You need to learn AI in 2024! (And here is your roadmap)
TLDRThe video discusses the current state of AI in 2024, dispelling hype and providing realistic guidance for getting started in the field. It emphasizes that AI skills will remain relevant for decades, with opportunities at various levels from using tools to advanced research. Core advice is to learn Python and PyTorch, get hands-on experience with basic supervised learning, and leverage free resources like Google Colab. The goal is to demystify AI, build intuition, and gain transferable skills to tap into the field's growth and impact.
Takeaways
- 😊 AI is advancing rapidly, with major improvements in areas like image generation.
- 💡 Even if you don't work at a big tech company, there are many AI applications solving real-world problems.
- 🤔 Understanding the fundamentals of AI will enable you to stay relevant even as tools change.
- 📈 The demand for jobs involving AI is growing quickly across industries.
- 👩💻 Python and PyTorch are the best entry points for getting hands-on with AI.
- 📚 Andrew Ng's Machine Learning course provides a solid base of AI knowledge.
- 😎 With dedication, you can gain job-ready AI skills in weeks/months rather than years.
- 🔨 AI tools like Segmentation Models and ChatGPT are ready to deploy out-of-the-box.
- 💰 Salaries tend to be high for roles involving AI development and applications.
- ☁️ Google Colab provides free access to GPUs for learning purposes.
Q & A
What are the three main groups of people that can benefit from AI according to the discussion?
-The three main groups are: 1) Non-technical people who can interact with AI systems through simple interfaces 2) Developers who can leverage AI APIs and create applications 3) AI researchers and experts who have advanced skills to build and train AI models from scratch
What basic skills does Mike recommend people should learn to get started with AI?
-Mike recommends learning Python programming language and PyTorch deep learning library to get started with AI. He also suggests taking Andrew Ng's machine learning course on Coursera to develop foundational knowledge.
How long does it take to gain a baseline level of AI knowledge according to Mike?
-Mike says it takes only a few weeks to months to gain a baseline level of AI knowledge that allows you to start training basic models. With consistent practice, you can rapidly pick up the skills needed to build practical AI applications.
What resources does Mike recommend for learning PyTorch?
-Mike recommends going through the PyTorch tutorials and examples on their GitHub page. He also suggests trying out models on Google Colab which provides free access to GPUs for running experiments.
Why does Mike emphasize understanding fundamentals rather than just using AI models out-of-the-box?
-Mike says it's important to learn fundamentals because AI models can change rapidly. With core knowledge, you can better adapt to new advancements instead of relying on prompt engineering or fixed APIs that may stop working.
What is the key benefit of starting with supervised learning according to Mike?
-Mike says supervised learning is the most intuitive approach to begin with. It allows you to easily provide labeled examples for a model to learn from. Once you grasp supervised learning, you can transition to more advanced techniques.
How can basic AI knowledge help even if you don't directly work in AI?
-Mike explains that basic AI knowledge allows you to better understand AI systems your company may use. It helps ensure predictable outcomes instead of blindly relying on black-box models. It also enables adapting to changes better.
Why does Mike advise against worrying too much about AI taking away jobs at the moment?
-Mike believes there will continue to be demand for people with AI skills in the foreseeable future. Rather than worry about job loss, it's better to skill up and position yourself to take advantage of the opportunities in this space.
What excites Mike the most about the current era of AI development?
-Mike finds it incredibly satisfying when an AI model he has trained actually works and produces the desired output. He enjoys the process of transforming data into useful insights even if the exact workings remain a black box.
What is Mike's advice for young people or career changers regarding getting into AI?
-Mike strongly advocates for people to jump into AI as it solves many real-world problems and is not going away anytime soon. The understanding you build today will enrich your career for decades. There are opportunities across levels from using AI interfaces to advanced research.
Outlines
😊 David and Mike discuss AI hype versus reality
David welcomes Mike back to discuss the current state of AI in 2024. They talk about the continued hype around AI versus the reality. Mike says AI continues to progress quickly, especially in areas like image generation. He notes the difficulty now in discerning real versus fake images. Mike cautions that while impressive, AI still has limitations when applied to complex real-world problems.
