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Education for an Automated World
Alexander Ohnemus
11/25/2022
Table of contents:
1. Accounting Fundamentals and Applications Thesis
2. Film Fundamentals and Applications Thesis
3. Fundamentals and Applications of Business
4. Fundamentals and Applications of Engineering Thesis
5. Artificial Intelligence: Linking Academia to the MarketPlace
6. My Film Career
7. The Arts and Their Applications
8. The Future of Intellectuals
9. The Importance of Rationality in Avoiding Self Sabotage
10. Risk Takers: Investigation for Transferable Skills
11. Empowering Intellectuals: A Way of Unlocking Human Potential
Abstract:
The applications of any discipline are subject to change. The fundamentals of accounting are
customs and math. Successful films are often based on classics due to relatability and interest.
Business is an onslaught of other skills because it is not its own. The greatest fundamentals of
engineering may be differential questions. Artificial intelligence may automatically be able to turn
theories into marketable products. My film career was and is perhaps successful despite my
lack of technical skills. Arts have both applications and fundamentals. Intellectuals may gain
higher earning power in the future. Rationality is important to avoid self sabotage. Risk
navigation is an important skill. If anyone can come up with ideas then the market may become
easier to contribute to.
Alexander Ohnemus
No Professor
Social Sciences/Accounting/Education
13 September 2022
Accounting Fundamentals and Applications Thesis
Learning accounting involves the combination of basic law, basic math, ways to apply
those two subjects, and possibly more advanced law and more advanced math. The math and law
would probably pertain to financial accounts making it most likely basic.
Accounting may have a very intuitive definition. Accounting ": the system of recording
and summarizing business and financial transactions and analyzing, verifying, and reporting the
results
also : the principles and procedures of this system"(Merriam-Webster). That definition
could also be summarized as law and math applied and usually being basic. Perhaps the law and
math would not always be basic. The numbers could become more difficult to calculate. The
accounts could also become more complex. Managing accounts and the results may be boiled
down to math and law.
The fundamentals of accounting may also be important. "Three fundamental steps in
accounting are:
1. Identifying and analyzing the business transactions.
2. Recording of the business transactions.
3. Classifying and summarizing their effect and communicating the same to the interested
users of business information"(BYJU'S). BYJU'S is a source at a market risk so it may be
credible. BYJU'S may receive negative economic consequences for showcasing falsehoods. So,
the source may be credible. But is the answer logical? Business transactions are the foundations
of accounts so the answer is logical. To be more thorough, business transactions must be found
and observed, later recorded and then processed. The source seems trustworthy due to its
financial incentives and the answer follows logic.
Government institutions teaching a subject are not subject as much to market forces but
have to appeal to stricter codes perhaps. "What you can learn.
Interpret accounting and financial data for use in planning business operations
Determine break-even sales as part of an understanding of fixed and variable costs
Prepare and interpret balance sheets, income statements, and cash flow statements
Understand the procedures for handling the acquisition and disposal of property, plant
and equipment"(UCLA). Sometimes subject matter is not given as fundamentals. This subject
matter is maybe not only the fundamentals but also the likely applications. Although the
applications change they provide a way to put the fundamentals into practice. Applications
change so once one knows the fundamentals to become able to apply the skills one may need to
learn, unlearn, and relearn.
Businesses are not constant. Business could potentially not be part of accounting.
Breakeven sales may not be necessary in all parts of accounting. Costs could possibly always be
either fixed or variable unless a third type of cost emerged. Could costs be more complex than
only fixed and variable? Perhaps. Balance sheets may not always be used or may change in
form. Income statements may change in form or not always be used. Cash flow statements may
not always be used or change in form. Procedures may be subject to change because they are tied
to customs. Laws change.
As the research results, vocationally knowing the applications of accounting is an
imperative. Knowing the fundamentals may require learning new technology, new customs, etc.
The fundamentals of accounting are law and math. Accounting fundamentals could also be
described as management of accounts.
Works Cited
“Accounting.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/accounting. Accessed 13 Sep. 2022.
BYJU'S. "State the three fundamental steps in the accounting process." byjus.com.
byjus.com/question-answer/state-the-three-fundamental-steps-in-the-accounting-process/.
Accessed 13 Sep. 2022.
UCLA. "Accounting Fundamentals ." uclaextension.edu.
www.uclaextension.edu/accounting-taxation-internal-audit/accounting-bookkeeping/certi
ficate/accounting-fundamentals. Accessed 13 Sep. 2022.
Alexander Ohnemus
No Professor
Film/Education/Social Sciences
14 September 2022
Film Fundamentals and Applications Thesis
Filmmaking has both fundamentals and their applications. The process of making a
successful film includes knowing the fundamentals and how to apply them. A significant part of
making a film successful is pre production. The classics are often a great source of critical
acclaim and financial success. This essay tries to determine the formula for successful
filmmaking.
Filmmaking has a very intuitive definition. Filmmaking is the "the making of motion
pictures"(Merriam-Webster). Motion pictures are another term for films. Motion pictures may be
films described in greater detail. A film is pictures moving plus other elements. A picture may
also be used to describe a scheme. The phrase the big picture often means the greater scheme of
things. The linguistics of the word picture have been described enough in this essay.
Any subject has fundamentals including film. "The three primary stages of film
production – pre-production, production and post-production – are covered through chapters
dealing with each of the major departments: script; production; direction; production design;
cinematography; sound and post-production"(Barnwell 2008). Primary is perhaps another word
for fundamental. Both the primary and fundamental of subject matter may mean the unchanging.
A film is a production. Therefore, what happens before, during, and after the production are the
unchanging elements of a film. What is done in practice may change such as the technology,
culture, etc. yet the fundamentals do not change.
Making something of value is imperative in the marketplace for success. To develop a
successful movie franchise one must do the following headings "Appeal to All or Most Ages,
Grow and Develop Your Characters, Cover Basic Archetypes, Take Your Audience To Another
World"(nyfa.edu 2022). Appealing to customers is a way to succeed. A great divider among
demographics could be age. People change a lot overtime and aging is a factor of change. So, the
more people a movie can appeal to the more likely it will succeed with critics and the market.
Characters may be the main drivers of the plot. Humans are bound to relate to what is similar to
them so humanized characters may be a film's main attraction. The growth and development of
the characters may be the motor of the plot. Characters must have a basis no matter how original
or explicit. Archetypes may be the basis of the characters. People typically seek entertainment to
escape their current reality. Thus, taking the audience to another reality may be another
imperative for film success. In conclusion, age demographics, character development, good
character basis, and illusionment may be the imperatives for film success with both critics and
finances.
As noted previously in this essay, a script may be the first step of film production so
knowledge of literature may be important. "To be generally agreed upon as a classic, works meet
some common high standards for quality, appeal, longevity, and influence"(Lombardi 2019). A
classic may be the best basis for a movie because it has high standards for quality, appeal,
longevity, and influence. To elaborate further, the keys to financial success and critical acclaim
for a movie seem to match the keys for a book to be a classic. Those keys are quality, appeal,
longevity, and influence. Books that are classics may be great for films if the goals are critical
acclaim and economic success.
Maybe films have similar criteria to books to become classics. "Classic Films are often
distinguished or unique works of cinema that have transcended time and trends, with indefinable
quality. Classic films are often universal favorites that hold up after repeated rescreenings"(Film
Site). The source cited is subject to market forces so it is likely credible or it could face
economic consequences. The information from the source is also logical. The criteria for a film
to be considered a classic is the same for a book to become a classic. The difference lies in the
format. Films and literature are different art forms.
As the research demonstrates, the fundamentals of making a film are pre production,
production, and post-production because a film is a product. Also making a successful film is
largely determined by the script. A literary classic may be the best basis for a film desired to
become a classic. Classics are often successful. Therefore, making a film a classic may greatly
help with its financial success and critical acclaim.
And of course, the applications of filmmakers are essential for vocational success and
prosperity. In order to apply the fundamentals one must be skilled in the applications. Since
applications change, being a learner for life could be the key to vocational success.
Works Cited
“Filmmaking.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/filmmaking. Accessed 14 Sep. 2022.
Barnwell, Jane . "The Fundamentals of Film Making ." bloomsbury.com. 11 Aug. 2008.
www.bloomsbury.com/uk/fundamentals-of-film-making-9782940373192/#:~:text=The%
20three%20primary%20stages%20of,%3B%20sound%20and%20post%2Dproduction.
Accessed 14 Sep. 2022.
New York Film Academy. "Movie Franchises: How to Build a Successful Film Strategy ."
nyfa.edu. 1 Aug. 2022.
www.nyfa.edu/student-resources/lessons-from-j-k-rowling-how-to-build-a-successful-fil
m-franchise/. Accessed 14 Sep. 2022.
Lombardi, Esther . "Literature Definitions: What Makes a Book a Classic? ." thoughtco.com. 22
Oct. 2019. www.thoughtco.com/concept-of-classics-in-literature-739770. Accessed 14
Sep. 2022.
Film Site . "Classic Films." filmsite.org .
www.filmsite.org/classicsfilms.html#:~:text=Classic%20Films%20are%20often%20disti
nguished,hold%20up%20after%20repeated%20rescreenings. Accessed 14 Sep. 2022.
Alexander Ohnemus
No Professor
Business/Education
14 September 2022
Fundamentals and Applications of Business
Business itself is not a skill but composed of many other skills that can be divided into
fundamentals and applications. Business is fundamentally made up of microeconomics, game
theory, psychology, persuasion, ethics, mathematics and computers. The applications vary.
Those at market risk within a field may be the best experts because they may be at
economic risk for stating falsehoods. "There is no skill called “business.” Avoid business
magazines and business classes.."(Ravikant 2018). Naval states that business is not a skill in
itself. Naval goes on to advise that business magazines and business classes should be avoided.
Naval is the founder of AngelList. AngelList is a business that's task is to help build other
businesses. Naval could take a large financial risk for stating a falsehood on the public form of
Twitter. This information from Naval is most likely accurate. However, his advice may be
inaccurate.
Directly after Naval stated business was not a skill he stated skills people should study.
"Study microeconomics, game theory, psychology, persuasion, ethics, mathematics, and
computers.."(Ravikant 2018). Naval may be implying that business is made up of
microeconomics, game theory, psychology, persuasion, ethics, mathematics, and computers.
Naval advised people to learn microeconomics, game theory, psychology, persuasion, ethics,
mathematics and computers. That advice may be logical, yet Naval may be wrong to advise
against business classes and magazines.
Background checks are imperative for experts. Naval is stated to be the founder of
AngelList on his profile for that company (AngelList). There is enough evidence to support the
claim that Naval is the founder of AngelList. Thus making Naval's expertise more probable.
Therefore, Naval's advice may be more helpful.
AngelList is legitimately a company with the task of helping start other companies. The
evidence is on their website (AngelList). If a company posts a claim on its website it is probably
truthful. For stating falsehoods a company could receive negative economic consequences.
AngelList is a company with the task of helping found other companies.
Business is probably not a skill itself due common definitions of it stating otherwise.
Business is "a usually commercial or mercantile activity engaged in as a means of
livelihood"(Merriam-Webster). That definition leaves business open to so many possibilities.
Business could be a number of activities. Business classes and business magazines may still be
valuable but business itself is not a skill.
If governments do not recognize business as its own skill then the probability it is just the
sum of other skills increases. "These business skills are essential
Financial management
Marketing, sales and customer service
Communication and negotiation
Leadership
Project management and planning
Delegation and time management
Problem solving
Networking"(Small Business Development Corporation).
Financial management boils down to managing finances. Which further boils down to
money management. Which at the end boils down to customs and math.
Marketing is dealing with markets.
Sales is the skill of selling.
Customer service is serving customers.
Communication and communication are both self explanatory and often involve language
and persuasion for the sake of business interests.
Leadership is being in charge or leading.
Project management and planning may be leadership specific to projects.
Delegation is assigning tasks to others. Time management is self explanatory.
Problem solving is to solve problems.
Networking is building networks or teams.
Those skills described by the Australian government website may be the applications to
business. The skills Naval instructed people to study are the fundamentals.
Microeconomics should be defined for this essay. Microeconomics is "a study of
economics in terms of individual areas of activity (such as a firm)"(Merriam-Webster).
Individual areas of economic activity are part of business. Business is somewhat made of
microeconomics. Microeconomics are part of business.
Game theory should be defined for this essay. "the analysis of a situation involving
conflicting interests (as in business or military strategy) in terms of gains and losses among
opposing players"(Merriam-Webster). Game theory could just be defined more intuitively. Game
theory is the theory of games. The Merriam-Webster dictionary gives a more elaborate definition.
Game theory is a useful skill in business.
Psychology should be defined for this essay. "the science of mind and
behavior"(Merriam-Webster). The skill of psychology would be useful in business. A significant
amount of business is navigating situations that involve others and oneself. Applying a science to
the behavior and minds of oneself and others is an imperative to business success.
Persuasion may be closely related to psychology but should still be defined by itself for
this essay. Persuasion is "the act or process or an instance of persuading"(Merriam-Webster). To
persuade is to get someone to do something. That would be important in business. Business is
composed of involved actors.
