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Exam Code: PL-200 Practice exam 2023 by team
PL-200 Microsoft Power Platform Functional Consultant

Exam ID : PL-200

Exam Name : Microsoft Power Platform Functional Consultant

Candidates for this exam perform discovery, capture requirements, engage subject matter experts and stakeholders, translate requirements, and configure Power Platform solutions and apps. They create application enhancements, custom user experiences, system integrations, data conversions, custom process automation, and custom visualizations.

Candidates implement the design provided by and in collaboration with a solution architect and the standards, branding, and artifacts established by User Experience Designers. They design integrations to provide seamless integration with third party applications and services.

Candidates actively collaborate with quality assurance team members to ensure that solutions meet functional and non-functional requirements. They identify, generate, and deliver artifacts for packaging and deployment to DevOps engineers, and provide operations and maintenance training to Power Platform administrators.

Power Platform Functional Consultants should be familiar with Dynamics 365 model-driven applications and should have experience using the Power Platform components to extend and customize Dynamics 365 model-driven applications.

Configure the Common Data Service (25-30%)

Create apps by using Power Apps (20-25%)

Create and manage Power Automate (15-20%)

Implement Power Virtual Agents chatbots (10-15%)

Integrate Power Apps with other apps and services (15-20%)

Configure the Common Data Service (25-30%)

Manage an existing data model

 assign a type for an entity including standard, activity, or virtual

 configure entity ownership

 create new entities or modify existing entities

 determine which type of relationship to implement including 1: N and N: N

 configure entity relationship behaviors including cascading rules

 create new relationships or modify existing relationships

 create new fields or modify existing fields

 create alternate keys for entities

 configure entity properties

Create and manage processes

 define requirements for business rules

 define and implement business rule logic

 define the scope for business rules

 configure and test business rules

 configure a synchronous classic workflow

Configure Common Data Service settings

 configure Relevance Search

 configure auditing

 perform data management tasks

 configure duplicate detection settings

Configure security settings

 create and manage business units

 create and manage security roles

 create and manage users and teams

 create and manage field security profiles

 configure hierarchy security

Create apps by using Power Apps (20-25%)

Create model-driven apps

 create and configure forms

 create and configure views

 create and configure charts

 create and configure dashboards

 configure site maps

 select applicable assets for an app including entities, forms, views, business process

flows, dashboards, and charts

 share a model-drive app

Create canvas apps

 create a canvas app

 configure the Common Data Service as a data source for an app

 create canvas app screens

 implement form navigation, formulas, variables and collections, and error handling

 build reusable components and component libraries

 configure offline capabilities for apps

 run Power Automate flows based on actions that occur in a canvas app

 interpret App Checker results and resolve identified issues

 test, monitor, and share apps

Create portal apps

 create a portal app

 expose Common Data Service data

 configure portal web pages, forms, and navigation

 configure portal security including web roles and page access

Create and manage Power Automate (15-20%)

Create flows

 describe types of flows and flow components

 trigger a flow by using Common Data Service connectors

 run actions by using the Common Data Service connector

 implement logic control

 implement dynamic content and expressions

 interpret and act on Flow Checker results

 activate and deactivate flows

 interpret flow analytic data

Create and manage business process flows

 configure a business process flow

 add business rules, workflows, and action steps to a business process flow

 define stages and steps

 configure parallel branches

 manage the business process flow entity for a business process flow

Build UI flows

 describe types of UI flows

 identify use cases for UI flows

 differentiate between attended and unattended UI flows

 record business process tasks

Implement Power Virtual Agents chatbots (10-15%)

Create chatbot

 assign a chatbot to an environment

 publish a chatbot

 share a chatbot

 add chatbots to Teams and other channels

 monitor and diagnose bot performance, usage, and subject usage

Configure Topics

 define subject conversation triggers

 create questions, messages, and conditions

 extract Topics from a web page

 implement greetings, escalations, error messages, and statuses

 call a Power Automate flow to run an action

Configure entities

 create custom entities

 implement entities in conversations

 implement variables to store data

Integrate Power Apps with other apps and services (15-20%)

Integrate Power BI with Power Apps

 create Power BI visualizations

 create data flows and schedule data flow runs

 filter data

 build reports and dashboards

 publish and share reports and dashboards

 add Power BI tiles to model-driven apps and canvas app

 add canvas apps to a Power BI dashboard

 trigger Power Automate flows from Power BI alerts

Implement AI Builder

 determine which AI Builder model type to use

 create an AI Builder model

 prepare source data for use by models

 train, test, and publish a model

 consume a model by using Power Apps

 consume a model by using Power Automate

Integrate Power Apps with Microsoft 365

 add apps to Microsoft Teams

 create a Teams app from a Power Apps app

 configure app policies

 create a Teams channel by using Power Automate

 configure and use Microsoft Word and Microsoft Excel templates

Implement Power Virtual Agents chatbots (10-15%)

Create chatbot

 assign a chatbot to an environment

 publish a chatbot

 share a chatbot

 add chatbots to Teams and other channels

 monitor and diagnose bot performance, usage, and subject usage

Configure Topics

 define subject conversation triggers

 create questions, messages, and conditions

 extract Topics from a web page

 implement greetings, escalations, error messages, and statuses

 call a Power Automate flow to run an action

Configure entities

 create custom entities

 implement entities in conversations

 implement variables to store data

Integrate Power Apps with other apps and services (15-20%)

Integrate Power BI with Power Apps

 create Power BI visualizations

 create data flows and schedule data flow runs

 filter data

 build reports and dashboards

 publish and share reports and dashboards

 add Power BI tiles to model-driven apps and canvas apps

 add canvas apps to a Power BI dashboard

 trigger Power Automate flows from Power BI alerts

Implement AI Builder

 determine which AI Builder model type to use

 create an AI Builder model

 prepare source data for use by models

 train, test, and publish a model

 consume a model by using Power Apps

 consume a model by using Power Automate

Integrate Power Apps with Microsoft 365

 add apps to Microsoft Teams

 create a Teams app from a Power Apps app

 create an app directly in Teams

 configure app policies

 create a Teams channel by using Power Automate

 configure and use Microsoft Word and Microsoft Excel templates

Microsoft Power Platform Functional Consultant
Microsoft Functional test prep
Killexams : Microsoft Functional test prep - BingNews Search results Killexams : Microsoft Functional test prep - BingNews Killexams : How to use the Not function Microsoft Excel

The Not function in Microsoft Excel is a built-in Logical function, and its purpose is to reverse the logic of its argument. It ensures that one value is not equal to another. When given TRUE, NOT returns FALSE. When NOT given FALSE, NOT returns TRUE. Logical values are used in spreadsheets to test whether the situation is TRUE or False.

How do I create a NOT formula in Excel?

The formula for the NOT function is =Not(logical).

The syntax below for the NOT function is below:

Logical: A value or logical expression that can be evaluated as TRUE or False.

Follow the steps below to use the Excel NOT function:

  1. Launch Microsoft Excel
  2. Create a table or use an existing table from your files
  3. Place the formula into the cell you want to see the result
  4. Press the enter key to see the result.

Launch Microsoft Excel.

Create a table or use an existing table from your files.

How to use the Not function Excel

Type the formula into the cell you want to place the result =NOT(A2=”Sherwin-Williams”).

Then press the Enter key to see the result.

The result would be the reverse, which is FALSE.

If placed into the cell =NOT(A2=”Clare”). The NOT function will return the result as TRUE.

In Microsoft Excel, not only can you use the NOT function by itself, but you can also use it with conjunctions such as IF, OR, and AND.

If you type into the cell =NOT(OR(A3=”Benjamin Moore”, A3=”Clare”)), the result will be FALSE.

If you choose to type =NOT(OR(A2=”Benjamin Moore”, A3=”Clare”)), the result would be TRUE.

There are two other methods to use the NOT function

Method one is to click the fx button on the top left of the Excel worksheet.

An Insert Function dialog box will appear.

Inside the dialog box in the section, Select a Category, select Logical from the list box.

In the section Select a Function, choose the NOT function from the list.

Then click OK.

A Function Arguments dialog box will open.

In the Logical entry box, input into the entry box cell A2=”Sherwin-Williams”.

Then click OK.

Method two is to click the Formulas tab, click the Logical button in the Function Library group.

Then select NOT from the drop-down menu.

A Function Arguments dialog box will open.

We hope this tutorial helps you understand how to use the NOT function in Microsoft Excel; if you have questions about the tutorial, let us know in the comments.

Read: How to link a part of data in Excel Worksheet in Microsoft PowerPoint.

Thu, 13 Jan 2022 23:37:00 -0600 en-us text/html
Killexams : Pytest for Functional Test Automation with Python

By Rahul Vala, Softnautics

Today’s modern businesses require faster software feature releases to produce high-quality products and to get to market quickly without sacrificing software quality. To ensure successful deployments, the accelerated release of new features or bug fixes in existing features requires rigorous end-to-end software testing. While manual testing can be used for small applications or software, large and complex applications require dedicated resources and technologies like python testing frameworks, automation testing tools, and so on to ensure optimal test coverage in less time and faster quality releases. PyTest is a testing framework that allows individuals to write test code in Python. It enables you to create simple and scalable test cases for databases, APIs, and user interfaces. PyTest is primarily used for writing API tests. It aids in the development of tests ranging from simple unit tests to complex functional tests. According to a report published by future market insights group, the global automation testing market is expected to grow at a CAGR of 14.3% registering a market value of US$ 93.6 billion by the end of 2032.

