Technology Articles

Article / Updated 06-21-2023

ChatGPT is a global phenomenon that sparks fear and excitement worldwide. Because it sounds human but is made of advanced AI software connected to an internet-sized database, people assume that it's better — or worse — than humans. Reactions run the gamut. Maybe this AI is the first machine overlord of sci-fi lore. Maybe it will save mankind. Perhaps it will take all our jobs and eliminate our purpose for living. Maybe it will make everything cheap and banish inflation. But ChatGPT is only a mindless tool. It's as good or as bad as its users’ prompts — notwithstanding a scary or offensive machine rant or hallucination or two. What ChatGPT is, and isn't To give ChatGPT’s seemingly sudden appearance in the world some much needed perspective, think back to past cycles when a new technology was assumed to be the end of a previous one. The advent of radio was going to eradicate newspapers. TV was thought to be a radio slayer. And the internet was supposed to be the TV killer. But that’s not what happened. Although these forms of communication have changed over the years, they all co-exist. ChatGPT is yet another communication medium. It generates narratives in a conversational format. But just like the communication media that came earlier — newspaper, radio, TV and the internet — ChatGPT isn’t going to replace much that came before. It certainly isn’t going to replace search engines. Each serves a distinct purpose the other can't recreate — although at some point, the two technologies might converge. ChatGPT isn’t going to replace people in their jobs, either. Rather, it will eliminate some jobs and create others. This is a cycle you’ve seen more than once. For example, both the invention of the assembly line and modern-day automation replaced some workers but also created jobs. ChatGPT will eventually spark a similar job cycle. ChatGPT is a powerful tool that can assist humans in doing many different kinds of work. And when its use is coupled with something capable of a physical manifestation, such as 3D printers or automated physical infrastructure , the effect in the real world can be astonishing. Workers who become adept at using it to increase their own productivity and creativity will be best positioned to advance in their existing careers or move to new ones. The good & bad in ChatGPT If what I’ve written so far seems reassuring, you're mistaken. ChatGPT is an existential technology, a hard pivot in human experience, a doorway to a far different kind of future. It's the harbinger of the AI Age and a pronounced mark of the arrival of the Fourth Industrial Revolution. If that terrifies you, you're still mistaken. Mankind, not software, is equal parts scary and saintly. AI in its current forms mostly mirrors that duality. ChatGPT certainly does. The most threatening aspect of ChatGPT is its capability to rapidly create and spread harmful disinformation in every aspect of human life, from politics to healthcare and everything in-between. It can lie, hallucinate, misinform, and make stuff up. How human of it. The most endearing aspect is its capability to help people create things that do not yet exist. ChatGPT can help authors and scriptwriters create stories, filmmakers make incredible special effects and new films on the tiniest of budgets, performance artists to consider new routines, musicians to create new scores, architects to design new kinds of buildings, and photographers to capture realistic images of decidedly unreal subjects. No aspect of human creativity is beyond the scope of ChatGPT’s enhancement and collaborative capabilities. Will ChatGPT replace jobs? Can ChatGPT be used to outright replace the work of creative people? Yes, it can. Note that publishers’ inboxes and self-publishing platforms are already filled to the brim with ChatGPT-generated books. But also note that most of them are truly awful reads. The people who jumped to create works using only ChatGPT were driven by greed, not by any desire to create true art. It takes a true artist to shape a prompt for ChatGPT that will render a magnificently original creative work. The lack of quality coupled with recent events in the U.S. that deny copyright protection of such works — while enforcing copyright protections for any plagiarism ChatGPT and its ilk use in the creation of such works — will soon rob the greedy of their dreams of easy wealth. And like the content farms of yesteryear that tried to game search engines by producing godawful reads full of keywords, those using ChatGPT to create fast content for cash will soon fall into obscurity. But do remember that the fatal fall for content farms came from human hands, in spite of the intense efforts at search engine optimization (SEO). Specifically, owners of search engines didn’t want to render lists of trash results to users, so they drove the work of content farms to the far outer corners of the search universe. Mainstream media, seemingly on its last leg and hemorrhaging badly, began to rebound almost immediately. We’ll likely see that pattern again with ChatGPT content. Human artists and experts will continue to thrive, with ChatGPT taking a permanent place as an important tool in their toolbox. The same will likely hold true for most knowledge workers. Using ChatGPT in place of or without the direct supervision of workers who possess the necessary knowledge will prove to be a horrendous and expensive mistake. Such careless reliance on ChatGPT alone will also open companies to a slew