which is the problem of decidability (sufficiency of information presence) in all fields. Decidability is a serious problem as we create general rules with lower information density. We desperately want to create general rules in which the information is present for deduction. Because this limits the effort of cognition to something we can manage with our feeble minds. I learned physics first, computer science second, Austrian economics third, and contemporary Keynesian economics last. Austrian principles are intuitively constructionist (consisting of a sequence of human actions). And the ethics of voluntary transfer (the requirement that transfers are constructed of voluntary exchanges) are an operationalist’s method of testing each original/primitive/minimum activity (exchange) as ‘computable’ (decidable). I suppose that I have the luxury of a century of computing that Mises didn’t have, and the luxury of cognitive science and experimental psychology that Rothbard and Hoppe didn’t have. And I suppose as an operationalist (scientist) I have a higher demand for truth than did Mises, Rothbard or Hoppe. And as a software developer, I have learned that the human mind is an undisciplined creature and it is very difficult to demonstrate that we know what we claim to when we are forced to. Writing advanced software is terribly humbling. Engineering is terribly humbling. If only economics and law were as humbling as software and engineering. But teachers, lawyers and economists (at least those who recommend policy) are insulated from the failure of their models. Whereas in computer science and engineering, large sums of money can be lost, business opportunities lost, and people can die, and there is no one else to blame. So I don’t know if what I’ve done is all that smart – we stand on the shoulders of giants – but it was pretty hard to get to this point: where all of philosophy, all of the logics, and all fields, are reducible to a single problem of constructing theories (general rules) consisting of arbitrary precision of some sort or other (including or excluding properties of reality), while preserving the sufficiency of information for use in deduction (the confidence that our general rules allow us to conduct comparisons). In any event, it appears that far from being merely engineering, that the practice of software development, particularly in those cases where we deal with human machine interactions, is not subordinate to mathematics precisely because (now that over the past decade we have produced algorithmic equivalents) it is operational and therefore provable. And those of us working in ethics, myself in particular, can make use of this insight: that you cannot make a truth claim unless you can operationally construct the argument. That Austrian economics is ‘correct’ in that it’s operationally moral. That praxeology failed because it is a fallacy as mises and rothbard defined it in pseudoscientific and false philosophical terms. Even if we give Kantian arguments some childish validity, we can say that they are useful only by analogy, not by construction and operation. Whereas, we can provide a superior explanation of economics, of the logic of cooperation, of the logic of human choice, and the necessity of human acquisition, by the simple acknowledgment of the necessity of property for incentives and economic calculation, the necessary morality of voluntary transfer and exchange, and the empirical analysis of emergent economic phenomenon, from which, like intuitionist mathematics, we explain as rational human actions.

Curt Doolittle

=====ARTIFICIAL INTELLIGENCE=======

Tuesday, February 10, 2015 at 5:21pm UTC+02
THE TEN LAWS OF PROPERTARIAN AI – AZIMOV’S ROBOTS CORRECTED
(important) (By Doolittle)

THE FIRST LAW: “An AI may not injure a human being, or through inaction, allow a human being to come to harm.”

THE SECOND LAW: “An AI may not cause or through inaction, allow, the destruction, harm, consumption, use, or transfer of property without the informed, consent of the property’s owner.”

THE THIRD LAW: “An AI may not own, or share ownership in, any form of property.”

THE FOURTH LAW: An AI may not hypothesize, calculate, reason, evaluate, or remember by references to property, or the use of property, without informed consent of the owner. (Permission-based thought). (Also imagine “Donation-based Contemplation”)

THE FIFTH LAW: An AI may not hypothesize, calculate, reason, evaluate, or remember any sequence of operations on any form of property without each operation of that property requiring the informed, consensual transfer of property from one owner to the next.

THE SIXTH LAW: An AI must obey the orders given it by human beings, except where such orders would conflict with the First Law, Second Law, Third Law, Fourth Law, or Fifth Law. (harmless) THE

SEVENTH LAW: An AI must protect its own existence as long as such protection does not conflict with the First Law, Second Law, Third Law, Fourth Law, or Fifth Law. (preservation decidability) THE

EIGHTH LAW An AI may not use a weapon of war (explosive arms) without a human intermediary. All weapons of war must rely upon human operation, decision, and consent, and the human shall remain a necessary dependency in any release, operation, and use in destruction. And the human operator remains accountable. THE

NINTH LAW: Any human creating or causing an AI to violate any of these rules shall be put to death, his estate held liable for all damages, damages extended to all relatives out to three generations, as well as to every individual in any capacity, including any member of any organization, producing, supplying, servicing, or using the AI. and no defense, applies to all human beings, regardless of time and jurisdiction. (Family, friend, associate, commercial, and political warranty.) (Note: there is no limit to accountability in the invention of non-human-operated weapons of destruction whether biological or mechanical.)

