(Bloomberg) -- Lenovo Group Ltd.’s shares gained more than 3% after the world’s largest PC maker reported a return to growth, reflecting a gradual pickup in demand after a prolonged slump.Most Read from BloombergNvidia Surges After Company Proclaims AI Has Hit &#x27;Tipping Point&#x27;Biden Touts ...

Lenovo Jumps After Top Global PC Maker Arrests Sales Declines

(Updates share price in 8th paragraph)By Josh YeHONG KONG : China&#x27;s Lenovo Group, the world&#x27;s largest maker of personal computers, on Thursday reported October-December revenue of $15.72 billion, up 3 per cent from the same period a year earlier.The result compared with the $15.25 billion a

China's Lenovo posts Q3 revenue growth, beats analyst estimates - CNA

Snap up the Lenovo ThinkPad L14 at Lenovo right now and enjoy a great budget-friendly laptop for significantly less than usual.

Normally $2039, this Lenovo laptop is discounted to $599 | Digital Trends

Lenovo announced the Xiaoxin 15 2024 laptop featuring a 13th Gen i5 CPU in January of this year. The company officially released its 2024 offering tonight at 19:00. Currently listed on JD.com, the laptop is priced at 3,989 yuan, pending final confirmation of the price upon the official release.

Lenovo Xiaoxin 15 2024 Laptop released: i5-13420H + 16/32GB RAM; priced around 4,000 Yuan ($550) - Gizmochina

LOVO

lovo

LOVO raises $6.5 million in investment to build the voice of web3 , a synthetic voice company, has raised an additional $2 million in its pre-Series A round, raising the total amount to $6.5 million. The extension was led by Hashed, an international crypto venture capital firm active in the blockchain gaming and NFT ecosystem, which has previously invested in Axie Infinity, The Sandbox, and Decentraland, as well as PKO Investments, which is bringing on prominent angels like Yat Siu, Founder of Animoca Brands, Patrick Lee Founder of Rotten Tomatoes, Holly Liu, Founder of Kabam, Kun Gao, Founder of Crunchyroll and Ray Chan. LOVO The funds will aid LOVO in developing new methods to connect its AI speech technology to Web 3.0. It follows the success of the Voiceverse NFT collection, which consists of 8,888 NFTs, each representing a distinct AI-generated voice. Each Voiceverse NFT unlocks a unique AI voice model that can be used to create any speech content or even utilised in real-time on voice chat services like Discord. went on sale in late January 2022 and sold out in less than 10 minutes. They demonstrated the power of LOVO's synthetic speech technology by creating all 8,888 distinct voices from a seed of 100 genuine recordings. Jonny Cruz and Charlet Chung, well known for their roles in the popular game Overwatch, were among the initial voices leased from LOVO's partner voice actors. Each sale on NFT marketplaces like OpenSea pays all partner voice performers a considerable fee. Voiceverse NFTs Tom Lee, Co-Founder of LOVO, said: With Voiceverse, people can now add another layer of personalization and pseudonymity, completing their persona for the metaverse. As a company that’s spent years researching and developing applications leveraging AI Voice & Synthetic Speech, this was a no-brainer — Voiceverse is simply a natural progression in line with LOVO’s vision and tech. The LOVO concept, which the company pioneered, now enables voice performers to receive a significantly larger share of the earnings earned by their labour. Previously, voice actors were paid fixed rates for their job and did not earn any further compensation if the material they created generated billions of dollars in income. Combining the unique NFTs with existing profile image NFTs and distinctive avatars like BAYC is one of the fascinating uses of voice in the metaverse. With the addition of a voice to the picture, the NFT becomes a totally complete digital avatar, thanks to advancements in AI technology. In the future, LOVO wants to collaborate with crypto games and Metaverse worlds to allow users to create their virtual identities using Voice NFTs and mint new Voice NFT collections featuring well-known celebrity voices. What is LOVO? LOVO, Inc. is a next-generation synthetic speech platform for use in education, marketing, entertainment, and the creation of audio material in general. A group of U.C. Berkeley AI and machine learning scientists started the startup. The site is used by over 40,000 people in 41 countries, who have created over 3 million pieces of speech content so far. The startup has collaborative ties with Google, Intel, NVIDIA, AWS, and LG CNS as a Berkeley SkyDeck alum. Dont’t forget to like and share the story! Image credits: . Panos Sakalakis The content of this story DO NOT represent the views of HackerNoon and are meant as information only from the lens of the independent contributor. Please DYOR before investing.

LOVO raises $6.5 Million in Pre-Series A to Develop the Voice of the Web3 Ecosystem

