Groups like Brothers in Arms and Peace Now accused Prime Minister Benjamin Netanyahu of endangering national security with the appointment.

Netanyahu appoints far-right MK as chairman of West Bank committee

It is right-wing lies and propaganda, of the reactionary US and Western mainstream media, to call Hamas a “terrorist organisation”, similar to the lies and Stratcom propaganda that were deployed to call those of us who,

Why I support Hamas

DANNY Cowley has stressed his next managerial move must be right for his family as he continues to be strongly linked with the Bradford City vacancy.

Potential Bantams target Cowley says family key to any move

At the epicenter of this judicial tempest is Anwar Usman, the Chief Justice of the Constitutional Court (MK), now under the scrutiny of the ... The allegations against Usman are rooted in decision number 90 PUU XXI 2023, which altered the minimum age ...

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Gauff and her coach Pere Riba achieved success earlier this year, acquiring her first Grand Slam at the US Open.

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So since she was born she's been in, like, scrutiny and public eye, and everyone having an opinion." He said of Blue's decision to perform on stage: "She wanted to do it. She wanted to do it the first night, and we was like, 'OK, if this is something you ...

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NEARLY a quarter of the clubs in League Two currently have “situation vacant” signs hung on the manager’s office.

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Former Pakistani opener Salman Butt on Tuesday gave his opinion on the umpiring decisions during the ongoing ICC World Cup 2023, calling them “pathetic”. Butt, 39, pointed out how there have been a number of questionable decisions in the mega event ...

Salman Butt slams umpiring decisions during ICC World Cup 2023

Gillingham close to decision on new manager after second interview with Stephen Clemence, who wants to be his own man after working with Steve Bruce. Keith Millen has been caretaker manager, with only two league wins, but Gillingham still in contention for playoffs.

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Adam Johnson, 29, died after appearing to suffer a serious cut to his neck from a skate during a Challenge Cup match against the Sheffield Steelers.

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MK Decision

mkdecision

HackerNoon Reporter: Please tell us briefly about your background. Absolutely. I am one of the first generations of Jewish Sikhs. My father's family hails from Ukraine in Eastern Europe, and my mother's family comes from England, Scotland, and France in Western Europe. In the early 1970s, they were on a path of soul searching and looking for peace within. They eventually got into Kundalini yoga, meditation, as well as Sikhism. In the late 1980s, when I was born, I was raised as a Sikh and performed yoga daily. I came into the United States culture with a very different background which enveloped lots of philosophy hailing from the East. I ultimately merged my Eastern influence with everything I learned from the West. What's your startup called? And in a sentence or two, what does it do? My startup is MK Decision, and we make decisions for financial institutions. We support their account opening for checking and savings accounts via their website and their loan and credit card origination. Community financial institutions rely on MK to take consumer application data and process their credit profile to determine if they deserve the loan they applied for. What is the origin story? Both my brother and I have a background in lending and building technology to support our lending efforts. We combined our backgrounds to create a financial technology company to provide software for financial institutions. Ultimately to help institutions to convert off of paper and automate back-office processes. What do you love about your team, and why are you the ones to solve this problem? Here at MK Decision, our first priority is always our team. Without our team, we would have no customers. Without any customers, we would not have problems to solve in the industry. We solve the biggest challenges in the world of finance for community banks and credit unions. Consumers are making important decisions every single day with the cell phone in their pockets, which can be scary. Here at MK, we empathize with the consumer's experience to safely and securely find the right banking products for them. Then, ensuring the experience of applying and approval is seamless and enjoyable. If you weren’t building your startup, what would you be doing? If I wasn't building MK Decision, I would probably build another startup. At the moment, how do you measure success? What are your core metrics? We always measure success through the satisfaction of our customers and the inspiration of our team. The magic happens when we make our customers feel successful and our team feels uplifted and motivated. A few metrics that we measure include the growth of our customers’ loan portfolios and the number of new consumers opening bank accounts every month. One of the core metrics we like to determine is how long it takes consumers to apply for a new loan or checking account. Then ultimately, the conversion percentage of how many people start the application versus those who complete the application. What’s most exciting about your traction to date? The most exciting thing is the reputation we are building in the banking and credit union industry. We are in it for our reputation. MK wants people to know we are people of our word. Even though we are helping our customers overcome major challenges in their business, at the end of the day our commitment is to their satisfaction using our products. What technologies are you currently most excited about, and most worried about? And why? I am most excited about integrating our products with mobile banking providers. The integration allows us to connect with existing customers or members of the financial institution. This segment can compare available financial products and apply with one tap; expanding the banking experience. Traditional financial institutions need to be concerned with major technology companies like Google, Apple, Facebook, and Amazon introducing their own banking products. In the digital banking world, any entity with strong technology can get banks to support their endeavors and launch a digital bank. We want community financial institutions to continue to serve the local towns they exist within. MK would be concerned about any technologies taking money out of small towns and restricting those economies from succeeding. What drew you to get published on HackerNoon? What do you like most about our platform? Ultimately, we always like to collaborate with technology-focused and technology-inspired content providers. For us, expanding our brand is a passion. It is an important part of our reputation to grow awareness for our company and mission. What I enjoy most about HackerNoon is its expansive reach across all areas of technology. It is impressive to find a platform supporting an incredible number of writers and content creators with an all-encompassing approach to covering the tech industry. It means so much to MK to be nominated as a candidate for 2021 Startup of The Year. What advice would you give to the 21-year-old version of yourself? Trust your gut. Trust your instincts. Always speak your peace and give your two cents. Lastly, people will remember you by how you make them feel. What is something surprising you've learned this year that your contemporaries would benefit from knowing? I have learned there are endless problems to be solved in financial services and the market is extremely vast. There is enough business for all the vendors who want to get involved. You must ensure there is a large, addressable market to build a successful business and gainfully employ the people who take a chance on you. But more than anything, there is enough business for all of us. Vote for MK Decision for Startup of the Year, San Diego, California

On Empowering Community Financial Institutions: Interview with Har Rai Khalsa, MK Decision's CEO

84 Stories To Learn About Finance And Banking

Was it Smart to Switch from Linux to ChromeOS Flex?

Introduction I want to use a Mac with Mac OS to develop my projects. Since I am a .NET developer, it is critically important for me to use windows based equipment. But since .NET core came out, I had the opportunity to use not only windows computers for development. At first, I faced different problems, but gradually I solved them, and now I am doing development using Visual Studio Mac. In this article, I will demonstrate step by step how to create a .NET Core MVC project, connect the project to an AWS RDS database, use the entity framework to create a connection between the database and the application, and all I will do on Mac OS and use Visual Studio for Mac, as well as Visual Studio code. In this article, I will not use mapping and any patterns such as repository pattern and unit of work because it is not connected with the theme of the article, and our application will be completely coupled by Entity Framework. Let’s Get Started First, we need to create a simple MVC project. To do this, open Visual Studio and create a new project (Image 1). Next, select .NET core 3.1 as Target Framework. Next, it will be a surprise for you, but here we will see an empty window, just a white space. Do not worry, that is just closed by Solution Explorer by default. Go to . Here, as in Visual Studio windows, in an MVC project, we have , , folders. That is what we need. View-Layout-Solution Explorer Models Controllers Views Next, we need to connect services for the operation of our application. Since we will use Entity Framework for communication with the database, we will need everything connected with it. An example of connected services is in Image 2. Creating Model and Context Let's create our model. It will be named for our application as . Create a new class in the folder. Also, do not forget to connect data annotations. It will have the following code: Hospitals Model public class Hospital
{

