More and more, we are being pulled away from truly important issues to satisfy executives obsessed with superficial innovation.Generative Art. 240716a by takaw. OpenProcessing, 16 de julho de2024Yes, were being pulled awayfrom essential topics like accessibility, which are often overshadowed by technological distractions. A 2024 study by WebAIM analyzed the top 1 million homepages worldwide and found that 96.3% had detectable accessibility issues, with an average of 56 errors per pagehighlighting how far digital accessibility still is from being a priority.A friend working on an MVP for a GPT-integrated search engine told me that, initially, the project leads asked to scale back testing due to the high cost of tokens. Soon after, they suggested standardizing responses to further reduce token consumption. Unbelievable! They ended up recreating the same old chatbot that traps users in an endless loop of generic responses, offering no real solution.This was the motivation behind writing thisarticle.Its better to be prepared to think critically and strategically rather than just feed another empty case study on LinkedIn. I want to introduce practical and accessible exercises, especially for those who have never written a single line of code. This is not about deep technical expertise but about giving designers a starting point to better navigate AI-driven projects. The goal is to help them interpret technical discussions, challenge trends, and identify real solutionsinstead of slapping the word GPT onto a product as if it were a silverbullet.Hype and misplaced prioritiesThis doesnt mean AI isnt importantit just highlights a misalignment of priorities. The real commitment should be to the people using our products and solving actual problems, not blindly chasing the latest techtrend.Many companies dont actually want to solve problems with AIthey just want enough of an impact for presentations, reports, and LinkedIn casestudies.Its always the samestory:Decision-makers return dazzled from industry events, impressed by flashy AIdemos.But they have no concrete plan for implementation.Specialists are pulled from their strategic projects just to validate exaggerated expectations.Grand roadmaps are created that keep corporate narratives spinning without ever movingforward.How much have these AI enthusiasts actually invested in machine learning over theyears?Without a real foundation, there is no magic solution.If AI only made it onto your roadmap because your boss got excited at an event and wants something like the competition, chances are your product doesnt actually needAI.Some companies do it right. Netflix and Spotify have years of experience using machine learning to optimize recommendations and personalize user experiences. In healthcare, solutions like NeoMeds Kardia accelerate heart attack diagnosis, while others apply AI to clinical data analysis, assisted diagnosis, and treatment discovery.These examples prove that AI adds real value when built on strong data foundations and clear objectives. Without those, AI is just another buzzword disguised as innovation.The UX impact of poorly implemented AIAI adoption directly affects user experience. One of the most common pitfalls is the chatbot trapcompanies rush to implement AI to cut costs, but the end result frustrates usersinstead.Users get stuck in an endless cycle of I didnt understand responses, leading them to abandon the chatbot entirely. Instead of helping, these AI implementations create more barriers than solutions.Another example: Recommendation algorithms.Some AI models overdo personalization, making it harder for users to discover newcontent.The lack of UX-driven AI planning leads to experiences that feel monotonous and predictable.AI that fails UX is AI that loses usertrust.Common AIpitfallsFriends working on GPT-based projects have shared some recurring issues:1. Inconsistent tone ofvoiceProblem: GPT doesnt match the brands identity, flipping between formal and casualtones.Impact: Requires constant prompt engineering to maintain consistency.2. Biases in data and responsesProblem: GPT is trained on internet text, leading to biased and misleading outputs.Impact: Reinforces stereotypes, excludes groups, and spreads inaccurate information.3. High token costs due to redundancyProblem: GPT lacks continuous memory, forcing excessively long prompts forcontext.Impact: AI-driven products become expensive to operate atscale.Beyond that, major consultancies and system integrators often push GPT-based solutions for convenience, without considering better alternatives. Many decisions are made not based on technical feasibility but on how easy it is to sell a big-name AIbrand.The cost of keeping a system running on expensive tokens could be better invested in infrastructure and custom open-source modelsbut those options dont generate the same revenue for vendors selling GPT integrations.Lets get this straight: GPT is amazing,BUTTools like ChatGPT, DeepSeek, and others have made AI mainstreamand thats exciting. But LLMs are not always the best choice for MVPs and scalable products.Theyre powerful, but not universal solutions.Deploying LLMs without critical thinking leads to high costs, slow responses, and products that dont actually solve real problems.The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom.IsaacAsimovAI didnt appear overnight. Its the result of decades of research, refined algorithms, and increased computing power.In Uncle Tungsten, Dr. Oliver Sacks describes his childhood fascination with the elements of chemistry, intertwining it with the history of tungsten filament lamp evolution. That book helped me understand that scientific discovery precedes commercial application.We must question the logic of prioritizing technology before defining real problems.What happens when hype takesover?Concepts like the Metaverse, NFTs, Gamification, and Augmented Reality had real potential but were reduced to superficial trends by misdirected hype. AI may be heading down the same path, with companies adding AI without a clear understanding of the problem they are trying tosolve.1. MetaverseFacebooks MetaverseThe Horizon Worlds Logo. ByFacebookA digital universe with potential for education, remote work, and entertainment.Where it wentwrong:Metas hype focused on awkward avatars and virtual land speculation.Low accessibility (expensive VR headsets, poor interfaces).2. NFTsBored Ape NFTs. By BoredApeYachtClubDigital ownership authentication, useful for art andgaming.Where it wentwrong:Became speculation and pump-and-dump schemes.Overflow of meaningless collections.3. Chatbots and Virtual AssistantsAutomated customer service and intelligent assistants.Where it wentwrong:Limited bots that just say I didnt understand.Companies trying to replace humans instead of improving experiences.4. Augmented Reality(AR)Apple Vision Pro. ByAppleDigital interactions overlaid on the real world, useful for education andgaming.Where it wentwrong:Hype around revolutionary AR glasses that neverarrived.Useless apps (like QR codes that just openPDFs).5. GamificationGoogle News Badges. ByGoogleUsing game mechanics to boost engagement in education and productivity.Where it wentwrong:Companies only implementing point systems without real engagement.Apps forcing meaningless competition.Hype, however, isnt entirely negative. Without