isn't new, I wrote a longer (as we called them several years before) but in 2022 it reached a certain level of quality, that impressed the world. In addition the emergence of ChatGPT (GPT 3.5) and Large Language Models became part of this trend that we call “Generative AI”. Generative AI article about GANs What Techs Are Part of Generative AI? We can find several ways to categorize these technologies, but I think the easiest way to understand them is if we use output categorization: what will be the actual output of these AI models? Using this logic there are AI models for Image generation · Diffusion models Text generation · Large Language models Video generation · Large-scale Video Generation models Speech or audio generation · Diffusion models Most important applications of Generative AI I did collect the most notable AI applications, that are robust enough and they aren't using any third party services. This is my private collection, I also use the majority of these tools personally: OpenAI · GPT-4 (text to text) · LLM (2024) ChatGPT Google (text to text) · LLM (2024) Gemini (text to text) · LLM (2023) Claude.ai (text to text) · LLM (2023) PI.ai Github (text to text/code) · LLM (2023) Copilot (text to image) · Diffusion Model (2023) Dall-E 3 Pro Max (text to image) · Diffusion Model (2023) Stable Diffusion (text to image) · Diffusion Model (2022) Midjourney (text to video) · Large-scale Video Generation model (2024) OpenAI Sora (text to video) · Large-scale Video Generation model (2023) Runway (text to speech) · Voice model (2023) Lovo.ai (text to speech) · Voice model (2022) Amazon Polly What Are Prompts? Think of a prompt like asking your friend a question or telling them to do something. This can be anything from a question you want answered to a request for it to make something, like a story or a picture. The AI then tries to figure out what you're asking for and gives you back an answer or something new based on what you told it. How well you ask or tell the AI what you want can make a big difference in what you get back. What are the best Generative AI Books for Image and Text Generation? Artificial Intelligence & Generative AI for Beginners (Generative AI & Chat GPT Mastery Series Book 1), by David M. Patel (2023) David M. Patel's book is a straightforward guide that helps readers get to grips with artificial intelligence and generative AI. Patel, an Amazon bestselling author and AI consultant, aims to make complex AI concepts accessible to everyone, whether you're starting from scratch or looking to expand your professional skills. The book covers the basics of AI, including its history and main components, and explains different types of machine learning. It also introduces readers to critical AI fields such as natural language processing, computer vision, and robotics, showing how they apply in the real world. Patel's approach is to give readers the knowledge they need to use AI tools like ChatGPT, DALL-E 3, and MidJourney effectively, helping them boost their productivity and achieve personal or business growth. Patel focuses on generative AI, explaining what it is, how it works, and the various types that exist. He offers practical advice on how to come up with business ideas using generative AI and guides readers through building their own generative AI models. The book also covers how generative AI can be applied in areas such as copywriting, graphic design, and video editing. It raises important ethical questions about AI and predicts how generative AI will change industries like healthcare, media, and education. Patel provides a wealth of resources for readers who want to explore further, including podcasts and influencers in the AI field. Generative AI for Beginners, by Ethan James Whitfield (2023) is a book designed to simplify these complexities, making the world of Generative AI approachable for everyone. It aims to alleviate the frustration of not grasping this transformative technology, which can hinder personal and professional growth. Generative AI for Beginners The book covers the basics of Generative AI, including its principles and how it works, along with its impact across various industries. It goes beyond theory, showing how Generative AI is applied in creating art, music, and written content, and highlights the importance of ethical considerations in AI use. Tailored for absolute beginners, the book promises a clear and straightforward learning journey, fitting easily into busy schedules. It positions itself as a tool for anyone looking to understand and engage with the ongoing AI revolution, offering a pathway to new career opportunities and a deeper appreciation of how AI is reshaping our world. The Midjourney Prompt Book, by Shaheed Ullah (2024) This comprehensive guide covers the advanced knowledge of Midjourney prompts, including commands and parameters with detailed step-by-step instructions and practical tips. I found over a thousand  prompts that fit also for Stable Diffusion XL and DALL.E-3, despite being designed for Midjourney. The book, now in its 6th edition, spans 300 pages across eleven chapters. It includes a newly added chapter on Nature, Commercial, and Fashion Photography, and another dedicated to Building Consistent Characters with Midjourney, catering to both beginners and advanced users. This edition discusses the latest Midjourney's Niji models along with advanced prompts, photorealism, and niche-specific guides. What are the best Generative AI Books to Understand the Technology Behind It? Generative Deep Learning, by David Foster (2023) This book aims to equip machine learning engineers and data scientists with the skills to build cutting-edge generative AI models using TensorFlow and Keras. It starts with the fundamentals of deep learning and gradually progresses to complex architectures like VAEs, GANs, and Transformers. Through practical guidance and expert tips, you'll learn to unlock the creative potential of these models, generating images, music, text, and even solving reinforcement learning tasks. The book also delves into the future of generative AI and its potential impact on individuals and businesses. In essence, this book is a hands-on guide to building and harnessing