    [Key]

    public int Hospitalid { get; set; }


    [StringLength(100)]

    [Display(Name = "Hospital name")]

    public string HospitalName { get; set; }


    [Required]

    [StringLength(100)]

    [Display(Name = "Dicision Maker")]

    public string Dicisionmaker { get; set; }


    [Required]

    [StringLength(100)]

    [Display(Name = "Email")]

    public string Email { get; set; }


    [Required]

    [StringLength(100)]

    [Display(Name = "Phone")]

    public string Phone { get; set; }
} Next, let's create our context. Go to , and then create a folder, and then an empty class. It should look like Image 3. Solution Explorer Data Further, to connect our application with the database, you need to add the following code to the class: Create class inherits from . DBContext DbContext public class DBContext: DbContext After that, add a link to our table in the database. public DbSet <Hospital> Hospitals {get; set; } Next, we will define the table with which there will be a connection when creating a model. A lot of o people do not use it, but I use it because, in my experience, I have had a lot of situations where the model class name is a different name in a database. protected override void OnModelCreating (ModelBuilder modelBuilder)
 {
     modelBuilder.Entity <Hospital> () .ToTable ("Hospitals");
 } So, then, create a constructor: public DBContext(DbContextOptions<VegaDbContext> options):base(options)
        {
        } Each .NET Core MVC project has a file in which you can configure the connection, and let's open this file (Image 4). appsettings.json Next, edit , but if is not created. Next the sample of connection string: ConnectionStrings {
  "ConnectionStrings":{
    "Default":"Server=YOUR ENDPOINT ,1433; Database=DATABASENAME;
     Integrated Security=False;Persist Security Info=True;
     Trust Server Certificate = True;User ID=LOGIN;Password=PASSWORD"
  }, Then create a static method that will return a new context to us. public static DBContext Create ()
{
    return new DBContext ();
} As a result, you should get a similar code: public class DBContext : DbContext

    {

        public DbSet<Hospital> Hospitals { get; set; }


        protected override void OnModelCreating(ModelBuilder modelBuilder)

        {

            modelBuilder.Entity<Hospital>().ToTable("Hospitals");

        }


        


      public DBContext(DbContextOptions<VegaDbContext> options):base(options)
        {
        }


        public static DBContext Create()

        {

            return new DBContext();

        }


    } For this step, we are done. Create a Controller We are now ready to create our controller. Next, we need to create three methods: show all records from the table, add a new record to the table and create an empty page to populate the model. I do not show it anymore since the purpose of this article is to show the very principle of operation. Let's create a new control and name it (Image 5). HospitalsController Next, Let us inherit our class the class by using code: Controller public class HospitalsController:Controller Next, create a database connection in the controller. private DBContext _context; And add code: public HospitalsController()

{

    _context = new DBContext();

}


protected override void Dispose(bool disposing) => _context.Dispose(); Next, for basic operations, let's create three methods. The first one will display the list of entities. In this code, we read the values from the database and return them to the view, which we will create later (Index, Save, New). Do not forget to use routing. Add the following code: [Route("Hospitals/Index")]

        public ActionResult Index()

        {

            var CustomersList = _context.Hospitals;

            return View("Index", HospitalsList);

        } The next, create the second method will create a new entity. Add the following code: public ActionResult New()

        {

            var hospital = new Hospital();

             return View("HospitalsForm", hospital);

        } Next, create the third that will save a new entity. Let's add the following code: public ActionResult Save(Hospital hospital)

        {

            if (!ModelState.IsValid)

            {

                var _hospital = hospital;                


                return View("CustomerForm", hospital);

            }


            _context.Hospitals.Add(hospital);


            _context.SaveChanges();


            return RedirectToAction("Index", "Hospitals");

        } For this step, we are done. Create Views Finally, we have to do is add the views to our application. Next, let's edit _Layout in the Shared folder. <li class = "nav-item">
 <a class="nav-link text-dark" asp-area="" asp-controller="Hospitals" asp-action="Index"> Hospitals </a>
</li> Next, for our example, we do with two. Views Visual Studio on Mac does not have standard functionality without connecting various extensions to immediately create a View from a controller (Image 6). To create this, go to the View tab and create a new folder that has Hospitals. Next, create two view files - Index-for list of hospitals and HospitalsForm - for creating a hospital. It will display us a list, and there will be a button with the creation of a new one. Add the following code to Index: @model IEnumerable<My_Code_first.Models.Hospital>

@{ ViewBag.Title = "Hospitals";

                Layout = "~/Views/Shared/_Layout.cshtml"; }

<h2>Customers</h2>

<p>
    @Html.ActionLink("New Hospital", "New", "Hospitals", null, new { @class = "btn btn-primary" })
</p>
<table class="table">

    <tr>
        <th>
            Hospital ID
        </th>
        <th> Hospital Name</th>
        <th> Dicisionmaker</th>
        <th> Phone</th>
        <th> Email</th>
        @foreach (var hospital in Model)
        {

    <tr>

        <td>
            @Html.DisplayFor(hospitalId => hospital.Hospitalid)
        </td>

        <td>
            @Html.DisplayFor(hospitalName => hospital.HospitalName)
        </td>

        <td>
            @Html.DisplayFor(hospitalDicisionmaker => hospital.Dicisionmaker)
        </td>

        <td>
            @Html.DisplayFor(hospitalPhone => hospital.Phone)
       </td>

        <td>
            @Html.DisplayFor(hospitalEmail => hospital.Email)
        </td>
    </tr>}
    </table> Next, add the code is in : HospitalForm @model My_Code_first.Models.Hospital

@using (Html.BeginForm("Save", "Hospitals"))

{

    @Html.ValidationSummary()

    <div class="form-group">

        @Html.LabelFor(m => m.HospitalName)

        @Html.TextBoxFor(m => m.HospitalName, new { @class = "form-control" })

        @Html.ValidationMessageFor(m => m.HospitalName)


    </div>


    <div class="form-group">

        @Html.LabelFor(m => m.Dicisionmaker)

        @Html.TextBoxFor(m => m.Dicisionmaker, new { @class = "form-control" })

        @Html.ValidationMessageFor(m => m.Dicisionmaker)


    </div>


    <div class="form-group">

        @Html.LabelFor(m => m.Email)

        @Html.TextBoxFor(m => m.Email, new { @class = "form-control" })

        @Html.ValidationMessageFor(m => m.Email)


    </div>

    <div class="form-group">

        @Html.LabelFor(m => m.Phone)

        @Html.TextBoxFor(m => m.Phone, new { @class = "form-control" })

        @Html.ValidationMessageFor(m => m.Phone)


    </div>


    <button type="submit" class="btn btn-primary"> Save </button>

    @Html.HiddenFor(m => m.Hospitalid)

    @Html.AntiForgeryToken()