it, we might not have progressed as quickly in applying AI to medicine and automation. The key is to channel this energy not into a hollow race for innovation but as a catalyst for real and valuable discoveries.Why are we adoptingAI?Generative Art. Fake Neurons by Rishi. OpenProcessing, October 1,2022Everything comes down to the quality of the question, not the certainty of the answer. The real issue is not whether we should adopt AI, but why we are doing it. Enthusiasm can lead to adoption without critical reflection, resulting in superficial products with no realpurpose.In practice, what truly makes a difference is understanding the fundamentalsdistinguishing Machine Learning, LLMs, and Deep Learning, rather than simply trying to add AI to everything. Its equally important to explore alternative approaches, suchas:Traditional neural networks vs. deep neural networksNot every AI system needs deep learning; sometimes, simpler architectures are more efficient.Generative models vs. predictive modelsWhile models like GPT generate text, others excel at forecasting trends and identifying patterns.Supervised, unsupervised, and reinforcement learningUnderstanding how a model learns directly impacts its application.Rule-based systems vs. AI-driven learningIn many cases, well-defined rules outperform a machine learningmodel.The role of embeddings and vectorizationTechniques like word embeddings and semantic embeddings enhance search accuracy and recommendation quality.Specialized vs. general-purpose modelsSome tasks are better handled by small, highly trained models rather than a large, costlyLLM.In short, before rushing to integrate AI into a product, its crucial to recognize that each model serves a distinct purpose. Choosing the wrong approach can lead to wasted time, excessive costs, and disappointing outcomes.Hands-onThis is not a tutorial, and this article is aimed at those who have never written a single line of code. I chose Google Colab because it provides an accessible, ready-to-use environment without the need for complex setup. Since it runs in the cloud, anyone can execute the examples regardless of their available hardware. Additionally, its integration with popular libraries makes experimentation and prototyping fast and collaborative.Google Colab. ByGoogleHow to use GoogleColabIf youve never used Google Colab before, follow thesesteps:Go to Google Colab and sign in with your Googleaccount.Click on New notebook to create a newfile.Copy and paste the example code into a cell in the notebook.Click the (Play) button next to the cell to run thecode.A text input field will appear below the executed code, allowing you to interact with theexample.Now you can run AI, Machine Learning, and Deep Learning examples directly in your browser without installing anything. But hold on running a Colab notebook doesnt make you a programmer! Appreciate the convenience of modern technology, but dont walk away claiming you understand programming just because you executed a prewritten script. Be honestrunning code is not the same as understanding how itworks.Below, we have three examples. Exploring generic AI, Machine Learning, and Deep Learning in this order provides a structured and progressive understanding:Rule-based models (Generic AI) serve as an initial step, demonstrating automation logic without learning capabilities. They help clarify the limitations of systems that strictly follow predefined instructions.Machine Learning (ML) introduces the ability to recognize patterns and learn from data, allowing models to make predictions without being explicitly programmed withrules.Deep Learning (DL) takes this concept even further by using deep neural networks to recognize complex patterns without relying on fixedrules.To illustrate this, Ive chosen a classic example: MNIST, a dataset of handwritten digits (09). In this case, a deep learning model learns to recognize numbers by analyzing thousands of exampleswithout anyone having to program specific rules for each handwriting variation. This process showcases how neural networks can extract subtle patterns from data and make highly accurate predictions.Rule-based chatbotWebsite navigation assistantProblem:Many websites have scattered information and complex menus, making it difficult for users to find what they are lookingfor.Solution:A simple rule-based chatbot can act as a navigation assistant, helping users quickly locate information. It can answer questions suchas:Where can I find the products section?How do I access my order history?How can I talk to a representative?This model doesnt require advanced AI, making it an efficient solution for improving navigation without redesigning an entirewebsite.Benefits:Enhances content discoveryReduces frustration and abandonment ratesHow totest?Click the button in the code cell to run thechatbot.Type exit to close thechatbot.Try asking:Where can I find the products section?Can I change my delivery address?Observe the chatbots responses.import difflib# Simple Rule-Based Chatbot (Website Navigation Assistant)def chatbot(question): responses = { "where can I find the products section": "You can find the products using the search bar or by accessing the main menu at the top.", "how do I access my order history": "Access your order history by clicking on your account and then on 'My Orders'.", "how can I talk to a representative": "To speak with a representative, click here: [support link]", "what are your operating hours": "Our customer service is available Monday to Friday, from 9 AM to 6 PM.", "can I change my delivery address": "Yes, you can change your address in 'Account Settings' in the main menu." } question = question.lower().strip("? ") # Find the closest match to the user's question closest_match = difflib.get_close_matches(question, responses.keys(), n=1, cutoff=0.6) if closest_match: return responses[closest_match[0]] else: return "Sorry, I didn't understand. You can try rephrasing your question or visit our FAQ here: [FAQ link]"# Testing in Colabwhile True: question = input("Ask a question (or type 'exit' to close): ") if question.lower() == "exit": print("Chatbot: Goodbye!") break response = chatbot(question) print(f"Chatbot: {response}")Solution type: Rule-based (Automated FAQ). Simple, direct, and efficient for specific and well-defined scenarios.Is it AI? Yes. Because it simulates an automated conversation using predefined responses.Is it ML? No. Because it cannot learn from new data or interactions.Is it Deep Learning? No. Because it does not use deep neural networks or advanced learning algorithms.Rule-Based ChatbotMachine LearningchatbotIntelligent andadaptiveProblem:Traditional FAQs often fail to cover all user questions or require them to browse long pages to find the rightanswer.Solution:A chatbot powered by Machine Learning (ML) can learn from user queries and suggest more relevant answers, making the FAQ more dynamic and adaptive. Instead of mapping only fixed questions, the model identifies similarities between different queries and adjusts responses over time. For example, if many users ask Where can I see my order status? and others ask When will my order arrive?, the chatbot understands that both can be answered similarly.Reduces user effort when searching for answers and makes the experience morenatural.How totest?Click the button in the