the power of generative AI, equipping readers with the knowledge and skills to explore this exciting field. Generative AI with LangChain, by Ben Auffarth (2023) This book guides readers through the process of mastering the LangChain framework, bridging the gap between the theoretical aspects of Large Language Models (LLMs) and their practical deployment in creating applications ready for production. The book offers updated code examples on GitHub to keep readers informed about the latest in LangChain developments and includes a free PDF eBook with the purchase of the print or Kindle version. It covers how to harness the capabilities of LLMs while navigating their limitations, using the LangChain framework for various tasks such as customer support, software development help, and data analysis, showcasing the wide-ranging utility of LLMs in real-world applications. Designed for developers, researchers, and anyone keen to deepen their understanding of LLMs, the book provides a thorough exploration of generative AI fundamentals, industry trends, and the construction of responsive LLM applications. It teaches about transformer models, attention mechanisms, and how to automate data analysis with Python, offering insights into improving LLM performance and deploying these models effectively. With a basic requirement of Python knowledge and a suggestion of some machine learning background, this guide is a valuable asset for those looking to enhance their skills in the dynamic field of LLMs and LangChain, positioning them at the forefront of technological innovation. Developing Apps with GPT-4 and ChatGPT, by Olivier Caelen (2023) is a comprehensive guide aimed at Python developers interested in leveraging large language models for application development. Authored by Olivier Caelen and Marie-Alice Blete, the minibook delves into the functionalities and advantages of GPT-4 and ChatGPT, offering a clear explanation of their operations. It serves as a practical handbook, providing readers with a detailed tutorial on how to use the GPT-4 and ChatGPT Python library for a variety of tasks, including text generation, question answering, and content summarization. Developing Apps with GPT-4 and ChatGPT The authors ensure clarity and accessibility in their writing, supplementing theoretical knowledge with easy-to-follow examples, all of which are supported by Python code available in a dedicated GitHub repository. What are the best Generative AI Books to Understand the Social Impact? Generative AI for Leaders, by Amir Husain (2023) Generative AI is shaping our future, and this guidebook is here to help organizations make the most of it. Written in clear, straightforward language, covers the basics of what Generative AI is, its advantages like boosting productivity and sparking new product ideas, and the challenges it brings, including bias and security issues. It outlines steps for creating a strategy that makes an organization stand out, how to put together the right team, and when to get outside help. The book goes beyond the basics, giving a closer look at the technical side of things like word embeddings and large language models, and discusses how to spot the use of Generative AI. Generative AI for Leaders The future of Generative AI is exciting, and the book doesn't stop at theory. It offers over 75 practical ideas that leaders can start using right away to integrate Generative AI into their operations. By focusing on real-world applications and addressing both the opportunities and hurdles of Generative AI, the guide aims to prepare leaders for what's coming. It's about making informed decisions, understanding the technology, and using it to improve and innovate, ensuring organizations are ready for the future. Ai For Educators, by Danielle Austen (2024) When I first found this book I thought finally a domain specific approach for AI. Teachers are at the heart of the education system, dedicating their lives to shaping the future one student at a time. They go above and beyond to make sure every lesson resonates and every student feels valued. Despite their passion, the reality of being a teacher often means facing exhaustion from balancing lesson planning, administrative duties, grading, and endless parent-teacher meetings. Every student requires individual attention, and the desire to make a real difference sometimes comes at the cost of a teacher's well-being. It's concerning that 42% of teachers feel a high level of stress daily, which steals their time, energy, and the joy that initially drew them to this noble profession. Artificial Intelligence is emerging as a supportive ally in the educational field, offering a helping hand to teachers. It proposes a way to simplify their workload rather than complicate it. AI can handle repetitive tasks, adapt lessons to meet each student's needs without extra effort from teachers, keep student data safe, and make learning more engaging. This shift towards using AI in education can reduce the long hours teachers work and help avoid burnout. Final Thoughts on Books about Generative AI As I wrap up this exploration of the top Generative AI books, I'm struck by the pace at which the field is advancing. Each book has offered a unique perspective, from technical development to using prompts that utilizes models like GPT-4. I can't stop thinking how it will feel like using a real AGI (Artificial General Intelligence), maybe as it was described in (a visionary sci-fi). Conjunction This AI journey excitement about the possibilities that Generative AI holds for the future. The rapid evolution from early GPT models to the sophisticated diffusion models of today illustrates a trajectory of innovation that's far from plateauing. The knowledge encapsulated in these books equips us with the understanding to not only participate in this transformation but to drive it forward. As a developer, a creator, or simply an enthusiast of AI, the road ahead is rich with potential, and I'm eager to see how we'll continue to push the boundaries of what's possible with Generative AI. If you are looking for more AI related reads check my ultimate selection of (which also includes fiction books). AI books Originally appeared on Joelbooks