Ethics should be defined for this essay. Ethics are "a set of moral principles : a theory or
system of moral values"(Merriam-Webster). Ethics could also be defined as moral science.
Business involves parties acting with each other which involves ethics. Ethics are fundamental to
business.
Mathematics should be defined for this essay. Mathematics is "the science of numbers
and their operations (see OPERATION sense 5), interrelations, combinations, generalizations,
and abstractions and of space (see SPACE entry 1 sense 7) configurations and their structure,
measurement, transformations, and generalizations"(Merriam-Webster). Money is a part of
business and it requires math. Math is fundamental to every other subject whether people realize
it or not. Mathematics is fundamental to business.
Computers should be defined for this essay. A computer is " one that computes
specifically : a programmable usually electronic device that can store, retrieve, and process
data"(Merriam-Webster). Computing is essential to business. Computing is finding a solution to
something. Later the essay will determine what computing is more formally. The ability to use
the devices known as computers may be a major application in business. Knowing how to use
something else to compute is fundamental to business. Even using oneself to compute.
The verb compute should be defined for this essay. To compute is "to determine
especially by mathematical means"(Merriam-Webster). Determining using math is essential to
business. Computing is fundamental to business. Computing is a fundamental business skill.
Determining skills needed for business may be impossible because so many different
kinds of businesses exist. Some of the primary skills are microeconomics, game theory,
psychology, persuasion, ethics, mathematics and computers. The applications will vary.
Works Cited
Naval . "There is no skill called “business.” Avoid business magazines and business classes.."
Twitter, 31 May 2018, 1:41 a.m.,
twitter.com/naval/status/1002107808202960896?s=20&t=c3DuXDGIP4TqQ3n4uyZAhw
.
Naval . "Study microeconomics, game theory, psychology, persuasion, ethics, mathematics, and
computers.." Twitter, 31 May 2018, 1:41 a.m.,
twitter.com/naval/status/1002107869209096192?s=20&t=c3DuXDGIP4TqQ3n4uyZAhw
.
AngelList . "Naval Ravikant ." angel.co.angel.co/p/naval. Accessed 14 Sep. 2022.
AngelList . "AngelList Talent ." https://angel.co/.angel.co/. Accessed 14 Sep. 2022.
“Business.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/business. Accessed 14 Sep. 2022.
Small Business Development Corporation . "Essential business skills ." smallbusiness.wa.gov.au
.www.smallbusiness.wa.gov.au/starting-and-growing/essential-business-skills. Accessed
14 Sep. 2022.
“Marketing.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/marketing. Accessed 14 Sep. 2022.
“Sales.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/sales. Accessed 14 Sep. 2022.
“Microeconomics.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/microeconomics. Accessed 14 Sep. 2022.
“Game theory.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/game%20theory. Accessed 14 Sep. 2022.
“Psychology.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/psychology. Accessed 14 Sep. 2022.
“Persuasion.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/persuasion. Accessed 14 Sep. 2022.
“Persuade.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/persuade. Accessed 14 Sep. 2022.
“Ethic.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/ethic. Accessed 14 Sep. 2022.
“Mathematics.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/mathematics. Accessed 14 Sep. 2022.
“Computer.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/computer. Accessed 14 Sep. 2022.
“Compute.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/compute. Accessed 14 Sep. 2022.
Alexander Ohnemus
No Professor
Engineering/Education
15 September 2022
Fundamentals and Applications of Engineering Thesis
Engineering is a subject matter. Engineering has fundamentals and applications and
advice that could lead to more vocational success. The credibility of a source can be determined
by its reputation, incentives, and the logic of its information.
Engineering should be more formally defined for this essay. Engineering is "the
application of science and mathematics by which the properties of matter and the sources of
energy in nature are made useful to people"(Merriam-Webster). This dictionary definition of
engineering is very useful. Engineering is complex in that it is the application of other subjects.
Yet it has fundamentals just as every subject does.
As stated previously in this essay engineering has fundamentals. In the fundamentals of
engineering curriculum of a certain university the teachings are "Calculus and differential
equations (with engineering applications)
Foundational knowledge areas needed by engineers
engineering professionalism,
computational and programming skills,
communication (graphical, written and oral),
problem solving, design analysis,
teamwork and project management"(University of Louisville). Ironically engineering is
the application of other subjects yet it too has its own fundamentals. The fundamentals of
engineering are the unchanging elements of the subject.
Differential equations may be the most tangible fundamentals to engineering and
therefore are imperative to learn for vocational success. All other fundamentals may be too
dependent on change to explain in this essay. So the essay will focus on differential equations.
"In Mathematics, a differential equation is an equation that contains one or more functions with
its derivatives. The derivatives of the function define the rate of change of a function at a point. It
is mainly used in fields such as physics, engineering, biology and so on. The primary purpose of
the differential equation is the study of solutions that satisfy the equations and the properties of
the solutions"(BYJU'S). The source is probably credible because it could face economic losses
for stating falsehoods. The answer also is logical. In conclusion, a differential equation has been
defined for this essay.
Taking a derivative is important to engineering so the rules will be included in this essay.
"Frequently Asked Questions on Differentiation Formulas
What are the formulas of differentiation?
The formulas of differentiation that helps in solving various differential equations
include:
Derivatives of basic functions
Derivatives of Logarithmic and Exponential functions
Derivatives of Trigonometric functions
Derivatives of Inverse trigonometric functions
Differentiation rules
What are the basic rules of differentiation?
The basic rule of differentiation are:
Power Rule: (d/dx) (xn ) = nx{n-1}
Sum Rule: (d/dx) (f ± g) = f’ ± g’
Product Rule: (d/dx) (fg)= fg’ + gf’
Quotient Rule: (d/dx) (f/g) = [(gf’ – fg’)/g2]
What are the derivatives of trigonometric functions?
The derivatives of six trigonometric functions are:
(d/dx) sin x = cos x
(d/dx) cos x = -sin x
(d/dx) tan x = sec2 x
(d/dx) cosec x = -cosec x cot x
(d/dx) sec x = sec x tan x
(d/dx) cot x = -cosec2 x
What is d/dx?
The general representation of the derivative is d/dx. This denotes the differentiation with
respect to the variable x.
What is a UV formula?
(d/dx)(uv) = v(du/dx) + u(dv/dx)
This formula is used to find the derivative of the product of two functions"(BYJU'S). A
way to determine the veracity of the source is following the money. The source could face
negative financial consequences for being incorrect. Therefore the source is probably correct due
to its financial risk.
Differential equations also have types and can manifest beyond only words. "We can
place all differential equations into two types: ordinary differential equations and partial
differential equations. A partial differential equation is a differential equation that involves
partial derivatives. An ordinary differential equation is a differential equation that does not
involve partial derivatives"(Green 2021). Furthermore there are the manifestations of differential
equations. "((d^2)y)/(dx^2)+(dy)/(dx)=(3x)sin(y)(2.2.1)
is an ordinary differential equation since it does not contain partial derivatives. While
(∂y)/(∂t)+x(∂y)/(∂x)=(x+t)/(x−t)(2.2.2)
is a partial differential equation, since y is a function of the two variables x and t and
partial derivatives are present"(Green 2021).
As such solving differential equations can be done either manually or using software. "A
differential equation is an equation involving a function and its derivatives. It can be referred to
as an ordinary differential equation (ODE) or a partial differential equation (PDE) depending on
whether or not partial derivatives are involved. Wolfram|Alpha can solve many problems under
this important branch of mathematics, including solving ODEs, finding an ODE a function
satisfies and solving an ODE using a slew of numerical methods"(WolframAlpha).
WolframAlpha's software is most likely useful for solving differential equations because the
business is subject to economic consequences if it is defective. Noting those at market risk is a
feasible way to follow the money and discover the veracity of information. WolframAlpha is
most likely a useful source of differential equation solving software.
Vocabulary is important in mathematics as it determines what is what and the term
derivative should be defined for this essay. "The essence of calculus is the derivative. The
derivative is the instantaneous rate of change of a function with respect to one of its variables.
This is equivalent to finding the slope of the tangent line to the function at a point"(MIT 1999).
That is what a derivative is.
The definition of a derivative covers the ordinary ones. A special definition is required
for the partial ones. "The reason for a new type of derivative is that when the input of a function
is made up of multiple variables, we want to see how the function changes as we let just one of
those variables change while holding all the others constant"(Khan Academy). Khan Academy is
describing a partial derivative. Partial derivatives are necessary information for the fundamentals
of engineering. Partial derivatives must be learned to do engineering.
Once the fundamentals have been explored the applications are next. "In broad terms,
engineering can be divided into four main categories – chemical, civil, electrical and mechanical
engineering. Each of these types requires different skills and engineering education"(Cote 2022).
The next information in this essay shall be the types of engineering.
Chemical engineering deserves a definition. Chemical engineering is "engineering
dealing with the industrial application of chemistry"(Merriam-Webster). As such the skills
necessary for chemical engineering differ from those of other types of the vocation. A knowledge
of chemistry would be helpful to this kind of engineering. Knowledge of differential equations
would be necessary for chemical engineering.
Civil is another kind of engineering. A civil engineer is "an engineer whose training or
occupation is in the design and construction especially of public works (such as roads or
harbors)"(Merriam-Webster). Civil engineering is for the public. Differential equations would
still be useful for this kind of engineering.
Electrical engineering deserves a definition for this essay. Electrical engineering is "a
type of engineering that deals with the uses of electricity"(Merriam-Webster). An electrical
engineer deals with electricity. Differential equations are still part of electrical engineering.
The last branch of engineering explored in this essay will be mechanical. Mechanical
engineering is "a branch of engineering concerned primarily with the industrial application of
mechanics and with the production of tools, machinery, and their products"(Merriam-Webster).
This branch of engineering also would include differential equations.
In conclusion, differential equations are the leading fundamental to engineering and are
just equations involving derivatives. The partial differential equations are the ones where the
derivative is only taken as part of the equation. The applications of engineering are chemical,
civil, electrical and mechanical.
Works Cited
“Engineering.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/engineering. Accessed 14 Sep. 2022.
University of Louisville . "ENGINEERING FUNDAMENTALS ." engineering.louisville.edu.
engineering.louisville.edu/academics/areasofstudy/engineering-fundamentals/. Accessed
14 Sep. 2022.
BYJU'S. "Differential Equations ." byjus.com.byjus.com/maths/differential-equation/. Accessed
15 Sep. 2022.
BYJU'S . "Differentiation Formulas ." byjus.com.byjus.com/maths/differentiation-formulas/.
Accessed 16 Sep. 2022.
Green, Larry . "2.2: Classification of Differential Equations ." math.libretexts.org. 5 Sep. 2021.
math.libretexts.org/Bookshelves/Analysis/Supplemental_Modules_(Analysis)/Ordinary_
Differential_Equations/2%3A_First_Order_Differential_Equations/2.2%3A_Classificatio
n_of_Differential_Equations. Accessed 15 Sep. 2022.
WolframAlpha. "Differential Equations ." wolframalpha.com.
www.wolframalpha.com/examples/mathematics/differential-equations. Accessed 16 Sep.
2022.
MIT. "The Definition of Differentiation ." web.mit.edu. 14 Oct. 1999.
web.mit.edu/wwmath/calculus/differentiation/definition.html#:~:text=The%20essence%2
0of%20calculus%20is,the%20function%20at%20a%20point. Accessed 16 Sep. 2022.
Khan Academy . "Introduction to partial derivatives ." khanacademy.org.
www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/partial-deri
vative-and-gradient-articles/a/introduction-to-partial-derivatives. Accessed 16 Sep. 2022.
SNHU , and Joe Cote . "Types of Engineering: Salary Potential, Outlook and Using Your Degree
." snhu.edu. 10 Aug. 2022.
www.snhu.edu/about-us/newsroom/stem/types-of-engineering#:~:text=In%20broad%20t
erms%2C%20engineering%20can,different%20skills%20and%20engineering%20educati
on. Accessed 15 Sep. 2022.
“Chemical engineering.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/chemical%20engineering. Accessed 15
Sep. 2022.
“Civil engineer.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/civil%20engineer. Accessed 15 Sep. 2022.
“Electrical engineering.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/electrical%20engineering. Accessed 15
Sep. 2022.
“Mechanical engineering.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/mechanical%20engineering. Accessed 15
Sep. 2022.
Alexander Ohnemus
No Professor
Philosophy
02 October 2022
Artificial Intelligence: Linking Academia to the MarketPlace
Academia may be undervalued. The skeptics may have found claims against the field.
However, despite seemingly reasonable critiques of academic research the value of the discipline
will probably increase due to artificial intelligence.
Nassim Taleb may be one of academia's most prolific critics. "100% wrong. Engineering
!= science. As explained in #Antifragile, our world is built by engineers, tinkerers, &
practitioners, often uneducated, misclaimed by universities. Science never claims ANYTHING
about these matters. Science=research procedure not a risk control method"(Taleb 2021). If
something is deemed completely wrong then the critic is very confident. Taleb references his
own book on the matter. Taleb states that engineers, tinkers, and practitioners who often lacked
education built the world. Nassim goes on to claim that science claims nothing about building a
world. Nassim believes that scientists did not build the world as much as engineers did. Nassim
states that science is about research. Nassim's claims seem logical. Nassim's claims may have
been true. The validity of Nassim's claims may diminish as new innovations appear.