code cell to run the chatbot. Type exit to close thechatbot.Test scenarios for the ML chatbot (Semantic Search)Test 1: Directly matching registered questionsWhere can I find the products section?How do I access my order history?How can I contact customer support?Objective: Ensure the correct answers are returned.Test 2: Questions with synonyms and different phrasingWhere are the products?I want to check my previous orders, how do I dothat?I need to contact support.Objective: Evaluate its ability to recognize semantically similar questions.Test 3: Implicit context in questionsCan I change my address?What time do youopen?Where is the productsmenu?Objective: Assess if the chatbot understands implied questions and maintains accuracy.Test 4: Out-of-scope questionsHow do I track my delivery?Do you have live chat support?Can I pay withPayPal?Objective: Evaluate how the chatbot handles questions without a direct pre-registered answer.Test 5: Robustness (typos and variations)how to accsess orderhistarycustomer support, how can Icontactchange delivery adress possible?Objective: Check its tolerance to spelling mistakes.!pip install -q sentence-transformers fuzzywuzzyfrom sentence_transformers import SentenceTransformer, utilfrom fuzzywuzzy import fuzz, process# Machine Learning model for semantic searchmodel = SentenceTransformer("all-MiniLM-L6-v2")# Expanded knowledge base for the chatbotfaq = { "products section": [ "You can find the products using the search bar or by accessing the main menu at the top.", "Our products are available in the designated section in the main menu." ], "order history": [ "Access your order history by clicking on your account and then on 'My Orders'.", "To view past orders, go to 'My Orders' under your account." ], "speak with customer support": [ "Need help? Our support team is here for you! Click here: [support link].", "Chat with our support team anytime! Click here: [support link]." ], "business hours": [ "Our customer support hours are from 9 AM to 6 PM, Monday to Friday.", "We are available Monday to Friday, from 9 AM to 6 PM." ], "change delivery address": [ "Yes, you can change your address in 'Account Settings' in the main menu.", "To update your delivery address, go to account settings and edit your details." ], "track my delivery": [ "You can track your delivery by going to 'My Orders' and clicking on the tracking option.", "To track your order, go to 'My Orders' and select the order you want to track." ], "pay with PayPal": [ "Yes, we accept PayPal as a payment method. At checkout, select 'PayPal' and follow the instructions.", "PayPal is available as a payment option. Simply choose it during checkout." ], "live chat support": [ "Yes, we offer live chat support. Visit our website and click on the support option.", "Our support team is available via live chat. Go to our website and start a conversation with a representative." ]}# Creating embeddings for the FAQ questionsfaq_questions = list(faq.keys())faq_embeddings = model.encode(faq_questions, convert_to_tensor=True)def correct_spelling(user_question): """Applies spelling correction for better accuracy.""" best_match, similarity = process.extractOne(user_question, faq_questions, scorer=fuzz.token_sort_ratio) return best_match if similarity > 80 else user_question # Adjust threshold as neededdef chatbot_ml(user_question): """Finds the best-matching response using semantic embeddings and fuzzy matching.""" corrected_question = correct_spelling(user_question) # Improve matching to avoid confusion between "products" and "orders" if "order" in user_question.lower() or "purchase" in user_question.lower(): return faq["order history"][0] # Always return the correct response for orders question_embedding = model.encode(corrected_question, convert_to_tensor=True) similarities = util.pytorch_cos_sim(question_embedding, faq_embeddings) best_match_index = similarities.argmax().item() response_key = faq_questions[best_match_index] return faq[response_key][0] # Returns the first response from the matched category# Function to run the chatbot interactively in Colabdef start_chat(): print("Chatbot started! Ask a question or type 'exit' to quit.\n") while True: question = input("You: ") if question.lower() == "exit": print("Chatbot: Goodbye!") break response = chatbot_ml(question) print(f"Chatbot: {response}\n")# Run the chatbotstart_chat()Solution type: Machine Learning with SemanticFocusIs it AI? Yes. Because it simulates an intelligent interaction by interpreting user intentions based on the questions asked.Is it ML? Yes. Because it learns patterns and recognizes intent, even with variations or different wording from the original trainingdata.Is it Deep Learning? No. Because while it understands basic semantics and word context, it does not use deep neural networks or advanced algorithms.Machine LearningChatbotDeep LearningWhen AI stops just responding and startslearningGenerative Art. Neural Network Visualization by Hgy. OpenProcessing, June 29,2022The fun was great while it lasted, and thats exactly what this experiment is aboutplaying around with concepts by pasting some code into Colab. But now, things get serious. It would be amazing to use deep learning for a chatbot that truly understands context, adapts its language to the user, and ensures accessibility. Imagine an assistant that adjusts responses for users with limited digital literacy or provides more descriptive content for visually impaired users. However, achieving this would require millions of interactions to train the model, a robust infrastructure with GPUs/TPUs, and continuous training to maintain accuracy and responsiveness.So, instead of forcing an artificially limited example, I chose something we can actually test: handwritten digit recognition with MNIST. This is one of the most iconic and educational examples of deep learning, as it demonstrates how a convolutional neural network (CNN) learns visual patterns without anyone having to program fixed rules. Unlike rule-based models or simple supervised learning, the network here understands the variations in handwriting styles and generalizes that knowledge to recognize new examples.In this example, youll see a bunch of numbers and statistics appearing in the promptdont be alarmed! That just means our deep learning model is learning. We will feed a neural network thousands of images of handwritten digits, and it will adjust its parameters until it can recognize them with high accuracy. Instead of defining fixed rules like the number 3 has two curves, the model learns patterns by analyzing examples. In the end, youll be able to test it by drawing a number, and the AI will try to guess what itis.Neural Network Learning inProgress1. ModeltrainingEach epoch represents a cycle where the model adjusts its weights to improve predictions. Here, we run 5 epochs to teach the AI how to recognize handwritten numbers.Epoch 1: The model starts learning, correctly identifying 91% of the images, but still makes significant errors (loss:0.2861).Epochs 25: Accuracy increases to 99.58%, with loss reduced to 0.0136indicating that the model has become highly proficient at recognizing patterns.2. Accuracy, loss, and validationAccuracy: Measures how often the model makes correct predictions.Loss: Indicates how incorrect the predictions arethe lower, thebetter.Validation: The model is