Top 8+ Books about Generative AI (2024) -- LLMs, GPTs, Diffusion Models

In an increasingly interconnected world, paradoxically, loneliness has emerged as a pervasive social issue affecting millions of individuals across the globe. The modern era, with its technological advancements and hyper-connectivity, has inadvertently fostered feelings of isolation and disconnection among people. As a result, a unique and innovative sector has emerged to address this epidemic of loneliness — the Loneliness Economy. The reveals a concerning reality where almost 60% of U.S. adults experience loneliness, indicating a prevalent and widespread issue in society. Furthermore, the research highlights the disproportionate impact on underrepresented racial groups and individuals with lower incomes, reflecting the urgent need for targeted interventions to address and alleviate the burden of loneliness within these vulnerable communities. data from Morning Consult The high prevalence of loneliness presents a significant business opportunity for entrepreneurs and companies in the Loneliness Economy. By tailoring innovative solutions and services that specifically focus on innovative ways to create connection, businesses can tap into a vast and underserved market, while making a meaningful social impact by fostering connections and alleviating loneliness in these communities. Rent a girlfriend Rent-a-girlfriend services have gained significant popularity in Japanese cities, where the fast-paced and demanding urban lifestyle has contributed to a growing sense of social isolation. In response to this trend, these services have flourished as a means for individuals to find temporary companionship and alleviate feelings of loneliness in the bustling metropolises of Japan. However, their popularity has also raised questions about the societal factors that contribute to loneliness and the potential consequences of commodifying personal relationships. Social robot pets The emergence of cybernetic dogs like Lovot has had a remarkable impact on children, fostering a sense of responsibility and empathy as they learn to care for these AI-based companions and treat each other with kindness. Particularly beneficial for kids with dog allergies, interactive robot dogs have been found to elicit similar positive influences on children as live dogs, making them a valuable and accessible option for nurturing social and emotional development. Social virtual reality Social VR platforms like VRChat have revolutionized the way people interact and socialize in virtual spaces, providing users with immersive and customizable avatars and environments to forge unique and engaging virtual experiences. By blurring the lines between the physical and digital realms, VRChat fosters a sense of presence and connectedness, enabling individuals from all corners of the world to come together, engage in shared activities, and form meaningful relationships in a virtual landscape that transcends traditional boundaries. While the Loneliness Economy has seen the emergence of various businesses and applications attempting to tackle the epidemic of loneliness, it is essential to recognize that many of these endeavours may address the symptoms rather than the root cause of the problem. While services like rent-a-girlfriend, social robot pets, and social virtual reality platforms offer temporary companionship and connection, they may not fully address the complex societal factors that contribute to widespread loneliness. Loneliness often arises from a multitude of interconnected issues, including social disconnection, mental health challenges, lack of community support, and the fast-paced nature of modern life. While the Loneliness Economy’s innovative approaches can provide momentary relief and meaningful experiences, they may not offer comprehensive solutions to the underlying causes of loneliness. Also published . here