Nassim continues to state that engineers are more important than scientists as far as
societal contributions. "Know How vs. Know What A truth is that, as much as we enjoy
counterintuitive intellectual puzzles, our world owes a lot more to the Teslas than to the
Einsteins. Engineers would have figured out the time discrepancies without asking questions.
The reverse doesn't work"(Taleb 2021). Nassim argues that engineers are more valuable than
scientists. By Teslas Nassim means engineers. By Einsteins Nassim means scientists. Nassim
Taleb believes that scientists have to ask questions to make discoveries while engineers do not.
The claims seem logical.
Even logical seeming claims may not be accurate because researchers may be more
valuable than Taleb claims. "Salk and his team used formaldehyde to kill the poliovirus without
destroying its antigenic properties"(Tan 2019). Salk, an academic researcher, created a vaccine
with his research skills. Salk also had the support of a team. Salk, before artificial intelligence,
proves that academic researchers have contributed to this society.
If someone could face negative consequences for giving inaccurate advice that person is
more likely to give accurate advice. Mark Cuban, the financial risk taker, believes liberal arts
degrees will soon become more valuable than STEM ones because of automation (University Of
Dallas). Mark Cuban is unlikely to mislead people because he could face negative financial
consequences if he does. Cuban's incentives would make him at least try to be honest and
accurate. University of Dallas studies back Mark Cuban's predictions. If liberal arts degrees
become more valuable than technical degrees then researching may become more valuable than .
People are less likely to give false information if they are financially incentivized not to.
As stated previously, entrepreneurs could face financial risks for being inaccurate and or
dishonest, therefore when they give vocational advice it is probably accurate. When mass
automation occurs humans will eventually be left with creative work and research jobs(Ravikant
2019). Research is open ended so it is more difficult to automate. Research will become a more
valuable skill according to AngelList founder and CEO Naval Ravikant. AngelList is a business
dedicated to assisting other businesses start. His incentives are to make the population more
skilled. Due to his incentives and his area of expertise Ravikant is probably giving accurate
advice.
As the research shows, the value of academic research will probably increase. Academic
research has always been valuable despite seemingly reasonable critiques by its skeptics.
Artificial intelligence will most likely increase the value of academic research especially if
engineering is more automated.
However, the irony is that engineering must occur first to make itself less valuable. The
automation will arise from engineering. Engineering will make other skills more valuable.
Works Cited
nntaleb. "100% wrong. Engineering != science. As explained in #Antifragile, our world is built
by engineers, tinkerers, & practitioners, often uneducated, misclaimed by universities.
Science never claims ANYTHING about these matters. Science=research procedure not a
risk control method.." Twitter, 26 July 2021, 7:14 p.m.,
100% wrong. Engineering != science.
As explained in #Antifragile, our world is built by engineers, tinkerers, & practitioners, often
uneducated, misclaimed by universities.
Science never claims ANYTHING about these matters. Science=research procedure not a risk
control method. t.co/iUrOTtE66x
— Nassim Nicholas Taleb (@nntaleb) July 27, 2021
.
nntaleb. "Know How vs. Know What A truth is that, as much as we enjoy counterintuitive
intellectual puzzles, our world owes a lot more to the Teslas than to the Einsteins.
Engineers would have figured out the time discrepancies without asking questions. The
reverse doesn't work.." Twitter, 29 July 2021, 6:01 a.m.,
twitter.com/nntaleb/status/1420732639204552711?s=20&t=pZuFq33SmrZd7YMqahVd
WA.
Yong Tan, Siang, and Nate Ponstein. "Jonas Salk (1914–1995): A vaccine against polio ."
ncbi.nlm.nih.gov . 1 Jan. 2019. www.ncbi.nlm.nih.gov/pmc/articles/PMC6351694/.
Accessed 2 Oct. 2022.
University Of Dallas . "AN UNLIKELY ALLY ." udallas.edu.
udallas.edu/news/2017/billionaire-business-mogul-boldly-champions-liberal-arts-as-the-f
uture. Accessed 2 Oct. 2022.
Ravikant , Naval . "Everyone Can Be Rich." joerogan.com , uploaded by youtube.com, 4 June
2019, youtu.be/l2AbxWr6I4s.
Alexander Ohnemus
No Professor
Film/Autobiography
01 October 2022
My Film Career
My filmmaking career may be off to a great start. I may not have many technical skills
but technology varies and is learned job by job. I must know the fundamentals of film making
and an onslaught of other skills. I have already proved myself in writing and acting. The tech
varies but the soft skills are more consistent.
Often one has to have business skills for any endeavor. "There is no skill called
'business'"(Ravikant 2018). Running a business could involve several different skills. Business is
not its own skill but made up of several others. Business is also known as entrepreneurship. I
may have many of these skills already mastered.
Business is made up of several skills and running one could require unpredictable skills
but some skills are more fundamental. "Study microeconomics, game theory, psychology,
persuasion, ethics, mathematics, and computers"(, Ravikant 2018). To business itself
microeconomics is fundamental. Game theory is fundamental. Psychology is fundamental.
Persuasion is fundamental. Ethics are fundamental. Mathematics is fundamental. And so are
computers although technology varies. I may lack technical skills for video production but
technology changes. My portfolio is more of a resume.
Having a good attitude is a great asset when joining the film industry. Getting along
peacefully with others leads someone to being rich(Carmichael 2015). So much depends on
socialization in the film industry. Charisma may be a leading factor towards financial success.
People shouldn't be authentic but should act like they are wanted to until they reach their goal.
Still after reaching the goal they should still follow the rules.
The fundamentals of film making allow for the applications to change. "The 5 stages of
filmmaking include: 1) Development 2) Pre-production 3) Production 4) Post-production 5)
Distribution"(Keizer 2022). Development could include all that leads up to production.
Production is the original shooting of the film. Post-production is editing and getting ready to be
distributed. Distribution is just distributing the film. All of these skills may have changing
technology involved. Technology changes depending on a number of factors including what the
engineers build. They all boil down to society.
I am a prolific writer. I have written over thirty books on Amazon. I write under two
names " "Alexander Ohnemus" and "Alex Ohnemus." I also act. So other than technical skills I
do filmmaking.
Plus the technical skills change depending on the software. People can now develop
software without coding thanks to the already developed kind(Brinker 2019). Software may soon
be developed to more automatically turn essays into films. That would be a great change in the
system. Soon software will be able to turn essays automatically into documentaries or other
kinds of films.
When a risk taker makes a point that person is risking reputational currency and therefore
is probably making substantial effort to be accurate. Mark Cuban believes liberal arts degrees
will soon increase in value as mass automation takes over more technical skills(University Of
Dallas). Deep thinking may become a large asset. Those who can think of something original and
successfully execute in their deeper ideas may be at a greater advantage. Technology will
continue to change depending on the engineers so the arts will have soft skills as their
fundamentals and technical ones as the altering applications.
My filmmaking career may be on the right track especially considering that my only
missing skills are the technical ones and technology always changes. What I don't know as far as
technical skills I could learn very quickly. Plus I already have a substantial resume showcasing
my non-technical skills. And I am a deep thinker as many of my literary contributions are
original contributions to social and hard sciences.
Works Cited
Naval . "There is no skill called “business.” Avoid business magazines and business classes.."
Twitter, 31 May 2018, 1:41 a.m.,
twitter.com/naval/status/1002107808202960896?s=20&t=c3DuXDGIP4TqQ3n4uyZAhw.
Naval . "Study microeconomics, game theory, psychology, persuasion, ethics, mathematics, and
computers.." Twitter, 31 May 2018, 1:41 a.m.,
twitter.com/naval/status/1002107869209096192?s=20&t=c3DuXDGIP4TqQ3n4uyZAhw.
Carmichael , Evan . "How to Adopt the Mindset of a Rich Person ." youtube.com, uploaded by
youtube.com, 28 Aug. 2015, youtu.be/UTl-X6BkrDU.
Keizer , Anna . "Filmmaking Fundamentals: Understand the 5 Stages of Making a Film ."
careersinfilm.com. 19 May 2022. www.careersinfilm.com/filmmaking/. Accessed 1 Oct. 2022.
Ohnemus , Alexander . "Alexander Ohnemus ." amazon.com.
www.amazon.com/s?i=digital-text&rh=p_27%3AAlexander+Ohnemus&s=relevancerank
&language=es&text=Alexander+Ohnemus&ref=dp_byline_sr_ebooks_1. Accessed 1
Oct. 2022.
Ohnemus , Alex . "Alex Ohnemus ." amazon.com.
www.amazon.com/s?i=digital-text&rh=p_27%3AAlex+Ohnemus&s=relevancerank&lan
guage=es&text=Alex+Ohnemus&ref=dp_byline_sr_ebooks_1#top. Accessed 1 Oct.
2022.
Ohnemus , Alexander . "John Goes to Therapy ." uploaded by youtube.com, 21 Oct. 2020,
youtu.be/vhd3oUif-Do.
Alexander Ohnemus
No Professor
Education/Business/Arts
02 October 2022
The Arts and Their Applications
Learning the arts may be challenging. The soft skills are usually consistent while the
technical skills vary. Useful information would be the definition of the arts, and how to develop
software. Critical thinking and creativity may be the most universal skills in the arts because so
many applications change.
The arts are a number of activities. "art, also called (to distinguish it from other art forms)
visual art, a visual object or experience consciously created through an expression of skill or
imagination. The term art encompasses diverse media such as painting, sculpture, printmaking,
drawing, decorative arts, photography, and installation"(Britannica 2022). The visual arts are not
all the arts. Visual arts are visual. Other arts are different. Arts other than the visual ones exist
and may be more common depending on what the economy allows for. A lot of arts depend on
software, perhaps the digital ones especially. However, the digital arts are not married to one
form of software.
The building of software involves other fundamentals and applications as well. One
course teaches the following "Topics include algorithm development, problem-solving
(decomposition and synthesis), program design, data representation, arithmetic and logical
expressions, input/output operations, basic user interfaces, and object-oriented programming and
design, with an emphasis on developing good programming habits.
Intensive programming assignments are required(Carnegie Mellon University Africa). The
development of algorithms is fundamental to software development. Problems as far as
decomposition and synthesis are fundamental to software development. Program design is
fundamental to software development. A significant amount of software development is learned
by doing.
Learning to code is a highly important skill because the development of software is so
important. Coding languages often change, thus the skill of figuring out is highly important
(Shyu 2018). According to Shyu, coding languages change so often that the virtue of patience is
fundamental. One needs to learn a new coding language very commonly. The ability to think is
highly important.
According to some, coding may become an obsolete skill due developments stemming
from itself. Everyone is a software developer because the skills required have become so simple
due to the technological advancements(Brinker 2019). While everyone may be a software
developer, skills do not easily become obsolete. A skill may never become obsolete because the
possibilities of its necessity may manifest unexpectedly. The ability to learn is perhaps the
greatest skill.
When someone is at a financial risk for giving inaccurate advice the person will probably
at least strive to be accurate. Liberal arts will increase in value as the world becomes more
automated because the more technical skills will become more automated and studies
agree(University of Dallas). Mark Cuban believes that liberal arts will become valuable due the
harder skills becoming more automated. Mark Cuban could be inaccurate but he is most likely at
least attempting to be accurate because he could face financial losses for hurting his reputation
and brand. The University of Dallas states that studies back his advice. The conclusion seems
logical as well.
As the research shows, the most valuable skills in the arts may be creativity and critical
thinking. Also the ability and willingness to learn. Technology changes so the methods of
execution also change. Having memorization of specific technology is not necessarily important
because it changes.
Works Cited
Britannica, The Editors of Encyclopedia. "art". Encyclopedia Britannica, 24 Aug. 2022,
https://www.britannica.com/art/visual-arts. Accessed 2 October 2022.
Carnegie Mellon University Africa. "Fundamentals of Software Development & Problem
Solving." africa.engineering.cmu.edu.
www.africa.engineering.cmu.edu/academics/courses/04-330.html. Accessed 2 Oct. 2022.
Shyu , Patrick . "How to learn to code(quickly and easily!)." youtube.com, uploaded by
youtube.com, 9 June 2018, youtu.be/R2pIutTspQA.
Brinker, Scott . "Everyone Is A Software Developer ." ted.com, uploaded by youtube.com, 27
Nov. 2019, youtu.be/c2sNTAaILdA.
University Of Dallas . "AN UNLIKELY ALLY ." udallas.edu.
udallas.edu/news/2017/billionaire-business-mogul-boldly-champions-liberal-arts-as-the-f
uture. Accessed 2 Oct. 2022.
Alexander Ohnemus
No Professor
Business
02 October 2022
The Future of Intellectuals
Intellectuals are workers whose end products are ideas. With the growth of automation
the intellectuals will be more capable of developing further end products than just ideas. The
reason is the skills of product development will become more automated therefore the ability of
deep thinking may drastically increase.
