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

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

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 14 of 38. Line of Effort 3: Enhance Security and Resilience of Undersea Cables Undersea cables carry more than 95 percent of the world’s digital traffic. As data continues to proliferate and increase exponentially, so too does demand for cables and other transmission systems. Disruption or destruction of the cables as a result of accidents, natural disasters, or malicious actions could isolate a county, threaten national security, and result in billions of dollars of damage to the economy. Choices made about which vendors to rely on for undersea cable infrastructure, maintenance, and repair operations can either drive development and innovation or lead to new forms of dependency and insecurity. As a result, the Department of State, in coordination with other agencies, will prioritize enhancing the security and resilience of undersea cables. U.S. firms and other trusted suppliers are leading producers of many network components, embedded technologies, and related services for undersea cables, and they are investing in and financing new undersea cables connecting all regions of the world. The U.S. government will continue to support U.S. and other trusted suppliers in the installation, operation, maintenance, and repair of secure infrastructure as well as to promote a regulatory environment that enables continued investments. Since 2021, the Department of State has implemented the CABLES program throughout the East Asia Pacific region, responsibly informing essential telecommunications and cables infrastructure stakeholders of the perils of choosing untrusted suppliers. The United States provided capacity building to support five countries using U.S. technology for the South-East Asia-Middle East-Western Europe 6 cable (SMW6), and separately it provided over $22 million in partnership with Australia and Japan to help fund the East Micronesia Cable being built by a Japanese firm. In October 2023, the United States announced that, working with Congress, it would provide, along with Australia, investments totaling $65 million to fund future undersea cable connectivity for Pacific Island countries in order to facilitate access to global markets and the realization of regional connectivity goals. In support of these policy objectives, the United States will continue engaging with the G7 and other multilateral groups to strengthen trusted, multi-layered global connectivity that provides data route diversity, resiliency, and redundancies. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 14 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 14 of 38. here Line of Effort 3: Enhance Security and Resilience of Undersea Cables Undersea cables carry more than 95 percent of the world’s digital traffic. As data continues to proliferate and increase exponentially, so too does demand for cables and other transmission systems. Disruption or destruction of the cables as a result of accidents, natural disasters, or malicious actions could isolate a county, threaten national security, and result in billions of dollars of damage to the economy. Choices made about which vendors to rely on for undersea cable infrastructure, maintenance, and repair operations can either drive development and innovation or lead to new forms of dependency and insecurity. As a result, the Department of State, in coordination with other agencies, will prioritize enhancing the security and resilience of undersea cables. U.S. firms and other trusted suppliers are leading producers of many network components, embedded technologies, and related services for undersea cables, and they are investing in and financing new undersea cables connecting all regions of the world. The U.S. government will continue to support U.S. and other trusted suppliers in the installation, operation, maintenance, and repair of secure infrastructure as well as to promote a regulatory environment that enables continued investments. Since 2021, the Department of State has implemented the CABLES program throughout the East Asia Pacific region, responsibly informing essential telecommunications and cables infrastructure stakeholders of the perils of choosing untrusted suppliers. The United States provided capacity building to support five countries using U.S. technology for the South-East Asia-Middle East-Western Europe 6 cable (SMW6), and separately it provided over $22 million in partnership with Australia and Japan to help fund the East Micronesia Cable being built by a Japanese firm. In October 2023, the United States announced that, working with Congress, it would provide, along with Australia, investments totaling $65 million to fund future undersea cable connectivity for Pacific Island countries in order to facilitate access to global markets and the realization of regional connectivity goals. In support of these policy objectives, the United States will continue engaging with the G7 and other multilateral groups to strengthen trusted, multi-layered global connectivity that provides data route diversity, resiliency, and redundancies. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