of liabilities while producing little in the way of efficiencies or revenue. Sampling the disruptions ahead That said, it’s time to look at the ways ChatGPT and its generative AI ilk are likely to further disrupt our world: Public trust will be further eroded. Generative AI can produce fake voice tracts that are identical to the original voice, provide tons of disinformation on nearly every subject, and manipulate human behavior through carefully formed narratives. It will be much harder for citizens in any country to discern reality from deception and truth from untruth. Even the results of fact-checking will be suspicious given that generative AI can mimic trustworthy sources. Security teams will be pushed to the limit to keep pace. Fortunately, AI models can be trained to help spot and stop these threats. Unfortunately, it’ll take much longer to rebuild public trust. The rise of superapps will begin. Until this point, apps were embedded with AI to make them faster and better at performing tasks requiring immense amounts of information. But ChatGPT can be considered a superapp in that it can do tasks that formerly required several separate apps, and it can do so by automatically selecting plug-ins to augment its own performance as needed. ChatGPT integrations will eventually be replaced with connected AI. Chat GPT is being integrated with search engines, apps, and other technologies that may or may not have other AI embedded or integrated. Soon, various forms of AI will be connected directly rather than indirectly through app integrations. Some think this is the path to the infamous singularity, while others see it as little different than integrating multiple apps. Smart automation will become more intelligent and versatile. Smart homes, smart phones, smart TVs, smart things of all kinds will become more helpful as virtual assistants like Siri and Alexa become supercharged with generative AI capable of understanding and even anticipating user needs. Knowledge work will be augmented. Workers will spend less time handling mundane functions in knowledge work and more time focused on creating more knowledge. For example, data analysts can use ChatGPT to do the needed analysis as well as write final reports. This will free data analysts to look deeper into the data for more refined insights and to unearth knowledge that may have been missed earlier. Likewise, lawyers can use ChatGPT to write legal briefs, contracts, and other legal documents. The signers can use ChatGPT to condense all the legal forms into an easy-to-understand summary. Opposing attorneys can do likewise, and thus free their time to develop a strategy to counter any challenges or sticking points that ChatGPT uncovers in evidence or legal agreements. Marketers and advertisers will produce more content in a continuous delivery cycle. Because companies seek to stay ahead of emerging trends and be the first-to-market on new ideas, marketers and advertisers will use ChatGPT and its siblings to produce content and ads extremely fast to sell in the moment. As the product changes with the moment, so too will the ads. Education will be disrupted. Long teetering on obsolescence, education as we know it will be disrupted by ChatGPT and generative AI of all forms. The nine-month education model designed to let farmer’s children out of school for planting and harvesting is no longer needed or useful. Neither is the mass conveyor-belt-like grade system or formalized testing. ChatGPT will enable educators to move to more customized learning models wherein students progress according to their competency with the subject rather than their age, school grade, or time spent on the subject matter. How can it do that? By enabling educators to analyze each student’s data and create a customized learning plan for each student. Homework, tests, and other measures of subject competence can also be assessed and reported by generative AI. Media will use ChatGPT to break news faster. ChatGPT can't do investigative reporting. That task requires human reporters to find hidden information and conduct interviews. However, ChatGPT can help fill in the background info for color and context and get the final piece to audiences faster. Journalists will be valued more for their ability to dig up or create additional insights rather than for simply reporting existing and often easily found facts. Customer service will become far more personalized and instantaneous. ChatGPT can discuss issues with customers, offer solutions, give instructions in product support, facilitate returns that fit the customer’s circumstances, and resolve issues faster and with less friction than typical customer service processes. For example, rather than a shopper searching a company's website for a given product, ChatGPT can become a personal shopper and deliver product selections that fit the taste, size, and style preferences of each individual shopper. Healthcare will improve. ChatGPT can provide doctors with information from the latest research and recommendations based on the probability of success for a specific patient. ChatGPT can act as a physician’s assistant in other ways too. In addition, ChatGPT can save lives by providing information for laypeople to help them prevent health issues, deliver emergency treatment, and accurately assess whether a doctor’s attention is warranted. ChatGPT and generative AI will change our daily lives and the future human experience in many more ways than this already long list, though not all of them can be foreseen.