Why choose Pytest?

Selection of the right testing framework can be difficult and relies on parameters like feasibility, complexity, scalability, and features provided by a framework. PyTest is the go-to test framework for a test automation engineer with a good understanding of Python fundamentals. With the PyTest framework, you can create high-coverage unit tests, complex functional tests, and acceptance tests. Apart from being an extremely versatile framework for test automation, PyTest also has a plethora of test execution features such as parameterizing, markers, tags, parallel execution, and dependency.

  • There is no boilerplate while using Pytest as a test framework
  • Pytest can run tests written in unittest, doctest, and nose
  • Pytest supports plugins for behaviour driven testing
  • There are more than 150 plugins available to support different types of test automation

The diagram below shows a typical structure of a Pytest framework.

Pytest root framework

As shown above in the structure, the business logic of the framework core components is completely independent of Pytest components. Pytest makes use of the core framework just like instantiating the objects and calling its functions in the test script. Test script file name should either start with `test_` or end with `_test`. The test function name should also be in the same format. Reporting in Pytest can be taken care of by Pytest-html reporting.

Important Pytest features

1. Pytest fixtures

The most prominently used feature of Pytest is Fixtures. Fixtures, as the name suggests are decorator functions that are used in pytest to generate a specific condition that needs to be arranged for the test to be run successfully. The condition can be any precondition like creating objects of the classes required, bringing an application to a specific state, bringing up the mockers in case of unit tests, initializing the dependencies, etc. Fixtures also take care of the teardown or reverting of the conditions that were generated after the test execution is completed. In general, fixtures take care of the setup and teardown conditions for a test.

Fixture scope

The setup and teardown do not have to be just for the test function. Scope of the setup may differ from a test function to as large as the whole test session. This means the setup-teardown is executed only once per defined scope. To achieve the same, we can define the scope along with the fixture decorator i.e., session, module, class, function.

Fixture usage

Pytest provides the flexibility to use a fixture implicitly or call it explicitly, with autouse parameter. To call the fixture function by default, the autouse parameter value needs to be set to True, else to False.


All the fixtures that are to be used in the test framework are usually defined in It is the entry point for any Pytest execution. Fixtures need not be autouse=True. All defined fixtures can be accessed by all the test files. needs to be placed in the root directory of the Pytest framework.

3. Pytest hooks

Pytest provides numerous hooks that will be called in to perform a specific setup. Hooks are generator functions that yield exactly once. Users can also write wrappers in conftest for the Pytest hooks.

4. Markers

Pytest provides markers to group a set of tests based on feature, scope, test category, etc. The test execution can be auto-filtered based on the markers. i.e., acceptance, regression suit, login tests, etc. Markers also act as an enabler for parameterizing a test. The test will be executed for all the parameters that are passed as the argument. Note, Pytest considers a test for one parameter as a completely independent test. Many things can be achieved with markers like marking a test to skip, skipping on certain conditions, depending on a specific test, etc.

5. Assertion

Pytest does not require the test scripts to have their assertions. It works flawlessly with Python inbuilt assertions.

6. Pytest.ini

All default configuration data can be put in pytest.ini and the same can be read by the conftest without any specific implementation.

PyTest supports a huge number of plugins with which, almost any level of a complex system can be automated. A major benefit of Pytest is that any kind of implementation of the structure is done using raw Python code without any boilerplate code. It means implementing anything in Pytest is as flexible and clean as implementing anything in Python itself.

Amidst shorter development cycles, test automation provides several benefits that are critical for producing high-quality applications. It reduces the possibility of unavoidable human errors taking place during manual testing methods. Automated testing improves software quality and reduces the likelihood of defects jeopardizing delivery timelines.

At Softnautics, we provide Quality Engineering Services for both embedded and software products to help businesses create high-quality solutions that will enable them to compete in the market. Our complete QE services include embedded software and product testing, DevOps and automated testing, ML platform testing, and compliance with industry standards such as FuSa - ISO 26262, MISRA C, AUTOSAR, etc. Our internal test automation platform, STAF, supports businesses in testing end-to-end solutions with increased testing efficiency and accelerated time to market.

Read our success stories related to Quality Engineering services to know more about our expertise in the domain.

About the Author

Rahul is working as a Principal Engineer at Softnautics and has a total of 10 years’ experience in Test Automation of different types of systems like embedded firmware, mobile and enterprise web applications. He has developed several complex Test Automation frameworks involving complex products and multiple components like boards, mobile devices, GPIO Controls, R Pi, cloud APIs, etc. He is passionate about pytest automation and loves to debug and find root cause of complex issues. In his free time, he loves to walk and play cricket and volleyball.

If you wish to download a copy of this white paper, click here

Tue, 03 Jan 2023 13:23:00 -0600 en text/html
Killexams : Microsoft Bing Chat

Microsoft has updated its Bing search engine with a generative AI feature dubbed Bing Chat. It provides a paragraph summary in response to your queries, powered by OpenAI’s powerful GPT-4 AI model, and lets you continue asking follow-up questions to refine the result. While this concept is similar to competitors ChatGPT and Google Bard, Bing Chat differentiates itself with its consistent source citations, making it the most trustworthy of the three. You can always click a footnote to vet the information or continue researching on your own.

Bing Chat also has drawbacks. It tends to give dry, occasionally inaccurate answers and it has bothersome ads in some results. ChatGPT beats it out as a creative writing companion, while Google Bard offers more helpful export options and eye-catching image responses. Additionally, Bing Chat works best in Microsoft Edge or the Bing mobile app, a hindrance to anyone who's a devotee of a different browser. Bard is my current favorite of the three, followed by Bing Chat, but none of these AI chatbots have been named an Editors’ Choice winner yet. Each is riddled with flaws and has features the others lack, though they are evolving quickly.

What Is Bing Chat?

You’ve probably already heard of Microsoft Bing, the search engine that launched in 2009 to compete with Google. What’s new is the AI-generated version of search results, which Microsoft added to the search engine in February 2023. It’s powered by ChatGPT’s most advanced AI model, GPT-4, thanks to Microsoft’s reported $10 billion investment in OpenAI.

The difference between a traditional Bing search result and a Bing Chat result is that the former suggests links for you to click, while the latter summarizes the main points of those linked web pages for you. Below, when I search “Oppenheimer movie reviews” on the main Bing page, I see a link to reviews published by different sites. When I give Bing Chat the same search words, I see a one-sentence summary of various publications that gives me a general feel for the movie’s reception. 

You can also carry on a conversation with Bing Chat, asking it to refine its output and build on the information it's already given you.

Clicking a link on a traditional search result page leads you to more in-depth information compared with the one-sentence summary Bing provides—in my case, noting that one publication called Oppenheimer a “hot mess.” The value, however, is that these AI-generated overviews can help when you aren’t sure what you want to go deep into just yet. Bing Chat makes it easy to keep researching because it gives you a footnote at the end of each idea that links to the source (and compensates the publication).

Microsoft undoubtedly hopes its new Bing Chat will help it compete with Google Search, which is still the top dog. Google fields 86% of global search volume compared with 8% for Bing, though Microsoft was first at adding AI to its core search engine. Google’s main chatbot product is Bard, and it's still labeled as an "experiment." Google plans to AI-ify its core search results page at an undisclosed, though likely imminent, date. 

How to Access Bing Chat

In the past, you could only access Bing Chat through the Edge browser. But as of August 7, Microsoft offers a pared-down version on third-party browsers like Google Chrome. Microsoft says you still need to use Edge for the "best-in-class" experience, including "longer conversations, chat history, and more Bing features built right into the browser."

You can also access Bing Chat on mobile devices with the Microsoft Bing or Edge apps. You need a Microsoft account, which is free. Bing Chat is available in 160 regions, slightly fewer places than ChatGPT (195 countries) and Google Bard (180).

Bing still prioritizes the traditional link-based search results as its first, default menu option. To get Bing Chat, go to the top menu bar and switch from Search to Chat. Microsoft entices you to do it on desktop by showing a small preview of what the AI-generated results for your search query would be. If you like it, a Let’s Chat button takes you to the full chatbot product to continue. 

Even though Microsoft made Bing Chat available on third-party browsers, I'd like to see the company offer it as a truly standalone product—otherwise it comes off as an advertisement for Edge. As long as AI is so early in development, it feels like a big leap to switch browsers just for a chatbot. My home browser, Google Chrome, is more familiar, and I've set up a customized interface with bookmarks, extensions, and other tools. Plus, I can fully use ChatGPT or Google Bard without changing browsers, and Google Bard has easy export options to Gmail, Docs, and other Google apps I rely on.

How Much Does Bing Chat Cost? 

Bing Chat is free as long as you have a Microsoft account. In exchange, your input helps train the AI, and you have to see ads in some of the answers.

Google Bard is also free to use with a Google account. ChatGPT has a free version, known as the “research preview,” and a ChatGPT Plus tier for $20 per month. That extra $20 gives you access to GPT-4, the more advanced model, which you get to try with Bing for free.

Bing Uses GPT-4, But You Probably Don’t Need Such an Advanced AI

Before you get too excited about getting free access to the best large language AI model around by using Bing Chat, keep in mind that you probably don’t need it for the vast majority of searches. 

As OpenAI notes, the free GPT-3.5 is faster and “great for most everyday tasks.” In this context, it quickly generates a response to your questions with robust answers, while GPT-4 may take 10 seconds to generate a similar result, though it has the capability of handling more complex STEM questions—or questions that require "reasoning and advanced creativity,” as OpenAI puts it. 

There are reasons you might prefer to use ChatGPT rather than Bing Chat. One is ChatGPT’s writing chops: It writes longer, higher-level sentences than Bing Chat. Conversely, Bing Chat has a few advantages over ChatGPT. It’s connected to the web, for example, so it can access the latest information, like Oppenheimer reviews, whereas ChatGPT does not know the 2023 movie exists because it only has information dating up to 2021.  

ChatGPT Plus used to have a web browser feature, but OpenAI disabled it after discovering people were using it to bypass paywalls. The company plans to bring it back, but the feature worked so poorly when I tested it that I recommend using Bing Chat or Bard anyway.

GPT-4, whether used within Bing Chat or ChatGPT, limits the number of exchanges you can have with it; it's 30 on Bing and 50 on ChatGPT. While both are ample for casual use, you may need more for professional work or extended projects. That said, you can always start a new subject to refresh the number of exchanges you can have, but then you lose the conversation history and may need to reask or reiterate some ground you previously covered.

Three Conversation Style Options

When you visit Bing Chat, the first thing you see are its three conversation modes: More Creative, More Balanced, and Less Creative. It's a unique approach to personalizing the chatbot, but it fails to deliver meaningful differences in its responses.

I tried the modes by asking Bing to wordsmith the opening of one of my recent articles. Changing work that's already written appears to be one of the most common uses of generative AI chatbots. All three conversation modes suggested breaking my first sentence into two shorter ones, and the More Precise and More Balanced modes opted for breaking it down even further to be more to the point. The tone and vocabulary of all three were similar. In other words, More Creative didn’t add any flowery vocabulary or imagery. Here's a comparison of my original opening sentence and the suggestions Bing Chat gave me in the different modes:

  • Original: "Tesla Superchargers may be the undisputed champion of EV charging networks in the US at the moment, but seven major automakers have teamed up to develop their own network, which will launch in the US in summer 2024 and Canada at a later date."
  • More Precise: “Tesla Superchargers currently dominate the EV charging network landscape in the US. However, a new challenger is on the horizon. Seven major automakers have joined forces to develop their own network, set to launch in the US in summer 2024, with plans to expand to Canada at a later date.”
  • More Balanced: "Tesla Superchargers have been the go-to EV charging network in the US for a while now. However, seven major automakers have teamed up to develop their own network that will rival Tesla’s. The new network will launch in the US in summer 2024 and Canada at a later date." 
  • More Creative: "Tesla may have the largest and fastest EV charging network in the US, but it will soon face some serious competition. Seven major automakers have joined forces to create a rival network that will offer more than 10,000 charging stations across the US by summer 2024 and in Canada shortly after."

The More Creative version introduced errors, as Tesla doesn't have the fastest network—it offers similar speeds as others. And the network will have 30,000 stations, not 10,000. I don't know where Bing Chat got this information, as it does not provide citations.

The More Creative style was the only one to offer an opinion when I asked, “What’s your take on global warming?” It answered, “My take on global warming is that it is a serious problem that requires urgent attention and action from all of us.” The others provided scientifically accepted definitions of global warming as a phenomenon.

It’s surprising that Microsoft features these three options so prominently in the interface when they are hardly different. I’d like to see Microsoft differentiate them more or create a singular AI engine that can adapt conversation style on the fly, programmed to respond in the way the human would like, similar to ChatGPT’s custom instructions feature.

Bing Chat Gets to the Point

No matter what conversation mode you use, Bing Chat’s answers are generally more concise and to the point than what you get from Google Bard or ChatGPT. Bing keeps answers to around 100 words, even when asked to write an essay (more on that below). The goal seems to be to provide clear and concise information rather than take every opportunity to showcase the extensiveness of its source data and expansive vocabulary. It no doubt has a large body of data to pull from, not to mention access to the entire English language, but it restrains itself.

Bing’s all-business vibe has one unsavory aspect to it: You see ads in many responses. Someone has to pay for all this AI computing power, right? Thus far Google and ChatGPT have not implemented ads, which makes sense given Bard is an “experiment” and ChatGPT has opted for a paid tier to bring in some cash.

The ads do not feel spammy and are on topic, but they take up nearly the entire screen so you need to scroll back up to see the actual response. As a result, Bing Chat is more cumbersome than its competitors, and I feel subtly deterred from using it regularly. The ad-free experience on Google Bard or ChatGPT is more pleasant.

The Best Citations of Any AI Chatbot

Bing Chat sets the standard for how an AI tool can responsibly cite the information it scrapes to generate a response. Most sentences end with a footnote that links to the source. Those references make it more trustworthy. With other chatbots, it’s hard to blindly accept what it tells you, with ChatGPT being the worst offender. Bard falls somewhere in between the two on citations. It appears to list its sources for most responses, but they often include erroneous sources. For example, Bard gave me links to product listings and defunct URLs when I asked it to write an essay about a historical figure.

Bing Chat is the only generative AI tool I can realistically use for work because it allows me to fact-check what it says. For other use cases, Bing Chat has the added benefit of providing citations that let you continue the research on your own. Sometimes, sifting through links and memorizing what’s out there feels better than crafting the perfect AI prompt or asking follow-up questions. 

Bing Chat Isn't Going to Write an Essay for You

Although Bing Chat is an intriguing option for quick search queries, especially on a blank slate subject where you don’t know where to begin, it’s not quite as good of a writer as its competitors. When I asked it to write a biography of Nelson Mandela, it gave a short, 129-word answer, compared with ChatGPT’s 558 words and Google Bard’s 452.  Bing Chat also ended its response with advertisements for books on Mandela.

The content across all three chatbots was similar, focusing on his political legacy as South Africa’s first Black president who helped bring an end to apartheid. Bing Chat, unsurprisingly, provided the best citations for me to follow up on my own. It pulled the information from, Wikipedia, the movie database IMDB, and It’s unlikely any teacher wants to receive an essay written by Wikipedia and IMDB, making it ill-advised to copy and paste Bing Chat’s ideas verbatim—or that of any chatbot. It’s more like an introduction to get you up to speed on a topic. It won’t replace creative thinking or do the more inventive work of a true historian to provide an alternative interpretation of an old story.

When I asked the same question in the More Creative style, Bing Chat gave a 507-word answer that was no more “creative,” only longer with more events from Mandela’s life. But I wanted creativity, so to spice it up I asked Bing Chat to rewrite the essay on Mandela in the style of Mark Twain.

“Nelson Mandela was a mighty fine fellow who stood up against the mean folks who wanted to keep black people down in South Africa,” it wrote after admitting it couldn’t certain it would be able to write as well as Twain’s original writings. “Mandela was always up to something, like protesting and campaigning against the bad laws,” it continued. It’s cringeworthy, made more off-putting by the large ads with books on Mark Twain that end the response.

For another test of its creativity, I asked Bing Chat to write a script for a TV scene about a woman rescuing a dog. I also asked Google Bard and ChatGPT to create their own versions. Of the three, ChatGPT's was by far the best. It included four characters across three different settings, and interpreted "rescue a dog" to mean adopt from a shelter. Bing and Bard both provided simplistic, painfully cheesy scripts featuring just one character who finds a dog outside and frees it from being stuck. The main character in all three versions is a young woman, described as "cheerful" and "compassionate," an example of how AI can perpetuate stereotypes.

Somewhat Helpful for Ideation, But Not a First Choice

Even if Bing Chat is not necessarily the most creative AI, it can still help with ideation, especially on subjects where you don’t know where to start. I asked Google Bard, Bing Chat, and ChatGPT for ideas on how to approach a two-week trip to Mongolia in the summer of 2024. I intentionally picked a country that’s less visited than, say, Italy or France, to test its “intelligence” at putting together a more niche travel plan.

ChatGPT (GPT-4) gave my least favorite answer: 350 words of bland travel advice, like getting a tourist visa and packing for variable weather. Bing Chat focused on specific sites to see. In a quick 150 words, most of which were bullet points, it suggested I start in Ulaanbaatar, the capital city, then fly to the Gobi desert, and finally see a national park. While Bing's answer was a bit better than ChatGPT's, it still isn't compelling enough for me to seriously consider building travel plans with it. Google Bard’s answer combined the advice from ChatGPT’s with Bing’s site-specific suggestions. I also liked how Bard's results include pictures and links to websites. Bard is the clear winner in this scenario.

For another ideation experiment, I asked each chatbot to help me come up with ideas for articles I might write. I typically cover electric vehicles, with a focus on the US, European, and Chinese markets. Could a generative AI chatbot get me started covering EVs in another area that I’m not familiar with, like India? I asked, “What are some article ideas about electric vehicles in India?”

Bard once again gave my favorite response. It provided five bullet points with demo headlines and a sentence on what the content in the article would discuss, which provided an overview of political and social challenges India will face in switching to EVs. It also suggested profiling a specific start-up, a more unique content type than the other chatbots suggested. 

ChatGPT gave a list of 20 bland headlines with no detail about what the articles would cover. Bing gave five bulleted headlines, like Bard, but it included links to where it got those ideas so I can explore more. Having citations was once again helpful, because upon clicking the links I could see that three of the five sites it referenced in the answer are from 2022 or earlier. I don't want a list of stale ideas, but at least Bing empowered me with the right tools to qualify the information on my own. Bard’s ideas may have been similarly dated, though without listing its sources I cannot know, proving how valuable citations are in an AI-generated world.