Enhancing the Security and Resilience of Undersea Cables

Strategies for Responsible AI Governance

দায়িত্বশীল এআই গভর্নেন্সের জন্য কৌশল

Strategien für eine verantwortungsvolle KI-Governance

Estrategias para una gobernanza responsable de la IA

Stratégies pour une gouvernance responsable de l’IA

उत्तरदायी एआई शासन के लिए रणनीतियाँ

責任あるAIガバナンスのための戦略

책임 있는 AI 거버넌스를 위한 전략

Estratégias para uma governança responsável da IA

Стратегии ответственного управления ИИ

Sorumlu Yapay Zeka Yönetişimine Yönelik Stratejiler

Chiến lược quản trị AI có trách nhiệm

负责任的人工智能治理策略

Invisible Online: A Family Guide to Private and Secure Online Living

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

Navigating Complex Search Tasks with AI Copilots: Opportunities

How to Run Your Own Local LLM: Updated for 2024 - Version 2

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

Ishan Pandey: Hi Mehdi, I am delighted to welcome you to our "Behind the Startup" series. Can you tell us what inspired you to create Exponential.fi? Thanks for having me, Ishan. The journey began during the 2018 bear market when I started guiding my family and friends into the crypto world. We aimed for a 10% exposure to , , and some Altcoins. By DeFi's summer of 2020, our collective portfolio had grown to eight figures. Mehdi Lebbar: Bitcoin Ethereum As passive investors, we wanted to earn yield on our crypto holdings. BlockFi and Celsius were not options for us because of the lack of transparency on where the yield comes from. We like DeFi’s transparency but investing in DeFi was too complex and inaccessible for most. This realization sparked the idea to build a platform that provides crypto holders with easy access to yield opportunities in DeFi. DeFi is a major leap towards productivity for humans, alongside AI and robotics, but it is the only one that allows most people to participate directly. This is why we care so much about making DeFi accessible. Ishan Pandey: Can you provide more details on how Exponential streamlines DeFi investment for users and expands access to opportunities that were once exclusive to traditional banking institutions? simplifies the DeFi investment process dramatically. Users can easily choose and invest in opportunities with a few clicks. We've implemented a from A to F for each yield opportunity with supporting research, so investors can make informed decisions. We also make moving and holding funds easier without the hassle of navigating through different blockchain networks or managing private keys. Our platform handles trade execution, and keeps client funds safe using advanced technologies and cold storage. Our platform makes DeFi investing as safe and easy as possible. Mehdi Lebbar: Exponential.fi risk rating system Ishan Pandey: Security is paramount in the DeFi space, especially considering the significant sums involved. Can you elaborate on the specific security measures implemented by Exponential.fi to safeguard user funds? is comprehensive —we are a safety-first platform. We use multi-party computation technology, which ensures that no single party can access funds without explicit permission and offers both security and fund recovery options. Additionally, we store assets in cold storage with stringent controls. Transactions are initiated, signed, and monitored across different systems. The initiating server only acts upon user requests, guaranteeing that all transactions performed are user-driven. This architecture is unique and ensures that users maintain full control over their funds at all times. Mehdi Lebbar: Our approach to security Ishan Pandey: How does Exponential navigate regulatory challenges to ensure compliance and foster long-term success for the platform and its users? As a US-based company registered with FinCEN, Exponential.fi proactively embraces compliance for long-term success. Transparency, trust, and adherence to rules are our guiding principles. While a compliant approach may be more challenging, it aligns with our commitment to make DeFi investing accessible to everyone. We carefully select decentralized contracts for assets that are not classified as securities, guided by a thorough legal framework to ensure we steer clear of securities listings. Mehdi Lebbar: Ishan Pandey: Can you discuss any strategies or mechanisms implemented by Exponential.fi to mitigate risks associated with smart contract vulnerabilities and potential exploits in the DeFi ecosystem? At Exponential.fi, we prioritize direct access to DeFi protocols. We do not take a cut from the yield to ensure our incentives remain aligned with our users' best interests. We've curated the top 5% of yield opportunities from extensive research on over 800 investments. I previously launched UberEats in London, and our philosophy was that whatever you ordered on the platform should be amazing and meet your specific needs. We are taking the same approach with Exponential. Mehdi Lebbar: Ishan Pandey: How do you ensure that users understand the risks associated with DeFi investments while still encouraging participation in yield-generation opportunities? We've developed an institutional-grade DeFi risk rating system, the most comprehensive of its kind, allowing users to gauge the risk level of each investment. Our platform offers detailed analyses of pools, protocols, assets, and chains, including an evaluation of interdependency risks within the DeFi ecosystem. We educate users through our reports available on our site, and also started a podcast named "Degen Responsibly," where we discuss risk with protocol builders in the public arena. This is very much in line with the DeFi principles of transparency and openness. Mehdi Lebbar: Ishan Pandey: Looking towards the future, how do you envision the role of cryptocurrency investing and DeFi in the broader financial ecosystem? Crypto has emerged as a new asset class, gaining recognition alongside stocks, bonds, commodities, and derivatives. Similarly, DeFi represents a new asset class, offering yield on top of crypto exposure. At Exponential.fi, we're laying the groundwork to make DeFi accessible to all and enable investors to discover, assess and invest in these new financial instruments — safely and easily. Mehdi Lebbar: Don’t forget to like and share the story!

