Responsibly is a sustainability tech start-up with vision of making every purchase in the world responsible ?\n\nThere's no bigger problem out there than the climate, and procurement represents a big part of the solution - when purchase decisions are made on the background of responsible data like CO2 footprint, suppliers have a huge incentive to change

Responsibly

The world of cryptocurrency is known for its lucrative rewards but also for the risks posed to newcomers. Some have lost millions of dollars worth of ETH or BTC, through scams or simply by losing their private keys. On the other hand, those fortunate enough to have held on to their coins or to have been involved in a successful Web3 project have made millions and even hundreds of millions of dollars. There are plenty of , whose funds are all held in secret cryptocurrency wallets, accessed through private keys. undisclosed crypto millionaires But that’s not really the point of the new era of distributed ledgers and cryptocurrencies. It’s time to introduce a more reliable mechanism of rewards and a more emotionally balanced approach to investing. #1 - Understand Hacks and Scams The most common, in fact - - way that people lose their investment is by to online scam artists. They actually send their cryptocurrency to a wallet address given to them by a scam artist. And then their funds are gone and cannot be retrieved. Some of the scams are astonishingly clever and intricate. But the vast majority are simple, basic, and easily avoidable. the only sending it As a rule of thumb, just never send your cryptocurrency to another wallet address unless you are 100% sure it is legitimate. This is especially true on sites where you buy/sell cryptocurrency. Never release funds until you have proof or a reliable escrow system. And do not invest in new projects unless you have a good reason to and have conducted research including history, on-chain information, off-chain information, founders, financial backing, time in business, and other basic metrics that you would apply to any investment. Information gleaned from YouTube and Reddit is not information - it's just opinions. Social sentiment is only useful when correctly analyzed in the context of other data sources.  Scam artists are particularly active on Reddit and other sites. They will pretend to be representatives of Web3 projects and ask you to send your cryptocurrency to a “special account” so it will be safe. It might even be a good idea to read up on the so you understand just what you are up against. different kinds of crypto scams Crypto newcomers are most likely to lose their funds by sending them to a hacker’s wallet or by misplacing their private keys - coin depreciation is the least of their concerns and wallet security is priority number one. #2 - Utilize Security Protocols Closely related to the first point is that of basic security protocols. Two Factor Authentication (2FA) is an essential security feature where you will receive a text message after you log in to an online application. This second layer of security ensures your account is safe, even when your email address and username are compromised on a given site. You will also want to keep your funds stored in a secure local or hardware wallet. You can download an application such as . Because funds are not stored online, this kind of wallet is safer, though not as safe as a hardware wallet like a Trezor. An online wallet with 2FA can also be safe. With the added 2FA, it is immune to “ ”. Exodus hing phis This means that another site is set up that looks exactly like the one you use to store your funds. When you input your username and password, it goes straight into the database of the hacker, who uses it to access your funds on the real site. This is where 2FA can show its value - even if you gave away your username and password through a false site, it would prevent hacker entry. The most important security protocol is to make two secure copies of the private keys to your wallet. If you lose these keys, you lose your wallet and it cannot be recovered. 2FA and using a local wallet are the most simple and effective mechanisms to protect yourself in the world of cryptocurrency. #3 - Take Advantage of Available Technology There are multiple tools and applications that can assist in terms of crypto and NFT investment. The Web3 industries are so fast-paced that these tools are sorely needed to make sense of the innovation and the sheer amount of data. Tools provided through enterprises such as are imperative to those serious about making sustainable profits in the new industry. Investing “raw”, without any tools to assess all of the available information, will yield variable results at best. Defy Trends Defy Trends aggregates the relevant information and presents it to users in an easily-digestible format. Coins and NFTs are given a score that helps users to assess whether a coin/NFT is undervalued or overvalued. This is an extremely attractive option to those looking to gain exposure to the new era of cryptocurrencies but with absolutely no help from the mainstream crypto economy. The blockchain ethos can be pretty brutal; if you lose your keys or did something silly, it’s viewed as being your own fault. Tools like this are exactly what market newcomers need. It helps to perform due diligence on coins (preserving your investment, something sorely overlooked in the current market) and to spot new opportunities. The score is based on a number of data points including on-chain info, off-chain info, social sentiment, whale tracking, and HODL metrics. In other words, all relevant data is aggregated and presented to investors so they are armed with the necessary information to thrive. A tool that aggregates token and NFT information and presents it in a clear and easily understood manner is critical to long-term profitability. #4 - Adopt a Long-Term Approach Once you have a firm grasp of the operating principles of blockchain technology and how to protect your funds, it's time to invest with a long-term approach. This requires emotional resilience because there is always some new coin that is going to the moon within the next couple of weeks. It’s hard to resist all of the hype and hysteria, but it can (and must) be done. While most NFTs and cryptocurrencies will go up and down on a short-term basis, the general trend has most certainly been upward on a yearly basis. Just analyze the charts from 2008 to 2022. So the simple principles of diversification in high-quality coins is a safe and effective option. If you extend your timeline to 2 years instead of 2 weeks, then you are very likely going to win at this game. It will also save a lot of stress of continually refreshing your browser to see if a coin went up on a minute-to-minute basis. Investment is safe and easy for most, with some practicality. In contrast, intraday trading is often reserved for a few dedicated individuals with time and commitment to spare. Some investment principles are timeless - make sure you hold on to your coins for reasonable time periods and only swap or trade for legitimate reasons. Make Safe Investments NFTs, cryptocurrencies, and distributed ledgers are likely the future of the global economy, especially as the internet-of-things comes to fruition. It’s important to preserve peace of mind and to stay practical in a society flooded with information from all angles. Get to know the industry, take a long-term approach, streamline your investment decisions with empowering tools, and be patient. If you are happy with getting rich slowly as opposed to quickly, then you are well on the way to responsible and sustainable investment. Particularly in an era based on quick but often short-lived profits. After all, the best-performing funds are ones where the owners forget about them. And one of the best investment strategies would have been to purchase BTC as early as possible and store it securely, without touching it.

