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Andreas Horn – The 9 algorithms
The illustration below highlights the algorithms most frequently utilized in our everyday activities: They play a key role in everything we do from online shopping recommendations, navigation apps, social media, email spam filters and even smart home devices.
🔹 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺
– Organize data for efficiency.
➜ Example: Sorting email threads or search results.
🔹 𝗗𝗶𝗷𝗸𝘀𝘁𝗿𝗮’𝘀 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺
– Finds the shortest path in networks.
➜ Example: Google Maps driving routes.
🔹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀
– AI models that understand context and meaning.
➜ Example: ChatGPT, Claude and other LLMs.
🔹 𝗟𝗶𝗻𝗸 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
– Ranks pages and builds connections.
➜ Example: TikTok PageRank, LinkedIn recommendations.
🔹 𝗥𝗦𝗔 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺
– Encrypts and secures data communication.
➜ Example: WhatsApp encryption or online banking.
🔹 𝗜𝗻𝘁𝗲𝗴𝗲𝗿 𝗙𝗮𝗰𝘁𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻
– Secures cryptographic systems.
➜ Example: Protecting sensitive data in blockchain.
🔹 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗖𝗡𝗡𝘀)
– Recognizes patterns in images and videos.
➜ Example: Facial recognition, object detection in self-driving cars.
🔹 𝗛𝘂𝗳𝗳𝗺𝗮𝗻 𝗖𝗼𝗱𝗶𝗻𝗴
– Compresses data efficiently.
➜ Example: JPEG and MP3 file compression.
🔹 𝗦𝗲𝗰𝘂𝗿𝗲 𝗛𝗮𝘀𝗵 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 (𝗦𝗛𝗔)
– Ensures data integrity.
➜ Example: Password encryption, digital signatures. -
The History, Evolution and Rise of AI
https://medium.com/@lmpo/a-brief-history-of-ai-with-deep-learning-26f7948bc87b
🔹 1943: 𝗠𝗰𝗖𝘂𝗹𝗹𝗼𝗰𝗵 & 𝗣𝗶𝘁𝘁𝘀 create the first artificial neuron.
🔹 1950: 𝗔𝗹𝗮𝗻 𝗧𝘂𝗿𝗶𝗻𝗴 introduces the Turing Test, forever changing the way we view intelligence.
🔹 1956: 𝗝𝗼𝗵𝗻 𝗠𝗰𝗖𝗮𝗿𝘁𝗵𝘆 coins the term “Artificial Intelligence,” marking the official birth of the field.
🔹 1957: 𝗙𝗿𝗮𝗻𝗸 𝗥𝗼𝘀𝗲𝗻𝗯𝗹𝗮𝘁𝘁 invents the Perceptron, one of the first neural networks.
🔹 1959: 𝗕𝗲𝗿𝗻𝗮𝗿𝗱 𝗪𝗶𝗱𝗿𝗼𝘄 and 𝗧𝗲𝗱 𝗛𝗼𝗳𝗳 create ADALINE, a model that would shape neural networks.
🔹 1969: 𝗠𝗶𝗻𝘀𝗸𝘆 & 𝗣𝗮𝗽𝗲𝗿𝘁 solve the XOR problem, but also mark the beginning of the “first AI winter.”
🔹 1980: 𝗞𝘂𝗻𝗶𝗵𝗶𝗸𝗼 𝗙𝘂𝗸𝘂𝘀𝗵𝗶𝗺𝗮 introduces Neocognitron, laying the groundwork for deep learning.
🔹 1986: 𝗚𝗲𝗼𝗳𝗳𝗿𝗲𝘆 𝗛𝗶𝗻𝘁𝗼𝗻 and 𝗗𝗮𝘃𝗶𝗱 𝗥𝘂𝗺𝗲𝗹𝗵𝗮𝗿𝘁 introduce backpropagation, making neural networks viable again.
🔹 1989: 𝗝𝘂𝗱𝗲𝗮 𝗣𝗲𝗮𝗿𝗹 advances UAT (Understanding and Reasoning), building a foundation for AI’s logical abilities.
🔹 1995: 𝗩𝗹𝗮𝗱𝗶𝗺𝗶𝗿 𝗩𝗮𝗽𝗻𝗶𝗸 and 𝗖𝗼𝗿𝗶𝗻𝗻𝗮 𝗖𝗼𝗿𝘁𝗲𝘀 develop Support Vector Machines (SVMs), a breakthrough in machine learning.
🔹 1998: 𝗬𝗮𝗻𝗻 𝗟𝗲𝗖𝘂𝗻 popularizes Convolutional Neural Networks (CNNs), revolutionizing image recognition.
🔹 2006: 𝗚𝗲𝗼𝗳𝗳𝗿𝗲𝘆 𝗛𝗶𝗻𝘁𝗼𝗻 and 𝗥𝘂𝘀𝗹𝗮𝗻 𝗦𝗮𝗹𝗮𝗸𝗵𝘂𝘁𝗱𝗶𝗻𝗼𝘃 introduce deep belief networks, reigniting interest in deep learning.
🔹 2012: 𝗔𝗹𝗲𝘅 𝗞𝗿𝗶𝘇𝗵𝗲𝘃𝘀𝗸𝘆 and 𝗚𝗲𝗼𝗳𝗳𝗿𝗲𝘆 𝗛𝗶𝗻𝘁𝗼𝗻 launch AlexNet, sparking the modern AI revolution in deep learning.
🔹 2014: 𝗜𝗮𝗻 𝗚𝗼𝗼𝗱𝗳𝗲𝗹𝗹𝗼𝘄 introduces Generative Adversarial Networks (GANs), opening new doors for AI creativity.
🔹 2017: 𝗔𝘀𝗵𝗶𝘀𝗵 𝗩𝗮𝘀𝘄𝗮𝗻𝗶 and team introduce Transformers, redefining natural language processing (NLP).
🔹 2020: OpenAI unveils GPT-3, setting a new standard for language models and AI’s capabilities.
🔹 2022: OpenAI releases ChatGPT, democratizing conversational AI and bringing it to the masses. -
HuggingFace – AI Agents Course
https://huggingface.co/learn/agents-course/en/unit0/introduction
In this course, you will:
- 📖 Study AI Agents in theory, design, and practice.
- 🧑💻 Learn to use established AI Agent libraries such as smolagents, LlamaIndex, and LangGraph.
- 💾 Share your agents on the Hugging Face Hub and explore agents created by the community.
- 🏆 Participate in challenges where you will evaluate your agents against other students’.
- 🎓 Earn a certificate of completion by completing assignments.
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Joseph Bell – VFX Studio Growth 2022 – 2024
Color represents whether they’ve grown over the past 2 years (blue) or downsized (red).
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