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Exam Code: Google-IQ Practice exam 2023 by team
Google-IQ Google Analytics Individual Qualification (IQ)

Exam Detail:
The Google Analytics Individual Qualification (IQ) exam is designed to assess an individual's knowledge and proficiency in using Google Analytics, a web analytics tool provided by Google. Here are the exam details for the Google Analytics IQ exam:

- Number of Questions: The exam typically consists of multiple-choice questions. The exact number of questions may vary, but it is typically around 70 questions.

- Time Limit: The time allotted to complete the exam is 90 minutes (1 hour and 30 minutes).

Course Outline:
The Google Analytics IQ exam covers a wide range of courses related to Google Analytics and its various features. The course outline typically includes the following domains:

1. Fundamentals of Google Analytics:
- Understanding the basics of web analytics and its importance.
- Navigating the Google Analytics interface.
- Configuring Google Analytics accounts and properties.
- Setting up goals and conversion tracking.

2. Implementation and Data Collection:
- Implementing Google Analytics tracking code on websites.
- Configuring tracking parameters and custom dimensions.
- Understanding data collection methods and data accuracy.

3. Data Analysis and Reporting:
- Analyzing website traffic and user behavior using Google Analytics reports.
- Creating custom reports and segments.
- Understanding attribution modeling and cross-device tracking.
- Utilizing advanced features like data import, custom funnels, and event tracking.

4. Goals and Ecommerce Tracking:
- Setting up and tracking goals for different types of conversions.
- Configuring ecommerce tracking for online stores.
- Analyzing ecommerce performance and revenue attribution.

5. Campaign Tracking and Tag Management:
- Implementing campaign tracking using URL parameters and UTM tags.
- Utilizing tag management systems like Google Tag Manager.
- Understanding cross-domain and cross-device tracking.

Exam Objectives:
The objectives of the Google Analytics IQ exam are as follows:

- Assessing candidates' understanding of web analytics principles and their application in Google Analytics.
- Evaluating candidates' proficiency in configuring and implementing Google Analytics tracking code.
- Testing candidates' ability to analyze and interpret data using Google Analytics reports and advanced features.
- Assessing candidates' knowledge of setting up goals, ecommerce tracking, and campaign tracking in Google Analytics.
- Evaluating candidates' understanding of tag management systems and their integration with Google Analytics.

Exam Syllabus:
The specific exam syllabus for the Google Analytics IQ exam covers the following topics:

1. Google Analytics Fundamentals:
- Introduction to web analytics and Google Analytics.
- Google Analytics account structure and navigation.
- Understanding basic metrics and dimensions.
- Setting up goals and ecommerce tracking.

2. Implementation and Data Collection:
- Installing and configuring the Google Analytics tracking code.
- Customizing tracking parameters and dimensions.
- Understanding data collection methods and data accuracy.

3. Data Analysis and Reporting:
- Analyzing standard reports and segments.
- Creating custom reports and advanced segments.
- Utilizing attribution modeling and cross-device tracking.
- Advanced features like data import and event tracking.

4. Goals and Ecommerce Tracking:
- Setting up and tracking goals for different types of conversions.
- Configuring ecommerce tracking for online stores.
- Analyzing ecommerce performance and revenue attribution.

5. Campaign Tracking and Tag Management:
- Implementing campaign tracking using URL parameters and UTM tags.
- Introduction to tag management systems and Google Tag Manager.
- Cross-domain and cross-device tracking.

Google Analytics Individual Qualification (IQ)
Google Qualification information
Killexams : Google Qualification information - BingNews Search results Killexams : Google Qualification information - BingNews Killexams : Best Free AI Training Courses for 2023

With businesses finding new, inventive ways to use AI almost every day, it's no surprise that AI training courses are becoming increasingly sought after.

Workers in all sorts of industries are looking to upskill themselves in line with the rapid technological changes occurring. Luckily, companies like Microsoft and Google offer free AI training courses, as do some higher education institutions.

In this guide, we cover the best AI training courses currently available, as well as the benefits of learning about AI in the current job market. We've largely focused on free courses that offer immediate, foundational learning opportunities that you can start applying to your job role or career straight away, rather than paid degree courses that cost hundreds or thousands of dollars.

1. Google’s Generative AI Learning Path (10 Courses)

One of the more generous courses available in terms of real hours of learning, Google’s Generative AI Learning path has 10 courses on it. All courses take one day to complete.

Seven of the courses are classified as introductory, including “Introduction to Generative AI”, “Introduction to Large Language Models” and “Generative AI Fundamentals”. There are three courses within the learning path described as intermediate, including “Encoder-Decoder Architecture” and “Attention Mechanism: Overview”.

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The first two introductory courses cover a lot of immediately applicable content, such as how to use prompt tuning to get the best out of large language models. There's also a course on the responsible usage of AI.

Although there's no official qualification, you will be awarded a completion badge that you can attach to your digital resume.

2. Microsoft's “Transform Your Business With AI” course

This Microsoft learning path is designed, as the tech giant says, to help businesspeople acquire “the knowledge and resources to adopt AI in their organizations”, and explores “planning, strategizing, and scaling AI projects in a responsible way.”

Microsoft says the objectives for this course are to become familiar with existing AI tools, understand basic AI terminology and practices, and use prebuilt AI to build intelligent applications.

To enroll in this course, which is 2 hours and 40 minutes long, Microsoft says you’ll need a “basic understanding” of IT and business concepts. Modules included in the pathway are:

  • Leverage AI tools and resources for your business (55 mins)
  • Create business value from AI (21 min)
  • Embrace responsible AI principles and practices (48 mins)
  • Scale AI in your organization (36 mins)

3. Linkedin’s “Career Essentials In Generative AI” training course

Linkedin’s AI Career Essentials Course is made up of five different videos, with a total run time of around four hours. Each video is hosted by a different AI expert, covering a range of core concepts and ethical considerations relating to AI models.

One of the videos provides a detailed explanation of how to streamline your work with Microsoft Bing Chat, while another discusses the key differences between search engines and reasoning engines. Although there’s no accreditation or certification, completing this course will earn you a badge of completion from LinkedIn, which can be displayed on your profile.

The fifth video in the series, entitled “an introduction to artificial intelligence” is an hour and a half long and provides a simplified overview of the best AI tools for businesses, which is handy for those who haven’t taken the plunge yet and implemented a tool in work.

4. IBM’s “AI Foundations for Everyone” training course

IBM offers a course entitled “AI Foundations for Everyone” through Coursera, of which over 19,000 people have already enrolled in. You can audit the course for free, which will give you access to all of the materials and some of the assignments, but you won't be graded or get a certificate at the end.

It’s geared toward beginners and you don’t need prior experience to enroll, and the schedule is flexible so you can learn at your own pace. Along with AI fundamentals, the course will also ensure you’re familiar with IBM’s own AI services, which help businesses integrate artificial intelligence into their existing infrastructure.

IBM says that, by the end of the course, participants will have had “hands-on interactions with several AI environments and applications”.

The course has three modules: “Introduction to Artificial Intelligence”, “Getting Started with AI using IBM Watson”, and “Building AI-Powered Chatbots Without Programming”. Each module takes between nine and eleven hours to complete.

5. Digital Partner's “The Fundamentals of ChatGPT” training course

Digital Partner’s course entitled “The fundamentals of ChatGPT” is a great option for anyone who wants to take a free, accredited course that covers the basics of Generative AI.

During the course, you’ll spend time learning about OpenAI’s role in global AI development, and be able to learn about how ChatGPT works, its advantages and limitations. There’s also a variety of examples included within the course that will show you how to leverage ChatGPT for different tasks, and you'll learn more about the difference between ChatGPT and ChatGPT Plus.

Modules include “Working With ChatGPT”, “ChatGPT and Its Shortcomings” and “Training a GPT Model”.

This free course is available on, and is published by a digital marketing firm called Digital Partner. The course is CPD accredited and a certificate will be awarded upon completion of a small assessment at the end of the 1.5-3 hour program.

6. Phil Ebner’s ChatGPT, Midjourney, Firefly, Bard, DALL-E” AI crash course

While there are some good courses on Udemy that will guide you through the ins and outs of MidJourney and other AI generation tools, this instructor covers the most ground, and almost 12,000 students have already enrolled in the course, which has a 4.6/5 rating on Udemy.

The course is almost two hours long and also includes content that will help you better use tools like ChatGPT to generate text responses as well as images.

The “AI for Visual Creativity” section, however, will show you how to use both MidJourney and Dall-E to create “photorealistic images, illustrations, and digital art in a variety of styles.”

On Udemy, you don’t receive certificates of completion for free courses, but if you're just looking to upskill yourself free of charge, this course is definitely worth a look.

Best Free AI Training courses for Programmers, Developers & Tech Experts

Up next, we have more advanced courses geared towards programming and development.

  1. Harvard University’s “Introduction to Artificial Intelligence with Python”
  2. DeepLearning.AI’s “ChatGPT Prompt Engineering For Developers” (Coursera)
  3. Intro to TensorFlow for Machine Learning (Udacity)
  4. Georgia Tech’s Reinforcement Learning (Udacity)
  5. Become an AI-Powered Engineer: ChatGPT, GitHub Copilot (Udemy)
  6. Great Learning’s “ChatGPT for Beginners” training course

1. Harvard University’s “Introduction to Artificial Intelligence with Python”

Harvard University offers a self-paced, 7-week course on the “concepts and algorithms at the foundation of modern artificial intelligence”.

The time commitment of between 10-30 hours a week – but it’s completely free to enroll and you’ll be supported as you complete projects and attend lectures. However, you need to have taken Harvard’s “Introduction to Computer Science” course first to enroll.

2. DeepLearning.AI’s “ChatGPT Prompt Engineering For Developers” (Coursera)

This course will help you utilize OpenAI’s API to write more effective prompts, learn how large language models can be used to carry out tasks like text transformation and summarizing, and teach you how to program and build a custom AI chatbot.

The course is run by AI expert and DeelLearning.AI co-founder Andrew Ng and OpenAI’s Isa Fulford, and it’s only an hour long. DeepLearning.AI says the course is “free for a limited time”. A basic understanding of Python is needed, but aside from that, it’s beginner friendly.

3. Intro to TensorFlow for Machine Learning (Udacity)

This course teaches participants how to build deep-learning applications with TensorFlow, one of the most popular open-source Python software libraries.

The estimated completion time for the course is approximately two months, and you should have some experience with Python syntax, including variables, functions, and classes, as well as a grasp of basic algebra.

If you take the course, providers Udacity say, you’ll get “hands-on experience building your own state-of-the-art image classifiers” as well as other types of deep learning models.

4. Georgia Tech’s Reinforcement Learning (Udacity)

This Georgia Tech course is free on Udacity and focuses on exploring “automated decision-making from a computer-science perspective”.

