Artificial intelligence Archive - Rock the Prototype - Softwareentwicklung & Prototyping

AI & Us – Software development & artificial intelligence

Sascha Block2024-06-28T12:38:58+02:00Categories: Artificial intelligence (AI)|Tags: AI, AI, AI applications, AI Applications, AI Frameworks, AI Future, AI software development, AI technologies, AI Technologies, AI Tools, AI Tools, Artificial intelligence, Artificial intelligence, Automation, Automation, Career IT, Coding, Deep learning, Digital transformation, Digital transformation, Innovation, IT Career, machine learning, neural networks, neural networks, neural networks, Programming, Robotics, Robotics, Software development AI, Technology trends|

Artificial intelligence and us Welcome to the first episode

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AI language models

Sascha Block2025-02-05T13:55:57+01:00Tags: AGI, AI ethics, AI for companies, AI Governance, AI in everyday life, AI in research, AI language models, AI Research, AI safety, AI-supported creativity, Alexa, Algorithmic Bias, Artificial intelligence, Attention mechanism, Autoencoding, Autoregressive models, BERT, Bias in AI, bidirectional models, Chatbots, CLIP, Cloud computing, Computational linguistics, Computing power, Conversational AI, DALL-E, Data ethics, Deep learning, DeepMind, digital assistants, Embeddings, Energy-efficient AI, Explainability, Fairness in AI, Few-Shot Learning, Few-Shot Prompting, Fine tuning, GDPR, generative AI, generative language models, GitHub Copilot, GloVe, Google AI, Google Assistant, GPT, GPT-2, GPT-3, GPT-4, GPUs, Hallucinations in AI, Hugging Face, Information retrieval, Interpretability, Knowledge Graphs, Language processing, Large Language Models, LLM, LSTM, machine learning, machine translation, Masked Language Modeling, Meta AI, Model compression, multimodal AI, multimodal models, Named Entity Recognition, natural language processing, neural networks, NLP, Open source AI, OpenAI, OpenAI Codex, Perplexity, Privacy, Prompt Engineering, Prompt Tuning, Regulation, Reinforcement Learning, Responsible AI, Retrieval-Augmented Generation, RNN, Scalability, Self-Attention, Sentiment analysis, Siri, Supervised Learning, synthetic data, T5, Text classification, Text generation, Tokenizer, TPUs, Training data, Transfer Learning, Transformer, Unsupervised Learning, Voice assistants, Word2Vec, Zero-shot learning|

AI language models - what's behind them? Artificial intelligence is changing the way we interact with machines. Language models enable machines not only to understand language, but also to generate meaningful, human-like responses. Find out more now...

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Artificial Intelligence Act – All the facts about the EU’s AI Act

Sascha Block2025-01-11T10:02:52+01:00Tags: AI, AI Act, Algorithms, Artificial intelligence, Artificial Intelligence Act, Automated facial recognition, Compliance, Decision-making processes, Ethical use of AI, EU, European Union, GDPR, KI Gesetz, KI Gesetze, KI Regularien, KI Regulatorik, KI Vorschriften, Potential of AI, Regulation of AI technologies, Social scoring, Training data|

What is the EU's Artificial Intelligence Act - AI Act for short? The Artificial Intelligence Act (AI Act) is the world's first binding law regulating artificial intelligence (AI). The AI Act was adopted by the European Commission on March 13, 2024, after being introduced as a legislative proposal in April 2021. The AI Act is thus part of a broader European Union (EU) AI strategy that aims to harness the potential of AI while minimizing risks, maximizing opportunities, combating discrimination and ensuring transparency in the use of AI technologies.

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Artificial intelligence: How does Chat GPT work?

Sascha Block2024-04-12T09:18:22+02:00Categories: Artificial intelligence (AI)|Tags: AI, AI, AI models, Artificial intelligence, Artificial intelligence, Big Data, BigData, Chat GPT, ChatGPT, Data Science, DeepLearning, Distortions, Embeddings, FineTuning, GPT, Hallucinations, machine learning, Machine learning, neural networks, neural networks, NLP, PositionalEncoding, PreTraining, Prototyping, SelfAttention, Software development, Transformer|

Artificial intelligence: How does Chat GPT work? I unravel the threads of semantic probabilities and reveal how AI technology can come so incredibly close to our human understanding. Join me on an exciting journey through the neural landscape of GPT and expand your boundaries between human creativity and machine learning... ✅ What exactly is GPT? ✅ How does a Generative Pretrained Transformer (GPT) work? ✅ Chat GPT - facts about this fascinating AI technology ✅ How does GPT work and why is this AI model so groundbreakingly revolutionary? ✅ How is a GPT model actually trained? ✅ The revolutionary difference between GPT AI technology and traditional AI models ✅ The power of combining self-attention and positional encoding ✅ Pre-training - the pre-training process of AI ✅ How does GPT process all this information? ✅ The embeddings strategy of the AI model

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Deep learning frameworks

Sascha Block2025-02-01T17:01:23+01:00Tags: Activation Functions, AI benchmarking, AI deployment in the cloud, AI ethics, AI for autonomous systems, AI for companies, AI for image recognition, AI for language models, AI for recommendation systems, AI for robotics, AI for word processing, AI Frameworks, AI in medicine, AI in production, AI in the automotive industry, AI in the financial sector, AI infrastructure, AI optimization, AI Performance, AI Pipeline, AI Research, AI toolkits, AI training, Algorithmic fairness, Algorithmic optimization, Artificial intelligence, artificial neural networks, Autoencoder, AutoML, Backpropagation, Batch Normalization, BERT, Bias in AI, Big Data, Caffe, Cloud-based AI, Clustering, CNTK, Computer Vision, Convolutional Neural Networks, Cross Validation, Data Augmentation, Data Parallelism, Data Science, Deep learning, Deep reinforcement learning, DeepMind, Deployment of AI models, Deployment strategies, Dropout, Edge AI, Edge Computing, Explainable AI, Feature engineering, Federated Learning, Few-Shot Learning, FPGAs, Generative Adversarial Networks, GPT, GPU acceleration, Gradient Descent, Graph Neural Networks, Hardware acceleration for AI, High-Performance Computing, Hyperparameter optimization, Hyperparameter tuning, Image classification, Industrial applications for AI, Inference, Interpretability of models, JAX, Keras, Language processing, Loss function, LSTM, machine learning, Machine Learning Operations, MobileNet, Model compatibility, Model compression, Model management, Model Parallelism, Model Quantization, Model Serving, Model validation, MXNet, natural language processing, neural networks, neuronal architecture, NVIDIA CUDA, Object recognition, ONNX, Open Source, OpenAI, OpenAI Gym, Optimization, PyTorch, Recurrent Neural Networks, Regularization, Reinforcement Learning, ResNet, Scalability, TensorFlow, Theano, TPUs, Training of models, Training Pipelines, Transfer Learning, Transformer models, VGG, Zero-shot learning|

What are deep learning frameworks? Deep learning frameworks are

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Gemini AI model

Sascha Block2023-12-09T19:11:54+01:00Tags: AI development, AI ethics, AI innovation, AI integration, AI model, AI regulation, AI technology, Artificial intelligence, Audio processing, Code generation, Data security, Deep learning, ethical AI, Gemini, Google DeepMind, Image analysis, Language processing, machine learning, Multimodality, neural networks, Privacy, Reinforcement Learning, Research and development, Supervised Learning, Technology innovation, Text analysis, Unsupervised Learning, Video analysis|

Gemini is an advanced AI language model developed by Google DeepMind. It serves as the successor to the earlier AI models LaMDA and PaLM 2 and was presented by google in December 2023. Gemini is characterized as an AI model by its multimodality, which means that it can process different types of data, including text, images, audio, video and computer source code. What can the gemini AI do? Gemini will be integrated into Google's search engine, the google advertising products and the Chrome browser. Learn all about google AI now...

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Genetic algorithms

Sascha Block2023-12-08T12:10:16+01:00Tags: Adaptability in AI, AI decision making, AI development, AI modeling, algorithmic innovations, algorithmic models, algorithmic pattern recognition, Algorithms in AI, Artificial intelligence, Automation in AI, Climate change prediction, Data-driven optimization, digital maps, Ecological data analysis, Effects of climate change, Environmental informatics, Environmental management, Environmental planning, Environmental strategies, evolutionary algorithms, Feature selection, genetic algorithm, Genetic programming, Hyperparameter tuning, Learning algorithms, machine learning, machine learning training, Nature conservation, neural networks, Optimization, Optimization algorithms, Resource management, Simulation, Sustainable decision-making, Sustainable use of resources|

Genetic algorithms are a class of optimization methods based on the principles of natural evolution and genetics. A genetic algorithm is ideal for finding solutions to complex problems by mimicking evolutionary processes such as selection, crossover and mutation. In the field of machine learning, genetic algorithms offer a unique and effective approach, especially when it comes to optimizing learning models and selecting features.

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Machine Learning

Sascha Block2023-12-10T19:11:37+01:00Tags: Alan Turing, Algorithms, Artificial intelligence, Artificial intelligence programs, artificial neural networks, Automation, Backpropagation, Big Data, Chess program, continuous learning, Convolutional Neural Networks (CNNs), Data, Decision making, Deep learning, Efficiency, Image processing, Logic Theorist, machine learning, Models, natural language processing, Pattern recognition, Perceptron, personalized recommendations, Predictions, Recurrent Neural Networks (RNNs), Turing machine|

Machine learning is a branch of artificial intelligence (AI) that deals with the development of algorithms and models that enable computers to learn from experience and data, recognize patterns and make predictions without being explicitly programmed. The aim of machine learning is to enable computers to learn independently from data and extract information in order to master complex tasks. We explain which concepts are used to do this and how predictions can be made by adapting models to existing data.

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Natural Language Processing (NLP)

Sascha Block2025-01-24T15:57:08+01:00Tags: AllenNLP, API integration, Artificial intelligence, Attention mechanism, Azure Text Analytics, BERT, Bias reduction, bidirectional models, Chatbot development, Computational linguistics, computational semantics, Constituent parsing, Context-aware models, Contextual embeddings, Conversational AI, Corpus analysis, Data preprocessing, Deep learning, Dependency parsing, Dialog systems, ethical AI, Fairness in AI, Gated Recurrent Unit (GRU), GloVe, Google Cloud NLP, GPT, Handling sensitive data, Hugging Face, IBM Watson, Information retrieval, Language comprehension, Language models, Lemmatization, Long Short-Term Memory (LSTM), machine learning, morphological analysis, Multilingual NLP, Named Entity Recognition (NER), natural language processing, neural networks, NLP, NLTK, NLU (Natural Language Understanding), OpenNLP, Parsing algorithms, Privacy, PyTorch, Question answer, recurrent neural networks (RNN), Self-Attention, semantic analysis, Sentiment analysis, Sentiment classification, Sequence modeling, spaCy, Speech recognition, Speech-to-text, Stemming, syntactic analysis, TensorFlow, Text analysis, Text classification, Text generation, Text mining, Text parsing, Text summary, Text-to-speech, Tokenization, Transformer models, virtual assistants, Word embeddings, Word type tagging, Word2Vec|

Natural Language Processing (NLP) - a highly specialized subfield of artificial intelligence (AI) How do machines understand human language? Natural Language Processing is the key to bridging the gap between humans and machines

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Artificial intelligence

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