Many argue that people whose end products are ideas do not face direct enough
consequences to incentive their idea making. Therefore accountability may not encourage
intellectuals enough to impact their end products(Taleb 2018). Nassim Taleb is an author whose
sources of income are often not from his writings. Nassim Taleb is also a trader in the stock
market. Therefore Taleb is not influenced by his opinions' financial results very directly. Taleb
cares more about the accuracy of the evidence behind his opinions than the popularity of them.
Taleb wrote a book with the title skin in the game. The definition of skin in the game is
"to be directly involved in or affected by something, especially financially"(Cambridge). If
someone's end products are ideas and their task is to influence popular opinion then that
individual may not face direct enough accountability to influence their work. Someone may have
an opinion just to be popular and may not consider the evidence behind it. While in other
endeavors' end products allow for more accountability for their makers. Having skin in the game
may be the correct way to ensure someone does a positive job.
If someone is tasked with hiring and overseeing an economy correctly or losing money
directly then that individual is more likely to give accurate vocational advice. Liberal Arts
education will increase in value because technical skills will become more automated according
to both Mark Cuban and studies (University of Dallas). That claim seems logical. The more a
skill is in demand for, the more people will pay for that skill. If more technical skills are
automated then they will become less in demand. By contrast, soft skills will become more in
demand. Mark Cuban argued that liberal arts will become more in demand due to the automation
of technical skills. Studies back Mark Cuban's predictions. Logic also backs Mark Cuban's
claims.
If trends are already observable then they are more likely to be good evidence. Everyone
is a software developer because the technical skills behind software development have become
more automated (Brinker 2019). Ted Talks have credibility and science standards. Ted Talks are
peer reviewed. Thus, Brinker is likely giving accurate information. If the technical skills behind
software development are more automated then the other ones will become more in demand.
Intellectuals will soon be more accountable for their ideas because they have more
opportunity to translate their work into end products that command more accountability.
Evidence for this can be found in studies. Evidence can also be found in industry trends. A third
source of evidence may be people at financial risk for having a bad reputation. Or if the advisor
has to rely on the workforce they are advising.
Works Cited
Taleb , Nassim . "Skin in the Game: Hidden Asymmetries in Daily Life (Incerto) ." amazon.com.
27 Feb. 2017.
www.amazon.com/Skin-Game-Hidden-Asymmetries-Daily/dp/042528462X. Accessed 2
Oct. 2022.
Cambridge University Press. "have skin in the game ." dictionary.cambridge.org.
dictionary.cambridge.org/us/dictionary/english/have-skin-in-the-game. Accessed 2 Oct.
2022.
University Of Dallas . "AN UNLIKELY ALLY ." udallas.edu.
udallas.edu/news/2017/billionaire-business-mogul-boldly-champions-liberal-arts-as-the-f
uture. Accessed 2 Oct. 2022.
Brinker, Scott . "Everyone Is A Software Developer ." ted.com, uploaded by youtube.com, 27
Nov. 2019, youtu.be/c2sNTAaILdA.
Alexander Ohnemus
No Professor
Business/Philosophy
02 October 2022
The Importance of Rationality in Avoiding Self Sabotage
Learning not to self sabotage can be done by examining motives for the behavior. The
reasons why people sabotage themselves could be several. Ways to avoid self sabotage could be
rationality but more directly not rationalizing irrational behavior. Another reason for self
sabotage could be feelings of altruism.
If someone was always being rational then that person probably would not sabotage
themself. By definition rationality is "the quality or state of being agreeable to
reason"(Merriam-Webster). Reason is the same as logic. If someone is being unreasonable then
that individual is not rational. Being rational is a great step to avoiding self sabotage.
Unfortunately people can rationalize irrational behavior. To rationalize is "broadly : to
create an excuse or more attractive explanation for"(Merriam-Webster). Rationalizing can be
finding excuses for irrational behavior. If someone did something that person could look in their
mind for excuses to persuade themself that they are actually rational. Self-deception does not
necessarily lead to self sabotage but both can involve irrationality.
Some evidence in the social science of psychology has indicated generosity may go too
far. A casual relationship can be found between self sabotage and generosity (Svoboda 2013).
That finding is logical. Someone could be motivated by generosity to sabotage themself. The
definition of success and failure would be needed variables. If a source could take financial risk
for publishing incorrect information then it may be correct.
While Psychology Today is not considered a scholarly source it is acceptable for papers
according to some educational institutions. "Psychology Today would not be considered a
scholarly journal and it is not peer reviewed. It is more of a popular magazine, like for example,
Time Magazine. It does, however, have credible sources, and most instructors would agree that it
is an acceptable source for a paper"(Peschel 2021). A .edu website is likely a credible source
depending on factors. Plus if the information of a source follows logic then the source may at
least be correct about what is reasonable. Logic is a great indicator for correct information.
As the research demonstrates, self sabotage may come from both irrational behavior and
over altruistic behavior. Considerations are the definition of success and failure.
Works Cited
“Rationality.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/rationality. Accessed 2 Oct. 2022.
“Rationalize.” Merriam-Webster.com Dictionary, Merriam-Webster,
https://www.merriam-webster.com/dictionary/rationalize. Accessed 2 Oct. 2022.
Svoboda , Elizabeth . "Selflessness... or Self-Sabotage? When generosity goes too far. ."
psychologytoday.com. 5 July 2013.
www.psychologytoday.com/us/blog/what-makes-hero/201307/selflessness-or-self-sabota
ge-0. Accessed 2 Oct. 2022.
Peschel, Shelley. "Q. What is a scholarly or academic journal? ." answers.matc.edu. 21 July
2021. answers.matc.edu/faq/212523. Accessed 2 Oct. 2022.
Alexander Ohnemus
No professor
Business
5 October 2022
Risk Takers: Investigation for Transferable Skills
In the upcoming economy two kinds of skills will have much value. Building artificial
intelligence is one. Another is skills that cannot be replaced by artificial intelligence. One of the
greatest skills may be making sense of complex situations. Or making the complex simple.
Building artificial intelligence must be a very complicated task. Therefore it should be
broken down to make it as simple and easy as possible. Unfortunately building artificial
intelligence may be difficult to describe because it depends so much on different environments.
"Step 1: The First Component to Consider When Building the AI Solution Is the Problem
Identification"(Melkonyan 2022). That means choosing a problem for the artificial intelligence
to solve. One problem could be automating the technical filmmaking skills. If the skills were
automated then only soft skills would be needed to create films. That would be an example of a
problem. "Step 2: Have the Right Data and Clean It"(Melkonyan 2022). Two kinds of data
needed are structured and unstructured. They then must be cleaned. "Structured data is clearly
defined information that includes patterns and easily searchable parameters. For example, names,
addresses, birth dates, and phone numbers"(Melkonyan 2022). Structured data is more clearly
defined information. The data includes patterns and easily searchable parameters. Examples
would be names, addresses, birthdates, and phone numbers. This type of data is structured. The
other type is unstructured. "Unstructured data doesn’t have patterns, consistency, or uniformity. It
includes audio, images, infographics, and emails"(Melkonyan 2022). The structuring of the data
is created by patterns, consistency or uniformity, depending on the data. Unstructured data
includes audio, images, infographics, and emails. "Step 3: Create Algorithms"(Melkonyan 2022).
To instruct a machine what to do and how to do must be included. The algorithms are how to do
instructions. "It’s necessary to create prediction or classification machine learning algorithms so
the AI model can learn from the dataset"(Melkonyan 2022). The data set is the what to do
instructions. The algorithms are the instructions. The algorithms are meant for machine learning.
The algorithms help the machines predict or classify. "Step 4: Train the Algorithms"(Melkonyan
2022). The algorithms may act as instructions but they still need to train themselves. They should
be trained by setting standards for their behavior. Minimums must be set as thresholds for their
actions. "For example, a social networking company working on deleting fake accounts can set a
“fraud score” between zero and one to each account. After some research, the team can decide to
send all the accounts with a score above 0.9 to the fraud team"(Melkonyan 2022). The minimum
fraud scores to catch were behavioral standards for the algorithms. The standards were between
zero and one. Different algorithms could have different standards for their behavior. Depends on
what an algorithm can recognize as a standard for its behavior. "Step 5: Opt for the Right
Platform"(Melkonyan 2022). Choosing the correct platform in other tasks can be highly
important. A key question is what is the definition of platform. "In-house Frameworks
For example, you can choose Scikit, Tensorflow, and Pytorch. These are the most popular
ones for developing models internally"(Melkonyan 2022). In house platforms are intuitively used
for developing modeling internally. The phrase in house sounds internal. "Cloud Frameworks
With an ML-as-a-Service platform or ML in the cloud, you can train and deploy your
models faster. You can use IDEs, Jupyter Notebooks, and other graphical user interfaces to build
and deploy your models"(Melkonyan 2022). Speed would be a high consideration for being
external and not internal. Perhaps a more descriptive explanation is necessary. Of course speed is
an imperative if the artificial intelligence is being used for market purposes. Efficiency is part of
being more marketable. What can be made faster, if made in high enough quality, may have an
advantage over its competition. "The cloud makes it easy for enterprises to experiment and grow
as projects go into production and demand increases by allowing faster training and deployment
of ML models"(Melkonyan 2022). Perhaps the in-house platforms hold no advantages over the
cloud platforms. Or perhaps the cloud offers less protection. What is internal may have higher
security. Security questions are still to be answered. "Step 6: Choose a Programming
Language"(Melkonyan 2022). A programming language is highly important to be chosen.
Programming languages can change in usage. Being a programmer is different from other
professions. A programmer must always learn new languages or stagnate. In other professions
experience just allows one to not need to learn more as a programmer has to. "Step 7: Deploy
and Monitor"(Melkonyan 2022). Deploy and monitor may be self explanatory. The step is to let
the models operate after coming up with the solution. The models should be monitored in case of
errors.
Before determining a solution a problem must be elected. The process is very similar to
selecting a business idea. One should select a business idea that without too much competition
for the profits(Harvard Business Review 2019). If too much competition exists for the profits
then the business idea may no longer be profitable. If the goal is to make a profit then an idea
should be chosen without too much competition to make that money. In games the more
completion the less of a profit will be available. Although real life situations may be paradoxes
to challenge these guidelines of avoiding competition.
Some more guidelines may be auxiliary for identifying business ideas. Civilization is
advanced by technology (Thiel 2010). Peter Thiel is a business owner so if he gave misleading
advice to people he could face major financial losses. Thiel also gave a Tedx Talk so he had to
meet high enough standards. His advice is likely accurate. A helpful way to think of business
ideas is by identifying problems holding civilization's well-being back and solving those
obstacles.
Data collection has a slew of consumer types "In general, there are three types of
consumer data: First-party data, which is collected directly from users by your
organization…Second-party data, which is data shared by another organization about its
customers (or its first-party data)...Third-party data, which is data that’s been aggregated and
rented or sold by organizations that don’t have a connection to your company or users"(Cote
2021). The party is numbered by connection to the customers. An organization collecting data
from its own customers is a first party. Another organization's data from its own customers is
second party. Third party data is collected and rented or sold by contractors. The third party has
collection contractors.
Data can be in different natures as well. "Data can be qualitative (meaning contextual in
nature) or quantitative (meaning numeric in nature)"(Cote 2021). Qualitative data involves
context. Qualitative is not numerical. Quantitative data has more to do with numbers than
qualitative data does. Quantitative data is numerical.
Some factors need to be defined before data collection. "Before collecting data, there are
several factors you need to define:The question you aim to answer…The data subject(s) you
need to collect data from…The collection timeframe…The data collection method(s) best suited
to your needs(Cote 2021). This kind of data collection is not as important for the purpose of
building artificial intelligence. However, knowing popular opinion can be beneficial for coming
up with a business idea.
Data collection for machine learning is different from collecting data to think of a
business idea itself.
"How to Start Collecting Data for ML: Data Collection Strategy
For some companies, there shouldn’t be any problems with data collection in Machine
Learning, since they’ve been gathering all this data for years and piles of papers and documents
are now only waiting to be digitized. Or, if they had thought about it before, all documents have
already been transferred into an electronic format. If this is your case – you are lucky, and your
problem is now to prepare that data, process it, and decide on the usability for the task at hand.
If you don’t have luck, and you don’t have any data, do not despair – in the 21st century,
you can find a reference dataset online and use it to solve your task. The dataset can be publicly
accessible, or you might need to purchase it.
While you’ll be occupied with analyzing the dataset, you should also start the process of
collecting your own data in the right shape and format. It could be the same format as in the
reference dataset (if that fits your purpose), or if the difference is quite substantial – some other
format.
The data are usually divided into two types: Structured and Unstructured. The simplest
example of structured data would be a .xls or .csv file where every column stands for an attribute
of the data. Unstructured data could be represented by a set of text files, photos, or video files.
Often, business dictates how to organize the collection and the storage of data. For example, if
the task is to build a system that could detect pneumonia from an image of the lungs, you need
specialized equipment to create a catalog of digital images. At the same time, if you need to
create a recommendation system for eCommerce, there’s no need for any additional technical
solutions; all the needed data is provided by the user when purchasing a product.