} For this step of creating a view, we are done. Connect the Database You can argue for a long time how to choose a database for work. However, to reduce the load on our working machine and not install various applications for working with a database, I will create a new database based on an instance in my Amazon account. I have already shown how to create a new database in Amazon in . It is a very similar step. one of my articles Immediately, we need an application that can communicate with the database on Amazon, and the best option is VS Code. Open it and go to the section. There, we put on the and install it (Image 7). Extensions SQLTools Next, we need to connect to our database on . Find the Endpoint of instance and copy it (Image 8). Amazon RDS Next, create a new connection, and select in and as a database, as well as a and (Image 9). Server Address Endpoint rdsadmin username password Although the allows you to create a database; I prefer to create a database manually. Next, to do this, run a query. entity framework CREATE DATABASE hospitalServises After that, let's go back and change our database connection (Image 10). For this step, we are done. Connect Entity Framework for Our Application Since we are using the approach, we need to use the migrations. In Visual Studio mac, we do not have a Package Manager console, and we can use the terminal to create the migration. CodeFirst For the test, let us open the project folder in the terminal and run the command you can see on Image 11. dotnet ef Next, I want to note that it is necessary to open exactly the folder with which is located; otherwise, the creation of the migration will not work. To create our migration, enter the command (Image 12): Startup.cs dotnet ef migrations add InitialUpdate Next, update the database using the command (Image 13). dotnet ef database update After successful completion of the database update, let's check the creation of tables. Open VS Code and open the database. As we can see, our table is created. For this step, we are done. Test Application Next for the test, launch our application and go to Hospitals (Image 14). Then, create a new hospital (Image 15): Then we'll save. As you can see, everything works (Image 16). Conclusion In conclusion, we created a new .NET MVC application, created a model, a controller, and a view. In addition, we prepared a context for creating a connection to the database. Also, we created a connection to the database, edited connection strings, and created a connection in our .NET MVC application with Entity Framework, which is not a difficult process if you follow all the suggested steps.

Get Used to Building a .Net core MVC Application on Mac Visual Studio

2001: A Bitcoin Odyssey

2001: 'n Bitcoin Odyssey

2001: একটি বিটকয়েন ওডিসি

2001: Bitcoinová odysea

2001: Una odisea de Bitcoin

2001. Բիթքոյնի ոդիսական

2001: Sebuah Pengembaraan Bitcoin

2001年: ビットコインの旅

2001: អូឌីសស៊ី Bitcoin

2001: Одисеја на Биткоин

2001 watapi: Huk Bitcoin Odisea

2001: Bitcoin Odisseý

Introduction Imagine sitting down for a two-hour conversation with an AI interviewer. You discuss your childhood, pivotal life moments, and even your views on social issues. Soon after, an eerily accurate digital version of yourself, a so-called "AI twin", is created. This clone mirrors your personality, preferences, and decision-making processes with up to 85% similarity. This isn’t science fiction. Groundbreaking research by Stanford University and Google DeepMind, as highlighted in James O’Donnell’s recent piece for MIT Technology Review “AI can now create a replica of your personality”, explores the fascinating and unsettling world of AI-powered replicas. This article dives into the research, its potential applications, and the ethical dilemmas of sharing your decision-making power with a machine. The Making of a Digital Twin Led by Joon Sung Park, the research team conducted interviews with 1,000 participants across a diverse demographic spectrum. The interviews covered personal histories, career journeys, and opinions on societal issues. From this data, the researchers created AI agents designed to mimic participants’ behaviors in personality tests, social surveys, and logic games. These digital doppelgängers achieved an impressive 85% similarity in their responses when compared to their human counterparts. Though not flawless, the results point to a near future where digital replicas could extend our influence into tasks we may not have time, or inclination, to handle. Park suggests these replicas might one day act as proxies in decision-making processes. “If you can have a bunch of small ‘yous’ running around and actually making the decisions that you would have made, that, I think, is ultimately the future,” he says. Applications Beyond Entertainment While the concept of having an AI clone may conjure images of personalized avatars in gaming or digital assistants, its implications extend far beyond entertainment. These simulation agents could become invaluable tools for research in social sciences, where ethical or logistical constraints often limit the scope of studies. John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” From analyzing how interventions combat misinformation to understanding the behavioral drivers of traffic jams, these agents promise to simulate complex human scenarios at scale, offering researchers a new lens for exploring societal challenges. Ethical Quandaries and Risks Despite its promise, this technology raises significant ethical concerns. Much like how image-generation AI has enabled deepfakes, personality replication tools could be misused. Imagine a digital twin endorsing products or political ideologies without your consent. The implications for privacy, consent, and misinformation are enormous. Additionally, critics have noted the limitations of current evaluation methods used in the study, such as the General Social Survey and Big Five personality assessments. While useful, these tools may fail to capture the intricacies of what makes us unique. For instance, AI agents struggled in behavioral tests like the “dictator game,” which explores fairness in decision-making. Reducing the Data Burden The research also introduces an efficient alternative to the vast datasets typically required to create digital twins. Current companies, such as Tavus, often rely on extensive datasets, emails, social media interactions, and more, to replicate personalities. This study, however, demonstrates that a focused interview might achieve similar results in less time and with fewer resources. Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” The Future of AI Replicas As AI advances, the line between human and machine behavior will continue to blur. Whether aiding researchers, enhancing productivity, or enabling creative exploration, simulation agents represent a powerful new frontier. However, their ethical implications cannot be overlooked. Would you trust an AI version of yourself to make decisions on your behalf? As technology like this becomes more accessible, the question isn’t just “if” but “when” we’ll confront these dilemmas in our daily lives. By leveraging the insights from James O’Donnell’s reporting and the latest AI research, we stand at the cusp of a transformative era, one where our digital selves may be as active as our physical ones. But as we move forward, a balanced approach will be critical, ensuring that these tools serve humanity without compromising the essence of individuality. Sources: James O’Donnell’s article in MIT Technology Review
Research from Stanford University and Google DeepMind What are your thoughts on living alongside your AI twin? Share them below! Introduction Introduction Imagine sitting down for a two-hour conversation with an AI interviewer. You discuss your childhood, pivotal life moments, and even your views on social issues. Soon after, an eerily accurate digital version of yourself, a so-called "AI twin", is created. This clone mirrors your personality, preferences, and decision-making processes with up to 85% similarity. This isn’t science fiction. Groundbreaking research by Stanford University and Google DeepMind, as highlighted in James O’Donnell’s recent piece for MIT Technology Review “AI can now create a replica of your personality” , explores the fascinating and unsettling world of AI-powered replicas. MIT Technology Review “AI can now create a replica of your personality” This article dives into the research, its potential applications, and the ethical dilemmas of sharing your decision-making power with a machine. The Making of a Digital Twin The Making of a Digital Twin Led by Joon Sung Park, the research team conducted interviews with 1,000 participants across a diverse demographic spectrum. The interviews covered personal histories, career journeys, and opinions on societal issues. From this data, the researchers created AI agents designed to mimic participants’ behaviors in personality tests, social surveys, and logic games. These digital doppelgängers achieved an impressive 85% similarity in their responses when compared to their human counterparts. Though not flawless, the results point to a near future where digital replicas could extend our influence into tasks we may not have time, or inclination, to handle. Park suggests these replicas might one day act as proxies in decision-making processes. “If you can have a bunch of small ‘yous’ running around and actually making the decisions that you would have made, that, I think, is ultimately the future,” he says. Applications Beyond Entertainment Applications Beyond Entertainment While the concept of having an AI clone may conjure images of personalized avatars in gaming or digital assistants, its implications extend far beyond entertainment. These simulation agents could become invaluable tools for research in social sciences, where ethical or logistical constraints often limit the scope of studies. John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” From analyzing how interventions combat misinformation to understanding the behavioral drivers of traffic jams, these agents promise to simulate complex human scenarios at scale, offering researchers a new lens for exploring societal challenges. Ethical Quandaries and Risks Ethical Quandaries and Risks Despite its promise, this technology raises significant ethical concerns. Much like how image-generation AI has enabled deepfakes, personality replication tools could be misused. Imagine a digital twin endorsing products or political ideologies without your consent. The implications for privacy, consent, and misinformation are enormous. Additionally, critics have noted the limitations of current evaluation methods used in the study, such as the General Social Survey and Big Five personality assessments. While useful, these tools may fail to capture the intricacies of what makes us unique. For instance, AI agents struggled in behavioral tests like the “dictator game,” which explores fairness in decision-making. Reducing the Data Burden Reducing the Data Burden The research also introduces an efficient alternative to the vast datasets typically required to create digital twins. Current companies, such as Tavus, often rely on extensive datasets, emails, social media interactions, and more, to replicate personalities. This study, however, demonstrates that a focused interview might achieve similar results in less time and with fewer resources. Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” The Future of AI Replicas The Future of AI Replicas As AI advances, the line between human and machine behavior will continue to blur. Whether aiding researchers, enhancing productivity, or enabling creative exploration, simulation agents represent a powerful new frontier. However, their ethical implications cannot be overlooked. Would you trust an AI version of yourself to make decisions on your behalf? As technology like this becomes more accessible, the question isn’t just “if” but “when” we’ll confront these dilemmas in our daily lives. By leveraging the insights from James O’Donnell’s reporting and the latest AI research, we stand at the cusp of a transformative era, one where our digital selves may be as active as our physical ones. But as we move forward, a balanced approach will be critical, ensuring that these tools serve humanity without compromising the essence of individuality. Sources : Sources James O’Donnell’s article in MIT Technology Review Research from Stanford University and Google DeepMind James O’Donnell’s article in MIT Technology Review MIT Technology Review Research from Stanford University and Google DeepMind What are your thoughts on living alongside your AI twin? Share them below!

