ΑΙhub.org
 

How to benefit from AI without losing your human self – a fireside chat from IEEE Computational Intelligence Society


by
02 December 2024



share this:

The image is a very detailed, black-and-white sketch-like illustration featuring a complex scene of interconnected figures and technology. The artwork portrays various individuals in different environments to represent the relationship between technology and humans. 

In the foreground, multiple people are surrounded by computer screens filled with data visualisations, charts, and technical information. A woman seated in an armchair appears deep in thought, surrounded by data-filled monitors. Beside her, a man leans over, using a tablet to assist with their inspection of a plant or tree. In the centre, a figure holds a large frame or screen displaying anatomical illustrations, representing the use of AI to analyse medical imagery. To the left, another person is intently observing a computer screen, while a second figure nearby is deeply immersed in analysing data. A woman dominates the right side of the composition, gazing upwards as if in contemplation or envisioning something beyond the immediate scene. The background features more people, including a family holding hands, and other abstract representations of data.Ariyana Ahmad & The Bigger Picture / Better Images of AI / AI is Everywhere / Licenced by CC-BY 4.0

In this fireside chat from IEEE Computational Intelligence Society, Tayo Obafemi-Ajayi (Missouri State University) asks Hava T. Siegelmann (University of Massachusetts, Amherst) about how to benefit from AI without losing your human self.

You can watch the chat in full below:




IEEE Computational Intelligence Society

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

‘Probably’ doesn’t mean the same thing to your AI as it does to you

  17 Apr 2026
Are you sure you and the AI chatbot you’re using are on the same page about probabilities?

Interview with Xinwei Song: strategic interactions in networked multi-agent systems

  16 Apr 2026
Xinwei Song tells us about her research using algorithmic game theory and multi-agent reinforcement learning.

2026 AI Index Report released

  15 Apr 2026
Find out what the ninth edition of the report, which was published on 13 April, says about trends in AI.

Formal verification for safety evaluation of autonomous vehicles: an interview with Abdelrahman Sayed Sayed

  14 Apr 2026
Find out more about work at the intersection of continuous AI models, formal methods, and autonomous systems.

Water flow in prairie watersheds is increasingly unpredictable — but AI could help

  13 Apr 2026
In recent years, the Prairies have seen bigger swings in climate conditions — very wet years followed by very dry ones.

Identifying interactions at scale for LLMs

  10 Apr 2026
Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencies and patterns.

Interview with Sukanya Mandal: Synthesizing multi-modal knowledge graphs for smart city intelligence

  09 Apr 2026
A modular four-stage framework that draws on LLMs to automate synthetic multi-modal knowledge graphs.

Emergence of fragility in LLM-based social networks: an interview with Francesco Bertolotti

  08 Apr 2026
Francesco tells us how LLMs behave in the social network Moltbook, and what this reveals about network dynamics.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence