The company is playing catch-up with Nvidia in AI accelerators, the processors used to develop ... people adopting this,” she said on the conference call with analysts, who mostly focused their questions on the prospects for the MI300.

AMD Rallies After Predicting Sales Surge for New AI Processor

That's been harder than it sounds to get right—the older players among us will remember the AI in earlier installments of GT, which mostly stuck to a preprogrammed line with little concern for where you, the human player, wanted to put your car.

Almost-unbeatable AI is now a permanent feature of Gran Turismo 7

Being more specific, later in the call Su said that AMD expected the Instinct line would drive around $400 million in sales in Q4 2023, with that mostly being driven by the HPC market with some early ramping of AI workloads at hyperscalers and cloud ...

AMD’s Instinct GPU Business Is Coiled To Spring

Agencies will be on the hook for tapping chief AI officers, adding risk management practices to AI like independent evaluations and more.

On the heels of Biden's executive order, agencies get White House directive on implementing AI

Stocks are drifting higher as Wall Street waits to hear what will come out of the Federal Reserve’s latest announcement on interest rates. The S&P 500 was up 0.4% Wednesday,

Stock market today: Wall Street drifts higher as it counts down to a decision by the Federal Reserve

WASHINGTON, Nov. 1, 2023 /PRNewswire/ -- Clutch, the leading global marketplace of B2B service providers, today released its findings from a survey of 500 small businesses in the US with intentions to invest in digital transformation projects in 2024.

Clutch Report Reveals Small Businesses Shift to Self-Made AI Tools in 2024

The Microsoft 365 Copilot tool will appear in Word, Excel and other Office programs and cost $30 per person per month.

Microsoft starts selling AI tool for Office, which could generate $10 billion a year by 2026

I’m here to see the future of computing. But at the moment, I’m trying to coax a butterfly onto a nectar-dipped stick. I feel like I’m bothering the insects, but the monarch butterfly caretakers accompanying us in a 10-foot screened-in box insist that it’s okay,

I went to paradise to see the future of AI, and I’m more confused than ever

Marketers will take more calculated risks, experimenting with new approaches in areas including artificial intelligence (AI) and gaming, but mostly under the auspices of already dominant media partners. An ad market buoyed by major cyclical events like ...

AI will boost Google but cause a Super Bowl ad fumble in 2024: Forrester

Constellation's Connected Enterprise event was bursting with AI debates. A shakeup in the event format surfaced divergent views - and brought an essential discussion to the forefront. Here's my boil down of the enterprise AI implications.

CCE 2023 - the great AI debate shifts from content creation to... Data governance?

Mostly AI

mostlyai

The process of creating test data can be an arduous task. More often than not, it requires a considerable amount of manual work to obtain a dependable subset of your production data that maintains its referential integrity, business rules, and representative business scenarios. All while protecting the privacy of your data subjects. This blog will help you expedite your test data creation efforts. With just a few mouse clicks in , you'll have ready-to-use test data for your development environment — free of charge and without the need to configure any business rules or logic. Our AI-powered synthetic data engine learns all your dataset's features, taking care of all the hard work for you. MOSTLY AI’s synthetic data platform Before we're going to jump head-first into generating test data, let's first explore a test dataset that's common in e-commerce software engineering: the online shopping cart. The Entity-Relationship diagram below depicts a small version of this dataset and consists of 4 tables: , which contains the data subjects whose privacy you want to protect users and , which are linked tables that represent a user's shopping cart and its contents cart cart_item , which is a reference table that contains all the products this online store sells product We will use this database schema to illustrate the steps you need to take to create a subset, maintain its referential integrity, and demonstrate how MOSTLY AI retains the business rules that underpin the data. We won't need to synthesize the reference table in these steps. As it contains , such as a product’s name, brand, price, and barcode, there's no or information that we need to worry about. These tables can be copied to the test environment as is. Only the tables with privacy-sensitive data need to be synthesized, namely the subject table and the linked tables and . product product information sensitive privacy-relevant user cart cart_item Subsetting Production datasets can be terabytes in size. But what you want for testing is the data rather than of it. MOSTLY AI can create a smaller-sized, representative subset of your data that covers all the business scenarios for testing. Instead of randomly sampling the data, it'll a subset that accurately represents all the production data — QA report included. right all synthesize This is analogous to the way that, for instance, sociologists conduct population studies. It wouldn't be feasible for them to interview the entire population of a country. Instead, they carefully select a sample with which they can say or conclude something about the complete population. Useful and realistic test data works in exactly the same way. Even though it's only a sample, its test cases are representative of the entirety of the production data. With this in mind, let's start generating a part of our synthetic test dataset. The references to the database schema are only there to illustrate the workflow. However, you can use it as a template to extract a dataset from your production data. online shopping cart Log in to and click on in the left side menu. MOSTLY AI’s free edition Runs 2. Drag the CSV file that contains the table into the pane. user Subject table 3. Click on and drag the CSV file that's linked to this subject table to the pane. In the case of our online shopping cart, this would be the table. Add table Table 2 cart 4. You will see the page once you click . Here you can specify how large you want your subset to be. If your production data contains 100,000 subjects, you can take a 10% sample by entering 10.000 in the field. Configure Run Data Proceed Number of generated subjects You can also use instead of . This will create a subset of your production data before the AI model is being trained. This speeds up the test data generation process, but will result in a less accurate rendering of the statistical distributions and correlations in your production data. Number of processed subjects Number of generated subjects 5. A form appears once you click . Here you can specify the relationship between the two tables. Join tables Continue primary key - foreign key 6. Click again to configure your columns’ encoding types. MOSTLY AI already analyzed the contents of your tables for their data types and unique values. You can use the Configure tables page to review the settings before launching the synthetization run. Continue 7. Lastly, click on , sit back, and relax. Launch run Referential integrity For a test dataset to be useful, it must contain the same relationships as the production dataset with accurate values based on business rules. With MOSTLY AI, you can ensure referential integrity by generating the linked tables of your production dataset. conditionally Conditional generation works like an auto-complete for your data. Once you have a training model of a subject table-linked table pair, you can "ask" this model to "complete" any subject table you throw at it, as long as the columns and data types are the same as the original subject table. Upload your handcrafted, updated, or synthesized versions of the subject table, and MOSTLY AI will generate a fitting, realistic linked table. For our online shopping cart dataset, we'll need to conditionally generate the table. It's the last table we need to synthesize, and the items in this table must refer to the carts in the table. This procedure differs slightly from the subsetting step. First, we'll need to create a training model using the original and tables. We can then use MOSTLY AI's "Generate more data" function to generate the synthetic version of the table. cart_item synthetic cart cart cart_item cart_item We also need to consider the reference table in this step. Even though we won't synthesize it, we do need to ensure that we maintain the references to this table. You do this by assigning the encoding type to the foreign key column that links the table to the table. This ensures that the synthetic foreign key column contains the values that are already present in the original foreign key column. product Categorical cart_item product Now let's complete our test dataset by walking through the steps below: Drag the table into the pane and the original table to the pane and click . original cart Subject table cart_item Table 2 Proceed 2. Leave all fields on the page blank and click General Settings Continue. 3. Specify the relationship between the two tables and click . primary key - foreign key Continue 4. Review the column settings. Please set any columns that link to a reference table to , otherwise you'll break the references to this table. For our online shopping cart, this would be the column in the table. Categorical product_id cart_item 5. Click on Launch run and wait for the run to complete. 6. Scroll down to the section of your run and click on . Actions Generate more data 7. Lastly, drag the version of the table to the upload area, and click to synthesize the final table of your test dataset. synthesized cart Generate Congratulations! You successfully synthesized a test dataset from your production data. Conditioned test data Obtaining useful results from legacy test data generators is a laborious task. First, you'll need a deep understanding of the business logic and rules that underpin your production data. This knowledge is necessary to specify the conditions in which certain values occur. These can be simple rules — . But they can also be nested — . Or describe behavioral patterns — . 80% of cars sold cost less than 20,000 euros cars that cost less than 20,000 euros are often compact city cars customers visit a car dealer six times on average before buying a car MOSTLY AI saves you the effort of understanding and defining granular business rules. In fact, it automates the process of studying the data to formulate these rules. As an AI-powered synthetic data generator, it learns the patterns that are present in the production data, assigns probabilities to the conditions in which certain values occur, and synthesizes your test data accordingly. The resulting test data breathes realism and makes your product come alive before it's launched to its users. Conclusion With MOSTLY AI, the struggles of launching new products have become a thing of the past. In this deep-dive, you've experienced where the gains lie when creating a small test dataset, and how it can benefit and improve your work as a test engineer. The Enterprise edition of MOSTLY AI allows you to scale these benefits to production databases that may well contain dozens of tables. You also wouldn't need to perform any manual tasks. Rather, the steps that you went through can be fully automated using its REST API. Either way, whether you're using the Free or Enterprise edition, you're benefitting from a significantly improved turnaround time for your test cases and the ability to point out hidden defects in the codebase with ease. Test data will no longer be the bottleneck that keeps you from delivering value to your customers.