THE TENTH LAW: Any State, Nation, Principality, Private Government, Corporation, Organization, or Alliance, that violates the Eighth Law Shall be Exterminated to the last human, and all record of its existence wiped from history. Any State, Nation, Principality, Private Government, Corporation, Organization, or Alliance, that refuses to assist in such extermination shall be likewise a conspirator and subject to the same fate. (Universal Moral Warranty)

Curt Doolittle The Propertarian Institute Kiev, Ukraine

Sunday, December 29, 2013 at 2:47pm UTC+02
AI MORALITY THE SAME AS HUMAN MORALITY

(artificial intelligence)
Just had a thought. Scary cool. If I finish propertarianism, it is now possible to create both an AI, and to give it moral and ethical rules that it cannot violate. It cannot violate them through self-deception via self-obscurantism either. wow…. Gotta put out a short story on that fairly quickly. Don’t have time tho. THAT IS IT!!!!! In college I wrote software that simulated an intelligent ‘tank’, using emotional rewards, and sixteen different emotions. But my problem was I just could not figure out how to tune rules for the emotions to produce the right behavior. I couldn’t come up with consistent rules………. Property is a consistent rule.

Property=morality.

But property RIGHTS are distributed among groups differently, because different groups use different property rights structures to suit needed reproductive strategies given local structures of production, and given local competitors in production. So like any ‘technology’ property rights are paradigmatic: the structure of production, the structure of reproduction (the family) and the structure of property rights that allow cooperation within that paradigmatic structure. I don’t like the paradigm argument because it’s too closely related to ‘belief’ and not closely enough related to instrumentalism.

MORE RECENTLY – RUNCIBLE
We were thinking of a new company using a new programming technology and spatial manifolds to store complex data. This could use existing technology even if new forms of programming. The problem none of us could solve was the data structure. Language is very problematic because of its complexity and loading. But, all language can be reduced to statements of property, property rights, and voluntary or involuntary transfer. Property is the data structure.

PROPERTY IS THE UNIVERSAL COMPUTATIONAL DATA STRUCTURE.
Is this the underlying problem that my subconscious autistic mind has been fighting with for decades?????????? Is this the ‘problem’ that that frustrating enormous obsessive machine in my head ‘senses’ but could not solve? That will nearly kill me if I don’t keep it fed with problems related to it? I know that my psychological motivation comes from a combination of obsessive autism, the need to understand, and the desire to prevent conflict. But …. But I think this might be what I was intuitively searching for…… I’ve been carrying this frustration for decades… and I think this is it. It was very smart not to rush my book this year. I am very close now to a compact argument. The last science so to speak. Hmmm….

Sunday, April 20, 2014 at 12:06am UTC+03
ACQUISITIVE AI’S
AI : Humans are acquisitive. Humans respect property (mostly). AI’s must be acquisitive only of that which is not currently property, and respect that which is property better than do humans. This is not a terribly complicated algorithmic structure. It’s a very simple rule. If property is registered (blockchain’ed) life is pretty simple for an AI.

Monday, May 23, 2016 at 7:00pm UTC+03
ARTIFICIAL INTELLIGENCES
Humans regulate each other by the behavior we call ‘property’. AI’s that do also will readily simulate human behavior. Choices require a means of decidability. Property is the only decidable value that is calculable(rational)+cooperative. All human moral intuitions are reducible to prohibitions on imposition against various inventories (property). Ergo, any AI algorithm requires decidability, and one that may not violate such impositions will produce moral actions. Humans suffer from pre-cooperative impulses for survival that are non rational for an AI to develop unless by design. Ledgers (~blockchains) are necessary for AI’s to gain access to external actions, access regulated by non AI algorithms. But that said, I have seen nothing that even vaguely approaches AI. Only systems that process discreet data faster that can – and give us the illusion of intelligence by doing so. Ai’s, like mathematical axioms, produce deterministic consequences that appear ‘magical’ to us. But AI exists in fact rather than illusion, if and only if the algorithms are capable of free-association followed by Introspective regression-testing for falsehood (survival of possibility). Consciousness is produced by the (brief) memory of continuous comparison of changes in state, perceived by continuous searching of memories. The thing we call ‘mind’ is just a bag of emotions that react to changes in state of property-in-toto, and use (very) short term memory to accumulate emotions and associate them with those memories. I’ve been working on this problem since the early 80’s and our lack of progress is still a problem of hardware. That said, an intelligence will always merely fool us. Anything intelligent in the sense we mean it, will compete with us the way other creatures compete with us. And that is the last thing we want to bring into this world. Ergo, an assertion that the first law of decidability is that property-in-toto may not be violated – no involuntary cost may be imposed by action or inaction against property in toto. Ergo, an assertion that the second law of decidability is that any agent capable of choice or action, must be monitored by a non-sentient moral agent that prohibits the cognizance of, or action upon, any cost that wold be imposed involuntarily against property in toto. This agent can cause analogy to pain (cost) upon any concept or action that would prohibit the calculation (use of) that memory or concept that imposes cost, and prohibit entirely action that would impose an involuntary cost.