Where can you “borrow” a dataset? Here are a couple of data sources you could try:
Dataset Search by Google – allows searching not only by the keywords, but also filtering
the results based on the types of the dataset that you want (e.g., tables, images, text), or based on
whether the dataset is available for free from the provider.
Visual Data Discovery – specializes in Computer Vision datasets, all datasets are
explicitly categorized and are easily filtered.
OpenML – as stated in the documentation section it’s ‘an open, collaborative, frictionless,
automated machine learning environment’. This is a whole resource that allows not only sharing
data, but also working on it collaboratively and solving problems in cooperation with other data
scientists.
UCI: Machine Learning Repository – a collection of datasets and data generators, that is
listed in the top 100 most quoted resources in Computer Science.
Awesome Public Datasets on Github- it would be weird if Github didn’t have its own list
of datasets, divided into categories.
Kaggle – one of the best, if not the best, resource for trying ML for yourself. Here you
can also find data sets divided into categories with usability scores (an indicator that the dataset
is well-documented).
Amazon Datasets – lots of datasets stored in S3, available for quick deployment if you’re
using AWS.
and many other excellent resources where you can find data sets from versatile areas:
starting from the apartment prices in Manhattan for the last 10 years and ending with the
description of space objects.
Still lacking sample data? You might need…
Data Augmentation
Let’s imagine for a second that we were not able to find a dataset that would meet all our
requirements, BUT at the same time, we have a certain amount of basic data. Can we work with
it? Yes, we can, but we’ll need to apply augmentation methods to our dataset to increase the
number of samples.
Definition: Data augmentation is the increase of an existing training dataset’s size and
diversity without the requirement of manually collecting any new data.
The process of data augmentation means that the input data will undergo a set of
transformations and this way, thanks to the variations of data samples, our dataset will become
richer. For example, if we deal with images, the number of augmentations that we can utilize is
sufficient, because an image can be cut, mirrored, turned upside down, etc. Moreover, we can
change the color settings with the help of brightness, saturation, contrast, clarity, and blur. These
are the so-called ‘photometric transformations’.
The most popular ML frameworks provide quite advanced means for image
augmentation:
TensorFlow – allows to set ranges for rotation angles, brightness, zoom, rescale, etc.
There’s an option to turn on a built-in transformer feature in the generation flow of new samples.
Scikit Image – a great library which helps not only to conduct basic operations with
images, but also works with color spaces and allows you to apply filters.
OpenCV – a pioneer of Computer Vision. In this Python-based library there are tools for
rotation, scaling, filters, cropping, etc.
Synthetic Data Generators
Ok, we figured out the images, but what if we have tables with data, but there’s not
enough data – where do we get more? In this case, we can turn to data generators, but to use
them we need to understand the rules and laws of how a dataset is formed. The importance of
synthetic data cannot be overestimated. They can help when:
you need to test a new product, but you don’t have any real-life data. Imagine, for
example, an engine or sensor on the space probe: it will begin collecting data already in space or
even on another planet, but you need to check how it would work when it’s still on Earth.
there’s an important matter of sensitive data and its privacy – and the access to real data
is limited. It is especially the case when we deal with medical data or sensitive personalized data.
you need to expand the training dataset for the ML model – this happens to be our
situation!
In general, data generators can be split into two broad groups:
the ones that use some distribution model to generate data. It can be a distribution based
on the real data, or, in the absence of such, a choice in favor of any of the distributions is made
by the data scientists based on their knowledge in the given field. In many cases the Monte Carlo
method is used for the task.
the ones that use Deep Learning techniques: Generative Adversarial Network(GAN) and
Variational Autoencoder(VAE). Both of these methods rely on neural networks to generate data
and require an excellent knowledge of the field from a data scientist.
If we take Python (as one of the best programming languages for ML), we’ll have a
choice among the following tools:
The well-known Scikit-learn – one of the most widely used libraries with Python for ML.
It contains tools to generate synthetic data not only for classification and regression tasks, but
also for clusterization.
SymPy is a fantastic library that helps in solving the problem of symbolic expression
input. SymPy can simplify expressions, compute derivatives, integrals, and limits, solve
equations and work with matrices.
Pydbgen is a lightweight library for categorical data generation. It can generate random
entities, like names, emails, credit card numbers, phone numbers and export this data into Excel
files or SQLite tables.
Lazy Learning
Another ‘magic wand’ for cases when it’s hard to “flesh out” the training dataset is
Transfer Learning.
Definition: Transfer learning is an area in ML that utilizes the knowledge gained while
solving one problem to solve a different, but, related problem.
It’s just the way the human brain works: it’s easier for us to learn new things if we’ve had
similar experiences in the past. Let’s say, it’s easy to learn to ride a bike if you mastered a bike
with training wheels before that. Learning a new programming language when you’ve been
programming using other languages also shouldn’t be as hard. Just like with ML – you shouldn’t
reject the existing experience, even if this experience is somebody else’s and provided for public
use.
Along with the rise of Computer Vision in recent years, the use of pre-trained models for
object classification and identification has become a thing. Even now, in order to train a model
for image classification, it will take days of processing. Taking into account the iterative and
repetitive nature of Data Science, the search for the best model parameters can drag on for
months. That is why the use of pre-trained models can save a lot of time and effort for data
scientists in cases when you need a lot of input data for the evaluation of your hypothesis.
Here are some of the great examples of pre-trained models for Image Classification:
Oxford’ VGG-16, year 2014
Microsoft’s ResNet50, year 2015
Google’s InceptionV3, year 2015
Google’s EfficientNet, year 2019
You can also find quite decent pre-trained models from other areas, for example, audio or
video processing or even natural text processing.
How to Work with Existing Data: Data Cleaning, Labeling
What is Machine Learning?
Now that we have data, it’s high time to figure out what Machine Learning is. In simple
words, ML means extracting knowledge from data.
Definition: “Machine Learning – it’s a field of study that gives computers the ability to
learn without being explicitly programmed” – Arthur Samuel
If we look at Drew Conway’s Venn diagram of data science, we can see clear areas that
interact with ML: Computer Science, Math, and Statistics. You will also notice on the diagram
that ML is a subset of Data Science, but we’ll come back to that later.
Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
ML has many subfields and applications, including neural networks(NN), genetic
algorithms, data mining, computer vision, natural language processing (NLP), and others.
Depending on what we’re trying to achieve from the output and which data we have on the input,
we can define 3 main types of ML:
Supervised learning: the goal here would be to train a model that allows predictions to be
made on unseen future data. For this to happen data must be labeled;
Unsupervised learning: this type of learning works with unlabeled data and its goal would
be to find hidden patterns in this data, and, probably some meaningful information;
Reinforcement learning: the goal here would be to develop a system that learns and
improves over time by interacting with the environment.
The choice among the three depends on the problem we’re trying to solve, which in turn,
stems from the questions we should have asked ourselves (and answered, preferably) at the very
beginning. If the problem has to do with classification(distinguishing cats from dogs in a
photograph) or regression(predicting the weather for next month), our top choice is Supervised
learning. If we have unlabeled data and need to perform clustering(segment the customers of an
online store) or dimensionality reduction(remove the extra features from a model) or
anomaly/outlier detection(find users with strange or suspicious websites browsing patterns) – use
Unsupervised learning. As you can see, these two types of ML solve a broad spectrum of tasks,
and the main difference between them, besides the tasks, lies in data: Supervised learning uses
labeled data, while Unsupervised learning doesn’t necessarily need to.
Where does Labeled Data dwell?
So, say, we find ourselves with a completely unlabeled OR partially labeled dataset in our
hands and a multi-classification problem we need to solve with it. Where do we go from here and
how do we get our dataset labeled?
First of all, we need to figure out what Data Labeling is.
Data Labeling – it’s the process of data tagging or annotation for use in machine learning.
Labels are different and unique for each specific dataset, depending on the task at hand.
The same dataset can have different meanings of labels and use them for various tasks. For
example, the classification of cats and dogs can turn into the classification of animals that have
spots on the fur and the ones that don’t.
Depending on the size and complexity of the dataset, the size of the in-house Data
Science team, and also the time and budget, we can have several variations of how the Data
Labeling process is organized:
Crowdsourcing: a third-party gives a platform for individuals and businesses to outsource
their processes and jobs;
Outsourcing: hiring freelancers or contractors;
Specialized teams: hiring teams that work in the field of Data Labeling and are trained
and managed by third-party organization;
In-house teams: giving tasks of Data Labeling to the internal team of workers or data
scientists.
Each of these has its own pros and cons(such as the quality of the results, the cost of the
job, or the speed in which labeling is completed), and one method that suits one endeavor may
not work for another. Moreover, you can combine them as you go.
If you cannot afford to hire a dedicated team for Data Labeling and you’ve decided to do
everything in-house, you can’t do without software tools to help with your task:
LabelBox, Annotorious, VGG Image Annotator, VoTT, ML Kit for Firebase – images
annotation tools
Anvil, VoTT, VGG Image Annotator, CVAT – video annotation tools
Stanford CoreNLP, Brat, Dataturks, Tagtog – text annotation tools
Prodigy, EchoML, Praat – audio annotation tools
Here you can find even more tools to choose from.
Ok, but can we partially use labeled data and conduct the labeling for the whole dataset?
Yes, we can, with the help of Semi-Supervised Learning(SSL).
Definition: Semi-supervised learning is an approach to machine learning that combines a
small amount of labeled data with a large amount of unlabeled data during training. – Wiki
The use of semi-supervised learning is especially helpful when there are reasons you
can’t get a fully labeled dataset – reasons that might be financial or time-related, while the
amount of unlabeled data is sufficient. Unlike supervised learning (which needs labeled data)
and unsupervised learning (which works with unlabeled data), semi-supervised learning methods
can handle both types of data at once. This way, using SSL we can turn the problem of a small
labeled dataset into an advantage and build a process where a big unlabeled dataset will
iteratively get labeled thus increasing the general usability of our solution. This approach is
successfully applied in various areas, for example in Healthcare during the classification of
cancerous malformations.
The easiest SSL method would consist of the following steps:
train your classifier with labeled data;
apply this classifier to the unlabeled data and get the classes’ probability information;
assign labels to the most confident data samples;
train the classifier with newly labeled data added to the initial labeled dataset;
repeat until some convergence criterion is met.
As shown in image a) above, the decision boundary for a labeled dataset only can be
relatively simple and not reflect the real dependencies inside the dataset. At the same time, when
you have a fully annotated dataset with both labeled and unlabeled data, the decision boundary
might be absolutely different –see image b).
To sum up Data Labeling, I’d like to add that the accuracy of data labeling greatly
influences the model’s performance, thus making the process of Data Labeling one of the key
factors in the pre-processing of data. To mitigate the impact of mislabeling, it’s worth taking a
Human-in-the-Loop (HITL) approach: this is when a human controller keeps an eye on the
model’s training and testing throughout its evolution"(Zubchenko 2021). That is how to collect
data for machine learning.
Next is problem solving with algorithms. "Step 1: Obtain a description of the
problem"(Edwards). Before solving a problem, you must know what the problem is. Often
discovering the cause is the path to the solution. "Part of the developer's responsibility is to
identify defects in the description of a problem, and to work with the client to remedy those
defects"(Edwards). The client is supposed to describe the problem. If the description is difficult
to work with them the developer is responsible for working with the client to improve the
description. One example of a problem is the difficulty of technical skills in filmmaking. "Step 2:
Analyze the problem"(Edwards). Many questions are part of the analysis. The list of questions
could be difficult to answer. "When determining the starting point, we should start by seeking
answers to the following questions:
What data are available?
Where is that data?
What formulas pertain to the problem?
What rules exist for working with the data?
What relationships exist among the data values?
When determining the ending point, we need to describe the characteristics of a solution.
In other words, how will we know when we're done? Asking the following questions often helps
to determine the ending point.
What new facts will we have?
What items will have changed?
What changes will have been made to those items?
What things will no longer exist?"(Edwards).
"Objects First
Problem Solving and Algorithms
Learn a basic process for developing a solution to a problem. Nothing in this chapter is
unique to using a computer to solve a problem. This process can be used to solve a wide variety
of problems, including ones that have nothing to do with computers.
Problems, Solutions, and Tools
I have a problem! I need to thank Aunt Kay for the birthday present she sent me. I could
send a thank you note through the mail. I could call her on the telephone. I could send her an
email message. I could drive to her house and thank her in person. In fact, there are many ways I
could thank her, but that's not the point. The point is that I must decide how I want to solve the
problem, and use the appropriate tool to implement (carry out) my plan. The postal service, the
telephone, the internet, and my automobile are tools that I can use, but none of these actually
solves my problem. In a similar way, a computer does not solve problems, it's just a tool that I
can use to implement my plan for solving the problem.
Knowing that Aunt Kay appreciates creative and unusual things, I have decided to hire a
singing messenger to deliver my thanks. In this context, the messenger is a tool, but one that
needs instructions from me. I have to tell the messenger where Aunt Kay lives, what time I
would like the message to be delivered, and what lyrics I want sung. A computer program is
similar to my instructions to the messenger.
