Evolution of AI, Large Language Models, and Finding the right Balance Between research and Application
An interview with Felix Tao, CEO of MindverseAI and ex Facebook & Alibaba researcher
Good morning fellow AI enthusiast! This week's iteration focuses on a new episode of the What's AI podcast discussing the recent evolution of AI, large language models, and finding the right balance between research and application with Felix Tao, an experienced ex-NLP researcher at Facebook and Alibaba and now CEO of MindverseAI. Auxane also shares great ethics insights regarding LLMs. We hope you enjoy this iteration!
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2️⃣ Evolution of AI, Large Language Models, and Finding the right Balance Between research and Application - What's AI episode 14
This new episode features Felix Tao, CEO of Mindverse AI, and his years of experience working as a researcher at Facebook and Alibaba, mostly involved in language applications and AI. In this interview, Felix gives his insights on the evolution of AI, large language models, and finding the right balance between research and application.
Here's a quick overview of the first few minutes of the interview before you commit to an insightful hour-long episode!
First, Felix reflects on the changing landscape of AI research during his time at Facebook, where he worked on content understanding tools, and how the field has now advanced with foundation models like GPT3 shaping the way for AGI (Artificial General Intelligence).
He answers questions such as: is a research-focused career valuable today? Felix says it depends on the area - foundational work and addressing large language model challenges remain critical, but job seekers might profit from gaining industry experience.
We discuss his time at Alibaba, and he shares his goals of "waking up the consciousness" in machines by combining large language models with human-like frameworks - including memory, perception, and motor control. Exciting times!
Felix highlights the back-and-forth pattern of AI development, moving between fragmented specialized tasks and unified general approaches, and believes the future may witness domain-specific AI for higher quality and better problem-solving abilities.
Check out Felix Tao's thought-provoking journey in AI research and development, and discover how MindverseAI's MindOS is aiming to employ specialized AI for domain-specific tasks, in this captivating interview. Don't miss out! (or Listen on Spotify, Apple podcasts)👉
3️⃣ AI Ethics with Auxane
Good day, fellow AI enthusiasts!
This edition delves into the fascinating realm of Large Language Models (LLMs) and examines the ethical considerations surrounding their utilisation and adaptation across various sectors. Let us consider the following arguments as we explore the possibilities and challenges.
On the positive side, LLMs have the potential to revolutionise user interactions, surpassing the limitations of current chatbot systems. We can enable engaging and informative conversations by adequately training the model and tailoring it to specific domains and users! For instance, LLMs can offer invaluable assistance to patients seeking answers about particular diseases, provided the model has been trained on relevant medical knowledge and their solutions are supervised and checked for severe accuracy. Similarly, students can benefit from LLMs when seeking clarification on intricate university administrative systems. By deploying LLMs in specific areas, we save time for individuals, enhance their overall experience, and provide support beyond generic information!
However, we must also address the negative aspects associated with LLMs. Data security remains a pressing concern. Implementing an LLM necessitates robust infrastructure to ensure data privacy, protecting sensitive information from unauthorised access or breaches.
Misinformation is another potential but very real pitfall. Inadequate training or a misperception of the AI's capabilities can lead to incorrect or misleading information dissemination. Hence, it is imperative to thoroughly train and test the AI, ensuring its accuracy and reliability before deploying it to end-users.
Furthermore, we must recognise the economic implications and job displacement that may arise from adopting LLMs. As these models become more sophisticated, there is a possibility of roles traditionally performed by humans being replaced partially. Organisations must assess the impact on employment and consider the ethical implications, ensuring that the AI system truly matches or exceeds the capabilities of the human workforce it replaces.
When contemplating the implementation of an LLM in a new sector or institution, several vital questions must be thus be posed. Here are a few lines of thinking as a starting point. But keep in mind that many more questions will have to be asked!
Is our infrastructure robust enough to safeguard data privacy and security?
Do we possess the necessary data to adequately train and test the AI, ensuring accurate responses to end-users?
Is the proposed application ethically appropriate, particularly in high-risk sectors like (mental) health or finance? How would we handle potential mishaps or risks associated with this application of AI?
Are we unjustly reducing jobs by incorporating this AI system? Can AI truly perform tasks as effectively and rightly as our human employees?
Is the supervision of this AI system sufficient to ensure responsible and ethical use?
Are we inadvertently stripping users of their autonomy by integrating AI into these contexts? How, and at which point, does it become a problem?
By thoughtfully addressing questions like those, we can navigate the ethical terrain and make informed decisions when harnessing the power of LLMs!
That's all for now, folks! Let me know your thoughts on the possible applications of LLM, and I look forward to hearing from you soon. Have a fantastic week!
- Auxane Boch (iuvenal research consultant, TUM IEAI research associate).
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