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Understanding the Landscape: Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks 𝗝𝗼𝗶𝗻 𝗺𝗲 𝗳𝗼𝗿 𝗮 𝗙𝗿𝗲𝗲 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝘄𝗼𝗿𝗸𝘀𝗵𝗼𝗽 𝗼𝗻 𝗮𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗚𝗲𝗻𝗔𝗜  in data analytics: ✳️ RSVP here - https://brij.guru/ai Artificial intelligence (AI) is the vast field of computer science dedicated to creating intelligent machines. Machine learning (ML) is a subfield of AI that empowers systems to learn and improve from experience, all without explicit programming. Deep learning (DL) takes machine learning a step further, utilizing artificial neural networks with many layers to uncover intricate data representations. 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗞𝗲𝘆 𝗧𝗲𝗿𝗺𝘀: • 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜): AI encompasses two main categories: applied AI and general AI. Applied AI, what we encounter most often today, tackles tasks like self-driving cars, facial recognition, and spam filtering. General AI, still under development, strives for human-level intelligence with the potential to drastically transform our world. • 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗠𝗟): ML algorithms learn from labeled datasets with desired outputs. Imagine an image recognition algorithm trained on a massive dataset of images labeled with the objects they contain. Once trained, the algorithm can identify objects in new, unseen images. • 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗗𝗟): A type of ML algorithm that leverages artificial neural networks. These networks, inspired by the human brain's structure, consist of interconnected layers of nodes, or artificial neurons. Each node receives a weighted sum of its inputs, then applies an activation function to generate an output. Through training the connections' weights within the network, deep learning algorithms can learn complex data patterns. • 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗔𝗡𝗡𝘀): The core building blocks of deep learning, ANNs mimic the structure and function of the human brain. They are comprised of interconnected layers containing artificial neurons that process information through weighted connections. By adjusting these weights during training, ANNs learn intricate relationships within data. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: • 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Spam filtering, fraud detection, recommendation systems, image recognition, speech recognition    • 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Self-driving cars, facial recognition, natural language processing, machine translation, generative art Deep learning offers a powerful tool for tackling various challenges. However, it's crucial to remember that deep learning algorithms can be intricate and require substantial training data for effectiveness.

As if AI-generated "user research" wasn't bad enough, now I am seeing designers advance LLMs for generating UI content - forgetting the most important design adage, "form follows function." The digital equivalent is not the function of clicking the button but the function of *accessing the content.* All the way back in 2002, Jesse James Garrett created this incredible model, which I use frequently to structure my work. And to explain to junior designers the extent of the depth they need to consider, because 22 years after this was published few of them even know what a "hypertext system" even is, and envision their work exclusively as a software interface. Without both of these pillars, your UX design will fall over. Some UXers understand this partially - but think that the interface is "more important" and the content can come afterwards. These are the people who champion ChatGPT as a great alternative to Lorem Ipsum. And they repeat the same mantra as every designer who invites AI into their workflow: "it's just for now, we will fix it later." You will *not* be able to fix it later. The elements you are generating are *foundational* to the way the product is used. They are not stacked on top. If you decided to outsource your thinking about the hypertext system, redoing it will require ripping out the guts of the software interface layers that you have built in parallel. The content - not the beautiful scroll animations - is the thing people use your product for. It is not lesser than. It is not an afterthought. It even has its own set of roles (content design, UX writing, information architecture, etc). The content is the scaffolding of the experience. When you minimize it, it is only to your own detriment.

Need data? No problem, let's create a million different viewpoints. 🤯 Synthetic data has become a cornerstone of modern AI development. But creating diverse, high-quality data at scale remains a challenge. Persona Hub is a new approach that leverages the knowledge and perspectives encapsulated within Large Language Models (LLMs). By curating 1 billion unique personas from web data, Persona Hub enables the synthesis of highly diverse and nuanced datasets across a wide range of domains. From mathematical reasoning problems to game NPCs - It's scalable, flexible, and easy to use. This could mark a shift in how we approach synthetic data creation, at least in areas that are all about user data. The persona approach isn't new per se, but LLMs let us scale it beyond anyting that was previously possible. ↓ Liked this post? Join my newsletter with 35k+ readers that breaks down all you need to know about the latest LLM research: llmwatch.com 💡

TOP 100 Generative AI Use Cases! 🎉👏🎉👏🎉 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲𝘀𝗲 𝗼𝘂𝘁...   ▶▶  𝟭. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗶𝗱𝗲𝗮𝘀.   ▶▶  𝟮. 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 / 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗼𝗻𝘀𝗵𝗶𝗽.   ▶▶  𝟯. 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵.   ▶▶  𝟰. 𝗘𝗱𝗶𝘁 𝘁𝗲𝘅𝘁.   ▶▶  𝟱. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗼𝗽𝗶𝗰𝘀 𝗼𝗳 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁.   ▶▶  𝟲. 𝗙𝘂𝗻 & 𝗻𝗼𝗻𝘀𝗲𝗻𝘀𝗲.   ▶▶  𝟳. 𝗧𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁.   ▶▶  𝟴. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.   ▶▶  𝟵. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.   ▶▶  𝟭𝟬. 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗮𝗱𝘃𝗶𝗰𝗲.   ▶▶  𝟭𝟭. 𝗗𝗿𝗮𝗳𝘁 𝗲𝗺𝗮𝗶𝗹𝘀.   ▶▶  𝟭𝟮. 𝗦𝗶𝗺𝗽𝗹𝗲 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗿𝘀.   ▶▶  𝟭𝟯. 𝗪𝗿𝗶𝘁𝗲 / 𝗲𝗱𝗶𝘁 𝗖𝗩 / 𝗿𝗲𝘀𝘂𝗺𝗲.   ▶▶  𝟭𝟰. 𝗘𝘅𝗰𝗲𝗹 𝗳𝗼𝗿𝗺𝘂𝗹𝗮𝗲.   ▶▶  𝟭𝟱. 𝗔𝗱𝗷𝘂𝘀𝘁 𝘁𝗼𝗻𝗲 𝗼𝗳 𝗲𝗺𝗮𝗶𝗹. Enjoy! #AI #ArtificialIntelligence #GenAI #TScottClendaniel

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Learn Gen AI with Retrieval-Augmented Generation (RAG) Join me for a free workshop on implementing RAG in your GenAI projects. ✅ 👉 RSVP here - https://brij.guru/ai Ever felt your large language model (LLM) responses were a bit...off?    That's because LLMs, while impressive, can struggle with accuracy. That's where Retrieval-Augmented Generation (RAG) comes in! 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗔𝗚? RAG combines the power of LLMs with external knowledge bases. Here's the gist: 1. 𝗨𝘀𝗲𝗿 𝗮𝘀𝗸𝘀 𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻. 2. 𝗥𝗔𝗚 𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗯𝗮𝘀𝗲 for relevant info. 3. 𝗧𝗵𝗲 𝗟𝗟𝗠 𝗴𝗲𝘁𝘀 𝘁𝗵𝗶𝘀 𝗶𝗻𝗳𝗼, along with the user's question. 4. 𝗧𝗵𝗲 𝗟𝗟𝗠 𝘂𝘀𝗲𝘀 𝗶𝘁𝘀 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲𝗱 𝗶𝗻𝗳𝗼 to craft a response. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗥𝗔𝗚? • 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗯𝗼𝗼𝘀𝘁: RAG ensures responses are grounded in factual data, minimizing misinformation. • 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗴𝗮𝗶𝗻𝘀: No more sifting through documents! RAG delivers info quickly. • 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗼𝗻 𝘁𝗮𝗽: RAG integrates seamlessly with your existing knowledge base, keeping info fresh. 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗼𝗳 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗥𝗔𝗚? • 𝗗𝗮𝘁𝗮 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: The quality of your knowledge base directly impacts RAG's effectiveness. • 𝗛𝘂𝗺𝗮𝗻 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗶𝘀 𝗸𝗲𝘆: RAG is a tool, not a replacement for human expertise. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲? Join this Free workshop by professor Tom Yeh! 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗻𝗼𝘄 - https://brij.guru/ai