Bing Chat Will Not Replace Your Assistant

Bing Chat’s focus on search inquiries limits its helpfulness as a productivity tool, which is one area Google Bard shines. Bard makes it easy to incorporate its responses into your workflow with one-click export to Docs, Sheets, and coding tools like Colab. You can even write an email and port it directly into Gmail, make some changes, and press send, which strikes me as an easy way to integrate AI into your daily routine. 

In contrast, Bing Chat offers export to Microsoft Word, a PDF, or text file, but given its short, shallow responses, I can hardly see a use case for them. Bing Chat, Bard, and ChatGPT all offer a basic copy-and-paste function as well.

Bing Chat cannot create tables. I asked Bard and ChatGPT to create a table of EVs on the market today, and while both were too short and contained inaccurate pricing information, at least they created a framework for me to build off of. But Bing Chat was not able to make a table, and instead pointed me to a site that had one. I had not seen that site before, and I value Bing’s ability to find such a comprehensive source. The table has some outdated prices, as did Bard and ChatGPT’s shortlists, but at least it has a "last updated" date, unlike the others. I can always copy and paste the table from that site, so it still helped me complete the same task.

Bing Chat's Best Feature: Free Image Generator

Bing Chat’s image generator is the one feature that compels me to minimize Google Chrome and pull up Microsoft Edge. Bard and ChatGPT do not have embedded image generators, so it sets Bing apart. 

I enjoy asking Bing to create images like “a dog in a canoe” or “the pope coding." To do the same on OpenAI’s Dall-E or Midjourney requires a subscription.

But as with all things AI, there are ethical issues with image generation. These services have been used to spread misinformation, even affecting the stock market and creating national security risks with fabricated images of Donald Trump getting arrested that went viral on social media. Bing knows about these risks, and it refuses to create images with well-known names or controversial topics. For the image of the pope, for example, it refused to specifically create an image with Pope Francis' likeness. Instead, it shows a man from behind wearing a religious robe, but is not recognizable as any one specific pope.

As a best practice, for any images you create with Bing Chat and decide to share, you should note that they are AI-generated.

Bing Chat Is a Reliable—Though Imperfect—Chatbot Worth Trying

If you’re looking for a concise, search-oriented AI chatbot, Bing Chat is the one to try. It’s perhaps the most ethical and responsible of the options available today with its consistent citations, which makes it easier to vet the information, recover from the occasional inaccuracy, and dive deeper into the sites it references. 

For non-Bing users, Bing Chat does not offer distinct enough search results to convince others, such as Google Search users, to make the switch—especially since Google's results page already offers a small text answer for simple searches. Bing Chat's competitors have notable strengths that it lacks, such as Google Bard’s productive export options and ChatGPT’s writing skills. In the end, the right chatbot depends on your task and digital habits, and none of the three hit enough marks or with enough consistency to be a clear Editors’ Choice winner. As such, Bing Chat likely won’t be your one-stop AI shop, but is certainly a compelling option to try.

Fri, 18 Aug 2023 08:16:00 -0500 en-gb text/html
Killexams : How Microsoft is changing its comms function because of AI

Microsoft has made two separate systemic changes to its communications function: one focused on the company’s response to AI and the other on its approach to global communications. 

In mid-July, Microsoft created a standalone team focused exclusively on helping the comms organization “reimagine its discipline in the age of AI,” said Microsoft chief communications officer Frank Shaw.

Steve Clayton, VP of comms strategy, is leading a team of 10 staffers and reporting to Shaw. Clayton was previously VP of public affairs; Brent Colburn is taking over that role.  

Clayton’s team is supporting Microsoft with cutting-edge AI tools and resources, “infusing the breadth of our AI technologies across the comms function,” said Shaw. 

The team will use the breadth of Microsoft’s AI technologies to enhance creativity, productivity and efficiency across measurement, reporting, media relations and more, Clayton said via email.

Clayton is also taking a leadership role in evangelizing Microsoft’s view of AI and serving as a company spokesperson on the topic, which includes sharing Microsoft’s insights on AI with comms organizations globally that are looking for guidance and support. 

“New tech has represented a big change for every industry, and we want to make sure the comms function is leading how we can best use these new tools,” said Shaw.

This change comes after Shaw, in March, offered communications guidance amid a series of AI moves at the tech giant.

Separately, Microsoft has changed its global approach, with GM of global comms Doug Dawson leading and reporting to Shaw. 

Dawson previously had the same title “but we have moved from a loosely federated model to a more centralized model,” Shaw said. Dawson has picked up accountability for Microsoft’s work in different countries and regions. 

Previously, Microsoft had a regional approach and each country had a local manager. The model was optimized to support local sales and business needs. 

Benjamin Lampe, senior director of global comms, is leading comms efforts in EMEA; Lauren Myers-Cavanagh, senior director of global comms, is heading comms in Asia; and Lisa Polloni, senior director of global comms, is leading comms in the Americas, which Microsoft defines as Latin America and Canada.

“A single, global communications organization will enable us to better meet the elevated expectations of our business and our brand, and truly put the 'world' into 'world-class,'” said Dawson via email.

Wed, 26 Jul 2023 03:39:00 -0500 text/html
Killexams : Demystifying AI

There is going to be a big gap between accountants who use artificial intelligence and those who don't, says Randy Johnston, executive vice president at K2 Enterprises, who shares ideas for leveraging this transformative technology.


Dan Hood (00:03):

Welcome to On the Air with the Accounting Today, I'm editor-in-chief Dan Hood. It's hard to describe how we all feel about artificial intelligence. It's terrified. We're optimistic, we're confused. We're maybe a little bit hungry now and then that's really not surprising given how confusing a subject it really is and how many conflicting messages we're getting about it from wild claims that it will inaugurate the rapture to equally wild claims that it will initiate Armageddon. Here to demystify AI a little bit and particularly as it relates to accounting, is Randy Johnston. He's the executive Vice president of K2 Enterprise and one of the foremost thinkers and experts on accounting technology in the field. Randy, thanks for joining us. 

Randy Johnston (00:34):

Dan. Thank you and welcome to you and all the listeners. 

Dan Hood (00:37):

Excellent. Well, first off, we should probably start by asking how do you define AI? Everyone has a slightly different definition. Everyone you talk to describes it slightly differently. How do you define it? 

Randy Johnston (00:47):

Yeah, I'm pretty straightforward on this, Dan, but you have to remember, technical background. So I have a computer science degree in addition to my other talents, so I tend to be very fussy about a true definition of artificial intelligence. But the whole field of artificial intelligence, which was first defined in 1950, has evolved today where people are talking about artificial intelligence in the terms of generative ai, the chat, G p T and so forth. But artificial intelligence is actually a collection of algorithms that simulates human intelligence. It's really that simple. 

Dan Hood (01:22):

I am always fascinated about it because there is a, at one point when people say, well, it's going to simulate or it imitates, or it attempts to reproduce human intelligence. I say, first off, what human intelligence, but too, sort of always wondering, and I'm always fascinated by this and I realize this wasn't a question we talked about, but I always like to dive into this a little bit. Why do they try to reproduce human intelligence? I mean, why was the thinking, let's reproduce human intelligence as opposed to saying, let's make something that's intelligent in a very different way, right? Computers have can in theory, think very differently from human beings. Why do we choose human intelligence as the model there? Do you know? 

Randy Johnston (01:57):

Well, we're really trying to leverage the talent that we have as people, and if you think about how you come up with new ideas, there is this goal to make artificial intelligence sentient. In other words, just being able to have kind of a personality, a consciousness, if you will. And that's a long-term goal that's been in AI for decades at this point. I figure it's going to be at least another 10 to 15 years before we get close. One of the tests for AI right now is being able to have a conversation like we do and not being able to figure out its machine on the other end. And the goal, 

Dan Hood (02:32):

That's the Turing test, right? 

Randy Johnston (02:33):

Yeah, it is the Turing test and being able to have that test for 20 minutes where you can't figure out that you're actually responding with a machine or conversing with a machine, but the human brain is fascinating if you have not been watching the developments in neurology and neuroscience, the way our brains work is just stunning the amount of energy that comes in the output that comes out, and that's the issue related to computing as well. See today, our computers, which are all Von Neumann, Turing type of concepts, they're ones and zeros. We're Studying to make a big jump with quantum computing, and I suspect the way quantum computing is going to be applied to artificial intelligence will give us even more capability than we see today. 

Dan Hood (03:18):

Alright, well good answer. That makes sense. Now I understand. Like I said, I always sort of wondered that. Well, with that, let's narrow it down a bit. This is a huge field and it's going to have a huge impact on humanity and the economy and all kinds of broader things like that. But let's narrow it down a little bit to accounting. How do you see it impacting, and I kind of want to break this into a couple of different time periods. How do you see AI impacting accounting today? And then maybe we can take a look a little further out a couple of years or maybe five years, whatever you're comfortable doing. But let's start with how it's impacting us today. 