tested with new images to ensure it hasnt just memorized the numbers but can generalize to unseendata.In our tests, the model achieved 98.77% accuracy, proving it can generalize well to newdata.3. Real predictionsIn my tests, the model correctly predicted all the numbers. Let me know if your results were different!By running this code, you trained a neural network to recognize handwritten numberssomething that once only humans could do. This highlights the power of deep learning: the ability to learn patterns without relying on predefined rules.import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltfrom tensorflow.keras import layers, modelsfrom tensorflow.keras.datasets import mnist# Load the MNIST dataset(x_train, y_train), (x_test, y_test) = mnist.load_data()# Normalize the data (from 0-255 to 0-1)x_train, x_test = x_train / 255.0, x_test / 255.0# Expand dimensions for CNN (28x28x1)x_train = np.expand_dims(x_train, axis=-1)x_test = np.expand_dims(x_test, axis=-1)# Create the Deep Learning model (CNN)modelo = models.Sequential([ layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), layers.MaxPooling2D((2,2)), layers.Conv2D(64, (3,3), activation='relu'), layers.MaxPooling2D((2,2)), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(10, activation='softmax') # 10 classes (digits 0 to 9)])# Compile the modelmodelo.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])# Train the modelmodelo.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))# Evaluate on the test settest_loss, test_acc = modelo.evaluate(x_test, y_test)print(f"\n Test set accuracy: {test_acc:.4f}")# Function to test the model's predictiondef predict_digit(index): img = x_test[index] plt.imshow(img.squeeze(), cmap='gray') plt.axis('off') plt.show() prediction = modelo.predict(np.expand_dims(img, axis=0)) print(f"The model predicted: {np.argmax(prediction)}")# Test: randomly select a number from the test setimport randomindex = random.randint(0, len(x_test) - 1)predict_digit(index)Solution Type: Deep Learning with Convolutional Neural Networks (CNN). Capable of identifying complex visual patterns without explicit rules, learning to recognize handwritten digits from examples.Is it AI? Yes. Because the model processes data and makes predictions automatically, simulating a human cognitive process to interpret images.Is it Machine Learning? Yes. Because it learns from labeled data (handwritten numbers) and generalizes this knowledge to predict new examples.Is it Deep Learning? Yes. Because it uses deep neural networks (CNNs), which analyze different levels of image details to recognize patterns and improve accuracy.MNIST ModelResultsConclusion: Promise questions, notanswersAlign expectations with decision-makers. How? Commit to understanding which questions truly matter instead of promising all the answers. Invest time in identifying these key questions and exploring what still needs to be answered. If youre the one leading the discussion, dont divert experts from critical projects just to chase AI hypecreate an environment where experimentation is encouraged, where trial and error are part of the process. This shifts the focus away from AI as a buzzword, reduces pressure to follow trends, and allows your team to discover meaningful solutions naturally and genuinely.Start small discussion groups within your company to share AI insights, fostering the exchange of experiences and diverse perspectives. The current moment is unique because everyone is learning together, creating opportunities for real collaborationwhere innovation isnt just a trend but something built collectively, driven by curiosity and the pursuit of what truly makessense.Index of resources used in theexamplesGeneral AIRule-based chatbotThis example used only pure Python, without machine learning. The chatbot follows a fixed set of rules to respond to questions.Resources used:Python https://www.python.org/String similarity matching (difflib) Python Standard Library -difflibDictionary structure (dict) https://docs.python.org/3/tutorial/datastructures.html#dictionariesString manipulation (.lower(),.strip()) https://docs.python.org/3/library/stdtypes.html#string-methodsMachine LearningSemantic searchchatbotThis example applies Machine Learning to find the most relevant answer based on the meaning of a question.Libraries used:Sentence Transformers (For semantic search) https://www.sbert.net/FuzzyWuzzy (For typo correction and fuzzy matching) https://github.com/seatgeek/fuzzywuzzyNumPy (For array manipulation) https://numpy.org/doc/stable/Model Used:all-MiniLM-L6-v2 (Pre-trained model for semantic search) https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2Deep LearningHandwritten digit recognition (MNIST)This example used Convolutional Neural Networks (CNNs) to recognize handwritten numbers.Libraries used:TensorFlow/Keras (Deep Learning framework) https://www.tensorflow.org/Matplotlib (For image visualization) https://matplotlib.org/NumPy (For array manipulation) https://numpy.org/doc/stable/Model used:Custom Convolutional Neural Network(CNN)Built using Conv2D, MaxPooling2D, Flatten, Dense, and Softmaxlayers.Dataset used:MNIST (Handwritten digit image dataset) https://keras.io/api/datasets/mnist/References & additional readingWebAIM. The WebAIM Million, https://www.w3.org/WAI/, March 28,2024MIT Technology Review. What is AI?, July 10,2024MIT Technology Review. What is Machine Learning?, November 17,2018MIT Technology Review. Deep Learning, October 16,2023Spotify. Machine LearningBusiness Insider. AI companies are copying each others homework to make cheap models, March 7,2025reddit. Using Open-Source LLM Models vs. Expensive OpenAI APIs: A Logical Choice for ConsumerApps?Forbes. Metas Awful Horizon Worlds Ad Helps Explain $70 Billion Metaverse Loss, February 17,2025The New Yorker. Was Linguistic AI Created by Accident?, August 23,2024Lifewire. How Deep Learning Is Revolutionizing Technology and Everyday Life, February 10,2025IBM. AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: Whats the Difference?, July 6,2023Hype-driven CEOs: when innovation misses the point was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

We may earn a commission from links on this page.Today, I'm throwing a lot of spring cleaning content at you, from checklists that can help you remember every spot that needs sprucing up, to suggestions for products that can make it all easier, but I am just one person who, for better or worse, cleans my house in a specific way. To make this series on spring cleaning more comprehensive, I decided to ask some pros for their advice and share expert tips I've received. Speed up drying time on your laundryWhen you're reading through my spring cleaning checklists today, you'll notice that the step-by-step guide for each room starts by suggesting you put the linens, fabrics, and clothes associated with that room in the laundry. You want them washing and drying while you clean the rest of the space, so on a spring cleaning day, you'll be laundering a lot of textiles. That doesn't have to take all day, though: Michelle Piombino, Principal Scientist at Purex, says you can toss a clean, dry towel in the dryer with a wet load; take it out after 15 or 20 minutes, "once it absorbs most of the water." Let it air dry while the remaining garments finish up in the machine. The fresh towel will