Article / Updated 06-21-2023

ChatGPT is a huge phenomenon and a major paradigm shift in the accelerating march of technological progression. Artificial intelligence (AI) research company OpenAI released a free preview of the chatbot in November 2022, and by January 2023, it had more than a million users. So, what is chatgpt? It's a large language model (LLM) that belongs to a category of AI called generative AI , which can generate new content rather than simply analyze existing data. Additionally, anyone can interact with ChatGPT (GPT stands for generative pre-trained transformer) in their own words. A natural, humanlike dialog ensues. ChatGPT is often directly accessed online by users, but it is also being integrated with several existing applications, such as Microsoft Office apps (Word, Excel, and PowerPoint) and the Bing search engine. The number of app integrations seems to grow every day as existing software providers hurry to capitalize on ChatGPT’s popularity. What is ChatGPT used for? The ways to use ChatGPT are as varied as its users. Most people lean towards more basic requests, such as creating a poem, an essay, or short marketing content. Students often turn to it to do their homework. Heads up, kids: ChatGPT stinks at answering riddles and sometimes word problems in math. Other times, it just makes things up. In general, people tend to use ChatGPT to guide or explain something, as if the bot were a fancier version of a search engine. Nothing is wrong with that use, but ChatGPT can do so much more. How much more depends on how well you write the prompt. If you write a basic prompt, you’ll get a bare-bones answer that you could have found using a search engine such as Google or Bing. That’s the most common reason why people abandon ChatGPT after a few uses. They erroneously believe it has nothing new to offer. But this particular failing is the user’s fault, not ChatGPT’s. What can ChatGPT do? This list covers just some of the more unique uses of this technology. Users have asked ChatGPT to: Conduct an interview with a long-dead legendary figure regarding their views of contemporary topics. Recommend colors and color combinations for logos, fashion designs, and interior decorating designs. Generate original works such as articles, e-books, and ad copy. Predict the outcome of a business scenario. Develop an investment strategy based on stock market history and current economic conditions. Make a diagnosis based on a patient’s real-world test results. Write computer code to make a new computer game from scratch. Leverage sales leads. Inspire ideas for a variety of things from A/B testing to podcasts, webinars, and full-feature films. Check computer code for errors. Summarize legalese in software agreements, contracts, and other forms into simple laymen language. Calculate the terms of an agreement into total costs. Teach a skill or get instructions for a complex task. Find an error in their logic before implementing their decision in the real world. Much ado has been made of ChatGPT’s creativity. But that creativity is a reflection and result of the human doing the prompting. If you can think it, you can probably get ChatGPT to play along. Unfortunately, that’s true for bad guys too. For example, they can prompt ChatGPT to find vulnerabilities in computer code or a computer system; steal your identity by writing a document in your style, tone, and word choices; or edit an audio clip or a video clip to fool your biometric security measures or make it say something you didn’t actually say. Only their imagination limits the possibilities for harm and chaos. Unwrapping ChatGPT fears Perhaps no other technology is as intriguing and disturbing as generative artificial intelligence. Emotions were raised to a fever pitch when 100 million monthly active users snatched up the free, research preview version of ChatGPT within two months after its launch. You can thank science fiction writers and your own imagination for both the tantalizing and terrifying triggers that ChatGPT is now activating in your head, making you wonder: Is ChatGPT safe? There are definitely legitimate reasons for caution and concern. Lawsuits have been launched against generative AI programs for copyright and other intellectual property infringements. OpenAI and other AI companies and partners stand accused of illegally using copyrighted photos, text, and other intellectual property without permission or payment to train their AI models. These charges generally spring from copyrighted content getting caught up in the scraping of the Internet to create massive training datasets. In general, legal defense teams are arguing the inevitability and unsustainability of such charges in the age of AI and requesting that charges be dropped. The lawsuits regarding who owns the content generated by ChatGPT and its ilk lurk somewhere in the future. However, the U.S. Copyright Office has already ruled that AI-generated content, be it writing, images, or music, is not protected by copyright law. In the U.S., at least for now, the government will not protect anything generated by AI in terms of rights, licensing, or payment. Meanwhile, realistic concerns exist over other types of potential liabilities. ChatGPT and ChatGPT alternatives are known to sometimes deliver incorrect information to users and other machines. Who is liable when things go wrong, particularly in a life-threatening scenario? Even if a business’s bottom line is at stake and not someone's life, risks can run high and the outcome can be disastrous. Inevitably, someone will suffer and likely some person or organization will eventually be held accountable for it. Then, there are the magnifications of earlier concerns, such as data privacy, biases, unfair treatment of individuals and groups through AI actions, identity theft, deep fakes, security issues, and reality apathy, which is when the public can no longer tell what is true and what isn’t and thinks the effort to sort it all out is too difficult to pursue. In short, all of this probably has you