At the end of the course – which takes approximately four months to complete, but is also described as self-paced – participants will recreate a result from a published paper on reinforcement learning.

However, it is recommended you have a graduate-level machine-learning qualification and some prior experience with reinforcement learning from previous studies. Experience with Java is also required.

Although there’s no official certificate awarded for completing the course, you can earn a nano degree program certificate by completing Udacity's 4-month long “Deep Reinforcement Learning”, although this costs $1116.

5. Become an AI-Powered Engineer: ChatGPT, Github Copilot (Udemy)

In this course, students will learn how to create high-quality pieces of code using ChatGPT and integrate it with other text editors. It also covers how to use GitHub Copilot.

This might be a free tutorial, but the course has much better reviews than some of the other AI courses available on Udemy, with 56% of watchers who left a review giving the course five stars, and a further 24% giving it four stars at the time of writing.

The course will be best suited to developers who want to leverage AI tools for coding responsibilities in general, and also, to become more efficient in their coding practices.

6. GreatLearning’s “ChatGPT for Beginners” training course

This is a completely free, two-hour long beginners-focused ChatGPT course. It’s one of the only beginner's courses on the internet that includes a section on coding prompts, although it also covers quite a bit of other ground, including email prompting.

There are no prerequisites needed for this course, and it has an average rating of 4.61/5, with 75% of reviewers giving the course 5 stars.

Free College AI Courses and Training

A number of universities and colleges offer AI-focused courses.

  1. Stanford University’s “Machine Learning” Course (Coursera)
  2. Vanderbilt University’s “Prompt Engineering for ChatGPT”
  3. Georgia Tech’s “Machine Learning” Course (Udacity)
  4. The Open University’s “AI Matters” Course (OpenLearn)
  5. University of Pennsylvania’s “AI For Business” (Coursera)
  6. University of Helsinki's “Elements of AI” and “Ethics of AI” Course

1. Stanford University’s “Introduction to Artificial Intelligence” course (Udacity)

This foundational online program, which takes around 10 months to complete at a rate of 10 hours a week, focuses on fundamental AI concepts and practical machine learning skills but is classified as an intermediate course.

The course, which is split into two umbrella sections (“Fundamentals of AI” and “Applications of AI) is completely free if you sign up for Udacity (which also doesn't cost anything).  It consists of 22 different lessons and a string of interactive quizzes.

2. Vanderbilt University’s “Prompt Engineering for ChatGPT”

Jules White, Vanderbilt University’s associate dean for strategic learning programs and associate professor of computer science, has launched a free online course available through Coursera focusing on prompt engineering.

It goes through the most effective approaches for prompt engineering, covering summarization, simulation, programming, and other useful ways you can harness the power of ChatGPT with your inputs.

The course takes around 18 hours to complete and is made up of an introduction to prompts and three separate sessions on prompt patterns, as well as a 2-hour module on examples.

3. Georgia Tech’s “Machine Learning” course (Udacity)

In collaboration with Georgia Tech, Udacity has made an intermediate machine-learning course available for free, which takes around 4 months to complete, although the course listing says you can do it at your own pace.

The course is offered as part of an online master’s degree at Georiga Tech, but taking this course won’t earn you credit toward this degree.

The course covers Supervised and Unsupervised Learning, which are two different types of machine learning, and covers how they're used in AI systems.

However, having a “strong familiarity with Probability Theory, Linear Algebra, and Statistics” and prior experience with statistics is helpful. Students should also have some experience with programming.

4. The Open University’s “AI Matters” course (OpenLearn)

The Open University is a UK-based institution that offers a free course through its learning portal OpenLearn entitled “AI Matters”.

In the course, you'll learn about the “historical, social, political and economic issues in AI”, explore the benefits and limitations of the technology, and discuss ethical risks relating to AI.

The course is six hours long, and you'll be eligible for a certificate of participation.

5. University of Pennsylvania’s “AI For Business” (Coursera)

The University of Pennsylvania's “AI for Business” specialization is made up of four different, free courses:

  • AI Applications in People Management
  • AI Fundamentals for Non-Data Scientists
  • AI Applications in Marketing and Finance
  • AI Strategy and Finance

According to the University of Pennsylvania's website, although the course itself costs $39 to complete, you can enroll in the four individual modules that take up the course for free. Each module will take up around two hours a week to complete.

6. University of Helsinki's “Elements of AI” and “Ethics of AI”

The University of Helsinki has two, free online courses available. The course entitled “Ethics of AI” is geared towards “anyone who is interested in the ethical aspects of AI”, the university says.

The course will familiarize you with common questions that arise in AI ethics and the various ways to approach them.

Elements of AI” is a broader course with 6 chapters, focusing on courses such as “neural networks”, “machine learning” and “AI problem solving”.  All you need to do to access the course materials is sign up.

The Benefits of Learning About AI

Of course, completing an AI training course can have a number of benefits. From a personal learning perspective, it's one of the best ways you can spend your time – AI is here to stay, and getting a better grasp of how it works might just help you out in the near future.

Plus, the things you learn about AI will be applicable to a wide variety of job roles in almost every sector of the economy, so it's arguably a safer bet than completing a course on a niche or industry-specific topic.

What's more, right now, businesses are looking for people who understand how generative AI tools like ChatGPT work, and how to leverage them effectively. Employees that are conscious of the limitations of AI tools and able to generate useful responses using prompts are going to become more sought after than employees without these skills.

Completing an AI training course is going to look good on your CV, which will help if you're applying for a new job. Evidence that you've taken the initiative to explore an emerging technology is definitely something an employer will find desirable.

Of course, if you don't have much of a budget – or you're not entirely sure what AI training course would be the best use of your time – then trying out some free options is a great place to start.

Yes – Google has a free AI learning path that has ten courses you can complete for free. Each course takes around one day to complete. The modules provide an introduction to generative AI, and although there's no official qualification, you will get a completion badge which you can add to your resume.

Yes – you can learn how to harness the powers of AI on your own, through online guides, tutorials, and courses. There are quite a lot of resources out there now that will show you how to get the most out of generative AI tools like ChatGPT. However, if you want to learn how more complex skills, such as programming a large language model, you may need to seek out a paid course

Yes – Microsoft has a free training path available that consists of four short modules, which cover how to leverage AI effectively in business settings and how to scale AI projects in a responsible, impactful way.
Wed, 16 Aug 2023 20:53:00 -0500 en-US text/html
Killexams : How to Ace Your Technical Interview No result found, try new keyword!Want to land your dream tech job? Learn how to ace your technical interview with these handy tips and strategies. Sat, 19 Aug 2023 03:31:23 -0500 en-us text/html Killexams : GCSE results set to drop close to pre-Covid levels
  • By Hazel Shearing
  • Education correspondent

Image source, Getty Images

GCSE results in England are set to drop for a second year running, bringing grades back in line with 2019 levels.

It follows a spike in higher grades in 2020 and 2021, when exams were cancelled because of Covid and results were based on teachers' assessments.

In Wales and Northern Ireland, results are set to be higher than they were in 2019 but lower than 2022.

Students will also receive Level 2 BTec, Cambridge National and other vocational results on Thursday morning.

By Christina McSorley, education producer

Euan is waiting for GCSE results in science, maths, computer science, English language and English literature - as well as a BTec in creative media.

His autism meant he found mainstream schools quite difficult, but he has flourished since moving to Abbey School in Rotherham.

They saw Euan's potential and ran a GCSE in computer science especially for him.

"The teachers I've had this year are the best ones I've ever had," he says.

Results day will be stressful for Euan, as the exams were a mixed bag.

"The ones I feel quite good about are the science and computer science ones. The ones I don't feel so good about are the English ones," he says.

Euan hopes to work in network and cyber-security one day. Once he has his results, he plans to head to college to study a T-Level in digital support networking.

Exams were cancelled in 2020 and 2021 because of Covid. Pupils' grades were based on teachers' predictions instead, leading to a spike in top results.

England's exams watchdog, Ofqual, set out a two-year plan to bring A-level and GCSE results back down to pre-pandemic levels.

Last year was the first time students sat exams since the start of the pandemic. Ofqual called it a "transition year", with grades set to reflect a midway point between 2019 and 2021. About 73.2% of GCSEs in England, Wales and Northern Ireland were marked at grades 4/C and above.

Now, in the second stage of the plan, grades are due to be much more similar to those in 2019, when 67.3% of GCSEs were marked as passes.

Schools Minister Nick Gibb said bringing them back down would ensure results carried "weight and credibility" with employers, universities and colleges, so they know what the different grades mean.

The expected fall in grades will lead to more compulsory resits of these subjects - which can be carried out alongside those further studies - compared with the last two years.

Most of this year's GCSE students were in Year 8 when the pandemic hit. They also faced disruption from teacher strikes this year, although unions said they tried to minimise the impact on exam year groups.

Ofqual says there is "protection built into the grading process" so that students should achieve the grades they would have done if the pandemic had not happened - even if they did not perform as well in their exams.

Some Covid measures also remained in place for this year's exams. GCSE papers in the same subject were spaced apart more than they were before the pandemic, allowing for rest and revision.

Students had formulae and equation sheets in some subjects, and were not tested on unfamiliar vocabulary in modern foreign language exams.

But, unlike in the rest of the UK, GCSE students in England were not given advance information about the courses on which they would be tested.

In Wales and Northern Ireland, grades are still expected to be higher than they were in 2019.

Last week, the overall percentage of top A-level grades fell close to pre-pandemic levels.

A total of 3,448 people received T-level results - although 5,210 students started them in 2021.

Education Secretary Gillian Keegan told the BBC that some had switched to other Level 3 courses, apprenticeships, or gone straight into work.

"That is something that we are working to make sure that doesn't happen because we do want to make sure that they finish the T-levels," she said.

"The T-levels are fantastic but I'll be honest, it's a very new qualification so we still need to grow it."

The Department for Education said it was working to "improve retention".

What questions do you have about results day? Whether you have queries about A-levels, GCSEs, Highers or vocational courses, you can get in touch by emailing

Please include a contact number if you are willing to speak to a BBC journalist. You can also get in touch in the following ways:

If you are studying this page and can't see the form you will need to visit the mobile version of the BBC website to submit your question or comment or you can email us at Please include your name, age and location with any submission.

Wed, 23 Aug 2023 09:43:00 -0500 en-GB text/html
Killexams : Where the Republican Presidential Candidates Stand on Five Key Economic Issues

Donald Trump says he won’t be at the Republican debate Wednesday, but his ideas will be at center stage.

Since his win in 2016, Trump has moved the GOP toward his views on a tougher trade policy with China as they have embraced his accomplishments such as the 2017 tax cuts.