absorb excess water and humidity in the dryer, saving you time and energy costs. Address stains firstBefore you start your first load of laundry on a spring cleaning day, go through all of it and pull out anything with a stain. I'm guilty of just tossing everything into the wash and vaguely hoping stains magically come out, which has a mixed success rate, but on a day when you're focused entirely on cleaning, you should really give those garments attention. Separate anything with a stain, then pre-treat those items while other fabrics go in the machine. Jennifer Rivera, Henkel Research and Development Director and laundry expert at Persil, suggests looking for detergents that have enzymes formulated to break down stains and following their directions. (Naturally, she recommends Persil Original Everyday Clean.)Tackle overlooked spotsTake a second to look around at your space and consider it from an outsider's perspective so you can find areas that need attention you wouldn't normally notice. I have two ways of doing this: I either take a photo of my space and examine it, which helps me compartmentalize my familiarity with the room and assess it objectively, or I call a friend over to help identify problem areas I've gone blind to. Spring cleaning is the best time to hit the spots you normally skip over. For Rosa Picosa, a CleanTokker with nearly 900,000 followers and a cleaning expert with Fabuloso, for instance, that means tackling the baseboards: "Honestly, they can be easily overlooked and I don't think any guests in my home are inspecting my baseboards," she says. But even so, eventually, it must be done. It can be as easy as using the mop to run over them when you're mopping your floor (which is one of the final steps on all of my room-by-room cleaning checklists). Naturally, she suggests double-concentrated Fabuloso for this, but so do I. Read my review of it here (in which I actually use it to clean my baseboards).Think prevention when you're cleaningHandling existing messes is primarily what tidying up is all about, but the beauty of spring cleaning is that you're starting over, in a sense, and preparing for the future. This year, instead of just addressing the problem areas you already have, think about how you can prevent future ones. For instance, my building's exterminator Xavier Maldonado gave me a great tip the last time he was in my apartment for my monthly service: Mixing one part ammonia with 24 parts water and wiping it across areas like your baseboards and behind your shelves and appliances will easily repel pests. It's something simple you can do in the course of your spring cleaning that will preemptively stop any bug-related issues from arising as they come out for the summer season. Remember, though, to not ever mix chemicals, and make sure your ammonia is sufficiently diluted.I also use this set of coatings from spotLESS. The set comes with spray-on coatings for for glass and mirrors, toilets, and sinks and fixtures. When you spray them on their respective surfaces, they prolong the time between cleanings by repelling dust and grimeplus they make it easier to wipe all that off when the surfaces do get dirty. Don't forget your carWhen you think "spring cleaning," you probably think of major household tasks like moving the fridge to clean underneath it or swapping all your heavy winter linens for lighter ones. But you should think outside the house, too. Amy Brooks, National Sales Executive at Chase Auto, reminds us that cleaning your vehicle can extend its longevity and keep it in top condition. Start by vacuuming in and around your seats, as dirt and debris can scratch and damage the surfaces. To get rid of smells, she suggests sprinkling baking soda on the seats, letting it sit overnight, then vacuuming it up. Wipe down your seat surfaces using a leather or cloth cleaner and a microfiber cloth. (I don't have a car with leather seats, but I do have a lot of leather boots. I use the Chemical Guys' Quick Detailer wipes, $8.99, and recommend them for a fast and easy fix.) Wipe down all your surfaces, use a steam cleaner to get deep into the seats if you need to, and wipe down your windows with glass cleaner.

If youre at all like me, spring cleaning can be something of a domino situation. I might start by cleaning the baseboards, but when I get to the stairs, Im like, wow these handrails are dirty. And so on. By the time Im done, Ive also discovered coffee splashes on walls, cobwebs in ceiling corners, and the dustiest blinds one has ever seenand Im feeling rather disgusted with myself. Im also feeling sore, because my back is not what it once was and it seems like the worst of the dirt is either way low or way high. You know who still has good backs, though? Our kids, thats who.Spring cleaning should, I believe, be a family affair. From the inside of the home to the outside, there is plenty to be done. And kids (at least when theyre young) generally are willingeven excitedto help. You could have them help you as you go from task to task, but sometimes that only serves to slow you down. Instead, if your kids are old enough, I suggest you try delegating some of the work this year: Divvy up what needs to be done and put them fully in charge of certain chores. If youre not sure what chores kids are generally capable of tacking, this age-by-age guide to kids chores is a good place to start. But spring cleaning goes beyond the usual bed-making and towel-folding, so this list will include some bigger, more infrequent projects you can assign.Best spring cleaning chores for young kidsYoure likely to get the most excitement and compliance from little kids when you announce its time for a spring cleaning party. Of course, for all their sweet enthusiasm, little kids are also going to be the least independent (and thorough) when it comes to tackling their tasks. If your kids are younger than about six years old and you want to get them involved, theyre going to need supervision and assistance, so plan for that. But by the time theyre at the age where you trust them to feed the pets and sweep the floor on their own, you can probably show them what to do and then send them off on their own. In my experience, this happens somewhere in the six- to eight-year-old range. Youll still want to consider a couple things, thoughnamely, what types of chores they like to do and how thoroughly you need something to be done. Any chore where you are happy to declare it good enough is a better fit for this age. Those might include:Vacuuming or sweeping up areas that tend to get overlooked during the regular week-to-week cleaning, such as basements, porches, vehicle floorboards, and garages.Vacuuming up the living room furniture, removing cushions and taking care of all those hidden crumbs.Raking up any old dried leaves that are still littering the yard from last fall.Pulling weeds.Cleaning the aforementioned baseboardsspare your back and let them get all bent over.Best spring cleaning chores for tweensTweens are going to be more self-sufficient than their younger counterparts, which means you can put their slightly more developed critical thinking skills to good use here. Tweens are old enough to do something that is often a major component of spring cleaningthe sorting and disposing or donating of Items you no longer want or need.Heres how tweens can pitch in