Contents
The main questions and challenges that arise as AI technologies develop, and promising areas of their application – in seven podcasts
Replacement for a person
To understand what artificial intelligence (AI) systems are and how they work, the Trends What’s Changed podcast series will help. So, in the fifth issue, experts help to understand the terminology, and also discuss whether the computer will replace us. And from the bonus episode it will become clear which specialists are not in danger of being replaced by machines.
AI against super bacteria
Artificial intelligence is used in many areas, and medicine is considered one of the most promising for using its capabilities. So, AI can help in the fight against antibiotic-resistant super-bugs and even against COVID-19. Bioengineer Jim Collins explains how in a short TEDTalks Technology podcast.
Teach a robot, learn from a robot
On the Artificial Intelligence podcast with Lex Friedman, UC Berkeley Associate Professor Sergey Levin talks about how artificial intelligence learns, how robots can help us understand what intelligence is in general, and also gives advice to students interested in AI.
ethical robot
Discussions about how to make AI useful, safe for humans, are carried out by a relatively small number of technology companies and scientific organizations around the world. Experts Chris Benson (Principal AI Strategist at Lockheed Martin) and Daniel Whittenack (co-host of the Practical AI podcast) talk about how to achieve ethical principles in AI that align with your own company norms and what it takes to implement these strategies.
fair choice
This episode of the O’Reilly Data Show podcast discusses how machine learning and analytics can help improve employee performance and promote fair recruitment.
A critical look
This episode of the DataFramed podcast is about critical thinking in data science and why it matters as AI permeates every area of our lives, infiltrating organizations and society.
Through trial and error
This episode of the Data Skeptic podcast talks about computer vision and how you can trick neural networks into understanding why they make mistakes.
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