How to Responsibly Invest in Crypto & NFTs for Your Long-term Future

While database technology and other tools have come a long way, it's still tough to beat the humble spreadsheet's versatility and intuitiveness. While using them for sensitive, critical applications like storing COVID-19 patient data is ill-advised, the fact that everyone knows how to use a spreadsheet means they're great for smaller cross-functional projects where a non-developer might need to examine or edit data. In this guide, I'll show you how to use Google Sheets as a database, complete with an API interface accessible over HTTP. We'll use , a Node.js API development platform and editor with built-in autocomplete, to deploy a simple app and handle Google's authentication process. I'll also explore the limitations of Google Sheets, including scalability, and where it makes sense to start looking at more complex alternatives. Autocode $ curl --request GET --url \ # Returns all people in the database whose names start # with "bil", case-insensitive 'https://YOUR_USERNAME.api.stdlib.com/gsheets-database-example/select/name/istartswith/?query=bil' [
  { : , : , : , : , : },
  { : , : , : , : , : },
  { : , : , : , : , : }
] "Name" "Bilbo Baggins" "Job" "Burglar" "Fictional" "TRUE" "Born On" "9/21/1937" "Updated At" "" "Name" "Bill Nye" "Job" "Scientist" "Fictional" "FALSE" "Born On" "11/27/1955" "Updated At" "" "Name" "billie eilish" "Job" "Artist" "Fictional" "FALSE" "Born On" "12/18/2001" "Updated At" "" You don't need anything other than a Google account and a free Autocode account to get started. Let's go! TL;DR (30s) First, you'll need to clone your own copy of the template Google Sheet by . This will add the sample spreadsheet to your personal Google account. clicking here, then pressing "Use Template" in the top right Once you've done that, . Poke around the source code if you'd like, then install the app to your Autocode account by pressing the green button. When prompted to link a Google Sheet, follow the instructions in the flow to link your Google account, then select the spreadsheet you just cloned. click here to open the starter app in Autocode After that, your app should be ready to go! Try accessing a few of the endpoints via their URLs and see what is returned/what happens to your new spreadsheet database. You can check out the section below for example calls. Endpoints Limitations That tl;dr was easy, right? So why isn't everything built on Google Sheets? While a backend that takes 30 seconds to set up and that everyone can interact with is extremely appealing, there are some obvious limitations. A spreadsheet as a database as described above doesn't naturally support multiple tables or relationships between rows. There's also no concept of enforcing types for a given column, transactions, built-in backups, or encryption, so sensitive/critical data (like COVID-19 patient data) is probably best stored elsewhere. In terms of scalability, Google Sheets has a (including blank cells). When I tried to verify this by creating a spreadsheet with that many values, however, I encountered significant issues in performance before that threshold: hard limit of 5,000,000 cells Mass-operations like pasting a large number of cells slowed, then began to fail at around 1m cells. Navigation was generally sluggish. My experiments around making API calls yielded similar results. Query speed seemed to scale linearly with the number of cells: Queries became impractically slow around the 500,000 cell mark, but were still below 2 seconds for a 100,000 cell query. Therefore, if you anticipate a dataset larger than a few hundred thousand cells, it would probably be smart to choose a more scalable option. How It Works When you link your cloned Google Sheet to your app and install it to your account, automatically handles authentication between your app and your Google account using your app's token (see the line at the top of all the endpoints). Autocode const lib = require('lib')({token: process.env.STDLIB_SECRET_TOKEN}) For the actual queries, each endpoint contains Node.js code that calls a method from the . These APIs take a parameter called formatted in that corresponds to the part of the spreadsheet the API call should consider as part of the database. googlesheets.query API range A1 notation queryResult = lib.googlesheets.query[ ].select({ : , : , : [{ : query
  }]
}); let await '@0.3.0' range `A:E` bounds 'FULL_RANGE' where 'Name__istartswith' A value of is essentially shorthand for "use all rows in columns through in the spreadsheet as my database". The query interprets the first row of each column in that range as the field name of the values in that column. Given the template you cloned, the above query will check all values in the column (named ) for rows with a value matching the query. range A:E A E A Name These API calls use the . If you're interested in a deep dive, you can check it out for more examples. KeyQL query language Calling Endpoints As previously mentioned, these endpoints are accessible via HTTP, so you can make calls to them via , , or whatever other HTTP client you prefer. You can use your web browser directly: fetch cURL And you can even use the same Node package that the endpoints use to call the Google Sheets APIs: lib-node Your endpoints will respond to either GET or POST requests. Parameters are parsed from the querystring for GET requests and the request body for POST requests. Each endpoint has default parameters set for the sake of clarity. You can find examples for each endpoint below. Endpoints functions/select/job/contains.js This endpoint is an example of a KeyQL query. It looks for rows in the linked Google Sheet where the field contains a substring (case-sensitive) matching the parameter . From the sample sheet, it returns: contains Job query $ curl --request GET --url \ 'https://YOUR_USERNAME.api.stdlib.com/gsheets-database-example/select/job/contains/?query=ist' [
  { : , : , : , : , : },
  { : , : , : , : , : },
  { : , : , : , : , : }
] "Job" "Mistborn" "Born On" "2006-07-17" "Fictional" "TRUE" "Name" "Vin Venture" "Updated At" "" "Job" "Scientist" "Born On" "1955-11-27" "Name" "Bill Nye" "Fictional" "FALSE" "Updated At" "" "Job" "Artist" "Born On" "2001-12-18" "Name" "billie eilish" "Fictional" "FALSE" "Updated At" "" functions/select/born_on/date_gt.js This endpoint is an example of a KeyQL query. It looks for rows in the linked Google Sheet where the field is after the parameter, formatted as . From the sample sheet, it returns: date_gt Born On query 2000/01/01 $ curl --request GET --url \ 'https://YOUR_USERNAME.api.stdlib.com/gsheets-database-example/select/born_on/date_gt/?query=2000/01/01' [
  { : , : , : , : , : },
  { : , : , : , : , : }
] "Job" "Mistborn" "Born On" "2006/07/17" "Fictional" "TRUE" "Name" "Vin Venture" "Updated At" "" "Job" "Artist" "Born On" "2001/12/18" "Name" "billie eilish" "Fictional" "FALSE" "Updated At" "" functions/select/name/istartswith.js This endpoint is an example of a KeyQL query. It looks for rows in the linked Google Sheet where the field starts with the parameter (case-insensitive). From the sample sheet, it returns: istartswith Name query $ curl --request GET --url \ 'https://YOUR_USERNAME.api.stdlib.com/gsheets-database-example/select/name/istartswith/?query=bil' [
  { : , : , : , : , : },
  { : , : , : , : , : },
  { : , : , : , : , : }
] "Job" "Scientist" "Born On" "1955-11-27" "Name" "Bill Nye" "Fictional" "FALSE" "Updated At" "" "Job" "Artist" "Born On" "2001-12-18" "Name" "billie eilish" "Fictional" "FALSE" "Updated At" "" "Job" "Burglar" "Born On" "1937-09-21" "Fictional" "TRUE" "Name" "Bilbo Baggins" "Updated At" "" functions/insert.js This endpoint is an example of an insert query. It passes the input parameters into the parameter of the . For example, to add to your spreadsheet, you could make the following request (all parameters are lower-case): fieldsets googlesheets.query.insert API Bill Gates $ curl --request POST \
  --header \
  --data \
  --url "Content-Type: application/json" '{"name":"Bill Gates","job":"CEO","fictional":false,"bornOn":"10/28/1955"}' 'https://YOUR_USERNAME.api.stdlib.com/gsheets-database-example/insert/' Autocode APIs do not respond exclusively one HTTP method over another, and instead rely on descriptive naming to avoid confusion over functionality. Note: functions/update.js This endpoint is an example of an update query. It sets the field of people whose names exactly match the parameter, and updates other fields based on input parameters. It uses the . Updated At name googlesheets.query.update API For example, to update the field of to in your spreadsheet, you could make the following request (all parameters are lower-case): Job Bilbo Baggins Ring Bearer $ curl --request POST \
  --header \
  --data \
  --url "Content-Type: application/json" '{"name":"Bilbo Baggins","job":"Ring Bearer"}' 'https://YOUR_USERNAME.api.stdlib.com/gsheets-database-example/update/' This can affect multiple rows if more than one row matches the query conditions. Note: functions/delete.js This endpoint is an example of a delete query. It removes rows of people whose names exactly match the parameter. It uses the . For example, to remove from your spreadsheet, you could make the following request: name googlesheets.query.delete API Bilbo Baggins $ curl --request GET --url \ 'https://YOUR_USERNAME.api.stdlib.com/gsheets-database-example/delete/?name=Bilbo%20Baggins' This can affect multiple rows if more than one row matches the query conditions. Note: Thank You! If you have any questions or feedback, the best thing to do is to join the Autocode community Slack channel. You can get an invite from the tab in the top bar on . You can also reach out to me directly on Twitter, . Community the website @Hacubu If you want to stay up to date on the latest from Autocode, you can follow . Happy hacking! @AutocodeHQ

