Supercharge Your Learning: 10 Game Changing ChatGPT Prompts To Upskill Faster

Rowan Cheung

ChatGPT can be your writing assistant, research buddy and partner in crime. With the right prompts, you can save time, make more money and have more fun doing your work. A brilliant use of OpenAI’s flagship platform is for learning. Just like a college tutor, chunky textbook or a wise old sage, but a whole lot faster. With a little knowledge, you can get ChatGPT to teach you things ten times more effectively than any other method.

Rowan Cheung is founder of The Rundown, a fast-growing AI newsletter providing an in-depth look at the latest developments in AI. In less than 4 months, The Rundown has gained a following of over 170,000 subscribers who rely on its content to stay informed about the latest advancements in artificial intelligence.

Cheung is on a mission to inform millions of people about the latest advancements in AI and highlight how technology is transforming the world. His AI database, Supertools, records the best tools mentioned in the newsletter. Cheung’s favourite way of using ChatGPT is for learning and he’s sharing these 10 best prompts to learn anything faster.

1. Simplify complex information

“Prompt: Break down (topic you’d like to understand) into smaller, easier-to-understand parts. Use analogies and real-life examples to simplify the concept and make it more relatable.”

Forget scrolling YouTube for videos or looking for explainer articles. Whenever you want to understand the building blocks of something new, or someone mentions a concept you don’t understand, learn the fundamentals quickly in a way that sticks in your mind.

2. Create a study schedule

“Prompt: I need help organizing my study time for (subject). Can you create a study schedule for me, including breaks and practice exercises?”

Even if you don’t have any upcoming exams, learn a new topic by sticking to a schedule. This might be something complex related to your field, marketing, or even AI itself.

3. Memorize key information

“Prompt: What are the most important facts, dates, or formulas related to (topic)? Help me create a memorization technique to remember them easily.”

If you’re preparing for an interview, about to go on a podcast, or just want to impress your next customer, learning and remembering key information about your topic can be done in minutes. With this prompt, your AI memory assistant will think of catchy rhymes, stories and mnemonics for you.

4. Ask for feedback

“Prompt: I’ve been studying (topic) on my own. Can you review my work and give me feedback on areas I need to improve?”

Ask ChatGPT to review your crib sheet, show notes, course content or resume. Treat it like the editor or professor you would love to have by your side. Even a few pointers in the right direction might vastly improve the impression you give.

5. Apply your knowledge

“Prompt: Use your knowledge of (topic you’re interested in) to solve a real-world problem. Explain your thought process and share your solution.”

Figure out how what you already know could improve your life. Take your areas of strength and see where they might be applied within a wider context. That interest in hot sauce or obsession with snooker might serve you well going forward.

6. Compare and contrast

“Prompt: Compare and contrast (concept 1) and (concept 2) to better understand their similarities and differences. Use examples to illustrate your points.”

Build better rapport with clients by finding the similarities between your interests or areas of work. Check their social media profiles to find what they’re into, then compare and contrast with your hobbies to find common ground for discussion.

7. Learn from mistakes

“Prompt: I made a mistake while practicing (skill). Can you explain what went wrong and how I can avoid making the same mistake in the future?”

Public speaking, that sales meeting that didn’t go ahead, or Twitter posts that are falling flat; improve everything you do by explaining what went wrong and getting a second opinion on where to correct your course.

8. Connect with others

“Prompt: Connect me with a community of learners and experts in (topic). How can I join a forum, social media group or other online community to share my knowledge and learn from others?”

To go fast, go alone. To go far, go together. Entrepreneurship can be a lonely game if you don’t find people traveling a similar path. Connect with people in your industry and find where they hang out using this prompt.

9. Summarize complex texts

“Prompt: I need to read a complicated article related to (topic). Can you help me summarize the key points and takeaways from the text?”

We’ve all been there. Someone sends an article they think we absolutely must read, but it’s five pages long. Get the TL:DR by asking for a summary, and continue the conversation without spending ages looking for the facts.

10. Stay updated

“Prompt: Help me stay updated on the latest developments and trends in (topic). What are some trustworthy resources I can follow to stay informed?”

Get a constant stream of relevant information straight into your inbox by getting ChatGPT to do the research. Instead of waiting for sites, podcasts and newsletters to appear on your radar, get recommendations all at once and tick off the task.

With these 10 clever prompts you can simplify complex information, create a study schedule, memorize key information, and ask for feedback. You can apply your knowledge, compare and contrast, learn from mistakes and connect with others. Finally, summarize complex texts and stay updated. It’s learning, on artificially intelligent steroids. Never fall behind again, and never let lack of knowledge get in the way of your success.

Founder of Coachvox.ai – we make AI coaches. Forbes 30 under 30 class of 2017. Post-exit entrepreneur and author of Ten Year Career.

Source: Supercharge Your Learning: 10 Game-Changing ChatGPT Prompts To Upskill Faster

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How The Power Of Predictive Analytics Can Transform Business

Tableau analytics visual

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:

  • How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
  • What prevents people from more deeply exploring and using data?
  • In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?

With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.

Current realities and barriers to scale smarter decision-making with AI 

With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.

Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.

At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.

Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.

The opportunity to solve data science challenges

This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:

  • Reducing data exploration and prep work
  • Empowering analyst experts to deliver data science outputs at lower costs
  • Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
  • Extending, automating, and accelerating analysis for business groups and domain experts
  • Reducing time and costs spent on deploying and integrating models
  • Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias

Business scenarios that benefit from predictive analytics 

There are several business scenarios where predictive capabilities can be immensely useful.

Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.

A solution to consider: Tableau Business Science

We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.

This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.

When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.

Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.

Source: How The Power Of Predictive Analytics Can Transform Business

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Critics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science,marketing,business management, sports/fantasy sports, insurance,policing, telecommunications,retail, travel, mobility, healthcare, child protection, pharmaceuticals,capacity planning, social networking and other fields.

One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.

See also

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