during your spring cleaning extravaganza:Sorting through their clothes, making piles of anything that no longer fits or they dont wear to hand down, donate or sell to another family.Sorting through their books and toys for items they no longer read or play with.Cleaning out their closet and rearranging or reorganizing their bedroom (with assistance moving any furniture, as needed).Steam-cleaning the kitchen or bathroom floors.Emptying out kitchen draws to wipe them down.Plus, any of the tasks you would have assigned to the younger group.Best spring cleaning chores for teenagersThis group may be the least interested in helping but theyve also got the greatest potential to be thoroughand put a little more muscle to put behind their effort. Here are a few tasks teenagers can be in charge of:Helping organize the garage.Washing the cars.Power-washing the trash cans.Moving indoor furniture for more thorough vacuuming.Helping with planting or mulching.Cleaning windows or mirrors.Cleaning the bathrooms.Assisting younger kids with their tasks.Plus, any of the tasks you would have assigned to the younger groups.I referred to it as a spring cleaning party earlier, and I was only half jokingwhen all is said and done and the winter grime has been wiped from every last window, celebrate your efforts and take advantage of your freshened space. Order a pizza and relax with a family movie night or get a fire going in the fire pit to roast some marshmallows and enjoy the newly spruced-up backyard.Im also not above dangling a little monetary incentive, particularly for the older kids, to help secure their cooperation during this process.

Alphabet is letting its laser-based internet company Taara fly and be free, according to reporting by Financial Times. Googles parent company is spinning off the service from X, its moonshot incubator system (not to be confused with X the social network.)Taara uses light beams to provide high-bandwidth internet and phone services to hard-to-reach areas of the world. This places it in direct competition with Elon Musks Starlink network of satellites.Taara now has two dozen staff members and is hiring aggressively. It has secured backing from Series X Capital. Alphabet will retain a minority stake, but it remains tight-lipped regarding seed funding details or potential financial targets.Theyre going to be able to get connected quickly to market capital, bring in strategic investors and generally be able to scale faster this way, said Eric Teller, Xs Captain of Moonshots.Taara already operates in 12 countries throughout the world and has provided supplemental service during events like Coachella. The tech works by firing beams of light to and from various terminals. Alphabet says the system can transmit data at 20 gigabits per second over an area of around 12 miles. The company also says that these systems are relatively easy to build and maintain, especially when compared to satellites in space.It has its origins in an Alphabet-created concept called Loon. This was a modified version of the same idea that shot beams of light to and from groups of balloons floating on the edge of space. There are strict governmental and regulatory hurdles to flying thousands of balloons near space, however, so it wound down in 2021. Loons loss was Taaras gain, as the newer entity uses the same lasers.Mahesh Krishnaswamy, Taaras general manager and a lead engineer, says that the next step is to develop a silicon photonic chip that will eliminate the need for many of the mirrors and lenses currently positioned on system terminals.The newly-minted startup has a long way to go to catch up to Starlink, which has over four million subscribers worldwide. Taara doesnt even sell subscriptions directly to consumers. Instead, it partners with existing telecommunication companies like T-Mobile to extend their networks to remote locations.This article originally appeared on Engadget at https://www.engadget.com/mobile/alphabets-starlink-competitor-taara-is-spinning-off-into-an-independent-company-154653176.html?src=rss

Although the future of TikTok remains uncertain, the app continues to get new features. The latest is called Security Checkup, which is a centralized dashboard designed to help you protect your account.You can find the tool by going to Settings and privacy > Security & permissions in the TikTok app. The idea behind Security Checkup is to make it easy for you to do things like link a phone number and email address to make sure you have a backup login method, turn on two-factor authentication, set up a passkey and enable biometric login options such as facial recognition.In addition, you'll be able to see which devices are logged into your account, so you can revoke access for any that you don't recognize or no longer use. Handily, TikTok will flag any unusual behavior that it detects on your account and you can review any oddities here.We've seen other major platforms introduce similar features in the past. Instagram, for instance, rolled out a tool that's also called Security Checkup back in 2021. Still, it's better late than never to add handy features, especially when it comes to helping users secure their accounts.This article originally appeared on Engadget at https://www.engadget.com/social-media/tiktok-rolls-out-a-security-checkup-tool-to-help-protect-your-account-152819724.html?src=rss

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The next time youre due for a medical exam you may get a call from someone like Ana: a friendly voice that can help you prepare for your appointment and answer any pressing questions you might have.With her calm, warm demeanor, Ana has been trained to put patients at easelike many nurses across the U.S. But unlike them, she is also available to chat 24-7, in multiple languages, from Hindi to Haitian Creole.Thats because Ana isnt human, but an artificial intelligence program created by Hippocratic AI, one of a number of new companies offering ways to automate time-consuming tasks usually performed by nurses and medical assistants.This March 2025 image from the website of artificial intelligence company Xoltar shows a demonstration of one of their avatars for conducting video calls with patients. [Photo: Xoltar via AP]Its the most visible sign of AIs inroads into health care, where hundreds of hospitals are using increasingly sophisticated computer programs to monitor patients vital signs, flag emergency situations and trigger step-by-step action plans for carejobs that were all previously handled by nurses and other health professionals.Hospitals say AI is helping their nurses work more efficiently while addressing burnout and understaffing. But nursing unions argue that this poorly understood technology is overriding nurses expertise and degrading the quality of care patients receive.Hospitals have been waiting for the moment when they have something that appears to have enough legitimacy to replace nurses, said Michelle Mahon of National Nurses United. The entire ecosystem is designed to automate, de-skill, and ultimately replace caregivers.This March 2025 image from the website of artificial intelligence company Xoltar, shows two of of their demonstration avatars for conducting video calls with patients. [Photo: Xoltar via AP]Mahons group, the largest nursing union in the U.S., has helped organize more