Future Jobs for the Cognition Economy

Cognitive Systems Architect

Knowledge Engineer

Cognitive Systems Trainer

Cognitive Systems SRE Safety and Realiability Engineering

AI Application Developer

AI Systems Administrator

The evolution of many other professional skills to incorporate AI: Project Management, Business Management and Team Leadership, Supply Chain Management, Network operations and administration.

The role of the 'Knowledge Engineer' is becoming paramount. It supersedes the Business Analyst and will be vital in AI applications.

Role: Knowledge Engineer

Job Description: A professional responsible for designing, implementing, and managing knowledge bases to support AI applications. They will work closely with AI developers and business analysts to translate business requirements into technical specifications.

Qualifications:

• Bachelor’s degree in Computer Science, AI, or related field.
• Familiarity with knowledge representation techniques.
• Proficiency in AI programming languages and tools.
• Excellent analytical and problem-solving skills.

Sample Job Ad:

"Looking for an experienced Knowledge Engineer to join our dynamic AI team. The ideal candidate will be skilled in knowledge representation, have a background in AI, and be adept at working in cross-functional teams. Apply now to be at the forefront of AI technology."

TOOLS AND SYSTEMS

AI's vast applications mean our Knowledge Engineers will need to familiarize themselves with various tools and systems.

Key Tools & Systems for Knowledge Engineers:

• Knowledge Representation Tools: Protégé, OWL, RDF.
• AI Programming Languages: Python, R, Java.
• Machine Learning Frameworks: TensorFlow, PyTorch.
• Data Visualization Tools: Tableau, PowerBI.
• Knowledge Bases & Databases: SQL, NoSQL, Neo4j.

HUMAN ASPECTS

AI isn't just about cold, hard logic. It's about understanding humans and their needs. These Knowledge Engineers need a blend of technical expertise and human understanding.

https://www.dhirubhai.net/pulse/soft-skills-revolution-knowledge-economy-odyssey-peter-sigurdson?trackingId=fO%2FZMkugx12AFIxRFaXa5w%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_recent_activity_content_view%3BuplAJUfNSUyr3K6vJLwBZQ%3D%3D&utm_source=coda&utm_medium=iframely

Key Skills & Qualities:

• Strong communication: Translating technical jargon for non-tech stakeholders.
• Empathy: Understanding user needs and designing knowledge bases accordingly.
• Teamwork: Collaborating with AI developers, business analysts, and other stakeholders.
• Continuous Learning: Keeping up with the fast-evolving AI industry.
• Ethical Judgment: Ensuring AI applications uphold human values and ethics.

MOCK JOB INTERVIEWS

Lean into your AI Agent: https://www.dhirubhai.net/pulse/chatgpt-prompt-use-do-practice-interview-sessions-peter-sigurdson/

Sample Interview Questions:

• "How do you ensure the AI applications you design are user-centric and ethical?"
• "Tell me about a time you faced a challenge while designing a knowledge base and how you overcame it."

# Emerging AI Job Categories: Knowledge Engineer

## Role Description

A professional responsible for designing and managing knowledge bases for AI applications.

## Key Qualifications

- Bachelor's in Computer Science, AI, or related field.

- Familiarity with knowledge representation.

- Proficiency in AI programming and tools.

## Tools & Systems

- Protégé, OWL, RDF.

- Python, R, Java.

- TensorFlow, PyTorch.

- Tableau, PowerBI.

- SQL, NoSQL, Neo4j.

## Key Skills & Qualities

- Strong communication.

- Empathy.

- Teamwork.

- Continuous learning.

- Ethical judgment.

## Sample Interview Questions

- "How do you ensure AI applications are user-centric?"

- "Describe a challenge faced while designing a knowledge base."

ROLE: AI APPLICATION DEVELOPER

The AI Application Developer focuses on designing and implementing AI-powered applications, bridging the gap between the potential of AI and its real-world applications.

Job Description: Responsible for designing, coding, and deploying AI-driven applications. They work in collaboration with Knowledge Engineers and AI System Administrators to ensure seamless integration of AI functionalities into business solutions.