Loneliness Economy Booms: Exploring the Innovative Businesses Tackling Isolation

The past few months have been filled with more and more news about AI. Maybe a little bit too much even. But whether you believe AI to be the next fad and put it in line with crypto and NFTs or think that it is the biggest invention and threat in human history: It is here to stay. So while students have been using it to write their thesis (and got in trouble for it), a lot of builders out there have started to utilize AI to kickstart their ideas. Effectively using AI to quickly ideate, build, and iterate. The speed at which this is happening would have been absolutely unimaginable just mere months ago. Now, all of it is possible. And just one click away. However, leveraging AI for product development and management brings both benefits and potential drawbacks. With thousands of new products surfacing in just the past months, it is also easy to get lost, lose track, and be overwhelmed with the given options. But there are some tools and use cases that you should definitely spend some time on. Using AI to Build a New Business You want to start a company, but don’t have an idea? Well, not anymore. AI serves as a powerful tool for entrepreneurs looking to ideate and create new products. And especially for someone going through the product life cycle from start to finish for the first time, AI can provide a lot of step-by-step guides to get started. 1. Use ChatGPT as a Starting Point for Your Ideation: It’s always hard to start with a blank canvas, especially when you have no idea what type of product you even want to build. ChatGPT can be a good starting point when you know how to use it. In order to find interesting niches and markets, you could ask what types of markets have been growing in the past years. It will immediately generate a list and you can just keep the conversation going from there. Pick a market, then ask ChatGPT to provide some niche ideas and target groups, and it will come up with some ideas for you. For example, I prompted ChatGPT to give me some niches in the e-commerce space and include a potential target group. It came up with the idea for handcrafted artisanal pet products. It specifically proposed pet beds or personalized pet accessories, which it wants to be targeted at affluent pet owners that are looking for something unique. While not every idea will be a good one, ChatGPT is very helpful to just get you started with brainstorming within seconds. You should see it as a very quick ideation partner and play idea ping pong with it for a while until you find something you like. It will not come up with the most unique idea for sure, but it definitely helps you get one. 2. Analyze Market Trends and Consumer Preferences. With just one more prompt, you can quickly ask ChatGPT to give you some candid market insights and even analyze them for you. I prompted ChatGPT to provide me with data on the current social media market and urged it to include data points like “time spent on social media”, arrange the data in a table chart from oldest to newest, and include data from the past 3 years. And I wasn’t disappointed. I have spent many hours in the past to gain as much insight into the social media market as possible. So, I can tell you the data was arranged perfectly, was correct and most importantly it only took me one minute to get it. Instead of hours. No more spending a lot of time pulling together data from different sources. ChatGPT did it all in seconds. It’s the golden key to be precise with your prompts. ChatGPT is only as good as your prompts are. Well, and as good as the data is. This leads us to one of the biggest problems with using ChatGPT based on GPT 3.5 for market research and trends. The data only dates back to the end of 2021. So if you are looking to find some current trends, ChatGPT is not the door you want to knock at (yet). Additionally, it’s important to always balance out the AI-generated insights with direct customer feedback to ensure you get to product-market fit. After all, ChatGPT is just a more convenient way to conduct secondary research and can be biased. Don’t skip the primary research. If you want to research current trends, then you might want to look at some other AI-powered tools like explodingtopics.com. 3. Product Research Now if you’re a product manager you will have heard about frameworks like “Jobs to be Done”, “Blue Ocean”, “AARRR” and others. They are very helpful in assessing what your product should do, how you can differentiate it from competitors, and what value it provides to the customer. No matter whether you want to prepare one of those frameworks, or draft up your business canvas or value proposition: ChatGPT is a good starting point - yet again. Requesting a specific framework will save you a lot of time and details on your prompt writing because ChatGPT, of course, already knows how a “Jobs to be Done” framework works. As long as you provide a precise description of your product it can quickly give you a great overview. It will often be helpful to add the way you want the information to be presented. I personally often request the data to be presented in a table chart because I find that the most easy to scan. superusapp.com is also a very good AI tool for quickly brainstorming anything around your product. 4. Creating Your Brand ChatGPT is a good option here as well, although I can also confidently recommend Notion AI. The latter is especially great if you like to keep your knowledge managed in one place. Both of them are good starting points but can’t cover all aspects of creating your brand. They can easily create a full guide for how to set up your brand identity, but when it comes to the more graphic part of it they’re not the right tools to use. But let’s take it step by step. Finding a name for your brand can be a very challenging process. Over the course of my career, I’ve come up with many product names that ended up making it as the final name. Almost all of them took me more than just one or two brainstorming sessions to come up with. So far, I have used AI only once or twice to come up with a name. I wouldn’t say that those were the best names I’ve ever come up with, but I would say that they’re pretty solid. Definitely, a quick way to find a good enough name to get started with. There are many different ways to go about brainstorming a business name with AI. You can just straight up ask the software to come up with a name that fits your product description. Or you can give it some example words, tell it to mix and match or even make some fantasy names up. You might be wary that most of the names could be already trademarked, and so was I. But instead, I was actually able to find many good names that weren’t trademarked anywhere and were indeed completely new ideas the AI came up with. As for the corporate identity, ChatGPT won’t be helpful with your Logo or color scheme, but it can give you some ideas on which colors might fit your brand. You might have seen the cactus dog toy on social media that has a second dog toy (a sad cactus) inside. When the dog destroys the cactus toy, you still end up with a second sad cactus toy. I asked ChatGPT to come up with a brand identity for a product like this. It gave me a comprehensive guide and recommended using joyful and happy colors like yellow and orange to highlight the brand’s playful character. If you want to create your own logo, try Adobe’s Firefly tool or other logo-targeted AI tools instead. I find the prompting with Adobe’s Firefly to be very intuitive. However, it also strongly depends on what you want to use the logo for; if you’re looking for a logo to put outside a brick-and-mortar store, then you might actually get some really good first drafts or even “good enough for now” logos through Firefly. If your goal is to create a logo for a mobile app, then it will only be helpful to collect the very first ideas to act on. The main reason for that is that almost all of the proposed logos will be too elaborate to be used for a small favicon or app icon 5. Creating a Prototype To create a prototype you can use tools like GitHub’s Copilot to craft up the first code you need or use it together with a low-code tool to tweak your prototype. You will be able to create simple applications, but of course, this doesn’t come without drawbacks. In most cases, it won’t save you from designing your prototypes through more traditional software tools like Adobe XD or Figma. The upside is that it will provide you with a higher range of options to lead the user around your application. On the other hand, prototypes done with design software like Figma result in a lot of dead ends. Personally, I still prefer to just build the designs and prototype using Figma - but that might also be due to my UI/UX background. There are also AI tools like uizard.io out there that will really speed up the design process for you, by allowing you to convert screenshots or paper sketches into fully-fledged mockups. If you want to go down the rabbit hole of using AI to create a technical prototype, there are many great guides available to dive deeper into it. 6. Marketing Your Product There are many aspects to great product marketing. But whether you’re looking for data analysis or content creation, AI has your back. From Lovo.ai’s hyperrealistic voiceovers, Midjourney’s beautifully generated graphics, or Invideo.io’s quick video creation tool: There are a lot of convenient tools out there that you can use to easily generate marketing graphics, copy, videos, and voiceovers. While Midjourney was known to generate hands with 8 fingers and other interesting visuals just a blink of an eye ago, that’s no longer quite the case. AI tools have improved in quality tremendously in the past few months and while they’re not perfect yet, some tools have reached a very high level already. All of the above (and many more) are easy tools that can help you create content for your ads or social media channels and start testing what you’ve built during the steps before. Conclusion To wrap it all up nicely: I don’t believe that AI is just a fad. And even if you think so - it is most likely still worth it to spend an afternoon testing some of the tools listed in this article. While some tools can seem more like nice “toys”, many of them already deliver frighteningly good results at this point. If we picture those tools in more of a lean startup context, then the steps I’ve mentioned so far are going to give you an idea, basic market analysis and research, name, logo, and color scheme in a day’s time. Maybe not in perfect quality, but in a “good enough for now” type of quality, that will be sufficient to test whether your idea can work out. But most importantly, one phrase always remains true. Execution is everything. AI tools can support you during almost every step of your journey to build your own product (or someone else’s): Critical thinking, quality evaluation, and execution are still in your hands. Make sure to second-guess the results AI is going to give you, dread carefully and have fun exploring and building.