than 20 demonstrations at hospitals across the country, pushing for the right to have say in how AI can be usedand protection from discipline if nurses decide to disregard automated advice. The group raised new alarms in January when Robert F. Kennedy Jr., the incoming health secretary, suggested AI nurses as good as any doctor could help deliver care in rural areas. On Friday, Dr. Mehmet Oz, whos been nominated to oversee Medicare and Medicaid, said he believes AI can liberate doctors and nurses from all the paperwork.Hippocratic AI initially promoted a rate of $9 an hour for its AI assistants, compared with about $40 an hour for a registered nurse. It has since dropped that language, instead touting its services and seeking to assure customers that they have been carefully tested. The company did not grant requests for an interview.AI in the hospital can generate false alarms and dangerous adviceHospitals have been experimenting for years with technology designed to improve care and streamline costs, including sensors, microphones and motion-sensing cameras. Now that data is being linked with electronic medical records and analyzed in an effort to predict medical problems and direct nurses care sometimes before theyve evaluated the patient themselves.In this photo provided by National Nurses United, nurses hold a rally in San Francisco on April 22, 2024, to highlight safety concerns about using artificial intelligence in healthcare. [Photo: National Nurses United via AP]Adam Hart was working in the emergency room at Dignity Health in Henderson, Nevada, when the hospitals computer system flagged a newly arrived patient for sepsis, a life-threatening reaction to infection. Under the hospitals protocol, he was supposed to immediately administer a large dose of IV fluids. But after further examination, Hart determined that he was treating a dialysis patient, or someone with kidney failure. Such patients have to be carefully managed to avoid overloading their kidneys with fluid.Hart raised his concern with the supervising nurse but was told to just follow the standard protocol. Only after a nearby physician intervened did the patient instead begin to receive a slow infusion of IV fluids.You need to keep your thinking cap onthats why youre being paid as a nurse, Hart said. Turning over our thought processes to these devices is reckless and dangerous.Hart and other nurses say they understand the goal of AI: to make it easier for nurses to monitor multiple patients and quickly respond to problems. But the reality is often a barrage of false alarms, sometimes erroneously flagging basic bodily functionssuch as a patient having a bowel movementas an emergency.Youre trying to focus on your work but then youre getting all these distracting alerts that may or may not mean something, said Melissa Beebe, a cancer nurse at UC Davis Medical Center in Sacramento. Its hard to even tell when its accurate and when its not because there are so many false alarms.Can AI help in the hospital?Even the most sophisticated technology will miss signs that nurses routinely pick up on, such as facial expressions and odors, notes Michelle Collins, dean of Loyola Universitys College of Nursing. But people arent perfect either.It would be foolish to turn our back on this completely, Collins said. We should embrace what it can do to augment our care, but we should also be careful it doesnt replace the human element.More than 100,000 nurses left the workforce during the COVID-19 pandemic, according to one estimate, the biggest staffing drop in 40 years. As the U.S. population ages and nurses retire, the U.S. government estimates there will be more than 190,000 new openings for nurses every year through 2032.Faced with this trend, hospital administrators see AI filling a vital role: not taking over care, but helping nurses and doctors gather information and communicate with patients.Sometimes they are talking to a human and sometimes theyre notAt the University of Arkansas Medical Sciences in Little Rock, staffers need to make hundreds of calls every week to prepare patients for surgery. Nurses confirm information about prescriptions, heart conditions and other issueslike sleep apneathat must be carefully reviewed before anesthesia.The problem: many patients only answer their phones in the evening, usually between dinner and their childrens bedtime.So what we need to do is find a way to call several hundred people in a 120-minute windowbut I really dont want to pay my staff overtime to do so, said Dr. Joseph Sanford, who oversees the centers health IT.Since January, the hospital has used an AI assistant from Qventus to contact patients and health providers, send and receive medical records and summarize their contents for human staffers. Qventus says 115 hospitals are using its technology, which aims to boost hospital earnings through quicker surgical turnarounds, fewer cancellations and reduced burnout.Each call begins with the program identifying itself as an AI assistant.We always want to be fully transparent with our patients that sometimes they are talking to a human and sometimes theyre not, Sanford said.While companies like Qventus are providing an administrative service, other AI developers see a bigger role for their technology.Israeli startup Xoltar specializes in humanlike avatars that conduct video calls with patients. The company is working with the Mayo Clinic on an AI assistant that teaches patients cognitive techniques for managing chronic pain. The company is also developing an avatar to help smokers quit. In early testing, patients spend about 14 minutes talking to the program, which can pickup on facial expressions, body language and other cues, according to Xoltar.Nursing experts who study AI say such programs may work for people who are relatively healthy and proactive about their care. But thats not most people in the health system.Its the very sick who are taking up the bulk of health care in the U.S. and whether or not chatbots are positioned for those folks is something we really have to consider, said Roschelle Fritz of the University of California Davis School of Nursing.The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institutes Science and Educational Media Group and the Robert Wood Johnson Foundation. The AP is solely responsible for all content.Matthew Perrone, AP Health Writer

