Retrospective XVI

Last year, I conducted a simple retrospective for 2023. Therefore, here is a retrospective for year 2024.

2024 Achievements

Transfer all Windows and Linux keyboard shortcuts and muscle memory to new Mac Book Pro Transfer all important Windows and Linux applications navigations for M1-powered MacBooks Build GitLab CI/CD pipelines extending DevOps skillset and streamline collaborative workflow Provision Kubernetes clusters for GitLab CI/CD pipelines e.g. Azure AKS, AWS-EKS, GCP-GKE Configure Doom open source port for Windows and Linux to debug step thru the source code Launch fulltime Python coding experience to learn AI focusing on RL Reinforcement Learning Experiment with OpenAI Gym project for RL research and build Atari available environments Investigate OpenAI Retro project for RL research on classic Sega 8-bit + 16-bit video games Note: building OpenAI projects for classic Sega 8/16-bit video games integration is a big achievement!

2025 Objectives

Document DevOps managed clusters provisioning experience with AWS / Azure / GCP providers Channel cloud computing knowledge toward software architecture or infrastructure certification Harness Python potential power invoking C/C++ [PyBind11] with code magnitudes times faster Extend OpenAI Gym and Retro projects for more Indie video games + Reinforcement Learning!

Artificial Intelligence
Artificial Intelligence refers to capability of machines to imitate human intelligence. AI empowers machines to acquire knowledge, adapt and independently make decisions like teaching a computer to act human like.

Machine Learning
AI involves a crucial element known as Machine Learning. ML is akin to training computers to improve tasks without providing detailed instructions. Machines utilize data to learn and enhance the performance without explicit programming and concentrates on creating algorithms for computers to learn from data to improve.

Deep Learning
Deep Learning involves artificial neural networks inspired by the human brain: mimicking how human brains work. DL excels at handling complex tasks and large datasets efficiently and achieves remarkable success in areas like natural language processing and computer vision despite complexity and interpretation challenges.

Generative AI
Generative AI is the latest innovation in the AI field. Instead of just identifying patterns GenAI goes one step further by actually attempting to produce new content that closely resembles what humans might create.

Outline

Artificial Intelligence	Artificial Intelligence is the "big brain"
Machine Learning	Machine Learning is its learning process
Deep Learning	Deep Learning is its intricate wiring
Generative AI	Generative AI is the creative spark

Gen AI and LLMs are revolutionizing our personal and professional lives From supercharged digital assistants to seemingly omniscient chatbots these technologies are driving a new era of convenience, productivity, and connectivity.

Traditional AI uses predictive models to classify data, recognize patterns, + predict outcomes within specific context whereas Gen AI models generate entirely new outputs rather than simply making predictions based on prior experience.

This shift from prediction to creation opens up new realms of innovation: in healthcare traditional predictive model can spot suspicious lesion in lung tissue MRI whereas GenAI could also determine the likelihood that patient will develop pneumonia or other lung diseases and offer treatment recommendations based on best practices gleaned from thousands of similar cases.

Example
GenAI powered healthcare chatbots can assist patients and healthcare providers and medical administrators:

01. Symptom Checker	07. Mental Health Support
02. Appointment Scheduling	08. Insurance and Billing Assistance
03. Medication Reminders	09. Virtual Consultations and Telemedicine
04. Health Tips and Preventive Care	10. Health Records Access
05. Lab Results Interpretation	11. Emergency Triage
06. Chronic Disease Management

By leveraging conversational AI healthcare chatbot can improve patient engagement and provide real-time support and optimize the workflow for healthcare providers. Finally, Reinforcement Learning From Human Feedback RLHF can be integrated to further improve model performance over original pre-trained version!

Future
Artificial Intelligence is changing industries across the globe from healthcare and finance to marketing and logistics. As we enter 2025, the demand for skilled AI professionals continues to soar. Start out by building strong foundations in Python and understand key concepts such as machine learning and neural networks.

Therefore, whether an AI beginner or seasoned tech professional, here are the top 10 AI skills for success:

No.	AI Skill	Key Tools
01	Machine Learning (ML)	Scikit-learn, TensorFlow, PyTorch
02	Deep Learning	Keras, PyTorch, Google Colab
03	Natural Language Processing (NLP)	NLTK, SpaCy, GPT-based models (e.g., GPT-4)
04	Data Science and Analytics	NumPy, Pandas, Jupyter Notebooks
05	Computer Vision	OpenCV, YOLO (You Only Look Once), TensorFlow
06	AI Ethics and Bias Mitigation	AI Ethics Courses, Fairness Indicators (Google)
07	AI Infrastructure and Cloud Computing	Amazon Web Services, Microsoft Azure, Google Cloud AI
08	Reinforcement Learning	OpenAI Gym, TensorFlow Agents, Stable Baselines3
09	AI Operations (MLOps)	Docker, Kubernetes, Kubeflow, MLflow
10	Generative AI	Generative Adversarial Networks, DALL-E, GPT models

Finally, the GenAI market is poised to explode, growing to $1.3 trillion over the next 10 years from market size of just $40 billion in 2022. Therefore, it would be extraordinary to integrate GenAI to build content for OpenAI-based retro video games only to be trained by Reinforcement Learning algorithms to beat them J