Can You Trust Your AI Twin to Think Like You?

Episode Four, a New York-based advertising agency, has introduced RYA (Radical Yet Acceptable), an AI tool designed to generate marketing campaign ideas using consumer behavior data. The platform processes 84 million data points weekly to analyze U.S. demographic preferences and spending patterns. The agency, projecting $20 million in revenue this year, developed RYA using proprietary data collected over a decade. The system analyzes where Americans spend their discretionary time and money, providing insights across 180 genres and 20 action categories. Mark Himmelsbach, Founding Partner of RYA and Episode Four, explained the tool's development: "What our agency used to do in six weeks we can now do in one day. The system generates insights and ideas in reduced time frames." The platform's technical capabilities include: Analysis of U.S. population samples


Data processing for demographic segments


Integration with client databases


Custom survey incorporation


Data clean room compatibility


Processing of unstructured data sources Current clients including Adobe, Charles Schwab, and Invesco QQQ have received early access to the platform. The system allows brands to: Access demographic data down to 0.5% of the U.S. population


Analyze consumer entertainment preferences


Track spending behavior patterns


Generate campaign concepts


Integrate proprietary data


Monitor audience engagement metrics Teddy Lynn, Founding Partner of RYA and Episode Four, noted the platform's role in campaign development: "RYA prompts broader thinking. With 180 genres, we can create concepts that align with audience preferences." The platform's development comes amid industry discussions about AI's impact on marketing roles. Episode Four positions RYA as an enhancement tool for human creativity rather than a replacement for marketing professionals. Technical features include: Consumer behavior analysis


Demographic segmentation


Data integration capabilities


Campaign concept generation


Performance tracking


Market trend analysis Future developments include plans for a marketplace connecting agencies, production companies, and marketing specialists. This expansion aims to facilitate campaign execution across various genres and platforms. Industry analysts note that AI-powered marketing tools represent a growing trend in advertising technology. The success of such platforms could influence how agencies approach campaign development and audience analysis. The tool's release reflects broader industry movements toward data-driven marketing decisions. Market observers suggest that integration of AI tools in creative processes could impact traditional agency workflows and campaign development timeframes. For the marketing industry, RYA's launch represents continued evolution in creative technology applications. The platform's performance could influence future developments in AI-assisted marketing tools and creative processes. 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 Episode Four , a New York-based advertising agency, has introduced RYA (Radical Yet Acceptable), an AI tool designed to generate marketing campaign ideas using consumer behavior data. The platform processes 84 million data points weekly to analyze U.S. demographic preferences and spending patterns. Episode Four RYA The agency, projecting $20 million in revenue this year, developed RYA using proprietary data collected over a decade. The system analyzes where Americans spend their discretionary time and money, providing insights across 180 genres and 20 action categories. Mark Himmelsbach, Founding Partner of RYA and Episode Four, explained the tool's development: "What our agency used to do in six weeks we can now do in one day. The system generates insights and ideas in reduced time frames." The platform's technical capabilities include: Analysis of U.S. population samples Data processing for demographic segments Integration with client databases Custom survey incorporation Data clean room compatibility Processing of unstructured data sources Analysis of U.S. population samples Analysis of U.S. population samples Data processing for demographic segments Data processing for demographic segments Integration with client databases Integration with client databases Custom survey incorporation Custom survey incorporation Data clean room compatibility Data clean room compatibility Processing of unstructured data sources Processing of unstructured data sources Current clients including Adobe, Charles Schwab, and Invesco QQQ have received early access to the platform. The system allows brands to: Access demographic data down to 0.5% of the U.S. population Analyze consumer entertainment preferences Track spending behavior patterns Generate campaign concepts Integrate proprietary data Monitor audience engagement metrics Access demographic data down to 0.5% of the U.S. population Access demographic data down to 0.5% of the U.S. population Analyze consumer entertainment preferences Analyze consumer entertainment preferences Track spending behavior patterns Track spending behavior patterns Generate campaign concepts Generate campaign concepts Integrate proprietary data Integrate proprietary data Monitor audience engagement metrics Monitor audience engagement metrics Teddy Lynn, Founding Partner of RYA and Episode Four, noted the platform's role in campaign development: "RYA prompts broader thinking. With 180 genres, we can create concepts that align with audience preferences." The platform's development comes amid industry discussions about AI's impact on marketing roles. Episode Four positions RYA as an enhancement tool for human creativity rather than a replacement for marketing professionals. Technical features include: Consumer behavior analysis Demographic segmentation Data integration capabilities Campaign concept generation Performance tracking Market trend analysis Consumer behavior analysis Consumer behavior analysis Demographic segmentation Demographic segmentation Data integration capabilities Data integration capabilities Campaign concept generation Campaign concept generation Performance tracking Performance tracking Market trend analysis Market trend analysis Future developments include plans for a marketplace connecting agencies, production companies, and marketing specialists. This expansion aims to facilitate campaign execution across various genres and platforms. Industry analysts note that AI-powered marketing tools represent a growing trend in advertising technology. The success of such platforms could influence how agencies approach campaign development and audience analysis. The tool's release reflects broader industry movements toward data-driven marketing decisions. Market observers suggest that integration of AI tools in creative processes could impact traditional agency workflows and campaign development timeframes. For the marketing industry, RYA's launch represents continued evolution in creative technology applications. The platform's performance could influence future developments in AI-assisted marketing tools and creative processes. 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

Episode Four Launches RYA AI Tool for Marketing Campaign Generation

Episode vier stel RYA AI-instrument bekend vir die generering van bemarkingsveldtogte

Afsnit fire lancerer RYA AI-værktøj til generering af marketingkampagner

Episode Four lanza la herramienta de inteligencia artificial RYA para la generación de campañas de marketing