Exponential.fi: Revolutionizing DeFi Accessibility for Crypto Investors

The launch of generative artificial intelligence (genAI) tools such as ChatGPT and DALL-E, making businesses explore how this technology can spark creativity and drive innovation. In fact, a 2021 survey by found that 86% of executives believe AI will become a mainstream technology in their company within the next 3-5 years. PwC But what exactly is Generative AI, and how can it help in the creative field? Generative AI isn't just about automating tasks, it inspires creativity and has practical applications in fields like drug discovery, virtual reality, and content creation. These AI systems can use deep learning algorithms to create entirely new content, from images and videos to music and text, that closely resembles human work. In this post, we'll explore how Generative AI is changing creativity and problem-solving in the business world. Generative AI as a creative assistant While automation is a one of the aspects of Generative AI, it is also used as a creative assistant; let's explore how it is changing creative processes across various fields. Brainstorm new product ideas Brainstorming sessions often need help to overcome creative blocks and create diverse ideas. Companies can use Generative AI tools like MarcomAI. By using vast datasets, these tools can create surreal product ideas based on keywords provided by the user. For example, PepsiCo uses Generative AI to create new flavor combinations. The AI analyzes consumer preferences, flavor profiles, and market trends to suggest novel and potentially delicious options humans might not have considered. Product and graphic design Generative AI is also changing the way designers explore concepts. Tools like AutoDesk Generative Design allow designers to input parameters and receive multiple design variations based on those constraints. It helps them to explore possibilities they might not have considered before for more innovative and efficient product development. Generates personalized marketing copy For a successful marketing campaign, writing compelling copy that resonates with different audiences can be a time-consuming task. Generative AI tools like Copy.ai analyze target audiences and product details to craft personalized marketing copy for different platforms. It allows marketers to tailor their messages for maximum impact without sacrificing creativity. Similarly, creating catchy headlines is also important for creating content assets. AI tools like Jasper can analyze your content and target audience to suggest attention-grabbing headlines.Thus, marketers can focus on refining the overall strategy while the AI does the creative heavy lifting. Compose music Generative AI is also making waves in the music industry. Amper Music can analyze a musician's style and generate melodic snippets to jumpstart the creative process. These AI-generated parts inspire musicians to create unique masterpieces. Moreover, platforms like LANDR analyze and build upon existing music to create new song sections or even full instrumental accompaniments. It allows musicians to explore new sonic parts and make music that might not have been possible alone. Popular GenAI platforms Generative AI tools like GPT-4, DALL-E 3, Sora, Apple's Realm, Google Gemini, and Anthropic's Claude are changing the creative landscape. Let's see how these platforms are being applied across various industries. by OpenAI, is used for many applications, from generating creative writing prompts to code development. For example, Duolingo, the popular language learning app, has integrated GPT-4 to create more personalized user learning experiences. GPT-4 understands significantly more nuance and detail, allowing you to translate your ideas into exceptionally accurate images easily. DALL·E 3 is built natively on ChatGPT, which lets you use ChatGPT as a brainstorming partner and refiner of your prompts. Just ask ChatGPT what you want to see in anything from a simple sentence to a detailed paragraph. DALL·E 3 Developed by OpenAI, Sora focuses on creating high-quality videos. Unlike prior models, which are limited by video size and resolution, Sora can generate impressive visuals in various formats. Google's Gemini excels at responding to your questions and requests in a comprehensive way, potentially serving as an effective research and writing assistant. Given Google's focus on global accessibility, Gemini can provide information and communication across multiple languages. Anthropic Claude exhibits near-human levels of fluency in complex tasks. It has increased capabilities in analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages like Spanish, Japanese, and French. These examples show how GenAI is becoming an invaluable assistant in the creative process. As AI continues to evolve, it's important to remember that it is not meant to replace human creativity but to augment it. By taking care of repetitive tasks, AI frees humans to focus on higher-level strategic thinking and emotional connection in the creative process. Building a Responsible AI Future: Challenges and Opportunities As we explore the incredible applications of Generative AI, it's also vital to address its challenges. Data privacy, bias, and the need for skilled professionals are key issues that require our attention. Data privacy Generative AI models are trained on vast amounts of data. To protect personal information, we need robust data governance frameworks and strict adherence to privacy regulations like GDPR and CCPA. The Partnership on AI (PAI) is a collaborative initiative that brings together diverse voices from the AI community, including leading technology companies, academic institutions, and civil society organizations. Their mission is to address important questions about our future with AI and to advance responsible AI practices. In January 2024, released a comprehensive report titled "AI Rules of the Road". This report provides a holistic approach to safety that includes bias, overreliance on AI systems, privacy, and worker treatment. It aligns with their ongoing efforts to establish best practices for responsible data collection and usage in AI development. PAI Bias in AI systems If the data used to train Generative AI models is biased, the outputs generated by these models can perpetuate and even amplify those biases. Ensuring diversity and inclusivity in the data sets used for training AI models is essential to mitigate this risk. Organizations like OpenAI have been working on techniques like "constrained language models" to reduce bias in generated content. Additionally, regularly auditing AI systems for potential biases and making necessary adjustments is necessary to build trust. The shortage of skilled professionals As the demand for AI talent continues to grow, there is a widening gap between the supply and demand of skilled professionals. To bridge this gap, we need to invest in education and training programs that equip individuals with the necessary skills to work with Generative AI technologies. Initiatives like the AI4ALL program, which focuses on increasing diversity and inclusion in AI education, are making great strides in this direction. Focusing on ethical AI development practices is another aspect of building a responsible AI future. It involves integrating ethical considerations into each stage of AI development, from data collection and model training to deployment and monitoring. The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems has developed a comprehensive framework called "Ethically Aligned Design" to guide the responsible development of AI systems. To implement these principles, organizations can establish internal ethics committees to oversee the development and deployment of AI systems. These committees can ensure that AI projects align with the organization's values and adhere to ethical guidelines. Regular audits and impact assessments can also identify and mitigate potential risks associated with AI systems. Wrapping up Generative AI is pushing human creativity and innovation like never before. We can accomplish many tasks in less time and create imaginative products with these tools and technology. However, we must also address AI's challenges. We can ensure AI systems are developed and deployed responsibly by prioritizing data privacy, mitigating bias, and nurturing a skilled workforce. Our best chance of building AI systems that support our values and benefit humanity is through collaboration and transparency. By working with researchers, developers, and policymakers, we can create a future where Generative AI is used for good. So, let's experiment with Generative AI tools, learn about the latest advancements, and contribute a unique perspective to AI adoption.