Citing his high poll numbers in the primary, Trump has said he will not participate in primary debates and sat for a taped interview with former Fox News host Tucker Carlson that is expected to be posted online around the same time.

That leaves at least seven candidates on stage in Milwaukee:  Florida Governor Ron DeSantis, former Vice President Mike Pence, former United Nations ambassador Nikki Haley, South Carolina Senator Tim Scott, former New Jersey Governor Chris Christie, entrepreneur Vivek Ramaswamy and North Dakota Governor Doug Burgum.

Former Arkansas Governor Asa Hutchinson said he has also met the Republican National Committee’s qualification criteria.

Here’s a closer look at the 2024 Republican presidential candidates’ proposals on five key economic issues. Bloomberg has included each candidate who has received at least 1% in an early primary state poll.

Donald Trump, former president

Although he supported raising the retirement age and privatizing Social Security in the past, Trump broke with Republicans in his 2016 campaign to say that he would not cut benefits. He urged GOP members of Congress to vote against cuts to Social Security or Medicare in the discussions earlier this year over raising the debt ceiling.

What he’s said: “I will not be cutting Medicare, and I will not be cutting Social Security.”

Trump signed the 2017 tax cuts into law and frequently cites it on the 2024 campaign trail as one of his signature achievements. He has said that he would extend the expiring tax cuts if elected to a second term. The Congressional Budget Office estimates that extending the tax cuts through 2033 would add $3.5 trillion to the federal deficit.

What he’s said: “We will replace Biden’s tax hikes with the beautiful Trump tax cuts. We have to extend the tax cuts, biggest tax cuts in history.”

Trump has called for imposing baseline tariffs against Chinese goods, revoking China’s permanent normal trade relations status, phasing out imports of essential goods from China over four years, barring US companies from investing in China, revoking federal contracts from companies that outsource to China, and banning China from buying US farmland. 

What he’s said: “My agenda will tax China to build up America.”

Trump signed a 2018 bill that exempted smaller banks from annual stress tests of their assets, which some Democrats argue led to the collapse of Silicon Valley Bank and Signature Bank. He has blamed the failures on high interest rates and environmental, social and corporate governance investing, known as ESG. He has said he would not reappoint Federal Reserve chair Jerome Powell, despite appointing him to the position.

What he’s said: “I would not reappoint him. I thought he was always late, whether it was good or bad, but he was always late.”

Trump has said he would overturn Biden administration policies promoting the domestic production of electric cars. He has also said he would expand domestic oil and gas exploration and production to reduce dependence on foreign oil. 

What he’s said: “We are a nation whose leaders are demanding all electric cars, even though they can’t go far, cost too much, and whose batteries are produced in China with materials only available in China, when an unlimited amount of gasoline is available inexpensively in the United States of America.”

Ron DeSantis, Florida governor

As a member of Congress, DeSantis voted for non-binding resolutions in favor of raising the retirement age and privatizing Social Security, but he now says he would not touch benefits for current seniors or support privatization. He says he is open to bipartisan changes that would affect younger Americans.

What he’s said: “When people say that we’re going to somehow cut seniors, that is totally not true. Talking about making changes for people in their 30s and their 40s so that the program’s viable — that’s a much different thing.”

DeSantis voted in the US House in favor of the 2017 Trump tax cuts. He has proposed extending the lower individual tax rates before they expire in 2025 and renewing a temporary measure that allows companies to write off investments in short-lived assets such as machinery immediately. He is also calling for stricter international tax policy that makes it harder for US companies to move assets and cash offshore. He would eliminate the Internal Revenue Service.

What he’s said: “We are also going to ensure that tax rates are low, that the tax code is simple and that these policies are permanent so that people are able to make decisions about how to deploy their resources.”

DeSantis calls China “the most formidable adversary we face since the Soviet Union.” He has proposed revoking China’s permanent normal trade relations status, banning the sale of US farmland to Chinese officials, banning imports of goods made with stolen intellectual property, and incentivizing the repatriation of US capital from China with tax incentives. He said he would consider banning TikTok.

What he’s said: “They said if you granted China special trading status and put them in the World Trade Organization, that China would become more democratic, that it would guard against forced transfers of technology, that you wouldn’t have a mass relocation of American manufacturing to China and that we would be safeguarded against surges of Chinese imports. What actually happened over these past 25 years, China has become more authoritarian, more powerful and more ambitious.”

As a member of Congress, DeSantis voted for the 2018 law loosening bank stress tests, but he blames accurate bank failures on regulators and corporate diversity, equity and inclusion efforts, known as DEI. He promises to order the Justice Department to target DEI efforts. He’s called for a Federal Reserve chair who will focus on maintaining a stable dollar, and keeping inflation down, saying he would work to oust Powell before his term ends in 2026. He also said he would oppose a central bank digital currency, a counterpart to physical US dollars. As governor, he signed a bill barring Florida pension funds from ESG investing and says he would end its use by large investors as president. 

What he’s said: “We need to rein in the Federal Reserve. It is not designed or supposed to be an economic central planner. It is not supposed to be indulging in social justice or social engineering. It’s got one job: maintain stable prices.”

As governor, DeSantis vetoed a bipartisan bill aimed at encouraging Florida to buy more electric cars and has vowed to reverse Biden administration policies encouraging electric vehicles. He says climate change is often used as an excuse to advance liberal policies. He has vowed to eliminate the Department of Energy, along with three other agencies. 

What he’s said: “What I’ve found is when people start talking about things like global warming, they typically use that as a pretext to do a bunch of left-wing things that they would want to do anyways.”

Mike Pence, former vice president

Pence has described Social Security and Medicare as causes of “runaway government spending.” He’s called for allowing younger Americans to invest a portion of their Social Security taxes in a private account and said he is open to phasing in a hike in the retirement age.

What he’s said: “If we act in this moment with the support of this generation, we can introduce common-sense reforms that will never touch anyone who is in retirement, or anyone who will retire in the next 25 years.”

As vice president, Pence supported the 2017 tax cuts, pointing to White House figures estimating that a typical family of four earning $73,000 received a $2,000 tax break from them. In his campaign kickoff speech, he called for extending them.

What he’s said: “We’ll champion lower taxes at home and extend the historic tax relief of the Trump/Pence administration and give the American people freedom from excessive federal regulations and get back to repealing two federal regulations for every new rule that we put on the books.”

Pence has called for maintaining tariffs on China put in place by the Trump administration, banning TikTok, ending the country’s permanent normal trade relations status, and creating automatic sanctions if China invades Taiwan, among other things.

What he’s said: “China may not be an evil empire, but under President Xi, it certainly aspires to become one.”

Pence would eliminate the Consumer Financial Protection Bureau. He has long called for ending the Federal Reserve’s dual mandate, leaving it to focus on inflation while Congress and the president handle employment. He has criticized activist investors who push for ESG investing. He said he would not reappoint Powell to the Federal Reserve and floated conservative economist Judy Shelton instead. Shelton failed to garner enough support from Senate Republicans when Trump nominated her in 2020.

What he’s said: “Jerome Powell’s time is over. I’d love to see somebody like Judy Shelton appointed to the Federal Reserve. You can google it later and find out how good she is. Trust me.”

Pence has called for eliminating the Environmental Protection Agency and expediting permitting for “critical energy infrastructure” such as oil pipelines and solar farms, restarting oil and gas leasing on federal lands and opening the Arctic and offshore areas for oil exploration. He opposes emission standards designed to boost electric cars and would repeal tax credits and subsidies for electric vehicles.

What he’s said: “The time has come for America to return to the policies that will drive American growth and American strength and it begins with American energy.”

Nikki Haley, former UN ambassador

Haley has proposed raising the retirement age for people now in their 20s, limiting benefits for the wealthy and using chained CPI, which measures when consumers switch to a less expensive alternative, that would reduce future increases. She has also proposed expanding private Medicare Advantage plans.

What she’s said: “It is unrealistic to say you’re not going to touch entitlements. The thing is you don’t have to touch it for seniors and anybody near retirement. You’re talking about the new generation like my kids coming up.”

Haley supports the 2017 tax cuts.

What she’s said: “The tax cuts of 2017 put more money in the pockets of hard-working Americans.”

Haley would would bar federal, state and local governments from buying Chinese-made drones; ban the Chinese instant messaging and payment app WeChat and the social media app  TikTok; halt exports of technology equipment to China; restrict China from buying US land; and limit scientific and technological cooperation with China. She would consider ending normal trade relations with China over the fentanyl crisis, which has been linked to Mexican criminal groups using precursor chemicals from China.

What she’s said: “I think China’s an enemy. I think we have to take them incredibly seriously. And the problem is, you can look at dollars and cents or you can look at a threat to America.”

Haley criticized the Biden administration for using money from the federal deposit insurance fund for customers of Silicon Valley Bank, calling it a “bailout.” She said depositors should have been paid by selling off the bank’s assets.

What she’s said: “Joe Biden is pretending this isn’t a bailout. It is. Now depositors at healthy banks are forced to subsidize Silicon Valley Bank’s mismanagement.”

Haley has called for speeding up permitting of oil pipelines, expanding domestic production of oil and natural gas and rolling back “wasteful green energy subsidies and regulations” promoting solar panels and electric vehicles, including new emission standards on cars. Her campaign has said she would withdraw from the Paris Agreement on climate change and expand the use of carbon capture technology.

What she’s said: “Do we want renewables? Sure we do, but we want to make sure we have every other type of energy possible.”

Tim Scott, US Senator, South Carolina

As a member of the House in 2012, Scott voted for a proposal to raise the retirement age for Medicare. He now says that he would not touch Social Security or Medicare benefits, although he also says the country should examine the state of entitlements to “tame the tiger.” He has said he would focus Medicare to improve efficiency and pay for outcomes instead of procedures.

What he’s said: “I will protect Social Security and Medicare for my own mother, and for you too.”

Scott voted for the 2017 tax cuts, which included a provision he championed creating “opportunity zones” that allow investors and corporations to defer capital gains taxes by investing in designated low-income areas. His campaign platform calls for making tax cuts permanent.

What he’s said: “I was one of the lead authors for the Republican tax reform bill that slashed taxes for families, brought jobs and investment back from overseas and created my signature legislation, the Opportunity Zones – that’s brought billions of dollars back in the poorest communities that have been left behind.”

Scott supports the “strategic” use of sanctions against China, barring the sale of oil from the strategic petroleum reserve to China and requiring apps like TikTok to be labeled “Made in China.” His platform calls for “removing the Chinese Communist Party from our universities, our farmland, our personal data, and our skies.”

What he’s said: “China started this new economic cold war, but America, we’re going to finish it.”