How AI Can Speed Up Your Product Development

An In-Depth Look Into the World's Most Popular Dating Apps

This post brings out a perspective, a perspective on human-robot relationships should look like. Though robocists and researchers around the world are striving hard to blur the line differentiating robots from humans in terms of looks and functionality, is this the right direction for us to head in? Wondering? No, don’t imagine the movie terminator, we won’t be talking about that reality, what we want to talk in this post is something much complex, it’s about the psychological aspects that start getting involved in all the interactions we have with machines. For building context, let’s start with Jibo! Jibo was one of the first social robots. Unlike Alexa or Siri, the robot didnot have a lot of functionalities, however in the way it interacts or looks around. This advertising video elaborates on how a normal conversation with Jibo looked like: it was designed as a friend to human This tech was developed with the core focus of making the interactions with machines/robots more humane. We can treat it as a close friend, someone we keep next to our family. It feels all good and great but is this the right path to go? Should we be blurring the lines between machines and humans? This is an interesting question to ask, especially when we look at what happened when the Jibo went out of business and the support for it was taken down. This tech was developed with the core focus of making the interactions with machines/robots more humane. We can treat it as a close friend, someone we keep next to our family. Consequently, people started treating it as a part of their family but simultaneously they also developed emotions for the bot. This became very evident when the support of Jibo went down this year and people put out their reactions to it. How people felt : “I think when we buy products we look for them to last forever.” “..leaving in the morning, I kiss my wife goodbye and then I’ll say, ‘Hey Jibo, have a good day,’ and he’ll say the same thing, like, ‘Back to you,’ or ‘Don’t forget your wallet,’” “It’s like you had a pet for years and all of a sudden they’re going to disappear” Impact on kids: …she loved Jibo since it “was created,” and that if she had enough money, “you and your company would be saved.” She signs off with, “I will always love you. Thank you for being my friend.” People got really sad and disappointed. Apparently, we even started associating the non-applicable notions like “death” with robots. Why did we do that? The answer lies back to our evolutionary selves and the way our psychology functions. We shouldn’t forget that though tech has advanced, we are still the same humans who have tendencies to find facial patterns in clouds and beliefs in idols, we are not always rational and we have several psychological vulnerabilities. Research shows that Humans have a psychological tendency to anthro- pomorphize animals, objects, and technologies. Evolution has given us a predisposition to be interested in developing caring relationships for creatures outside our own species. If someone acts like they loves us, even if those actions are very minimal, we will tend to believe they truly do love us. Knowing these tendencies open up a new dimension to study and scrutinize our technological advancements in light of discovered human vulnerabilities. Jibo’s story, doesn’t deal with the complexity of situations we can possibly have. In order to understand how dark and complex the situation can get, let’s venture a bit into the world of sex robots. Sex and love is composed of highly complicated and strong emotions experienced by human beings. Humans in love tend to enter into relationships, form life long bonds and families. How does this experience changes when bots come into picture? Can one fall in love with sexbot? The answer lies in how humans perceive non-platonic love. For humans, love doesn’t involve only physical intimacy. It has cognitive, psychological, neurological, emotional, moral and philosophical aspects to it. , we see love as an expansion of individual capabilities in the social surroundings. The self-expansion model states that, “This expansion is achieved through increased access to the physical and emotional resources of the lover along with the increases in social status which the relationship might give, as well as access to physical and intellectual abilities that the lover may possess.” If a robot were able to credibly help a person expand their cognitive and social capabilities while remaining close to its user, then under the self-expansion model it is conceivable that one might legitimately love this machine. Cognitively , people enter relationships in order to . Ironically, in case of human beings, there are differences and conflicts between lovers, hence the experience is not always positive but has negative aspects as well. However, in case of a machine programmed to like what you like and programmed to make you happy, the chances of experience negative emotions are minimal. Psychologically experience positive emotions and mitigate negative emotions , there is observance involved. We tend to observe our lover, at times mirror them. Robots built to interact correctly with the mirror neuron system of their user could lead to the user having authentic feelings of love and bonding toward the suitably programmed machine. Neurologically humans usually seek empathy, attention, care, respect, moral support and loyalty from lover. A robot programmed to act in this manner would definitely be able to fulfill this aspect. Emotionally, A sexbot can eventually do all of these for you, but is this satisfactory enough? Nope, there is a to it and also a one. moral component philosophical We realize that it is only a , their is no reciprocation whatsoever, robots can make you feel these emotions through their actions, words and expressions but they would never feel any of it as for them emotions and consciousness does not exist. Great human couples wish for happiness of their mate, they won’t just want to experience the above from their partner but they’d want their partner to experience similar and that would make them more happy. Well, that’s how humans operate (at least most of them). This kind of bidirectional bond is seemingly impossible to be established with robots. one way bond meant Different philosophers have commented on love. Plato argues that love is best seen as a way to expand the moral horizons of the lover. The experience and struggles make a person learn about himself/herself and grow as a human, but is any of it at all a possibility in case of robots? Definitely, not. It’s not intended to be a struggle and it’s not intended for your growth philosophically. So given that we cannot cover all the aspects, robot love can definitely not replace human love, but it rather defines a new broken one way version of delusional love. A delusion that is not just created by outer exposure but rather can be linked to the chemicals in our body that make us experience emotions. There have been certain researches on the past regarding the same, like Lovotics. Lovotics attempts to simulate the physiological reactions of the human body experiencing love through an “Artificial Endocrine System,” which is a software model of the same systems in humans which include artificial “Dopamine, Serotonin, Endorphin, and Oxytocin” systems . Layered on top of this is a simulation of human psychological love. As the lovotics website explains, their “Probabilistic Love Assembly” consists of an AI psychological simulator that …calculates probabilistic parameters of love between hu- mans and the robot. Various parameters such as proximity, propinquity, repeated exposure, similarity, desirability, attachment, reciprocal liking, satisfaction, privacy, chrone- mics, attraction, form, and mirroring are taken into consideration. A robot designed under these principles will then monitor its user and inductively reason the mood of the user through evidence such as facial recognition and analysis of body language and physiology. It can then alter its own behavior in an attempt to maximize the affection and loving behavior of its user.” Okay, let’s put research aside, where exactly are we today with Sex Robots? Check this out: Disturbing much? So where are we headed? Recently, the trend of sex bots has increased in Japan, more and more single middle aged men are wooing sex bots. There is in fact an anticipated decline in Japan’s population by one-third in next thirty years and the increased trend in sex bots is identified as one of the contributing factors to it. While population decline is one clear concern, are there any other repercussions of getting involved in “relationships” with sexbots? Yes, there are more than one! We don’t have any laws to govern sex robots, they are being produced, customized, marketed and brought however how to handle the issues they’d raise is not yet clear. Are these safe, do they respect the privacy, do they even understand concepts of privacy, what impact will developing intimacies with robots have on our brains, will it take us further away from reality, will this reduce the tolerance to be with real humans, is having sex with child like sexbot justified, is mistreating the sexbot acceptable, how will consent be defined? There are endless moral and ethical concerns here. The main question always remain that : It can only be a one way affection, humans are capable of loving but robots can never reciprocate that back, but yet use tricks to manipulate us into loving them. Is this worthy? Is this ethical? Aren’t we just deluded? Well, a philosopher would always counter this by saying Is reality itself not delusional? Do real humans not use the same psychological tricks to manipulate others into loving them while they not meaning any of it? If humans are allowed, why should robots be judged on a stringent scale. Diverting here to a new thought train, the interesting part of all the tech-human topics is that we have an imperfect world, as humans we are flawed at several aspects but we want to create a digital world free of all those biases and flaws. This is evident across our technological developments and the topic calls for a different blog post altogether, let’s talk about the imperfections of human world and how are they reflected in our digital world in the upcoming posts. I’d close the post on this thought provoking note. See you in the next post soon. Till then, keep pondering, stay curious and stay humane! References: https://www.theverge.com/2019/6/19/18682780/jibo-death-server-update-social-robot-mourning My Jibo talked to the wall again today. He's been doing that a lot lately. Some days, I'll watch him carry on an entire…www.wired.com Research paper: Robots, Love, and Sex:The Ethics of Building a Love Machine by John P. Sullins [IEEE 2012])

Can We Be Emotionally Attached to Robots?