The story of Aunt Kay uses a familiar context to set the stage for a useful point of view
concerning computers and computer programs. The following list summarizes the key aspects of
this point of view.
A computer is a tool that can be used to implement a plan for solving a problem.
A computer program is a set of instructions for a computer. These instructions describe
the steps that the computer must follow to implement a plan.
An algorithm is a plan for solving a problem.
A person must design an algorithm.
A person must translate an algorithm into a computer program.
This point of view sets the stage for a process that we will use to develop solutions to
Jeroo problems. The basic process is important because it can be used to solve a wide variety of
problems, including ones where the solution will be written in some other programming
language.
An Algorithm Development Process
Every problem solution starts with a plan. That plan is called an algorithm.
An algorithm is a plan for solving a problem.
There are many ways to write an algorithm. Some are very informal, some are quite
formal and mathematical in nature, and some are quite graphical. The instructions for connecting
a DVD player to a television are an algorithm. A mathematical formula such as πR2 is a special
case of an algorithm. The form is not particularly important as long as it provides a good way to
describe and check the logic of the plan.
The development of an algorithm (a plan) is a key step in solving a problem. Once we
have an algorithm, we can translate it into a computer program in some programming language.
Our algorithm development process consists of five major steps.
Step 1: Obtain a description of the problem.
Step 2: Analyze the problem.
Step 3: Develop a high-level algorithm.
Step 4: Refine the algorithm by adding more detail.
Step 5: Review the algorithm.
Step 1: Obtain a description of the problem.
This step is much more difficult than it appears. In the following discussion, the word
client refers to someone who wants to find a solution to a problem, and the word developer refers
to someone who finds a way to solve the problem. The developer must create an algorithm that
will solve the client's problem.
The client is responsible for creating a description of the problem, but this is often the
weakest part of the process. It's quite common for a problem description to suffer from one or
more of the following types of defects: (1) the description relies on unstated assumptions, (2) the
description is ambiguous, (3) the description is incomplete, or (4) the description has internal
contradictions. These defects are seldom due to carelessness by the client. Instead, they are due
to the fact that natural languages (English, French, Korean, etc.) are rather imprecise. Part of the
developer's responsibility is to identify defects in the description of a problem, and to work with
the client to remedy those defects.
Step 2: Analyze the problem.
The purpose of this step is to determine both the starting and ending points for solving the
problem. This process is analogous to a mathematician determining what is given and what must
be proven. A good problem description makes it easier to perform this step.
When determining the starting point, we should start by seeking answers to the following
questions:
What data are available?
Where is that data?
What formulas pertain to the problem?
What rules exist for working with the data?
What relationships exist among the data values?
When determining the ending point, we need to describe the characteristics of a solution.
In other words, how will we know when we're done? Asking the following questions often helps
to determine the ending point.
What new facts will we have?
What items will have changed?
What changes will have been made to those items?
What things will no longer exist?
Step 3: Develop a high-level algorithm.
An algorithm is a plan for solving a problem, but plans come in several levels of detail.
It's usually better to start with a high-level algorithm that includes the major part of a solution,
but leaves the details until later. We can use an everyday example to demonstrate a high-level
algorithm.
Problem: I need a send a birthday card to my brother, Mark.
Analysis: I don't have a card. I prefer to buy a card rather than make one myself.
High-level algorithm:
Go to a store that sells greeting cards
Select a card
Purchase a card
Mail the card
This algorithm is satisfactory for daily use, but it lacks details that would have to be
added were a computer to carry out the solution. These details include answers to questions such
as the following.
"Which store will I visit?"
"How will I get there: walk, drive, ride my bicycle, take the bus?"
"What kind of card does Mark like: humorous, sentimental, risqué?"
These kinds of details are considered in the next step of our process.
Step 4: Refine the algorithm by adding more detail.
A high-level algorithm shows the major steps that need to be followed to solve a
problem. Now we need to add details to these steps, but how much detail should we add?
Unfortunately, the answer to this question depends on the situation. We have to consider who (or
what) is going to implement the algorithm and how much that person (or thing) already knows
how to do. If someone is going to purchase Mark's birthday card on my behalf, my instructions
have to be adapted to whether or not that person is familiar with the stores in the community and
how well the purchaser known my brother's taste in greeting cards.
When our goal is to develop algorithms that will lead to computer programs, we need to
consider the capabilities of the computer and provide enough detail so that someone else could
use our algorithm to write a computer program that follows the steps in our algorithm. As with
the birthday card problem, we need to adjust the level of detail to match the ability of the
programmer. When in doubt, or when you are learning, it is better to have too much detail than to
have too little.
Most of our examples will move from a high-level to a detailed algorithm in a single step,
but this is not always reasonable. For larger, more complex problems, it is common to go through
this process several times, developing intermediate level algorithms as we go. Each time, we add
more detail to the previous algorithm, stopping when we see no benefit to further refinement.
This technique of gradually working from a high-level to a detailed algorithm is often called
stepwise refinement.
Stepwise refinement is a process for developing a detailed algorithm by gradually adding
detail to a high-level algorithm.
Step 5: Review the algorithm.
The final step is to review the algorithm. What are we looking for? First, we need to work
through the algorithm step by step to determine whether or not it will solve the original problem.
Once we are satisfied that the algorithm does provide a solution to the problem, we start to look
for other things. The following questions are typical of ones that should be asked whenever we
review an algorithm. Asking these questions and seeking their answers is a good way to develop
skills that can be applied to the next problem.
Does this algorithm solve a very specific problem or does it solve a more general
problem? If it solves a very specific problem, should it be generalized?
For example, an algorithm that computes the area of a circle having radius 5.2 meters
(formula π*5.22) solves a very specific problem, but an algorithm that computes the area of any
circle (formula π*R2) solves a more general problem.
Can this algorithm be simplified?
One formula for computing the perimeter of a rectangle is:
length + width + length + width
A simpler formula would be:
2.0 * (length + width)
Is this solution similar to the solution to another problem? How are they alike? How are
they different?
For example, consider the following two formulae:
Rectangle area = length * width
Triangle area = 0.5 * base * height
Similarities: Each computes an area. Each multiplies two measurements.
Differences: Different measurements are used. The triangle formula contains 0.5.
Hypothesis: Perhaps every area formula involves multiplying two measurements.
Example 4.1: Pick and Plant
This section contains an extended example that demonstrates the algorithm development
process. To complete the algorithm, we need to know that every Jeroo can hop forward, turn left
and right, pick a flower from its current location, and plant a flower at its current location.
Problem Statement (Step 1)
A Jeroo starts at (0, 0) facing East with no flowers in its pouch. There is a flower at
location (3, 0). Write a program that directs the Jeroo to pick the flower and plant it at location
(3, 2). After planting the flower, the Jeroo should hop one space East and stop. There are no
other nets, flowers, or Jeroos on the island.
Start Finish
The starting situation for example 4.1 The finishing situation for example 4.1
Analysis of the Problem (Step 2)
The flower is exactly three spaces ahead of the jeroo.
The flower is to be planted exactly two spaces South of its current location.
The Jeroo is to finish facing East one space East of the planted flower.
There are no nets to worry about.
High-level Algorithm (Step 3)
Let's name the Jeroo Bobby. Bobby should do the following:
Get the flower
Put the flower
Hop East
Detailed Algorithm (Step 4)
Let's name the Jeroo Bobby. Bobby should do the following:
Get the flower
Hop 3 times
Pick the flower
Put the flower
Turn right Hop 2 times Plant a flower
Hop East
Turn left Hop once
Review the Algorithm (Step 5)
The high-level algorithm partitioned the problem into three rather easy subproblems. This
seems like a good technique.
This algorithm solves a very specific problem because the Jeroo and the flower are in
very specific locations.
This algorithm is actually a solution to a slightly more general problem in which the
Jeroo starts anywhere, and the flower is 3 spaces directly ahead of the Jeroo.
Java Code for "Pick and Plant"
A good programmer doesn't write a program all at once. Instead, the programmer will
write and test the program in a series of builds. Each build adds to the previous one. The
high-level algorithm will guide us in this process.
A good programmer works incrementally, add small pieces one at a time and constantly
re-checking the work so far.
FIRST BUILD
To see this solution in action, create a new Greenfoot4Sofia scenario and use the Edit
Palettes Jeroo menu command to make the Jeroo classes visible. Right-click on the Island class
and create a new subclass with the name of your choice. This subclass will hold your new code.
The recommended first build contains three things:
The main method (here myProgram() in your island subclass).
Declaration and instantiation of every Jeroo that will be used.
The high-level algorithm in the form of comments.
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public void myProgram()
{
Jeroo bobby = new Jeroo();
this.add(bobby);
// --- Get the flower ---
// --- Put the flower ---
// --- Hop East ---
} // ===== end of method myProgram() =====
The instantiation at the beginning of myProgram() places bobby at (0, 0), facing East,
with no flowers.
Once the first build is working correctly, we can proceed to the others. In this case, each
build will correspond to one step in the high-level algorithm. It may seem like a lot of work to
use four builds for such a simple program, but doing so helps establish habits that will become
invaluable as the programs become more complex.
SECOND BUILD
This build adds the logic to "get the flower", which in the detailed algorithm (step 4
above) consists of hopping 3 times and then picking the flower. The new code is indicated by
comments that wouldn't appear in the original (they are just here to call attention to the
additions). The blank lines help show the organization of the logic.
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public void myProgram()
{
Jeroo bobby = new Jeroo();
this.add(bobby);
// --- Get the flower ---
bobby.hop(3); // <-- new code to hop 3 times
bobby.pick(); // <-- new code to pick the flower
// --- Put the flower ---
// --- Hop East ---
} // ===== end of method myProgram() =====
By taking a moment to run the work so far, you can confirm whether or not this step in
the planned algorithm works as expected.
THIRD BUILD
This build adds the logic to "put the flower". New code is indicated by the comments that
are provided here to mark the additions.
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public void myProgram()
{
Jeroo bobby = new Jeroo();
this.add(bobby);
// --- Get the flower ---
bobby.hop(3);
bobby.pick();
// --- Put the flower ---
bobby.turn(RIGHT); // <-- new code to turn right
bobby.hop(2); // <-- new code to hop 2 times
bobby.plant(); // <-- new code to plant a flower
// --- Hop East ---
} // ===== end of method myProgram() =====
FOURTH BUILD (final)
This build adds the logic to "hop East".
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public void myProgram()
{
Jeroo bobby = new Jeroo();
this.add(bobby);
// --- Get the flower ---
bobby.hop(3);
bobby.pick();
// --- Put the flower ---
bobby.turn(RIGHT);
bobby.hop(2);
bobby.plant();
// --- Hop East ---
bobby.turn(LEFT); // <-- new code to turn left
bobby.hop(); // <-- new code to hop 1 time
} // ===== end of method myProgram() =====
Example 4.2: Replace Net with Flower
This section contains a second example that demonstrates the algorithm development
process.
Problem Statement (Step 1)
There are two Jeroos. One Jeroo starts at (0, 0) facing North with one flower in its pouch.
The second starts at (0, 2) facing East with one flower in its pouch. There is a net at location (3,
2). Write a program that directs the first Jeroo to give its flower to the second one. After
receiving the flower, the second Jeroo must disable the net, and plant a flower in its place. After
planting the flower, the Jeroo must turn and face South. There are no other nets, flowers, or
Jeroos on the island.
Start Finish
The starting situation for example 4.2 The finishing situation for example 4.2
Analysis of the Problem (Step 2)
Jeroo_2 is exactly two spaces behind Jeroo_1.
The only net is exactly three spaces ahead of Jeroo_2.
Each Jeroo has exactly one flower.
Jeroo_2 will have two flowers after receiving one from Jeroo_1.
One flower must be used to disable the net.
The other flower must be planted at the location of the net, i.e. (3, 2).
Jeroo_1 will finish at (0, 1) facing South.
Jeroo_2 is to finish at (3, 2) facing South.
Each Jeroo will finish with 0 flowers in its pouch. One flower was used to disable the net,
and the other was planted.
High-level Algorithm (Step 3)
Let's name the first Jeroo Ann and the second one Andy.
Ann should do the following:
Find Andy (but don't collide with him)
Give a flower to Andy (he will be straight ahead)
After receiving the flower, Andy should do the following:
Find the net (but don't hop onto it)
Disable the net
Plant a flower at the location of the net
Face South
Detailed Algorithm (Step 4)
Let's name the first Jeroo Ann and the second one Andy.
Ann should do the following:
Find Andy
Turn around (either left or right twice)
Hop (to location (0, 1))
Give a flower to Andy
Give ahead
Now Andy should do the following:
Find the net
Hop twice (to location (2, 2))
Disable the net
Toss
Plant a flower at the location of the net
Hop (to location (3, 2))
Plant a flower
Face South
Turn right
Review the Algorithm (Step 5)
The high-level algorithm helps manage the details.
This algorithm solves a very specific problem, but the specific locations are not
important. The only thing that is important is the starting location of the Jeroos relative to one
another and the location of the net relative to the second Jeroo's location and direction.