Generative AI: The Future of Creativity and Problem-Solving is Here

Artificial intelligence is exciting; the fresh technological breakthrough that took over our lives in less than a year hasn't stopped since. Personally, I have some issues with AI tools and how they are being used at the moment. With that, I cannot deny that they are a mighty instrument when given to the right person to do the right job - but as we all know, with great power comes great responsibility. Amidst the excitement and semi-fulfilled promises - did we ever stop to think about the cost of running 1,2,3 billion parameter models for hours on end, scaled across multiple instances, for millions of users, all at once? I was always an advocate of the "minimal web" concept (I will write an article about the concept in the future; there is a lot to say). We live in an era where on one side, everyone seems extremely concerned about the health of the planet (and for good reason). Yet on the other hand, no one is stopping to think how much the internet in its current state is costing us in terms of raw energy. Is it efficient? Is it green? Far from it. Traffic and Energy A while ago, I wrote a tiny TUI tool using Perl ( ), that monitors my internet traffic and aggregates the byte-length of packets that are sent and received while the tool is running. webmeter Just by opening Reddit's front page, my tool logged a whopping 5.85 megabytes of data transferred between my interface and Reddit's servers. Reddit as a website is a combination of text, low-to-mid-quality images, and the occasional video. It seems extremely unreasonable that just by opening Reddit's front page I transferred so much data across the planet (auto-playing videos were off during testing, this result excludes video content). Reddit's front page, a page that could have concluded with 10 kilobytes of content, javascript, and CSS (perhaps a few more kilobytes for additional images), needed 300 times the bandwidth just to load up. In comparison (considering the moon landing did not take place in a Hollywood basement), the software written for the Apollo 11 mission was only 72 kilobytes in size. Just for the kick of it, imagine billions of people, opening Reddit's front page, all together, at every moment, all the time. Try and think: what is the direct cost of transferring the data? What is the secondary cost? How much energy are we wasting as internet-consuming beings? This concept has been dwelling in my mind for the past year or so which was the main reason I hacked together my Perl tool in the first place. One "minimal web" thought led to another, and I realized that we are so consumed by the hype (or hate) that we are ignoring the cost of our artificial intelligence endeavors. How Can We Calculate the Energy Cost of AI As a Service (or Stable Diffusion for Example)? Accurately? We can't. Not without knowing how these services operate: which CPUs or GPUs (or dedicated chips) they are using. How many instances are they running, what is their load, what is the exact power consumption of the critical hardware, etc? But we can come to a pretty realistic (although not as accurate) conclusion if we put our minds to it. I chose for this example which is the most straightforward (and I believe popular) stable diffusion GUI out there. Let's start simple: Fooocus Using Fooocus, it took my old-timer GPU (a Quadro P2000) about 10 minutes to generate an image. According to the performance table on Fooocus's repository, we know that using an Nvidia RTX 4XXX GPU would give us the fastest result. My Quadro is somewhat comparable to an Nvidia GTX 1060, therefore, we know that using an Nvidia GTX 1060 GPU, we can generate an image in approximately 10 minutes. After some searching online, I concluded that the most commonly used (enterprise) GPU for AI/ML is the Nvidia A100 (up to 400W under load). The Nvidia A100 is a massively more powerful GPU than the Nvidia GTX 1060; it is also built specifically for AI and ML. Therefore, let's assume that generating an image using an Nvidia A100 will take 5 seconds (if anyone reading this has accurate data about the A100 and its performance, please share). Using the formula E = P * T, an Nvidia A100 GPU power consumption under load for 5 seconds costs around 0.5 watt-hours. Adding on to this example: Midjourney (or other AI image generation services) generates 4 images per prompt. Let's assume the service is using an array of Nvidia A100s to generate the images which means that the service is probably wasting around 2 watt-hours of energy for each prompt, for each user. As of November 2023, Midjourney has 2.5 million DAUs. 2Wh x 2,500,000 Users x 24 Hours = 120,000,000Wh (per day, considering every DAU is executing a prompt per hour, in reality, it is much more than that).
120,000,000Wh = 120,000MWh 120,000 Mega watt hours could potentially power over 100,000 households for a whole month. Modestly, services such as Midjourney, Dall-E, or even self-hosted solutions such as Fooocus, waste at the very least 50 million watt-hours per day, each (considering their GPUs are state-of-the-art and each DAU uses the service for a few hours every day). This amount of energy could potentially power over 100,000 average-sized households for a month, per day. Keep in mind that we only talked about image generation services; let's not forget about all other services, such as ChatGPT, Gemini, Bard, and all of their versions and flavors. The list, and waste, goes on. What Should We Do? How should we, as developers, come together to address this emerging issue? How can we take measures to educate others to use energy responsibly? I would love to hear your thoughts about this topic and its possible solutions. Also published here