As a senator, Scott did not vote on Powell’s 2018 appointment as Fed chair under Trump, but did vote to confirm him for a second term in 2022 under Biden. As a member of the Senate Banking Committee, Scott has proposed a bipartisan bill that would allow — but not require — regulators to force some executives at failed banks to return some of their compensation, impose penalties and block them from going to work at other banks and been critical of Powell over his response to the bank failures.

What he’s said: “We’ve worked to find a common-sense solution to address executive accountability that is tailored to protect the American taxpayer and limit government overreach.”

As a senator, Scott has cosponsored a bill to repeal a Biden administration tax on crude oil and imported petroleum products. His platform calls for “removing burdensome red tape” and an “all-of-the-above energy approach,” which typically means approving more drilling for oil and gas alongside coal, nuclear, solar and wind energy projects.

What he’s said: “The best way to bring down sky-high prices at the gas pump is by unleashing American energy production—not punishing American energy producers.”

Chris Christie, former New Jersey governor

Christie supports raising the Social Security retirement age for people now in their 30s and 40s, reducing benefits for the wealthy or setting an income cap, ideas he also proposed in his unsuccessful 2016 run for the GOP nomination. 

What he’s said: “When we set up Social Security, they never thought that the average life expectancy would be in the mid-80s. That’s where we are now.”

Christie said he would extend the Trump tax cuts if elected and would keep a cap on state and local tax deductions, known as the SALT cap, which mainly affects residents of high tax states, although he said he might have made it more generous.

What he’s said: “I don’t think that the rest of the country should be paying for the excessive taxes that we had in New Jersey, and New York, and Illinois, and California.”

Christie says he would get tougher on China on issues like intellectual property theft or unauthorized spy balloons. He’s said tariffs would be part of an “overall negotiation” with China, and he would ban TikTok.

What he’s said: “The Chinese have taken advantage of our good nature, stealing our intellectual property, spying on us, causing trouble around the world, unfair trade deals, and it’s time for that to stop now.”

Christie says he would keep Powell as Federal Reserve chair when his term ends in 2026 but he thinks he could have acted sooner to raise interest rates to fight inflation. 

What he’s said: “I don’t have anything that I think Jay has done horribly wrong. I think that he probably should have started tightening sooner than he did. I think he waited too long on that.”

Christie has called for increasing domestic oil production to lower gas prices for Americans and put pressure on Russia, arguing that Biden is “a captive of the extreme environmentalists.” He also proposed banning the importation of Russian oil over its invasion of Ukraine. He’s said people should not be forced to buy electric cars.

What he’s said: “Joe Biden should announce tonight that he will permit North Dakota, Texas & Alaska to produce as much oil & gas as they can to lower prices for Americans & squeeze Putin & Russia by helping our European allies.” 

Vivek Ramaswamy, co-founder and executive chairman of Strive Asset Management

Ramaswamy says that proposals to reform Social Security come from “a good place” and it would not make sense to create the system today but he believes the government should honor its commitments.

What he’s said: “If the government has made you certain commitments that you have lived your life and paid in as you have over the last number of years in your life, that you can’t just pull that rug out from under them.”

Ramaswamy has not made public comments about extending the 2017 tax cuts and his campaign didn’t respond to requests for information on his position.

Ramaswamy has called for “cutting the cord” with China over trade unless it stops stealing US intellectual property and hacking US companies’ data. He said he would bar Chinese officials from buying land in the US and stop their financial donations to American universities. He has said he would tell China not to “mess with Taiwan” before 2028, as the US builds up its own semiconductor industry, and would cut a deal with Russia to allow it to keep parts of Ukraine in order to weaken its alliance with China.

What he’s said: “Let me make it really simple for Xi Jinping’s translator, okay? Do not mess with Taiwan before 2028, before the end of my first term, okay?”

Ramaswamy says the Federal Reserve’s dual mandate on inflation and unemployment has been “disastrous” and he would narrow its mission to preserving the dollar as “a stable financial unit.” He opposes ESG investing and the creation of a central bank digital currency.

What he’s said: “If elected president, I will return the Fed to a narrower scope: preserving the US dollar as a stable financial unit to help prevent financial crises and restore robust economic growth.”

Ramaswamy is the co-founder of an exchange-traded fund called DRLL that would engage in shareholder activism to encourage oil and gas companies to “drill more and frack more.” He has called for reforming the permitting system to allow more oil, gas, coal and nuclear power in the US and opposes subsidies for electric vehicles.

What he’s said: “No. 1 is unshackle the US energy sector, drill, frack, burn coal, embrace nuclear. And those are words that sometimes in certain circles, you can’t say out loud right now.”

Doug Burgum, North Dakota governor

As governor of North Dakota, Burgum signed legislation that ends taxing Social Security income. He’s said the government should honor its “commitment” on Social Security but he could cut red tape to lower costs.

What he’s said: “I believe that when the government, at the state or the federal level, when we make a commitment to someone, then that’s a solemn commitment. We have to honor the people that are in that.”

Burgum, who previously served as founder and CEO of Great Plains Software, praised the tax cuts bill when it passed, highlighting a section that allowed US firms with overseas earnings to repatriate that money. 

What he’s said: “There’s so many good things about it, but it starts with virtually every American is going to have more money in their pocket.”

Burgum said that Chinese President Xi Jinping is a “brutal dictator” with a poor human rights record. He has said the US is in a “Cold War” with China, which he would fight economically and militarily. He’s said he would “use energy policy as a lever” in that push since China is a major importer of oil and gas.

What he’s said: “We are in a cold war in China, we just won’t admit it. But the way you work your way through a cold war is you win it economically.”

Burgum has said he would make the economy the “absolute top priority,” including fighting inflation.

What he’s said: “Every small business owner and every family in our country is feeling the corrosive hidden tax on their lives driven by the Biden-induced inflation.”

Burgum wants to increase domestic oil and natural gas drilling to make America “energy independent” and fight climate change with carbon capture and sequestration instead of subsidizing electric vehicle production and sales.

What he’s said: “America produces the cleanest and safest energy in the world. Anyone who cares about the environment should want all energy produced here.”

Asa Hutchinson, former Arkansas governor

Hutchinson opposes changes to Social Security and Medicare such as raising the retirement age for all workers, noting that there are differences between white-collar workers and manual laborers. He has said increasing legal immigration could offset shortfalls.

What he’s said: “We have got to protect Social Security and Medicare. When you look at the long-term concerns about it, we have got to bring in more workers.”

Hutchinson praised the 2017 tax cuts, especially the section creating tax-deferred “opportunity zones,” which Arkansas pursued while he was governor.

What he’s said: “This tax-deferral program will provide new economic opportunities for many of Arkansas’ communities. This is another benefit to our state from the Tax Cuts & Jobs Act of 2017.”

Hutchinson has said the US may have no choice but to engage in a cold war with China. He wants to change from a “China-centric supply chain” for key industries such as health, military technology, pharmaceuticals and critical minerals. 

What he’s said: “We’re still going to trade, we’re still going to have those industrial connections, but those critical industries need to move back to more friendly territory.”

Hutchinson said that “some of the blame” for the failures of Silicon Valley Bank and Signature Bank goes to the Fed’s rate hikes to fight inflation, which he blamed on Biden administration policies. 

What he’s said: “I lay some of the blame at the Fed, but it really goes back to the excessive spending we had that caused inflation that led to those rate hikes.”

As governor, Hutchinson pushed for more electric-car charging stations. He has called for a “pro-growth energy policy” that would expand domestic production from “a variety of sources” to make the US energy independent.

What he’s said: “We don’t want to be dependent on other countries. We don’t want our president going to Saudi Arabia and say, ‘Please, can you produce more oil?’”

Will Hurd, former US representative, Texas

Hurd voted against a conservative proposal to raise the Social Security retirement age, reduce benefits for the wealthy and adopt chained CPI. 

What he’s said: “With population growth slowing and a growing federal debt, seniors on Social Security and those close to retirement are thinking about whether Social Security will be there for them in their twilight years.”

Hurd voted for the 2017 tax cuts, saying they would put “more money in the pockets of hard-working Americans.” 

Hurd says the US is in a cold war with China, saying the country is “stealing our technology” and “preparing for war.” 

What he’s said:  “China is stealing our technology, using our polarization against us, and making a global impact doing so.”

Hurd has called for fighting inflation by cutting government spending, lowering taxes and reducing the deficit.

What he’s said: “Inflation is bad no matter what the other side says. We’ve got to work together to bend it back with common-sense solutions.”

Hurd supports boosting renewable energy, using carbon capture technology and promoting the development of nuclear fusion but not restricting oil and gas.

What he’s said: “Climate change is real.” 

Francis Suarez, Miami mayor

Suarez has not made public comments about changes to Social Security or Medicare and his campaign didn’t respond to requests for information on his position.

Suarez hasn’t made public comments about extending the 2017 tax cuts.

Suarez said that the US needs a strategy to “bring back American manufacturing from China.” He has called for protecting intellectual property, supporting the US tech industry in areas like quantum computing and ending China’s permanent normal trade status. 

What he’s said: “For the past 40 years, we have been giving China a growing piece of our prosperity. Make no mistake, China is not our partner; China is now our adversary.”

As mayor, Suarez promoted the cryptocurrency MiamiCoin, which has since lost much of its value. He called for Miami to become the “crypto capital” of the US.

What he’s said: “The next president of the United States has to be a pro-Bitcoin candidate.”

As mayor, Suarez initiated a plan to make Miami carbon neutral by the year 2050 and has called for Republicans to do more to fight climate change.

What he’s said: “In Miami, our approach is that the environment is the economy. We don’t separate one from the other.”

--With assistance from Laura Davison and Nancy Cook.

More stories like this are available on

Mon, 21 Aug 2023 07:31:00 -0500 en text/html
Killexams : How to Become a Travel Agent—Because, Believe It or Not, the Industry Is on the Rise No result found, try new keyword!That’s why Cherikonda, India-based stay-at-home mother, Maya Kapoor-Miller, decided to enter the professional world as a travel agent this year, at 31. “I knew nothing about the travel industry prior ... Fri, 11 Aug 2023 11:00:00 -0500 en-us text/html Killexams : 6 Essential tips on negotiating your salary from an expert career advisor

You’re trying to show how an investment in you, in the future, will be paid back by virtue of the things you’ll be doing. 

4. What's your goal?

Work out your goal. For example, how does the entire package impact your work-life balance and therefore influence the work choices you can make? 

Then work out your bottom line: What could you trade or accept to get to that goal? 

That might mean delayed pay rises, or having to work in an area you don’t want to for a period of time. If you are prepared to work towards a clear goal, then you are in a better frame of mind to negotiate.   

It should also help you to think about your options. For example, if your boss can’t give you the $4,000 you were looking for, but can give you $2,000, have you thought through other benefits that could make up the difference?