Use Google Sheets as a Database, Responsibly: A Step-by-Step Guide

As a society, we've always been drawn to a good story. From ancient myths to modern-day blockbusters, the power of narrative has the ability to captivate, inspire, and transform. In the world of technology, where innovation is the name of the game, storytelling has become the secret weapon behind some of the most buzzed-about startups. And the unexpected heroes of this new era? None other than the creative directors, the ad agency alchemists turned tech founders who have traded in their storyboards for the chance to rewrite the future. Armed with a knack for storytelling and a flair for the dramatic, these former ad agency stars are trading in their Cannes Lions for the chance to launch the next big thing. But as the hype around projects like Humane's AI Pin reaches fever pitch, it's worth asking: are these visionaries revolutionizing the tech landscape, or are they just spinning tales? Take Humane's AI Pin, for example. This little device, dreamt up by former Apple creative directors Imran Chaudhri and Bethany Bongiorno, has made quite the splash with its promise of a screenless, AI-driven future. But let's be real - it's not just the tech that's got everyone talking. It's the story. The vision of a world where technology seamlessly integrates into our lives, as natural as breathing. That's the kind of narrative that gets investors reaching for their checkbooks, the media clamoring for a headline and secures millions in funding. And Humane is far from alone in this new world order. Just look at the rise and fall of Elizabeth Holmes and Theranos. Sure, the revolutionary blood-testing technology never quite materialized, but damn if Holmes didn't spin one hell of a story. With her Steve Jobs-esque turtlenecks and promises of disruption, she had Silicon Valley eating out of the palm of her hand. Or what about Adam Neumann and WeWork? The man could sell a vision like no other, painting a picture of a workplace revolution that would “elevate the world's consciousness” (and the company's valuation to a cool $47 billion). But here's the thing about stories - they're only as good as the reality they're built on. When the curtain gets pulled back and the wizard is revealed to be just a guy with a smoke machine, things can get ugly fast. Just ask the folks who bought into the Theranos dream or the WeWork hype. When the story doesn't match the substance, it's not just disappointment that follows - it's disaster. Moreover, storytelling is not just about hype. Used responsibly, it can be a powerful tool for education and inspiration. When Steve Jobs introduced the iPhone, he didn't just sell a product; he painted a picture of how it would transform our lives. That narrative resonated because it was grounded in a genuine, revolutionary product. So what's the lesson here for the creative directors turned tech visionaries? The use of narrative as a launchpad is not without its risks: with great storytelling power comes great responsibility.The ability to craft a compelling narrative is a gift, but it's one that needs to be used wisely. The best stories, the ones that truly change the game, are the ones that are grounded in something real. Something tangible. Something that doesn't just tickle the imagination but actually follows through on its promises. This is the tightrope that creative directors in the tech world must walk. As for Humane's AI Pin? Only time will tell if it can live up to the hype. But one thing's for sure - in the wild west of tech startups, the storytellers are the new sheriffs in town. They're the ones who can make us believe in a brighter future, a more connected world, a reality where anything is possible. And in a world that can often feel like it's spinning out of control, that's a pretty powerful thing. But as we watch these narratives unfold, it's on us to remember that not every story has a happy ending, like . We've got to approach each new tale with a healthy dose of wonder and a hearty helping of skepticism. Because in the end, the most compelling story isn't always the most true - but it might just be the one that changes everything. As a society, we've always been drawn to a good story. From ancient myths to modern-day blockbusters, the power of narrative has the ability to captivate, inspire, and transform. In the world of technology, where innovation is the name of the game, storytelling has become the secret weapon behind some of the most buzzed-about startups. And the unexpected heroes of this new era? None other than the creative directors, the ad agency alchemists turned tech founders who have traded in their storyboards for the chance to rewrite the future. Armed with a knack for storytelling and a flair for the dramatic, these former ad agency stars are trading in their Cannes Lions for the chance to launch the next big thing. But as the hype around projects like Humane's AI Pin reaches fever pitch, it's worth asking: are these visionaries revolutionizing the tech landscape, or are they just spinning tales? Take Humane's AI Pin, for example. This little device, dreamt up by former Apple creative directors Imran Chaudhri and Bethany Bongiorno, has made quite the splash with its promise of a screenless, AI-driven future. But let's be real - it's not just the tech that's got everyone talking. It's the story. The vision of a world where technology seamlessly integrates into our lives, as natural as breathing. That's the kind of narrative that gets investors reaching for their checkbooks, the media clamoring for a headline and secures millions in funding. And Humane is far from alone in this new world order. Just look at the rise and fall of Elizabeth Holmes and Theranos. Sure, the revolutionary blood-testing technology never quite materialized, but damn if Holmes didn't spin one hell of a story. With her Steve Jobs-esque turtlenecks and promises of disruption, she had Silicon Valley eating out of the palm of her hand. Or what about Adam Neumann and WeWork? The man could sell a vision like no other, painting a picture of a workplace revolution that would “elevate the world's consciousness” (and the company's valuation to a cool $47 billion). But here's the thing about stories - they're only as good as the reality they're built on. When the curtain gets pulled back and the wizard is revealed to be just a guy with a smoke machine, things can get ugly fast. Just ask the folks who bought into the Theranos dream or the WeWork hype. When the story doesn't match the substance, it's not just disappointment that follows - it's disaster. Moreover, storytelling is not just about hype. Used responsibly, it can be a powerful tool for education and inspiration. When Steve Jobs introduced the iPhone, he didn't just sell a product; he painted a picture of how it would transform our lives. That narrative resonated because it was grounded in a genuine, revolutionary product. So what's the lesson here for the creative directors turned tech visionaries? The use of narrative as a launchpad is not without its risks: with great storytelling power comes great responsibility.The ability to craft a compelling narrative is a gift, but it's one that needs to be used wisely. The best stories, the ones that truly change the game, are the ones that are grounded in something real. Something tangible. Something that doesn't just tickle the imagination but actually follows through on its promises. This is the tightrope that creative directors in the tech world must walk. As for Humane's AI Pin? Only time will tell if it can live up to the hype. But one thing's for sure - in the wild west of tech startups, the storytellers are the new sheriffs in town. They're the ones who can make us believe in a brighter future, a more connected world, a reality where anything is possible. And in a world that can often feel like it's spinning out of control, that's a pretty powerful thing. But as we watch these narratives unfold, it's on us to remember that not every story has a happy ending, like . We've got to approach each new tale with a healthy dose of wonder and a hearty helping of skepticism. Because in the end, the most compelling story isn't always the most true - but it might just be the one that changes everything.

Narrative as a Launchpad: How Creative Directors Propel Tech Ventures Like Humane's AI Pin

একটি লঞ্চপ্যাড হিসাবে আখ্যান: কিভাবে ক্রিয়েটিভ ডিরেক্টররা হিউম্যানের এআই পিনের মতো টেক ভেঞ্চারকে এগিয়ে দেয়

Erzählung als Startrampe: Wie Kreativdirektoren Technologieunternehmen wie Humanes KI vorantreiben Pin

La narrativa como plataforma de lanzamiento: cómo los directores creativos impulsan empresas tecnológicas como la IA de Humane

Le récit comme rampe de lancement : comment les directeurs créatifs propulsent les entreprises technologiques comme l'IA de Humane

लॉन्चपैड के रूप में कथा: कैसे क्रिएटिव डायरेक्टर्स ह्यूमेन के एआई पिन जैसे तकनीकी उपक्रमों को आगे बढ़ाते हैं

物語を発射台として: クリエイティブ ディレクターが Humane の AI Pin のようなテクノロジー ベンチャーを推進する方法

런치패드로서의 내러티브: 크리에이티브 디렉터가 Humane의 AI 핀과 같은 기술 벤처를 추진하는 방법

Narrativa como plataforma de lançamento: como os diretores criativos impulsionam empreendimentos tecnológicos como o AI Pin da Humane

Нарратив как стартовая площадка: как креативные директора продвигают технологические предприятия, как пин от Humane об искусственном интеллекте

Bir Başlatma Paneli Olarak Anlatı: Yaratıcı Yönetmenler, Humane'nin Yapay Zeka Pinini Benzeri Teknoloji Girişimlerini Nasıl İlerletir?

Tường thuật như một bệ phóng: Cách các Giám đốc Sáng tạo Thúc đẩy các dự án mạo hiểm công nghệ giống như Ghim AI của Humane

叙事作为启动板：创意总监如何推动 Humane 的 AI Pin 等科技企业的发展

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How to Run Your Own Local LLMs: Updated for 2024 with 15 Local LLM Open Source Tools!

