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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
Engaging topics at a glance
Join us in this episode featuring Anupam Datta, Co-founder and Chief Scientist, TruEra, as we dive into the evolution of LLMs and what they hold for the future!
This world of generative AI has caught us by storm. And as enterprise leaders in your companies, understanding the technology behind generative AI will give you a competitive advantage as you plan your companies and businesses. And to help you do this, we will unpack a technology, large language models (LLMs), that powers AI today and represent a paradigm shift in this field of Artificial Intelligence.
LLMs can craft meaningful responses across many domains. Their performance has notably improved recently thanks to the substantial increase in model size and data volume.
With the increasing acceptance of this technology, numerous companies are unveiling various Large Language Models (LLMs). It's important to recognize that opting for the largest or highest-performing LLM isn't always the most suitable approach. Instead, one might prefer LLMs that excel in specific tasks relevant to their application. As a leader in the enterprise, it's crucial to integrate this understanding into your company's strategy, aiding in identifying the appropriate LLMs to match and adapt for your applications. Achieving equilibrium between LLM selection, cost considerations, and latency considerations stands as a pivotal concern for enterprises. Equally essential is the thorough validation and assessment of generative outputs, serving as a safeguard prior to embarking on consequential choices. Hence, the undertaking of reliability testing at this current juncture is paramount.
Furthermore, enterprises need to consider a few other key aspects in this evolving landscape of LLMs as they build out LLMs. Starting with a well-defined business use case that offers real value is crucial. As LLMs move from development to production, it's important to establish thorough evaluations and observability throughout their lifecycle. Education across the organization is vital to implement LLMs effectively. Companies should train their workforce to adapt to this changing technology stack. Fostering a community around responsible AI development and evaluation can contribute to a better understanding and management of LLMs. With these steps, enterprises can navigate the complexities of LLMs and harness their potential for positive impact.
Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha
Engaging topics at a glance
Explore the vital link between AI and sustainability as we discuss strategies for eco-conscious AI deployment with Dr. Eng Lim Goh, Senior vice president of Data & AI at Hewlett Packard Enterprise
In this episode of Unpacked, Arvind introduces Dr. Eng Lim Goh, SVP of Data and AI at Hewlett Packard Enterprises, to discuss the topic of sustainable AI. They agree on the importance of being conscious of the planet's well-being while charting business growth and profitability.
Dr. Goh explains the need for corporations to consider sustainability due to their net-zero goals, the conscious younger generation of employees, and the economic implications of power consumption in supercomputing. He shares his experience of working on the International Space Station and how he realized the importance of a sustainable approach to technology.
Similarly, he suggests that businesses should consider long-term goals while investing in AI and related technologies, adding that it is important to measure the impact of such efforts quantitatively. He also talks about the importance of collaboration between businesses, governments, and academia to achieve sustainable progress. The conversation then moves on to the topic of energy consumption in AI, and Dr. Goh explains how the power consumption of large models has been a challenge in the supercomputing industry. He suggests that businesses should consider using more efficient hardware and software to reduce energy consumption and how they can approach this. He also mentions the importance of using renewable energy sources to power data centers.
The conversation concludes with Dr. Goh’s vision for the future of AI and sustainability. Dr. Goh emphasizes the need for businesses to consider the long-term impact of their actions and to invest in sustainable technologies. He believes that AI can play a crucial role in achieving sustainability goals and that it is important for businesses to collaborate and share knowledge to achieve sustainable progress.
Overall, the conversation highlights the need for businesses to consider sustainability while investing in AI and related technologies. It emphasizes the importance of transparency, collaboration, and measurement in achieving sustainable progress.
Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha
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