Qualifications:

• Bachelor’s degree in Computer Science or a related discipline.
• Proficiency in AI programming languages and tools.
• Experience in developing AI-driven applications.
• Familiarity with cloud platforms and DevOps practices.

Sample Job Ad:

"Seeking an AI Application Developer to design and deploy state-of-the-art AI solutions. The ideal candidate will be a skilled coder with a deep understanding of AI principles and cloud integration. Join our team and shape the future of AI-powered applications!"

ROLE: AI SYSTEM ADMINISTRATOR (CLOUD DEVOPS)

AI System Administrators are the backbone of AI operations. They're the ones ensuring our AI solutions run smoothly, especially in a cloud environment.

Role: AI System Administrator (Cloud DevOps)

Job Description: Manage and maintain AI-driven systems, especially on cloud platforms. They optimize system performance, oversee security measures, and ensure continuous delivery and integration of AI applications.

Qualifications:

• Accreditation and training in IT, Computer Science, or related discipline.
• Certifications in cloud platforms (e.g., AWS, Azure, Google Cloud).
• Strong understanding of DevOps principles and tools.
• Experience in AI system administration.

Sample Job Ad:

"We are on the hunt for a skilled AI System Administrator with a focus on Cloud DevOps. The role demands expertise in cloud platforms, an understanding of AI systems, and a knack for DevOps. Be part of our team and ensure our AI systems achieve peak performance!"

TOOLS AND SYSTEMS

For these roles, a range of tools and platforms will be indispensable."

Key Tools & Systems for AI Roles:

• Development Frameworks: TensorFlow, PyTorch, Keras.
• Cloud Platforms: AWS Lambda, Azure Machine Learning, Google AI Platform.
• DevOps Tools: Docker, Kubernetes, Jenkins, Terraform.
• AI Monitoring & Management: Datadog, New Relic, Prometheus.

HUMAN ASPECTS

The best tech in the galaxy means nothing without the right people behind it. Here's what they'll need."

Key Skills & Qualities for AI Roles:

• Adaptability: The AI landscape is ever-evolving.
• Problem-Solving: Identifying and addressing system and application issues.
• Collaboration: Working with cross-functional teams.
• Security Awareness: Protecting AI systems from breaches and vulnerabilities.
• Ethical Considerations: Ensuring AI applications are built and maintained responsibly.

# AI Roles of the Future

## AI Application Developer

- Description: Designing and deploying AI applications.

- Qualifications: Bachelor's in Computer Science, coding expertise, AI tools proficiency, cloud integration knowledge.

## AI System Administrator (Cloud DevOps)

- Description: Managing AI systems on cloud platforms.

- Qualifications: Bachelor's in IT, cloud platform proficiency, DevOps knowledge, AI system admin experience.

## Key Tools & Systems

- Development: TensorFlow, PyTorch, Keras.

- Cloud: AWS Lambda, Azure ML, Google AI Platform.

- DevOps: Docker, Kubernetes, Jenkins, Terraform.

- Monitoring: Datadog, New Relic, Prometheus.

## Key Skills & Qualities

- Adaptability.

- Problem-solving.

- Collaboration.

- Security awareness.

- Ethical considerations.

Mathematics is the foundation upon which artificial intelligence and machine learning are built. To comprehend these advanced subjects, one must first understand various mathematical concepts.

Let's structure the curriculum sequentially, building on foundational knowledge to more advanced topics.