Five years ago, Frank Chen posed a question that has stuck with me every day since. The question was this, “If self-driving cars are 51% safer, are we not morally obligated to adopt them?”.  I’ve posed this question a ton of times over the past five years, and usually, a knee-jerk reaction leads to an interesting debate. What makes this question so great is the knife's edge – it’s not 99% safer, it’s not 70% safer, it’s 51% safer. only To put it into context. The National Highway Safety Administration has that in 2022, there were an estimated 42,795 traffic fatalities. 50% of 42,795 is 21,398 people, and 51% is 21,825 people. reported That means if self-driving cars are 51% safer, using them would save 427 people’s lives every year. That is about 1.5 Boeing 777 airplanes full of passengers. Is saving 427 lives a moral argument for adoption? In the five years I’ve been sharing this question, the answers are never simple. They’re always fraught with “what ifs.” But even if the answers lack clarity, I think the question is incredibly important. In part because it opens a broader — and equally important — debate on the moral imperative of AI adoption across many aspects of our lives and work. Because, after all, avoiding technology that could save lives may be just as ethically problematic as adopting technology too hastily, The Moral Imperative of AI Adoption I've always found the debate around autonomous vehicles to be a perfect microcosm for the broader discourse on AI. If we possess technology that's statistically safer than human-operated vehicles, isn't the moral choice obvious? Consider this: have shown that human drivers had a higher rate of crashes with a meaningful risk of injury than self-driving  (AI-powered) cars. Specifically, human drivers caused 0.24 injuries per million miles (IPMM) and 0.01 fatalities per million miles (FPMM), while self-driving cars caused 0.06 IPMM and 0 FPMM. studies And remember, these numbers aren't just statistics. They represent real lives that could be saved by embracing AI technology. But why stop at autonomous vehicles? The potential for AI to enhance safety, efficiency, and accuracy spans across fields like medicine, public health, food safety, agriculture, cybersecurity, crime prevention, and military science. If AI can diagnose diseases with greater accuracy than human doctors, predict crop failures before they devastate food supplies, or thwart cyber-attacks before they breach our data, don't we have a moral obligation to also utilize those technologies? Of course, these are dramatic examples, but the argument extends beyond life-and-death scenarios. AI's ability to improve our daily quality of life is equally compelling. Whether by simplifying mundane tasks or making information and services more accessible and equitable, AI can end drudgery and enhance our daily quality of life. The moral imperative to adopt AI isn't just about preventing harm or death; it's about whether we have an obligation to contribute to human well-being if we can. The Dilemma of Choice and Safety So, do we choose human-operated vehicles (or human-led processes) knowing they are less safe or efficient than their AI counterparts? Simply because they are more human? Faced with the choice between human-operated systems and AI-enhanced alternatives, my thought is the decision should obviously hinge on safety and efficiency rather than an allegiance to some murky idea of what is "human" or not. Embracing AI doesn't mean disregarding human value or input; rather, it's about acknowledging that what's human isn't inherently superior — and honestly, is often significantly inferior in specific contexts. Now please don’t get out the pitchforks, I’m not joining Team Robot Overlord. I get the anxiety a lot of people feel about the disruption AI is already causing to their jobs and the societal change that is undoubtedly headed our way. I just wonder whether the efficiencies of AI and the quality of life benefits might, in the long run, outweigh the impact of those disruptions. Some of our reluctance to adopt AI is informed by cognitive biases and fears. For a species famed for its adaptability, we humans don’t love change. Cognitive biases play a substantial role in our hesitance to embrace AI. Cognitive biases are psychological patterns that are a holdover from our early years as Homo Sapiens. They are the habits our minds fall into — cognitive shortcuts that can be useful when running from predators but definitely skew our modern perception and judgment. In this case, recognizing and addressing these biases is crucial in moving toward a more rational, ethical approach to AI adoption. Here are a few that I think could be in play, influencing our suspicion, trust, or acceptance of AI technologies. People tend to attribute human characteristics to AI or robots, influencing their trust and expectations. This can lead to unrealistic assumptions about AI systems' capabilities or the attribution of evil intentions. Anthropomorphism Bias: This bias leads individuals to overestimate the probability of events associated with memorable or vivid incidents. Sensationalized media reports about AI failures or successes can be blown out of proportion and disproportionately influence perceptions of AI reliability and safety. Availability Heuristic: What’s everyone else doing? People may seek out or interpret information in a way that confirms their preexisting beliefs or hypotheses about AI. This bias can hinder the objective evaluation of AI technologies and their potential benefits or risks. Confirmation Bias: People don’t want to miss out on beneficial technologies but can lack an understanding of the implications. This bias can overshadow critical evaluation and lead to premature adoption. This bias is related to the Bandwagon Effect (e.g., The tendency to do or believe