Key terms like algorithm, machine learning, deep learning, foundation models, and large language models (LLMs) are defined to establish a common understanding.
AI models inherit biases from their training data, which can lead to unethical outcomes like offensive chatbots if not carefully managed.
Many businesses claim to use AI when they are only using simple algorithms, leading to misapplication and wasted resources on overhyped solutions.
Large language models quickly become outdated and can be exploited without proper security guardrails, posing significant ethical risks like malicious image generation.
Current AI is comparable to a toddler that repeats what it hears without true reasoning, meaning it is not yet capable of replacing complex developer roles.
Historical examples like Microsoft's Tay chatbot and biased facial recognition systems demonstrate the critical need for guardrails and diverse testing data.
Developers have a responsibility to build accountable and publicly auditable AI systems while ensuring end-users are informed and have the right to opt out.
Adhering to regulations like GDPR and using technical solutions for prompt filtering, data anonymization, and hallucination mitigation are key to building ethical AI.
27:26Cassie Kozyrkov
25:17Scott Hanselman
28:54Torsten Stiller
28:05Toju Duke
41:06Pascal Reddig, TJ Griffiths, Fabian Schmidt, Oliver Winzenried & Matthias Niehoff & Mirko Ross
32:26Krzysztof Cieślak
17:01Christian Heilmann
25:14Jonas Andrulis
Jobs that call for the skills explored in this talk.
TRUSTEQ GmbH
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