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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.

Due to scientific and technological advancements, the economy of the future will increasingly require professionals with advanced degrees, but graduate costs have increased 233% since 2000. The current trajectories of cost and debt put graduate education out of reach for too many students. Grad PLUS loans are an important resource for many graduate students who may not otherwise have access to private lending. But they can also be a source of excessive debt—limited only by the cost of attendance, an amount that universities set with few incentives to rein in costs. Minimizing the risks to borrowers will require closer regulatory scrutiny and greater transparency about graduate program outcomes. Without clear information about degree programs’ debt and earnings outcomes, prospective students are unable to make informed choices about their graduate education and future careers, and policymakers are unable to hold graduate programs accountable for leaving program completers in precarious financial positions. Georgetown University Center on Education and the Workforce's new report, “Graduate Degrees: Risky and Unequal Paths to the Top,” supported by Arnold Ventures, proposes improvements to transparency and accountability in graduate education through a new regulatory framework for Grad PLUS loan eligibility, which would enable high-value programs to continue operating while putting the brakes on runaway costs and borrowing. https://bit.ly/3NaFnzn

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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