wondering: Is ChatGPT safe? The potential to misuse it accelerates and intensifies the need for the rules and standards currently being studied, pursued, and developed by organizations and governments seeking to establish guardrails aimed at ensuring responsible AI. The big question is whether they’ll succeed in time, given ChatGPT’s incredibly fast adoption rate worldwide. Examples of groups working on guidelines, ethics, standards, and responsible AI frameworks include the following: ACM US Technology Committee’s Subcommittee on AI & Algorithms World Economic Forum UK’s Centre for Data Ethics Government agencies and efforts such as the US AI Bill of Rights and the European Council of the European Union’s Artificial Intelligence Act. IEEE and its 7000 series of standards Universities such as New York University’s Stern School of Business The private sector, wherein companies make their own responsible AI policies and foundations How does ChatGPT work? ChatGPT works differently than a search engine. A search engine, such as Google or Bing, or an AI assistant, such as Siri, Alexa, or Google Assistant, works by searching the Internet for matches to the keywords you enter in the search bar. Algorithms refine the results based on any number of factors, but your browser history, topic interests, purchase data, and location data usually figure into the equation. You’re then presented with a list of search results ranked in order of relevance as determined by the search engine’s algorithm. From there, the user is free to consider the sources of each option and click a selection to do a deeper dive for more details from that source. By comparison, ChatGPT generates its own unified answer to your prompt. It doesn't offer citations or note its sources. You ask; it answers. Easy-peasey, right? No. That task is incredibly hard for AI to do, which is why generative AI is so impressive. Generating an original result in response to a prompt is achieved by using either the GPT-3 (Generative Pre-trained Transformer 3) or GPT-4 model to analyze the prompt with context and predict the words that are likely to follow. Both GPT models are extremely powerful large language models capable of processing billions of words per second. In short, transformers enable ChatGPT to generate coherent, humanlike text as a response to a prompt. ChatGPT creates a response by considering context and assigning weight (values) to words that are likely to follow the words in the prompt to predict which words would be an appropriate response. Some ChatGPT basics here: User input is called a prompt rather than a command or a query, although it can take either form. You are, in effect, prompting AI to predict and complete a pattern that you initiated by entering the prompt. If you'd like a comprehensive ChatGPT guide, including more detail on how it works and how to use it, check out my book ChatGPT For Dummies. Peeking at the ChatGPT architecture As its name implies, ChatGPT is a chatbot running on a GPT model. GPT-3, GPT-3.5, and GPT-4 are large language models (LLMs) developed by OpenAPI. When GPT-3 was introduced, it was the largest LLM at 175 billion parameters. An upgraded version called GPT-3.5 turbo is a highly optimized and more stable version of GPT-3 that's ten times cheaper for developers to use. ChatGPT is now also available on GPT-4, which is a multimodal model, meaning it accepts both image and text inputs although its outputs are text only. It's now the largest LLM to date, although GPT-4’s exact number of parameters has yet to be disclosed. Parameters are numerical values that weigh and define connections between nodes and layers in the neural network architecture. The more parameters a model has, the more complex its internal representations and weighting. In general, more parameters lead to better performance on specific tasks. ChatGPT for beginners Here, you'll learn the basics of how to use ChatGPT and why it relies on your skills to optimize its performance. But the real treasure here are the tips and insights on how to write prompts so that ChatGPT can perform its true magic. You can learn even more about writing prompts in my book ChatGPT For Dummies. Writing effective ChatGPT prompts ChatGPT appears deceptively simplistic. The user interface is elegantly minimalistic and intuitive, as shown in the figure below. The first part of the page offers information to users regarding ChatGPT’s capabilities and limitations plus a few examples of prompts. The prompt bar, which resembles a search bar, runs across the bottom of the page. Just enter a question or a command to prompt ChatGPT to produce results immediately. If you enter a basic prompt, you’ll get a bare-bones, encyclopedic-like answer, as shown in the figure below. Do that enough times and you’ll convince yourself that this is just a toy and you can get better results from an Internet search engine. This is a typical novice’s mistake and a primary reason why beginners give up before they fully grasp what ChatGPT is and can do. Understand that your previous experience with keywords and search engines does not apply here. You must think of and use ChatGPT in a different way. Think hard about how you’re going to word your prompt. You have many options to consider. You can assign ChatGPT a role or a persona, or several personas and roles if you decide it should respond as a team, as illustrated in the figure below. You can assign yourself a new role or persona as well. Or tell it to address any type of audience — such as a high school graduating class, a surgical team, or attendees at a concert or a technology conference. You can set the stage or situation in great or minimum detail. You can ask a question, give it a command, or require specific behaviors. A prompt, as you can see now, is much more than a question or a command. Your success with ChatGPT hinges on your ability to master crafting a prompt in such a way as to trigger the precise response you seek. Ask yourself these questions as you are writing or evaluating your prompt. Who do you want ChatGPT