The Energy-Inefficient AI Era Is Already Here: The Cost of AI

শক্তি-অদক্ষ এআই যুগ ইতিমধ্যেই এখানে: এআই-এর খরচ

Das Zeitalter der energieineffizienten KI ist bereits angebrochen: Die Kosten der KI

La era de la IA energéticamente ineficiente ya está aquí: el coste de la IA

L’ère de l’IA économe en énergie est déjà là : le coût de l’IA

ऊर्जा-अकुशल एआई युग पहले ही आ चुका है: एआई की लागत

エネルギー効率の悪い AI 時代はすでに到来: AI のコスト

에너지 비효율적인 AI 시대는 이미 도래했습니다: AI의 비용

A era da IA com ineficiência energética já chegou: o custo da IA

Эра энергоэффективного искусственного интеллекта уже наступила: стоимость искусственного интеллекта

Enerji Açısından Verimsiz Yapay Zeka Çağı Zaten Geldi: Yapay Zekanın Maliyeti

Kỷ nguyên AI sử dụng năng lượng kém đã đến: Chi phí của AI

能源效率低下的人工智能时代已经到来：人工智能的成本

Linux is one of the most popular operating systems (technically, Linux is just the Kernel) and has many types (distribution/distro). It may be unclear to choose which one to use, from beginner-friendly distro to those used for special needs. Linux users who use Linux as a daily driver usually have tried a lot of distros before sticking to one. Distro Hoppers Distro Hoppers is a term for users who often change distros regularly. The reason can vary: try new challenges, different features, and different purposes of the distro. Distro hopping is sometimes considered bad because it requires much time and offers little benefit. This article will discuss tools and advice for Distro Hoppers. Virtual Machine: Virtualbox/KVM The virtual Machine application is useful for trying the Linux distro before you even consider installing it or burning it on your flash disk. It is important to have a taste before installing it so you're not wasting so much time if you don't like the distro in the first place. Backups Backup is important to prevent data loss after installing a new Linux distribution. There are several ways. Using backup tools, e.g., , , etc. rsync Timeshift Separate partition for data, or separate partition for directory. With this approach, you must mount that partition after installing the new distribution. /home Save all data to the cloud. Several are out there, e.g., pCloud and MEGA. Just remember only to use a provider that has a Linux Client app. providers I recommend the last approach because saving important data on a local computer is not good practice. USB Bootable: Ventoy is an open-source tool to create a bootable USB drive for ISO/WIM/IMG/VHD(x)/EFI files. Using Ventoy, we can create live USB for multiple distros in one flash disk. This is a revolutionary feature compared to other tools, e.g., Etcher, Rufus, etc., that can only create one live USB at a time. This tool is so important. Don't call yourself a distro hopper if you don't know this tool. Ventoy Package Manager: Flatpak Hopping from one distro to another with a different package manager might require some time to adapt. Using a package manager that can be installed on most distro is one way to help you get to work faster. is one of them; other alternatives are , , or . Flatpak is a good starter, and if you have a bunch of free time, I suggest trying Nix. Flatpak Snap Nix Homebrew Development: Docker With DevContainer If you are using Linux, I think it's safe to assume that your job is somewhat related to programming. Setting up a development environment for a Software Engineer can be a hurdle. The development environment might require installing many tools, e.g., programming language, database, monitoring tools, etc. Docker with DevContainer is one of the fastest solutions to create a development environment faster, even after hopping from several distros. is a containerization tool that ships your app with all the necessary dependencies. Using makes developing software inside a container possible. Several editors, e.g., Visual Studio Code and JetBrains, already support DevContainer. If you use other editors like VIM or Helix, you might develop software directly inside the container or need to find another workaround. But if you are too lazy to configure VIM like me, use VSCode. Docker DevContainer Honorable Mentions Distrobox is a tool that enables us to try Linux distro CLI, including their package manager. This requires a containerization tool (e.g., Docker). In Windows, this can be achieved using WSL (Windows Subsystem for Linux) Distrobox EasyBCD is a Windows app that can modify the Bootloader if you dual-boot with Windows. I use this tool to remove Linux Grub when distro hopping. EasyBCD Conclusion Distro Hopping is not always bad, especially if you want to learn something new. Do this responsibly so it doesn't bother your work or your time with family. Utilize the tools above to minimize the time wasted, or even better if there is a separate computer just for that. OK, that's all for now. I hope you gain some insight and become better Distro Hoppers. Thank you. Also published here