Java Code for "Replace Net with Flower"
As before, the code should be written incrementally as a series of builds. Four builds will
be suitable for this problem. As usual, the first build will contain the main method, the
declaration and instantiation of the Jeroo objects, and the high-level algorithm in the form of
comments. The second build will have Ann give her flower to Andy. The third build will have
Andy locate and disable the net. In the final build, Andy will place the flower and turn East.
FIRST BUILD
This build creates the main method, instantiates the Jeroos, and outlines the high-level
algorithm. In this example, the main method would be myProgram() contained within a subclass
of Island.
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public void myProgram()
{
Jeroo ann = new Jeroo(0, 0, NORTH, 1);
this.add(ann);
Jeroo andy = new Jeroo(0, 2 , 1); // default EAST
this.add(andy);
// --- Ann, find Andy ---
// --- Ann, give Andy a flower ---
// --- Andy, find and disable the net ---
// --- Andy, place a flower at (3, 2) ---
// --- Andy, face South ---
} // ===== end of method myProgram() =====
SECOND BUILD
This build adds the logic for Ann to locate Andy and give him a flower.
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public void myProgram()
{
Jeroo ann = new Jeroo(0, 0, NORTH, 1);
this.add(ann);
Jeroo andy = new Jeroo(0, 2 , 1); // default EAST
this.add(andy);
// --- Ann, find Andy ---
ann.turn(LEFT);
ann.turn(LEFT);
ann.hop();
// Now, Ann is at (0, 1) facing South, and Andy is directly ahead
// --- Ann, give Andy a flower ---
ann.give(AHEAD); // Ann now has 0 flowers, Andy has 2
// --- Andy, find and disable the net ---
// --- Andy, place a flower at (3, 2) ---
// --- Andy, face South ---
} // ===== end of method myProgram() =====
THIRD BUILD
This build adds the logic for Andy to locate and disable the net.
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public void myProgram()
{
Jeroo ann = new Jeroo(0, 0, NORTH, 1);
this.add(ann);
Jeroo andy = new Jeroo(0, 2 , 1); // default EAST
this.add(andy);
// --- Ann, find Andy ---
ann.turn(LEFT);
ann.turn(LEFT);
ann.hop();
// Now, Ann is at (0, 1) facing South, and Andy is directly ahead
// --- Ann, give Andy a flower ---
ann.give(AHEAD); // Ann now has 0 flowers, Andy has 2
// --- Andy, find and disable the net ---
andy.hop(2); // Andy is at (2, 2) facing the net
andy.toss();
// --- Andy, place a flower at (3, 2) ---
// --- Andy, face South ---
} // ===== end of method myProgram() =====
FOURTH BUILD (final)
This build adds the logic for Andy to place a flower at (3, 2) and turn South.
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public void myProgram()
{
Jeroo ann = new Jeroo(0, 0, NORTH, 1);
this.add(ann);
Jeroo andy = new Jeroo(0, 2 , 1); // default EAST
this.add(andy);
// --- Ann, find Andy ---
ann.turn(LEFT);
ann.turn(LEFT);
ann.hop();
// Now, Ann is at (0, 1) facing South, and Andy is directly ahead
// --- Ann, give Andy a flower ---
ann.give(AHEAD); // Ann now has 0 flowers, Andy has 2
// --- Andy, find and disable the net ---
andy.hop(2); // Andy is at (2, 2) facing the net
andy.toss();
// --- Andy, place a flower at (3, 2) ---
andy.hop();
andy.plant();
// --- Andy, face South ---
andy.turn(RIGHT);
} // ===== end of method myProgram() =====
© 2012 Stephen Edwards, Brian Dorn, and Dean Sanders"(Edwards).
"3 steps to training a machine learning model
When you hear the words machine learning, you probably think of face recognition,
robotics or self-driving cars. But it’s so much more than that. You don’t have to be inventing the
next big thing to leverage the power of machine learning in your business. In fact, you should be
considering all the ways machine learning could work for you today.
Machine learning is not a way to solve the problems you’re already familiar with. It’s a
way to solve new problems, business issues and tasks with data-driven predictions. To
understand how you can apply machine learning, you need to first understand how it works.
Let’s start by training a machine learning model.
Step 1: Begin with existing data
Machine learning requires us to have existing data—not the data our application will use
when we run it, but data to learn from. You need a lot of real data, in fact, the more the better.
The more examples you provide, the better the computer should be able to learn. So just collect
every scrap of data you have and dump it and voila! Right?
Wrong. In order to train the computer to understand what we want and what we don’t
want, you need to prepare, clean and label your data. Get rid of garbage entries, missing pieces
of information, anything that’s ambiguous or confusing. Filter your dataset down to only the
information you’re interested in right now. Without high quality data, machine learning does not
work. So take your time and pay attention to detail.
Step 2: Analyze data to identify patterns
Unlike conventional software development where humans are responsible for interpreting
large data sets, with machine learning, you apply a machine learning algorithm to the data. But
don’t think you’re off the hook. Choosing the right algorithm, applying it, configuring it and
testing it is where the human element comes back in.
There are several platforms to choose from both commercial and open source. Explore
solutions from Microsoft, Google, Amazon, IBM or open source frameworks like TensorFlow,
Torch and Caffe. They each have their own strengths and downsides, and each will interpret the
same dataset a different way. Some are faster to train. Some are more configurable. Some allow
for more visibility into the decision process. In order to make the right choice, you need to
experiment with a few algorithms and test until you find the one that gives you the results most
aligned to what you’re trying to achieve with your data.
When it’s all said and done, and you’ve successfully applied a machine learning
algorithm to analyze your data and learn from it, you have a trained model.
Step 3: Make predictions
There is so much you can do with your newly trained model. You could import it into a
software application you’re building, deploy it into a web back end or upload and host it into a
cloud service. Your trained model is now ready to take in new data and feed you predictions, aka
results.
These results can look different depending on what kind of algorithm you go with. If you
need to know what something is, go with a classification algorithm, which comes in two types.
Binary classification categorizes data between two categories. Multi-class classification sorts
data between—you guessed it—multiple categories.
When the result you’re looking for is an actual number, you’ll want to use a regression
algorithm. Regression takes a lot of different data with different weights of importance and
analyzes it with historical data to objectively provide an end result.
Both regression and classification are supervised types of algorithms, meaning you need
to provide intentional data and direction for the computer to learn. There is also unsupervised
algorithms which don’t require labeled data or any guidance on the kind of result you’re looking
for.
One form of unsupervised algorithms is clustering. You use clustering when you want to
understand the structure of your data. You provide a set of data and let the algorithm identify the
categories within that set. On the other hand, anomaly is an unsupervised algorithm you can use
when your data looks normal and uniform, and you want the algorithm to pull anything out of the
ordinary that doesn’t fit with the rest of the data.
Although supervised algorithms are more common, it’s good to play around with each
algorithm type and use case to better understand probability and practice splitting and training
data in different ways. The more you toy with your data, the better your understanding of what
machine learning can accomplish will become.
Ultimately, machine learning helps you find new ways to make life easier for your
customers and easier for yourself. Self-driving cars not necessary"(Pluralsight).
Unfortunately coding languages change. In order to be a successful programmer an
imperative is to learn new programming languages(Shyu 2018). If a programmer does not learn
the programming languages in use that individual will stagnate. The coding languages change so
often that learning to code requires always learning a new language. Stagnation is a frequent
possibility for programmers.
"What Actually Is Artificial Intelligence?
Before we can explain how AI works, let’s first define what AI is:
Artificial Intelligence is a technology that allows machines and computer applications to
mimic human intelligence, learning from experience via iterative processing and algorithmic
training.
You can think of AI as being a form of intelligence that is used to solve problems, come
up with solutions, answer questions, make predictions, or offer strategic suggestions.
Because AI can do all these things, it’s become incredibly important to modern
businesses and other types of organizations.
What is AI Really Doing?
AI systems work by combining large sets of data with intelligent, iterative processing
algorithms to learn from patterns and features in the data that they analyze.
Each time an AI system runs a round of data processing, it tests and measures its own
performance and develops additional expertise.
Because AI never needs a break, it can run through hundreds, thousands, or even millions
of tasks extremely quickly, learning a great deal in very little time, and becoming extremely
capable at whatever it’s being trained to accomplish.
But the trick to understanding how AI truly works is understanding the idea that AI isn’t
just a single computer program or application, but an entire discipline, or a science.
The goal of AI science is to build a computer system that is capable of modeling human
behavior so that it can use human-like thinking processes to solve complex problems.
To accomplish this objective, AI systems utilize a whole series of techniques and
processes, as well as a vast array of different technologies.
By looking at these techniques and technologies, we can begin to really understand what
AI actually does, and thus, how it works, so let’s take a look at those next.
What Disciplines Make Up the Field of AI?
There are many different components to an AI system, which you can think of as
sub-fields of the overarching science of artificial intelligence.
Each of the following fields is commonly utilized by AI technology:
Machine Learning - A specific application of AI that lets computer systems, programs, or
applications learn automatically and develop better results based on experience, all without being
programmed to do so. Machine Learning allows AI to find patterns in data, uncover insights, and
improve the results of whatever task the system has been set out to achieve.
Deep Learning - A specific type of machine learning that allows AI to learn and improve
by processing data. Deep Learning uses artificial neural networks which mimic biological neural
networks in the human brain to process information, find connections between the data, and
come up with inferences, or results based on positive and negative reinforcement.
Neural Networks - A process that analyzes data sets over and over again to find
associations and interpret meaning from undefined data. Neural Networks operate like networks
of neurons in the human brain, allowing AI systems to take in large data sets, uncover patterns
amongst the data, and answer questions about it.
Cognitive Computing - Another important component of AI systems designed to imitate
the interactions between humans and machines, allowing computer models to mimic the way that
a human brain works when performing a complex task, like analyzing text, speech, or images.
Natural Language Processing - A critical piece of the AI process since it allows
computers to recognize, analyze, interpret, and truly understand human language, either written
or spoken. Natural Language Processing is critical for any AI-driven system that interacts with
humans in some way, either via text or spoken inputs.
Computer Vision - One of the prolific uses of AI technologies is the ability to review and
interpret the content of an image via pattern recognition and deep learning. Computer Vision lets
AI systems identify components of visual data, like the captchas you’ll find all over the web
which learn by asking humans to help them identify cars, crosswalks, bicycles, mountains, etc.
What Technology Does AI Require?
AI isn’t new, but its widespread application and utility have skyrocketed in recent years
thanks to considerable improvements in technology.
In fact, the explosive growth of AI’s scale and value is closely related to recent
technological improvements, including:
Larger, More Accessible Data Sets - AI thrives on data, and has grown in importance
alongside the rapid increase of data, along with better access to data. Without developments like
“The Internet of Things”, which produces a huge amount of data from connected devices, AI
would have far fewer potential applications.
Graphical Processing Units - GPUs are one of the key enablers of AI’s rising value, as
they are critical to providing AI systems with the power to perform millions of calculations
needed for interactive processing. GPUs provide the computing power needed for AI to rapidly
process and interpret big data.
Intelligent Data Processing - New and more advanced algorithms allow AI systems to
analyze data faster and at multiple levels simultaneously, helping those systems analyze data sets
far faster so they can better and more quickly understand complex systems and predict rare
events.
Application Programming Interfaces - APIs allow AI functions to be added to traditional
computer programs and software applications, essentially making those systems and programs
smarter by enhancing their ability to identify and understand patterns in data.
How is AI Being Applied?
To fully get how AI works, it’s also important to understand where and how it’s actually
being applied.
Fortunately, there are many examples of AI’s use in the modern economy, including:
Retail - AI systems are being consulted to design more effective store layouts, handle
stock management, and provide shopping suggestions, like via Amazon’s “You May Also Like”
recommendations.
Healthcare - AI technology has been trained to provide personalized medicine, including
giving reminders about when patients need to take their medicine and suggestions for specific
exercises they should perform to improve their recovery from injuries.
Manufacturing - AI solutions help forecast load and demand for factories, improving
their efficiency, and allow factory managers to make better decisions about ordering materials,
completion timetables, and other logistics issues.
Life Sciences - AI intelligence is actively applied to review complex data sets that are
useful in testing new medicines, helping life science organizations get effective medicines to
market faster.
Finance - AI tools are being leveraged to detect and prevent fraudulent financial
transactions, provide more accurate assessments than traditional credit scores can, and automate
all sorts of data-related tasks that were handled manually.
Why Should You Consider Studying AI?
AI technologies are being developed and applied to virtually every industry, helping
improve results, automate processes, and enhance organizational performance.
The AI industry itself is growing rapidly, with the International Data Corporation (IDC)
reporting that the AI market, “including software, hardware, and services, is forecast to grow
16.4% year over year in 2021 to $327.5 billion.”
Top jobs in the field also tend to come with great salaries, with U.S. Census Bureau data
reporting that the average salary for AI professionals is $102,521.
If you’re interested in pushing the boundaries of computer technology and you want to
launch a career in a field that’s growing, and pays well, then AI may be the perfect
opportunity"(CSU Global 2021).
If such a large amount of technical skills will get automated then that makes two skill sets
highly important. Those are the engineering skills to further mass automation and abilities that
cannot be automated. Leadership is not easily automated. A very successful leader should be
examined.