😃 Opportunities to apply AI are expanding
Mike discusses how opportunities to apply AI are expanding beyond advanced research to more practical applications. With the release of foundation models and APIs, those without a lot of expertise can still leverage the power of AI. Mike advises starting simple when possible, rather than using complex models that may be overkill.
😊 Advice for learning enough to be useful
In response to David's questions, Mike gives advice for learning enough about AI to be useful. He recommends starting by learning Python and PyTorch. Andrew Ng's machine learning course provides a good base of knowledge. Mike suggests focusing first on supervised learning for its simplicity before tackling more advanced techniques.
🤓 Understanding AI will help guide appropriate usage
Mike notes that while today's AI capabilities are impressive, the models have limitations in terms of robustness and reliability. By gaining more understanding of how they work, people can better evaluate strengths versus shortcomings and guide appropriate usage. Rather than just hype, realistic understanding is key.
😊 Specialized AI likely needed for complex tasks
Mike discusses the recent release of Meta's Segment Anything model for image segmentation and analysis. For complex specialized tasks like medical imaging, dedicated customized AI solutions still outperform general purpose models. Understanding fundamentals helps determine the best fit for different applications.
😁 Controlling AI behavior takes some expertise
Mike cautions against building products solely relying on AI model behaviors, since retraining and other changes can alter responses in unpredictable ways. Some expertise is needed to better control model functioning. Understanding fundamentals helps ensure more predictable performance.
🙂 Start basic, then specialize
Mike suggests an effective learning progression is to start by getting basic Python and PyTorch fundamentals, then training simple supervised models. Once comfortable with core concepts, moving to more complex tasks like unsupervised learning is easier. Understanding keys like data preparation and monitoring training are broadly applicable.
😊 Now is the time to jump on the AI wave
Mike strongly encourages jumping into AI now, as concepts carry forward and it continues advancing while solving many real-world problems. Unlike transient technologies, AI capabilities build on existing knowledge. He advises starting basic then working up expertise to be able to advance over time.
😃 AI will transform many industries
Mike observes how AI is already quietly revolutionizing many industries. While hype focuses on the newest consumer applications, less glamorous but impactful transformations are happening continuously behind the scenes. Understanding AI unlocks opportunities across domains.
Mindmap
Keywords
💡Python
💡PyTorch
💡supervised learning
💡segmentation
💡foundational models
💡prompt engineering
💡model training
💡data loaders
💡online resources
💡lifelong learning
Highlights
AI will continue to rapidly advance, with better chatbots and image generation, but likely not reach artificial general intelligence soon
Large language models like ChatGPT are impressive but hard to apply to specialized tasks; smaller, targeted models can be more useful
There are huge opportunities now for a wider community to leverage powerful AI models via APIs without extensive expertise
Understanding AI fundamentals helps one not get taken in by hype and choose the right tools for the job
With a baseline AI knowledge, one can start training basic networks in just weeks and have proper tools in months
Python and PyTorch are currently the primary entry points for getting started with AI
Start simple with supervised learning as it's the most intuitive; unsupervised learning and other advanced topics build on that base
Google Colab provides free access to GPUs for running AI experiments and exploring models
Jobs in AI companies involve various roles like data engineering, cluster/GPU management, web interfaces, and more - not just hardcore AI research
With hype around AI, there's a tendency to jump into trendy tools without understanding fundamentals, which will cause issues later
An AI understanding from 2024 will be relevant for a lifetime as the field is here to stay and will keep rapidly advancing
Quiet AI applications behind the scenes across industries are having massive transformative impact, not just the hyped consumer products
The AI job market pays very well and has huge demand even if you don't end up working on the latest hyped models
The core AI concepts don't change rapidly so foundational knowledge lasts, while applications build on top at a fast pace
With basic skills, one can quickly become productive in AI jobs and continue rapidly advancing from there
Transcripts
5.0 / 5 (0 votes)
Thanks for rating: