Episode 14  |  58 Min  |  May 31

Reasoning and evolution of GenAI with Dr. Srinivas Padmanabhuni, testAIng.com

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Engaging topics at a glance

  • 00:13:00
    Introduction
  • 00:15:00
    Why Artificial General Intelligence (AGI) is still very far
  • 00:16:30
    Are AI founders hyping up AI
  • 00:18:00
    Types of reasoning
  • 00:20:00
    Why current AI based on LLMs perform so well
  • 00:22:05
    What is Abductive reasoning
  • 00:25:10
    Does current AI only do inductive reasoning
  • 00:25:30
    Will the current architecture suffice in spawning abductive reasoning
  • 00:27:15
    Does current AI summarize documents in a way that a human does
  • 00:30:30
    Does statistics play a role in human reasoning
  • 00:33:35
    About emotional reasoning
  • 00:36:10
    Biggest risks of current AI models
  • 00:36:50
    Applications of current AI and use-case for inductive reasoning
  • 00:41:00
    Creativity and reasoning
  • 00:46:35
    Concluding thoughts

This podcast offers valuable insights about the limitations of current AI reasoning, including common sense reasoning, and debunks the hype around AGI, which viewers can apply in their field.

In this podcast, Arvind interviews Dr. Srinivas Padmanabhuni, who has a PhD in common sense reasoning from the University of Alberta and is a founder of an AI startup testAIng.com.

Dr. Srinivas explains that the concept of the old Aristotelian logic is just not enough to capture human-level reasoning. The traditional logic that is taught in schools is based on zero one, which is just one part of our reasoning. Human beings reason a lot more than that, and the way AI reasons today is a very narrow, myopic way of reasoning.

They (LLMs) are very poor at inventing new knowledge unless it is actually coming from somewhere, some large corpus existing somewhere.

– Dr. Srinivas Padmanabhuni

Dr. Srinivas then shares his thoughts on AGI (Artificial General Intelligence). He thinks that it is too premature to talk about AGI, and it’s only marketing. He says that AI is not able to invent anything new unless it is actually coming from somewhere, some large corpus existing somewhere. He says that unless you really have a common sense level agent, which can think about reasoning, you cannot just think about AGI.

The podcast also discusses the limitations of AI and what we can expect from it in the future. Dr. Srinivas explains that AI is not capable of inventing anything new, but is only capable of processing information and making predictions based on existing data. He suggests that AI is best used for tasks that involve large amounts of data processing, such as financial analysis, medical diagnosis, and weather forecasting.

Dr. Srinivas also talks about the importance of common sense reasoning and how it is an essential part of human-level reasoning, which is missing in AI today. He explains that common sense reasoning is necessary for tasks such as understanding jokes, sarcasm, and metaphors.

Technically it is nothing but the biggest statistical engine out there is; hardly any brain, hardly any logic, hardly any intelligence, so it is the dumbest of the dumbest application.

– Dr. Srinivas Padmanabhuni

Towards the end of the podcast, Arvind and Dr. Srinivas discuss the future of AI and its potential applications. They suggest that AI has the potential to revolutionize industries such as healthcare, retail, and transportation. However, they also caution that the development of AI should be done in an ethical and responsible manner, with consideration for the potential risks and consequences.

Overall, the podcast is very informative for anyone who wants to learn about the field of AI. It gives a good understanding of the limitations of AI and what we can expect from it in the future. The podcast also highlights the importance of common sense reasoning and how it is an essential part of human-level reasoning, which is missing in AI today. Lastly, the podcast emphasizes the need for ethical and responsible development of AI, with consideration for the potential risks and consequences.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

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Trailer  |  01 Min  |  May 31

Unpacked with Arvind Ravishunkar

Unpacked with Arvind Ravishunkar

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In this series, Unpacked, I explore and unpack the most important concepts that business leaders need to know about emerging technologies. I connect with reputed scholars, industry experts and leaders through conversations. I also do short 5 min episodes on key concepts. I am your host Arvind Ravishunkar and this is Season 1 : Generative AI

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Episode 12  |  56 Min  |  May 31

Uncovering GenAI tools and infrastructure with Rajat Monga, Co-Founder, TensorFlow

Uncovering GenAI tools and infrastructure with Rajat Monga, Co-Founder, TensorFlow

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Engaging topics at a glance

  • 00:16:10
    Google Brain and TensorFlow
  • 00:19:10
    TensorFlow for AI world
  • 00:21:55
    Tooling and infrastructure needs of previous AI models vs. GenAI
  • 00:25:40
    Trade-offs, Open-source library and framework vs. private company's framework
  • 00:31:30
    Present quality of tool and Infrastructure available for GenAI
  • 00:32:55
    How do I build a GenAI team?
  • 00:37:50
    Vendors and the Infrastructure available today to take GenAI into production
  • 00:41:51
    How to differentiate in the GenAI world?

Join us in exploring the evolving space of GenAI tools and infrastructure, featuring Rajat Monga, Co-Founder of TensorFlow and Google Brain.

A good power tool can make the difference of easily six to seven hours of work when you're doing woodworking. And the world of AI is no different. The types of tools and infrastructure that are developed to help you as enterprise leaders build artificial intelligence products and features are very, very important. In this episode, with our guest's help, we will unpack the infrastructure that surrounds you and the tooling that will help you as enterprise leaders build AI products and services.

We will look at how tooling and infrastructure needs are changing in the world of AI with the increasing adoption of GenAI. One key change that emerged from the talk is that now things have evolved to the extent that we don’t need to train the models from scratch. We already have foundation models available that know our world up to some level, reducing the burden of training the model with tonnes of data. However, models today have become so large that they sometimes don’t fit in a single machine.

As connecting your database with these models is very important, we also discussed the trade-offs between the open-source and private libraries. Should companies manage data on their own or outsource it? When you are not training your model, the easiest and fastest way is to use API, and if you want your data on-prem, then it will mostly cost you more. In the end, it boils down to what core to you is, and often, not all part of the infrastructure is core to companies. So, if your data is not your core strength, then better outsource it.

This episode also uncovered the current tools and infrastructure available for GenAI. The current tools and infrastructure available for large-level deployments are going through a rapid evolution; they are not very hard to rebuild or replace, and new companies are emerging in the tooling and infrastructure for GenAI space.

When looking at the talent and skills needed for GenAI implementation in your organization, it is important to have technically sound people with domain expertise in the organization's particular area.

For the differentiation in the market domain knowledge in your area, relationship with the customers, distribution channel, your execution, etc., today plays an even bigger role. However, in this data-driven world, having proprietary data and knowing how to leverage it can be an added advantage. To find out more, tune in to the full podcast.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

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