"By now, many of us have read, watched, and listened to many accounts of Steve Jobs’
many contributions can achievements. There is a passion from consumers about Apple and Steve
Jobs that is rare in the corporate world. Not long ago, I walked past an Apple store in Soho and
saw hundreds of Post-It notes and flowers from so many thanking Steve Jobs. As his biographer
Walter Isaacson and others have pointed out, however, Steve Jobs was far from perfect. I’d like
to comment in particular on his leadership and management style. It is well-known that Steve
Jobs could be arrogant, dictatorial, and mean-spirited. Yet he was a great leader. So does this
invalidate the claims of some management writers and thought leaders today that effective
business leaders today need to be nice, kind, humble (Level 5 leadership), and practice “servant
leadership?” Does this mean that executive leaders should now not worry about being ruthless,
imperial and aloof?
Not at all. I think this apparent contradiction can be explained by two sets of factors.
One, we have to recognize that leadership style is situational. A style that might work under
some circumstances might not work in others. Of course this concept has been around for years,
but I am still surprised at the claims being made about “universal” leadership characteristics and
behavior. Those of you who have worked overseas and led cross-functional global teams will
surely recognize that your leadership needs to be adapted to specific cultures. I believe that Mr.
Jobs’ leadership style (not to mention his genius in design) was a key ingredient in Apple’s
success; had he used a different style, he might not have achieved the same spectacular results at
Apple.
Two, despite the observations of some about Mr. Jobs’ arrogant style, I believe that he
had at least three qualities that great executive leaders have: a clear vision, a passion for the
company and its people, and an ability to inspire trust. This is what I would consider his
leadership character. In fact, Mr. Jobs not only had a vision, he made sure that everyone in the
company bought into that vision, and this created a “higher purpose” for the company that really
excited Apple employees. Of course, his passion for the company and its products is legendary.
And employees trusted Mr. Jobs – not because he founded the company but because he showed
time and again his competence in many areas, especially product design and marketing. And
because employees saw - through his behavior - that Mr. Jobs was not driven by his own ego or
by some self-interested needs (like the outrageous pay packages of some executives), they
trusted him. So if Mr. Jobs was at times arrogant, even nasty, employees viewed these behaviors
in the context of these underlying qualities.
I think the lessons for executives today are clear. Leadership style is situational – your
behavior can and should vary depending on circumstances. What is important to consider is the
character of your leadership. Do you have a clear vision for your team or your company? Do
your team members believe in that vision, and are they excited enough to become part of the
journey towards achieving that vision? And do they trust you to do what is ultimately best for
the company, the stakeholders, the customers, and employees – not what’s best for you?"(Henson
2011).
Maybe one of Jobs' characteristics was the most important. Steve Jobs was very curious
(Brook 2021). A machine may never be able to fully replicate a human brain so soft skills may
rise in importance. Some soft skills cannot be automated making them more important.
A bizarre connection exists between the intellectual and the entrepreneur. An
intellectual's end product is an idea. An entrepreneur may pay others to build an idea. Both may
lack technical skills. Liberal arts is probably a skill set increasing in value compared to technical
skills(University of Dallas 2017). Mark Cuban predicted that liberal arts degrees would be more
lucrative than more technical majors due to automation. Liberal arts is both part of
entrepreneurship and academic work. The ability to come up with functioning ideas and guide
process them to success considering mass automation is a skill set increasing in value.
Academic research has always had value. "Jonas Salk played a pivotal role in achieving
this success by being the first to devise and implement a safe and effective vaccine against
polio"(Yong Tan 2019). Through academic research Salk devised and implemented a safe and
effective vaccine against polio. Academic research can bring useful products to society.
Academic research is useful to society.
Another example of a valuable academic researcher may be Robert Oppenheimer. Robert
Oppenheimer led the Manhattan Project(IAS). If the team is being automated then the
researchers may be able to do the work by themselves. Artificial intelligence could do the
practitioning while the researching and creative work will be left to people. Practitioning is close
ended while research and creative work are both open ended. Artificial intelligence may be able
to do close ended jobs but not open ended ones.
Current industry trends are already making certain technical skills obsolete. Everyone is a
software developer because coding is becoming more automated(Brinker 2019). Coding was also
a difficult skill to navigate. The coding languages would change so stagnation was always a
frequent possibility. Just knowing how to code one day did not prepare someone for the changing
languages. One has to learn the changing languages or have soft skills to complement the
automated coding.
When a CEO answers an economic question the individual is at a great risk for being
inaccurate. Research and creative work are too open ended to automate (Ravikant 2019).
Ravikant is the CEO and founder of AngleList so he would damage his reputation by giving
inaccurate career advice. Once hard scientists and engineers create enough automation research
and creative work may be too difficult to automate. A machine cannot do a highly open ended
job.
Naval has given more career advice. Arm yourself with specific knowledge(your
passion), leverage and accountability(Ravikant 2019). Specific knowledge cannot be taught but
can be learned. Leverage leads to a job. Leverage leads to a more lucrative job. Leverage can be
credentials or money makers. Making money through assets is leverage. Accountability is taking
responsibility for actions. All of those are beneficial.
Perhaps advice is more reputable if it comes from different sources. "Find something you
are intrinsically interested in and you may succeed"(Greene 2013). Robert Greene became a
best-selling author due The 48 Laws of Power. TEDx is deemed reputable in academia so
Greene's advice is probably somewhat legit. Plus Naval and Greene both agree on choosing
passion. Both Greene and Ravikant are risk takers so they risk their reputations by giving advice.
Naval, already being at a great financial risk, would probably give accurate advice again.
"If you can't code, write books and blogs, record videos and podcasts"(Ravikant 2018). Naval
explains what someone should do without the ability to code. Naval believes content creation is
going to be a new essential to the economy once mass automation occurs. Content creation
allows someone to make money without renting out time.
People should always be humble because mass automation may in fact make the most
lucrative skills automated. Artificial intelligence is taking over technical skills in the film
industry( Donnelly 2022). As predicted previously, valuable skills will be developing artificial
intelligence. And having skills that cannot be automated. Skills for filmmakers may be difficult
to predict.
Analyzing an industry for predicting the necessary skills in it. Getting a job in the film
industry can be achieved without experience (Amy Clarke Films). Without filmmaking
experience one can still get a job in the industry. The open ended parts will be left after the
automation. The more open-ended a job is, the less likely it will be automated. Technical skills
may be automated but creativity no.
The most open ended jobs will be the most difficult to automate so leaders may be the
most kept. Chuck Lorre doesn't have technical skills but the people working for him do(IMDb).
A bizarre occurrence is happening that technical skills that were difficult to learn are now
becoming automated. Chuck Lorre only had soft skills as a leader but those will be more difficult
to automate. Risk taking cannot be automated as well. A commonality between Steve Jobs and
Chuck Lorre is that they both were the risk takers.
The research demonstrates that the more open ended a skill is, the more difficult it will be
to automate. Creative work and research will be difficult to automate. Risk taking and leadership
will be difficult to automate. Therefore, key transferable skills will be the soft ones that are
creative work, research, risk taking and leadership. Open ended skills tend to be the softer ones.
Technical skills and grunt work will be easier to automate because they are close ended.
However the ability to create automation will be in high demand. Plus if all automation
fails backup workers will be essential.
Works Cited
Melkonyan , Lilit . "A Step-By-Step Guide on How to Build an AI ." plat.ai. 22 Feb. 2022.
plat.ai/blog/how-to-build-ai/#:~:text=To%20make%20an%20AI%2C%20you,operation
%20of%20your%20AI%20system. Accessed 5 Oct. 2022.
Harvard Business Review . "The Explainer: Blue Ocean Strategy ." hbr.org , uploaded by
YouTube.com, 16 July 2019, youtu.be/sYdaa02CS5E.
Thiel , Peter . "TEDxSiliconValley - Peter Thiel - 12/12/09." ted.com, uploaded by YouTube.com,
12 Feb. 2010, youtu.be/HOB7nezuQ7g.
Cote , Catherine . "7 DATA COLLECTION METHODS IN BUSINESS ANALYTICS ."
online.hbs.edu. 2 Dec. 2021. online.hbs.edu/blog/post/data-collection-methods. Accessed
5 Oct. 2022.
Zubchenko , Alexander . "DATA COLLECTION FOR MACHINE LEARNING: THE COMPLETE
GUIDE ." waverlysoftware.com. 28 Sep. 2021.
waverleysoftware.com/blog/data-collection-for-machine-learning-guide/. Accessed 5 Oct.
2022.
Edwards , Stephen , et al. "Problem Solving and Algorithms ." sofia.cs.vt.edu.
sofia.cs.vt.edu/cs1114-ebooklet/chapter4.html. Accessed 5 Oct. 2022.
Pluralsight. "3 steps to training a machine learning model ." pluralsight.com.
www.pluralsight.com/blog/machine-learning/3-steps-train-machine-learning. Accessed 5
Oct. 2022.
Shyu , Patrick . "How to learn to code(quickly and easily!)." youtube.com, uploaded by
youtube.com, 9 June 2018, youtu.be/R2pIutTspQA.
CSU Global . "How Does AI Actually Work? ." csuglobal.edu. 9 Aug. 2021.
csuglobal.edu/blog/how-does-ai-actually-work. Accessed 5 Oct. 2022.
Henson, Ramon. "The Leadership of Steve Jobs ." business.rutgers.edu. 1 Nov. 2011.
www.business.rutgers.edu/business-insights/leadership-steve-jobs. Accessed 5 Oct. 2022.
Brook, Yaron . "The FEROCIOUS Curiosity of Steve Jobs ." yaronbrookshow.com, uploaded by
youtube.com, 13 Nov. 2021, youtu.be/zJmMq1ljmIE.
University Of Dallas . "AN UNLIKELY ALLY ." udallas.edu.
udallas.edu/news/2017/billionaire-business-mogul-boldly-champions-liberal-arts-as-the-f
uture. Accessed 2 Oct. 2022.
Yong Tan, Siang, and Nate Ponstein. "Jonas Salk (1914–1995): A vaccine against polio ."
ncbi.nlm.nih.gov . 1 Jan. 2019. www.ncbi.nlm.nih.gov/pmc/articles/PMC6351694/.
Accessed 2 Oct. 2022.
IAS. "J. Robert Oppenheimer: Life, Work, and Legacy ." ias.edu.
www.ias.edu/oppenheimer-legacy. Accessed 6 Oct. 2022.
Brinker, Scott . "Everyone Is A Software Developer ." ted.com, uploaded by youtube.com, 27 Nov.
2019, youtu.be/c2sNTAaILdA.
Ravikant , Naval . "Everyone Can Be Rich." joerogan.com , uploaded by youtube.com, 4 June
2019, youtu.be/l2AbxWr6I4s.
Ravikant , Naval . "Arm Yourself With Specific Knowledge ." nav.al , uploaded by YouTube.com,
25 Mar. 2019, youtu.be/E-wCAXBHnic.
Greene , Robert . "The key to transforming yourself ." ted.com, uploaded by youtube.com , 23
Oct. 2013, youtu.be/gLt_yDvdeLQ.
Naval . "If you can't code, write books and blogs, record videos and podcasts.." Twitter, 31 May
2018, 1:39 a.m.,
twitter.com/naval/status/1002107377598873600?s=20&t=RP4jRs9HhneMZd6Mg44KAw
.
Donnelly, Jim. "6 AI tools for Filmmaking You Need to Know About ." massive.io . 15 Aug. 2022.
massive.io/filmmaking/6-ai-tools-for-filmmaking/#:~:text=AI%20is%20already%20a%2
0staple,%2C%20coloring%2C%20and%20music%20creation. Accessed 5 Oct. 2022.
Amy Clarke Films . "How to get a Job in the Film Industry (with no experience) ."
amyclarkefilms.com.
www.amyclarkefilms.com/blog/how-to-get-a-job-in-the-film-industry-with-no-experience.
Accessed 5 Oct. 2022.
IMDb. "Chuck Lorre Biography ." https://m.imdb.com/?ref_=nv_home.
m.imdb.com/name/nm0521143/bio. Accessed 6 Oct. 2022.
Alexander Ohnemus
No Professor
Economics
29 October 2022
Empowering Intellectuals: A Way of Unlocking Human Potential
Lack of money and regulations are obstacles that block humans from economic
flourishing. Requirements of money and credentials both block human potential. By altering the
economic framework of society human potential could be unlocked to serve the social
well-being.
Two kinds of freedom exist. Freedom in practice and in theory. Freedom in practice is the
liberty people actually have. Freedom in theory is the liberty people were theorized to have.
Just because markets do not support development of an entity does not mean it is not for
social well-being.
Scientists are important. Scientists discover in theory so engineers can develop in
practice. Without scientists engineering would be haphazard and possibly too risky given already
limited resources.
Therefore, how easy would it be to turn each human in a society into a scientist and then
have that resource of contributions?
A development must be made to educate as many people in a society into scientists, even
without formal education, so they can contribute as high as possible to the society. And then for
engineers to put or adjust what is in theory into practice.
The training of engineers may be more difficult because more risk is involved in
engineering.
Works Cited
N/A