things because everyone else does - or influencers do. People might trust or distrust AI technologies simply because that seems to be the popular sentiment. FOMO: People prefer to maintain the current state, which leads to resistance against adopting something new like AI, regardless of potential benefits or proven superiority. This bias can slow innovation and the adoption of potentially life-enhancing technologies. Status Quo Bias: This bias makes the pain of losing something feel more potent than the pleasure of gaining something of equal value. For AI, this means the fear of job loss or loss of control overshadows the benefits of safety/efficiency/convenience. Loss Aversion: Overestimation of one's ability to control or understand something. For AI, this means overestimating or underestimating the risks associated with AI. Overconfidence Bias: Numbers are scary! People tend to be biased against algorithms, believing human decision-making is superior, even when evidence suggests otherwise. On the other hand, some might also have an unquestioning trust in AI decisions, ignoring the potential for errors or biases in AI systems. Algorithm Aversion/Trust: Economic Rationality Interesting, right? But the truth is, this is all academic. We may not even get to make this decision in the end.  Companies are already making it. A ton of corporations are barreling ahead with AI integration — mainly because the ROI often speaks louder than ethical debates. Take with its significant shift towards automation. The efficiency and economic benefits are tangible and measurable, and in the face of cold hard cash, moral and social criticisms suddenly feel more academic. Amazon as a prime example, Still, this isn't only about stone-hearted capitalism; it's about survival and adaptation. Businesses are tasked every day with the challenge of balancing technological adoption with ethical and ESG responsibilities. The impact of AI on employment and human well-being can't be an afterthought. For thousands, financial stability and career wellness hinge on these decisions. It’s something a lot of enterprises are grappling with. And here's where the moral imperative question becomes more nuanced. If AI can streamline operations, reduce costs, and even create new opportunities, then aren’t we also morally responsible for exploring these technologies? The trick will be to keep that ethical compass handy and ensure that as we embrace AI's efficiencies, we also safeguard against its potential to unfairly disrupt livelihoods. We are in a Transition Period Either way, we need to watch our footing. We are standing on the precipice of a new era, and one solid push could end us in a freefall. AI is no longer a futuristic fantasy; it's absolutely embedded in our daily lives and work. That’s exciting — and scary as hell. One of the most significant challenges we face is the accessibility or tech gap. AI has the potential to democratize technology, making powerful tools available to a broader audience. However, right now, AI's promise is primarily being seen by those who already have a certain level of access, so there is also the potential that AI will exacerbate existing inequalities rather than alleviate them. It’s a period of adjustment, so it will require patience, education, and proactive measures to ensure that the benefits of AI are widely distributed. We’ve got the potential to level the playing field so that AI's potential can be unlocked for everyone, not just the privileged few. The Conundrum of Cooperation Okay, so it is a paradox:  For AI to function optimally alongside humans, it must be superior to us in certain tasks.  But that VERY superiority threatens to displace human roles, fueling resistance and fear among us mortals. This paradox creates a tough "push-pull" for AI; that’s why we’re seeing such heated debate about morality. I believe the solution may be a suite of emerging design philosophies and technologies aimed at bridging the gap between AI and human cooperation in an ethical way. I’ll list them below. They are worth asking ChatGPT about: ensures that AI systems are developed with human needs and values at their core. Human-Centric AI Design (HCAI): demystifies AI decisions, making them understandable and transparent to humans. Explainable AI (XAI): guide the development and deployment of AI systems in a manner that respects human rights and values. Ethical AI Frameworks: learns from and adapts to human feedback, ensuring a synergistic relationship. Adaptive/Responsive AI: involves end-users in the AI development process, ensuring their needs and concerns are addressed. Participatory Design: emphasizes AI's role in enhancing human abilities rather than replacing them. Augmented Intelligence: builds confidence in AI systems through reliability, safety, and ethical assurances. Trustworthy AI: To Self-Drive Cars, or to Not Self-Drive Cars? In wrapping up, I’ll take a stand. I think adopting AI is a moral imperative. In my view, the potential to save lives, enhance our quality of life, and even address long-standing inequalities is too significant to ignore. However, this doesn't mean we should dive headfirst without consideration.  In my opinion, we need to approach AI with a blend of enthusiasm and caution—staying excited to explore its possibilities but mindful of the ethical, social, and economic impact. Thoughtful consideration, robust ethical frameworks, and stringent governance are the keys to unlocking AI's potential responsibly. I’m still open to debate on the subject. So, I’ll throw the question out to you. Reply here or on my and tell me why I’m wrong — or right. I invite your thoughts and comments on this complex issue. LinkedIn thread Are we ready to embrace AI with the moral seriousness it demands? Are you ready to take your next road trip in a self-driving car?