Mathematics Curriculum for AI and Machine Learning

1. Foundations of Mathematics

a. Algebra

• Linear equations
• Polynomials
• Factoring
• Quadratic equations

b. Calculus

• Limits and continuity
• Differentiation and integration
• Multivariable calculus
• Partial derivatives

c. Probability and Statistics

• Descriptive statistics
• Probability distributions (e.g., binomial, Poisson, normal)
• Statistical inference (hypothesis testing, confidence intervals)
• Correlation and regression

d. Linear Algebra

• Matrices and vectors
• Matrix multiplication
• Eigenvalues and eigenvectors
• Vector spaces

2. Discrete Mathematics and Data Structures

a. Logic and Propositional Logic

• Boolean algebra
• Logic gates
• Truth tables and logical equivalences

b. Set Theory

• Basic set operations (union, intersection, difference)
• Venn diagrams
• Counting principles

c. Graph Theory

• Trees, graphs, and networks
• Shortest path problems
• Graph traversals (DFS, BFS)

d. Data Structures

• Arrays and linked lists
• Stacks and queues
• Trees (binary trees, AVL trees) and graphs

3. Advanced Topics in Mathematics for AI

a. Optimization

• Convex optimization
• Lagrange multipliers
• Gradient descent and stochastic gradient descent

b. Probability and Statistics (Advanced)

• Bayesian statistics
• Maximum likelihood estimation
• Non-parametric statistics

c. Computational Complexity

• Big O notation
• Complexity classes (P, NP)
• Analysis of algorithms

4. Data Science Specifics

a. Exploratory Data Analysis (EDA)

• Data visualization techniques
• Outlier detection
• Correlation analysis

b. Time Series Analysis

• Autoregression (AR)
• Moving Average (MA)
• ARIMA models

c. Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-SNE

d. Distributions & Transformations

• Data normalization and standardization
• Box-Cox and Yeo-Johnson transformations

This curriculum provides students with a comprehensive understanding of the mathematical concepts essential for AI, machine learning, and data science. By mastering these subjects, students will be well-prepared to delve into the intricacies of AI algorithms and machine learning models. I recommend that students engage with these topics sequentially, ensuring mastery at each level before progressing to the next.

Mathematics Curriculum for AI and Machine Learning with R Programming

1. Foundations of Mathematics

a. Algebra

Skills:

• Solving linear equations.
• Handling polynomials.
• Factoring techniques.

Sample Test Question in R:

RCopy code        

# Solve the quadratic equation x^2 - 5x + 6 = 0

roots <- polyroot(c(6, -5, 1))

print(roots)

b. Calculus

Skills:

• Understanding limits.
• Differentiating functions.
• Integrating functions.

Sample Lab in R:

RCopy code        

library(pracma)

# Differentiate the function f(x) = x^2 + 2x

f <- function(x) x^2 + 2*x

df <- difffun(f)

df(1) # Should return 4, the derivative at x=1

c. Probability and Statistics

Skills:

• Calculating mean, median, mode.
• Understanding distributions.
• Hypothesis testing.

Sample Test Question in R:

RCopy code        

# Given a set of numbers, calculate its mean

numbers <- c(2, 3, 5, 7, 11)

mean(numbers)

d. Linear Algebra

Skills:

• Matrix operations.
• Vector manipulations.
• Eigenvectors and eigenvalues.

Sample Lab in R:

RCopy code        

# Find the eigenvalues of the matrix [[2, 1], [1, 3]]

m <- matrix(c(2, 1, 1, 3), nrow=2)

eigenvalues <- eigen(m)$values

print(eigenvalues)

2. Discrete Mathematics and Data Structures

a. Logic and Propositional Logic

Skills:

• Creating truth tables.
• Logical operations.

Sample Test Question in R:

RCopy code        

# Determine the result of the AND operation between two logical vectors

vector1 <- c(TRUE, FALSE, TRUE)

vector2 <- c(FALSE, FALSE, TRUE)

result <- vector1 & vector2

print(result)

b. Set Theory

Skills:

• Performing set operations.
• Creating Venn diagrams.

Sample Lab in R:

RCopy code        

# Find the union of two sets

set1 <- c(1, 2, 3, 4, 5)

set2 <- c(4, 5, 6, 7, 8)

union(set1, set2)

c. Graph Theory

Skills:

• Understanding trees and graphs.
• Implementing traversal algorithms.

Sample Test Question in R:

RCopy code        

library(igraph)

# Create a simple graph and determine if it's connected

g <- graph(c(1,2, 2,3, 3,1, 3,4))

is.connected(g)

d. Data Structures

Skills:

• Implementing and using arrays, lists.
• Understanding stacks and queues.