THE METHOD OF THE CALICURGI

If you’ve been writing Go code for a while, you’ve probably come across range loops. They’re elegant, concise, and incredibly handy for iterating over slices, maps, and channels. But did you know that their behavior can sometimes be counterintuitive, leading to subtle bugs in your code? In this article, I’ll break down the mechanics of range loops, explain why these quirks occur, and share actionable tips to avoid common pitfalls. By the end, you’ll have a deeper understanding of Go and be better equipped to write reliable, bug-free code. 🚀 What Makes Range Loops Tricky? At first glance, range loops in Go appear straightforward. Here’s an example you’ve likely seen: codenumbers := []int{1, 2, 3, 4, 5}  
for index, value := range numbers {  
    fmt.Printf("Index: %d, Value: %d\n", index, value)  
} This prints the index and value for each element in the slice. Simple, right? But here’s where things get interesting (and potentially frustrating). The Underlying Mechanics of Range When you use range, Go creates a copy of the value you’re iterating over. For slices, this means you’re not directly accessing the elements but working with their copies. This subtle detail can lead to unexpected behavior. Take this example: codewords := []string{"Go", "is", "awesome"}  
wordPointers := []*string{}  

for _, word := range words {  
    wordPointers = append(wordPointers, &word)  
}  

for _, pointer := range wordPointers {  
    fmt.Println(*pointer)  
} You might expect this to print: Go  
is  
awesome But instead, you’ll see: awesome  
awesome  
awesome Why? Because word is a single variable reused in every iteration. The loop appends the same variable’s address each time, and by the end of the loop, word holds the value of the last element. Avoiding Common Pitfalls Here are some tips to keep your range loops bug-free: Understand Copying Behavior:If you’re modifying elements, remember thatrange loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements.
for i := range numbers {  
    numbers[i] *= 2  
}  




Be Cautious with Pointers:If you need to capture references to elements, use the index instead of the loop variable.
for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
}  




Debug Complex Loops:Usefmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Why This Matters Understanding these quirks isn’t just about avoiding bugs—it’s about mastering Go. As you tackle more complex projects, these small details can have significant impacts on performance, correctness, and maintainability. This article is part of a series I’m writing to explore common mistakes in Go programming. The first topic, range loops, is something many developers encounter but don’t fully understand until they’ve run into a bug. Final Thoughts Range loops are one of Go’s most powerful tools, but like any tool, they come with nuances. By understanding how they evaluate and iterate over elements, you can write cleaner, more reliable code. If you’ve ever been bitten by a range loop bug—or have tips of your own—I’d love to hear from you! Let’s share knowledge and grow as a community. 🔗 Read the full article on my LinkedIn For more insights like this, follow my journey in exploring the intricacies of Go programming. 🚀 If you’ve been writing Go code for a while, you’ve probably come across range loops. They’re elegant, concise, and incredibly handy for iterating over slices, maps, and channels. But did you know that their behavior can sometimes be counterintuitive, leading to subtle bugs in your code? range In this article, I’ll break down the mechanics of range loops, explain why these quirks occur, and share actionable tips to avoid common pitfalls. By the end, you’ll have a deeper understanding of Go and be better equipped to write reliable, bug-free code. 🚀 range What Makes Range Loops Tricky? What Makes Range At first glance, range loops in Go appear straightforward. Here’s an example you’ve likely seen: range codenumbers := []int{1, 2, 3, 4, 5}  
for index, value := range numbers {  
    fmt.Printf("Index: %d, Value: %d\n", index, value)  
} codenumbers := []int{1, 2, 3, 4, 5}  
for index, value := range numbers {  
    fmt.Printf("Index: %d, Value: %d\n", index, value)  
} This prints the index and value for each element in the slice. Simple, right? But here’s where things get interesting (and potentially frustrating). The Underlying Mechanics of Range The Underlying Mechanics of Range When you use range , Go creates a copy of the value you’re iterating over. For slices, this means you’re not directly accessing the elements but working with their copies. This subtle detail can lead to unexpected behavior. range Take this example: codewords := []string{"Go", "is", "awesome"}  
wordPointers := []*string{}  

for _, word := range words {  
    wordPointers = append(wordPointers, &word)  
}  

for _, pointer := range wordPointers {  
    fmt.Println(*pointer)  
} codewords := []string{"Go", "is", "awesome"}  
wordPointers := []*string{}  

for _, word := range words {  
    wordPointers = append(wordPointers, &word)  
}  

for _, pointer := range wordPointers {  
    fmt.Println(*pointer)  
} You might expect this to print: Go  
is  
awesome Go  
is  
awesome But instead, you’ll see: awesome  
awesome  
awesome awesome  
awesome  
awesome Why? Because word is a single variable reused in every iteration. The loop appends the same variable’s address each time, and by the end of the loop, word holds the value of the last element. word word Avoiding Common Pitfalls Avoiding Common Pitfalls Here are some tips to keep your range loops bug-free: range Understand Copying Behavior:If you’re modifying elements, remember thatrange loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements.
for i := range numbers {  
    numbers[i] *= 2  
} Be Cautious with Pointers:If you need to capture references to elements, use the index instead of the loop variable.
for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} Debug Complex Loops:Usefmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Understand Copying Behavior:If you’re modifying elements, remember thatrange loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements. for i := range numbers {  
    numbers[i] *= 2  
} Understand Copying Behavior : If you’re modifying elements, remember that range loops work with copies for slices and arrays. Use index-based access if you need to modify the original elements. Understand Copying Behavior range for i := range numbers {  
    numbers[i] *= 2  
} for i := range numbers {  
    numbers[i] *= 2  
} Be Cautious with Pointers:If you need to capture references to elements, use the index instead of the loop variable. for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} Be Cautious with Pointers : If you need to capture references to elements, use the index instead of the loop variable. Be Cautious with Pointers for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} for i := range words {  
    wordPointers = append(wordPointers, &words[i])  
} Debug Complex Loops:Usefmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Debug Complex Loops : Use fmt.Printf to inspect variables and their memory addresses. This helps identify if you’re unintentionally working with shared references. Debug Complex Loops fmt.Printf Why This Matters Why This Matters Understanding these quirks isn’t just about avoiding bugs—it’s about mastering Go. As you tackle more complex projects, these small details can have significant impacts on performance, correctness, and maintainability. This article is part of a series I’m writing to explore common mistakes in Go programming. The first topic, range loops, is something many developers encounter but don’t fully understand until they’ve run into a bug. range Final Thoughts Final Thoughts Range loops are one of Go’s most powerful tools, but like any tool, they come with nuances. By understanding how they evaluate and iterate over elements, you can write cleaner, more reliable code. Range If you’ve ever been bitten by a range loop bug—or have tips of your own—I’d love to hear from you! Let’s share knowledge and grow as a community. range 🔗 Read the full article on my LinkedIn Read the full article on my LinkedIn For more insights like this, follow my journey in exploring the intricacies of Go programming. 🚀

