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Learning Skills for Engineering Career in AGI

Source: My person notes and thoughts on How to Future-Proof Your Software Engineering Career for the Age of AGI - victoria.dev

Article describes concepts and learning to prepare for a career in artificial intelligence, including artificial general intelligence (AGI) in 2025. It suggests places to find learning.

Why prepare for Artificial General Intelligence (AGI)?

Section titled “Why prepare for Artificial General Intelligence (AGI)?”
  • AGI can do any task a human can do with their brain, learn and adapt, and improve itself
  • For people working on AGI, engineers can create systems that can do tasks by themselves and mean new ways of system development
  • AGI combined with hardware can do any tasks a human does by itself and learn and improve over time

Conclusions

Section titled “Conclusions”
  • Future roles can require knowledge in multiple areas like technology and science and require adaptation
  • Do learn and talk with people regularly to update skills and be informed

Below are areas to look at for preparing yourself

Machine Learning and Deep Learning

Section titled “Machine Learning and Deep Learning”
  • Allow systems to learn from data, see patterns and make decisions
  • Concept help you know how to create models that handle unstructured data and make decisions

Topics

Section titled “Topics”
  • Supervised learning
  • Reinforcement learning, agents
  • Unsupervised learning - systems find hidden patterns in data without specific guidance
  • Neural networks
  • Deep learning

How to learn

Section titled “How to learn”
  • Courses like at freeCodeCamp, Coursera, edX, or Udacity
  • Projects:
    • Build machine learning models and experiment with different types of data
    • Kaggle Competitions

  1. Examples

AI Integration and Engineering, Multiple Disciplines

Section titled “AI Integration and Engineering, Multiple Disciplines”
  • Integrate software and hardware with AI
  • Might be APIs, platforms
  • Integrate knowledge from other fields:
    • Sciences: biology, bioinformatics, psychology, neuroscience

How to learn

Section titled “How to learn”
  • Work on AI components in software and hardware
  • Understand cloud AI services like AWS SageMaker or Google AI Platform
  • Projects: Integrate applications with AI like chatbots, predictive analytics, interdisiplinary projects where AI is applied in another area
  • Talking to experts and taking courses in other disciplines

Ethics and AI Governance

Section titled “Ethics and AI Governance”
  • Using AGI like any general purpose tools will require ethics and governance with policy and legal requirements

Topics

Section titled “Topics”
  • Accountability
  • Transparency
  • Statistics
  • Policy
  • Law

How to learn

Section titled “How to learn”
  • Read about policy at organisations and government
  • Learn social sciences
  • Learn regulations, law, policy related to technology and AI
  • Join industry groups like IEEE that looks at policy and ethics

Human Computer Interaction (HCI)

Section titled “Human Computer Interaction (HCI)”
  • Need for ways for humans to interact with systems

Topics

Section titled “Topics”
  • Psychology
  • User experience and user interface design

How to learn

Section titled “How to learn”
  • Courses: AI system like above with focus on design and interaction. See relevant courses on Interaction Design Foundation and Coursera
  • Projects: Design user interfaces for AI integrated applications like conversational agents and dashboards

Autonomous Systems and Robotics

Section titled “Autonomous Systems and Robotics”
  • Autonomous robots through improving robotics with artificial intelligence
  • Robots that can do tasks in unstructured environments, learn, and work with humans

Topics

Section titled “Topics”
  • Self-driving vehicles
  • Drones
  • Robotics

How to learn

Section titled “How to learn”
  • Courses: autonomous systems, computer vision, AI integration
  • Projects: build an autonomous vehicle, programming a robot for tasks

Hardware Development

Section titled “Hardware Development”
  • Design hardware like chips that are like the structure and function of the human brain’s neurons and synapses

How to learn

Section titled “How to learn”
  • Neuromorphic and quantum computing
  • Commercial developments (IBM, Intel)
  • Projects on Field Programmable Gate Arrays (FPGAs), quantum computing platforms

Cyber Security

Section titled “Cyber Security”

Topics

Section titled “Topics”
  • IT security
  • Data privacy

How to learn

Section titled “How to learn”
  • Courses: Cyber security courses related to AI and machine learning (freeCodeCamp, Cybrary, and Coursera)
  • Do cybersecurity challenges (Capture the Flag (CTF) competitions, secure systems)

Data, Infrastructure

Section titled “Data, Infrastructure”

Topics

Section titled “Topics”
  • Big data infrastructure and technologies (Hadoop, Spark, real time processing with Apache Kafka)
  • Data pipelines - data ingestion, storage and processing - batch and real time
  • Cloud computing (AWS, Google Cloud, Microsoft Azure), systems
  • Infrastructure as code (IAC) (Terraform, Ansible)
  • Scaling systems, high data volume

How to learn

Section titled “How to learn”
  • Courses on topics listed above
  • Cloud provider certifications
  • Contributing to open source projects in those technologies
  • Projects with setting up data pipelines and infrastructure