Randy Johnston (03:49):

And I am happy to take those additional windows and I think I've got pretty good guesses for you, but today we can leverage it for a lot of routine tasks in accounting. Now I'm going to frame a lot of my discussion around the privacy concerns because the way it works right now is if you provide a prompt, an input if you will, to ai, the prompt and anything that you put in becomes the property of the company that's providing the AI platform. So if you're using chat G p T, it actually becomes the property of OpenAI. So you don't want to put client confidential information inside these systems. You don't want to put in financials, you don't want to put in names, but doing simple things like responding to emails or perhaps even creating proposals or scheduling people, many of those types of things are quite doable today. One of the things that you'll want to learn is how to create prompts. And this whole area right now is called prompt engineering, and there are techniques and tools to get that job done. But in fact, a recent column I wrote was about how to do a business plan strategy and tactics on using ai. And I actually used AI engines to generate those strategies and tactics and for the record, less than 45 seconds to do the initial draft. 

Dan Hood (05:12):

Wow. But no, is it safe to assume you're pretty good at the using right at the prompting? I mean this is a little bit like you used for Google, you used to have to know if you knew bule and operators and stuff like that. That was a really useful skill for that. I was a similar kind of thing, like how to use the system to ask it the right questions to deliver the answers you need. 

Randy Johnston (05:30):

That's exactly right. And it turns out creativity here is really the key. If you can dream of it, there's a good chance that AI can produce an answer in it. And I've been asking accountant to generally think about the things that are time consuming to them and ask the generative AI engines to help them respond. And see, I think there's going to be a major difference in accountants that use AI versus accountants that use ai and good accountants will be able to leverage their skills and be able to check the AI results. But realistically, I don't think I start any of my tasks now that I don't ask an AI engine for some guidance. And it really eliminates that blank paper syndrome where you're starting with nothing at all. You get to start with something. Now, I worry about that a little bit, Dan, because we know that human intelligence generates creative new ideas seemingly out of midair, but the fact of the matter is AI is actually going to narrow and channel, and that's a risk of using AI for this starting point. 

Dan Hood (06:37):

But then there are plenty of things where we talk about human creativity and it is a beautiful and great thing and we want to maintain it, but there's plenty of tasks that people do every day that really don't require, it's not Michelangelo painting the ceiling of the Sistine Chapel it, it's a marketing email or it's an introduction to a request for proposal kind of thing where it's just you just need, as you say, the blank page, you like, I need some words to start this. And they don't need to be perfect. They don't need to be the preamble of the constitution. It just needs to be adequately correct in English and get my point across. So there's a lot of room for that, 

Randy Johnston (07:10):

A lot of room for that. In fact, I usually prompt accountants just think about that mundane and boring stuff that they have to get done and assume that AI is going to be an intelligence assistant that you can train to become even better over time. And as you use AI more and teach the AI engine how you work and think the AI responses will be even better for you. So the simple way for most of you is just think about the top five or top 10 things that you just kind of, I wish I didn't have to do. And start using AI to do those things and you'll find you can do 'em in much less time. Now, Dan, you know us from being public speakers and writers and that type of thing, but I can tell you in this year when I wrote my C P E courses, I did not write a single review question. 


And in the CPE world, you actually have to generate five review questions per hour and all that stuff and match 'em to objectives. I've written over 500 review questions this year, all in ai, and I only had to edit one of them. Other than that, they were frankly better than I could write. And I'm actually trained in educational behavioral psychology. So I actually know how to write objectives correctly and how to write test questions correctly. And it's like this stuff is stunning. Now you've met my wife too, and she's also trained in education. I showed her how these bloody questions were being generated and she said, oh my, so maybe we'll do George. Oh my, oh 

Dan Hood (08:42):


Randy Johnston (08:43):

It was 

Dan Hood (08:44):

Great. So basically I'm assuming you're feeding in your course materials to it and saying, give me some questions based on this. 

Randy Johnston (08:52):

And actually in many cases, if I'm going to write a course, I'll say, I'm going to write a course on this topic. I'm going to focus it on accounting professionals. I have a description that looks roughly like this. give me objectives, that behavioral objectives that meet the criteria that we have to use, and it'll generate the objectives. And I'll look at those and say, yeah, not quite. We'll fix these a little bit. Okay, now I'm going to use these objectives. Now write 10 questions, multiple choice that meet these objectives, identify the objectives, identify the right answer, how they come. It is stunning how good it is, but more on the point of day-to-day accounting tasks. If you begin to realize that things that are thrown onto your plate or that you delegate to others in your organization, you could actually delegate some of those things to a generative AI tool and it will come back with an answer in almost real time. 


And that part of it could even be more efficient than a delegation to another staff member. Or if you've got staff members that are trained in using ai, they can come up with far better draft results for you to review in a very, very short period of time. And let's just use the proposal that you had. There's a lot of proposals that are generated for different activities, and you can write proposals in the style of your business or firm by simply providing a prior proposal and say, I need to write a proposal. Here's an example of one I've written in the past. This is Randy writing again, here's my writing style, or this is firm A, B, or C, and here's our style. Write a proposal for these situations with this parameters in the same style. It'll fill it out. 

Dan Hood (10:41):

That's amazing. Now we could spend a lot of time on a lot of things here. There's a lot of rabbit holes I could go down, but one quick one I want to go with on this one is right now everyone's right, big model of AI is chat, g p T and chat G B T. And correct me if I'm wrong, I could well be, it seems like a lot of the function of chat g t is language generation, as you say, it can take your style, your writing style, and your presentation style and mimic it. I've seen people say, write me this speech in the style of a cockney urchin from the 1890s, and it did a pretty good job of bringing in cockney slang, that sort of stuff. But it's not necessarily, for instance, it doesn't know, I dunno how good it is at math for instance. I know it's very good at breaking down language and reproducing it and finding patterns within language and that sort of stuff. But is chatt BT really the model we should be looking at, or is it should be one model of something that would apply in a lot of different ways? 

Randy Johnston (11:41):

Yeah, it is clearly poor at math and accounting. Okay, so the current generative models you should not use in that way. Now I want to fork people's thinking in this because what you'll discover is predictive analytics can be an AI that is useful in these types of financial pieces, but because of the way the large language models that are behind these generative AI products work, they really work in four separate steps. They do tokenization first, then they do embedding, then they have what's known as an attention model to select the words that they're going to respond with, and then they do completion. It's a four step process in most of these current models. And the issue is it's just a game of statistics. How often does this word come up in the context of these other words? So just like you and I are talking here, Dan, if you, and notice you probably completed the word you before I said it, and I paused with intent for our listeners on that. 


But notice you have a pretty good idea of what's going to be said next because you understand the English language in the context of our sentence. Well, that's what's happening in ai. But it is smart because it is crawled a predefined section of the internet. The large crawl model that was used between 2016 and 2019 to feed most of the generative AI engines right now have extracted a lot of the content of the internet. And notably, it is English speaking biased because of the crawl itself, but it is limited in scope in that way. So you have to watch in many of the models chat G P T in specific, it has not supplied things past September, 2021, the application of chat G P T into the binging. Microsoft search engines is completing some of the more current pieces. But you have to recognize until we get full internet search and completion capability through current, you always have a little bit of a historical bias, 

Dan Hood (13:52):

Right? Well, and among going to things you say, if it's only 2021, it won't know the latest accounting standards or the latest tax regulations or that sort of thing. And then even if it does know them, it might just make them up. It might just hallucinate them, which is a whole other terrifying, terrifying issue. But I want to move from now because I think that's a great picture of where we are now. Maybe start to look forward and maybe as we look forward just to, because we could speculate for, I could speculate wildly, you could give informed speculation as to the future in a wide range of things. But maybe if we focus out on a couple of areas, one would be the issue of ownership. You talked about if you put stuff in chat G P T, it's effectively owned by OpenAI in terms of how those things, how that's going to work out. 


Maybe we talk a little bit about the hallucination issue and whether that's going to be fixed or how that might be fixed in part because you say when you're putting in your courses to get your questions, your courses, so you can those questions and go, oh yeah, that's terrible. Why did they put that in there? That makes no sense. Whereas there's the famous story of the lawyer who did a brief, had AI do a brief for a case and it made up a case just at a whole cloth, not even misinterpreted, made it up, which is bizarre, but also suggests that the computers are dreaming. So maybe those two in particular, but then also and then maybe what happens when we move beyond chat G P T as our main idea of what AI is. 

Randy Johnston (15:20):

Yeah, so let's take it out three to five years, as you had suggested earlier. And there is already indications that the current large language models have reached their limits. So there's going to have to be new models built and they are being built on a regular basis. Last numbers I saw, Dan, was that there've been 29 new commercial models this year, three new educational models, and I think there's probably more than that. Those are just the ones I'm aware of. Now what you can expect is that the precision of the models will become better. You can expect that there will be more private models. And of course, Microsoft Copilot 365 pricing is finally available, $30 per user per month on top of the E three E five platforms. So many of us will have it as part of our Microsoft Word, Excel, PowerPoint, OneNote Suite. 


So you could expect that if you're writing a document in Word, that you can prepare a presentation from it just by saying go do it. And that has actually been demonstrated and is live today in the Microsoft 365 platform. But what I believe will happen is the privacy will begin to kick in and there are a number of models that are available. The alpaca model from Stanford, the LAMA model of Facebook has said that they're going to keep their AI engine public and so forth. You'll be able to install your own AI engine for your firm or for your business and keep the data private. And that's actually a big step forward. Now, the other part of these models, Dan, is tuning them up today in chat G P T, there is a feature known as reinforcement learning from human feedback, R L H F. 