Tools for Linux Distro Hoppers: A Beginner-Friendly Guide

Bitcoin Ordinals Explained: A Comprehensive Guide

Crypto Update 04/01 TLDR Market's big! Total market cap at a whopping $2.6 trillion, but remember, crypto is a rollercoaster. Fear & Greed Index at 82/100. Bitcoin's on an upward swing, but don't get swept up in the hype. Research first! Trading volume is high across the board - be cautious; it could be FOMO. Positive news today isn't a guarantee for tomorrow. Crypto can be exciting but risky - DYOR (do your own research)! Buckle up, crypto enthusiasts, because Ether's on a tear. We've witnessed a 3.5% surge over the weekend, pushing the price past $3,630 – a sweet 18.75% jump from its recent low just over a week ago. But what's the secret sauce behind this sudden price action? Let's peel back the layers and see if we can find some rational reasons amidst the potential frenzy. First, there's a clear shift in investor sentiment. Money's flowing into the Ether market, flexing its muscles against both the U.S. dollar and Bitcoin. The ETH/BTC ratio is up 2.5%, hinting at a possible short-term rotation of investment strategies. Ether's even stealing the spotlight from its altcoin brethren, with its dominance index rising a cool 2.16% in the past 48 hours. It's like everyone's RSVP'd to the Ether party, leaving the other cryptos with a case of FOMO (Fear Of Missing Out). The Great Capital Churn: Speaking of FOMO, the crypto whales – those deep-pocketed investors – seem to be feeling it too. Data suggests these financial Goliaths have been accumulating ETH like crazy, with their reserves increasing by 1.15% in March. When the whales start piling in, it often precedes a price surge. After all, nobody likes to miss out on a potential windfall, right? This whale activity adds fuel to the fire, potentially igniting a self-fulfilling prophecy as others jump in to avoid missing the boat. Whales on the Prowl: The FOMO isn't just a whale thing. Regular traders are getting in on the action, too. Funding rates for Ether's perpetual contracts are spiking. In simpler terms, it's costing more to hold onto long positions (essentially betting the price will go up). But hey, if you truly believe the price will keep climbing, that extra cost might be a small price to pay. This trend, combined with a steady open interest of around $14 billion, suggests traders are borrowing more to buy, essentially doubling down on their bullish bets. It's a classic case of market sentiment feeding on itself, potentially creating a positive feedback loop. Traders Feeling the Fever: Now, let's not forget the world of technical analysis – the land of charts and squiggly lines that some swear by. Ether's recent price action has seen it rebound after testing a key support level. And some technical analysts are poring over fancy wedge patterns, predicting a price target of either $3,280 or a glorious breakout beyond $4,000 by April's end. Remember, folks, these are just predictions, not magic spells. Technical analysis can be a helpful tool, but it's important to remember that the market is a complex beast, and past performance is no guarantee of future results. A Technical Tinkerbell: The Final Word: Tread Carefully So, what can we take away from all this? Ether's on a roll, fueled by a mix of capital movement, whale activity, bullish traders, and a sprinkle of technical optimism. But the crypto market, as always, is a wild west. It's a land of opportunity but also a land fraught with risk. Do your own research, don't let the fear of missing out cloud your judgment, and tread carefully, my friends. Remember, even the most well-informed predictions can be wrong, and even the most euphoric markets can experience corrections. Invest responsibly, and never bet more than you can afford to lose.