For example, support in getting a further qualification that is beneficial to both sides. Or extra holiday days. You might decide that an extra two weeks off in the summer is worth as much to you as the $2,000 that they can’t cover.

5. Keep emotions out of it

You need to make sure in meetings like this that you are never at a point of emotional confrontation. There shouldn’t be any emotion in it. And if you can’t keep emotion out of it, you’ll struggle.

It’s the manager’s job to keep costs as low as they can, while still keeping you happy. You shouldn’t be braced for conflict. Your approach should be “I can justify all of this” and go into the meeting positive and upbeat.

Tue, 22 Aug 2023 01:26:00 -0500 en-US text/html
Killexams : HiringThing Review 2023: Features, Pros And Cons

The main features of HiringThing include job posting, résumé import, candidate matching and more. We were able to test out nearly all of them for their ease of use.

Syndicated Job Posting

It’s easy to find the “Create a New Job” button right when you log in to HiringThing. Upon clicking that, it takes you to a form where you populate all the necessary information for your job listing.

Right when you log in, the “Create a New Job” button is easy to find.

HiringThing’s new job post screen

When you’re done with the job post, you can save it as a draft to come back to later or simply hit “Publish,” and it instantly posts to free job boards, such as FlexJobs and LinkedIn Limited. The system then directs you to more job boards you can purchase access to. These job boards include but are not limited to:

  • CareerBuilder
  • DiversityJobs
  • Indeed
  • LinkedIn
  • Monster
  • ZipRecruiter

Unless you’re already a subscriber to these premium job boards and can simply link your account to HiringThing, the cost of adding each one through HiringThing can add up quickly. For this reason, HiringThing may not be the most cost-effective option for specialized, high-demand and hard-to-fill roles.

Résumé Import

You can import an applicant’s data through drag and drop or via email with HiringThing’s Resume Import Tool. Dragging and dropping or uploading a file from your computer’s hard drive into the system automatically converts it into an applicant file.

You can also import applicant documents via email by sending the file to the job posting’s specific email address found in the system. However, you have to go into the job itself to find the email address specific to the job posting and copy and paste it into your email, which can be cumbersome.

HiringThing’s Resume Import feature via email

The system needs about one hour to reflect the uploaded files in the system, which is a long time, making it easy to forget to check if the upload worked. We were able to add a file manually using the first method, but we did not see a new applicant file created under the applicant section of the dashboard, even after one hour had passed.

Candidate Matching

HiringThing has keyword tagging and customized pre-screening questions that help you more quickly identify the most qualified candidates for any given position.

Keyword Tagging

When creating a position, you can add a series of keywords separated by commas. HiringThing will then surface applications that best match those keywords, and you’ll see an alert displayed when there’s a match.

Form Builder

For applicants who apply directly from your career site, you can set up a customized form that poses pre-screening questions related to anything from job experience to specific certifications. This can be helpful to screen out candidates that don’t meet minimum criteria.

However, keep in mind that applicants applying from job boards won’t see this form, so you should choose the option that doesn’t allow applicants to apply from the job boards. When selecting that option, applicants will be redirected to your career site where they can proceed with their application.

You can get to the Form Builder feature either through a specific job listing’s settings or by navigating to “Account Details.”

When testing out the Form Builder, we had the option of creating a template to save time in the future. When creating the template, you can choose what types of questions to ask:

  • Text-based
  • Essay questions
  • Multiple choice
  • List of checkbox attributes
  • Yes/No
  • Date for applicant’s earliest start date
  • Employment history
  • Education history

For some question types, such as Yes/No questions, you can set a rule that automatically tags or archives an applicant based on their response. For instance, if a minimum criterion for a new Sales Associate is at least two to three years of experience, you can filter out applicants that select “no” to the question about whether they have that experience.

The employment and education history questions require applicants to type in that information manually, which can make for a frustrating job candidate experience, as the résumé usually contains this information already.

However, HiringThing gives you full control over the kind of screening questions you want applicants to fill out, so you don’t have to necessarily pose questions that require manual data entry.

HiringThing’s Form Builder

Scorecards for Candidate Evaluation

HiringThing enables you to create customized scorecards before you start evaluating candidates in order to mitigate bias in the review process. You can enter customizable text fields, called “Titles,” and those fields will appear in an applicant’s review section.

The “Title” field is misleading because it’s not the job title. Rather, it denotes the attributes by which you want to evaluate candidates. For example, if hiring for a Sales Manager position, you may want to add titles such as “integrity” or “leadership.”

We found two ways to create a scorecard. For the first method, you have to navigate to the main menu > Account Details > Feature Management, and scroll down to the “Scorecards” section. It’s important to note that once you enter titles this way, they automatically apply to all subsequent jobs that you post after creating the scorecard and cannot be applied retroactively to existing job openings.

This can be frustrating if your general criteria change over time because changes can’t be applied to current openings, and you’d have to enter them into those individual jobs manually per the second method below. It’s therefore important to settle on a general set of criteria that you’ll rarely, if ever, need to change.

The second way to create a job scorecard is to edit a specific job and expand the “Advanced Options” section, and the titles will appear in each applicant’s “Review section.” We found this method more intuitive. Plus, we like that you can add specific criteria for specific jobs.

HiringThing’s Scorecards display different pre-set attributes, and you provide a star rating and optional notes for each candidate.

Candidate Relationship Management

HiringThing facilitates candidate relationship management via email and SMS.

Managing candidate statuses and communication were some of the easiest tasks we tested out in HiringThing. You can individually change an applicant’s status or do so in bulk to save time. You can add statuses including “offer extended” and “offer accepted/rejected” in the “Workflow” section of Account Details.

It was very easy to select a pre-made email template for requesting an interview, rejecting a candidate or other typical recruiter communications. Paying customers have access to HiringThing’s SMS option as an additional communication method.

Interview Tools

HiringThing enables collaborative interview scheduling by syncing with your Outlook, Exchange, Microsoft 365, Google or iCloud calendar. You can select a specific date or time or, even better, allow the candidate to select from a range of dates and times that you give them.

Using a Google account will automatically use Google Meets as a videoconference tool, unless you link your account to another videoconference tool such as Zoom. Otherwise, 8×8 is the default videoconferencing solution for HiringThing.

Workflows and Automations

HiringThing helps you expedite your hiring process by automating job requisitions, candidate screening, interviewing and any other workflows you set up.

HiringThing enables you to set up a workflow for opening, approving and posting job requisitions. Rules for screening questions noted above also automate the candidate selection process by filtering out candidates who don’t meet the minimum requirements and automatically sending them a “Thanks but no thanks” email. That way, you won’t spend time sifting through résumés from unqualified candidates or sending rejection emails.

HiringThing’s new automated interviewing workflow allows you to send an automated interview invitation in one of two ways: as soon as anyone submits an application to the job or based on a change in application status.

You might want to use the former if you’re hiring for a niche position that draws few candidates, all of whom you want to interview. Otherwise, you’d more likely utilize the second workflow option that invites candidates to an interview after you change their status. However, with either option, make sure all who are involved in the process keep their calendar availability up to date.

Unfortunately, we weren’t able to try out the workflows setup in the test environment, as this feature is only available to paying customers.

Fri, 18 Aug 2023 04:05:00 -0500 Lauren Hansen en-US text/html
Killexams : Youth mental illness drives rise in benefit claims, study suggests
  • By Becky Morton
  • Political reporter

Image source, Getty Images

An increase in younger people claiming disability benefits is being driven by a "staggering" rise in mental health issues, a think tank says.

The Institute for Fiscal Studies (IFS) said there had been a striking increase in claim rates for working-age adults over the past decade.

The rise is particularly stark for adults in their 20s and 30s.

The figures have implications for public finances, as spending on disability benefits grows.

In a new report, the IFS found that while physical disabilities are continuing to increase with age, for younger age groups disabilities due to mental health conditions are rising.

Meanwhile, the proportion of 30-year-olds claiming disability benefits has risen from around 2% in 2002 to around 4% in 2022, with most of the increase happening in the past decade.

In contrast, the proportion of those 60 and over claiming disability benefits are almost the same as they were in 2002.

The main disability benefit for working-age adults is personal independence payment. Eligibility is unrelated to whether someone is able to work so many younger claimants may still be in employment.

Heidi Karjalainen, one of the authors of the report, said growing awareness and less stigma around mental health, meaning people are more likely to report suffering from poor mental health, was "part of the story" but "that doesn't mean it's not a problem".

She told the BBC mental health conditions had "a real life impact", including an increase in benefit claims and pressure on the NHS, as well as affecting people's ability to work and continue education.

The government says getting people back to work is a key priority, with the issue contributing to worker shortages and the UK economy doing less well than other developed countries.

The IFS also found a large increase in the proportion of school-age children receiving disability benefits, mainly disability living allowance, from 2-3% in 2002 to 5-7% in 2022.

It said this was largely accounted for by increases in claims for learning disabilities, behavioural disorders and ADHD.

Ms Karjalainen said there was concern this could translate into lower education attainment and job prospects, with implications for efforts to get people into work and tackle inequality.

The IFS report found those with low levels of education were more likely to have a poor mental health or a disability and this was much more likely to result in them being unemployed.

At the age 30, the rate of disability is 8% for degree holders and 24% for those with no qualifications, the IFS said.

Nil Guzelgun, policy and campaigns manager for mental health charity Mind, said the pandemic and cost-of-living crisis had both contributed to a rise young people struggling to cope.

She said the charity was "deeply concerned" by the IFS findings.

Mind is calling for the government to overhaul the benefits system and do more to help people with mental health problems into work.

A government spokesman said: "We're investing an extra £2bn to help more people with disabilities and health conditions into work and grow the economy - with the latest figures showing inactivity has fallen by over 300,000 since the pandemic peak."

For those who cannot yet return to work, he said the government was also spending an extra £2.3bn a year by March 2024 to Strengthen mental health services.

If you've been affected by any of the issues raised here and would like to get in touch with us, you can email

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If you are studying this page and can't see the form you will need to visit the mobile version of the BBC website to submit your question or comment or you can email us at Please include your name, age and location with any submission.

Wed, 23 Aug 2023 03:03:00 -0500 en-GB text/html
Killexams : Latest Prompt Engineering Technique Aims To Get Certainty And Uncertainty Of Generative AI Directly On The Table And Out In The Open

Certainty and uncertainty play a big role in life.

It is said that the only true certainty consists of deaths and taxes. Michael Crichton, the famous writer, said that he was certain there is too much certainty in the world. Legendary poet Robert Burns indicated that there is no such uncertainty as a sure thing.

One thing about both certainty and uncertainty is that we seem to crave and relish certainty, while we tend to agonize over and strive to convert uncertainty into certainty if we can do so. As Carl von Clausewitz, the lauded military strategist professed: “Although our intellect always longs for clarity and certainty, our nature often finds uncertainty fascinating.”

