I.KNOW CONVERSATIONS
In this section, we present "podcast videos" that cover the most exciting
and relevant topics of the Academy.
Our videos offer informative yet engaging, AI-generated conversations,
making even complex topics easy to understand.
Enjoy discovering and learning!
The History of AI
Die The history of artificial intelligence (AI) is marked by technological breakthroughs and pivotal moments that have profoundly shaped our understanding of machine learning, decision-making, and human-machine interaction.
Since the initial concepts of the 1950s, AI has evolved from a theoretical discipline to a key technology impacting nearly every aspect of modern life.
The story of AI is a fascinating journey from early theoretical ideas to today’s advanced systems. It reflects humanity’s desire to create intelligent machines while highlighting the need to develop and apply these technologies responsibly.
Machine Learning
Machine learning has become a core component of modern technology, applied across nearly every area of life—from speech recognition to medicine and autonomous driving.
There are three main methods of machine learning, which train machines to analyze provided data and acquire useful skills:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
In our podcast video, you’ll learn more about these three methods of machine learning.
How do LLM´s learn?
Large Language Models (LLMs), widely used in today’s AI applications, are based on neural networks trained on vast amounts of text data.
These models can generate human-like text, answer complex questions, perform translations, and much more.
The development of an LLM involves several stages, including systematic data collection, preprocessing, machine learning, and fine-tuning.
How do LLM´s generate text?
LLMs LLMs generate text using a process called autoregressive prediction, where the model predicts the next word in a sequence based on the preceding context.
This is made possible by the understanding of language patterns gained during pre-training. Using the transformer architecture and attention mechanisms, the model can weigh the significance of individual words within the context of an entire sentence, maintaining coherence across longer text passages.
For instance, if you provide the model with a question or an incomplete sentence, it analyzes the context and generates a fitting and cohesive response word by word.
The model processes inputs through multiple layers of neural networks, performing mathematical operations such as matrix multiplication. Each layer refines predictions until an appropriate word choice is made.
This process continues until the model has fully answered the input or generated the intended text.
Prompt Components
Prompt-Komponenten bieten einen strukturierten Rahmen, um die Antworten von Sprachmodellen gezielt zu beeinflussen.
Grundsätzlich bestehen Prompts aus drei Hauptkomponenten:
- Task
- Instruktionen
- Kontext
Je nach Anwendungsfall kann zusätzlicher Input hinzugefügt werden, um die gewünschte Richtung zu verstärken.
Zudem können optionale Parameter wie Temperatur oder Tokens definiert werden, um die Ausgabe des Modells weiter zu spezifizieren.
Viele interessante Details zu den Prompt Komponenten und der effizientesten Prompting Struktur findest Du in unserem PodCast Video.
Prompting Strategies
Prompt components provide a structured framework to guide language model responses effectively.
In general, prompts consist of three main components:
- Task
- Instructions
- Context
Depending on the application, additional input can be added to reinforce the desired direction.
Optional parameters, such as temperature or token limits, can also be defined to further customize the model's output.
You can find many interesting details about prompt components and the most effective prompting structure in our podcast video.
AI Risks & Limitations
Artificial Intelligence (AI) has revolutionized many industries by enhancing automation, improving decision-making, and increasing efficiency. However, alongside these benefits, AI also presents significant risks.
Major challenges include phenomena such as hallucinations, overfitting, and algorithmic biases. These issues can lead to unreliable and unfair outcomes.
To minimize these risks, it is crucial to understand these problems and apply appropriate mitigation strategies. Only through careful oversight and continuous development of AI systems can their use remain safe and responsible.
Awareness of these challenges is essential to harness the full potential of AI without overlooking ethical and technical risks.
AI Evolution: From ANI to ASI?
Artificial Intelligence (AI) is divided into three stages of development: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).
ANI is already widely used in various fields and is specialized in specific tasks, such as voice assistants.
AGI, however, remains a theoretical concept. It describes an AI system with human-like understanding and learning abilities, capable of independently solving a wide range of tasks.
ASI refers to a hypothetical intelligence far surpassing human capabilities, capable of independently finding answers to questions that we may not even know how to ask.
Currently, ANI dominates, while research on AGI is advancing rapidly. Further development of AI systems holds the promise of solutions to global challenges like climate change, poverty, and energy and resource shortages. However, it also carries significant risks if AI’s goals do not align with ours.
Therefore, a clear governance framework is essential to ensure that AI research progresses safely and responsibly, in harmony with key legal and ethical considerations.
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