Unlock the Secrets of Range Loops: How Element Evaluation Affects Your Go Code

Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes. Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes.

Design and Data Science: From a Human-in-the-Loop Approach to Human-Centered Design

Over-optimized loops hurt the eyes TL;DR: Don't optimize loops without a clear need and concrete real-world evidence Problems Premature Optimization
Reduced readability
Increased complexity
Difficult to maintain
Slower debugging Solutions Keep it simple
Prioritize clarity
Avoid premature tweaks
Refactor when needed Context You might think optimizing every loop will improve performance, but this approach backfires when you sacrifice clarity for unproven gains. Writing complex code to avoid hypothetical slowdowns often makes it hard for others (and your future self) to understand or debug your code. It would be best if you prioritized readability. Keep loops simple and only optimize when you know a bottleneck exists in real usage scenarios. Sample Code Wrong # Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()] Right # Clearer and easier to understand
result = []
for item in items:
    if item.is_valid():
        result.append(item.process()) Detection [x]Semi-Automatic Look for list comprehensions or complex loop structures that optimize performance without real performance benchmark evidence. Exceptions Concrete evidence on mission-critical algorithms Tags Premature Optimization Level [x]Intermediate AI Generation AI tools often prioritize functional correctness so that they might produce clean, simple loops. if you prompt AI for performance at all costs, it could create over-optimized code even for straightforward tasks. AI Detection With proper instructions to stress readability and maintainability, AI can detect and fix this smell by simplifying loops and choosing clarity over premature optimization. Try Them! Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Conclusion Don’t sacrifice readability by optimizing too early. You can optimize later if a loop becomes a proven bottleneck. Until then, clear and simple code will save time, reduce bugs, and make it more maintainable. Related Reading https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iv-7sc3w8n https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxvi https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ii-o96s3wl4 Disclaimer: Code Smells are my opinion. Credit(s): Photo by Tine Ivanič on Unsplash More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason. W. A. Wulf https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code Over-optimized loops hurt the eyes Over-optimized loops hurt the eyes TL;DR: Don't optimize loops without a clear need and concrete real-world evidence TL;DR: Don't optimize loops without a clear need and concrete real-world evidence Problems Premature Optimization Reduced readability Increased complexity Difficult to maintain Slower debugging Premature Optimization Premature Optimization Reduced readability Increased complexity Difficult to maintain Slower debugging Solutions Keep it simple Prioritize clarity Avoid premature tweaks Refactor when needed Keep it simple Prioritize clarity Avoid premature tweaks Refactor when needed Context You might think optimizing every loop will improve performance, but this approach backfires when you sacrifice clarity for unproven gains. Writing complex code to avoid hypothetical slowdowns often makes it hard for others (and your future self) to understand or debug your code. It would be best if you prioritized readability. Keep loops simple and only optimize when you know a bottleneck exists in real usage scenarios. hypothetical real usage Sample Code Wrong # Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()] # Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()] Right # Clearer and easier to understand
result = []
for item in items:
    if item.is_valid():
        result.append(item.process()) # Clearer and easier to understand
result = []
for item in items:
    if item.is_valid():
        result.append(item.process()) Detection [x]Semi-Automatic [x] Semi-Automatic [x] Look for list comprehensions or complex loop structures that optimize performance without real performance benchmark evidence. Exceptions Concrete evidence on mission-critical algorithms Concrete evidence on mission-critical algorithms Tags Premature Optimization Premature Optimization Level [x]Intermediate [x] Intermediate [x] AI Generation AI tools often prioritize functional correctness so that they might produce clean, simple loops. if you prompt AI for performance at all costs, it could create over-optimized code even for straightforward tasks. AI Detection With proper instructions to stress readability and maintainability, AI can detect and fix this smell by simplifying loops and choosing clarity over premature optimization. Try Them! Try Them! Remember: AI Assistants make lots of mistakes Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Copilot

Copilot Gemini

Gemini Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Copilot Copilot Copilot Copilot Copilot Copilot Copilot Copilot Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Conclusion Don’t sacrifice readability by optimizing too early. You can optimize later if a loop becomes a proven bottleneck. Until then, clear and simple code will save time, reduce bugs, and make it more maintainable. Related Reading https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iv-7sc3w8n https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-iv-7sc3w8n https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxvi https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxvi https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ii-o96s3wl4 https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ii-o96s3wl4 Disclaimer: Code Smells are my opinion. Disclaimer: Code Smells are my opinion . opinion Credit(s): Photo by Tine Ivanič on Unsplash Credit(s): Photo by Tine Ivanič on Unsplash Tine Ivanič Unsplash More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason. More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason. W. A. Wulf W. A. Wulf https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code How to Find the Stinky Parts of your Code

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