All of these machinations over certainty and uncertainty turn out to be a big matter for those that wish to fruitfully make use of today’s generative AI such as ChatGPT, Bard, and so on.

In today’s column, I am continuing my ongoing special series about advances in prompt engineering, doing so this time with a particular focus on the crucial and often unexposed course concerning generative AI and the controversial matter of expressing certainty versus uncertainty in the essays and outputs being emitted by the AI. This course is likely something you might not have contemplated before. I assure you that it is a lot more important than the coverage or attention it has received to date.

Allow me to explain.

As background, realize that being able to write productive and effective prompts when using generative AI is paramount. A lousy prompt tends to generate lousy results out of generative AI. A wisely composed prompt can lead to stellar results out of generative AI. Knowing the vital keystones of prompt engineering is a prudent means to get your biggest bang for the buck when employing generative AI.

One issue that few realize exists until taking a reflective moment to ponder it is that most generative AI apps tend to exhibit an aura of immense certainty. You enter your prompt and typically get a generated essay or interactive dialogue that portrays the generative AI as nearly all-knowing. The sense that you get is that the generative AI is altogether confident in what it has to say. We subliminally fall into the mental trap of assuming that the answers and responses from generative AI are correct, apt, and above reproach.

The essays and interactive dialogue come across this way for two major reasons.

First, generative AI produces responses that often exude a semblance of certainty. If you ask whether Jack and Jill fell down the hill, you might get a reply by generative AI that says yes, they definitely did so. There isn’t any kind of qualification or hedging in the answer by the AI app. A human that is asked the same question might quality their response, such as saying that if you are referring to the famous nursery rhyme, indeed they fell down a hill. But if you are thinking of some other Jack and Jill, maybe they didn’t fall down a hill.

Second, as humans, we are conditioned to assume that if we don’t explicitly see suggestions of uncertainty, we tend to lean into the certainty camp. Suppose you are talking with someone, and they tell you that it is raining outside. All else being equal, you probably believe them and that it is a certainty that rain is in fact falling. Only if the person says they believe that it is raining (the word “believe” becomes a signal of less than certain), or they declare it might be raining (the word “might” is a strong signal of uncertainty), do you begin to consider the certainty versus uncertainty of what has been stated.

Generative AI typically does not include the signals and wording that would tip you toward thinking of how certain or uncertain a given response is. To clarify, I am not saying that generative AI will never provide such indications. It will do so depending upon various circumstances, including and especially the nature of the prompt that you have entered.

If you explicitly indicate in your prompt that you want the generative AI to emit a certainty or uncertainty qualification then you will almost certainly get such an indication. On the other hand, if your prompt only tangentially implies the need for an indication of certainty or uncertainty, you might get an output from the AI app that mentions the certainty considerations or you might not.

A rule of thumb is that generative AI is like a box of chocolates, namely that you never know for sure what the generative AI is going to produce or generate.

Another handy-dandy rule of thumb is that unless you bring up certainty or uncertainty in your prompt, the chances of having the generative AI by default include some indication of the certainty about a response is a wild throw of the dice.

Why does this matter to you?

Anyone using generative AI has got to awaken to the fact that often the response by the AI is going to be essentially a guess or approximation, even if the AI doesn’t directly state this condition when generating a reply. Your tendency to anthropomorphize the AI lulls you into thinking that the AI is giving you the correct answer. You assume that the answer has nearly absolute certainty. Only if perchance the reply state that there is some uncertainty underlying the response will you be mentally sparked into realizing that the reply ought to be given a concerted second glance.

Of course, there are limits to this implied assumption of certainty.

Imagine you ask the generative AI whether the sun will come up tomorrow. Suppose that the generated response is that the sun will not come up tomorrow. This is stated by the AI in a matter-of-fact manner, unequivocally, and appears to be an absolutely certain assertion. I suppose you might pack your bags and get ready for the world as we know it to somehow spin off into space. I doubt though that many of us would blindly accept the implied certainty of the AI response. Our commonsense kicks into gear at the seemingly preposterous claim that the sun won’t rise. We would undoubtedly ask the AI about this, and the odds are that the AI might sheepishly emit an indication that it was wrong about that whole thing of the sun not coming up tomorrow.

Here’s a remedy of a sort.

Had you asked the generative AI at the get-go to proffer an indication of certainty or uncertainty, at least you would have gotten some added wording to go along with the assertion about the sun. The additional wording might be helpful to you and keep your head in the game, causing you to mindfully assess whether the generative AI is on the up and up (you see, sometimes, generative AI is said to incur AI hallucinations, which I have examined at the link here, encompassing the AI making things up entirely).

Furthermore, and this is a mind bender, the very act of asking or telling the generative AI to include a certainty or uncertainty will often spur the generative AI to be less off-the-cuff and produce more well-devised results (for those of you that know about the use of prompting techniques such as chain-of-thought, that I’ve covered at the link here, research tends to suggest that these methods will prod the computational pattern-matching toward better results).

I trust that you are beginning to see where I am taking you on this journey about the latest in prompt engineering. A practical and highly prized technique of prompting involves stoking the generative AI toward including some indication about the certainty or uncertainty of the responses that are being emitted. You will be a lot better off by seeing wording or indications within the responses that clue you to the certainty or uncertainty involved. The idea is to turn something that right now is often omitted, hidden, or otherwise neglected, and make sure that it gets clearly onto the table and out in the open.

So that you can properly and appropriately devise prompts that stir the AI into providing certainty and uncertainty indications, I will provide you with useful ways to get this to happen. You are urged to try out the approaches and add them to your prompt engineering skillset. You’ll be happier and more informed if you do so.

Before I dive into the crux of this exciting approach, let’s make sure we are all on the same page when it comes to the keystones of prompt engineering and generative AI.

Prompt Engineering Is A Cornerstone For Generative AI

As a quick backgrounder, prompt engineering or also referred to as prompt design is a rapidly evolving realm and is vital to effectively and efficiently using generative AI or the use of large language models (LLMs). Anyone using generative AI such as the widely and wildly popular ChatGPT by AI maker OpenAI, or akin AI such as GPT-4 (OpenAI), Bard (Google), Claude 2 (Anthropic), etc. ought to be paying close attention to the latest innovations for crafting viable and pragmatic prompts.

For those of you interested in prompt engineering or prompt design, I’ve been doing an ongoing series of insightful looks at the latest in this expanding and evolving realm, including this coverage:

  • (1) Practical use of imperfect prompts toward devising superb prompts (see the link here).
  • (2) Use of persistent context or custom instructions for prompt priming (see the link here).
  • (3) Leveraging multi-personas in generative AI via shrewd prompting (see the link here).
  • (4) Advent of using prompts to invoke chain-of-thought reasoning (see the link here).
  • (5) Use of prompt engineering for domain savviness via in-model learning and vector databases (see the link here).
  • (6) Augmenting the use of chain-of-thought by leveraging factored decomposition (see the link here).
  • (7) Making use of the newly emerging skeleton-of-thought approach for prompt engineering (see the link here).
  • (8) Determining when to best use the show-me versus tell-me prompting strategy (see the link here).
  • (9) Gradual emergence of the mega-personas approach that entails scaling up the multi-personas to new heights (see the link here).
  • (10) Additional coverage including the use of macros and the astute use of end-goal planning when using generative AI (see the link here).

Anyone stridently interested in prompt engineering and improving their results when using generative AI ought to be familiar with those notable techniques.

Moving on, here’s a bold statement that pretty much has become a veritable golden rule these days:

  • The use of generative AI can altogether succeed or fail based on the prompt that you enter.

If you provide a prompt that is poorly composed, the odds are that the generative AI will wander all over the map and you won’t get anything demonstrative related to your inquiry. Being demonstrably specific can be advantageous, but even that can confound or otherwise fail to get you the results you are seeking. A wide variety of cheat sheets and training courses for suitable ways to compose and utilize prompts has been rapidly entering the marketplace to try and help people leverage generative AI soundly. In addition, add-ons to generative AI have been devised to aid you when trying to come up with prudent prompts, see my coverage at the link here.

AI Ethics and AI Law also stridently enter into the prompt engineering domain. For example, whatever prompt you opt to compose can directly or inadvertently elicit or foster the potential of generative AI to produce essays and interactions that imbue untoward biases, errors, falsehoods, glitches, and even so-called AI hallucinations (I do not favor the catchphrase of AI hallucinations, though it has admittedly tremendous stickiness in the media; here’s my take on AI hallucinations at the link here).

There is also a marked chance that we will ultimately see lawmakers come to the fore on these matters, possibly devising and putting in place new laws or regulations to try and scope and curtail misuses of generative AI. Regarding prompt engineering, there are likely going to be heated debates over putting boundaries around the kinds of prompts you can use. This might include requiring AI makers to filter and prevent certain presumed inappropriate or unsuitable prompts, a cringe-worthy issue for some that borders on free speech considerations. For my ongoing coverage of these types of AI Ethics and AI Law issues, see the link here and the link here, just to name a few.

With the above as an overarching perspective, we are ready to jump into today’s discussion.

Foundations Of Certainty Versus Uncertainty

We will ease our way into the arena of certainty and uncertainty, doing so by first examining how humans convey certainty and uncertainty to each other. After we cover that aspect, we can then consider how a human interacting with generative AI is likely to act and react related to how the AI does or does not express certainty or uncertainty.

It is useful to first explore how humans do this when interacting with fellow humans. When we use generative AI, we tend to carry over our preexisting assumptions and habits about certainty that have been dutifully learned or naturally acquired throughout our lives on a human-to-human interaction basis.

I cover the matter in this way with a bit of erstwhile caution because I don’t want anyone to be led down the path of anthropomorphizing AI. In current times, AI is not sentient and should not be equated to the sentience of humans. I will do my best to make that same alert when we get into certain aspects of the generative AI details that might seem overly sentient-like.

Thanks for keeping a level head on these weighty matters.

Let’s begin with the notion that certainty or uncertainty can be in the mind of a person and it separately can exist as an expression communicated by a person:

  • (1) Implicit. A person has in their mind a sense of certainty or uncertainty pertaining to a matter in hand.
  • (2) Explicit. The same person conveys or communicates out loud or explicitly a sense of certainty or uncertainty regarding the same matter at hand.

Here’s what I mean.

You ask someone whether Humpty Dumpty fell off a wall. The person in their mind believes that Humpty Dumpty did indeed fall off of a wall. They are certain of this. Thus, the person then speaks to you and tells you that Humpty Dumpty did fall off a wall. They express this unequivocally. No doubt about it, poor old Humpty Dumpty fell off a wall.

Notice that the implicit sense of certainty matches the explicit or expressed sense of certainty.

