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You need to learn AI in 2024! (And here is your roadmap)

David Bombal・52 minutes read

AI technology is continuously evolving and solving global issues, making it a valuable skill to acquire for individuals looking to make an impact beyond major tech companies. Despite concerns about AI taking away jobs, current tools have limitations, with Python and PyTorch being essential for starting in AI and creating supervised models for practical experience.

Insights

AI technology is continuously evolving and solving global issues, showcasing its transformative impact beyond major tech companies. Despite concerns about AI taking jobs, current tools have limitations, highlighting opportunities for individuals to make a significant impact.
Access to AI models through APIs enables a wider community to leverage AI power without extensive expertise, eliminating the need for deep resources to train models. Python serves as a crucial entry point for AI, with supervised learning recommended as a starting point before delving into more complex topics, offering a practical roadmap for those interested in AI careers.

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Recent questions

What are the benefits of learning AI?
AI technology solves problems, offers opportunities beyond tech companies, and assists in various tasks.
How can one quickly get started in AI?
Starting with Python and supervised learning is recommended for beginners in AI.
What resources are available for learning AI?
Coursera offers machine learning courses, PyTorch tutorials, and GitHub examples for AI learning.
What is the role of Python in AI?
Python is essential for running AI models and serves as the primary language for AI tasks.
What are the key considerations for AI development?
AI development involves learning AI basics, gaining practical experience, and staying updated on AI technologies.

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Summary

00:00

AI Technology: Transformative, Accessible, and Evolving

AI technology is continuously improving and solving various problems, making it a valuable skill to acquire.
The understanding of AI gained in 2024 is likely to remain relevant for a lifetime due to its continuous development.
Numerous smaller AI and Deep learning applications are quietly solving global issues, showcasing the technology's transformative impact.
Despite media hype, AI offers significant opportunities beyond working for major tech companies, allowing individuals to make a substantial impact.
Concerns about AI taking away jobs are present, but current AI tools have limitations, especially in complex tasks.
AI tools can assist in speeding up tasks like boilerplate code generation but may also introduce vulnerabilities.
The industry is moving towards better chatbots and image generation, with regulatory challenges arising due to the realism of generated content.
Artificial General Intelligence (AGI) is not expected to be achieved soon, with a focus on enhancing existing AI capabilities.
Access to AI models through APIs enables a wider community to leverage AI power without deep expertise, opening up new opportunities.
Utilizing APIs eliminates the need for extensive resources and expertise required to train AI models, making AI more accessible for various applications.

10:41

Python: Essential for AI, no PhD needed.

To quickly get into AI, one doesn't necessarily need a PhD but can start by learning Python, which is crucial in the AI community.
Mathematical knowledge is not extensively required for AI, although a basic understanding of linear algebra might be beneficial for comprehending network papers.
Python serves as the primary entry point for AI, handling network operations and serving as the interface for machine learning tasks.
Understanding fundamental Python concepts like lists, dictionaries, and data structures is essential before delving into specific libraries like PyTorch.
Supervised learning is recommended as a starting point in AI, followed by more complex topics like unsupervised learning and hybrid models.
PyTorch tutorials and examples on GitHub are valuable resources for learning, focusing on concepts like tensors and auto differentiation.
Coursera offers courses on machine learning and deep learning, with a preference for starting with the machine learning course for fundamental knowledge.
The AI field is rapidly evolving, with an overwhelming amount of research papers and new technologies emerging, making it both challenging and exciting.
Domain-specific AI models, like Segment Anything by Meta, are becoming more prevalent, offering tailored solutions for specific tasks like image segmentation.
While general AI models like ChatGPT are intriguing, domain-specific models may be more practical for specialized tasks like medical image analysis.

21:21

AI Applications and Training in Various Fields

AI can quickly identify common scenes in everyday photos, though may require specific training for medical images or specialized datasets.
Companies utilize AI for various tasks like financial data analysis, networking, security, and image processing.
Diverse job roles exist within AI companies, from network engineering to data management and interface development.
Andrew Ng's machine learning course is highly recommended for technical individuals to gain a foundational understanding of AI.
Learning Python is essential for running AI models, but understanding the underlying processes is crucial for troubleshooting.
Familiarity with AI terminology and technologies aids in selecting the most suitable tools for different tasks.
Basic AI models may suffice for simple, consistent tasks, avoiding unnecessary complexity.
AI applications are becoming mainstream, necessitating involvement and understanding of AI concepts.
Three groups of AI users exist: those who interact with AI casually, programmers utilizing APIs, and individuals deeply involved in AI development.
Cloud resources like Azure or AWS are beneficial for companies engaging in extensive AI training, while platforms like Google CoLab are ideal for learning and experimentation.

32:15

"CoLab: Python, GPU, AI Learning Platform"

CoLab offers a notebook-style interface with text boxes for Python code and GPU access.
The platform includes pre-installed libraries like PyTorch for easy use.
Detectron 2, a model for object detection, is available on CoLab with a link on the Git repo for quick access.
CoLab is a low-cost option, with free access but potential delays for GPU usage.
Monthly access for about eight or nine British pounds provides comprehensive GPU resources.
CoLab is recommended for experimentation and learning before potentially transitioning to personal systems or cloud computing.
Recommended books for Python and PyTorch include shorter introductions for quicker understanding.
Learning AI basics can be achieved in a few weeks or months, with practical experience being a key component.
Python and PyTorch are highlighted as essential tools for starting in AI, with PyTorch currently favored in research and industry.
AI offers a fast-moving and rewarding career path with continuous learning opportunities and high-paying job prospects.

42:50

Training AI Models with Python and PyTorch

To train the next ChatGPT, the recommended roadmap involves learning Python, specifically PyTorch, and creating a supervised model using any dataset, whether public or self-collected. Starting with supervised learning is advised due to its intuitive nature, allowing for progression to more complex problems like segmentation.
Despite concerns about AI taking over jobs, there remains a need for individuals skilled in AI. The process of training AI models can be both overwhelming and rewarding, with the satisfaction of seeing the model successfully perform tasks, even if the inner workings may not be fully understood.

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