Come pensa un’intelligenza artificiale? | Giulio Deangeli | TEDxBari

()

Traditional AI (Discriminative AI)

Traditional AI involves techniques like deep learning and neural networks.
Deep learning neural networks perform classification (assigning labels to data) and regression (estimating numerical values).
Neurons, the basic units of neural networks, process electrical signals and generate output signals.
The complexity of life and the brain arises from the non-linear nature of neurons and their massive interconnectedness.
Training neural networks enables them to perform classification and regression tasks, resulting in pattern recognition and prediction capabilities.
Traditional AI has applications in medicine, such as drug discovery and development.

Generative AI, like the Transformer architecture, uses attention mechanisms to filter important information and generate new text.
The scalability of generative AI models like GPT has exceeded expectations, with increased computational power leading to significantly improved performance.
Unlike traditional AI models, generative AI models do not necessarily perform better with increased scale.
GPT (Generative Pre-trained Transformer) generates human-like text by predicting the next word in a sequence based on preceding words.
GPT introduces a probabilistic element, making its outputs more varied and human-like, mimicking the non-deterministic nature of the human brain.
GPT has applications in medicine, such as analyzing medical literature, creating standardized clinical datasets, and assisting doctors in decision-making.

AI is already integrated into healthcare, with patients interacting with AI-powered systems for triage and other tasks.
AI has a significant societal impact and is an integral part of our lives, presenting both challenges and opportunities.
AI techniques can significantly increase productivity, and tools like CoPilot enhance programmers' work.
The widespread adoption of AI has societal implications, potentially leading to rapid job displacement similar to the Industrial Revolution but on a shorter timescale.
While some jobs will become obsolete, AI can democratize access to certain professions by reducing the need for extensive memorization and technical knowledge.
The focus of human work will shift towards decision-making and evaluation, while routine tasks can be automated.
There is a risk of social inequality if access to AI tools is limited, so governments must ensure universal access.
AI is an integral part of the world and can be used by others to gain a competitive advantage.