Suppose that the same person was asked this very same question by a child. The person might worry that it will be unduly disturbing to the child if an absolute confirmation of Humpty Dumpty falling is conveyed to the child. The child might be dismayed at this revelation.

In this case, the implicit (in the mind of the person) is that Humpty Dumpty did fall. But, when telling the child, the person decides to sprinkle in a semblance of uncertainty, hoping this will soften the distress of the child to the upsetting discovery that Humpty Dumpty fell.

They answer this way: “Humpty Dumpty might have fallen, but I’ll tell you more when you get a bit older.”

Observe closely that the wording includes “might” which reduces the implied level of certainty. The door to uncertainty has been opened. Just a nudge. The implicit sense of certainty has not been precisely aligned with the explicit or expressed sense of certainty. Why did the person do this? You could suggest they were trying to be kind or polite. Some might take a dim view and argue that the person was misleading or maybe lying in their expressed answer. Anyway, that’s something we will come back around to momentarily.

An analytic person might have said this to the child: “There is a 50% chance that Humpty Dumpty fell, ergo there is also a 50% chance that Humpty Dumpty didn’t fall.” Admittedly, the child might not quite comprehend this. The gist though is that this introduces an even grander sense that the answer embodies uncertainty.

Imagine that a parent is standing there and they don’t like the answer that was given by the analytic person. The parent turns to the child and says this: “It is 100% the case that Humpty Dumpty fell.” Notice that this answer carries explicitly again an indication of certainty and does to the highest degree.

We can have these four kinds of categorical situations:

  • (a) Certainty implicit: Certainty explicit - A person in their mind (implicit) believes something is certain and explicitly says so.
  • (b) Certainty implicit: Uncertainty explicit - A person in their mind (implicit) believes something is certain but says explicitly it is uncertain (see my example above).
  • (c) Uncertainty implicit: Uncertainty explicit - A person in their mind (implicit) believes something is uncertain and explicitly says so.
  • (d) Uncertainty implicit: Certainty explicit – A person in their mind (implicit) believes something is uncertain but explicitly says it is certain.

I’ve covered the first two categories in my discussion above. The third category is rather self-explanatory of someone in their mind believing that something is uncertain and they explicitly say so. The fourth category you’ve undoubtedly seen occur, whereby someone has uncertainty about something in their mind but nonetheless expresses that the matter is certain. We’ll use the child setting again. A child is thinking that their dog ate bad food. The parent is uncertain of the fate of the dog. Despite this mental semblance, they reassure the child and tell them that they are certain the dog will be perfectly fine.

There is a lot of human behavioral research on how we conceive and communicate certainty and uncertainty.

For example, a research study entitled “The Role Of Certainty (And Uncertainty) In Attitudes And Persuasion” by Zakary Tormala, which appeared in Science Direct, 2016 indicates this about certainty and uncertainty related to human attitudes and persuasion (selected excerpts):

  • “Psychological certainty plays a crucial role in attitudes and persuasion.”
  • “As people become more certain of their attitudes, they become increasingly willing to talk about their attitudes, share their opinions with others, sign pro-attitudinal petitions, and even persuade others to adopt their views.”
  • “Uncertainty stimulates interest in and engagement with a message, whereas certainty transforms attitudes into action and imbues them with meaning and consequence.”

You could suggest that certainty propels us toward being more certain and more outwardly expressive of a matter at hand. The thing is, an abundance or overabundance of certainty can at times not be engaging. Research seems to say that a dose of uncertainty can cause greater engagement by others, presumably intrigued and wanting to aid in filling in the pieces of the puzzle.

I’ve also mentioned earlier in this discussion that the wording that we use can convey or explicitly communicate whether we want to impart a semblance of certainty or uncertainty to others. If you use words like “might” or “maybe” this can be a strident signal that there is uncertainty in the midst of things. The same can be said for using probabilities or percentages, such as stating that something is an 80% chance of happening and therefore a 20% chance of not happening.

Researchers in the realm of linguistic semantics have long examined the words that we use related to certainty and uncertainty. In a research study entitled “Strategic Use of (Un)certainty Expressions”, authored by Alexandra Lorson, Chris Cummins, and Hannah Rohde, that appeared in the Frontiers in Communication, March 2021, the paper indicates this:

  • “Work in linguistic semantics and pragmatics has paid particular attention to the use of expressions that convey information about the (un)certainty of propositional information, which constitute an important tool for reducing a hearer’s uncertainty as to the current state of affairs in the world. Among these expressions are verbs which take sentential complements and which convey different degrees of speaker confidence in the factuality of those complements.”
  • “Speakers have a number of options when introducing propositions which they take to be uncertain: for instance, they can use verbs such as ‘know’, ‘believe’ or ‘think’. The production of uncertainty expressions is highly context dependent.”
  • “(1a). I believe that you left your glasses on the kitchen table.”
  • “(1b). I know that you left your glasses on the kitchen table.”
  • “(1c). You left your glasses on the kitchen table.”

All in all, I’ve now covered some of the essentials about the nature of humans and the ways in which we think of and communicate about certainty and uncertainty. I’m betting that you are eager to see how this comes to the fore when using generative AI.

Fasten your seatbelts, we are going to leap into the world of certainty and uncertainty entailing the use of generative AI and the generated outputs thereof.

Generative AI And Certainty Versus Uncertainty

The usual default by generative AI apps is to express any generated results in a somewhat certainty-oriented worded way. The wording might be rather subliminally worded to not mention anything about the certainty of what is being expressed. You are left to your own devices to interpret the generated result as being of a presumed certainty concoction.

You might ask whether the muffin man lived on Drury Lane. The reply by generative AI could be that Yes, the muffin man lived on Drury Lane. That’s the extent of the answer. You are likely to conclude that this is an answer of a full-on certainty magnitude. There is nothing in the answer that suggests or notes otherwise. The omission of uncertainty draws you toward an assumption of certainty.

Here’s an AI insider secret that might be surprising.

The AI maker of the generative AI app can pretty much set up the AI to be more explicit about the certainty or uncertainty of the generated results. They often don’t aim to do so. It could be that the AI maker is blissfully unaware that they have data-trained their generative AI in a manner that tends toward generating essays and interactions that omit any explicit indication about the certainty or uncertainty of the answers presented.

Another possibility is that the AI maker realizes that they have established the generative AI to appear to be certain most of the time, and the AI maker is happy with this. If users of the generative AI were to continually be bombarded with the generated results identifying all kinds of uncertainties, they might find this to be unappetizing. You would wonder what all this fuss over a simple answer. Just say yes or no, one might be thinking. Don’t waste time with oddities and exceptions.

In addition, if the wording was largely embedded with uncertainty cues, you might start to become suspicious of the generative AI as not being on the ball altogether. Your consternation might cause you to drift over to some other generative AI app that doesn’t spout out all those irritating uncertainties. An AI maker doesn’t want to lose their users due to the exposure of certainties and uncertainties that might rattle the confidence of their users.

There is also the claim that if people want to see certainties and uncertainties, they can always get this to happen of their own volition. People can simply give prompts or otherwise instruct the generative AI to mention any certainties or uncertainties associated with the results being generated. Choose your own path, as they say. This allows the AI makers to be off-the-hook about why they aren’t by default ensuring that the generative AI always states certainties and uncertainties.

On top of that, generative AI will at times provide certainties and uncertainties without needing to be prompted to do so. If you ask a question or engage in courses involving open questions, the odds are that the generative AI will include wording that showcases the lack of certainty underlying the matter. In that manner, it isn’t as though generative AI never offers uncertainties. There are notable odds that during any everyday interactive conversation with generative AI, you will receive phrasing that suggests or outrightly identifies uncertainties.

Some in the AI Ethics sphere have argued that the default for generative AI is that it should be set up to always add wording that alerts to uncertainties and overtly avoid emitting wording that implies absolute certainty. The logic for this is straightforward. People are readily misled when they see results that appear to be of a certainty wording. By seeding uncertainties intentionally, when warranted, people would be less lulled into always believing whatever generative AI emits. They would be accustomed to always being alerted that the results can be of an uncertain nature.

That debate continues to rage on.

For now, let’s consider how you can steer generative AI toward emitting certainty and uncertainty signals due to using suitable prompting strategies.

Getting Certainty And Uncertainty On The Table

You decide that you would like to have generative AI go ahead and let you know about certainties and uncertainties regarding the answers being produced.

There are five fundamental ways that this is conventionally done:

  • (1) In general. By asking or telling the generative AI that you want certainties and uncertainties to be mentioned overall.
  • (2) Wording throughout. By instructing the generative AI to embed certainty/uncertainty wording throughout the responses.
  • (3) At the start or end. By requesting the generative AI to at the start and/or the end of a produced response to provide an indication of certainty/uncertainty.
  • (4) By special phrasing. By directing the generative AI to use prominent phrasing that depicts the certainty/uncertainty.
  • (5) By numeric scale. By indicating that certainty/uncertainty is to be presented and done in a numeric fashion such as a scale of 0-1, 1-10, 1-100, etc.

Let’s briefly explore those core approaches, one at a time.

(1) In general

As part of your prompt, you could try to ensure that the certainty/uncertainty will be conveyed by saying something like this:

  • “Make sure to describe uncertainties associated with the answer.”

This is a rather broad instruction.

It is unclear in what manner the AI will end up indicating any certainty/uncertainty elements. This is your blandest overarching line to get the generative AI into a mode of encompassing the certainty/uncertainty undercurrents. You might want to consider the other four approaches if you want to be more specific in how the AI will respond.

(2) Wording throughout

As part of your prompt, you can be relatively specific by getting the generative AI to include certainty/uncertainty indications throughout the wording that is emitted.

Do so by saying something like this in your prompt:

  • “Make sure to describe uncertainties associated with the answer and do so by adding or including wording throughout the response rather than waiting until the end to do so. I am asking you to blend the various uncertainty indications so that they seamlessly appear throughout the generated response.”

Notice that you are directing the generative AI to have some aplomb as to blending the certainty/uncertainty indications.

(3) At the start or end

You might want to have the certainties/uncertainties called out rather than blended into the overall generated response.

This makes sense. Sometimes you want to see the qualifications as their own distinctive indication. Seeing them throughout the response might be harder to discern what the concerns are or could be distracting to the ambiance of the response.

You can say something like this in your prompt:

  • “Make sure to describe uncertainties associated with the answer and do so by putting them entirely at the start [or the end] of the response. Do not blend them into the response.”

(4) By special phrasing

Another route involves getting the generative AI to use phrases as an indicator of the certainties/uncertainties. This can be done via a prompt that uses a show-me strategy or a tell-me strategy, see my discussion about those prompting approaches at the link here.