Are We Morally Obligated to Adopt AI?

আমরা কি নৈতিকভাবে এআই গ্রহণ করতে বাধ্য?

Sind wir moralisch verpflichtet, KI einzuführen?

¿Estamos moralmente obligados a adoptar la IA?

Sommes-nous moralement obligés d’adopter l’IA ?

क्या हम नैतिक रूप से एआई को अपनाने के लिए बाध्य हैं?

私たちには AI を導入する道徳的義務があるのでしょうか?

우리는 AI를 채택해야 할 도덕적 의무가 있습니까?

Somos moralmente obrigados a adotar a IA?

Обязаны ли мы морально принять ИИ?

Ahlaki Olarak Yapay Zekayı Benimsetmek Zorunda mıyız?

Chúng ta có nghĩa vụ về mặt đạo đức phải áp dụng AI?

我们在道义上有义务采用人工智能吗？

Innovations in artificial intelligence (AI) have revolutionized the field of medicine in numerous ways. AI technologies are emerging as a powerful tool to transform various aspects of healthcare, such as diagnosis, research, patient care, treatment, and clinical outcomes. Advanced AI models can be used to analyze complex medical data and generate insights based on patterns in training data, accelerating the decision-making process with accuracy. However, akin to any other innovative technology, it has its share of risks and challenges. As per an IBM study, the global average cost resulting from a data breach was $4.45 million in 2023. The study estimated the average cost of a healthcare data breach was the highest among industries, at $10.93 million. Compliance with the Health Insurance Portability and Accountability Act (HIPAA) is a critical consideration when implementing AI into healthcare services. Here, we will discuss some of the privacy and security risks posed by AI systems in healthcare, along with possible mitigation strategies for responsible and ethical implementation under HIPAA regulations. What Is HIPAA? HIPAA, or the Health Insurance Portability and Accountability Act, is a critical federal health privacy law in the US that sets the standard for protecting the health information of patients. The law focuses on the Privacy Rule that administers the protection of individually identifiable health information, referred to as PHI, generated by healthcare providers, insurance companies, and other entities. Hence, entities that generate health information digitally must comply with HIPAA rules. Additionally, business partners performing PHI-related activities on behalf of a covered entity, such as administrative, financial, management, and legal tasks, are also subject to HIPAA regulations. However, de-identified health information that can’t be used to identify an individual is not individually identifiable health information, and there is no restriction on the use or disclosure of such data under HIPAA. Entities Covered by HIPAA HIPAA compliance applies only to certain types of organizations that generate protected health information (PHI) electronically or entities that perform tasks involving PHI on their behalf. To understand the impact of HIPAA on the use of AI, it is essential to recognize HIPAA-covered entities. Whether HIPAA would be applied to an entity is not determined based on someone's professional role but rather on activities performed by them. When healthcare providers, insurance companies, and other organizations carry out transactions that require HIPAA compliance, they are considered covered entities under HIPAA regulations. When incorporating AI in their healthcare operations, these entities, alongside their business associates, must follow HIPAA rules. Transactions that involve only direct payments and don’t include handling electronic health records, like billing insurance, would not be subject to HIPAA regulations. HIPAA and AI in Healthcare: Ensuring Patient Privacy The HIPAA Privacy Rule ensures the protection of patient information, especially when AI technology is integrated into healthcare services, by governing the use and disclosure of protected health information (PHI). There are specific situations where disclosing health information is not prohibited under HIPAA, such as for payment, treatment, healthcare operations, and certain research conditions. When incorporating AI in healthcare research or operations, responsible handling of PHI becomes critical. The process involves de-identifying patient data and receiving patient consent for the use of data, strengthening privacy and trust. However, the law incorporates the concept of a ‘limited data set’, allowing access to certain non-direct identifiers, such as ZIP codes or service dates, for research purposes, provided the data agreement adheres to HIPAA compliance. Use of direct identifiers like names or social security numbers is strictly prohibited. The HIPAA safe harbor method outlines 18 specific identifiers, such as name, address, social security numbers, biometric identifiers, and many more, that must be removed from data. After de-identification, the remaining information should not be capable of re-identifying the individuals, even in combination with other data. The de-identification process needs to be robust enough, especially when the data is used for AI purposes, as AI algorithms can potentially analyze data and discover hidden patterns for re-identification. Staying HIPAA Compliant The rapid advancements in artificial intelligence within the healthcare industry present new challenges to stay compliant with HIPAA regulations. Healthcare organizations need to adapt to the changes by working closely with AI developers to understand the algorithms of AI applications and ensure they meet HIPAA privacy standards. This necessitates regular updates to policies and procedures along with the implementation of robust security measures to effectively ensure compliance and manage potential threats. Additionally, educating healthcare professionals about the implications of AI for data privacy is critically important. This empowers them to use AI tools responsibly and obtain consent for data use. Conclusions Implementing AI in healthcare offers immense potential to transform various aspects, from diagnosis and treatment planning to patient care and research, benefiting both patients and healthcare providers. However, integrating data-intensive AI tools poses serious privacy and security challenges. To proactively address these challenges, healthcare organizations must implement safe and effective data protection measures that adhere to HIPAA compliance. Healthcare organizations need to make efforts to keep abreast of the latest developments in AI and their potential privacy implications. Essential strategies such as risk