Sample Lab in R:

RCopy code        

# Create a stack and perform basic operations

stack <- vector("list", 5)

stack <- append(stack, "pushed_item")

tail(stack, 1) # Peek at the top

stack <- stack[-length(stack)] # Pop

3. Advanced Topics in Mathematics for AI

a. Optimization

Skills:

• Convex optimization techniques.
• Implementing gradient descent.

Sample Test Question in R:

RCopy code        

# Implement a simple gradient descent for f(x) = x^2

gradient_descent <- function(start, lr, epochs) {

?x <- start

?for (i in 1:epochs) {

? ?grad <- 2 * x

? ?x <- x - lr * grad

?}

?return(x)

}

gradient_descent(5, 0.1, 100)

b. Probability and Statistics (Advanced)

Skills:

• Bayesian inference.
• Non-parametric statistics.

Sample Lab in R:

RCopy code        

# Bayesian updating with a Beta prior and Binomial likelihood

alpha_prior <- 2

beta_prior <- 2

observations <- c(4, 6) # 4 successes and 6 failures

posterior <- dbeta(seq(0, 1, by=0.01), shape1=alpha_prior+observations[1], shape2=beta_prior+observations[2])

plot(seq(0, 1, by=0.01), posterior, type="l", ylab="Density", xlab="Theta", main="Posterior Distribution")

c. Computational Complexity

Skills:

• Understanding Big O notation.
• Analyzing algorithmic complexity.

Sample Test Question in R:

RCopy code        

# Implement a basic sorting algorithm and analyze its time complexity

basic_sort <- function(vec) {

?for (i in 1:(length(vec) - 1)) {

? ?for (j in (i + 1):length(vec)) {

? ? ?if (vec[i] > vec[j]) {

? ? ? ?temp <- vec[i]

? ? ? ?vec[i] <- vec[j]

? ? ? ?vec[j] <- temp

? ? ?}

? ?}

?}

?return(vec)

}

vec <- sample(1:100, 100, replace=FALSE)

sorted_vec <- basic_sort(vec)

print(sorted_vec)

4. Data Science Specifics

a. Exploratory Data Analysis (EDA)

Skills:

• Data visualization.
• Handling missing values.

Sample Lab in R:

RCopy code        

library(ggplot2)

data <- data.frame(x = rnorm(100), y = rnorm(100))

ggplot(data, aes(x=x, y=y)) + geom_point() + theme_minimal()

b. Time Series Analysis

Skills:

• Decomposing time series data.
• Forecasting.

Sample Test Question in R:

RCopy code        

library(forecast)

ts_data <- ts(rnorm(120, 0, 1), frequency=12)

decomposed <- decompose(ts_data)

autoplot(decomposed)

c. Dimensionality Reduction

Skills:

• Implementing PCA.
• Understanding t-SNE.

Sample Lab in R:

RCopy code        

library(FactoMineR)

data <- data.frame(x = rnorm(100), y = rnorm(100), z = rnorm(100))

pca_res <- PCA(data, graph=FALSE)

print(pca_res$eig)

With these lessons, labs, and test questions, students will be well-equipped to understand the mathematical foundations of AI, machine learning, and data science, and to apply them using the R programming language. This curriculum is based on a logical progression of knowledge, and is only the starting point of a life long learning endeavor.

The curriculum provided is foundational, but there are additional concepts and topics of relevance that Data Scientists and Machine Learning practitioners should be aware of.

Here are some additional areas of focus for a comprehensive understanding:

Advanced Data Science Concepts

1. Feature Engineering

• Techniques for feature selection.
• Creating interaction terms.
• Polynomial features.
• Encoding categorical variables.

2. Regularization Techniques

• Ridge regression (L2 regularization).
• Lasso regression (L1 regularization).
• Elastic net.

3. Model Evaluation and Validation

• Cross-validation methods: k-fold, stratified k-fold, time series split.
• Precision, recall, F1 score, ROC curve, AUC.
• Model interpretability techniques like SHAP, LIME.