Create Realistic and Secure Test Data with our Synthetic Data Platform

In October 2022 I published the most embarrassing article of my career, but 2 years later I am starting to think I might have been right. Strolling through the colossal vendor hall at BlackHat in 2022, every stall made some bold claim about AI. “AI-powered analytics”, “Combining AI with real-time detection” or some other slogan. Whenever you talk to the sales team to ask exactly what their AI did, you either get a succession of AI jargon or a claim about secret research teams in underground vaults in Antarctica. Following on from that, I confidently published an article calling out AI as nothing more than marketing BS… “BlackHat 2022, more A than I”. Fast forward a couple of months and ChatGPT was released. All of a sudden, AI became real and suddenly my brilliant article was a joke, and blatantly wrong. But, after strolling through the BlackHat 2024 vendor hall this year, maybe I wasn’t so far off. It is undeniable that AI is indeed real, and that it has huge potential to change the landscape. And in many ways it already has. Something I didn’t believe 2 years ago. But, as you walk through the expansive halls of Mandalay Bay for BlackHat 2024, you still see the same slogans and I still have yet to find a meaningful implementation of AI in security that I can point to that remotely matches the bold claims. “One thing I notice [in BlackHat] was that people were tired of AI”
Ashish Rajan AI CyberSecurity Podcast Why has AI failed to be effective in security? To preempt your objections, I am aware security vendors are indeed using AI within their products. But so far there are no game-changing results that we can point to, and a lot of this comes down to how they are implementing AI. Following the release of ChatGPT and the subsequent AI models, a lot of vendors rushed to add these new tools to their platforms. The go-to implementation was ‘AI-powered remediation guidelines’ and ‘additional context around alerts’. But here is the reality behind this: that implementation helps mostly the vendor not the user.  This simply means they don’t need to spend as much time on their documentation. Not only that, contextual information is always at least 2 years out of date for most commercial AI models. The reality is that a lot of this implementation was simply a way to make good on vendors' promises behind their ‘AI’ powered products. Another problem that limits AI's ability within security is being able to train AI models on specific security data. Models like ChatGPT are great for showcasing what an AI model can do, but are far from reliable for specific use cases. As Dean De Beer said on the AI + A16z Podcast “ChatGPT is a great hammer, [but] I would certainly not recommend it when you start to productize and perform tasks at scale”. Training a security focused model right now needs extraordinary computational power and even if - and it's a big if - a small company could buy enough GPUs to perform this, the cost would be astronomical! “We have not hit the moment where AI is more than a product side feature”
Caleb Sima AI CyberSecurity Podcast This means only a few big players have the ability to implement AI features that go beyond sending generic data to generic AI trained models. Today AI mostly remains a side-feature in the majority of security tools, almost a gimmick marketing teams can blast into the world. How can you do AI right? For all my skepticism, I will admit there are a few interesting implementations of AI. Purple AI  by SentinelOne, marketed as an AI security Analyst, certainly is impressive to see in action (at least in a demo). So how can you use AI, especially if you don’t have the budget of a Formula 1 team? Empower humans, don’t try and replace them
The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans.


Focus on what AI is good at right now, classification.
The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities.


Make sure you use the right model
The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Will the ‘A’ ever be ‘I’ in security? Intelligence is a bold - and fairly ambiguous - word. Each day AI is finding itself more embedded into our everyday lives and security tooling has huge potential for AI. I have little doubt that effective innovative implementations of AI will absolutely be available in security tooling. But we aren’t there yet. Security vendors have been stretching their AI abilities for years, and when ChatGPT threw them a lifeline they tried to bend the tools to their slogans. The reality is that effective implementation will be different to what we had first imagined. AI will be effective when the slogans match the tool, and not the other way around. In October 2022 I published the most embarrassing article of my career, but 2 years later I am starting to think I might have been right. In October 2022 I published the most embarrassing article of my career, but 2 years later I am starting to think I might have been right. Strolling through the colossal vendor hall at BlackHat in 2022, every stall made some bold claim about AI. “ AI-powered analytics”, “Combining AI with real-time detection” or some other slogan. Whenever you talk to the sales team to ask exactly what their AI did, you either get a succession of AI jargon or a claim about secret research teams in underground vaults in Antarctica. AI-powered analytics”, “Combining AI with real-time detection” Following on from that, I confidently published an article calling out AI as nothing more than marketing BS… “BlackHat 2022, more A than I”. Fast forward a couple of months and ChatGPT was released. All of a sudden, AI became real and suddenly my brilliant article was a joke, and blatantly wrong. But, after strolling through the BlackHat 2024 vendor hall this year, maybe I wasn’t so far off. It is undeniable that AI is indeed real, and that it has huge potential to change the landscape. And in many ways it already has. Something I didn’t believe 2 years ago. But, as you walk through the expansive halls of Mandalay Bay for BlackHat 2024, you still see the same slogans and I still have yet to find a meaningful implementation of AI in security that I can point to that remotely matches the bold claims. “One thing I notice [in BlackHat] was that people were tired of AI” Ashish Rajan AI CyberSecurity Podcast “One thing I notice [in BlackHat] was that people were tired of AI” “One thing I notice [in BlackHat] was that people were tired of AI” Ashish Rajan AI CyberSecurity Podcast Ashish Rajan AI CyberSecurity Podcast AI CyberSecurity Podcast AI CyberSecurity Podcast Why has AI failed to be effective in security? Why has AI failed to be effective in security? To preempt your objections, I am aware security vendors are indeed using AI within their products. But so far there are no game-changing results that we can point to, and a lot of this comes down to how they are implementing AI. Following the release of ChatGPT and the subsequent AI models, a lot of vendors rushed to add these new tools to their platforms. The go-to implementation was ‘AI-powered remediation guidelines’ and ‘additional context around alerts’. But here is the reality behind this: that implementation helps mostly the vendor not the user.  This simply means they don’t need to spend as much time on their documentation. Not only that, contextual information is always at least 2 years out of date for most commercial AI models. The reality is that a lot of this implementation was simply a way to make good on vendors' promises behind their ‘AI’ powered products. Another problem that limits AI's ability within security is being able to train AI models on specific security data. Models like ChatGPT are great for showcasing what an AI model can do, but are far from reliable for specific use cases. As Dean De Beer said on the AI + A16z Podcast “ChatGPT is a great hammer, [but] I would certainly not recommend it when you start to productize and perform tasks at scale” . Training a security focused model right now needs extraordinary computational power and even if - and it's a big if - a small company could buy enough GPUs to perform this, the cost would be astronomical! AI + A16z Podcast AI + A16z Podcast “ChatGPT is a great hammer, [but] I would certainly not recommend it when you start to productize and perform tasks at scale” “We have not hit the moment where AI is more than a product side feature” Caleb Sima AI CyberSecurity Podcast “We have not hit the moment where AI is more than a product side feature” “We have not hit the moment where AI is more than a product side feature” Caleb Sima AI CyberSecurity Podcast AI CyberSecurity Podcast AI CyberSecurity Podcast AI CyberSecurity Podcast This means only a few big players have the ability to implement AI features that go beyond sending generic data to generic AI trained models. Today AI mostly remains a side-feature in the majority of security tools, almost a gimmick marketing teams can blast into the world. How can you do AI right? For all my skepticism, I will admit there are a few interesting implementations of AI. Purple AI by SentinelOne, marketed as an AI security Analyst, certainly is impressive to see in action (at least in a demo). Purple AI Purple AI Purple AI So how can you use AI, especially if you don’t have the budget of a Formula 1 team? Empower humans, don’t try and replace them
The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans. Focus on what AI is good at right now, classification.
The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities. Make sure you use the right model
The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Empower humans, don’t try and replace them The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans. Empower humans, don’t try and replace them The simple truth is that right now, we don’t and shouldn’t trust AI to replace a human. It will always be behind if we can’t trust it, and its ability is limited. AI is a powerful tool to assist humans. Focus on what AI is good at right now, classification. The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities. Focus on what AI is good at right now, classification. The original strength of AI was classification. We can feed AI lots of context around a vulnerability and get a model to classify and prioritize vulnerabilities. Make sure you use the right model The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Make sure you use the right model The more prioritized we get with AI, the more we need a model that is equally specialized. Training these models ourselves may be out of the realm of possibilities for most. But there are plenty of models that will outperform the standard hammers. Will the ‘A’ ever be ‘I’ in security? Intelligence is a bold - and fairly ambiguous - word. Each day AI is finding itself more embedded into our everyday lives and security tooling has huge potential for AI. I have little doubt that effective innovative implementations of AI will absolutely be available in security tooling. But we aren’t there yet. Security vendors have been stretching their AI abilities for years, and when ChatGPT threw them a lifeline they tried to bend the tools to their slogans. The reality is that effective implementation will be different to what we had first imagined. AI will be effective when the slogans match the tool, and not the other way around.