Crypto Market Update: Ether's Weekend Escapade

As an AI expert, let me be blunt - cyber threats are a massive, unrelenting risk that no business with an online presence can afford to ignore these days. Whether you're running an e-commerce site, logistics operation, or tech firm, phishing, ransomware attacks, or data breaches - these scourges can cripple a company, leading to devastating financial losses, irreparable brand damage, and even regulatory penalties that could bury you. I've personally seen the aftermath of cyber strikes on SMBs with limited security resources and tight budgets. In those cases, traditional cybersecurity just doesn't cut it against today's attack of threats. But that's exactly where artificial intelligence (AI) emerges as a total game-changer. discovered that organizations leveraging AI and automation extensively saved a mind-blowing $1.76 million on average compared to their less AI-savvy counterparts. IBM's 2023 Cost of Data Breach Report And I can testify that AI-powered cyber defense solutions truly earn those savings. By unleashing cutting-edge AI techniques like machine learning, deep learning models, and natural language processing, modern AI cybersecurity acts like an indispensable digital force multiplier reinforcing our defenses against even the wiliest adversaries. As an AI expert, I've deployed these capabilities for clients - it's like having an entire squad of cybersecurity sentries patrolling the digital premises 24/7 to keep the bad actors at bay. Moving forward, I don't believe adopting advanced AI will be a luxury for organizations - it's a flat-out necessity we can't ignore. Whether you're a small business or a giant enterprise, you'd better get proactive and start leveraging AI's offensive cybersecurity potential to safeguard your vital digital assets and ensure continuous operations. In my opinion, AI is quickly separating the cybersecurity haves from the have-nots. AI's Cyber Offense: How It Turbocharges Threat Detection I can't overstate how AI is revolutionizing cybersecurity by unleashing cutting-edge threat detection and response capabilities never before possible. Just look at the rapidly growing market - projects that AI in the cybersecurity sector will explode from $22.4 billion in 2023 to $60.6 billion by 2028. And there are good reasons behind that meteoric growth. MarketsandMarkets Machine learning algorithms excel at quickly identifying patterns and anomalies within massive datasets - making them invaluable assets for recognizing potential cyber threats amid tons of network traffic, user activity logs, and other data. By training these algorithms on historical data, we can have them recognize known attack signatures and deviations signaling new threats before they can wreak havoc. One study I find particularly compelling is from MDPI, showing machine learning models achieved up to 99% malware detection accuracy using techniques like Decision Trees, Convolutional Neural Networks, and Support Vector Machines. That's the level of precision AI can deliver against emerging threats. There are deep learning models that I've deployed to rapidly analyze and classify malware samples with incredible accuracy. The latest deep-learning architectures can see patterns invisible to the naked eye. However, some of the most exciting AI cybersecurity applications my team has tested involve reinforcement learning for adaptive threat response. These self-learning systems can dynamically adjust defensive actions on the fly based on their experiences against evolving attack vectors. Allied Market Research predicts reinforcement learning will see explosive 41.5% annual growth from 2023 to 2032, with cybersecurity as a prime use case. Let's not overlook natural language processing (NLP) for automating incident analysis. I've personally overseen NLP deployments that can rapidly comprehend human language data from reports, intelligence feeds, and more - streamlining our incident response workflows and prioritizing genuine threats over false positives. Combining all these AI disciplines is delivering incredible offensive capabilities to counter even the trickiest cyber adversaries. As an expert and founder in this space, I truly believe AI's ability to autonomously detect and shut down threats before impact will utterly transform cybersecurity over the coming years. Why UEBA and User Behavior Monitoring Matter (And How AI Excels) The cyber risk from malicious insiders or compromised user accounts is one we can't overlook either. That's why I consider user and entity behavior analytics (UEBA) powered by advanced AI/ML models to be paramount for comprehensive, 360-degree security visibility. At a basic level, these AI-driven UEBA solutions leverage machine learning to establish baselines modeling each user's normal behavior based on their activity patterns. Once those baselines are established, the AI can continuously monitor for deviations that may indicate insider threats, privilege abuse, compromised accounts, and other insider risk vectors. What makes this application of AI so powerful is its ability to dynamically score the risk level of unusual user actions based on contextual factors like their role, access privileges, the sensitivity of data/systems involved, and more. This enables real-time flagging of potentially malicious insider activity for further investigation - something that was highly prone to human error with traditional, rigid rule-based approaches. Another area where I've seen UEBA with AI shine is detecting compromised privileged user accounts, which are prime targets for external threat actors seeking a foothold. By understanding the unique behavior patterns of different privileged user roles, AI can rapidly identify the subtle anomalies of a privileged account under hostile control - giving our teams a chance to shut down that breach before catastrophic damage occurs. In my opinion, deploying AI for insider and privileged user threat detection by understanding human behavior is an absolute game-changer. Monitoring our people and accounts is just as vital as external threat monitoring - without it, we're leaving a gaping hole in our defenses that skilled adversaries will inevitably find and exploit. Of course, my teams always emphasize that AI-powered UEBA is just one layer in a comprehensive, defense-in-depth strategy. But it's a crucial layer that lets us get in front of insider incidents before they escalate - buying time that can mean the difference between a contained issue and a full-blown crisis. The Flip Side: Defending Against AI-Powered Threats As amazing as AI's cybersecurity offensive capabilities are, I have to address the flip side too: hackers are actively working on ways to weaponize AI against us. Adversarial attacks designed to bypass, poison, or steal our AI models are a clear and present danger we can't ignore. One disturbing forecast from research analysts indicates: Up to 30% of AI cyberattacks will leverage training data poisoning, model stealing, or adversarial examples to attack AI-powered systems. But we're not defenseless - there are proven ways to harden AI systems against these tactics if we invest in robust AI security controls from the start. Approaches like adversarial training, where models are intentionally exposed to deceptive inputs during the learning process, can improve resilience against evasion. Differential privacy techniques can also obscure model behavior to thwart extraction attacks. We follow best practices around secure AI development Life Cycle, subjecting our models to rigorous adversarial testing and employing defensive AI techniques to stay ahead of emerging threats. I don't mean to sound alarmist, but the cybersecurity industry at large needs to prioritize battling AI-powered threats just as much as leveraging AI for defense. It's a two-way street - we must secure the AI models underpinning our cyber defenses with the same vigor we apply AI offensively. Neglecting AI security WILL