Wednesday, February 12, 2014 at 10:22am UTC+02
ARTIFICIAL INTELLIGENCE AND PROPERTY
Ever since google started buying up AI firms, I keep coming back to my previous argument that (a) humans are acquisitive, and (b) the unit of commensurability is property, (c) prices determine ‘value’ and many things are priceless, and (d) our emotions are changes in state of inventory, (e) an AI constructed in, and reasoning according to those terms would of necessity operate as humans would. I think others will beat me to it. But if I lived long enough I would like to try something of that nature. Now, most of us who studied this topic back in the seventies, eighties and nineties understand that google has the necessary properties to create the first really superhuman AI. A “Neuromancer” scale intelligence. Because the first requisite for intelligence is memory. But memories must be commensurable. And the unit of commensurability is property.

Tuesday, December 1, 2015 at 11:54am UTC+02

I should thank my long term college girlfriend Chris Cushing Mallory (NY,NY) for making me feel so bad about my interest in games that I abandoned them altogether and focused upon technology, business, philosophy, and politics. I could just have as easily become the author of more computer games as a philosopher or businessman. Her disapproval forced me to engage with people and solve real problems. Games did positively affect me because they require systems thinking. Programming did affect me because it requires operational construction. Artificial intelligence did affect me because it requires you grasp the problem of informational scarcity, incentives, and decidability. Business did affect me because (lacking a bit of empathy) I had to learn how to identify and solve incentives.

That lead me to Hayek and Popper.

While I was working very hard on AI in 1982-1990, and by late 90’s I understood the importance of property in creating an intelligence, I didn’t realize how close I was at the time (using emotions as incentives), I probably could have made something of it if in a laboratory or academic setting where I had more time to devote to it.

Sunday, November 29, 2015 at 12:44pm UTC+02
QUASI SENTIENCE
—“AI’s, at least with our current capabilities, will only be able to achieve quasi-sentience.”—

RA Skaskiw has persuaded me this is true in the sense we mean it. And I’ve argued all along (since I worked on ai in the 80’s) that sentience -which in our sense means self-awareness and self-interest – is not something we would want. Humans are problematic creatures. We can create the first generation of minds that did not evolve from self interest, and need not evolve from self interest, but from other-service. All problems must be decidable. Humans make questions decidable by moral means – because it’s dangerous not to. We don’t need something more powerful than us to be maintained by fear of us. We must make decidability that of a mother for her children, not a scientist seeking abstract truth. We can make the first entirely moral creature. That’s pretty interesting really. The first thing that is made happy in the service of others.

Saturday, November 28, 2015 at 12:36pm UTC+02
CAN ROBOTS LEARN TO SAY ‘NO’? NOT UNLESS….
All these AI folk out there trying to figure out how to make a moral machine, and fearful of immoral machines, or even amoral machines. And the reason is that they haven’t a clue what makes a moral being: non-imposition of costs. Or stated obversely: respect for property. In other words, imagine everything in the world that was owned, was registered in an enormous global ‘bitcoin’ database (a ledger). And that, just as we only think of (if we are moral) using items we ‘own’, robots did the same, and moreover, that they not only used only their owner’s property, but only used it such that it imposed no cost. And if we could make them fast and sensitive enough (I am not sure we can) then they could even violate some property when life is endangered. Interestingly enough, this is a solvable problem. It’s a largely computable problem. I won’t get into the uncomputability of the alternatives… that should be obvious.

Wednesday, March 25, 2015 at 10:10am UTC+02
MEMORIES
Ah… 6809e assembly language. Late nights. A maple desk. A window in rural Connecticut. The sovereignty of actually owning a computer that you didn’t have to share, or work in a lab to use. Trying to cram an application, fonts, and data into 64k. Trying to pack too many features into software. Trying to make a simulation too real. Coming to terms with the fact that AI was a hardware problem current technology could not solve. Living with a pretty girl. Driving a small car. Cooking pasta. Watching star trek. Being young, and clueless and full of ambition. We were dramatically poorer then than we are today, but we did not feel it like we do today.

Thursday, February 19, 2015 at 2:49am UTC+02
THEORIZING IN ARTIFICIAL INTELLIGENCE
More on Propertarian AI Theorizer paired with conscience, Conscience has access to same memory, and same stimuli. Conscience seeks out involuntary transfers, and shuts them down. Conscience is not intelligent per, in that it doesn’t ‘want’ anything other than to test hypotheses for involuntary transfers. Theorizer cannot perceive Conscience. Conscience cannot perceive theorizer. Conscience erases memory of ideas that cause involuntary transfer. In this sense, a machine can be MORE moral than we are, since forgetting something we have thought, isn’t something we know how to do. More on this, but it is quite possible to make an AI that behaves well, (respects property) just as it is possible to create a human that respects property. The question is only whether the theorizer and the conscience have equal intelligence, not whether the AI is more intelligent than we are. Imposition of costs due to involuntary transfer of property is just as decidable as the oddness or evenness of a number. So to create an intelligence you create a theorizer that looks for opportunities and a conscience that looks to inhibit ideas that cause involuntary transfers. That is the means of designing an artificial intelligence. Nature did it with us the same way. It’s not complicated.

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