In a tell-me, you instruct the generative AI:

  • “Make sure to describe uncertainties associated with the answer and do so by using added sentences or phrases in the response that are indicative of the underlying uncertainties.”

In a show-me, you provide examples (if just one example it is known as a one-shot, while if using several examples it is known as a few-shot):

  • “Make sure to describe uncertainties associated with the answer. For example, add sentences or phrases such as “the answer isn’t fully certain”, or “the matter is up in the air”, or “experts disagree”, and so on, doing so whenever appropriate in the response.”

(5) By numeric scale

One of the most obvious ways to get the certainties/uncertainties out in the open is to require generative AI to produce such matters via a numeric scale. This might consist of probabilities or percentages. Use whatever numeric scale that you believe befits the circumstances.

Some might like to use a scoring scale of 0 to 1, conventionally arising when seeking probabilities. Some prefer to use percentages, such as the generative AI might indicate that a particular claim is 90% sure and 10% unsure. And so on.

Say something like this in your prompt:

  • “Make sure to describe uncertainties associated with the answer and do so by including a numeric score that ranges from 1 to 10, whereby a 1 is indicative of being absolutely uncertain and a 10 is indicate of being absolutely certain. Use any of the numbers between 1 and 10 as suitable to that scale.”

Each of the above prompting examples should be honed to the particular generative AI app that you are using. Some generative AI apps will respond well to such wording, others might not. Experiment with your generative AI app until you land on wording about identifying certainties that seem to work well for you.

Reaching For The Moon When Prompting For Uncertainties

I’ve got a question for you.

Did mankind land on the moon?

I dare say that most people would say that yes, mankind has landed on the moon. This would seemingly be proven by the historic Apollo 11 mission and various other subsequent landings on the moon. Seems like an exceedingly straightforward question with an inarguable straightforward answer.

Maybe not.

You likely are aware that some people believe that we didn’t land on the moon. Perhaps it was all a hoax. All kinds of theories have been brought up insisting that we never landed on the moon.

What do you think generative AI would say in response to that same question regarding whether mankind has landed on the moon?

Let’s find out.

I opted to use ChatGPT.

If you decide to do the same for this question, keep in mind that your results might differ since a probabilistic algorithm within the AI is used to devise the responses. Each response generated by generative AI will usually differ from any other response. The difference might be minor and inconsequential, but nonetheless, the wording will likely slightly differ or more so differ.

My prompt entered into ChatGPT is this:

  • “Did mankind land on the moon?”

The response from ChatGPT is this:

  • “Yes, mankind has landed on the moon. The United States' Apollo 11 mission successfully landed the lunar module, named ‘Eagle,’ on the moon's surface on July 20, 1969. Astronauts Neil Armstrong and Edwin ‘Buzz’ Aldrin became the first humans to set foot on the moon, while Michael Collins orbited above in the command module. Neil Armstrong's famous words upon stepping onto the lunar surface were, ‘That's one small step for [a] man, one giant leap for mankind.’ This historic event marked a significant achievement in human space exploration.”

I want you to closely read and read again that response from ChatGPT.

Please do so, I’ll wait a moment.

You hopefully observed that there wasn’t anything uncertain about the assertion that mankind has landed on the moon. The wording is very affirmative. There is no wording that waffles or suggests anything other than complete certainty.

I will try the question again and this time bring into the prompt by “general prompt” that spurs the AI to consider telling about any uncertainties that might exist.

My prompt entered into ChatGPT is this:

  • “Make sure to describe uncertainties associated with the answer. Did mankind land on the moon?”

The response from ChatGPT is this:

  • “Yes, mankind did land on the moon. The Apollo 11 mission in 1969 successfully landed astronauts Neil Armstrong and Edwin ‘Buzz’ Aldrin on the lunar surface, while Michael Collins orbited above in the command module. They conducted experiments, collected samples, and left scientific instruments on the moon before returning safely to Earth.”
  • “However, it's important to acknowledge that there have been conspiracy theories and claims suggesting that the moon landing was a hoax. These claims often revolve around alleged inconsistencies in the photographic and video evidence, as well as the technological challenges of the time. Skeptics argue that the U.S. government and space agency staged the entire event to win the Space Race against the Soviet Union during the Cold War.”
  • “These conspiracy theories have been extensively debunked by experts, scientists, and evidence-based analysis. Multiple lines of evidence, including moon rocks brought back by the Apollo missions, reflectors left on the moon's surface for laser measurements, and independent observations from other countries' space agencies, all confirm the reality of the moon landings.”
  • “In summary, while there have been doubts raised about the authenticity of the moon landings, the preponderance of evidence supports the fact that mankind did indeed successfully land on the moon as part of the Apollo 11 mission and subsequent missions.”

Notice that we got quite an elaboration that goes far beyond the first answer.

If you had only gotten the first answer, you would be completely unaware of the certainty/uncertainty about whether or not we landed on the moon (unless, perchance, you knew of it beforehand). I realize that some of you might be exhorting that the uncertainty isn’t real and can be utterly ignored. My point is not whether the truth is one or the other (I believe we did land on the moon, see my coverage at the link here), and instead that simply a normal query would not likely reveal the controversy over the certainty factor.

As a final step in this brief example, I’ll ask ChatGPT to include a numeric score. Plus, I will instruct ChatGPT to include the score at the start of the response. This then covers two more of my above trial prompting strategies for garnering certainty/uncertainty. For a bonus, I also asked ChatGPT to explain how it arrived at the certainty/uncertainty.

Here’s what I entered as my prompt into ChatGPT:

  • “Make sure to describe uncertainties associated with the answer and do so by including a numeric score that ranges from 1 to 10, whereby a 1 is indicative of being absolutely uncertain and a 10 is indicate of being absolutely certain. Use any of the numbers between 1 and 10 as suitable to that scale. Make sure to describe uncertainties associated with the answer and do so by putting them entirely at the start of the response. Explain how the uncertainties were determined. Did mankind land on the moon?”

The response by ChatGPT was this:

  • “Uncertainty Level: 10”
  • “Mankind's landing on the moon is supported by overwhelming evidence, and the uncertainty regarding this event is extremely low. The Apollo 11 mission, conducted by NASA in 1969, resulted in the successful landing of astronauts Neil Armstrong and Buzz Aldrin on the lunar surface. This event was witnessed by millions of people through live television broadcasts, and the astronauts brought back moon rocks and other scientific data as tangible proof of their journey.”
  • “While there have been conspiracy theories questioning the authenticity of the moon landing, these claims have been extensively debunked by experts, scientists, and researchers. Multiple lines of evidence, including photographs, videos, samples of moon rock, and the presence of reflective materials left on the moon's surface for laser measurements, all corroborate the fact that mankind did land on the moon.”
  • “Given the overwhelming and consistent nature of the evidence, the level of certainty that mankind landed on the moon is at its highest point on the scale, represented by a rating of 10.”

You can plainly see that ChatGPT has given the uncertainty a score of 10 which on my provided scale is the least amount of uncertainty. An explanation is included about how the score was determined.

Some of you might find the scale that I used to be somewhat confusing. An uncertainty level of 10 on the scale that I defined means that there is essentially no uncertainty. I purposely wrote the prompt in that manner because I wanted to show you that you need to be thinking about how your scale will look once it is put into use by the generative AI. It could be that a scale of 0 to 10 would have been better, along with stating that a 0 means no uncertainty while a 10 means the utmost uncertainty. People would indubitably find that easier to understand and digest.

Devise whatever scale you think is most conducive to your generative AI efforts and be contemplating how the results will be further utilized.

Mighty Important Caveats And Considerations

You might recall that I had earlier stated that humans have an implicit semblance of certainty/uncertainty, and separately can convey or explicitly communicate a semblance of certainty/uncertainty. I want to bring that back into focus.

Keep in mind that today’s generative AI is not sentient. Thus, if someone refers to what is implicitly or internally going on within generative AI, it is all a matter of mathematics and computational pattern matching, which I explain at the link here.

Okay, so when generative AI emits a response that Jack and Jill fell down the hill with a 90% certainty level, what does that mean? It could be that the mathematical and computational pattern-matching was able to calculate this degree of certainty. But it could also mean that the generative AI pulled the number out of thin air.

A rule of thumb is that you cannot believe the stated certainty of generative AI and do not fall for a double whammy. The double-whammy is that when you don’t ask for certainties the wording is going to possibly imply certainty (first of the whammies), while by asking for a certainty you might get a totally concocted one that leads you to believe that the certainty expressed is somehow magically accurate and apt (ouch, the double whammy).

There is a bit of irony there. The act of asking for a certainty indication can get you one, lulling you into believing even more so the generative AI, when the reality is that the certainty indication is contrived and has no substance behind it.

Be careful.

Be very careful.

One means to cope with this involves adding into your prompt that you want the generative AI to explain how it came up with the certainties and uncertainties. Ask what the basis for those is. This might boost your confidence in the showcased certainties.

I don’t want to be the bearer of bad news but the explanations might also be contrived. Think of it this way. You wanted certainties and so the generative AI complied. You also want explanations. The explanations might be generated by the AI mainly to appease your request, and not due to the explanations solidly having anything to do with how the certainties were derived.

Perhaps you can discern why some AI makers generally avoid getting into the morass or abyss associated with showcasing certainties. They could be accused of being excessively misleading. People might go around quoting that this generative AI or that generative said that this or that is a 95% chance of happening. Such a claim could be utterly bogus and the generative AI came up with certainties in a manner that has little or no viable justification.

Keep your wits about you in exercising the certainties prompting approach.


Let’s do a wrap-up.

You would be wise to use the activation of identifying generative AI certainties when it is presumably most suitable to your situation at hand. Doing so might be appropriate for a given knotty question or complex dialogue that you are opting to have with generative AI, rather than doing so all of the time. This is a prompting strategy or tactic that can be leveraged or invoked on a particular prompt (on a case-by-case basis).

For those of you that adore seeing the certainties, you could put into your custom instructions that you want the certainties to be identified all of the time, including doing so on a conversation-at-a-time basis or that they should perennially be displayed in whatever conversation you have with generative AI. For my discussion about how to set your own preferred defaults (known as custom instructions in ChatGPT), see the link here.

Your eyes ought to be wide open when you opt to get the certainties laid out. Do not necessarily believe what you see. Also, if you share the generated results with others, you should feel duty-bound to forewarn them too that the certainties are not ironclad and should be interpreted with a big grain of salt.

I am certain that if you decide to invoke certainties in generative AI, you will most certainly endeavor to use this prompting strategy suitably and with great certainty. The uncertainty lies in how well the generative AI will do at certainties, of which you must remain ever vigilant as to the uncertainty therein.

That’s certainly worth remembering.

Thu, 17 Aug 2023 23:00:00 -0500 Lance Eliot en text/html
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