be exploited by sophisticated hackers. So while I'm incredibly confident about AI's potential to revolutionize cyber threat detection and response, I'm also pragmatic. We simply can't ignore the dark side and must proactively invest in developing and deploying AI securely to maintain a decisive strategic advantage. Integrating AI Across Security Operations Of course, AI alone is not a silver bullet for cybersecurity. Its true power comes from thoughtful integration across existing processes, teams, and technology stacks. As an enthusiastic AI Expert, I emphasize this force-multiplier effect to clients constantly. AI should augment and empower human expertise, not replace it entirely. That's why the most successful deployments I've seen use AI to offload tedious, repetitive tasks to machines - freeing up skilled personnel to focus on higher-level analysis, investigation, and decision-making that requires human judgment. That's also why AI is rapidly becoming essential for SOC analysts and incident response teams: by automating alert triage, log analysis and basic response workflows, AI drastically reduces burnout from low-value "grunt work" while improving overall efficiency. Research from Capgemini revealed organizations using AI for security operations saw a 12% increase in breach detection rates and an impressive 19% reduction in time-to-respond. Those are major gains I've validated through my own startup's AI deployments. AI and machine learning are also driving huge advances in security orchestration, automation, and response (SOAR) tools that streamline entire incident response processes. With SOAR, AI handles tasks like correlating disparate security signals, surfacing relevant threat intelligence, and even executing semi-automated response playbooks. It's a huge step towards smarter, more autonomous incident handling. Ultimately though, AI is a complement to skilled human teams, not a wholesale replacement. Cybersecurity operations will always require human expertise, analysis, and judgment. But by intelligently augmenting our people with AI automation and assistance, we can concentrate their talents on higher-impact duties while optimizing productivity and efficiency. I already see this human-machine symbiosis enhancing the industry's ability to outpace threats. As AI integration continues maturing, I'm confident embracing this collaborative model is the only way we'll realistically close the cybersecurity skills gap plaguing our field. AI's Cybersecurity Superpowers By rapidly analyzing network data, user activities, system logs, and more, AI can identify malicious behavior patterns and potential threats with superhuman speed - giving us an early warning to shut down attacks before escalation. Threat Detection & Prevention: While phishing lures and malware grow more devious, AI algorithms can precisely distinguish malicious files and emails from legitimate ones by evaluating content, sender metadata, behavioral signals, and other artifacts that often evade human analysis. Phishing and Malware Detection: Rather than rigid rules, AI lets us dynamically model normal user activity baselines. It can then rapidly detect anomalies indicative of insider threats, compromised accounts, data exfiltration attempts, and more - all in real-time. User Behavior Analytics: I've deployed AI to prioritize vulnerabilities based on their risk and exploit potential, automating the triaging process. AI can also correlate vulnerabilities with active threats, showing us which exposures need patching first. Automated Vulnerability Management: Modern AI-powered SIEM solutions can correlate security events across networks, endpoints, and other sources to rapidly identify genuine incidents amidst the noise. AI also streamlines response workflows based on alert contexts. Smart SIEM & Incident Response: Across incident triage, alert analysis, threat hunting, and more, AI is automating repetitive, labor-intensive security tasks - amplifying the impact of human personnel while reducing burnout and fatigue. Security Ops Automation: I could go on, but you get the idea. As an AI expert and entrepreneur, I've seen these capabilities utterly transform cybersecurity operations into an amazingly proactive, autonomous counter-threat force. And AI's offensive cyber capabilities are still rapidly evolving. The Way Forward: Practical AI Cyber Defense But when I gaze into the future of AI-powered cybersecurity, I see incredible potential beyond just better threat detection and response. Used wisely, I believe AI can fundamentally reshape and elevate our entire profession into a truly proactive, predictive security posture - finally outpacing even the wiliest cyber adversaries. However, realizing this AI-driven future won't happen automatically. It requires us as an industry to thoughtfully and securely integrate AI across our people, processes, and technology stacks with a keen focus on augmenting human expertise and capabilities rather than wholesale automation. At an operational level, this will mean embracing increasingly autonomous security workflows offloaded to AI systems - handling tedious triaging and basic response while escalating only the most critical issues to skilled analysts for analysis and key decisions. It's a pragmatic balance of human and machine capabilities. We'll also need a new class of cybersecurity professionals fluent in interpreting AI system outputs and translating them into strategic guidance. These AI-savvy analysts must be adept at not just using but explaining and justifying AI system behaviors and decisions to leadership stakeholders. It plays a crucial role in building organizational trust and accountability around AI systems. Additionally, organizations must adopt secure AI system development lifecycles, proactively battling adversarial AI threats through approaches like robust model training, adversarial testing, privacy-preserving analytics, and defensive AI architectures. We simply can't leave our AI systems vulnerable to adversarial subversion. Thankfully, I see positive momentum towards these goals across the AI security startup ecosystem and cybersecurity community. But we must keep pressing forward holistically - investing in skilling our people, refining our processes, and pioneering new AI-centric technologies securely and ethically. Only then can we responsibly wield AI's incredible cyber defense potential without enabling worse nightmares. Despite all the work ahead, I'm hugely optimistic as an AI expert and founder. I've already deployed these transformative AI capabilities in the field, experiencing their game-changing impact firsthand. AI gives us a truly decisive advantage in combating even the most sophisticated threat actors - if we successfully fuse machine intelligence and human expertise. It won't be easy, but I'm convinced the payoff will be a safer, more secure digital world actively hardening itself against cyber threats in real-time. A world where novel attacks are neutralized before impact, where vulnerabilities are patched before exploitation, and where malicious insiders and compromised accounts are stopped in their tracks. Best of all, AI cyber defense done right should free up our human personnel for higher-impact strategic work, not tedious data grunt work. AI should empower us to be the cybersecurity experts, creative problem-solvers, and strategic leaders we always aspired to be - not menial attack analysts. So to my fellow AI professionals, founders, and innovators - the path ahead is clear. Let's blaze this trail TOGETHER, pioneering the new era of practical, intelligent, and ethical AI-powered cyber defense. The future of digital safety depends on us doing this right. And with our unique combination of human ingenuity and machine intelligence, I'm confident we're up to the immense challenge. Let's get to work.

Applications of Artificial Intelligence in Cybersecurity: Boosting Threat Defense System

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