This morning, my LinkedIn feed lit up with anniversary congratulations, marking two remarkable years as a founding faculty at The Fast Future Executive. These moments of celebration offer a perfect…

Finance Meets the Future: Reflections on Two Years at Fast Future Executive | by Efi Pylarinou | Dec, 2024

Today, November 14, is Finance Day at COP29. We caught up with Leslie Labruto, EDF’s Managing Director for Sustainable Finance, about what she’s watching for at COP29, the United Nations’ climate change talks in Baku, Azerbaijan. Follow Leslie on LinkedIn.

Climate Finance and Accountability at COP29 - Climate 411

18, 2024 (GLOBE NEWSWIRE) -- TILT Holdings Inc. (“TILT” or the “Company”) (Cboe CA: TILT) (OTCQB: TLLTF), a global provider of cannabis business solutions that include inhalation technologies, cultivation, manufacturing, processing, brand development and retail, today announced a leadership ...

Cringey LinkedIn Posts Are Getting Dragged on Fast-Growing Subreddit

Following these professionals on LinkedIn can help you stay informed, learn from experts, and expand your network.

Cybersecurity Pros to Follow on LinkedIn - Spiceworks

LinkedIn shares your activity on the platform by default. Change this setting and you can snoop on other people&#x27;s profiles without them knowing.

LinkedIn Tells People if You Look at Their Profile. Here's How to Turn That Off | WIRED

LinkedIn News | 14,839,382 followers on LinkedIn. Bringing you the business news and insights you need to stay informed. | LinkedIn News is powered by 250+ editorial team members who are creating, curating and cultivating the news and insights professionals need to know now, reaching about ...

McKinsey pays staff to find new jobs

Login to LinkedIn to keep in touch with people you know, share ideas, and build your career.

To Answer the Question of Why I Invest in China

The professional social media network is testing a feature that lets users scroll through bite-sized videos.

LinkedIn is testing a TikTok-like feed for short-form videos | ZDNET

Listen closely to how the hiring manager responds. More than likely, Raman says, they&#x27;ll be able to tell you about the onboarding process, periodic training for cybersecurity and legal purposes, and maybe access to online courses like LinkedIn or Coursera.

The most important question to ask in a job interview, according to a LinkedIn expert

AMG Critical Materials N.V. ("AMG", EURONEXT AMSTERDAM: "AMG") will release its third quarter 2023 financial results on Wednesday, November 8, 2023, at approximately 18:00 CET. AMG will host a conference call to discuss its financial results for the third quarter of 2023 at 19:00 CET (18:00 GMT / 1:00PM EST) on Wednesday,

AMG Schedule for Third Quarter 2023 Earnings Release

LinkedIn (Microsoft)

linkedin

You can’t make this stuff up. So, here I am doing my thing on LinkedIn. I put so much thought into my posts. Turns out the best-performing one is a meme⁉️ So much for value, quality, and insights. C'mon... I posted this one in one of the LinkedIn groups requiring previous admin approval and got 100K+ impressions and 200+ engagements in 48 hours. OK. So, this is how it works. If you have a small number of followers and connections, you only stand a chance by posting in LinkedIn groups. It is what it is. Moving on. So, I don’t know how, when, and where, but I stumbled upon the AI “hallucinations” phenomenon, I just couldn’t stop thinking about it. I thought to myself, that’s a nice one to post on LinkedIn and spark a discussion in some of the groups that are mainly about artificial intelligence. I did my own research, and I found the simplest explanation. I already have a “Question For Group” in my head. Now, all that’s left to do is to find an appropriate illustration. I’m on my phone. You know, not making a big deal about it. I’m just curious what the legit AI experts have to say about it on LinkedIn. While I was searching, I stumbled upon a funny meme. I liked it. I used it. So, I had all three elements for a LinkedIn post: An interesting question,
A brief, but great quote to give the context, and
A funny-looking illustration (meme). I wasn’t expecting much in terms of engagement or impressions. I really wanted to learn more about these so-called AI “hallucinations.” The next thing I knew, not only this post but all of my previous posts in all of these AI-related LinkedIn groups were gone. It didn’t take me long to realize that I was banned and removed from these groups. I wanted to know why. So, I posted again in some other AI LinkedIn groups the same question and quote BUT WITHOUT A MEME. Guess what? No problem. It’s still there. So, if it’s not the question, and it’s not The Verge’s definition of AI “hallucinations,” it has to be the meme that got me in trouble on LinkedIn. Again, I was on my phone, and I was searching for the first one available (relevant). So, what’s wrong with this picture (meme)? Please enlighten me. Maybe, I’m missing something. I didn’t come up with this question, but I like it. It made me wonder: is it the meme’s question that’s the problem or who’s asking this question? In this case, a raptor from Jurassic Park. If you see a lizard person, then you’re hallucinating. Look, you don’t have to be an AI expert to know that artificial intelligence still has a long way to go in both development and adoption. Yes, it’s not perfect. Yes, it’s potentially dangerous. Yes, it’s making mistakes, but it’s a work in progress. I have to add, an impressive one and at a mind-blowing speed. I’m not being AI-brutal because I’m AI-neutral. Let’s sit and wait for what’s waiting for us on an AI’s plate. I don’t know about you, but this whole thing is laughable to me. AI “hallucinations” took me back right away to the iconic movies, such as “Fear and Loathing in Las Vegas” with Johnny Depp and Benicio Del Toro. Now that I mentioned it, I have to rewatch it. What about AI “hallucinations?” I don’t know, and honestly, I don’t care. Let all those AI Top Voices on LinkedIn keep their heads wrapping about it while I enjoy revisiting a timeless movie classic. You can’t make this stuff up. So, here I am doing my thing on LinkedIn. I put so much thought into my posts. Turns out the best-performing one is a meme⁉️ So much for value, quality, and insights. C'mon... I posted this one in one of the LinkedIn groups requiring previous admin approval and got 100K+ impressions and 200+ engagements in 48 hours. OK. So, this is how it works. If you have a small number of followers and connections, you only stand a chance by posting in LinkedIn groups. It is what it is. Moving on. So, I don’t know how, when, and where, but I stumbled upon the AI “hallucinations” phenomenon, I just couldn’t stop thinking about it. I thought to myself, that’s a nice one to post on LinkedIn and spark a discussion in some of the groups that are mainly about artificial intelligence. I did my own research, and I found the simplest explanation. I already have a “Question For Group” in my head. Now, all that’s left to do is to find an appropriate illustration. I’m on my phone. You know, not making a big deal about it. I’m just curious what the legit AI experts have to say about it on LinkedIn. While I was searching, I stumbled upon a funny meme. I liked it. I used it. So, I had all three elements for a LinkedIn post: An interesting question, A brief, but great quote to give the context, and A funny-looking illustration (meme). An interesting question, A brief, but great quote to give the context, and A funny-looking illustration (meme). I wasn’t expecting much in terms of engagement or impressions. I really wanted to learn more about these so-called AI “hallucinations.” The next thing I knew, not only this post but all of my previous posts in all of these AI-related LinkedIn groups were gone. It didn’t take me long to realize that I was banned and removed from these groups. I wanted to know why. So, I posted again in some other AI LinkedIn groups the same question and quote BUT WITHOUT A MEME. Guess what? No problem. It’s still there. So, if it’s not the question, and it’s not The Verge’s definition of AI “hallucinations,” it has to be the meme that got me in trouble on LinkedIn. Again, I was on my phone, and I was searching for the first one available (relevant). So, what’s wrong with this picture (meme)? Please enlighten me. Maybe, I’m missing something. I didn’t come up with this question, but I like it. It made me wonder: is it the meme’s question that’s the problem or who’s asking this question? In this case, a raptor from Jurassic Park. If you see a lizard person, then you’re hallucinating. Look, you don’t have to be an AI expert to know that artificial intelligence still has a long way to go in both development and adoption. Yes, it’s not perfect. Yes, it’s potentially dangerous. Yes, it’s making mistakes, but it’s a work in progress. I have to add, an impressive one and at a mind-blowing speed. I’m not being AI-brutal because I’m AI-neutral. Let’s sit and wait for what’s waiting for us on an AI’s plate. I don’t know about you, but this whole thing is laughable to me. AI “hallucinations” took me back right away to the iconic movies, such as “Fear and Loathing in Las Vegas” with Johnny Depp and Benicio Del Toro. Now that I mentioned it, I have to rewatch it. What about AI “hallucinations?” I don’t know, and honestly, I don’t care. Let all those AI Top Voices on LinkedIn keep their heads wrapping about it while I enjoy revisiting a timeless movie classic.

AI "Hallucination" Post Got Me Banned in All Artificial Intelligence Groups on LinkedIn

One product that I’ve used every day over the past few years is LinkedIn. I used to prefer using LinkedIn over other social media platforms like Instagram due to the rich and high-quality professional content and limited spam. I used to post a lot of content as well, especially during the pandemic but I stopped posting as content overload increased over time. I used LinkedIn as a way to improve my writing as well as to learn from other professionals on a completely new perspective on topics that I write on. The content overload has negatively impacted my user experience. Few examples of the recent content and feature clutter problem I've observed on LinkedIn: New AI tools and features have been coming out every day which is exciting to me as a tech enthusiast but the number of people who post the same type of content repeatedly on LinkedIn defeats the purpose for someone like me who wants to gain valuable insights and relevant updates.Contribute Expertise is a really great feature on LinkedIn in my opinion to learn things and experiment in my day-to-day work but the number of plagiarized answers has put me in a position to not use the feature at all. The positive intent of the feature has been overshadowed by the content practices that stray from LinkedIn's mission.I am still trying to understand the need for games/puzzles on LinkedIn. It reminds me of good old Farmville days on Facebook although I don't see the relevance on LinkedIn. Let's take a step back and look at the mission and vision of LinkedIn before diving deep into the pain points and potential solutions. The mission of LinkedIn is simple: connect the world's professionals to make them more productive and successful. LinkedIn’s vision is to create economic opportunity for every member of the global workforce. Now that I have provided the context and background, the following seem to be the top pain points I've noticed on LinkedIn today. Please note that these are based on personal experiences and the reality might be far-fetched due to the lack of quantitative and qualitative research here. Data Clutter - Content Overload, Low Quality, and Irrelevant Content: The rapid pace of content generation on LinkedIn has led to the repetition of low-quality or irrelevant content. Users are bombarded with ads, notifications, posts, and updates from connections, and relevant industry insights get lost in the noise.Algorithmic Challenges: LinkedIn prioritizes engagement-driven posts that favor sensational or "viral" posts over genuinely insightful ones that might not align with the user's professional goals.Feature Overload: LinkedIn’s variety of features—such as Games, Contribute Expertise, LinkedIn News, Groups, Articles, Events, and Media—adds to the platform’s clutter. This is a genuine problem not just on LinkedIn but for any company. Even if I start a competing product that has a simple user experience today, it might evolve into another LinkedIn in probably another 10 years due to limited growth potential after a saturation point.Limited Customization: The LinkedIn feed currently has limited flexibility in filtering the relevant content that interests the user. There is no tailor-made content that meets relevance to the users' needs.User Experience Impact: All the above pain points in summary reduce LinkedIn's effectiveness as both a professional networking and knowledge-sharing tool. Addressing these problems effectively will lead to better user experience and help LinkedIn to be more in alignment with its mission and vision. With the recent advancements in AI capabilities, LinkedIn can leverage some of the following tools for a more personalized and clutter-free user experience. Generative AI for Content SummarizationAI for Content Quality ScoringEnhanced AI Search and RecommendationsConversational AI for In-Depth Engagement My goal here is not to overuse the term ‘AI’ but to suggest practical AI applications to address these issues. Combining the above tools can help LinkedIn generate a cohesive user experience aimed at reducing content clutter and more user-centric experience. LinkedIn already has implemented some AI tools for its features like AI-Powered Feed Summarization and Generative AI for Content Summarization. Please check the linkfor more details. There are some other potential ideas that I could think of to reduce the clutter problem on both content and features. Customizable Notification Controls: The current notification center is too extensive and hard to use. Analyzing user behavior to suggest relevant notification preferences can help improve the user experience. Personalized Feed Filters: Advanced filter options allow users to adjust their feeds based on relevant topics, industries, and connection levels, all powered by AI for a more dynamic feed. Tags do help alleviate this issue to a certain extent but it's not foolproof. LinkedIn Games: Since LinkedIn hasn't really been a platform that I really want to play games on, I think it can tune the current games feature in order to align with its mission. They can gamify so that users can learn or update their skills in various topics. Taking the Duolingo approach for learning product management skills, software development skills, marketing, etc. would be really nice to explore. Similar to the LinkedIn top Voice badge, gamification can add more badges would really encourage users to create more genuine content or learn new skills that could benefit a lot of users. I personally have a lot of ideas that I would love to see on the topic. One of my side projects has been on similar lines which I could speak for pages in a different blog post. AI for Content Quality Scoring is another feature that I highly value. Similar to how ChatGPT content can be upvoted or downvoted, LinkedIn could add this feature for users other than the author of the post to upvote or downvote and this data doesn't need to be shared with the author. But LinkedIn could use it to recommend or bring this content up the order. This doesn't mean this has to be the only way to determine the content quality but can be used as an additional parameter that can be considered for their algorithmic challenges. In summary, there are still a lot more ideas that I could think of like optimizing or changing the Contribute Expertise feature and Article Feature. Job-related features are a different beast altogether to tackle, but overall, I feel LinkedIn has done a pretty good job so far, and I hope they continue improving the overload problem on both the content and feature set by leveraging AI. References: LinkedIn AI Features Prompt for DALL.E Image Generation - Generate an image for LinkedIn decluttering One product that I’ve used every day over the past few years is LinkedIn. I used to prefer using LinkedIn over other social media platforms like Instagram due to the rich and high-quality professional content and limited spam. I used to post a lot of content as well, especially during the pandemic but I stopped posting as content overload increased over time. I used LinkedIn as a way to improve my writing as well as to learn from other professionals on a completely new perspective on topics that I write on. The content overload has negatively impacted my user experience. Few examples of the recent content and feature clutter problem I've observed on LinkedIn: New AI tools and features have been coming out every day which is exciting to me as a tech enthusiast but the number of people who post the same type of content repeatedly on LinkedIn defeats the purpose for someone like me who wants to gain valuable insights and relevant updates. Contribute Expertise is a really great feature on LinkedIn in my opinion to learn things and experiment in my day-to-day work but the number of plagiarized answers has put me in a position to not use the feature at all. The positive intent of the feature has been overshadowed by the content practices that stray from LinkedIn's mission. I am still trying to understand the need for games/puzzles on LinkedIn. It reminds me of good old Farmville days on Facebook although I don't see the relevance on LinkedIn. New AI tools and features have been coming out every day which is exciting to me as a tech enthusiast but the number of people who post the same type of content repeatedly on LinkedIn defeats the purpose for someone like me who wants to gain valuable insights and relevant updates. Contribute Expertise is a really great feature on LinkedIn in my opinion to learn things and experiment in my day-to-day work but the number of plagiarized answers has put me in a position to not use the feature at all. The positive intent of the feature has been overshadowed by the content practices that stray from LinkedIn's mission. I am still trying to understand the need for games/puzzles on LinkedIn. It reminds me of good old Farmville days on Facebook although I don't see the relevance on LinkedIn. Let's take a step back and look at the mission and vision of LinkedIn before diving deep into the pain points and potential solutions. The mission of LinkedIn is simple: connect the world's professionals to make them more productive and successful. mission LinkedIn LinkedIn’s vision is to create economic opportunity for every member of the global workforce. Now that I have provided the context and background, the following seem to be the top pain points I've noticed on LinkedIn today. Please note that these are based on personal experiences and the reality might be far-fetched due to the lack of quantitative and qualitative research here. Data Clutter - Content Overload, Low Quality, and Irrelevant Content: The rapid pace of content generation on LinkedIn has led to the repetition of low-quality or irrelevant content. Users are bombarded with ads, notifications, posts, and updates from connections, and relevant industry insights get lost in the noise. Algorithmic Challenges: LinkedIn prioritizes engagement-driven posts that favor sensational or "viral" posts over genuinely insightful ones that might not align with the user's professional goals. Feature Overload: LinkedIn’s variety of features—such as Games, Contribute Expertise, LinkedIn News, Groups, Articles, Events, and Media—adds to the platform’s clutter. This is a genuine problem not just on LinkedIn but for any company. Even if I start a competing product that has a simple user experience today, it might evolve into another LinkedIn in probably another 10 years due to limited growth potential after a saturation point. Limited Customization: The LinkedIn feed currently has limited flexibility in filtering the relevant content that interests the user. There is no tailor-made content that meets relevance to the users' needs. User Experience Impact: All the above pain points in summary reduce LinkedIn's effectiveness as both a professional networking and knowledge-sharing tool. Data Clutter - Content Overload, Low Quality, and Irrelevant Content: The rapid pace of content generation on LinkedIn has led to the repetition of low-quality or irrelevant content. Users are bombarded with ads, notifications, posts, and updates from connections, and relevant industry insights get lost in the noise. Algorithmic Challenges: LinkedIn prioritizes engagement-driven posts that favor sensational or "viral" posts over genuinely insightful ones that might not align with the user's professional goals. Feature Overload: LinkedIn’s variety of features—such as Games, Contribute Expertise, LinkedIn News, Groups, Articles, Events, and Media—adds to the platform’s clutter. This is a genuine problem not just on LinkedIn but for any company. Even if I start a competing product that has a simple user experience today, it might evolve into another LinkedIn in probably another 10 years due to limited growth potential after a saturation point. Limited Customization: The LinkedIn feed currently has limited flexibility in filtering the relevant content that interests the user. There is no tailor-made content that meets relevance to the users' needs. User Experience Impact: All the above pain points in summary reduce LinkedIn's effectiveness as both a professional networking and knowledge-sharing tool. Addressing these problems effectively will lead to better user experience and help LinkedIn to be more in alignment with its mission and vision. With the recent advancements in AI capabilities, LinkedIn can leverage some of the following tools for a more personalized and clutter-free user experience. Generative AI for Content Summarization AI for Content Quality Scoring Enhanced AI Search and Recommendations Conversational AI for In-Depth Engagement Generative AI for Content Summarization AI for Content Quality Scoring Enhanced AI Search and Recommendations Conversational AI for In-Depth Engagement My goal here is not to overuse the term ‘AI’ but to suggest practical AI applications to address these issues. Combining the above tools can help LinkedIn generate a cohesive user experience aimed at reducing content clutter and more user-centric experience. LinkedIn already has implemented some AI tools for its features like AI-Powered Feed Summarization and Generative AI for Content Summarization. Please check the link for more details. link There are some other potential ideas that I could think of to reduce the clutter problem on both content and features. Customizable Notification Controls: The current notification center is too extensive and hard to use. Analyzing user behavior to suggest relevant notification preferences can help improve the user experience. Personalized Feed Filters: Advanced filter options allow users to adjust their feeds based on relevant topics, industries, and connection levels, all powered by AI for a more dynamic feed. Tags do help alleviate this issue to a certain extent but it's not foolproof. LinkedIn Games: Since LinkedIn hasn't really been a platform that I really want to play games on, I think it can tune the current games feature in order to align with its mission. They can gamify so that users can learn or update their skills in various topics. Taking the Duolingo approach for learning product management skills, software development skills, marketing, etc. would be really nice to explore. Similar to the LinkedIn top Voice badge, gamification can add more badges would really encourage users to create more genuine content or learn new skills that could benefit a lot of users. I personally have a lot of ideas that I would love to see on the topic. One of my side projects has been on similar lines which I could speak for pages in a different blog post. AI for Content Quality Scoring is another feature that I highly value. Similar to how ChatGPT content can be upvoted or downvoted, LinkedIn could add this feature for users other than the author of the post to upvote or downvote and this data doesn't need to be shared with the author. But LinkedIn could use it to recommend or bring this content up the order. This doesn't mean this has to be the only way to determine the content quality but can be used as an additional parameter that can be considered for their algorithmic challenges. In summary, there are still a lot more ideas that I could think of like optimizing or changing the Contribute Expertise feature and Article Feature. Job-related features are a different beast altogether to tackle, but overall, I feel LinkedIn has done a pretty good job so far, and I hope they continue improving the overload problem on both the content and feature set by leveraging AI. References: LinkedIn AI Features LinkedIn AI Features Prompt for DALL.E Image Generation - Generate an image for LinkedIn decluttering

 How to De-clutter LinkedIn

 LinkedIn-də qarışıqlığı necə aradan qaldırmaq olar

 Com desordenar LinkedIn

 Jak odstranit nepořádek na LinkedIn

 Πώς να αποφορτίσετε το LinkedIn

 Cómo ordenar LinkedIn

 लिंक्डइन को अव्यवस्थित कैसे करें

 LinkedIn を整理する方法

 Како да го разбиете LinkedIn

 Maitiro ekuDe-clutter LinkedIn

 LinkedIn-i nädip bulaşdyrmaly

 Ndlela yo De-clutter LinkedIn

 如何整理 LinkedIn 的杂乱

So, why even bother to write LinkedIn posts with ChatGPT? Writing a few posts a week for LinkedIn isn’t such a big deal, is it? Well, it can be, especially when you have a lot of other responsibilities and want to stay consistent. And consistency equals following, so something we all more or less strive for when building a professional or personal brand. That’s why I take every chance I get to make the posting process easier and less time-consuming. Now, I manage to write actionable and engaging LinkedIn posts in under 5 minutes just by having a general idea for them and using some proper ChatGPT commands. But first, what does ‘a good LinkedIn post’ even mean? Searching for the happy medium, I found out that: A good headline should have no more than 50 characters and needs to include a catchy hook.


Body of the text - for best results, short, informative, or branding posts, range around 150-300 characters, and for longer, standard ones, I stick to 1,300-2,000 characters.


The best call to action should be clear, include a verb (like subscribe, click, read, think, etc.), and evoke some stronger emotions. When deciding on one I think about what response I want to get from the reader: a comment, a like, answering some question or maybe sharing it further.


Hashtags: I don’t overuse them. 3-5 is enough. 2 general/popular ones and 1 specific for my topic. Practical Part - What are the most efficient commands for ChatGPT? I keep my commands simple - 4 points regarding Hook, Content (Initial Statement and Counterargument), Call to Action, and Text Organization (Hashtags and Characters’ Limits). The process is repeatable, easy, and effective. Ready to copy-paste ChatGPT input: Write a LinkedIn post based on "Hook-Initial Statement-Counterargument-CTA” framework on the topic of “(here put your topic)”:
"Hook": 1 strong sentence to grab the readers attention. Not more than 50 characters.
“Hook” example: AI will make your boss happier than you do
Body of the text:
“Initial Statement: 2 sentences that refer to the initial statement on the topic of the post that are controversial and what most people fall for, but not really true. Then move on to introduction to the “Counterargument” part of the post.
“Counterargument”: 2 sentences that are opposing the “Initial Statement” and explaining why the reality is not the way it seems at first. Making readers change their initial thoughts on the topic, going against the crowd. Ends with a strong statement underlining the opposing idea.
“CTA”: 1 sentence encouraging readers to comment on the topic. Not more than 50 characters.
“CTA” example: You think your job might be at risk?
Make the post fit around 1500 characters. Stick to the proposed length of each part of the text.
Keep language simple.
Use 3 hashtags: 2 popular ones and 1 specific. Here is what I get on the topic of dogs’ friendship: Hook: Dogs aren't humans' sole best friends!
Initial Statement: While the saying goes, "Dogs are humans' best friend," let's challenge this common belief. While our canine companions offer unparalleled loyalty, there's more to companionship than wagging tails and wet noses. Exploring beyond this conventional wisdom reveals that other animals and even fellow humans can provide unique forms of support and friendship.
Counterargument: It's not about diminishing the special bond we share with dogs, but expanding our perspective on companionship. Cats, for instance, bring a different kind of comfort with their independent yet affectionate nature. Additionally, human connections offer emotional support and understanding that no pet can replicate. Embracing a broader definition of friendship enriches our lives and relationships.
CTA: What's your take on companionship? Share your thoughts below! 💬
#CompanionshipPerspectives #BeyondDogs #FriendshipDiversity How to make it sound personal? If what I get still doesn’t sound personal enough to me, I can always achieve better versions of the output by adding some new commands. I found those helpful: Write it now from the 1st person perspective.
Make the language more personal and less formal.
Avoid overexcited, AI like tone. So, now I get: Hook: Dogs aren't my only best friends!
Initial Statement: You know the saying, "Dogs are humans' best friend," but I've been thinking. While my furry pals bring joy, there's more to companionship than wagging tails. Exploring beyond this common belief, I've realized that other animals and even fellow humans offer unique forms of support and friendship.
Counterargument: It's not about diminishing the bond with dogs; it's about expanding my view on companionship. Cats, with their independent yet affectionate nature, bring a different kind of comfort. Also, human connections provide a depth of understanding that no pet can replicate. Embracing a broader definition of friendship enriches my life and relationships.
CTA: What's your take on companionship? Share your thoughts below! 💬 #CompanionshipPerspectives #BeyondDogs #FriendshipDiversity Sometimes, if I’m still not fully happy with the results, I just manually replace peculiar words with their synonyms to make them sound more like the language I speak daily. To Sum Up The whole process takes less than 5 minutes, with all additional improvements included. All you need is a general idea for your publication and copy-pasting of the provided input. I made my life much easier using this strategy, and I’m sure it’ll help you keep your LinkedIn posting consistent while saving time. Thanks for reading Productive Impact! Click subscribe on the left to receive new posts and support my work. Check out my newsletter. And other socials: @productiveimpct So, why even bother to write LinkedIn posts with ChatGPT? Writing a few posts a week for LinkedIn isn’t such a big deal, is it? Well, it can be, especially when you have a lot of other responsibilities and want to stay consistent. And consistency equals following, so something we all more or less strive for when building a professional or personal brand. That’s why I take every chance I get to make the posting process easier and less time-consuming. Now, I manage to write actionable and engaging LinkedIn posts in under 5 minutes just by having a general idea for them and using some proper ChatGPT commands. But first, what does ‘a good LinkedIn post’ even mean? Searching for the happy medium, I found out that: A good headline should have no more than 50 characters and needs to include a catchy hook. Body of the text - for best results, short, informative, or branding posts, range around 150-300 characters, and for longer, standard ones, I stick to 1,300-2,000 characters. The best call to action should be clear, include a verb (like subscribe, click, read, think, etc.), and evoke some stronger emotions. When deciding on one I think about what response I want to get from the reader: a comment, a like, answering some question or maybe sharing it further. Hashtags: I don’t overuse them. 3-5 is enough. 2 general/popular ones and 1 specific for my topic. A good headline should have no more than 50 characters and needs to include a catchy hook. A good headline should have no more than 50 characters and needs to include a catchy hook. Body of the text - for best results, short, informative, or branding posts, range around 150-300 characters, and for longer, standard ones, I stick to 1,300-2,000 characters. Body of the text - for best results, short, informative, or branding posts, range around 150-300 characters, and for longer, standard ones, I stick to 1,300-2,000 characters. The best call to action should be clear, include a verb (like subscribe, click, read, think, etc.), and evoke some stronger emotions. When deciding on one I think about what response I want to get from the reader: a comment, a like, answering some question or maybe sharing it further. The best call to action should be clear, include a verb (like subscribe, click, read, think, etc.), and evoke some stronger emotions. When deciding on one I think about what response I want to get from the reader: a comment, a like, answering some question or maybe sharing it further. Hashtags: I don’t overuse them. 3-5 is enough. 2 general/popular ones and 1 specific for my topic. Hashtags: I don’t overuse them. 3-5 is enough. 2 general/popular ones and 1 specific for my topic. Practical Part - What are the most efficient commands for ChatGPT? I keep my commands simple - 4 points regarding Hook, Content (Initial Statement and Counterargument), Call to Action, and Text Organization (Hashtags and Characters’ Limits). The process is repeatable, easy, and effective. Ready to copy-paste ChatGPT input: Write a LinkedIn post based on "Hook-Initial Statement-Counterargument-CTA” framework on the topic of “(here put your topic)”: "Hook": 1 strong sentence to grab the readers attention. Not more than 50 characters. “Hook” example: AI will make your boss happier than you do Body of the text: “Initial Statement: 2 sentences that refer to the initial statement on the topic of the post that are controversial and what most people fall for, but not really true. Then move on to introduction to the “Counterargument” part of the post. “Counterargument”: 2 sentences that are opposing the “Initial Statement” and explaining why the reality is not the way it seems at first. Making readers change their initial thoughts on the topic, going against the crowd. Ends with a strong statement underlining the opposing idea. “CTA”: 1 sentence encouraging readers to comment on the topic. Not more than 50 characters. “CTA” example: You think your job might be at risk? Make the post fit around 1500 characters. Stick to the proposed length of each part of the text. Keep language simple. Use 3 hashtags: 2 popular ones and 1 specific. Write a LinkedIn post based on "Hook-Initial Statement-Counterargument-CTA” framework on the topic of “(here put your topic)”: "Hook": 1 strong sentence to grab the readers attention. Not more than 50 characters. “Hook” example: AI will make your boss happier than you do Body of the text: “Initial Statement: 2 sentences that refer to the initial statement on the topic of the post that are controversial and what most people fall for, but not really true. Then move on to introduction to the “Counterargument” part of the post. “Counterargument”: 2 sentences that are opposing the “Initial Statement” and explaining why the reality is not the way it seems at first. Making readers change their initial thoughts on the topic, going against the crowd. Ends with a strong statement underlining the opposing idea. “CTA”: 1 sentence encouraging readers to comment on the topic. Not more than 50 characters. “CTA” example: You think your job might be at risk? Make the post fit around 1500 characters. Stick to the proposed length of each part of the text. Keep language simple. Use 3 hashtags: 2 popular ones and 1 specific. Here is what I get on the topic of dogs’ friendship: Hook: Dogs aren't humans' sole best friends! Initial Statement: While the saying goes, "Dogs are humans' best friend," let's challenge this common belief. While our canine companions offer unparalleled loyalty, there's more to companionship than wagging tails and wet noses. Exploring beyond this conventional wisdom reveals that other animals and even fellow humans can provide unique forms of support and friendship. Counterargument: It's not about diminishing the special bond we share with dogs, but expanding our perspective on companionship. Cats, for instance, bring a different kind of comfort with their independent yet affectionate nature. Additionally, human connections offer emotional support and understanding that no pet can replicate. Embracing a broader definition of friendship enriches our lives and relationships. CTA: What's your take on companionship? Share your thoughts below! 💬 #CompanionshipPerspectives #BeyondDogs #FriendshipDiversity Hook: Dogs aren't humans' sole best friends! Initial Statement: While the saying goes, "Dogs are humans' best friend," let's challenge this common belief. While our canine companions offer unparalleled loyalty, there's more to companionship than wagging tails and wet noses. Exploring beyond this conventional wisdom reveals that other animals and even fellow humans can provide unique forms of support and friendship. Counterargument: It's not about diminishing the special bond we share with dogs, but expanding our perspective on companionship. Cats, for instance, bring a different kind of comfort with their independent yet affectionate nature. Additionally, human connections offer emotional support and understanding that no pet can replicate. Embracing a broader definition of friendship enriches our lives and relationships. CTA: What's your take on companionship? Share your thoughts below! 💬 #CompanionshipPerspectives #BeyondDogs #FriendshipDiversity How to make it sound personal? If what I get still doesn’t sound personal enough to me, I can always achieve better versions of the output by adding some new commands. I found those helpful: Write it now from the 1st person perspective. Make the language more personal and less formal. Avoid overexcited, AI like tone. Write it now from the 1st person perspective. Make the language more personal and less formal. Avoid overexcited, AI like tone. So, now I get: Hook: Dogs aren't my only best friends! Initial Statement: You know the saying, "Dogs are humans' best friend," but I've been thinking. While my furry pals bring joy, there's more to companionship than wagging tails. Exploring beyond this common belief, I've realized that other animals and even fellow humans offer unique forms of support and friendship. Counterargument: It's not about diminishing the bond with dogs; it's about expanding my view on companionship. Cats, with their independent yet affectionate nature, bring a different kind of comfort. Also, human connections provide a depth of understanding that no pet can replicate. Embracing a broader definition of friendship enriches my life and relationships. CTA: What's your take on companionship? Share your thoughts below! 💬 #CompanionshipPerspectives #BeyondDogs #FriendshipDiversity Hook: Dogs aren't my only best friends! Initial Statement: You know the saying, "Dogs are humans' best friend," but I've been thinking. While my furry pals bring joy, there's more to companionship than wagging tails. Exploring beyond this common belief, I've realized that other animals and even fellow humans offer unique forms of support and friendship. Counterargument: It's not about diminishing the bond with dogs; it's about expanding my view on companionship. Cats, with their independent yet affectionate nature, bring a different kind of comfort. Also, human connections provide a depth of understanding that no pet can replicate. Embracing a broader definition of friendship enriches my life and relationships. CTA: What's your take on companionship? Share your thoughts below! 💬 #CompanionshipPerspectives #BeyondDogs #FriendshipDiversity Sometimes, if I’m still not fully happy with the results, I just manually replace peculiar words with their synonyms to make them sound more like the language I speak daily. To Sum Up The whole process takes less than 5 minutes, with all additional improvements included. All you need is a general idea for your publication and copy-pasting of the provided input. I made my life much easier using this strategy, and I’m sure it’ll help you keep your LinkedIn posting consistent while saving time. Thanks for reading Productive Impact! Click subscribe on the left to receive new posts and support my work. Check out my newsletter . newsletter And other socials: @productiveimpct @productiveimpct

Writing a LinkedIn Post With ChatGPT and Keeping It Personal: A Guide

Cold outreach isn’t easy. There's always the risk of coming across as something we definitely don't want to be: a spammer. As I wrapped up my coffee order and went to my favorite spot, a message popped up from someone I cold DM’d on LinkedIn. A heartfelt message: “Hey, I don’t need your tool. But thanks to you, now I know that I need to find a way to block spammers like you.” "Gosh," I sweat. “Wait, was I just scolded? Did I just become the very thing I don’t like the most - a spammer?” How did this happen? It was the early days of our startup journey. We had just finished the first prototype and were eager to find our first users. Inspired by the gospel of "doing things that don’t scale," I decided it was time to start reaching out. Aware of how irritating it can be to receive unsolicited messages, I was determined to approach outreach in the most appropriate manner possible. 1. Only target those who fit our ideal audience profile 2. Craft a message addressing their potential pain points and how our product can help them Then I put in the work. First, I registered for LinkedIn Premium, using its tools to find the best matches based on our ideal persona. I scoured the platform, wholeheartedly selecting the top 50 potential users. Then, I spent more days crafting the perfect outreach message, obsessing over every comma, and double-checking for any grammar errors. I even compared my message to the best templates on LinkedIn lol. Filled with both hope and worry, I finally hit send. About 5 to 10 people accepted InMail, and only 2 or 3 replied. Unfortunately, none of the responses were positive. Then I immediately found the solution and get 80% CVR! No, it didn’t happen. After that incident, I turned my efforts to Reddit, as my previous first post mentioned, and I got some traction there. Quit hard selling As time passed, I looked at LinkedIn again and reflected on it. As I stepped into the shoes of the recipients, it dawned on me: no matter how well-crafted the message, problems, and solutions, or how perfectly their profiles aligned with the target persona, if they didn't have the need, hard selling them something would only feel like spam. That’s why I find LinkedIn more valuable for a different reason: Understanding target users. It's about understanding their challenges with note-taking and what they're looking to solve. It's not optimal to sell anything when you're clueless about users’ pain points. With this mindset, I shifted gears and saw remarkable outcomes (with hard work, of course; nothing is easy at this stage, haha): I got hundreds of interview hours with our target audience, helping us understand their problems, their pain points, and what we need to serve them better with Saner.ai And there you have it, LinkedIn - from being scolded to having hundreds of insightful conversations. Next up, User interviews: Uhm, do I really have to do them? I'm kinda shy… Cold outreach isn’t easy. There's always the risk of coming across as something we definitely don't want to be: a spammer. As I wrapped up my coffee order and went to my favorite spot, a message popped up from someone I cold DM’d on LinkedIn. A heartfelt message: “Hey, I don’t need your tool. But thanks to you, now I know that I need to find a way to block spammers like you.” "Gosh," I sweat. “Wait, was I just scolded? Did I just become the very thing I don’t like the most - a spammer?” How did this happen? It was the early days of our startup journey. We had just finished the first prototype and were eager to find our first users. Inspired by the gospel of "doing things that don’t scale," I decided it was time to start reaching out. Aware of how irritating it can be to receive unsolicited messages, I was determined to approach outreach in the most appropriate manner possible. 1. Only target those who fit our ideal audience profile 2. Craft a message addressing their potential pain points and how our product can help them Then I put in the work. First, I registered for LinkedIn Premium, using its tools to find the best matches based on our ideal persona. I scoured the platform, wholeheartedly selecting the top 50 potential users. Then, I spent more days crafting the perfect outreach message, obsessing over every comma, and double-checking for any grammar errors. I even compared my message to the best templates on LinkedIn lol. Filled with both hope and worry, I finally hit send. About 5 to 10 people accepted InMail, and only 2 or 3 replied. Unfortunately, none of the responses were positive. Then I immediately found the solution and get 80% CVR! No, it didn’t happen. After that incident, I turned my efforts to Reddit , as my previous first post mentioned, and I got some traction there. turned my efforts to Reddit Quit hard selling As time passed, I looked at LinkedIn again and reflected on it. As I stepped into the shoes of the recipients, it dawned on me: no matter how well-crafted the message, problems, and solutions, or how perfectly their profiles aligned with the target persona, if they didn't have the need, hard selling them something would only feel like spam. if they didn't have the need, hard selling them something would only feel like spam. That’s why I find LinkedIn more valuable for a different reason: Understanding target users. Understanding target users. It's about understanding their challenges with note-taking and what they're looking to solve. It's not optimal to sell anything when you're clueless about users’ pain points. With this mindset, I shifted gears and saw remarkable outcomes (with hard work, of course; nothing is easy at this stage, haha): I got hundreds of interview hours with our target audience, helping us understand their problems, their pain points, and what we need to serve them better with Saner.ai And there you have it, LinkedIn - from being scolded to having hundreds of insightful conversations. Next up, User interviews: Uhm, do I really have to do them? I'm kinda shy…

How I Succeed With Cold Outreach On LinkedIn

“I know you hate the idea of a personal brand, but it’s the best way to stand out and get noticed in a noisy crowd.” I stand by the idea. It’s surely not easy or comfortable to put yourself out there – just as much for the attention as for the scrutiny of it all. But for the love of books, I can’t seem to understand how the term “personal branding” came to be equated with posting on LinkedIn. I’ve seen people share endless frameworks. I’ve seen irrelevant pictures (read: fake candids at a cafe 🫠) attached to posts with regurgitated advice masked as unique insights. I’ve seen engagement tactics; a dime a dozen at that. I’ve seen the self-proclaimed “LinkedIn experts” trying to convince people to use this trick or that trick to game the algorithm and boost credentials: more likes, more comments, more shares. I may sound condescending here, but I don’t necessarily think there’s anything wrong with doing any of the above. Except for maybe the irrelevant pictures part. They need to go. Big Time. Posting on LinkedIn like it’s some type of ritual isn’t building a personal brand. It can get you to destination A, for sure, but you won’t go far. And I’ll tell you why. But What Even is a Personal Brand? Personal brand is how you want your audience to see you and talk about you. Unlike reputation, it’s not about what others think of you. It’s about the image you’ve cultivated and how others relate to this image. The idea is simple. You get to control the narrative about who you are, what you do, and the values you stand for. You’re the captain of the ship. You get to steer it in the direction you want to take it, instead of following the currents. And LinkedIn happens to provide a platform to do just that. Pre-LinkedIn (or social media) days, networking events and conferences used to be the platform to develop your brand. Now, it’s social media, and especially LinkedIn on the professional front. It’s a lot easier to develop your personal brand using social media. You can dictate and direct the way people perceive you and attract the right audience to win new business, get your dream job, and land other profitable gigs. The opportunities are endless. At the same time, it’s a lot easier to fabricate and blow up the truth to sell a specific image. Because at the end of the day, LinkedIn is a social media site, no better than Instagram or Twitter. But if you’ve got no credentials to walk the talk, you’re just another copy of a copy. I won’t take content strategy advice from someone who has zero experience scaling content teams. I won’t trust someone with a time-sensitive project if they’re always missing the deadlines. Would you? What You’re Building is a Mirage Erik Vesser, CEO of Hamlet Protein, has been consistently posting on LinkedIn since 2021. Of his many admirers and critics, many were wondering what someone like him was getting out of writing on LinkedIn. Answering why he dedicates so much time and effort to the activity, Erik writes in a : LinkedIn article "These posts aren’t about showing off or bragging. They’re instead about taking a personal approach from my position as CEO to drive brand awareness for Hamlet Protein……..I believe creating a personal connection with those within (and potentially outside) my network can and will help Hamlet Protein.” His posts and insights are focused around leadership, company operations, and the area of agriculture; domains he has first-hand experience in. That’s why he’s a true thought leader. He can walk the talk. You see, it’s never been about how many times you post in a week or at what time or how you engage with others’ posts or who you tag. These are tactics to get noticed by LinkedIn’s algorithm. Or as I like to call them: cheap tricks. Please don’t mistake virality for authority or authenticity. It’s a vanity metric. Wrapping Up If you have ambition to become a thought leader, remember the proof is always in the pudding. Rather than sharing mediocre, regurgitated content to demonstrate expertise, let your body of work speak for your expertise. Talk from experience, share learnings from the work you’re doing, not Google’s top 10 results. Because with LinkedIn, it’s always been about WHAT you post, and how you bring value and learnings to the people you’re targeting. It’s never been about the frequency or timing of the posts. If you’re adding value to someone’s life, they’ll remember you the way you want. And that’s what personal branding is all about.

You're Not Building a Personal Brand; You're Just Posting on LinkedIn

আপনি একটি ব্যক্তিগত ব্র্যান্ড নির্মাণ করছেন না; আপনি শুধু লিঙ্কডইনে পোস্ট করছেন

Sie bauen keine persönliche Marke auf; Sie posten gerade auf LinkedIn

No estás construyendo una marca personal; Solo estás publicando en LinkedIn

Vous ne construisez pas une marque personnelle ; Vous postez simplement sur LinkedIn

आप एक व्यक्तिगत ब्रांड का निर्माण नहीं कर रहे हैं; आप बस लिंक्डइन पर पोस्ट कर रहे हैं

あなたは個人的なブランドを構築しているわけではありません。 LinkedIn に投稿しているだけです

당신은 개인 브랜드를 구축하는 것이 아닙니다. LinkedIn에 글을 올리시네요

Você não está construindo uma marca pessoal; Você está apenas postando no LinkedIn

Вы не строите личный бренд; Вы просто публикуете сообщения в LinkedIn

Kişisel Bir Marka Oluşturmuyorsunuz; Sadece LinkedIn'de paylaşım yapıyorsunuz

Bạn không xây dựng thương hiệu cá nhân; Bạn vừa đăng bài trên LinkedIn

你不是在建立个人品牌；而是在建立个人品牌。您只是在 LinkedIn 上发帖

What is a skills-based economy, and how is LinkedIn moving from vision to implementation? As LinkedIn Director of Engineering Sofus Macskássy shares, there’s AI, taxonomy, and ontology involved in building the Skills Graph that powers the transition. Skills are the new currency. That’s a bold statement coming from LinkedIn CEO Ryan Roslansky. Roslansky makes the case for the so-called skills-first economy based on both anecdotal evidence and data. Skills-first hiring was mentioned in the 2022 State of the Union address, and a growing number of CEOs are calling on the need for companies to shift how they hire. In addition, that the skill sets for jobs have changed by around 25% since 2015. By 2027, this number is expected to double. That means jobs are changing on you even if you aren’t changing jobs, just as business demands are changing on you even if you’re not changing your business. recent LinkedIn data shows That was not the first or the last time Roslansky made that point. In 2021, to help transition the hiring market from focusing solely on titles and companies, degrees, and schools to also focusing on skills and abilities. In his 2021 post, Roslansky announced new LinkedIn features and services. He also referred to AI-driven changes, a prescient point . LinkedIn’s CEO outlined a vision brought forward again in 2023 Roslansky is not alone in identifying this shift. In 2018, we posited that . In 2019, we followed that up with more analysis on the relationship between the , , and knowledge graphs. in a rapidly shifting job market, being able to formalize skills is a requirement for job seekers and employers going forward future of work skills In 2021, Roslansky first referred to the LinkedIn Skills Graph. The Skills Graph was introduced to help create a common skills language, and it’s powering a multitude of LinkedIn features and services, as well as . In 2022, LinkedIn Director of Engineering Sofus Macskássy elaborated on how . Microsoft Viva LinkedIn’s Skills Graph is being built to power a skills-first world https://www.youtube.com/watch?v=tiE9ZMgfJKM&embedable=true Further details on were shared earlier in 2023. Today, Macskássy and his team are sharing more details on how they are . We caught up with Macskássy to discuss the journey from vision to implementation. building and maintaining the skills taxonomy that powers LinkedIn’s Skills Graph extracting skills from content to fuel the LinkedIn Skills Graph LinkedIn skills taxonomy: Helping everyone speak the same “skills language.” As Macskássy pointed out, the story of skills on LinkedIn goes way back. LinkedIn users have long been able to add skills to their profiles and endorse each other. LinkedIn understood that skills represent an important vocabulary by which people communicate and understand each other. But at some point the realization settled in that this goes beyond just finding a job or finding talent. When the CEO highlighted LinkedIn should move into a skills-first world, there was a question – what does that even mean? From a technical perspective, the team needed to understand what the vision was. As the team started digging into the various product lines as well as the news feed, advertising, recommendation, and search, they realized that skills could be leveraged as signals to aid ranking and recommendation. But going from vision to implementation has not been without obstacles. Many people didn’t have skills listed on their profiles. Even when they did, people did not necessarily use the same vocabulary. It was not easy to figure out whether the two skills were related to each other or even synonyms of each other. This is what Roslansky’s mention of helping everyone speak the same “skills language” alluded to. To resolve ambiguity, the first approach was to build out the . The skills taxonomy is where LinkedIn organizes and categorizes skills based on their hierarchical relationships with each other. LinkedIn skills taxonomy Each skill is represented by a “node” in the skills taxonomy, and nodes are linked together to form a hierarchical skill network through “edges” called knowledge lineages. Knowledge lineages reflect how two skills relate to each other. Skills may relate for various reasons, such as the skills are both part of a career specialization or one skill is for a tool that is used to apply another skill. To create a stronger network of connected skills, a framework called “Structured Skills” is utilized. This framework increases understanding of every skill by mapping the relationships it has to other skills around it. Building the Skills Graph with AI and humans in the loop The connected skills taxonomy is curated by a combination of human taxonomists and machine learning. This Human-In-The-Loop approach to building the Skills Graph helps grow the taxonomy at scale while ensuring the skills data meets the required quality and standards. “It is a feedback loop where we see new skills and new ways of mentioning skills across the board. We use this to expand the Skills Graph dynamically as we see new ways of phasing a skill or even a new skill we never had seen before. For example, prompt engineering is now a new skill that really popped up over the last couple of quarters. So we want to dynamically add it to the Skills Graph. We then use these new skills that can subsequently be tagged in the content”, Macskássy said. That is a continuous process, and the cadence by which it’s done depends on the type of content. The skill harvesting process happens on demand as content is updated. Potential additions or improvements to the Skills Graph are batched, so reviews and updates don’t have to be executed every time any single update happens. In 2022, LinkedIn’s taxonomy consists of over 39,000 skills spanning 26 languages, over 374,000 aliases (different ways to refer to the same skill – e.g., “data analysis” and “data analytics”), and more than 200,000 links between skills. In 2023, there are more than 41,000 skills. Instead of relying only on taxonomists to manually curate over 41k skills, LinkedIn applies machine learning techniques to help scale the taxonomy construction. This includes a tool LinkedIn developed, KGBert which was inspired by , a supervised model that applies a deep semantic understanding of skills to predict relationship lineages. KG-Bert From taxonomy to ontology By utilizing machine learning models, LinkedIn can extract and map skills from diverse content sources and collect feedback for continuous model improvement and member value. To do this, large pieces of text (such as job descriptions and resumes) first need to be segmented out into meaningful parts. Mentions of skills can then be removed from each piece of the text. Once extracted, they are normalized into canonical/single representations (i.e., “data analytics” and “data analysis” are the same type of skill), represented in the skills taxonomy. Attention also has to be paid to where a skill sits in a piece of content and what type of content it is. Skills are often represented differently in resumes, member profiles, or job descriptions, so models are fine-tuned to learn the specifics of those types of content. To address these nuances, LinkedIn built an architecture and platform that caters to the challenges of extracting skills and mapping them onto the Skills Graph. The details the AI model workflow that extracts and maps skills from raw text, such as job postings, as well as the architecture for serving models at scale. post that Macskássy’s team published As former AI Division Technical Lead for Taxonomies and Ontologies at LinkedIn Mike Dillinger points out, however, . They seem simple, flexible, and familiar. They are widely used. And they seem to work across many use cases and many domains. taxonomies are the duct tape of connected data But when looked at in more detail, taxonomies turn out to be crude tools for knowledge organization that are difficult to create, to scale, to adapt, to align, and to build on. A key reason is the fact that they are limited to modeling hierarchical relationships, which are only a small part of the rich relations connecting entities in the real world. Some of the technical details shared on the Skills Graph allude to harvesting and exploiting more than hierarchical relationships. Macskássy verified that LinkedIn utilizes an ontology used not only for mapping skills between branches but also for other concepts. That has provided great improvements when it comes to ranking and recommendation, with more details to be unveiled in due course. Explicit and implicit skills Applications across LinkedIn include career-relevant skills and job-important skills. The approach has resulted in performance improvements in Job Recommendation, Job Search, and Job Member Skills matching. As part of the process, recruiter skill feedback and seeker skill feedback are collected. When a recruiter manually posts a job on LinkedIn, a list of skills, pulled by LinkedIn’s AI model, is suggested after they fill in the posting content. A recruiter can edit this list depending on if they believe a skill is important. Similarly, when a job seeker opens a job posting on LinkedIn, a feature will show how many skills overlap between their profile and the job. Seekers can review the top 10 skills used for skill matching calculation, and if a certain skill is irrelevant to the job, they can provide feedback. Obviously, harvesting and utilizing those “implicit” skills, in addition to skills explicitly listed by members is something LinkedIn has put a lot of effort into. And there are anchors to surface those implicit skills and feedback from job seekers and recruiters on both ends of the recruiting process. But what about LinkedIn members who are not actively recruiting or being recruited? Wouldn’t they benefit from the opportunity to see the skills LinkedIn harvested from their content and perhaps be prompted to add them to their profiles too? Furthermore, explicit skills are part of a member’s profile and, therefore, get to be exported if members so choose. What about implicit skills? Macskássy noted that there are surfaces and flows by which members can add skills. LinkedIn continues to improve those flows where suggestions are made based on member resumes and profiles. Members are asked whether they might want to add certain skills and even associate certain skills with certain of their jobs as well. However, prompting is not done in an overly aggressive way. As for implicit skills, Macskássy said those are not necessarily surfaced because they are more fluid in nature. It makes less sense to surface all of these, particularly as LinkedIn is moving forward into how to think about skills more dynamically and more in-depth, he added. So, those skills are not considered as part of member profiles and are not exported either. Skill provenance, credibility, and depth There are more dimensions of implicit skill harvesting that are worth highlighting: provenance, credibility, depth, and interoperability. Since implicit skills are extracted from content that members themselves provide, is there a way to evaluate the weight this content carries or does it have to be taken at face value? The content that members provide is how they want to represent themselves, Macskássy said. So that has to be taken somewhat at face value, as it all comes down to trust. LinkedIn has not observed that members are stretching the truth because there are a lot of people looking at their professional profiles, and they would be called out, Macskássy added. https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/how-linkedin-is-moving-towards-a-skills-based-economy-with-the-skills-graph?embedable=true However, there is a mechanism through which the depth of member expertise can be assessed. (SAs) are adaptive assessments designed by LinkedIn Learning experts to evaluate and validate skills across a range of domains. LinkedIn Skill Assessments These short-form assessments are accessible through the profile skills section, where members can click on the “Take skill quiz” button to access a list of SA recommendations. Upon successfully passing a SA with a 70th percentile or higher score, members are awarded a “verified skill” badge that they can display on their profile page and is visible to recruiters. Skill assessments, as well as other learning material, align with skills in the skills taxonomy. It is represented internally in member data and can be part of the ranking. The amount of SAs and certificates is still very lightweight compared to other content. LinkedIn is considering how to best elicit and leverage it, for example, by evaluating member interactions. Interoperability, Open Badges, and Future Work Something that could potentially help both in terms of interoperability, as well as content enrichment, is . Open Badges is a data specification initiated by Mozilla and the MacArthur Foundation. The goal is to provide a simple but flexible format for documenting and showcasing skills. A badge corresponds to a skill a person has, as recognized by a third party. Open Badges That means that by using Open Badges, LinkedIn members would be able to both import skills documented by third parties as well as export their skills in a standardized format. Macskássy said that the team is aware of Open Badges, however, the focus is primarily on what can help with the member experience on the platform. Open Badges is on the radar but has a low priority at this point. LinkedIn is investing in continuously improving skill understanding capabilities in approaches such as leveraging Large Language Models. Examples include using LLMs to provide rich descriptions of skills, fine-tuning LLMs to improve skill extraction, or leveraging embedding for skill representation. As the team notes, the LinkedIn Skills Graph is at the center of powering the skills-first transformation. The tech stack for mapping content to the Skills Graph enables constant updates and evolution of the Skills Graph to stay up to date on the always-changing skills landscape. Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives.Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. Also published . here

LinkedIn's Skills Graph: Paving the Way for the Skills-First Economy with AI and Ontology

This week, I had the honor of receiving , 2-times LinkedIn top voice, experienced product manager at Amazon, and avid AI startup investor and advisor. We explore the evolution of content creation and sharing in the AI space, particularly on LinkedIn. Greg Coquillo A Talk With Greg Coquillo Greg shares his journey from using LinkedIn as a job-seeking platform to becoming a prominent voice in the AI community. His initial posts, driven by curiosity and a passion for learning Python and AI, resonated with many on a similar path. This conversation offers insights into the value of sharing knowledge, engaging with a community, and the importance of authenticity in social media. Greg emphasizes the significance of being open to learning and changing opinions based on community feedback. His consistent engagement and response to comments have helped him build a strong, interactive community on LinkedIn. This has not only enhanced his learning but also allowed him to connect and provide value to others. He stresses the responsibility that comes with having a high-profile platform, particularly in ensuring the accuracy and potential impact of his posts. We also discuss the challenges of content creation across different platforms. Greg chose LinkedIn over other platforms like YouTube or Twitter due to time constraints and the effort involved in creating content. He reflects on the importance of time management and balancing his professional life, family time, and personal well-being with his online presence. This choice highlights the reality that content creators must navigate – balancing their passion for sharing knowledge with the practical aspects of content production and platform dynamics. Furthermore, Greg and I touch on the saturation of content in today's digital landscape. The challenge for both creators and consumers is to navigate through the noise to find or provide accurate, valuable information. We observe how platforms like YouTube can become overwhelmed with content that, while relevant in keywords, may not deliver the depth or accuracy desired by users. This saturation not only impacts content creators' reach but also the quality of information accessible to the audience. Of course, this is all covering only around 15 minutes of the hour + conversation we had with many topics I left off for you to tune in this week’s episode of the What’s AI podcast, like tips for AI startups, how to understand the market, knowing when to pivot and more. This conversation with Greg Coquillo offers a behind-the-scenes look at the motivations, challenges, and strategies of content creation in the AI field. For those interested in understanding the dynamics of social media, content creation, and the AI community, this episode is a goldmine of insights. Listen to the full episode on , or : Spotify Apple Podcasts YouTube https://youtu.be/PS25tWfWnfs?embedable=true&transcript=true

How to Succeed on LinkedIn and Investing in Startups

Let's learn about via these 58 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Linkedin /Learn Repo 1. How to Land an Internship at Zomato by Cold Messaging on LinkedIn This blog covers everything from the importance of LinkedIn in getting an internship to a detailed hiring process I went through to get an internship at Zomato. 2. Why You NEED to Personalize Your LinkedIn Connection Requests and Messages Personalization is a term that has become so popular recently. Many people are familiar with it but not many of them know what exactly it means. 3. 5 Practices to Create an Amazing LinkedIn Profile That Gives Results Your LinkedIn profile is the first thing that grabs people's attention so make sure it's worth it. Here are 5 ways you can create a  profile that stands out. 4. 4 Tools to Schedule LinkedIn Posts Here are some tools that allow you to schedule LinkedIn posts for the future 5. The Dark Side of LinkedIn: A Capitalist’s Pipe Dream LinkedIn has become a cesspool of toxic productivity, virtue signaling, and corporate worship. It is time to call for a change! 6. If a Tree Falls and it's not on Linkedin, Who Knows? It's quite ironic how my reasons for signing up to write an article at Hacker Noon was to purge the frustrations caused by social networks of the day. What happens to be the first thing asked of me as a new member?  It is to edit my profile, and add my social media links: Linkedin, Twitter, Facebook, etc. 7. Why I Use YouTube as a LinkedIn Replacement For those lucky few who have not heard of recruiter spam, it is the torrential flow of DMs called “InMail” that recruiters send to nearly anyone. 8. How to Find and Message Investors on LinkedIn Here's how to look for investors on LinkedIn, and what should be in the first email to get interested in your project. 9. How Colleges Can Use Verifiable Credentials to Forge 21st-Century Leadership From Google career certificates to online academies to LinkedIn badges, 21st-century higher education is striving towards coherence. Prospective Bachelor's students increasingly balk at the debt associated with the degree, yet it remains the most credible way to prove employability. Legions of new certifications and vocational programs clutter the higher education ecosystem, yet lack accreditation. 10. Don't Look Like a Spammer: 3 Tips for LinkedIn Communication Since its launch in 2003, LinkedIn has become the largest professional network that is growing each passing day. 11. Dear Aspiring Data Scientists: Skip the Certificates, Do This Instead If you've been on LinkedIn anytime in the past several months, you've probably come across the infamous "certification post." 12. LinkedIn Algorithm 2023: Tips & Tricks You Must Know to Go Viral Sick of posting on LinkedIn only to have your content disappear into the black hole of its algorithm-driven newsfeed? No worries, just read this article... 13. Clubhouse Is Just LinkedIn Built Better LinkedIn may have been the center of business relations for the past decade, but Clubhouse is the Next Gen networking tool we've all been waiting for. 14. Top Reasons Why Your Brand Isn’t Making Money From LinkedIn Every social media has its own voice and unique language. For your brand to stand out, you need to take them into cognizance and also approach them effectively 15. Responding to Online War Propaganda on LinkedIn: Who Do the Rules Protect? I am from Ukraine, and I’m trying to use my LinkedIn in order to spread the truth about the war that is going on right now. 16. How to Use LinkedIn for Cybercrime How cybercriminals are currently using LinkedIn to hack lucrative Web 3 companies. 17. Build Your Personal Brand on LinkedIn with These 5 Effective Tips Check these five super effective tips to build your personal brand on LinkedIn,  boost your visibility, and advance your career! 18. The Clubhouse Guide to Running Terrible Meetings Joined Clubhouse thinking you'd be inspired by thought leadership talks? Instead, the Clubhouse experience is a good lesson on how to run an awful meeting... 19. How To Easily Validate Startup Ideas The first step in building a startup is validating the idea. It is a huge risk building a product without know if there are demand for it. The easiest and cheapest way to validate a start up idea is always cold email people at first. 20. 5 Basic LinkedIn Tips Every Marketer Should Follow How to get the best out of LinkedIn marketing and lead generation. 21. How to Build Your Startup's Brand on LinkedIn Read this article to learn how you can promote your startup on LinkedIn! 22. A New LinkedIn Vulnerability Exposes Jobseekers to Phishing Attacks The latest LinkedIn vulnerability can be exploited by con artists for massive phishing attacks, identity theft, and employment-related scams. 23. 7 Best Clubhouse Alternatives for Audio Chats If you are looking for a Clubhouse alternative, you have many options out there, and these are not invite-only apps. You can use them freely. 24. Successful Decentralization Lives Right Under Our Nose The successful decentralization of the Internet is a profound example to help other channels proceed in the same direction (social media included). 25. How Does the GDPR Affect B2B Data? - A Simple Explanation You can reach out to prospects and collect B2B data even under the GDPR. However, there are some rules and regulations. 26. Hacking Hacker Noon: Social Media Distribution Hacker Noon manually and programmatically posts top stories on all our social media channels to our combined over half a million users. 27. Running a Python Script to Scrape LinkedIn Profiles From Google LinkedIn is a great place to find leads and engage with prospects. In order to engage with potential leads, you’ll need a list of users to contact. However, getting that list might be difficult because LinkedIn has made it difficult for web scraping tools. That is why I made a script to search Google for potential LinkedIn user and company profiles. 28. How Do I Build a LinkedIn Scraper For Free? Check out this step-by-step guide on how to build your own LinkedIn scraper for free! 29. LinkedIn Enablement is Critical to Sales Readiness Marketing is essential for creating events and content, but relationship building depends upon each person reaching out and taking time to establish trust. 30. The Social Media Platform That Can Boost Your B2B Marketing (it’s not LinkedIn) Social media has come by leaps and bounds from its original intention. From what started as a pedestal for self-promotion and online socialization, it’s now bec 31. Upcoming Twitter, Instagram, and LinkedIn Features: Unrelease Updates with Nima - Vol. 3 The unreleased updates of famous apps like Twitter and Instagram - 3rd Issue 32. LinkedIn 101: What Social Media Managers Need to Know With 85% of the jobs found through networking, LinkedIn is at the heart of social media marketing, and LinkedIn 101 serves as a perfect guide for social media m 33. 5 Latest Awesome Java Frameworks As an object-oriented and class-based programming language, Java is loaded with numerous opportunities – it is portable, distributed, secure and dynamic. All the latest technologies depend on Java to support various functionalities. Which is why it comes as no surprise that Java reigns as the most popular programming language. It goes the same way with Java Frameworks which are very widely being used by Java developers today. 34. How to Leverage LinkedIn & Quora for Building Your Brand's Presence Online? “SEO is dead. Social media works only when you pay for
ads. Content distribution is harder than ever!” 35. Here's What It Takes to Become a LinkedIn Influencer in 2022 Being a public figure on LinkedIn increases the engagement on your LinkedIn posts and engages a more relevant audience for you to network with. 36. 5 Career Hacks to Landing a Job in Software Development Your LinkedIn profile can also serve as the first point of contact for your recruiters. But when you want to create one, bear in mind that a good LinkedIn... 37. Things You Need To Know About Our Redesigned Google Chrome Extension Last week, we have launched a brand new user interface for our Google Chrome extension. Take a look! 38. 8 STEPS TO GETTING 2 MILLION LINKEDIN VIEWS IN UNDER 24 HOUR In this article, I’m going to share some tips on how to become a LinkedIn influencer and create viral content. So, let’s get started. 39. Cracking Crypto Social Media Marketing - An Overview It would seem that everything is obvious and understandable. We use social networks every day and know how they work. In addition, it seems that it will be easy for the project to tell about its product there. No matter how it is! But in fact, social networks are the most tricky and unstable channels. 40. Pixelated Social Media Icon GIFs: Facebook, Twitter, LinkedIn, GitHub, YouTube and Instagram Hacker Noon has a new Giphy account. Our first 30 GIFs have created over 2.2 million views. These popular social media icons have been pixelated, greenified and GIF-ed: 41. Playback Theatre and the Startup Mindset I have always been fascinated with the theatre and aspired to "learn" theatre (still do). Between 2017 - 2019, I was part of a Playback Theatre group in Bangalore called The Actors Collective. I was involved passively for a few months and some months very very passionately. As I revisited some of those experiences, it shed light on a few core principles that intersect nicely with the Startup mindset. The "Startup mindset" is something I think helps startup employees and founders, as well as folks who are starting a new role or dimension in their career. I'll go even as far as to say it is one of the most potent traits shared by a band of people that operate well together. 42. Web Scraping and the Battle for Open Internet A few years ago, Cambridge Analytica made netizens concerned regarding the gathering of their online data. At that time, affected or interested users had little knowledge of how big the big-data industry actually was. 43. Connecting on LinkedIn: A Guide to Getting Interviews and Landing Job Offers Go to LinkedIn. Scroll through jobs. Find postings you may have a good chance of getting hired for. Click on a job posting. Upload resume. Submit. Repeat, over and over. 44. How Six Tech Giants and Four Startups Use Scala Nowadays, quite a few companies are banking on new technologies and taking on the risks that come with them. Blockchain is hyped up but expensive and precarious, mobile app development is easy to pull off but throws you into a highly competitive market, etc. There’s no shame in using trusted and effective technologies, making a choice in favor of things like Scala and its contemporaries. So to prove that Scala is a viable choice, let’s take a look at successful companies and startups using Scala for product development. 45. Why a Decentralized Social Network with Passive Income Potential Will Emerge, Eventually Social media changed the way you are browsing the Internet, but it didn’t change the way the Internet is being controlled by a few companies that are having a monopoly. While you are under the illusion of using platforms like Google, Facebook, and Twitter at no cost, their pockets are getting heavier and heavier. 46. LinkedIn Pod Software Options, Compared LinkedIn engagement pod software has changed the game for many small businesses and entrepreneurs. Now there are new engagement pod softwares we’re able to choose, making it feel more confusing. 47. 8 Social Media Scheduler Tools to Grow Your Online Audience Social media marketing: this term has certainly become one of the most important for everyone who wishes to have a digital presence. Especially when you are a blogger, influencer, artist, coach, a new startup, or a solo entrepreneur - social media is the place when you can get the maximum attention from your target audience. But, for that, you need to be active on all your social media platforms. You need to post consistently and interact with your followers. This can be a little overwhelming as there are a lot of social platforms to manage at the same time. However, this task can be made easy by using a social media scheduler! 48. Inbound Vs Outbound Sales: Which Path is Right for Your B2B Business? At the most fundamental level, the major difference between outbound sales and inbound sales is in who starts the sales relationship. 49. Brian Wallace AMA on Infographics, Startups and LinkedIn Optimization Brian Wallace is the Founder and President of NowSourcing , an industry leading infographic design agency based in Louisville, KY and Cincinnati, OH which works with companies that range from startups to Fortune 500s. Brian has been named a Google Small Business Advisor for 2016-present and joined the SXSW Advisory Board in 2019. 50. The Rise of Tech Influencers, Monetization, and the Future of LinkedIn Today, we share our thoughts on B2B influencer marketing, efficiently managing multi-channel distribution and the future of content creators on LinkedIn. 51. Beware of Huge Rise in LinkedIn Bogus Boss Scams In this thread, our community discusses their thoughts on the latest scams, including the LinkedIn Bogus Boss scam on the rise. 52. How To Leverage LinkedIn’s Potential for B2B Lead Generation If you are a marketer or a business owner and struggling to generate potential leads, this is definitely a go-to platform for you and your business. 53. 7 Growth Hacking LinkedIn Chrome Extensions to Up Your Sales Game LinkedIn is one of the top sales and marketing tools for 2021. LinkedIn is apt for prospecting, networking, email collection, and connecting with clients. 54. Ultimate Guide to LinkedIn: How to Harness the Professional Network LinkedIn is more than just a place to dump your resume and log out, and if you’re still using it that way you’re missing out on a lot of value. There are features rolling out all the time that few know even exist, let alone how to use to their advantage, and it’s time to take another look at this platform that is bringing business people together from all around the world in real, meaningful ways. If you’ve never used live and native video, document sharing, or attended a LinkedInLocal or Global Meetup for LinkedIn event, it’s time to take another look at just what LinkedIn can do for you. 55. 5 Proven LinkedIn Lead Generation Strategies That I Use For Growing My Business In the B2B space, lead generation through LinkedIn dominates all other social platforms. 56. Scraping Information From LinkedIn Into CSV using Python In this post, we are going to scrape data from Linkedin using Python and a Web Scraping Tool. We are going to extract Company Name, Website, Industry, Company Size, Number of employees, Headquarters Address, and Specialties. 57. How to Craft Your LinkedIn Recruitment Strategy Recruitment today is not the same as it was a few years ago. The traditional recruiting tools are taking a back seat now in this candidate-driven market. 58. 7 LinkedIn Automation Tools for B2B Digital Marketers to Generate Leads Use these 7 best effective LinkedIn automation tools for digital marketers to generate leads and improve your online business at a relatively low cost. Thank you for checking out the 58 most read stories about Linkedin on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

58 Stories To Learn About Linkedin

’s feed has come a long way since the early days of assembling the machine-learning infrastructure that powers it. Recently, a major update to this infrastructure was released. We caught up with the people behind it to discuss how the principle of being people-centric translates to technical terms and implementation. LinkedIn How do data and machine learning-powered algorithms work to control newsfeeds and spread stories? How much of that is automated, how much should you be able to understand and control, and where is it all headed? That was the introduction to my , exploring what was then a newly revamped LinkedIn newsfeed in terms of how the underlying machine learning algorithm worked and what the principles governing it were. Six years is a long time in technology. Back then, it was the early days of machine learning (aka “AI”) entering the mainstream. Today we live in the ChatGPT era. 2017 article for ZDNet In the last year interest in AI has peaked, and it seems like there are new developments almost on a weekly basis. LinkedIn could not possibly have stayed behind, and its models and infrastructure have evolved significantly. Recently, LinkedIn Engineering published a blog post titled “ “. machine-learning Enhancing homepage feed relevance by harnessing the power of large corpus sparse ID embeddings This blog post is a deep dive into LinkedIn’s newly revamped machine learning models and infrastructure that power its newsfeed. Unlike 2017, these changes are not directly perceptible. There are no new features per se, but rather, improvements in the underlying technology with the goal of enhancing the feed’s relevance. We caught up with Jason Zhu, Staff Software Engineer, and Tim Jurka, Senior Director of Engineering at LinkedIn, to discuss how the principle of being people-centric translates to technical terms. Click to listen to the conversation on the here Orchestrate all the Things podcast Human in the loop The gist of the update is that LinkedIn’s model, powering the feed, can now handle a larger number of parameters, resulting in higher-quality content delivery. In the process, Zhu’s team also ended up upgrading the hardware infrastructure that powers the model. Zhu is a member of the LinkedIn Foundational AI Technologies team. He defines the team’s mission as “prototyping and building the foundations behind those advanced algorithms that can benefit multiple vertical use cases at LinkedIn”. Zhu provided a good overview of LinkedIn’s previous major update to the feed in 2021, how that led to the latest update, as well as insights into the inner workings of LinkedIn’s approach. However, we were also interested in finding out what else has changed compared to LinkedIn’s 2017 incarnation of the feed. Most notably, LinkedIn’s spokespeople in 2017 emphasized the , and we were wondering if that is still a thing. As Jurka put it, it’s definitely still a thing, probably even more of a thing now than it was then: human-in-the-loop approach “It boils down to two applications for the feed. The first is we’re really trying to prioritize insightful, authentic, knowledge-oriented content in the LinkedIn feed, and it’s a very hard problem to get right just with AI alone. But we have an entire team of award-winning journalists led by our editor-in-chief, Dan Roth. We’ve actually created AI algorithms that train on the content that they curate and believe is really the cream of the crop in terms of LinkedIn insights. That AI algorithm then tries to say – Would an editor promote this on LinkedIn’s platform? And if so, let’s try to distribute on the platform and see which audiences resonate with that particular piece of content. The second is just with large language models, really taking the world by storm. The way that we make those high quality is by fine-tuning them using techniques like reinforcement learning with human feedback. And so large language models, again, are presenting yet another way where human loop is much more deeply integrated into the AI workflow versus where it was maybe five or six years ago”, Jurka said. The human-in-the-loop approach also applies to spam detection. While LinkedIn employs AI-powered spam detection, there is also the option for users to report spam as well as content moderators to look out for anything the AI may have missed. Jurka went on to add that as far as spam detection for the feed is concerned, some things have stayed the same, while others have changed. The three-bucket classification system described in 2017 has not changed much. What has changed is that LinkedIn has invested a lot more in deeper content understanding, aiming to understand the intent behind each post. For example, is it somebody trying to share a job opportunity? Is it somebody trying to share their opinion about the news? Getting more granular about the content helps LinkedIn know how to distribute it across the platform. From linear to neural, from single to multiple points of view As Jurka shared, the evolution of the feed ranking algorithm really follows the evolution of the LinkedIn feed. Compared to 2017, there are . A lot of the complexity is about making sure that the AI models can capture all those diverse use cases. And this is where understanding intent is also relevant. more members using the feed and more content creators publishing on LinkedIn “For example, you might have a post from a member mentioning that they raised their Series A for their startup, and now they’re hiring. And that post can be perceived through three very different lenses. If you’re a job seeker, that post might actually be an entry point for you to reach out and get a job, and the AI model has to understand that value to you. If you’re a first-degree connection of that individual, it might be just to congratulate them and say, Hey, I’m just checking to see you raised Series A. Congrats. If it’s somebody who’s in the venture capital space, this is actually an insight where they are like, I didn’t know that this particular company raised Series A”, Jurka said. Picking up on that, Zhu explained that LinkedIn’s feed contains a heterogeneous list of updates from members, connections and a variety of recommendations such as jobs, people, and articles. The objective is to provide a personalized ranked list for each member, to help professionals on the platform be more productive and successful. To achieve this, LinkedIn has adopted a two stage ranking process. There is something called the first pass ranker which is applied for each type of update. So the top-k candidates of each type of update are selected by individual algorithms before being sent to the second pass ranker for a final green ranking. LinkedIn’s models are presented with a set of features from both member and content sets. The model is trying to predict a set of likelihood of responses such as the likelihood of starting a conversation or the likelihood of performing certain actions on a particular post. Previously, LinkedIn transitioned its ranking from a linear-based model to a deep learning / neural network-based model. Zhu noted that the deep learning model is more effective because it introduces non-linearities through activation functions. That enables the models to capture more complex and non-linear relationships in the data and learn more powerful representations. Instead of an individual linear model to predict each different response separately, a multitask deep learning model shares parameters between different tasks. So this learning of each task can benefit the other tasks through transfer learning, Zhu said. However, he added, LinkedIn’s data is highly skewed between different types of responses. There are more click responses than engagement, for example. Therefore, cautious sampling and re-weighting is needed to avoid negative task interference during the model training process, because all those tasks are sharing parameters. Harnessing the power of large corpus sparse ID embeddings Zhu introduced LinkedIn’s previous major update of the feed, . As he shared, LinkedIn’s model size has grown by a factor of 500, while the data sets have also grown by a factor of 10. In addition, the training period was expanded and more training data is now sampled. This also presents some challenges in terms of overcoming bias. namely using multi-task learning using TensorFlow However, this was just the starting point for the latest update Zhu’s team implemented. Their objective was to optimize for engagement, which means improving the prediction of likelihood in terms of how users will interact with different posts. Then by enhancing the prediction of this likelihood, LinkedIn can better rank feed items to provide more engaging experience to users. The key to this was sparse . Let’s see what these are and how they are relevant. embedding vectors As a LinkedIn user interested in AI, you may have interacted with the #AI and #ML hashtags. Another user has interacted with the #generativeAI hashtag. These are different hashtags. However, via embeddings, their semantic meanings in the embedding space are close. As a result, the model can understand you may have similar tastes as mine, and the model can recommend similar content in the AI domain to both users. For each content item on LinkedIn, and for each user, the recommendation system uses a vector of embeddings. This means that those vectors will be very large, but also very sparse. They will be large, i.e. have many dimensions, because there are many different topics and corresponding hashtags. But they will also be sparse because not all users have interacted with all hashtags. Zhu’s focus is on transforming large corpus sparse ID features (such as hashtag ID or item/post ID) into embedding space using embedding lookup tables with hundreds of millions of parameters trained on multi-billions of records. For example, Zhu noted, that there are millions of dimensions for member IDs because there are millions of members on LinkedIn. “For each member the goal is to learn a personalized tensor representation. This tensor representation will encode user preferences, such as interaction with other users or hashtag preferences in a fixed period. All this information will be encoded in a dense vector during training. By presenting the model with such a personalized vector, the model will be able to capture the dynamic, shifting world as well as member preferences better”, Zhu said. Sparse features can be interpreted as categorical one-hot representations where the cardinality is several millions, Zhi noted. In these representations, all entries are zero except one, which corresponds to an ID index. That could be a hashtag ID or a member ID, for example. These are transformed into a low dimensional continuous dense space, called the embedding space, using embedding lookup tables with hundreds of millions of parameters trained on multibillion records. As Zhu noted, the embedding table itself is highly coupled with the data set and the problem at hand. However, the technique is definitely generalizable. The same approach is already leveraged in different use cases within LinkedIn such as job recommendation and Ads recommendation. “We are adopting a very similar strategy of converting those sparse IDs to the dense representation. We believe that’s a scalable way of developing AI at LinkedIn”, Zhu said. Scaling up, going forward Zhu’s work also touched upon hardware upgrades. As he explained, the team was facing challenges in serving dozens of large models in parallel on their serving host after enlarging the model size by 100x. The host was just not designed to be able to handle such a scale, so the team chose to serve the model with a two-stage strategy. In the first stage, a global model is trained in the usual way. ID features are converted to dense vectors from large embedding tables first and then these dense vectors are sent to a deep along with existing features for implicit feature interactions. neural network In the second stage, the global model is split at the boundary of the embedding table and deep neural network. From the deep neural network’s perspective, it only needs a set of dense vectors, which is the embedding representation of those sparse features for predicting the result. The ID conversion step is not necessary to present it on the critical path of model serving. Embeddings are converted offline and pushed to high-performance key-value stores. Therefore, these embedding tables don’t have to be hosted in the serving host which has limited memory. This strategy is used while in parallel LinkedIn is working on upgrading the host. Once the upgrade is complete, Zhu said, everything will be moved in memory and memory optimizations will be applied. The team adopted and extended the open-source framework . Horovod Zhu identified a few directions his team would like to explore going forward. In addition to serving both memory-intensive and compute-intensive models, scaling model size by adding more sparse features is another objective. The team is also exploring something called continuous training or incremental training to capture the dynamics of a shifting world. As Zhu explained, embeddings currently are trained on a fixed period basis. This could potentially introduce the problem of inconsistent offline vs. online results or diminished gains over a long period of time. Zhu thinks that by having more frequent retrieval, embeddings can capture the dynamics of the system and better predict a personalized feed for LinkedIn members. Also published . here Click to listen to the conversation on the here Orchestrate all the Things podcast

LinkedIn Feed Evolution: More Granular and Powerful Machine Learning, Humans Still in the Loop

With the recent waves of layoffs in the tech industry, having an optimized LinkedIn profile is now more critical than ever for job seekers. An optimized profile gives you an edge in standing out and attracting potential employers. Optimizing your LinkedIn can lead to new opportunities even as an employed professional. social media But creating a stellar LinkedIn profile takes time and effort. This is where AI tools like ChatGPT can help streamline the process and generate impressive initial results to work from. In this article, I'll share practical tips on to optimize key sections of your LinkedIn profile. leveraging ChatGPT How To Optimize Your LinkedIn Profile With ChatGPT Your LinkedIn profile is a standard online resume. So before we get started, list the job roles you're interested in and get a copy of your current resume as guidance. Then create a ChatGPT account if you haven't already done so. And once you're good, it's time to initiate conversations to optimize your profile with ChatGPT in the following ways. Find Relevant Industry Keywords When , they use specific keywords. To get found, incorporate relevant keywords naturally throughout your profile. Rather than manually analyzing job posts, have ChatGPT quickly suggest relevant keywords based on your background and target roles. Refine the list to align with your skills and experience. employers search for candidates First, give the AI a quick intro about yourself to get started. Write an introduction that covers your educational background, job experiences, and achievements, if any. In response, ChatGPT will give positive feedback and ask if it could assist with anything. Paste this prompt after the following: Prompt: I am applying for <insert job title> in <insert industry> in <insert location>. Give me SEO-friendly keywords for my LinkedIn profile relevant to my skills, experience, and career aspirations. Like the example in the image, . Select the ones that align best with your profile and incorporate them naturally into your profile to improve your visibility. ChatGPT will suggest relevant keywords Write a Standout Headline Your LinkedIn headline is your one-liner that hooks your viewers and gives them a reason to look further. People don't have the luxury of time to go over multiple profiles, so making the first contact impressive is your best shot. That said, your headline should state your value proposition—think of it as your mini-pitch that says, "This is who I am" and "This is what I do." Here are some frameworks you can adopt: ●        I help [audience] do [thing]| [position] at [company] ●        Unique Value Proposition | Job Title | Industry ●        [Niche + what you do] for clients like [your top clients for social proof]| Service 1| Service 2 | Service 3 To get started, use this prompt in ChatGPT: Create multiple variations of attention-grabbing LinkedIn headlines that showcase my skills, experience, and professional brand using this [framework]. Here’s my resume: [paste resume] My target job is [mention role] Remember to stick to the Linkedin character Limit. Didn't get results close to what you need? You can always prompt the AI with a different framework or regenerate multiple versions till you get a better outcome. Use ChatGPT to Write an Engaging About Section The About section is your chance to showcase your personality. but add your flair. Lead with an exciting story and end with a solid call to action. Remember to keep it focused on the value you offer. You also want your viewers to see what makes you different from other professionals. You have max 2000 characters, and I'll advise you to lead with a story instead of a summary that reads like a resume. ChatGPT can provide a solid starting point, Here’s a prompt to use: Write a search optimized About section for my LinkedIn profile representative of my brand in 300 words. Open with a hook in the first paragraph, use a narrative tone, and end with a call to action. The target job is <Insert role> Here is my resume <paste resume> From the example, ChatGPT gives a tailored result that lacks personality. So I suggest you take it as a first draft and modify it to read more engaging with a personal touch. Use ChatGPT to Write Your Experience Section Well-written experience summaries attract more profile views and connections. ChatGPT can produce initial drafts based on your target role and resume details. Take its content as a baseline and refine it to tell your authentic story. People neglect the experience session or even fill it out like a resume and wonder why their profile views are at all-time lows. a technical recruiter at Uber, has this to say: Nikita Gupta , "While searching , the parts of LinkedIn Profiles that I pay most attention to are the Headline, the About section, and, of course, the Experience section." candidates on LinkedIn What does that tell you? The experience section is equally important. And when done right, adding work experience can lead to 8x time profile views and more connection requests on Linkedin. Here’s the prompt to use: Craft a search-optimized work history in the first-person narrative for the experience section of my LinkedIn profile based on my target job and resume. Target role is: <insert Job role> Here is my resume <paste resume> Remember to finetune and add your personal touch to the output. "Your career story is more authentic when you tell it." - LinkedIn. Update Your LinkedIn Skill Section With ChatGPT The vast majority of recruiters use Linkedin. And if you don't have the right listed skills, you won't turn up in recruiters' searches. Fortunately, you can add up to 50 skills in the 'Key Skills' section. But not any skill will do; you have to choose the skills in most demand and relevant to your industry. Have ChatGPT recommend relevant hard and soft skills to add, tailored to your industry and goals. Vet the list carefully to ensure accuracy. Here’s a prompt you can use: Prompt: Can you suggest 50 relevant skills for the 'Skills and Endorsements' section of my LinkedIn profile? I am looking for <insert target job title> roles in <specific industry> industry Here are some examples of my target jobs: <paste relevant job descriptions> Once the output is ready, review the skills and ensure they align with your skill sets. One caution, though, is to make sure you have the skills you're listing. Lying on your LinkedIn profile is a no-no. Ethical Considerations You best write your story. While , it can replace the magic of the human touch in writing. Neither does it understand the context of your experience, so it may sometimes generate inaccurate content. Also, Chat GPt knowledge is cut off from 2021, so you may have to fact-check and update the output it spits out. After all, you don't want to pose as unprofessional. ChatGPT is an excellent tool for optimization, but it should use with caution. ChatGPT can help streamline the process Set Yourself Up For Future Success Optimizing your LinkedIn account is vital to staying on the radar of hiring managers and clients. ChatGPT is a powerful asset that can help you achieve that. It can generate content that can serve as a jumping-off point, which you can refine by infusing your unique personality and perspective. The human touch is irreplaceable, so edit its suggestions to highlight what makes you stand out.

Stories, News & Mentions about LINKEDIN

Linkedin’s Stories on HackerNoon

Linkedin's News & Mentions