Dr. Mike Pound, an expert in AI and computer vision, shares his insights on the future of artificial intelligence. He discusses the need for a multidisciplinary approach to AI, especially in fields like cybersecurity. Exploration of AI's limitations highlights the importance of hands-on experience and a critical assessment of new technologies. Mike also addresses privacy concerns and the societal divide created by AI advancements. Finally, he tackles fears about job security, emphasizing that human insight remains indispensable despite automation.
AI is increasingly integrated into systems for improved accuracy, leveraging techniques like retrieval augmented generation for enhanced performance.
Large language models have significant limitations in reasoning and context understanding, necessitating a refined approach to utilizing their strengths.
A multidisciplinary approach, combining knowledge of AI and cybersecurity, is essential for innovating solutions to complex security challenges in today's job market.
Deep dives
Current State of AI
AI remains a relevant field worth learning, despite ongoing hype and debates about its capabilities. Recently, the focus has shifted from merely increasing the size of language models to integrating them into systems that produce more accurate results using real-time data. This includes techniques like retrieval augmented generation, where data is brought into the model to enhance performance. Such advancements indicate that while AI isn't solving every problem independently, it is being effectively utilized as a part of larger systems.
The Limitations of Large Language Models
Large language models (LLMs) exhibit significant limitations when it comes to understanding context and reasoning. Research has shown that when posed with questions that include unnecessary information, these models can produce incorrect answers, failing to navigate complexities like humans can. This reinforces the notion that LLMs are not inherently capable of reasoning but rather generate text based on patterns found in training data. As a result, a more refined approach in utilizing these models is required, focusing on their strengths rather than assuming they can replace human cognitive processes.
The Importance of Diverse Skills
Learning about multiple fields is crucial in today’s evolving job market, especially in areas like AI and cybersecurity. Individuals with knowledge of both disciplines are well-positioned to leverage AI to solve complex security issues, as both fields continue to grow in significance. For example, basic AI skills can enhance tasks like phishing detection, which currently faces challenges despite technological advancements. Therefore, professional development that spans various topics can lead to more innovative and effective solutions.
Navigating the AI Hype Cycle
There is a noticeable hype surrounding the integration of AI into existing products and services, which can lead to superficial implementation rather than substantial improvements. Many companies are incorporating AI features for competitive advantage, sometimes resulting in products that don’t deliver significant benefits. The current buzz might diminish as consumers begin to recognize the actual effectiveness of these AI applications. This shift in perception may lead to a more pragmatic approach to implementing AI, focusing on genuine utility rather than trendy features.
Preparing for Future Skills in AI
For those starting their journey into AI, there remains ample opportunity, and it is advisable to begin with foundational skills like programming in Python and understanding machine learning principles. Engaging in hands-on projects using platforms like Google Colab can help users familiarize themselves with practical applications without feeling overwhelmed. Additionally, online courses, particularly those offering a broad grasp of machine learning, can provide a solid educational framework for further exploration. This proactive approach is essential as the AI landscape continues to rapidly evolve, shaping future career prospects and skill requirements.
Big thanks to Brilliant for sponsoring this video! To try everything Brilliant has to offer for free for a full 30 days and 20% discount visit: https://Brilliant.org/DavidBombal
// Mike SOCIAL //
X: / _mikepound
Website: https://www.nottingham.ac.uk/research...
// YouTube video reference //
Teach your AI with Dr Mike Pound (Computerphile): • Train your AI with Dr Mike Pound (Com...
Has Generative AI Already Peaked? - Computerphile: • Has Generative AI Already Peaked? - C...
// Courses Reference //
Deep Learning: https://www.coursera.org/specializati...
AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for...
Pytorch Tutorials: https://pytorch.org/tutorials/
Pytorch Github: https://github.com/pytorch/pytorch
Pytorch Tensors: https://pytorch.org/tutorials/beginne...
https://pytorch.org/tutorials/beginne...
https://pytorch.org/tutorials/beginne...
Python for Everyone: https://www.py4e.com/
// BOOK //
Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP
// PyTorch //
Github: https://github.com/pytorch
Website: https://pytorch.org/
Documentation: / pytorch
// David's SOCIAL //
Discord: discord.com/invite/usKSyzb
Twitter: www.twitter.com/davidbombal
Instagram: www.instagram.com/davidbombal
LinkedIn: www.linkedin.com/in/davidbombal
Facebook: www.facebook.com/davidbombal.co
TikTok: tiktok.com/@davidbombal
// MY STUFF //
https://www.amazon.com/shop/davidbombal
// SPONSORS //
Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com
// MENU //
0:00 - Coming Up
0:43 - Introduction
01:04 - State of AI in 2025
02:10 - AGI Hype: Realistic Expectations
03:15 - Sponsored Section
04:30 - Is AI Plateauing or Advancing?
06:26 - Overhype in AI Features Across Industries
08:01 - Is It Too Late to Start in AI?
09:16 - Where to Start in 2025
10:20 - Recommended Courses and Progression Paths
13:26 - Should I Go to School for AI?
14:18 - Learning AI Independently with Resources Online
17:24 - Machine Learning Progression
19:09 - What is a Notebook?
20:10 - Is AI the Top Skill to Learn in 2025?
23:49 - Other Niches and Fields
25:05 - Cyber Using AI
26:31 - AI on Different Platforms
27:13 - AI isn’t Needed Everywhere
29:57 - Leveraging AI
30:35 - AI as a Productivity Tool
31:55 - Retrieval Augmented Generation
33:28 - Concerns About Privacy with AI
36:01 - The Difference Between GPU’s, CPU’s, NPU’s etc.
37:30 - The Release of Sora38:56 - Will AI Take Our Job?
41:00 - Nvidia Says We Don’t Need Developers
43:47 - Devin Announcement
44:59 - Conclusion
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Disclaimer: This video is for educational purposes only.
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