4. Ensemble Methods

• Bagging (Bootstrap Aggregating) e.g., Random Forests.
• Boosting e.g., AdaBoost, Gradient Boosting, XGBoost.
• Stacking.

5. Advanced Machine Learning Techniques

• Neural networks and deep learning.
• Reinforcement learning.
• Transfer learning.
• Generative adversarial networks (GANs).

6. Unsupervised Learning Techniques

• Clustering methods: K-means, hierarchical clustering, DBSCAN.
• Association rule mining.

Advanced Data Structure and Algorithms Concepts

1. Advanced Trees and Graphs

• Red-black trees, B-trees.
• Dijkstra’s algorithm, Floyd-Warshall algorithm.
• Maximum flow problems.

2. Hashing

• Hash tables, hash functions.
• Collision resolution strategies: open addressing, separate chaining.

3. Advanced Sorting and Searching Algorithms

• Merge sort, quicksort, radix sort.
• Binary search, interpolation search.

4. Dynamic Programming

• Memoization.
• Problems like knapsack, longest common subsequence.

5. Design Patterns

• Singleton, factory, observer patterns for algorithmic designs.
• Strategy and decorator patterns.

While the curriculum presented covers foundational concepts essential for AI, machine learning, and data science, diving deeper into these advanced topics will further enhance one's expertise and capability in these fields. It is logical to consider both foundational and advanced concepts for a well-rounded education.

R labs for these additional topics, and sample test questions.

Advanced Data Science Concepts with R Labs

1. Feature Engineering

Lab: Encoding Categorical Variables

RCopy code        

data <- data.frame(

?color = c('red', 'blue', 'green', 'red')

)

data$color_encoded <- as.numeric(as.factor(data$color))

print(data)

Sample Test Question:

RCopy code        

# Convert the following factor variable to numeric

factor_var <- factor(c('A', 'B', 'A', 'C'))

numeric_var <- ?

print(numeric_var)

2. Regularization Techniques

Lab: Lasso Regression

RCopy code        

library(glmnet)

data <- data.frame(

?x1 = rnorm(100),

?x2 = rnorm(100)

)

data$y = 1.5 data$x1 - 2 data$x2 + rnorm(100)

model <- glmnet(as.matrix(data[,c('x1', 'x2')]), data$y, alpha=1)

print(coef(model))

Sample Test Question:

RCopy code        

# What does the alpha argument in glmnet function represent?

# a) Ridge regression

# b) Lasso regression

# c) Elastic net

# d) Polynomial regression

3. Model Evaluation and Validation

Lab: k-Fold Cross-Validation

RCopy code        

library(caret)

data(iris)

ctrl <- trainControl(method = "cv", number = 10)

model <- train(Species ~ ., data = iris, method = "glm", trControl = ctrl)

print(model$results)

Sample Test Question:

RCopy code        

# In k-Fold Cross-Validation, if k = n (where n is the number of data points), what is this special case called?

# a) Stratified sampling

# b) Bootstrapped sampling

# c) LOOCV

# d) Random sampling

4. Ensemble Methods

Lab: Random Forest

RCopy code        

library(randomForest)

data(iris)

model <- randomForest(Species ~ ., data = iris, ntree = 100)

print(importance(model))

Sample Test Question:

RCopy code        

# In a Random Forest algorithm, why are multiple trees used instead of a single decision tree?

# a) To increase the depth of the tree

# b) To decrease bias

# c) To decrease variance

# d) To reduce computational cost

5. Advanced Machine Learning Techniques

Lab: Neural Networks

RCopy code        

library(neuralnet)

data <- data.frame(x = rnorm(100))

data$y = data$x^2 + rnorm(100)

nn <- neuralnet(y ~ x, data, hidden = 2, threshold = 0.01)

print(nn$result.matrix)

Sample Test Question:

RCopy code        

# In neural networks, what is the main purpose of an activation function?

# a) To normalize input data

# b) To introduce non-linearity

# c) To speed up training

# d) To regularize weights

6. Unsupervised Learning Techniques

Lab: K-means Clustering

RCopy code        

library(stats)

data <- data.frame(

?x = c(rnorm(50, mean = 0), rnorm(50, mean = 5)),

?y = c(rnorm(50, mean = 0), rnorm(50, mean = 5))

)

clusters <- kmeans(data, centers = 2)

print(clusters$cluster)

Sample Test Question:

RCopy code        

# In K-means clustering, how are initial cluster centers typically chosen?

# a) Random data points

# b) Origin of the coordinate system

# c) Centroids of the data

# d) Highest density data points

These labs and sample test questions should provide learners with hands-on experience and insights into the key advanced data science concepts using R. Continual practice and exploration are key to mastery in this field.

The psychological and sociological changes required. A plan for building psychological conditioning training to teach how to adapt to functioning in this new cognition economy.

Insights from a psychological and sociological standpoint.

Adjusting to the new cognition economy will require a fundamental shift in our understanding of ourselves, our capabilities, and our role in society."

1. Mental Flexibility & Continuous Learning

Sociological Implication: As jobs evolve, individuals will have to continuously learn new skills. The society that values lifelong learning will thrive.

Psychological Training: Emphasize the importance of a growth mindset.

People need to believe that their abilities can be developed through dedication and hard work.

Regular brain exercises and cognitive training can also help in enhancing mental flexibility.

2. Emotional Intelligence & Human-centric Skills

Sociological Implication: In an AI-driven world, human-centric skills will become increasingly valuable. This includes empathy, understanding, and collaboration.

Psychological Training: Encourage active listening, empathy exercises, and training in conflict resolution. Mindfulness and meditation practices can also enhance self-awareness and emotional regulation.

3. Resilience & Adaptability

Sociological Implication: Changes can induce stress. The ability to bounce back from adversity, adapt to change, and keep going in the face of challenges is crucial.

Psychological Training: Develop resilience training programs. This could include exposure therapy to deal with fears and uncertainties, and cognitive-behavioral techniques to challenge negative thought patterns.

4. Digital Well-being

Sociological Implication: As we become more connected, there's a risk of digital overload, which can lead to burnout and mental health issues.

Psychological Training: Teach digital hygiene. This includes setting boundaries for screen time, understanding the impact of social media on mental health, and techniques to detox digitally.

5. Societal Collaboration

Sociological Implication: The cognition economy doesn't just require individual adaptability but also societal collaboration. This means working together across different sectors and cultures.

Psychological Training: Promote team-building exercises, cultural exchange programs, and interdisciplinary learning. Develop communication skills and an appreciation for diverse viewpoints.

6. Ethical Considerations and Morality

Sociological Implication: With the rise of AI, there will be numerous ethical dilemmas. Societies will have to grapple with decisions about privacy, data rights, and the moral implications of AI decisions.

Psychological Training: Encourage ethical training and moral philosophy. Scenario-based learning can help individuals think critically about complex issues.

It's crucial to understand that while technology is changing our environment, it's the human spirit and our inherent qualities that will determine our future.

We need to invest not just in technological education but also in developing the holistic well-being of our youth.

And remember, there's no replacement for genuine human connection, empathy, and understanding, no matter how advanced our tech becomes."

The potential of the human spirit.

Here are five psychological conditioning exercises to help implement these insights and navigate the challenges of the cognition economy."

1. The Growth Mindset Journaling Exercise

Objective: To foster a growth mindset and combat fixed mindset tendencies.

Exercise:

• Have individuals maintain a journal.
• Every evening, they should write down a challenge they faced that day, their initial thoughts about the challenge, and then reframe those thoughts from a growth mindset perspective.
• For example:
• Challenge: Struggled with a new coding problem.Initial Thought: "I'm not good at coding."Growth Mindset Reframe: "I faced a challenging problem today, but with practice and study, I can improve my coding skills."

2. The Active Listening Partner Exercise

Objective: To enhance emotional intelligence and improve understanding and empathy.

Exercise:

• Pair up participants.
• One person shares a recent personal experience or challenge.
• The other person listens actively without interrupting, then summarizes what they've heard and expresses empathy.
• Participants switch roles.

3. The Resilience Reflection Exercise

Objective: To develop resilience and learn from past experiences.

Exercise:

• Ask individuals to think about a past challenge or setback.
• Have them reflect on:
• What they learned from the experience.How they grew as a result.What strategies they used to cope and overcome.
• Encourage sharing and discussion to foster a collective understanding of resilience.

4. Digital Detox Day Challenge

Objective: To promote digital well-being and reduce dependency on digital devices.

Exercise:

• Designate one day a week as a 'Digital Detox Day.'
• On this day, individuals avoid non-essential digital devices and platforms.
• Instead, they engage in offline activities like reading, nature walks, or face-to-face social interactions.
• After the detox day, have them reflect on the experience, noting changes in mood, stress levels, and overall well-being.

5. The Ethical Dilemma Group Discussion

Objective: To enhance ethical reasoning and promote understanding of complex moral issues.

Exercise:

• Present a group with a hypothetical (or real-world) ethical dilemma related to technology and AI. For example, the implications of using facial recognition in public places.
• Divide the group into smaller teams, asking each to discuss and come up with a stance or solution.
• After discussion, teams present their viewpoints, followed by a collective debate.
• Conclude with a reflection on the importance of ethics in technology and the challenges of navigating moral gray areas.

These exercises aim to engage the mind and the heart. By incorporating them into educational or professional settings, we can help individuals prepare not just skill-wise, but also emotionally and ethically for the future. Remember, it's our humanity that'll always set us apart.

The computer systems engineering aspects needed to do cloud dev ops.

1. Foundational Knowledge

Computer Systems:

• Dive deep into understanding operating systems, especially Unix and Linux systems. They’re the backbone of many cloud platforms.
• Get a grasp of computer networking concepts. Know the difference between TCP and UDP, understand IP addressing, DNS, load balancers, and more.

Cloud Platforms:

• Start with one of the big cloud providers: AWS, Azure, or Google Cloud.
• Enroll in certification courses for these platforms. They offer structured paths and cover both basics and advanced topics.

2. Infrastructure as Code (IaC)

• Familiarize yourself with tools like Terraform, CloudFormation, or ARM templates.
• The idea is to define and provide data center infrastructure using code and automate the process.

3. Continuous Integration and Continuous Deployment (CI/CD)

• Delve into tools like Jenkins, Travis CI, GitLab CI, and others.
• Understand the principles of automating the build and deployment process.
• Know the difference between continuous integration, continuous delivery, and continuous deployment.

4. Containerization and Orchestration

• Begin with Docker. It's essential to grasp how containers work, their advantages, and how to use Dockerfiles.
• Move on to orchestration tools like Kubernetes. Understand pods, services, deployments, and more.

5. Configuration Management

• Tools like Ansible, Puppet, Chef, and SaltStack are pivotal.
• Learn to automate the configuration of software applications using these tools.

6. Monitoring and Logging

• Familiarize with monitoring tools such as Prometheus, Grafana, and Nagios.
• Dive into logging tools like ELK Stack (Elasticsearch, Logstash, and Kibana) or Graylog. Understanding system health and being proactive rather than reactive is key.

7. Security

• Understand the principles of the shared responsibility model.
• Delve into tools and practices like IAM, encryption (both at rest and in transit), secure key management, and vulnerability assessment.

Hands-on Practice

• Set up your own virtual lab environment using tools like VirtualBox or VMware.
• Deploy a simple application on a cloud provider using CI/CD tools, containerize it with Docker, and orchestrate with Kubernetes.
• Try breaking and fixing things – there's no better way to learn than hands-on experience!

To the budding engineers out there, remember, every mistake is a lesson. The universe of cloud dev ops is vast and ever-changing, just like the galaxy we explore. Keep learning, keep experimenting, and never be afraid to push the envelope. After all, as I've always said, 'Give me a wee bit more time, and I'll give you the results!