It's actually presented with a little finger or thumb, and if it's right, you're supposed to actually click that and it improves the model. But a lot of people aren't doing that right now. And there is concern on alignment problems. That's really what you're talking about when we're dealing with hallucination and making stuff up from whole cloth. We know that techniques used for war gaming or red teaming are being applied, but there is concern from the very best of the developers that it's going to be hard to make the models more accurate. And Sam Bowman from New York University and philanthropic has said it's going to get harder as the systems get better. So I'm actually concerned about that. And even with the newly formed AI group that's trying to set AI standards right now, the computer scientists behind these models just don't know how they can do some of the things that they're doing. And that's another one of those like, huh, how is that possible? 

Dan Hood (18:18):

Well then stop doing it if you don't know how you're doing it. This is how we got dinosaurs at Jurassic Park. I'm just saying. 

Randy Johnston (18:25):

Yeah, I think so. And those talents are called emergent capabilities. But here's what I'd like for you to think about From a county, you can expect that the models will become predictive, that you'll be able to do things that are routine, like forecasting in confidence and in private. So that'll become very natural. I'm a big fan of advisory services. I believe that you'll actually be able to analyze businesses and apply appropriate strategies to make them more profitable, make 'em run easier, Improve their processes. Things like scheduling will become easier to do. So what I want you to do is start thinking about processes that you're doing today and how they can be optimized by ai. 

Dan Hood (19:10):

Excellent. Alright, there's so much more to talk about, but we need to take a quick break and then we'll come right back. Alright, and we're back. We're talking with Randy Johnston of K two Enterprises, all about artificial intelligence, what it is, where it's going, and we've got a great sense of its direction and I'm encouraged by, should talk about things like better security or better able to control your data when you work with ai. Being able to have your own private AI as encouraging from that perspective. Little discouraged about the whole, we're not sure how we do this and we're not sure how to fix it kind of thing, but hopefully they're working on that. Now let's go a little bit even more narrowly into accounting, and I want to talk specifically about accounting software because it seems like every day some vendor comes out and says, Hey, we've got ai. 


We're using ai, we're all about ai. And you're never sure how much AI they're actually using and what they're applying it to. And there are a gazillion vendors and some of them genuinely are right out at the cutting edge or the bleeding edge of AI research. And then there's others who've basically bought a license to chat G P T and integrated that into their thing. So it's a huge spectrum. I realize that. So this is a wildly unfair question, but in general, do you have a sense of where the vendors in the accounting space are? Have they really wrapped their heads around ai? Obviously, like I said, I know there'll be outliers on either end, but where are they in terms of this? 

Randy Johnston (20:36):

Well, I appreciate the wildly unfair question, Dan, because I think I can actually give you an opinion on it and I think some basis in fact, which is probably the more important thing here. So first for our listeners, just to get 'em think about this, the algorithms behind ai, the quantity of algorithms developed are pretty stunning. Early on there were only about 30 to 50 AI algorithms, and today there's well over 500, many of these are in the public space and can be used and reused openly. On the other hand, a number of vendors have been developing their own private AI algorithms. Now, when it comes to all of this ai, there's actually a chain of neural networks which work kind of like our brains machine learning, which a lot of the vendors are using and calling it ai, but I don't consider machine learning ai. 


It's a branch of, but it's not the real deal. And then there's the AI algorithms. So the question really becomes, when I'm vetting products, Dan, how many AI algorithms are you using? Number one and number two, how many have you developed yourself? Okay, now that as the framework, then we can go back to the providers, to the profession, the big boys, the Walters cls, the Intuits, the Thomson Reuters, and we can ask what algorithms are inside their platforms. And we do see evidence of AI being applied, let's say inside checkpoint engage. We also see that inside some of the Walters CLR tools, we see much more machine learning inside the Intuit platforms. Now, I know in another podcast we are discussing accounting software, but I'll just throw accounting software vendors into a pool together. And most accounting software products that are out there don't really have ai. Everybody's trying to hook their bandwagon to the AI moniker. And I politely just call that fake ai, like we heard of fake news or fake advisory in the past. It's really just marketing bss and you have to be very, very cautious saying they've got ai and it might be machine learning and it might be some predecessor, but it may not be a true ai. 

Dan Hood (23:07):

Gotcha. So that's something, but in theory that's something we should expect them to be doing more and more. I mean, would you expect over time a lot of that fake AI to go away and be replaced by genuine ai or are they just never going to get it? 

Randy Johnston (23:21):

No, they actually will get it. And this is under nondisclosure, so I can't speak about it directly, but I am aware of a new platform that is completely AI driven that will support the tax marketplace. It'll be announced later this year, but I'm also aware of a number of products that you would know of and their AI efforts. And the best way to think it is the developers are looking at ways to apply AI with their engines, and they're basically peeling off one of the public domain models. They're plugging their product against it, and then they're using that AI engine in a private fashion to generate responses. So as an example here, Dan, the COR V tax planning product actually has connected a true AI engine to produce tax planning guidance. Well, that's kind of interesting. Now, is it still early in the process? Sure, but I could probably now name 10 or 12 products that have good solid beta tests running of applying AI to do specialized items inside their software platform. 

Dan Hood (24:36):

Excellent. Good. And as you say, we can expect more and more of them to be adding this, right? So it's not just fake AI forever and real AI over here, but they'll all converge on something real. 

Randy Johnston (24:47):

A good way for our listeners to think about that, Dan is think about the evolution of the web and think what websites were early on and how they got better and how mobile ops were clunky and got better and so forth. We're at a very, very early stage of ai, and right now many of the development teams were caught flatfooted. I think the reason we saw the letter that was signed by tens of thousands of developers were they actually got caught flatfooted and wanted six months to be able to catch up. Well, we're in a period where a lot of these companies now get it and they're trying to catch up. You've got some innovators, you've got some laggards, and in fact, I refer to that as type one and type two AI type one's, enhancing your product and type two is new revolutionary types of results, 

Dan Hood (25:40):

And we can expect to see more and more of those as more and more people get up to speed on it once that six month elapses. So it's interesting, you're bring this up, who's developing an internally, there are accounting firms, mostly the big four firms that are developing their own proprietary ais internally. And this leads me to my next question, which is when accountants think about this thing, do accountants need to be building AI themselves? Is this a thing they need to be focusing on or is it the kind of thing where they can just expect it, that it will be built into a lot of the tools they're going to use and maybe there'll be AI engines that they can sort of buy off the shelf to supply other needs? I mean, do they need to be working? Does Affirm need to have its own proprietary ai? I'm leaving aside the big four 

Randy Johnston (26:23):

Understood on that, and I think my simple answer is most of us do not need to build these platforms ourselves. I believe that you will find enough high quality AI tools to use that you can train on your own methodologies that they'll produce favorable results, your firm or business, and we can go back to the simple Microsoft Copilot 365 example, but we can also go to these private generative tools that we might host. And so today, if you use the three five chat OpenAI three five Chat G P T model, you could put up a private database and load it with all of your proposals and your methodologies and so forth, and it will begin acting like your firm acts. And that's a very simple installation. At the risk of sounding a little too technical, it's only like a 600 gig library to install and it runs very, very straightforward and you can privatize it. 


So I expect some of it to be done that way, but the vendors themselves will each have an offering and they'll be responsible for controlling the privacy, but just recognize that privacy still an issue. There's currently, I would call it a rumor, but it's actually got confirmation that even Microsoft is having trouble controlling the AI content across instances of 365. So I have my K two instance of 365. You have yours at Eant, you're running there, you put in something in your engine, and all of a sudden I can see it over in my engine. Not good, right? Yeah. Now it's not real common, but there are situations like that. So this whole issue of privacy and hallucinations, those two are the biggest threats on these platforms, but I expect three to five years, a lot of those will get worked out 

Dan Hood (28:26):

And in the meantime, there's plenty you can do without putting your private information, private client information, as we talked about all kinds of marketing things or just general writing tasks that you do every day that don't require any proprietary information or data or anything like that. But it makes things a lot easier. 

Randy Johnston (28:41):

Dan, it's very straightforward on this one, by the way. Everyone should have an AI initiative regardless of your size if you're even a sole proprietary. But if you're a small firm, you need to have a little bit of time set aside to understand what the tool can do for you. And I believe that you will be rewarded with time leverage because the time you invest in it will come back to you in results that you can use. So it's time to be learning, and I can't imagine any business that shouldn't have a little bit of an AI initiative regardless of size. 

Dan Hood (29:16):

No, it certainly will. Just in the same way that at some point, somebody recently said, all businesses are now technology businesses to a certain extent, all businesses are going to be AI businesses one way or another. It's going to touch everything. I want to just sort of give us a final thought of how accountants could be talking about it. I think you've given us some clear clues, right? One is it sounds like you don't need to be building your own ai. You don't need to know how to build an ai, but you do need to know how to use it, whether it's prompt engineering learning that now, and I always wonder about prompt engineering, whether it'll be sort of like I said, like Boolean operators for Google. No one needs to do any of those anymore because Google's gotten a lot better at finding things for you. And so a lot of that early stuff went away, but for now, prompt engineering, but then for later on, it's just that things like what can it do for us? How can this impact your business? What can we hand off to ai given the war for talent and all that sort of stuff? Are there other things you think accountants should be thinking about just as a final takeaway that they should be thinking about it when they think about ai? 