Չորրորդ դրվագը գործարկում է RYA AI գործիքը մարքեթինգային արշավի ստեղծման համար

エピソード 4 がマーケティング キャンペーン生成用の RYA AI ツールをリリース

მეოთხე ეპიზოდი იწყებს RYA AI ხელსაწყოს მარკეტინგული კამპანიის თაობისთვის

Episode ya minei ebandi RYA AI Tool pona génération ya campagne ya marketing

Fizarana efatra dia namoaka fitaovana RYA AI ho an'ny famoronana fanentanana ara-barotra

Episodi i katërt lëshon Mjetin RYA AI për gjenerimin e fushatës së marketingut

Avsnitt fyra lanserar RYA AI-verktyg för generering av marknadsföringskampanjer

Dördüncü Bölüm, Pazarlama Kampanyası Oluşturma için RYA AI Aracını Başlatıyor

Xiphemu xa Vumune Xi Sungula Xitirhisiwa xa RYA AI xa ku Tumbuluxa Pfhumba ro Xavisa

GRAND CAYMAN, Cayman Islands, November 22nd, 2024/Chainwire/--Sui drives blockchain innovation with support from global leader in asset management. The Sui Foundation, the independent organization dedicated to the advancement and adoption of Sui, the innovative Layer 1 and smart contract platform, today announced a strategic partnership with Franklin Templeton Digital Assets. Franklin Templeton Digital Assets has been investing in the Sui ecosystem, and believes this strategic partnership will provide further benefit toward that effort by seeking value creation opportunities to allow builders of Sui blockchain-based solutions to deploy novel technologies leveraging the Sui blockchain protocol. “Sui was originally inspired by some of the challenges Franklin Templeton Digital Assets is helping to solve, particularly those that exist within decentralized finance today,” said Jameel Khalfan, Head of Ecosystem Development. “Their interest in Sui’s groundbreaking technology feels not only validating, but as though Sui has come full circle.” Franklin Templeton Digital Assets has been engaged for the past few years in building blockchain-based technology solutions, running node validators, and developing a wide range of investment strategies. The firm’s dedicated digital assets research team leverages fundamental “tokenomic” analysis, insights from an embedded data science team and deep industry connections to help inform product development and investment decisions. “For the past decade, blockchain technology has captured public attention from technologists and economists alike, but often runs up against technical limitations. We are excited for what the Sui team is building,” said Tony Pecore, SVP / Director of Digital Asset Management at Franklin Templeton. Examples of impressive projects being built in the Sui ecosystem include DeepBook on Sui, called DeFi’s answer to a central limit order book, or what exchanges use to facilitate buying and selling between traders by recording bids and offers, as well as Karrier One, the decentralized mobile carrier behind the Karrier One DePin network, and Ika, a parallel MPC network that enables seamless and secure cross-chain interactions. As a secure and scalable platform for building the future of intelligent assets, Sui has already become a premier destination for application builders in gaming, DeFi, memecoins, as well as real-world applications such as 3D printing and tokenized securities. Most recently, native USDC launched on Sui, enabling users to have direct access to one of the most widely used stablecoins globally while removing the added complexities and risks caused by bridged assets. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here GRAND CAYMAN, Cayman Islands, November 22nd, 2024/Chainwire/--Sui drives blockchain innovation with support from global leader in asset management. The Sui Foundation, the independent organization dedicated to the advancement and adoption of Sui , the innovative Layer 1 and smart contract platform, today announced a strategic partnership with Franklin Templeton Digital Assets. Sui Sui Franklin Templeton Digital Assets has been investing in the Sui ecosystem, and believes this strategic partnership will provide further benefit toward that effort by seeking value creation opportunities to allow builders of Sui blockchain-based solutions to deploy novel technologies leveraging the Sui blockchain protocol. “Sui was originally inspired by some of the challenges Franklin Templeton Digital Assets is helping to solve, particularly those that exist within decentralized finance today,” said Jameel Khalfan, Head of Ecosystem Development. “Sui was originally inspired by some of the challenges Franklin Templeton Digital Assets is helping to solve, particularly those that exist within decentralized finance today,” said Jameel Khalfan, Head of Ecosystem Development. “Their interest in Sui’s groundbreaking technology feels not only validating, but as though Sui has come full circle.” “Their interest in Sui’s groundbreaking technology feels not only validating, but as though Sui has come full circle.” Franklin Templeton Digital Assets has been engaged for the past few years in building blockchain-based technology solutions, running node validators, and developing a wide range of investment strategies. The firm’s dedicated digital assets research team leverages fundamental “tokenomic” analysis, insights from an embedded data science team and deep industry connections to help inform product development and investment decisions. “For the past decade, blockchain technology has captured public attention from technologists and economists alike, but often runs up against technical limitations. We are excited for what the Sui team is building,” said Tony Pecore, SVP / Director of Digital Asset Management at Franklin Templeton. “For the past decade, blockchain technology has captured public attention from technologists and economists alike, but often runs up against technical limitations. We are excited for what the Sui team is building,” said Tony Pecore, SVP / Director of Digital Asset Management at Franklin Templeton. Examples of impressive projects being built in the Sui ecosystem include DeepBook on Sui , called DeFi’s answer to a central limit order book , or what exchanges use to facilitate buying and selling between traders by recording bids and offers, as well as Karrier One, the decentralized mobile carrier behind the Karrier One DePin network, and Ika, a parallel MPC network that enables seamless and secure cross-chain interactions. DeepBook on Sui DeepBook on Sui DeFi’s answer to a central limit order book DeFi’s answer to a central limit order book As a secure and scalable platform for building the future of intelligent assets, Sui has already become a premier destination for application builders in gaming, DeFi, memecoins, as well as real-world applications such as 3D printing and tokenized securities. Most recently, native USDC launched on Sui, enabling users to have direct access to one of the most widely used stablecoins globally while removing the added complexities and risks caused by bridged assets. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Sui Foundation Enters Into Strategic Partnership With Franklin Templeton Digital Assets

Sui Fondu Franklin Templeton Digital Assets ilə Strateji Tərəfdaşlığa Daxildir

La Fundación Sui firma una alianza estratégica con Franklin Templeton Digital Assets

بنیاد Sui با دارایی های دیجیتال فرانکلین تمپلتون وارد مشارکت استراتژیک شد

Sui Foundation aloittaa strategisen kumppanuuden Franklin Templeton Digital Assetsin kanssa

Sui հիմնադրամը ռազմավարական համագործակցության մեջ է մտնում Franklin Templeton Digital Assets-ի հետ

La Fondazione Sui stipula una partnership strategica con Franklin Templeton Digital Assets

קרן Sui נכנסת לשותפות אסטרטגית עם נכסים דיגיטליים של פרנקלין טמפלטון

Sui Foundation、フランクリン・テンプルトン・デジタル・アセットと戦略的提携を締結

Sui Foundation Franklin Templeton Digital Assets менен стратегиялык өнөктөштүккө кирет

Sui Foundation dia miditra amin'ny fiaraha-miasa stratejika miaraka amin'ny Franklin Templeton Digital Assets

ሱይ ፋውንዴሽን ምስ ፍራንክሊን ቴምፕልተን ዲጂታላዊ ንብረት ናብ ስትራተጂካዊ ምሕዝነት ኣትዩ።

Sui Foundation Yi Nghena Eka Ntirhisano Wa Maqhinga Na Franklin Templeton Digital Assets

Recently, my friend with a strong desire to make a difference in the world and deeply interested in the political future of society—asked me a question that sounded both paradoxical and provocative: “How can corruption be put to work for the state?” At first glance, the idea of using corruption as a tool for the state’s benefit seems controversial, but it raises an important question about the nature of governance and approaches to achieving public good in modern societies. These reflections, however, led me to another, more interesting idea: that the best path to building a prosperous society for everyone is through integrating economic growth and environmental protection as equal goals, handing these tasks over to technologies that will emerge very soon. I am convinced that only artificial general intelligence (AGI) can reliably and effectively manage the achievement of these two essential objectives in the future. But let’s start with corruption, one of the most deeply rooted problems in various countries. Corruption can be found in both democratic and authoritarian systems, in nations with high and low levels of development. It functions as a parallel power structure, where personal interests replace public ones and distort democratic principles. In research Corruption and Government: Causes, Consequences, and Reform, Susan Rose-Ackerman and Bonnie J. Palifka emphasize that corruption infiltrates even those structures meant to uphold justice and transparency, undermining public trust in institutions—a phenomenon we can observe in many countries, such as Russia, China, and North Korea. Transparency International, which publishes the Corruption Perceptions Index annually, confirms that fighting corruption remains a pressing issue on a global scale. Despite the efforts of anti-corruption organizations and reformers, corruption often remains a persistent and seemingly unavoidable part of political systems. It is worth noting that history has seen attempts at “managed” corruption, where instead of eradicating it completely, authorities aimed to minimize its harmful effects. For example, in some authoritarian regimes, corruption is tolerated as a way to maintain elite loyalty and system stability. Michael Johnston, in his book Syndromes of Corruption: Wealth, Power, and Democracy, explores the phenomenon of corruption and argues that such “managed” corruption may temporarily help sustain stability but ultimately deepens social inequality and increases public dissatisfaction in the long run. In short, corruption cannot be turned into something beneficial, as the idea of "useful" corruption encounters several fundamental issues: Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy.


Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management.


Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. An interesting aspect of our discussion is that the ideal economic model for humanity should combine sustainable economic development with environmental protection. The United Nations, through its Sustainable Development Goals (SDGs), emphasizes that only balanced and sustainable economic growth can improve quality of life on the planet while minimizing environmental impact. Thus, a crucial aspect here is the integration of economic growth with environmental goals, which modern scientists and researchers already consider the only path to long-term well-being. Economist Jeffrey D. Sachs, in his book The Age of Sustainable Development, argues that a society’s happiness can be measured not only by GDP growth but also by its level of environmental sustainability, air and water quality, access to green spaces, and social equality. The combination of these factors helps create a society in which each individual feels part of a shared prosperity, while the environment remains viable for future generations. But what does AGI, technocracy, and the coming technological revolution have to do with this? In light of the above conclusions about corruption and the challenges of sustainable development, it’s clear that traditional management models, based on human decisions, are often unable to effectively address issues of sustainable growth. Sadly, people alone cannot fully help people. Modern technologies, especially artificial intelligence, have the potential to offer more flexible and precise solutions. I argue that AGI will be capable of managing the economy by striking a balance between economic growth and environmental sustainability. In book Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark notes that AGI will be able to process massive amounts of statistical data, which is essentially what the economy is built on—data analysis and interpretation. Works like The Second Machine Age by Erik Brynjolfsson and Andrew McAfee emphasize that AI has the potential to transform the management of social and economic processes, helping to reduce the impact of human weaknesses and biases. To me, this means that AGI could effectively allocate resources in the future to serve the public good, taking into account both current and long-term needs of society and our planet’s environment. I believe that the development of AGI will pave the way for a technocratic model of governance, where the economy becomes not just a tool for growth, but a system focused on creating conditions for societal happiness and well-being. In conclusion, I believe that humanity's best path forward lies not in attempts to adapt corruption for state needs, but in the pursuit of sustainable development grounded in advanced technologies. Corruption, even when "under control," undermines trust in institutions, slows progress, and deepens inequality. Instead of seeking "useful" corruption, humanity should focus on technologies that can provide long-term economic growth while also promoting environmental protection—key aspects upon which the well-being of future generations depends. The development of artificial general intelligence (AGI) could be the starting point for this progress. AGI has the potential to bring a new level of efficiency and transparency to governance, where traditional problems like corruption would cease to exist, as all processes would be focused on the public good. Furthermore, AGI could become a tool for balancing economic development with the preservation of natural resources. In a world where sustainable management is essential for the very possibility of life on the planet, such a model could be a solution and lifeline for future generations. In the face of global challenges such as climate change and the depletion of natural resources, it is clear that only the alignment of economic and environmental goals will create a harmonious future. Sustainable development, managed by AGI, has the potential to ensure long-term stability in which both public welfare and the environment are preserved for the benefit of future generations. Thus, AGI could offer humanity a completely new paradigm of governance, where corruption becomes obsolete, and steady economic growth along with environmental stewardship form the solid foundation for the happiness and prosperity of society as a whole. Recently, my friend with a strong desire to make a difference in the world and deeply interested in the political future of society—asked me a question that sounded both paradoxical and provocative: “How can corruption be put to work for the state?” At first glance, the idea of using corruption as a tool for the state’s benefit seems controversial, but it raises an important question about the nature of governance and approaches to achieving public good in modern societies. These reflections, however, led me to another, more interesting idea: that the best path to building a prosperous society for everyone is through integrating economic growth and environmental protection as equal goals , handing these tasks over to technologies that will emerge very soon. I am convinced that only artificial general intelligence (AGI) can reliably and effectively manage the achievement of these two essential objectives in the future. as equal goals But let’s start with corruption, one of the most deeply rooted problems in various countries. Corruption can be found in both democratic and authoritarian systems, in nations with high and low levels of development. It functions as a parallel power structure, where personal interests replace public ones and distort democratic principles. In research Corruption and Government: Causes, Consequences, and Reform, Susan Rose-Ackerman and Bonnie J. Palifka emphasize that corruption infiltrates even those structures meant to uphold justice and transparency, undermining public trust in institutions—a phenomenon we can observe in many countries, such as Russia, China, and North Korea. Corruption and Government: Causes, Consequences, and Reform , Corruption and Government: Causes, Consequences, and Reform Corruption and Government: Causes, Consequences, and Reform Transparency International, which publishes the Corruption Perceptions Index annually , confirms that fighting corruption remains a pressing issue on a global scale. Despite the efforts of anti-corruption organizations and reformers, corruption often remains a persistent and seemingly unavoidable part of political systems. Corruption Perceptions Index Corruption Perceptions Index Corruption Perceptions Index annually annually It is worth noting that history has seen attempts at “managed” corruption, where instead of eradicating it completely, authorities aimed to minimize its harmful effects. For example, in some authoritarian regimes, corruption is tolerated as a way to maintain elite loyalty and system stability. Michael Johnston, in his book Syndromes of Corruption: Wealth, Power, and Democracy, explores the phenomenon of corruption and argues that such “managed” corruption may temporarily help sustain stability but ultimately deepens social inequality and increases public dissatisfaction in the long run. Syndromes of Corruption: Wealth, Power, and Democracy , Syndromes of Corruption: Wealth, Power, and Democracy Syndromes of Corruption: Wealth, Power, and Democracy In short, corruption cannot be turned into something beneficial, as the idea of "useful" corruption encounters several fundamental issues: Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Ethical contradictions. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Economic inefficiency. Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Social inequality . Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Social inequality An interesting aspect of our discussion is that the ideal economic model for humanity should combine sustainable economic development with environmental protection. The United Nations, through its Sustainable Development Goals ( SDGs ), emphasizes that only balanced and sustainable economic growth can improve quality of life on the planet while minimizing environmental impact. SDGs SDGs SDGs Thus, a crucial aspect here is the integration of economic growth with environmental goals, which modern scientists and researchers already consider the only path to long-term well-being. Economist Jeffrey D. Sachs, in his book The Age of Sustainable Development, argues that a society’s happiness can be measured not only by GDP growth but also by its level of environmental sustainability, air and water quality, access to green spaces, and social equality. The combination of these factors helps create a society in which each individual feels part of a shared prosperity, while the environment remains viable for future generations. The Age of Sustainable Development , The Age of Sustainable Development The Age of Sustainable Development But what does AGI, technocracy, and the coming technological revolution have to do with this? In light of the above conclusions about corruption and the challenges of sustainable development, it’s clear that traditional management models, based on human decisions, are often unable to effectively address issues of sustainable growth. Sadly, people alone cannot fully help people. Modern technologies, especially artificial intelligence, have the potential to offer more flexible and precise solutions. I argue that AGI will be capable of managing the economy by striking a balance between economic growth and environmental sustainability. In book Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark notes that AGI will be able to process massive amounts of statistical data, which is essentially what the economy is built on—data analysis and interpretation. Works like The Second Machine Age by Erik Brynjolfsson and Andrew McAfee emphasize that AI has the potential to transform the management of social and economic processes, helping to reduce the impact of human weaknesses and biases. Life 3.0: Being Human in the Age of Artificial Intelligence , Life 3.0: Being Human in the Age of Artificial Intelligence Life 3.0: Being Human in the Age of Artificial Intelligence The Second Machine Age The Second Machine Age The Second Machine Age To me, this means that AGI could effectively allocate resources in the future to serve the public good, taking into account both current and long-term needs of society and our planet’s environment. I believe that the development of AGI will pave the way for a technocratic model of governance, where the economy becomes not just a tool for growth, but a system focused on creating conditions for societal happiness and well-being. In conclusion, I believe that humanity's best path forward lies not in attempts to adapt corruption for state needs, but in the pursuit of sustainable development grounded in advanced technologies. Corruption, even when "under control," undermines trust in institutions, slows progress, and deepens inequality. Instead of seeking "useful" corruption, humanity should focus on technologies that can provide long-term economic growth while also promoting environmental protection—key aspects upon which the well-being of future generations depends. The development of artificial general intelligence (AGI) could be the starting point for this progress. AGI has the potential to bring a new level of efficiency and transparency to governance, where traditional problems like corruption would cease to exist, as all processes would be focused on the public good. Furthermore, AGI could become a tool for balancing economic development with the preservation of natural resources. In a world where sustainable management is essential for the very possibility of life on the planet, such a model could be a solution and lifeline for future generations. In the face of global challenges such as climate change and the depletion of natural resources, it is clear that only the alignment of economic and environmental goals will create a harmonious future. Sustainable development, managed by AGI, has the potential to ensure long-term stability in which both public welfare and the environment are preserved for the benefit of future generations. Thus, AGI could offer humanity a completely new paradigm of governance, where corruption becomes obsolete, and steady economic growth along with environmental stewardship form the solid foundation for the happiness and prosperity of society as a whole.

Could Artificial General Intelligence Make Government Corruption Obsolete? 

Može li vještačka opća inteligencija učiniti korupciju vlade zastarjelom? 

¿Podría la Inteligencia Artificial General hacer obsoleta la corrupción gubernamental?

Inteligentzia Orokor Artifizialak Gobernuaren ustelkeria zaharkitu al dezake? 

Podería a intelixencia xeral artificial facer obsoleta a corrupción do goberno? 

क्या कृत्रिम सामान्य बुद्धिमत्ता सरकारी भ्रष्टाचार को ख़त्म कर सकती है?

A mesterséges általános intelligencia elavulttá teheti a kormányzati korrupciót? 

האם בינה כללית מלאכותית יכולה להפוך את השחיתות השלטונית למיושנת? 

汎用人工知能は政府の腐敗をなくすことができるか？

ປັນຍາທຽມທົ່ວໄປສາມາດເຮັດໃຫ້ການສໍ້ລາດບັງຫຼວງຂອງລັດຖະບານລ້າສະໄຫມໄດ້ບໍ? 

Может ли искусственный интеллект сделать коррупцию в правительстве ненужной?

Sirdoonka Guud ee macmalka ahi ma ka dhigi karaa musuqmaasuqa dawladda mid duugoobay? 

Kan artificiell allmän intelligens göra statlig korruption föråldrad? 

Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? 1. Enabling Automatic Cart Audits Some retailers have employees standing near store exits to check shoppers’ receipts as they leave, verifying that the list of items matches their cart contents. However, this process often becomes cumbersome during busy periods, requiring people to wait in lines. Leaders at Sam’s Club believe AI can do the task faster. As of April 2024, more than 120 locations used AI exit scanning technology, and the overall figure matched executives’ goal of adding AI to 20% of its stores by the end of the first quarter. Sam’s Club represents the largest deployment of such customer-facing technology in the retail industry. Customers who engage with it can leave stores 23% faster, and internal data shows more than 50% of shoppers use it. Notably, before rolling out this solution, Sam’s Club employees got feedback from customers about current pain points and what they wanted from their experiences. Participants mentioned they’d like faster and more convenient shopping trips and consistently brought up the waiting times associated with the previous cart audits as a primary downside.  Now, computer-vision cameras and digital technologies work in the background to check that cart contents match a customer’s payment records. This high-tech approach aligns with the company’s other improvements, such as its Scan & Go offering, which allows people to bypass the traditional checkout process when buying their items. The market value for AI products in retail was approximately $5 billion in 2023, and it’s expected to grow. Decision-makers are even more likely to reap the benefits if they do as Sam’s Club leaders did and gather customer feedback. 2. Curbing Potentially Harmful Employee Preferentiality Most people think of customers as the main people behind retail theft, but workers can exacerbate the problem, too. Sometimes, they might slip small items into their pockets during slow periods, believing no one will notice and they will never get caught. The circumstances can also become more complicated. One example is what some in the retail industry call “sweethearting.” It happens when employees give discounts or free items to friends or relatives. One tech company, Corsight AI, began offering AI technology to detect such interactions. More specifically, it spots unusual behavior by analyzing how close customers stand to employees. Because the tool tracks engagements over time, it can also determine if specific shoppers keep going to the same employee’s cash register or otherwise show a preference. The system flags unusual behaviors to the relevant store decision-makers, letting them decide whether to act. Critics have pointed out that regularly engaging with the same employees when shopping at a store doesn’t necessarily mean anything suspicious. Perhaps a customer appreciates a worker’s friendliness, efficiency or product knowledge, causing them to naturally interact when opportunities arise. However, increasing theft problems have caused some retail leaders to scrutinize things they might have previously ignored. That is particularly true for retailers selling high-value items. For example, jewelry companies’ crime-related losses exceeded $129 million in 2022. These retailers usually have high-tech security systems, but those defenses probably won’t catch occasional instances of store workers offering unusually generous discounts. AI tools might. 3. Providing Better Awareness Although circumstances vary, some potential shoplifters target locations before attempting to steal things. During each visit, they gather information such as the number of workers in the store at particular times, whether certain departments or products have dedicated security measures and the locations of entrances and exits. Someone who goes to a well-staffed store, notices numerous security cameras and sees how employees seem continually aware of their surroundings may decide against proceeding with theft attempt plans. Conversely, stores that seem virtually deserted and feature few or no visible security cameras are much more appealing to would-be thieves. Because AI products can work in the background and detect unusual occurrences, they help loss prevention officers and others become more aware of what happens in individual stores. For example, Everseen creates AI tech that can be used to help reduce self-checkout theft. Part of its approach involves processing footage from ceiling-mounted cameras, often gathering tens of millions of customer interactions daily. Another helpful aspect of the technology is that it can scan shelves to determine when specific products are almost out of stock. Additionally, retail leaders can use this product to get near-real-time analytics about what’s happening in a store and receive prompts about handling certain situations. For example, if the AI spots a frustrated customer, it might prompt a worker to engage with them to learn what’s wrong. Many retailers already use artificial intelligence creatively by incorporating it into their demand-planning processes. For example, automating supply chain activities could reduce costs by as much as 10%, making that goal a worthy pursuit for cost-conscious parties. In any case, improved awareness provided by specialty tech products could help retailers manage theft. 4. Analyzing Shoppers’ Gestures Although many retailers use facial recognition technologies, some consumers dislike the perceived lack of privacy. An AI alternative is potentially less invasive because it does not analyze people’s appearance, but rather whether they make suspicious movements commonly linked to theft. Wild Duck Wine & Spirits in Boston uses an AI product from company Veesion on all 24 of its security camera feeds. The technology checks shoppers’ gestures to see if they match those frequently used during shoplifting. For example, someone might try to conceal something in a clothing pocket. Another “red flag” occurs when people hide items in baby strollers. Finally, people may eat or drink while shopping, potentially consuming what they have not paid for yet. If the AI spots something specific, it alerts workers approximately 15 seconds later, providing a video clip of the event. Those employees can then choose whether to confront the person or see if they try to skip the checkout counter. A representative from the AI company explained that this solution is about more than stopping people from stealing single items. It sends them a message that they should not try to repeat their actions because they’ll get caught. Minimizing Theft in Retail These fascinating examples show how AI can help store managers, loss prevention officers and other responsible parties reduce the frequency of theft in their locations. Choosing the appropriate tools and learning how to use them properly is crucial for success. Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? 1. Enabling Automatic Cart Audits 1. Enabling Automatic Cart Audits Some retailers have employees standing near store exits to check shoppers’ receipts as they leave, verifying that the list of items matches their cart contents. However, this process often becomes cumbersome during busy periods, requiring people to wait in lines. Leaders at Sam’s Club believe AI can do the task faster. As of April 2024, more than 120 locations used AI exit scanning technology, and the overall figure matched executives’ goal of adding AI to 20% of its stores by the end of the first quarter. Sam’s Club represents the largest deployment of such customer-facing technology in the retail industry. Customers who engage with it can leave stores 23% faster , and internal data shows more than 50% of shoppers use it. Notably, before rolling out this solution, Sam’s Club employees got feedback from customers about current pain points and what they wanted from their experiences. leave stores 23% faster leave stores 23% faster Participants mentioned they’d like faster and more convenient shopping trips and consistently brought up the waiting times associated with the previous cart audits as a primary downside.  Now, computer-vision cameras and digital technologies work in the background to check that cart contents match a customer’s payment records. This high-tech approach aligns with the company’s other improvements, such as its Scan & Go offering, which allows people to bypass the traditional checkout process when buying their items. The market value for AI products in retail was approximately $5 billion in 2023 , and it’s expected to grow. Decision-makers are even more likely to reap the benefits if they do as Sam’s Club leaders did and gather customer feedback. approximately $5 billion in 2023 approximately $5 billion in 2023 2. Curbing Potentially Harmful Employee Preferentiality Most people think of customers as the main people behind retail theft, but workers can exacerbate the problem, too. Sometimes, they might slip small items into their pockets during slow periods, believing no one will notice and they will never get caught. The circumstances can also become more complicated. One example is what some in the retail industry call “sweethearting.” It happens when employees give discounts or free items to friends or relatives. One tech company, Corsight AI, began offering AI technology to detect such interactions. More specifically, it spots unusual behavior by analyzing how close customers stand to employees. Because the tool tracks engagements over time, it can also determine if specific shoppers keep going to the same employee’s cash register or otherwise show a preference. The system flags unusual behaviors to the relevant store decision-makers, letting them decide whether to act. spots unusual behavior spots unusual behavior Critics have pointed out that regularly engaging with the same employees when shopping at a store doesn’t necessarily mean anything suspicious. Perhaps a customer appreciates a worker’s friendliness, efficiency or product knowledge, causing them to naturally interact when opportunities arise. However, increasing theft problems have caused some retail leaders to scrutinize things they might have previously ignored. That is particularly true for retailers selling high-value items. For example, jewelry companies’ crime-related losses exceeded $129 million in 2022. These retailers usually have high-tech security systems, but those defenses probably won’t catch occasional instances of store workers offering unusually generous discounts. AI tools might. 3. Providing Better Awareness Although circumstances vary, some potential shoplifters target locations before attempting to steal things. During each visit, they gather information such as the number of workers in the store at particular times, whether certain departments or products have dedicated security measures and the locations of entrances and exits. Someone who goes to a well-staffed store, notices numerous security cameras and sees how employees seem continually aware of their surroundings may decide against proceeding with theft attempt plans. Conversely, stores that seem virtually deserted and feature few or no visible security cameras are much more appealing to would-be thieves. Because AI products can work in the background and detect unusual occurrences, they help loss prevention officers and others become more aware of what happens in individual stores. For example, Everseen creates AI tech that can be used to help reduce self-checkout theft. Part of its approach involves processing footage from ceiling-mounted cameras, often gathering tens of millions of customer interactions daily. Another helpful aspect of the technology is that it can scan shelves to determine when specific products are almost out of stock. Additionally, retail leaders can use this product to get near-real-time analytics about what’s happening in a store and receive prompts about handling certain situations. For example, if the AI spots a frustrated customer, it might prompt a worker to engage with them to learn what’s wrong. Many retailers already use artificial intelligence creatively by incorporating it into their demand-planning processes. For example, automating supply chain activities could reduce costs by as much as 10% , making that goal a worthy pursuit for cost-conscious parties. In any case, improved awareness provided by specialty tech products could help retailers manage theft. by as much as 10% by as much as 10% 4. Analyzing Shoppers’ Gestures Although many retailers use facial recognition technologies, some consumers dislike the perceived lack of privacy. An AI alternative is potentially less invasive because it does not analyze people’s appearance, but rather whether they make suspicious movements commonly linked to theft. Wild Duck Wine & Spirits in Boston uses an AI product from company Veesion on all 24 of its security camera feeds. The technology checks shoppers’ gestures to see if they match those frequently used during shoplifting. For example, someone might try to conceal something in a clothing pocket. Another “red flag” occurs when people hide items in baby strollers. Finally, people may eat or drink while shopping, potentially consuming what they have not paid for yet. If the AI spots something specific, it alerts workers approximately 15 seconds later , providing a video clip of the event. Those employees can then choose whether to confront the person or see if they try to skip the checkout counter. approximately 15 seconds later approximately 15 seconds later A representative from the AI company explained that this solution is about more than stopping people from stealing single items. It sends them a message that they should not try to repeat their actions because they’ll get caught. Minimizing Theft in Retail These fascinating examples show how AI can help store managers, loss prevention officers and other responsible parties reduce the frequency of theft in their locations. Choosing the appropriate tools and learning how to use them properly is crucial for success.

With Black Friday Coming Up, Retailers Are Better Off Using AI to Combat Theft

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