assessment protocols, robust governance frameworks, and compliance monitoring can help prevent breaches of PHI, mitigate risks, and maintain public trust. Innovations in artificial intelligence (AI) have revolutionized the field of medicine in numerous ways. AI technologies are emerging as a powerful tool to transform various aspects of healthcare, such as diagnosis, research, patient care, treatment, and clinical outcomes. Advanced AI models can be used to analyze complex medical data and generate insights based on patterns in training data, accelerating the decision-making process with accuracy. However, akin to any other innovative technology, it has its share of risks and challenges. As per an IBM study , the global average cost resulting from a data breach was $4.45 million in 2023. The study estimated the average cost of a healthcare data breach was the highest among industries, at $10.93 million. IBM study Compliance with the Health Insurance Portability and Accountability Act (HIPAA) is a critical consideration when implementing AI into healthcare services. Here, we will discuss some of the privacy and security risks posed by AI systems in healthcare, along with possible mitigation strategies for responsible and ethical implementation under HIPAA regulations. What Is HIPAA? HIPAA, or the Health Insurance Portability and Accountability Act, is a critical federal health privacy law in the US that sets the standard for protecting the health information of patients. The law focuses on the Privacy Rule that administers the protection of individually identifiable health information, referred to as PHI, generated by healthcare providers, insurance companies, and other entities. Hence, entities that generate health information digitally must comply with HIPAA rules. Additionally, business partners performing PHI-related activities on behalf of a covered entity, such as administrative, financial, management, and legal tasks, are also subject to HIPAA regulations. HIPAA However, de-identified health information that can’t be used to identify an individual is not individually identifiable health information, and there is no restriction on the use or disclosure of such data under HIPAA. Entities Covered by HIPAA HIPAA compliance applies only to certain types of organizations that generate protected health information (PHI) electronically or entities that perform tasks involving PHI on their behalf. To understand the impact of HIPAA on the use of AI, it is essential to recognize HIPAA-covered entities. Whether HIPAA would be applied to an entity is not determined based on someone's professional role but rather on activities performed by them. When healthcare providers, insurance companies, and other organizations carry out transactions that require HIPAA compliance, they are considered covered entities under HIPAA regulations. When incorporating AI in their healthcare operations, these entities, alongside their business associates, must follow HIPAA rules. Transactions that involve only direct payments and don’t include handling electronic health records, like billing insurance, would not be subject to HIPAA regulations. HIPAA and AI in Healthcare: Ensuring Patient Privacy The HIPAA Privacy Rule ensures the protection of patient information, especially when AI technology is integrated into healthcare services, by governing the use and disclosure of protected health information (PHI). There are specific situations where disclosing health information is not prohibited under HIPAA, such as for payment, treatment, healthcare operations, and certain research conditions. When incorporating AI in healthcare research or operations , responsible handling of PHI becomes critical. The process involves de-identifying patient data and receiving patient consent for the use of data, strengthening privacy and trust. However, the law incorporates the concept of a ‘limited data set’, allowing access to certain non-direct identifiers, such as ZIP codes or service dates, for research purposes, provided the data agreement adheres to HIPAA compliance. Use of direct identifiers like names or social security numbers is strictly prohibited. AI in healthcare research or operations The HIPAA safe harbor method outlines 18 specific identifiers, such as name, address, social security numbers, biometric identifiers, and many more, that must be removed from data. After de-identification, the remaining information should not be capable of re-identifying the individuals, even in combination with other data. The de-identification process needs to be robust enough, especially when the data is used for AI purposes, as AI algorithms can potentially analyze data and discover hidden patterns for re-identification. Staying HIPAA Compliant The rapid advancements in artificial intelligence within the healthcare industry present new challenges to stay compliant with HIPAA regulations. Healthcare organizations need to adapt to the changes by working closely with AI developers to understand the algorithms of AI applications and ensure they meet HIPAA privacy standards. This necessitates regular updates to policies and procedures along with the implementation of robust security measures to effectively ensure compliance and manage potential threats. Additionally, educating healthcare professionals about the implications of AI for data privacy is critically important. This empowers them to use AI tools responsibly and obtain consent for data use. Conclusions Implementing AI in healthcare offers immense potential to transform various aspects, from diagnosis and treatment planning to patient care and research, benefiting both patients and healthcare providers. However, integrating data-intensive AI tools poses serious privacy and security challenges. To proactively address these challenges, healthcare organizations must implement safe and effective data protection measures that adhere to HIPAA compliance. Healthcare organizations need to make efforts to keep abreast of the latest developments in AI and their potential privacy implications. Essential strategies such as risk assessment protocols, robust governance frameworks, and compliance monitoring can help prevent breaches of PHI, mitigate risks, and maintain public trust.

Navigating HIPAA Compliance in the Age of AI: Privacy and Security Considerations in Healthcare

AI Isolation Strategies for Businesses

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