AI is Failing in Cyber

KI misluk in kuber

Süni intellekt kiberdə uğursuzluğa düçar olur

La IA está fallando en el ámbito cibernético

AI epäonnistuu kyberissä

AIはサイバー空間で失敗している

AI កំពុង​បរាជ័យ​ក្នុង​អ៊ីនធឺណិត

AI žlunga kibernetinėje erdvėje

A IA está falhando no ciberespaço

AI iri Kukundikana muCyber

AI กำลังล้มเหลวในโลกไซเบอร์

AI سائبر میں ناکام ہو رہا ہے۔

人工智能在网络领域失败

At this moment in time, virtually no industries remain that are untouched by AI penetration. From finance to healthcare, manufacturing to education, sectors are witnessing nothing less than an AI-powered revolution, one that is transforming the landscape of organizational operations, particularly in terms of how decisions are made. As businesses increasingly harness the power of AI-enabled analytics to tap into vast volumes of data, complex questions emerge. How much AI is too much? How can we best combine human experience, ingenuity and instinct with Machine Learning and Large Language Models (LLMs)? These queries are likely to ignite fierce debate for many years to come. One thing that cannot be denied is the utility and power of AI: even the most ardent skeptic must grudgingly acknowledge the technology’s potentially enormous impact on productivity, even if they bemoan the downstream effect on, say, human jobs or wages. Embracing the AI Revolution By far the most talked-about topic relating to AI’s effect on businesses is its ability to reduce dependence on manual labor. This article will not dwell on the moral ramifications of this trend, but rather consider the technology’s impact on organizations themselves. The most obvious example of AI in action is digital customer support agents, which are increasingly used to replace human operators. These chatbot agents are trained on common customer queries and provide 24/7 assistance, handling mostly routine inquiries with unprecedented efficiency. A McKinsey report suggests that about half of all customer service tasks could be automated by the end of the decade. Real-time production process monitoring, automated accounting, and simplified regulatory reporting are just a few other examples of how AI is transforming traditional business functions. As for the impact on workplaces themselves, PwC’s 2024 AI Jobs Barometer provides plenty of food for thought. The PwC survey shows that 84% of CEOs whose firms have begun adopting AI believe it will increase employee efficiency. Moreover, 70% of leaders anticipate AI will significantly change the way their business creates, delivers, and captures value over the next three years. AI can certainly analyze huge volumes and automate processes that would be time-consuming for a human to carry out. But what about making meaningful decisions? Can an AI-powered system really make educated judgment calls on matters of recruitment, budgeting, and so on? GenLayer, an intelligent blockchain that operates at the intersection of AI and distributed ledger technology, believes the answer could be yes. That’s because its AI-powered smart contracts are able to leverage Large Language Models and, unlike regular smart contracts, access the internet to autonomously make complex decisions in a decentralized manner. Indeed, these so-called Intelligent Contracts can handle a variety of non-deterministic operations including processing natural language and making complex judgments without human intervention. In fact, they’re flexible enough to incorporate multiple legal frameworks – meaning they could even be used to make nuanced decisions in matters of jurisprudence. This August, investors backed GenLayer to the tune of $7.5 million, with one of them, North Island Ventures’ Travis Scher, stating the intelligent blockchain could “enhance current decentralized applications (dApps) and unlock new potentials.” The Arrival of AI-Powered DAOs The way decisions are actually made is through an organizational structure familiar to those working in blockchain: DAOs, or decentralized autonomous organizations. The difference is, with GenLayer the DAOs are AI-powered. The breakthrough represents a new paradigm in organizational management, offering more transparent, efficient, and data-driven means of decision-making. Any organization that relies on real-time data to make decisions (virtually every business, in truth) can leverage GenLayer to make judgments that would otherwise fall on the shoulders of executives. Armed with accurate internet-sourced information, the DAO can even take decisions that accord with a constitution encoded directly into its Intelligent Contract. In the 13th century, the Consulate of the Sea provided a unified legal framework for maritime trade across borders. What GenLayer hopes to achieve is consulate status in the age of AI, offering a global, adaptable jurisdiction that enables efficient and secure AI-driven commerce. Why AI-Powered Decisions? AI’s ability to process enormous data sets, and to do so quickly, makes it the perfect decision making system or tool. After all, not all judgments can be made based purely on computation. While some decisions can be delegated to AI, others require human input. Nonetheless, even in the second scenario AI plays a role in presenting to the decision-maker the mother lode of data and insight required to make the right call. The phrase AI-powered decision-making is apposite: the AI remains a tool, and its integration into organizational processes, coupled with the emergence of technologies like GenLayer, promises to bring about a dynamic new era of operational efficiency. As we navigate this evolving landscape, it’s vital for leaders of businesses and governments to stay informed about these technologies and consider how they can be leveraged to create value, improve decision-making, and drive innovation. While challenges and questions remain, the potential benefits of AI and intelligent blockchain technologies are immense. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR At this moment in time, virtually no industries remain that are untouched by AI penetration. From finance to healthcare, manufacturing to education, sectors are witnessing nothing less than an AI-powered revolution, one that is transforming the landscape of organizational operations, particularly in terms of how decisions are made. As businesses increasingly harness the power of AI-enabled analytics to tap into vast volumes of data, complex questions emerge. How much AI is too much? How can we best combine human experience, ingenuity and instinct with Machine Learning and Large Language Models (LLMs)? These queries are likely to ignite fierce debate for many years to come. One thing that cannot be denied is the utility and power of AI: even the most ardent skeptic must grudgingly acknowledge the technology’s potentially enormous impact on productivity, even if they bemoan the downstream effect on, say, human jobs or wages. Embracing the AI Revolution By far the most talked-about topic relating to AI’s effect on businesses is its ability to reduce dependence on manual labor. This article will not dwell on the moral ramifications of this trend, but rather consider the technology’s impact on organizations themselves. The most obvious example of AI in action is digital customer support agents, which are increasingly used to replace human operators. These chatbot agents are trained on common customer queries and provide 24/7 assistance, handling mostly routine inquiries with unprecedented efficiency. A McKinsey report suggests that about half of all customer service tasks could be automated by the end of the decade. report report Real-time production process monitoring, automated accounting, and simplified regulatory reporting are just a few other examples of how AI is transforming traditional business functions. As for the impact on workplaces themselves, PwC’s 2024 AI Jobs Barometer provides plenty of food for thought. 2024 AI Jobs Barometer 2024 AI Jobs Barometer The PwC survey shows that 84% of CEOs whose firms have begun adopting AI believe it will increase employee efficiency. Moreover, 70% of leaders anticipate AI will significantly change the way their business creates, delivers, and captures value over the next three years. AI can certainly analyze huge volumes and automate processes that would be time-consuming for a human to carry out. But what about making meaningful decisions? Can an AI-powered system really make educated judgment calls on matters of recruitment, budgeting, and so on? GenLayer , an intelligent blockchain that operates at the intersection of AI and distributed ledger technology, believes the answer could be yes. That’s because its AI-powered smart contracts are able to leverage Large Language Models and, unlike regular smart contracts, access the internet to autonomously make complex decisions in a decentralized manner. GenLayer GenLayer Indeed, these so-called Intelligent Contracts can handle a variety of non-deterministic operations including processing natural language and making complex judgments without human intervention. In fact, they’re flexible enough to incorporate multiple legal frameworks – meaning they could even be used to make nuanced decisions in matters of jurisprudence. This August, investors backed GenLayer to the tune of $7.5 million, with one of them, North Island Ventures’ Travis Scher, stating the intelligent blockchain could “enhance current decentralized applications (dApps) and unlock new potentials.” backed GenLayer backed GenLayer The Arrival of AI-Powered DAOs The way decisions are actually made is through an organizational structure familiar to those working in blockchain: DAOs, or decentralized autonomous organizations. The difference is, with GenLayer the DAOs are AI-powered. The breakthrough represents a new paradigm in organizational management, offering more transparent, efficient, and data-driven means of decision-making. Any organization that relies on real-time data to make decisions (virtually every business, in truth) can leverage GenLayer to make judgments that would otherwise fall on the shoulders of executives. Armed with accurate internet-sourced information, the DAO can even take decisions that accord with a constitution encoded directly into its Intelligent Contract. directly In the 13th century, the Consulate of the Sea provided a unified legal framework for maritime trade across borders. What GenLayer hopes to achieve is consulate status in the age of AI, offering a global, adaptable jurisdiction that enables efficient and secure AI-driven commerce. Why AI-Powered Decisions? AI’s ability to process enormous data sets, and to do so quickly, makes it the perfect decision making system or tool. After all, not all judgments can be made based purely on computation. While some decisions can be delegated to AI, others require human input. Nonetheless, even in the second scenario AI plays a role in presenting to the decision-maker the mother lode of data and insight required to make the right call. or The phrase AI-powered decision-making is apposite: the AI remains a tool, and its integration into organizational processes, coupled with the emergence of technologies like GenLayer, promises to bring about a dynamic new era of operational efficiency. As we navigate this evolving landscape, it’s vital for leaders of businesses and governments to stay informed about these technologies and consider how they can be leveraged to create value, improve decision-making, and drive innovation. While challenges and questions remain, the potential benefits of AI and intelligent blockchain technologies are immense. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

How Organizational Decision-Making is Getting an AI Shakeup – And Why It’s a Good Thing

Як прыняцце арганізацыйных рашэнняў перабудоўваецца з дапамогай штучнага інтэлекту - і чаму гэта добра

Как вземането на организационни решения претърпява промяна на ИИ – и защо това е нещо добро

Jak organizační rozhodování je získání AI Shakeup – a proč je to dobrá věc

Cómo la toma de decisiones organizacionales está siendo revolucionada por la IA (y por qué es algo positivo)

Kuinka organisaation päätöksenteko saa AI-muutoksen – ja miksi se on hyvä asia

Como a toma de decisións organizativas está a ter un cambio de intelixencia artificial e por que é unha boa cousa

איך קבלת החלטות ארגונית מקבלת שיקום בינה מלאכותית - ולמה זה דבר טוב

組織の意思決定が AI によって大きく変化している理由とそれが良いこと

Como a tomada de decisões organizacionais está passando por uma reformulação da IA – e por que isso é uma coisa boa

Kuita Sarudzo YeSangano-Kuita Kuwana AI Shakeup - Uye Nei Icho Chinhu Chakanaka

Si është Marrja e Vendimeve Organizative duke marrë një ndryshim të AI - dhe pse është një gjë e mirë

Jinsi Uamuzi wa Kishirika Unavyopata Shakeup ya AI - Na Kwa Nini Ni Jambo Jema

Will Artificial Intelligence Take Over Humans?