Randy Johnston (30:08):

Yeah. So first thing I'll do is just respond to saying, think about your own personal situation. Think about all the things that are problematic. We've kind of identified this earlier in the discussion, but I would suggest you go after your top three time consuming items, whatever those are. So I would personalize that first, but once you get past that and you begin to learn how the platform works, then I would turn it towards your firm's needs or perhaps downstream to your client's needs. If you're in public practice or your customer's need, if you're in industry and say, what is it that our clients could benefit from? And I would begin building tools and methodologies to support your clients or your customers. To me, that's where it really works. Now, I'll flip it up with just one little other piece. We know that talent is short, and I have taken a much more team first, our employee first mentality on this. Look around at your team and say, how could I make all of my team members efforts easier? And I think you will discover right away that AI can do that. It doesn't necessarily have to be a young tech savvy person. You can almost go push by person, say, what could I use AI for with that person and help build that and help teach them that. 

Dan Hood (31:36):

Wow. Very cool. Excellent. I hadn't even thought about when you throw out the idea of what can your clients, your customers do with it, that's a whole advisory service right there, right? We'll come in and tell you how AI can Improve your business, and it's only going to be a bigger and bigger opportunity as time goes by. Well, this is a great conversation. I could pump you for information and ideas and thoughts for days and days and days because you've got a lot of 'em, and it's always fascinating to talk to you, but unfortunately, we have to go. So I want to say Randy Johnson, awesome stuff. Thank you for joining us. 

Randy Johnston (32:07):

Very pleased to do so, Dan, and I look forward to speaking with you and your listeners again. 

Dan Hood (32:11):

Cheers. Alright, and thank you all for listening. This episode of On the Air was produced by Accounting Today with audio production by Kevin Parise. Rate or review us on your favorite podcast platform and see the rest of our content on Thanks again to our guests, and thank you for listening.

Sun, 13 Aug 2023 21:10:00 -0500 en text/html
Killexams : 15 Worst Things About Retro Gaming

Passwords you will never remember, memory cards that constantly needed to be managed, on-cartridge saves that were easily corrupted, accidentally overwriting limited save spots, games that only let you save in very specific places…trying to pick up where you left off in most retro games was often either impossible or so frustrating that you eventually learned to accept that any amount of progress could easily be lost forever. 

Friday the 13th NES

2. Unnecessarily Difficult and Confusing Games

At this point, it seems fair to say that the subject of retro gaming difficulty is destined to be divisive. For some, the “NES Hard” era (and subsequent variations of that generational concept) was a glorious time for gamers who wanted to constantly test their skills and limits. For others, it was a time of seemingly impossible games that felt like a punishment.  

As is typically the case, the truth of this debate falls somewhere in the middle. Yes, a well-crafted level of difficulty can enhance an experience, but let’s not pretend that is what all retro games were offering. One-hit deaths, new player traps, and frustrating platforming puzzles were all hangovers from an era of arcade gaming where most machines were designed to deprive you of as many quarters as possible while leaving you with the false impression that you can get it right on the next run. 

However, I’ve never heard anyone successfully defend the era of genuinely confusing video game design. A stunning number of retro titles regularly presented you with challenges that defied all logic and were essentially impossible to overcome if you didn’t have a guide. Mind you, I’m not just talking about adventure game puzzles. Trying to simply get from one area of a screen to another in certain NES and SNES titles was often as challenging as overcoming some of the most glorified difficult games of those eras. 

Harvest Moon SNES

1. Not Being Able To Find The Game You Really Want

Although much of this list is not presented in any particular order, I really do feel like this was the worst thing about retro gaming. At the very least, it’s the worst thing about retro gaming that few people talk about. 

Let’s say you’ve just read about this game that sounds absolutely incredible and you need to have it right away. For me, it was Harvest Moon. I read about the game in Nintendo Power, and something about it spoke to my SimCity-loving heart. Unfortunately, I was never able to find a copy of Harvest Moon. I couldn’t find want to rent, I couldn’t find one to buy, and I never knew anybody who owned or even played it. 

Sat, 12 Aug 2023 05:00:00 -0500 en-US text/html
Killexams : Thyroid Function Test Market Size Report 2023-2030 | 112 Pages Report


"Thyroid Function Test Market" Insights Report 2023 | Spread Across 112 Pages Report which provides an in-depth analysis Based on Regions, Applications (Hospitals, Diagnostic Laboratories, Research Laboratories and Institutes, Other), and Types (TSH Tests, T4 Tests, T3 Tests, Other Tests). The report presents the research and analysis provided within the Thyroid Function Test Market Research is meant to benefit stakeholders, vendors, and other participants in the industry. The Thyroid Function Test market is expected to grow annually by magnificent (CAGR 2023 - 2030).

Who is the largest manufacturers of Thyroid Function Test Market worldwide?

Thermo Fisher
Cortez Diagnostics
Autobio Diagnostics

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Short Description About Thyroid Function Test Market:

The Global Thyroid Function Test market is anticipated to rise at a considerable rate during the forecast period, between 2023 and 2030. In 2022, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

Thyroid function test measures the levels of thyroid-stimulating hormone (TSH) and thyroxine (T4) in the blood.

Report Overview

Due to the COVID-19 pandemic and Russia-Ukraine War Influence, the global market for Thyroid Function Test estimated at USD 1226.3 million in the year 2022, is projected to reach a revised size of USD 1490.1 million by 2028, growing at a CAGR of 3.3Percent during the forecast period 2022-2028.

The USA market for Thyroid Function Test is estimated to increase from USD million in 2022 to reach USD million by 2028, at a CAGR of Percent during the forecast period of 2023 through 2028.

The China market for Thyroid Function Test is estimated to increase from USD million in 2022 to reach USD million by 2028, at a CAGR of Percent during the forecast period of 2023 through 2028.

The Europe market for Thyroid Function Test is estimated to increase from USD million in 2022 to reach USD million by 2028, at a CAGR of Percent during the forecast period of 2023 through 2028.

The global key companies of Thyroid Function Test include Thermo Fisher, Abbott, Roche, DiaSorin, Danaher, Kronus, Merck, Cortez Diagnostics and bioMÃrieux, etc. In 2021, the global top five players had a share approximately Percent in terms of revenue.

Get a demo Copy of the Thyroid Function Test Report 2023

What are the factors driving the growth of the Thyroid Function Test Market?

Growing demand for below applications around the world has had a direct impact on the growth of the Thyroid Function Test

Diagnostic Laboratories
Research Laboratories and Institutes

What are the types of Thyroid Function Test available in the Market?

Based on Product Types the Market is categorized into Below types that held the largest Thyroid Function Test market share In 2023.

TSH Tests
T4 Tests
T3 Tests
Other Tests

Which regions are leading the Thyroid Function Test Market?

North America (United States, Canada and Mexico)

Europe (Germany, UK, France, Italy, Russia and Turkey etc.)

Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)

South America (Brazil, Argentina, Columbia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

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This Thyroid Function Test Market Research/Analysis Report Contains Answers to your following Questions

What are the global trends in the Thyroid Function Test market? Would the market witness an increase or decline in the demand in the coming years?

What is the estimated demand for different types of products in Thyroid Function Test? What are the upcoming industry applications and trends for Thyroid Function Test market?

What Are Projections of Global Thyroid Function Test Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit? What Will Be Market Share, Supply and Consumption? What about Import and Export?

Where will the strategic developments take the industry in the mid to long-term?

What are the factors contributing to the final price of Thyroid Function Test? What are the raw materials used for Thyroid Function Test manufacturing?

How big is the opportunity for the Thyroid Function Test market? How will the increasing adoption of Thyroid Function Test for mining impact the growth rate of the overall market?

How much is the global Thyroid Function Test market worth? What was the value of the market In 2020?

Who are the major players operating in the Thyroid Function Test market? Which companies are the front runners?

Which are the recent industry trends that can be implemented to generate additional revenue streams?

What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for Thyroid Function Test Industry?

Thyroid Function Test Market - Covid-19 Impact and Recovery Analysis:

We were monitoring the direct impact of covid-19 in this market, further to the indirect impact from different industries. This document analyzes the effect of the pandemic on the Thyroid Function Test market from a international and nearby angle. The document outlines the marketplace size, marketplace traits, and market increase for Thyroid Function Test industry, categorised with the aid of using kind, utility, and patron sector. Further, it provides a complete evaluation of additives concerned in marketplace improvement in advance than and after the covid-19 pandemic. Report moreover done a pestel evaluation within the business enterprise to study key influencers and boundaries to entry.

Our studies analysts will assist you to get custom designed info to your report, which may be changed in phrases of a particular region, utility or any statistical info. In addition, we're constantly inclined to conform with the study, which triangulated together along with your very own statistics to make the marketplace studies extra complete for your perspective.

Final Report will add the analysis of the impact of Russia-Ukraine War and COVID-19 on this Thyroid Function Test Industry.


