Top 10 Challenges of LLMs – Part 2

Welcome to AI Masters Voice, the podcast where we connect minds to dive into the future of AI. 

In the last episode Martin Jokub and David Mataciunas continue discussion about TOP10 LLMs challenges. In the first part only 2 challenges were covered.

So this week, without any introductions, we jump into the other 8 challenges of LLMs:

• Multilingual Capabilities
• Bias and Fairness. Challenges, ethical considerations.
  
• Interpretability and Explainability. Importance, approaches.
• Data Privacy and Security. Risks, mitigation strategies.
• Environmental Impact. Energy consumption, sustainability efforts.
• Dependency on Large Datasets. Challenges, alternatives.
• Generalization across Domains. Issues, solutions.
• Robustness and Reliability. Vulnerabilities, solutions.

Key Highlights

• Multilingual AI Exploration
  Dive into the advancements and challenges of multilingual AI capabilities, featuring insights from David Matačiūnas.
  
  
• Innovative AI Projects
  Learn about AI's linguistic projects like "Liepa2" and EU OpenGPT-X.
  
  
• Addressing AI Challenges
  Discussing bias, fairness, and the critical importance of explainability in AI technologies.
  
  
• Intellectual Property Challenges
  Explore the legal complexities surrounding AI-generated content and the use of training data in AI development.
  
  
• Environmental and Data Concerns
  Tackling AI's environmental impact and the crucial role of large datasets in AI development.
  
  
• The Path Forward
  nsights into enhancing AI robustness and reliability, charting the course for future AI innovations.
  

This episode delves into the crucial role of multilingual capabilities in AI, exploring innovative projects like "Liepa2" and the ambitious EU OpenGPT-X project.

David shares his expertise on overcoming AI's linguistic hurdles, ensuring AI's accessibility worldwide, and addresses pressing issues like bias, fairness, and the future of AI adoption.

00:00 Trailer
01:04 AI Masters Voice Podcast Opener
01:11 LLMs challenge No3: Multilingual Capabilities
02:41 Lithuanian language was not represented
04:00 Cohere for AI: AYA project
06:50 Governments involvement
07:45 "Liepa2": Pioneering Lithuanian Language AI
09:18 Where to publish or find datasets?
10:39 The process of research projects run by EU and universities
12:05 Huge problem with projects in EU AI projects
14:04 Testing transcribing Lithuanian zoom call
16:16 AI Masters Agency practical adoption of Custom models and Datasets
17:10 HuggingFace and EU OpenGPT-X Projects: A Vision for Europe's Multilingual AI
19:15 Why EU is lacking of innovations?
20:49 EU 5 supercomputers and no LLM models developed: message to EU AI politics
23:10 Do we have time? Can Europe afford to go slow?
24:08 Sweden model as a good example
25:47 Bias and Fairness.
29:32 Interpretability and Explainability.
31:25 Data Privacy and Security.
35:17 Environmental Impact.
40:02 What we need to reach AGI?
40:37 Dependency on Large Datasets.
42:45 Generalization across Domains (Multimodality)
44:31 Robustness and Reliability.
45:02 Conclusion and Future Directions
45:44 Neurolink groundbreaking accomplishment
46:00 Personal stories of moments with AI paradigm shift (Midjourney)
47:47 Request to viewers
48:14 End titles

CohereForAI > aya-101

The Aya model is a massively multilingual generative language model that follows instructions in 101 languages. Aya outperforms mT0 and BLOOMZ a wide variety of automatic and human evaluations despite covering double the number of languages. The Aya model is trained using xP3x, Aya Dataset, Aya Collection, a subset of DataProvenance collection and ShareGPT-Command. We release the checkpoints under a Apache-2.0 license to further our mission of multilingual technologies empowering a multilingual world.

David Mataciunas

Founder and CEO of a pharmacy/AI startup AQ22, technical data analyst, ML engineer, and chairman of the board of the AI Association of Lithuania, founder of LT AI Research Group.