The videos roll through TikTok in 30-second flashes.Migrants trek in camouflage through dry desert terrain. Dune buggies roar up to the United States-Mexico border barrier. Families with young children pass through gaps in the wall. Helicopters, planes, yachts, tunnels, and Jet Skis stand by for potential customers.Laced with emojis, the videos posted by smugglers offer a simple promise: If you dont have a visa in the U.S., trust us. Well get you over safely.Illustration of migrants climbing over a border barrier with emojis overlayed on the scene, based on hundreds of TikTok videos reviewed by the AP. [Art: Peter Hamlin/AP Illustration]At a time when legal pathways to the U.S. have been slashed and criminal groups are raking in money from migrant smuggling, social media apps like TikTok have become an essential tool for smugglers and migrants alike. The videostaken to cartoonish extremesoffer a rare look inside a long elusive industry and the narratives used by trafficking networks to fuel migration north.With Gods help, were going to continue working to fulfill the dreams of foreigners. Safe travels without robbing our people, wrote one enterprising smuggler.As President Donald Trump begins to ramp up a crackdown at the border and migration levels to the U.S. dip, smugglers say new technologies allow networks to be more agile in the face of challenges, and expand their reach to new customersa far cry from the old days when each village had its trusted smuggler.In this line of work, you have to switch tactics, said a woman named Soary, part of a smuggling network bringing migrants from Ciudad Juarez to El Paso, Texas, who spoke to the Associated Press on the condition that her last name would not be shared out of concern that authorities would track her down. TikTok goes all over the world.Soary, 24, began working in smuggling when she was 19, living in El Paso, where she was approached by a friend about a job. She would use her truck to pick up migrants who had recently jumped the border. Despite the risks involved with working with trafficking organizations, she said it earned her more as a single mother than her previous job putting in hair extensions.Depiction of migrants with faces covered by emojis giving testimony that they arrived safely to the U.S. as part of the smugglers social media campaign to build their brand of trust, based on hundreds of TikTok videos reviewed by the AP. [Art: Peter Hamlin/AP Illustration]As she gained more contacts on both sides of the border, she began connecting people from across the Americas with a network of smugglers to sneak them across borders and eventually into the U.S.Like many smugglers, she would take videos of migrants speaking to the camera after crossing the border to send over WhatsApp as evidence to loved ones that her clients had gotten to their destination safely. Now she posts those clips to TikTok.TikTok says the platform strictly prohibits human smuggling and reports such content to law enforcement.The use of social media to facilitate migration took off around 2017 and 2018, when activists built massive WhatsApp groups to coordinate the first major migrant caravans traveling from Central America to the U.S., according to Guadalupe Correa-Cabrera, a professor at George Mason University focused on the migrant smuggling industry.Later, smugglers began to infiltrate those chats and use the choice social media app of the day, expanding to Facebook and Instagram.Migrants, too, began to document their often perilous voyages north, posting videos trekking through the jungles of the Darien Gap dividing Colombia and Panama, and after being released by extorting cartels.A 2023 study by the United Nations reported that 64% of the migrants they interviewed had access to a smart phone and the internet during their migration to the U.S.Around the time of the studys release, as use of the app began to soar, that Correa-Cabrera said she began to see smuggling ads skyrocket on TikTok.Its a marketing strategy, Correa-Cabrera said. Everyone was on TikTok, particularly after the pandemic, and then it began to multiply.Last year, Soary, the smuggler, said she began to publish videos of migrants and families in the U.S. with their faces covered and photos of the U.S.-Mexico border with messages like: Well pass you through Ciudad Jurez, no matter where you are. Fence jumping, treks, and by tunnel. Adults, children, and the elderly.Hundreds of videos examined by the AP feature thick wads of cash, people crossing through the border fence by night, helicopters and airplanes supposedly used by coyotes, smugglers cutting open cacti in the desert for migrants to drink from and even crops of lettuce with text reading The American fields are ready!The videos are often layered over heavy northern Mexican music with lyrics waxing romantically about being traffickers. Videos are published by accounts with names alluding to safe crossing, USA destinations, fulfilling dreams, or polleros, as smugglers are often called.Narratives shift based on the political environment and immigration policies in the U.S. During the Biden administration, posts would advertise getting migrants access to asylum applications through the administrations CBP One app, which Trump ended.Amid Trumps crackdown, posts have shifted to dispelling fears that migrants will be captured, promising American authorities have been paid off. Smugglers openly taunt U.S. authorities: one shows himself smoking what appears to be marijuana right in front of the border wall; another even takes a jab at Trump, referring to the president as a high-strung gringo.Comments are dotted with emojis of flags and baby chickens, a symbol meaning migrant among smugglers, and other users asking for prices and more information.Cristina, who migrated because she struggled make ends meet in the Mexican state of Zacatecas, was among those scrolling in December after the person she had hired to smuggle her to the U.S. abandoned her and her partner in Ciudad Jurez.In a moment of desperation, I started searching on TikTok and, well, with the algorithm videos began to pop up, she said. It took me a half an hour to find a smuggler.After connecting, smugglers and migrants often negotiate on encrypted apps like WhatsApp and Telegram, doing a careful dance to gain each others trust. Cristina, now living in Phoenix, said she decided to trust Soary because she was a woman and posted videos of families, something the smuggler admitted was a tactic to gain migrants trust.Smugglers, migrants and authorities warn that such videos have been used to scam migrants or lure them into traps at a time when cartels are increasingly using kidnapping and extortion as a means to rake in more money.One smuggler, who asked to only be identified by his TikTok name The Corporation due to fear of authorities tracking him down, said other accounts would steal his migrant smuggling networks videos of customers saying to camera they arrived safely in the U.S.And theres not much we can do legally. I mean, its not like we can report them, he said with a laugh.In other cases, migrants say that they were forced by traffickers to take the videos even if they havent arrived safely to their destinations.The illicit advertisements have fueled concern among international authorities like the U.N.s International Organization for Migration, which warned in a report about the use of the technology that networks are becoming increasingly sophisticated and evasive, thus challenging government authorities to address new, non-traditional forms of this crime.In February, a Mexican prosecutor also confirmed to the AP that they were investigating a network of accounts advertising crossings through a tunnel running under the border fence between Ciudad Juarez and El Paso. But investigators would not provide more details.In the meantime, hundreds of accounts post videos of trucks crossing border, of stacks of cash and migrants, faces covered with emojis, promising they made it safely across the border.Were continuing to cross and were not scared, one wrote.Illustrations are based on hundreds of videos posted on TikTok examined by the AP that advertise travel to the U.S. to migrants. Videos are often laced with emojis, make bold promises of success and promise safe travel.Megan Janetsky, Associated Press