Detailed TOC of Global Thyroid Function Test Market Research Report, 2023-2030

1 Market Overview
1.1 Product Overview and Scope of Thyroid Function Test
1.2 Classification of Thyroid Function Test by Type
1.2.1 Overview: Global Thyroid Function Test Market Size by Type: 2017 Versus 2021 Versus 2030
1.2.2 Global Thyroid Function Test Revenue Market Share by Type in 2021
1.3 Global Thyroid Function Test Market by Application
1.3.1 Overview: Global Thyroid Function Test Market Size by Application: 2017 Versus 2021 Versus 2030
1.4 Global Thyroid Function Test Market Size and Forecast
1.5 Global Thyroid Function Test Market Size and Forecast by Region
1.6 Market Drivers, Restraints and Trends
1.6.1 Thyroid Function Test Market Drivers
1.6.2 Thyroid Function Test Market Restraints
1.6.3 Thyroid Function Test Trends Analysis

2 Company Profiles
2.1 Company
2.1.1 Company Details
2.1.2 Company Major Business
2.1.3 Company Thyroid Function Test Product and Solutions
2.1.4 Company Thyroid Function Test Revenue, Gross Margin and Market Share (2019, 2020, 2021 and 2023)
2.1.5 Company recent Developments and Future Plans

3 Market Competition, by Players
3.1 Global Thyroid Function Test Revenue and Share by Players (2019,2020,2021, and 2023)
3.2 Market Concentration Rate
3.2.1 Top3 Thyroid Function Test Players Market Share in 2021
3.2.2 Top 10 Thyroid Function Test Players Market Share in 2021
3.2.3 Market Competition Trend
3.3 Thyroid Function Test Players Head Office, Products and Services Provided
3.4 Thyroid Function Test Mergers and Acquisitions
3.5 Thyroid Function Test New Entrants and Expansion Plans

4 Market Size Segment by Type
4.1 Global Thyroid Function Test Revenue and Market Share by Type (2017-2023)
4.2 Global Thyroid Function Test Market Forecast by Type (2023-2030)

5 Market Size Segment by Application
5.1 Global Thyroid Function Test Revenue Market Share by Application (2017-2023)
5.2 Global Thyroid Function Test Market Forecast by Application (2023-2030)

6 Regions by Country, by Type, and by Application
6.1 Thyroid Function Test Revenue by Type (2017-2030)
6.2 Thyroid Function Test Revenue by Application (2017-2030)
6.3 Thyroid Function Test Market Size by Country
6.3.1 Thyroid Function Test Revenue by Country (2017-2030)
6.3.2 United States Thyroid Function Test Market Size and Forecast (2017-2030)
6.3.3 Canada Thyroid Function Test Market Size and Forecast (2017-2030)
6.3.4 Mexico Thyroid Function Test Market Size and Forecast (2017-2030)

7 Research Findings and Conclusion

8 Appendix
8.1 Methodology
8.2 Research Process and Data Source
8.3 Disclaimer

9 Research Methodology

10 Conclusion


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Mon, 14 Aug 2023 13:28:00 -0500 text/html
Killexams : Ackley Function


Dimensions: d

The Ackley function is widely used for testing optimization algorithms. In its two-dimensional form, as shown in the plot above, it is characterized by a nearly flat outer region, and a large hole at the centre. The function poses a risk for optimization algorithms, particularly hillclimbing algorithms, to be trapped in one of its many local minima.

Recommended variable values are: a = 20, b = 0.2 and c = 2π.

Input Domain:

The function is usually evaluated on the hypercube xi ∈ [-32.768, 32.768], for all i = 1, …, d, although it may also be restricted to a smaller domain.

Global Minimum:



Adorio, E. P., & Diliman, U. P. MVF - Multivariate Test Functions Library in C for Unconstrained Global Optimization (2005). Retrieved June 2013, from http://

Molga, M., & Smutnicki, C. Test functions for optimization needs (2005). Retrieved June 2013, from

Back, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press on Demand.

For questions or comments, please email Derek Bingham at:




Tue, 17 May 2022 10:46:00 -0500 text/html
Killexams : Mom worries about European exclusion

The past couple of years, The Garden Guy has fallen head over heels for a plant called Jamesbrittenia.

Microsoft just underlined it in red, pointing out two things.

One: This is going to be bothersome to me as I write this.

And two: If Microsoft doesn't know it, you probably don't either.

Proven Winners is out to change that with the new Safari series of Jamesbrittenia, or South African phlox.

Let's chase some taxonomic rabbits.

Although it's called "South African phlox," it is not a phlox. You look at a photo of Jamesbrittenia grandiflora, and by George, it looks like a phlox.

It is, however, in the Scrophulariaceae, or figwort, family.

Proven Winners has introduced three Safari Jamesbrittenia or South African phlox: Safari Dawn, Safari Sky and next year's new Safari Dusk.

When you see any of them in mixed baskets or containers, you immediately realize what a jewel they are as a component plant. Without the part they play, the design loses its effectiveness.

If you look up Jamesbrittenia online, you will see a boatload of species, including one called Jamesbrittenia jurassica. They are from places like South Africa, India, Egypt, Sudan and Bangladesh. Thus, the Safari series are hybrids. They are, however, award-winning hybrids at that.

Safari Dawn is called rosecolored with a soft yellow eye, but I say it reveals more colors than that.

Each in the series gets about 12 inches tall with a 24-inch spread. It is that spread that allows them to be such a dazzling component plant.

When Jamesbrittenia varieties not named Safari first showed up in trials, they were pretty, but they couldn't take the heat of a long Southern summer.

Safari Dawn, on the other hand, is a multi-award winner, taking honors like Perfect Score in Oregon State and Top Performer at Oklahoma State and the University of Tennessee. That's a nice spread geographically.

Everyone attending the Young's Plant Farms 2022 Garden Tour in Auburn, Alabama, got their heart racing when they saw the mix showing Supertunia Royal Magenta petunia, Superbells Double Amber calibrachoa and Safari Dawn Jamesbrittenia. It was like a crown of rare jewels.

Young's Plant Farm summer 2023 Garden Tour brought the house down again with another magical recipe. This one had Supertunia Royal Velvet petunia, Superbells Double Amber calibrachoa and Safari Sky Jamesbrittenia. I can imagine a gardener anywhere in the country swooning over this combination.

Safari Sky is also an award winner, showing versatility with its sky blue/purple color, a white center and a tiny orange eye. It too won Perfect Score at Oregon State and Top Performer at the University of Tennessee and Michigan State.

Next year's addition, Safari Dusk, rounds out the perfect trifecta. It has shown The Garden Guy how beautiful the Jamesbrittenia can be as it reaches its 2-foot potential falling out of a basket.

That I am featuring it in mid-August is also a statement about its longevity in the South. The flowers are purple with a yellow to orange eye in the center of a tiny white halo. I have also been thrilled to see butterflies from swallowtails to tiny skippers hitting on it as well as bumblebees and metallic green sweat bees, too.

Jamesbrittenia varieties need sun to part sun and good drainage, like so many other plants. One thing you have to appreciate with mixed baskets and containers with a good potting soil: Drainage is hardly ever an issue.

They aren't heavy feeders, either; I've been using a water-soluble mix every two to three weeks.

If I might quote a favorite Beach Boys tune, come on and safari with me — not surfing but gardening with the Safari Jamesbrittenia, three knockout South African phlox. I promise your containers will be dazzling with a new floral design.

Follow Norman Winter on Facebook @NormanWinterTheGardenGuy.

Sun, 13 Aug 2023 18:25:00 -0500 en text/html
Killexams : How to use the TAN function in Microsoft Excel

The TAN function is a Math and Trigonometry function, and its purpose is to return a tangent of a number or angle. In this post, we show you how to use the TAN function in Microsoft Excel.

What is the TAN formula?

In Microsoft Excel, the formula for the TAN function is:


The syntax for the TAN function is:

Number:  The angle for radians for which you want the tangent.

What is Math and Trig functions in Excel?

In Microsoft Excel, the Math and Trig functions allow users to do many mathematical calculations including basic arithmetic, sums and products, exponent and logarithms, and trigonometric ratios. The Math and Trig functions are not the only math-related functions in Excel, depending on the math they use; math functions can also be found in the Statistical function and Engineering functions categories.

Follow the steps below to use the TAN function in Microsoft Excel:

  1. Launch Microsoft Excel.
  2. Create a table or use an existing table from your files.
  3. Place the formula into the cell you want to see the result.
  4. Press the Enter Key.

Launch Microsoft Excel.

Create a table or use an existing table from your files.

TAN function in Microsoft Excel

Finding a tangent of a particular number; click the cell where you want to place the result.

Type into the cell =TAN(A2) and press Enter. The result will be equal to 1.222056.

If your argument is in the degrees multiply by PI()/180 or use the Radian function to convert it to radians.

In the cell you want to place the result, type =TAN(30*PI()/180)  and press Enter. The result is 0.57735.

If using the Radian function use the formula =TAN(RADIANS(30)), press enter. The result is 0.57735.

There are two other methods to use the TAN function

1] Method one is to click the fx button on the top left of the Excel worksheet.

An Insert Function dialog box will appear.

Inside the dialog box in the section, Select a Category, select Math and Trigonometry from the list box.

In the section Select a Function, choose the TAN function from the list.

Then click OK.

A Function Arguments dialog box will open.

In the Number entry box, input into the entry box cell A2.

Then click OK.

2] Method two is to click the Formulas tab, then click the Math and Trigonometry button in the Function Library group.

Then select TAN from the drop-down menu.

A Function Arguments dialog box will open.

We hope this tutorial helps you understand how to use the TAN function in Microsoft Excel; if you have questions about the tutorial, let us know in the comments.

Now read: How to create a Gauge Chart in Excel.

Thu, 31 Mar 2022 10:11:00 -0500 en-us text/html
PL-200 exam dump and training guide direct download
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