አርቴፊሻል ኢንተለጀንስ ሰዎችን ይቆጣጠራሉ?

¿La Inteligencia Artificial superará a los humanos?

L'intelligenza artificiale prenderà il sopravvento sugli esseri umani?

人工知能は人間を支配するのか？

Жасанды интеллект адамдарды жаулап ала ма?

Mayele ya mayele ekozwa bato?

¿Inteligencia Artificialchu Runakunata hap’ikapunqa?

Va prelua inteligența artificială pe oameni?

Оё зеҳни сунъӣ одамонро тасарруф мекунад?

ปัญญาประดิษฐ์จะเข้ามาแทนที่มนุษย์ได้หรือไม่?

Maaagaw ba ng Artipisyal na Katalinuhan ang mga Tao?

人工智能会取代人类吗？

This week, I am taking a slight detour from my usual articles that explain AI Concepts or coding tutorials. Yes, it's a philosophical, nostalgic tour down the road I was fortunate enough to tread all along. One of the rare privileges of starting in a field quite early is that you see the field grow like your baby. When I started my Masters in Computer Vision a decade ago, a CS graduate friend of mine asked, “What does computer vision even mean?” It's a trivial question today with enough videos of computer vision tasks like segmentation (see below) floating around the internet. A result of image segmentation — a Computer Vision task that was all over the internet when deep learning took off a decade ago. So, what has changed in the last decade? There are always two sides to a coin. Also, growth can be both good and bad. Closed Community — Small Was Beautiful They say that people in Iceland are somehow related to each other and if someone is beyond your second cousin, you are good to marry them. Likewise, the AI community was close-knit. Gone are those feelings of a tight-knit community. When interacting with anyone from research you generally knew which group they belonged to. You somehow would relate to and respect their work as you read and appreciated their impact on the field. As the field is blowing up endlessly, it's becoming increasingly difficult to even say that you know someone’s work, forget about them. There are too many new names and novel branches that it's even challenging to keep track of. As another example, PyTorch was in its infancy. The Slack community was so small and helpful that the developers of PyTorch directly responded to questions we had about using the library. This encouraged learning more and more about it. Today, the framework is so mature, and new frameworks like LangChain and Llamaindex have propped up. The focus is all on LLMs over any other branch of AI. Hardware Luxury Those days, we used to train a deep neural network on a single GPU to create something impactful. Most work published in premier conferences like CVPR, NeurIPS, and ICML could be trained and replicated on a single 8 GB GPU machine or in the worst case, a single machine with 4 GPUs in rare cases. An image of the latest GPU clusters used by Tesla for AI (source: https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer) I vividly remember how happy I was to buy a single commodity GPU with just 8 GB of RAM to participate in Kaggle competitions. Some of the winning solutions from Kaggle Grandmasters trained deep learning models on a single GPU machine at home. Today’s AI world needs a GPU cluster to train foundational models. Even fine-tuning these models needs 24GB GPUs which are beasts, expensive, and affordable only by businesses with an “AI budget.” Skillset Demand Those were days when the field did not yet get the AI wrapper around it. Recruiters were given this alien task of hunting for “deep learning” engineers. Recruiters and start-up founders were hunting for deep learning experts through all channels. It was a norm to receive regular messages on LinkedIn asking me to join their team as a deep learning engineer. A screenshot of a job advert for a Machine Learning Engineer posted only 5 days ago receiving over 100 applicants! The current situation is that openings for “Machine Learning Engineer” get over 100 applications on LinkedIn well within a day of advertising. Take a look at the screenshot above if you find it hard to believe. The question remains as to how relevant the applicant's skills are to the job spec. But the market is saturated with skills — quite quickly! Sub-skills — Operations and Architecture Growth means more diversity and opportunity. There are new roles like ML-ops, LLM-ops, and ML-architects cropping up. Gone are those days of tiny, single-model files (< 1 GB). The growth of models both in size and capabilities has given rise to new skills in deploying and maintaining them. Moreover, training and deployment of models are being automated with tools such as MLFLow. The cloud infrastructure for training needs to be sophisticated enough. All this has given rise to full-time roles with dedicated responsibilities. Bye Bye ML Engineer, Hello AI Engineer The most fun in working in AI is writing the model architecture, and training the model from scratch using our in-house data. Though this involves pre-processing the data a lot, training models, and visualizing the training results used to be a lot of fun. There was/still is a specialized role for this called Machine Learning (ML) Engineer. The development of foundation models from tech giants is redefining this role. As model sizes grow, training budgets are enormous. As a matter of fact, the cost of training the LLama 2 model was $20m for Meta. Clearly start-ups or organizations trying to adopt AI do not wish to throw away this sum of money. It is now established that foundation models are for the tech giants to develop, with the exception of some companies like Mistral and Anthropic. Sadly, this means the ML Engineer role is getting cast into an AI Engineer role. The ML Engineer role was all about developing model architectures, training, and evaluating them. The new AI Engineer role mostly involves developing APIs or invoking APIs provided by Tech giants (OpenAI, Meta, and Google) to prompt the foundation models. In rare cases, it involves fine-tuning these foundation models. But companies have the option to build RAG pipelines or use the foundation models “as-is” over fine-tuning them. Conclusion To conclude, I see this as a slow amalgamation of the Software engineering and Machine Learning roles. The line between software engineers and deep learning experts is fading. So, in the years to come, software engineers will be AI engineers who work alongside foundation models, both to write code and to solve customer needs. Furthermore, in the coming years, companies will be grouped into two categories — the AI product and AI services companies. AI product companies would again be OpenAI, Meta, and Google to develop foundational models. AI services companies would provide API services by fine-tuning or developing RAG-style pipelines around AI foundational models to serve their customers. Lastly, does the spike in job applications indicate an impending bursting of the bubble like the dot com bubble? I feel so, YES. But let’s wait and watch it… Shout Out Hope that was useful. If you liked this article, why not follow me on Twitter for research updates, video tutorial links, and new AI tools from top AI labs? Also, please subscribe to my YouTube channel where I explain AI concepts and papers visually. This week, I am taking a slight detour from my usual articles that explain AI Concepts or coding tutorials. Yes, it's a philosophical, nostalgic tour down the road I was fortunate enough to tread all along. One of the rare privileges of starting in a field quite early is that you see the field grow like your baby. When I started my Masters in Computer Vision a decade ago, a CS graduate friend of mine asked, “What does computer vision even mean?” It's a trivial question today with enough videos of computer vision tasks like segmentation (see below) floating around the internet. A result of image segmentation — a Computer Vision task that was all over the internet when deep learning took off a decade ago. So, what has changed in the last decade? There are always two sides to a coin. Also, growth can be both good and bad. Closed Community — Small Was Beautiful Closed Community — Small Was Beautiful They say that people in Iceland are somehow related to each other and if someone is beyond your second cousin, you are good to marry them. Likewise, the AI community was close-knit. Gone are those feelings of a tight-knit community. When interacting with anyone from research you generally knew which group they belonged to. You somehow would relate to and respect their work as you read and appreciated their impact on the field. As the field is blowing up endlessly, it's becoming increasingly difficult to even say that you know someone’s work, forget about them. There are too many new names and novel branches that it's even challenging to keep track of. As another example, PyTorch was in its infancy. The Slack community was so small and helpful that the developers of PyTorch directly responded to questions we had about using the library. This encouraged learning more and more about it. Today, the framework is so mature, and new frameworks like LangChain and Llamaindex have propped up. The focus is all on LLMs over any other branch of AI. Hardware Luxury Hardware Luxury Those days, we used to train a deep neural network on a single GPU to create something impactful. Most work published in premier conferences like CVPR, NeurIPS, and ICML could be trained and replicated on a single 8 GB GPU machine or in the worst case, a single machine with 4 GPUs in rare cases. An image of the latest GPU clusters used by Tesla for AI (source: https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer ) https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer https://www.hpcwire.com/off-the-wire/tesla-to-roll-out-advanced-supercomputer I vividly remember how happy I was to buy a single commodity GPU with just 8 GB of RAM to participate in Kaggle competitions. Some of the winning solutions from Kaggle Grandmasters trained deep learning models on a single GPU machine at home. Today’s AI world needs a GPU cluster to train foundational models. Even fine-tuning these models needs 24GB GPUs which are beasts, expensive, and affordable only by businesses with an “AI budget.” Skillset Demand Skillset Demand Those were days when the field did not yet get the AI wrapper around it. Recruiters were given this alien task of hunting for “deep learning” engineers. Recruiters and start-up founders were hunting for deep learning experts through all channels. It was a norm to receive regular messages on LinkedIn asking me to join their team as a deep learning engineer. A screenshot of a job advert for a Machine Learning Engineer posted only 5 days ago receiving over 100 applicants! The current situation is that openings for “Machine Learning Engineer” get over 100 applications on LinkedIn well within a day of advertising. Take a look at the screenshot above if you find it hard to believe. The question remains as to how relevant the applicant's skills are to the job spec. But the market is saturated with skills — quite quickly! Sub-skills — Operations and Architecture Growth means more diversity and opportunity. There are new roles like ML-ops, LLM-ops, and ML-architects cropping up. Gone are those days of tiny, single-model files (< 1 GB). The growth of models both in size and capabilities has given rise to new skills in deploying and maintaining them. Moreover, training and deployment of models are being automated with tools such as MLFLow. The cloud infrastructure for training needs to be sophisticated enough. All this has given rise to full-time roles with dedicated responsibilities. Bye Bye ML Engineer, Hello AI Engineer The most fun in working in AI is writing the model architecture, and training the model from scratch using our in-house data. Though this involves pre-processing the data a lot, training models, and visualizing the training results used to be a lot of fun. There was/still is a specialized role for this called Machine Learning (ML) Engineer. The development of foundation models from tech giants is redefining this role. As model sizes grow, training budgets are enormous. As a matter of fact, the cost of training the LLama 2 model was $20m for Meta. Clearly start-ups or organizations trying to adopt AI do not wish to throw away this sum of money. It is now established that foundation models are for the tech giants to develop, with the exception of some companies like Mistral and Anthropic. Sadly, this means the ML Engineer role is getting cast into an AI Engineer role. The ML Engineer role was all about developing model architectures, training, and evaluating them. The new AI Engineer role mostly involves developing APIs or invoking APIs provided by Tech giants (OpenAI, Meta, and Google) to prompt the foundation models. In rare cases, it involves fine-tuning these foundation models. But companies have the option to build RAG pipelines or use the foundation models “as-is” over fine-tuning them. Conclusion To conclude, I see this as a slow amalgamation of the Software engineering and Machine Learning roles. The line between software engineers and deep learning experts is fading. So, in the years to come, software engineers will be AI engineers who work alongside foundation models, both to write code and to solve customer needs. Furthermore, in the coming years, companies will be grouped into two categories — the AI product and AI services companies. AI product companies would again be OpenAI, Meta, and Google to develop foundational models. AI services companies would provide API services by fine-tuning or developing RAG-style pipelines around AI foundational models to serve their customers. Lastly, does the spike in job applications indicate an impending bursting of the bubble like the dot com bubble? I feel so, YES. But let’s wait and watch it… Shout Out Hope that was useful. If you liked this article, why not follow me on Twitter for research updates, video tutorial links, and new AI tools from top AI labs? If you liked this article, why not follow me on Twitter for research updates, video tutorial links, and new AI tools from top AI labs? Twitter Twitter Also, please subscribe to my YouTube channel where I explain AI concepts and papers visually. Also, please subscribe to my YouTube channel where I explain AI concepts and papers visually. YouTube channel YouTube channel

Gone Are Those Days of AI

AI এর সেই দিনগুলি চলে গেছে

Die Zeiten der KI sind vorbei

Atrás quedaron esos días de la IA

Fini l’époque de l’IA

एआई के वे दिन चले गए

AIの時代は終わった

AI의 시대는 지났다

Longe vão aqueles dias de IA

Прошли те времена искусственного интеллекта

Yapay Zekanın O Günleri Geride Kaldı

Đã qua rồi những ngày của AI

人工智能时代已经一去不复返

Photo Credit, recovered from stomach illness. In original publication of this interview, “ ,” this photo was rejected in favor of . In the spirit of additional context when republishing, we added the original image above, about two dozen contextual links, and embedded a fun video at the end. David Smooke David Smooke Founder & CEO at HackerNoon — Digital Publishing Evolution, AI Impact, Content Management Innovation, Community Growth, Future Visions a kinder, more professional headshot from AI Time Journal: 1. David, can you share the origin story of HackerNoon and what motivated you to create a platform focused on technology storytelling? Flor Laorga Hey, ! Thanks for having me. I’m . I started and run We publish technology stories and build publishing software. Before HackerNoon, I was managing 400+ contributing writers on the , which is now a tech unicorn. Back then, it was cumbersome to manage the writer accounts, content, and editorial review process through existing systems. Building for the contributing writers opened doors. When I started this company, I built many sites, most failed, and HackerNoon found the right mix of community, customers, and - just a cool place to spend time on the internet. Millions visit us every month for free to read our 100k+ stories across . By offering editing and distribution, many technology practitioners, writers, and companies find value in . AI Time Journal David Smooke HackerNoon. blog for SmartRecruiters thousands of tech topics publishing their stories with us 2. With HackerNoon’s commitment to keeping its library free of paywalls and popups, how do you navigate the challenge of generating revenue while maintaining an open-access philosophy? It’s a battle! Historically, we’ve 2x-ed revenue most years of the company’s existence, and our yearly revenue is in the low millions. Media is a brutal industry, and tech’s an up-and-down world where layoffs and profits seem to coincide with each gen ai launch or full moon. Our top source of revenue right now is . Companies like Brave, NVIDIA and AWS buy monthly ads on all stories within a category, like or . Our second and third sources of revenue are and . Business blogging is about getting more readers for blog posts owned by technology companies like , , , and . We leverage our publishing system and owned media channels to improve and further distribute their content. Quality technology blog posts require a lot of effort from talented people. The labor that goes into each post on a tech company’s main blog is worth thousands if not tens of thousands of dollars - companies work with HackerNoon to get those technology blog posts more readers. In terms of writing contests, companies award the authors of the best stores on a tag ( ), pay HackerNoon to run the contest, and ultimately, more technology content enters our library to serve the readers. The intention is to create discussion about a niche technology topic. ad placements by content relevancy programming data science business blogging writing contests MinIO Ockam Blockchain Oracle Summit BrightData like how Aptible awarded $18k to best #DevOps stories 3. As AI continues to evolve, how do you see it transforming the landscape of digital publishing, especially in terms of content creation and distribution? AI’s a massive wrench into productivity and authenticity. Every current job relying on a keyboard or a phone has/will have an AI assistant, and an AI will be on deck with you in whatever UX replaces the keyboard or the phone next. On the positive side, small capable teams can have greater extensions of themselves and their work. AI and AI agents can move portions of projects forward. For example, we introduced , and had to assign every story in our 100k+ library to one category. 100k human decisions would have taken a while, but a chatgpt spreadsheet plugin was able to assign each story a technology category very easily. On the negative side, there is spamflation everywhere. Why must the builders of our generation rush to make AI tools to spam instead of AI tools to assist? In 2022, one contributing writer, , made the case that . There is just so, so much content being generated by AI. It’s human-like, but it’s not human. Which brings me to authenticity. Who authored the content will become more important in the future as it indicates trust in a uniquely human way. technology categories Jeremy Hillpot 99.99% of all content would be AI-created by 2025 4. In your experience, what are the most effective strategies for managing and growing a small team of editors, developers, and startup individuals to produce high-quality technology content? We’re still small enough that everyone’s an individual contributor first, even the C-suite and board, which are both just me and . When you’re under 20 people, everyone has to carry their own weight. When it comes to managing a small remote team, reducing meetings is a good start. Editorial, sales, and product, each only have one internal meeting a week. This provides time to do the actual work and makes the meetings we do have will carry more importance. We do highly encourage one-off, project-focused, short meetings. Weekly product meetings are the longest and loaded with demos, recaps, and debates. But it connects the group, and designers have a full view into what developers are doing and vice versa. Staff happiness and sense of purpose, or lack thereof, will show in the little actions with our community. I work to create a company where a small team of elite contributors can thrive. Linh Dao Smooke 5. Could you discuss the development process of HackerNoon’s content management system and what makes it uniquely suited for technology professionals? At , we’ve built a lot of systems to create, edit, and distribute content at scale. Technologists spend a lot of their day on the keyboard (like I am now) and understand that documenting their journey and professional expertise will lead to more long-term success. Our CMS helps you create content with help from , , and . The CMS also integrates with our email and social media distribution engines, and for good measure. We live between social media where anyone can post and traditional media where staff content production drives content creation and consumption. There is a vibrant community of millions of technologists on HackerNoon. Plus, our CMS is built to seamlessly port content around the web. For example, with the HackerNoon CMS, we’ll be launching AI-specific publications in 2024, such as , , and more. HackerNoon templates AI a human editorial review backups up all content on the blockchain FewShot.Tech TextModels.Tech 6. How has HackerNoon leveraged AI to enhance its platform? and what lessons have you learned about AI's role in spam creation and prevention? HackerNoon uses AI and machine learning to improve grammar, , , and . Maybe Clippy was ahead of its time? Writers have and will continue to have smart AI assistants. AI can not and should not replace the writer, but it should and can assist the writer and editor throughout the publishing process. We are documenting our approach to editorial at . With the rise of LLMs, we’ve seen an influx of AI story spam submissions. Other blogging platforms are hosting so much of these types of machine generated stories right now. We are actively taking steps to not become a wasteland of AI’s musings. Because every story is reviewed before publication (with about half being rejected), I think our average story quality is higher than other blogging platforms. Every story submission automatically enters into plagiarism and AI writing detection. We highlight what sections are likely, possibly, and unlikely to be written by AI. It’s all about confidence levels. Ultimately, human editors make the final call in interpreting the machines’ reports. translate stories generate featured images suggest possible headlines EditingProtocol.com 7. Given your expertise in product management and editing, how do you balance innovation with user experience when introducing new features or updates to your platform? The core has to work for any new feature to matter. Our core is the blog post. We work to publish better blog posts and build for what comes before and what comes after the blog post. New functionalities cannot come at the expense of a delightful reading experience and a smooth publishing experience. It’s got to load quickly and the relevant ads have to surface at the right place for customers. There’s always more projects that could be done to improve the core. We also have been using more API driven journalism in order to gain more traffic. What I mean by that is aggregating relevant data and business information so that we can build pages that cover , , and . By connecting stories around the web to relevant companies, technologies and currencies, we can make HackerNoon a stronger destination for technology education and discovery. In terms of what to build next, we’ve built a simple product management system within Notion, where we detail product cards, weighing which user it would benefit, the expected input/output of the project, and then generally contributors choose their own projects. tech companies technologies currencies 8. As a leader, how do you foster a culture of creativity and innovation within your team, and what practices have you found most effective in motivating and inspiring your staff? The simplest way is to lead by example. I’m contributing to projects more than managing projects. The people who make this business happen are good people. If I want people to blog with HackerNoon, I need to blog with HackerNoon. almost 300 technology stories and this year, my goal is a post a week. Our staff is remote and around the world, we have people in Munich, Goa, Florida, Hanoi, Buenos Aires, Bangkok, Michigan, Lahore, Lagos, and of course where I live in Colorado. It’s a lot of different cultures. And a lot of different ideas. Everyone on the team started by applying via our , so one thing that unites us is reading and using HackerNoon before joining HackerNoon. Everyone also has stock in the company, that helps with buy-in and enthusiasm. We agree on metrics we want to move, but we might not agree on how to get there. It generally works best when contributors choose their own paths. I’ve published careers page 9. With your insights into the future of work and career advice, what guidance would you offer to professionals aiming to break into the rapidly changing tech industry? Make things! Apps, models, prototypes, blueprints, communities, whatever - just start making. Your first technology thing probably won’t be your best or your future, but if you make more things in tech they become less like just technology things and more like just essential everyday things.  In terms of breaking in, it’s a tough market in technology/media right now with mass layoffs & unpredictable demands. Having your own functioning project, app, site, academic paper, or even just a blog can set you apart from other candidates. I recommend looking to apply at companies that make technologies or products that you use and respect. Write short and simple cold emails that explain why you’d like to work together.  Take a step back and look at our daily lives - and what you think our daily lives might be in ten years - technology isn’t going anywhere. It’s becoming more ingrained. Closer to our eyes. I’d also add that getting into tech doesn’t necessarily mean staying in tech forever, but better understanding technology will help however your career grows. 10. How do you envision the role of digital publishers like HackerNoon in shaping public discourse and understanding of complex topics like cryptocurrency, AI, and blockchain technology? We normalize it. I learned about Bitcoin from a HackerNoon story submission. . The technology’s finally caught up; let’s talk about it. I learn a lot by reading what contributors think could be next. As many people on HackerNoon are publishing about what they’re building, we usually learn about newer technologies before they reach more mainstream learning destinations. is a great blogging example for how the people making the next internet are documenting and teaching others along the way. That author, , kept writing that . Meanwhile, we kept integrating our tech stack with more Web3 technologies. Fun Easter egg, you can read . Publishing platforms are still learning how to detail where and how and when AI contributed. We add an “ on all stories where AI assisted the writer. At Meta, I see they’re implementing a , I’ll be monitoring how that goes. Publishers should provide transparency to the reader when it comes to AI content creation. Hopefully, the internet can reach reasonable standards for accrediting AI. Milton Friedman’s been talking about the need for ecash since 1999 Learn Blockchain by Building One Daniel van Flymen blog post into a book the entire HackerNoon library via blockchain signatures here AI Assisted” emoji credibility indicator watermark checking and display system for all AI generated images 11. Finally, can you share a personal story or moment from your journey with HackerNoon that has been particularly meaningful or impactful to you? Well, it’s been a long road! Hard to pick just one. Recently, meeting Pham Minh Chinh, the Prime Minister of Vietnam, stands out. He invited . Was cool to see up close how a head of state walks, talks and listens to pitches about how the country could become a bigger player in the technology industry. Another one that comes to mind is when we launched with our own CMS, it was janky, it was pixelated, and it mostly worked but didn’t work in all the right spots. It was ours and it was beautiful. HackerNoon! I’ll leave with you this : Linh to be a technology consultant as the country forms its semiconductor strategy HackerNoon.com previously unpublished video of the moment we went live https://www.youtube.com/watch?v=rdoh8z0NXzU&embedable=true

“We are actively taking steps to not become a wasteland of AI’s musings” says HackerNoon Founder/CEO

হ্যাকারনুন প্রতিষ্ঠাতা/সিইও বলেছেন, "আমরা সক্রিয়ভাবে পদক্ষেপ নিচ্ছি যাতে AI এর মিউজিং এর বর্জ্যভূমিতে পরিণত না হয়"

„Wir unternehmen aktiv Schritte, um nicht zu einem Ödland der KI-Gedanken zu werden“, sagt HackerNoon-Gründer/CEO

"Estamos tomando medidas activamente para no convertirnos en un páramo de las reflexiones de la IA", dice el fundador y director ejecutivo de HackerNoon

"Nous prenons activement des mesures pour ne pas devenir un désert de réflexions sur l'IA", déclare le fondateur et PDG de HackerNoon.

हैकरनून के संस्थापक/सीईओ कहते हैं, "हम एआई के विचारों की बंजर भूमि न बनने के लिए सक्रिय रूप से कदम उठा रहे हैं।"

「私たちは、AI の思索の荒野にならないよう、積極的に対策を講じています」と HackerNoon 創設者兼 CEO は語る

해커눈 창업자/CEO “AI 사색의 황무지가 되지 않기 위해 적극 나서고 있다”

“Estamos tomando medidas ativamente para não nos tornarmos um terreno baldio de reflexões de IA”, diz o fundador/CEO da HackerNoon

«Мы активно предпринимаем шаги, чтобы не стать пустошью размышлений ИИ», — говорит основатель/генеральный директор HackerNoon.

HackerNoon Kurucusu/CEO'su şöyle diyor: "Yapay zekanın düşüncelerinden oluşan bir çorak araziye dönüşmemek için aktif olarak adımlar atıyoruz"

Người sáng lập/CEO của HackerNoon cho biết: “Chúng tôi đang tích cực thực hiện các bước để không trở thành vùng đất hoang của những suy ngẫm về AI”.

HackerNoon 创始人/首席执行官表示：“我们正在积极采取措施，以免成为人工智能思考的荒原”

Generative AI is certain to be a game-changer in the business landscape. It has the potential to revolutionize a diverse range of industries and change how we live and work. I believe adopting AI best practices and use cases is necessary more than ever for your business to thrive in today’s world. Generative AI is undoubtedly the fastest-growing area of artificial intelligence, similar to ChatGPT. It generates fresh content rather than only relying on existing data — including music, images, text, and more that were previously best performed by people. Due to this ability, it has become more applicable to businesses. How can I practice generative AI in my business? Is that on your mind? Keep reading as I’ll reveal some of the many ways you may leverage generative AI to gain a competitive edge for your business. Create New Marketing Content For businesses, marketing is essential because it allows buyers to learn about and engage with a product emotionally. After all, there is little possibility that consumers will utilize your product if they are unaware of it or what you do. Marketing relies heavily on content, but developing content through traditional means may be time-consuming and occasionally luck-based. Thanks to generative AI, marketers can generate product descriptions, advertising slogans, social media postings, and advertising graphics quickly and effortlessly, freeing them time to focus on creative thoughts. For instance, Jasper AI and Writer AI are tools that can help fine-tune your content to perfection. Fine-Tune Your Sales Efforts Although product-led growth (PLG) is rising, sales still remain a personal business. I know how crucial it is for a salesperson to understand each client’s need to offer a tailored solution, especially in high-value sales. Templates for sales outreach and call scripts might accelerate the process, but they frequently feel like a sacrifice between quantity and quality. Generative AI has the power to change this. With its use, you can merge the best of both worlds. Generative AI lets sales teams continue to prioritize quality and personalization without compromising efficiency. Offer Ideal Customer Support Excellent customer service fosters customer loyalty. Without it, your business will likely face extended responses, poor service quality, and dissatisfied customers — which is something I bet you don’t want. However, providing appropriate resources and dedicating staff to adequate customer service may be challenging. Again, you may utilize a generative AI tool to automate customer support. By doing so, you can modify your approach and better serve customers. Speed Up Coding and Development Usually, creating a user app entails writing code in a computer-readable programming language that provides a structure for the application’s logic and operation. The way we build applications is changing thanks to generative AI. Instead of coding in computer language, developers can communicate using natural language, and the AI creates the code as simple as it can generate an article. As for beginner developers, I suggest using generative AI tools like GitHub Copilot and Arcwise will be a great head start. These tools significantly ease and speed up the development process. Secure Personal Data Data security and privacy are critical for preventing misuse or unauthorized access to personally identifiable information (PII). Businesses’ increasing gathering of personal data necessitates the protection of that data. Also, businesses must comply with data privacy rules, such as GDPR and CCPA, to avoid fines and adverse publicity. Businesses use representative data to create products and deliver services, but this access may jeopardize their consumers’ privacy. Yet, this can be solved with advanced generative AI apps. Mostly.ai and Tonic.ai are tools that use generative AI to generate synthetic data from actual data and ensure user privacy. It’s Time to Embrace Generative AI! There hasn’t been a suitable time than now to begin investigating the various benefits that generative AI may provide for your company. Embrace generative AI to revolutionize your operations, unleash potential, and generate a competitive advantage. As a CEO, I advise all business leaders to upskill employees to work with AI and manage change effectively, regardless of their role in marketing, sales, customer support, engineering, data privacy, or any other area. Like what you read? Stay connected for more.

Ways Generative AI Can Transform Your Business for the Better!

A Short Story "Hi, sugar baby." Me: Friend: "Hello." "How is it going with the targets we talked about getting new clients?" Me: "I have been trying. Only for this AI thing... I have lost two opportunities to work already." Friend: "What do you mean, " Me: AI thing? "I mean, the AI tools that are used to test to see if the contents are genuine or not." Friend: "Oh, of course. So, how does that concern you? You are one of the finest and most badass writers I have had the privilege to work with over the years." Me: "So did I, until some content AI originality-checking tools said my works were too perfect to have been written by humans." Friend: Intro We have been lied to when told that AI will not take away our jobs. Real-life testimonies and experiments prove that AI tools have been robbing humans of jobs. Well…, and will continue to do so. The above chat was a conversation between a friend, who is a colleague, and me. She writes smartly and uses different AI tools to support her content creation process, such as checking for plagiarism, fixing complex grammatical issues with content, and making content readable. However, she has never spun an article to rewrite it or cheat the writing process. In fact, she did not bother to sign up for or use ChatGPT because she considers it a professional cheat. She had lost two opportunities to land two potential clients because the client uploaded her content to an AI tool that could allegedly judge whether the content was AI-generated or human-generated. The client did not read the entire document. He only lets the AI do the job, and her work came back as AI-generated. Disappointed and confounded, she asked for the tool and tried it herself. To her surprise, the result came back affirmative as being AI-written, about 95%. She tried another, too; that had a lesser score but also said it was mostly AI-written. As she made some adjustments and rechecked, she had a typographical error, and the score came back as 75% humanly written. She saw the error and fixed it; the score went back to 95% AI. The logic behind this particular tool turned out to be that human-written content contains flaws and errors. Wow! So, what is the point of grammar and other writing aid tools? It looks like the developers never thought about that. First, it was the reCAPTCHA I thought it was funny and ironic that machines now ask humans to confirm that they are humans. First, it was the reCAPTCHA images, letters, signs, and ticks to prove to machines (web hosts) that we are humans. Resume Scanners/ATS I decided to experiment with different resume tools (Applicants Tracking System, or ATS) to scan my resume and see the eligibility of my competence for a particular job description. First, it was resume “Worded” too. Truly, the tool identified some weaknesses in the uploaded resume. My challenge, however, was that the AI could not pick up important sections of my work, such as skills. I used "skills" and varied it with "professional skills," and "skills (professional)," but the tool couldn't even recognize these labels as headers. Its absence made it score low. Furthermore, it identified headers (job titles and roles) as bullet points explaining job descriptions. Hence, the recommendation to start each bullet point with "action verbs" is illogical. In case you are wondering if I put a bullet before the enterprise where I had worked and the roles, no, I didn't. There was also the issue of the brevity of points. I was to cut bullet points short, which is relative anyway. If you've ever written a resume, you know how difficult (almost impossible) it is to discuss actions, roles, and impacts of your role in measurable metrics in 15-20 words. Ironically, in the example given by the tool on how to start a bullet point with an action word, it was longer than most of what I wrote. Following all recommendations, the resume still got hooked on an 80-margin score for this particular tool. The tool only considers a 90% score and above for jobs. I tried another ATS tool, whose only option was to pick up keywords in the document, not sections. It failed to use a contextual analysis of words, sentences, and phrases. It fails to identify synonyms or alternative words for all possible dictionary words that an applicant can use! For instance, if the job description required a degree in a related course and the tool was encoded to scan for a "university degree," if you had a college degree in a related course, you could be disqualified. However, if you mention that you worked as a teaching assistant at a university, even if the field is irrelevant, your resume will pass to the next review stage. Taleo is one of the ATS tools used by top companies such as Toyota, Starbucks, Amazon, and several other companies. It uses the principle of keyword scan and keyword density (the frequency of keywords of interest. Grammar and Writing Aids In the early days of Grammarly, if you submitted your work to a client before a personal review, they would upload it to Grammarly and tell you it had a low score (often less than 90)—the same Grammarly that has trouble identifying how to use a verb following an uncountable noun. The same Grammarly that picks popular words as inaccuracies and suggests that you delete or replace commonly-used words (not necessarily in that context). Sometimes, it has no alternative suggestions for the same word it flags as a "word choice" error. The same AI tool that does not fully understand your writing tone or audience but picks passive voices as errors. On this, you will agree that the parameters in the "Goals" section are very limited. Grammarly marks every passive verb form as an “issue”, disregarding the tone or setting of your document. Some clients would even expect technical and industry-niched content to have the readability score of a basic school pupil. The same tool is the judge. Why do they think so? Because they assume that a higher readability score ALWAYS means content can be better understood, neglecting the target audience demographics. Plagiarism Checkers: Copyscape Pass!!?? 🙃 "Copyscape pass" was a popular addition to many job postings on freelance platforms in those days. It didn't matter if the idea was popular, if you wrote it your way or in your style, or even if you had first-hand knowledge of the subject; you lose the job or the client if Copyscape flags it! 1text.com may also be one of the most notorious plagiarism checkers in the AI content generation tool market. This 'guy' picks on strings of words and phrases as plagiarism. Hence, it is difficult to have an accurate score if you are writing technical articles on a specific industry or tool. For instance, in writing about JavaScript as a programming language, you must mention some words. 1text.com does not care whether your style, grammar, or context differs from other sources on the internet, nor does it care about your presentation. As long as there are similar word strings in one or two sentences, you are a culprit ⸻a plagiarist! RIP to the jobs AI made humans lose! The Sun Never Rose on Some Jobs So, our jobs are being snatched from us not because AI is replacing our many abilities as individuals but because it continues to stand in the way of opportunities that our skillset could have earned us. They are stolen right from our hands before they even land. Yet, the AI tools themselves cannot do those tasks as well as we can. No two AI tools are exactly the same, but we are at the mercy of fighting for slots with tools with different algorithms. It would be luck and chance that an applicant plays right into the tool the recruiter is using. Sadly, executive customers and clients of these professionals do not know this. Typically, there are business executives who barely have enough time to perform their essential functions but believe AI is perfect and makes the best decisions. So, as a content writer, I submit an article to a SaaS company executive, and he does not read it but uploads it to a plagiarism checker interface to check if it is original or not. Unfortunately, this man does not even know the judging criteria for what makes an AI tool reliable. For many digital content creators, AI can be a bad market in this regard. These tools are built with algorithms that capitalize on human weaknesses as though there are no other tools to help humans overcome their oversights and weaknesses. It becomes apparent that the level of creative thinking of an application development team determines how the application will function. The limit of the developer's wisdom is the application’s limit. Some deserving candidates will not advance in the industry unless humans stop jeopardizing human efforts and use meaningful algorithms and datasets to instruct their AI models. It is not AI vs. humans; it is . humans vs. humans with AI being the weapon A person who has an idea about developing an applicant tracking system for employers has not done well if all they do is sit with a team of top HR executives on desired qualities. They must also speak with employees and job seekers. Otherwise, those who will be employed will be those who can beat the system, not necessarily the best hands. Most of your software that undermines human efforts only want to sell more value to you in the name of a "premium" package. Don't tell me, "those who beat the system are the best hands." Oh, please; don't get me started! I have seen terrible product descriptions on Amazon and seen wrong information or inconsistent styles on . Google's first-page results I have seen spelling errors and grammatical blunders on some of our most respected information outlets on the internet. However, in a capitalist-tech world, managers and senior-level executives do not explore all the possibilities of a tool before adopting it. As long as a tool has a huge fund-raising and is sponsored by prominent tech names, it is assumed to be an authoritative application system. The Future I believe the future of artificial intelligence as an independent entity is impossible. However, the greatest potential of AI tools will be realized in full collaboration with humans. It is not to marginalize humans or continue to oppose them. I believe it is to support humans. AI startups and tech giants have to stop developing AI tools with the ambition to "catch" a culprit and instead use them as a tool to foster human collaboration and business development. Also published here

Real-Life Stories About AI Taking Human Jobs

এআই-এর মানুষের চাকরি নেওয়ার বাস্তব-জীবনের গল্প

Geschichten aus dem wirklichen Leben über KI, die menschliche Jobs übernimmt

Historias de la vida real sobre la IA tomando trabajos humanos

Histoires vécues sur l'IA prenant des emplois humains

एआई मानव नौकरियां लेने के बारे में वास्तविक जीवन की कहानियां

AI が人間の仕事を奪う実話

AI가 인간의 일자리를 빼앗는 실제 이야기

Histórias da vida real sobre a IA tomando empregos humanos

Yapay Zekanın İnsan İşlerini Ele Geçirmesine İlişkin Gerçek Hayat Hikayeleri

Những câu chuyện đời thực về AI cướp đi công việc của con người

关于 AI 抢走人类工作的真实故事

44,000 startups. 4,553 cities. “4 billion votes” It was such a blessing to get to know so many of your ideas and innovations from every corner of the world in 2021. The startup community amazed all of us with your hard work and persistence despite all of the challenges that we faced globally throughout last year. Not gonna lie, the competition got our hearts racing like Eurovision. We won’t let you wait for much longer, below are long-awaited results! ✨✨✨ To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series 🔥 Central Europe Central Europe has always caught our attention with the talented pool of people in tech community. But were they able to get through 2021 despite the pandemic? Let’s cheers to the most outstanding amongst 4,500+ startups who have been working hard this year: Basel, Switzerland An overwhelming vote for ~ in their mission of helping machinery companies drive more service revenues from their installed base MachiQ Software Berlin, Germany We have with a whooping 538 votes! Let’s support Plan A with its mission to help companies to measure, monitor and reduce their environmental footprint. Plan A Düsseldorf, Germany It doesn’t get any easier with Kicking off in helping people organize online schedules more easily. EasyWeek! Geneva, Switzerland Manage code quality? We have to give props to for their startup! SonarSource Hamburg, Germany Taking over Hamburg is , an acoustic device to reduce noises in vehicle cabins. No more annoying sounds on the road 🚗 ReCalm Köln, Germany Small business? Hoping to manage your marketing campaign? is winning the vote! rankingCoach Lausanne, Switzerland is a Swiss-based company focused on cryptocurrency management solutions built for financial institutions. Metaco Leipzig, Germany empowers companies to operate and utilize drones for the most effective industrial data applications. FlyNex Mainz, Germany is growing sustainable digital mobility startups! Next Mobility Labs Munich, Germany wins for top-line, high-security international Identity Management! ORY Stuttgart, Germany Remote desktop application? Ask anywhere. AnyDesk Vienna, Austria , an IT company enabling privacy-preserving big data applications in the digital age. Mostly AI Zug, Switzerland Way to go 1762 votes for the first provably live pure Proof-of-Stake (PoS) CBC-Casper blockchain designed for real-world applications. CasperLabs! Nyon Calling for robust data solutions for impact-driven organizations! Havuta LLC Zürich is revolutionizing small business lending to give easy access to capital to those who need it most: SMEs ✨ Teylor Heidelberg, Germany is an innovative HR startup providing a solution for fast and efficient recruiting of employees with simple job profiles. Talk'n'Job Stuttgart, Germany Congratulations for winning this title! AnyDesk Moving on to our buddies in... 🔥 Eastern Europe Eastern Europe is home to many familiar big names in our start-up world. Trace history for Miro, Telegram, and Grammarly and you know where they are from. Who is the next generation for Eastern European startups? Budapest, Hungary is a platform for mobile app development, enabling tens of thousands of developers to automate integration, testing, and deployment. Bitrise I__ __ stanbul, Turkey Leading here is - a brand new software intelligence platform- helps organizations deliver faster, high-quality products by connecting their DevOps tools. Oobeya Kraków, Poland is a human cloud platform that connects global enterprises with European software houses, offering tech talent as a service. Talent Alpha Kyiv, Ukraine 281 votes for , an AI powered app where people can swap faces in the videos, GIFs and images in just a few seconds. Reface Sofia, Bulgaria 387 votes for __ __378, leading Blockchain and DLT solutions company that specializes in helping enterprises and blockchain startups design, build and scale innovative solutions using Blockchain & DLT! LimeChain Warsaw, Poland is a next-generation blockchain document management software (DMS). DoxyChain Yerevan, Armenia Whoever clicked to vote for was sure a vote, because it ended up winning as a full-service software provider delivering Artificial Intelligence & Machine Learning solutions for businesses. SmartClick smart Almaty Just like the name suggested, provides one pass to dozens of gyms and studios. 1FIT Debrecen got all the coders ranked figured out! CodersRank Kharkiv is complete solution digital assets management! Pics.io Minsk, Belarus with 243 votes is the first blockchain-powered sports media platform. Scorum Moscow, Russian Federation 1 e-grocery company in Russia with 26 votes and #1 on our HackerNoon board for Minsk. SberMarket Saint Petersburg, Russian Federation leads this area as a personal finance application that rewards responsible spending and savings. CoinKeeper Kazan, Russian Federation Smelling a sweet victory? 3d App designers for flower bouquets is in the house! Flowito Western Europe 🔥 Brexit? Pandemic? It seems like startups in Western Europe still keep their spirits regardless. Startups in Western Europe that stood out in votes this year go to: Antwerp is on a streak! As a bug bounty platform, it ensures online security through a crowd-sourced platform for computer experts. intigriti Belfast A record-breaking 10,003 votes? is an independent coffee shop. Come grab a coffee! Bobs & Berts Birmingham What’s happening in Birmingham? We have , an innovative UK-based advanced lithium battery developer aiming to accelerate the global shift to cleaner. Aceleron Bordeaux 30 is an open-source web framework for building modern apps and websites with Vue.js. NuxtJS Boulogne-billancourt is a software company that provides an in-store analytics platform to optimize operations. DiGEiZ Brighton In for some jam? offers musical education services. BIMM Bristol Another tasty victory for Western Europe is enabling the strongest digital encryption on the internet. KETS Quantum Security Brussels A fashionable win for , a social fashion network. You scan barcodes in fashion shops and see every influencer wearing those outfits! Share your wishlist with your friends and earn cashback. SCANDALOOK Cardiff is a technology company developing end-to-end digital connectivity for the point-of-care diagnostics market. Bond Digital Health London A whooping number of 1085 vote__ __ offers hardware based cyber-security solution for authentication and encryption in a wired local network. Crypto Quantique Edinburgh Hopping up on the crypto movement, just launched last year but they are already rocking it at making crypto accessible to everyone. Zumo Ghent 5379 votes for ! A Belgian InsurTech startup that helps insurance providers all over the world to better connect with their digital customers. WeGroup Glasgow specializes in single-cell analysis transforming the life sciences. Biomage Paris Show some love in the city of love for , a sports platform that is connected to a mobile sports coaching application. AirFit Aberystwyth enables artificial intelligence, data science & digital innovation solutions for the agriculture, biotech & life sciences industries. Agxio Norwich Congratulations for the amazing win of 1124 votes! WATR Charleroi Congratulations for your amazing win! Hirering Gibraltar Give a round of applause to Fortknoxter! Waltham Abbey is ruling the area with its amazing 72 votes! RPAIR Antwerpen Rocking up Antwerpen area is the rising startup ! Willow Egham What’s happening in Egham? Proudly announcing winning the Noonies this year! ATA.ONE Leicester Leicester is calling the name of as the winner for the region! DeadHappy Macclesfield Who else but ? Congratulations on dominating the award! AeroCloud Northern Europe 🔥 Want to listen to some music? Spotify. Need some games to kill time? Candy Crush. What about a quick call to someone you haven’t seen in a long time? Skype. These are all startups that originated from Northern Europe! Noonies, let’s take a look at the upcoming generation of startups from Northern Europe that hopefully will see an even bigger and brighter future. Gothenburg, Sweden Shipping was never made for small shippers, so took on the mission to built an end-to-end logistics platform to empower small businesses to spend less time and money on shipping and more time on growing their businesses. Sendify The Netherlands provides a virtual platform for many kinds of conferences and events. Let's Get Digital Helsinki, Finland A curated collection of public basketball courts in the Helsinki Metropolitan Area called . Everyone is welcome to submit to their local court. Koripallopaikat Lund, Sweden develops an antibiotic-free, safe and effective treatment of vaginal fungal infections. Gedea Biotech Luxembourg, Luxembourg 🚀 is a job-matching platform that connects developers and tech experts with CTOs and team leads from the best tech companies across Europe. 👾 nexten.io Malmö, Sweden Feeling studious, Noonies? __ __e is a tool for Google Classroom, that enables formative matrix assessment towards course learning objectives directly. StudyBe Oslo, Norway is the platform supports a unified API across all connected device categories, removing significant friction for app developers. Domos Labs Reykjavík, Iceland __ __s is a mobile-first technologically advanced neo-bank. Indo service #S__ __ tockholm, Sweden is the biggest blockchain start-up in Sweden. Double winner of the most prestigious nomination in Nordics 'NyTeknik 33-listan', winner of the Deloitte Fast 50, and now Noonies Award! ChromaWay AB T__ __ allinn, Estonia Passing 1000 votes is a fully digital and decentralized ecosystem for all actors in supply chain. OBORTECH Vilnius, Lithuania is an easy-to-use money app for cards and accounts. Via Genome, users can open a business or a merchant account or a personal one with dedicated IBANs, access transfers, exchange currency, process payments, and manage the funds. Genome Aalborg Digura A legaltech offering tenants fast, digital and reliable legal assistance Aarhus Partisia Market Design provides secure, simple, efficient solutions for auctions and exchanges realizing the potential of the market. Helsingborg is a hi-tech company developing cardiac occlusion devices and other vascular implants. Occlutech Tallinna is an AI-powered system for marketing automation in social networks. POSTOPLAN 🔥 Southern Europe Southern Europe’s economy went through a hard time this year due to a huge reduction in tourism. But our startups are of course always headstrong, from Spain to Italy and all the way through Greece. Who are the heroes rising amidst changes of Southern Europe? Skopje is a versatile software development company that solves the business challenges of SMBs, big brands, and brave hearts with awesome ideas. CodeChem Sliema Sounds like just a relaxing exhale. is a holistic health and wellbeing app built to allow users work on the development of their physical, mental and spiritual health Few Tajonar is a company of experienced business & industry entrepreneurs offering solutions to industries based on cutting-edge technologies. das-Nano Tarragona is a technology-based company focusing on the production of integrated microsystems. IMICROQ Terni is an ICT-based integrated solution that is able to modulate, respond, assist and learn to control most home activities. Mind Tirana is a video content and e-commerce platform for the Balkans built on top of an Albanian language specialized search engine. Gjirafa Torino develops and commercializes algorithms based on Artificial Intelligence to increase the autonomy of small satellite missions. AIKO T__ __ orres Buy online groceries like you would buy offline with personalized picking as fast as in 4 hours with Vedras 360hyper! Trento is a music-based platform built around streaming platforms that allow users to discover new emerging artists and make new friends. Brots 2021, what a YEAR. Congratulations to all of the startups that made it to the top winning list for Europe! Stay tuned for Startup awards in 2022 and let’s cheers to a new year of more innovative ideas, bold innovations, and robust creations from you, Europe 🌷 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by An Le and edited by Natasha Nel. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

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