to be? Where, when, and what is the situation or circumstances you want ChatGPT’s response framed within? Is the question you're entering in the prompt the real question you want it to answer, or were you trying to ask something else? Is the command you're prompting complete enough for ChatGPT to draw from sufficient context to give you a fuller, more complete, and richly nuanced response? And the ultimate question for you to consider: Is your prompt specific and detailed, or vague and meandering? Whichever is the case, that’s what ChatGPT will mirror in its response. ChatGPT’s responses are only as good as your prompt. That’s because the prompt starts a pattern that ChatGPT must then complete. Be intentional and concise about how you present that pattern starter — the prompt. Starting a chat To start a chat, just type a question or command in the prompt bar, shown at the bottom of the figure below. ChatGPT responds instantly. You can continue the chat by using the prompt bar again. Usually, you do this to gain further insights or to get ChatGPT to further refine its response. Following, are some things you can do in a prompt that may not be readily evident: Add data in the prompt along with your question or command regarding what to do with this data. Adding data directly in the prompt enables you to add more current info as well as make ChatGPT responses more customizable and on point. You can use the Browsing plug-in to connect ChatGPT to the live Internet, which will give it access to current information. However, you may want to add data to the prompt anyway to better focus its attention on the problem or task at hand. However, there are limits on prompting and response sizes, so make your prompt as concise as possible. Direct the style, tone, vocabulary level, and other factors to shape ChatGPT's response. Command ChatGPT to assume a specific persona, job role, or authority level in its response. If you’re using ChatGPT-4, you'll soon be able to use images in the prompt too. ChatGPT can extract information from the image to use in its analysis. When you’ve finished chatting on a particular topic or task, it’s wise to start a new chat (by clicking or tapping the New Chat button in the upper left). Starting a new dialogue prevents confusing ChatGPT, which would otherwise treat subsequent prompts as part of a single conversational thread. On the other hand, starting too many new chats on the same topic or related topics can lead the AI to use repetitious phrasing and outputs, whether or not they apply to the new chat’s prompt. To recap: Don't confuse ChatGPT by chatting in one long continuous thread with a lot of topic changes or by opening too many new chats on the same topic. Otherwise, ChatGPT will probably say something offensive or make up random and wrong answers. When writing prompts, think of the topic or task in narrow terms. For example, don't have a long chat on car racing, repairs, and maintenance. To keep ChatGPT more intently focused, narrow your prompt to a single topic, such as determining when the vehicle will be at top trade-in value so you can best offset a new car price. Your responses will be of much higher quality. ChatGPT may call you offensive names and make up stuff if the chat goes on too long. Shorter conversations tend to minimize these odd occurrences, or so most industry watchers think. For example, after ChatGPT responses to Bing users became unhinged and argumentative, Microsoft limited conversations with it to 5 prompts in a row, for a total of 50 conversations a day per user. But a few days later, it increased the limit to 6 prompts per conversation and a total of 60 conversations per day per user. The limits will probably increase when AI researchers can figure out how to tame the machine to an acceptable — or at least a less offensive — level.

Article / Updated 06-21-2023

In this article you will learn: What the problem is with legacy data loss prevention systems How modern data loss prevention works How to download a free eBook to learn more about moving to a modern data loss prevention solution That successful data breaches can have devastating consequences for a business is not new news. The risks from insiders (whether malicious or negligent) are as dangerous to your business as attacks from nefarious outside actors. All threaten to expose sensitive information — personal data/information of customers and employees, financial documents, intellectual property, and so on. This is why your company needs a modern data loss prevention (DLP) system. Security professionals were once confident that the valuable data they protected was safely tucked away inside heavily fortified data centers. But as businesses, both large and small, undergo digital transformation, moving their data to the cloud and across distributed locations, the demands placed on legacy data protection systems have changed drastically. The reality is that most legacy data loss prevention tools are not designed to handle cloud and hybrid work use cases, which require integrations and capabilities with cloud services that legacy DLP systems simply don’t readily support. Consequently, you need to rethink your approach to DLP and consider using modern DLP security technologies. These are systems designed to automatically discover and protect the storage, flow, and use of sensitive data — anywhere across an organization’s networks, users, and services. The problem with legacy DLP systems Although legacy data loss prevention solutions have been around for more than ten years, they’ve gained a reputation for being complex to implement and manage. They’re also considered costly, limited in scope, less and less accurate, and not able to provide the comprehensive coverage needed for today’s current work-from-anywhere world. Legacy data loss prevention software was designed with a perimeter-based security model that assumes all data is stored within the corporate network and managed environments, a model that is no longer sufficient. We are now in the cloud era, when data is stored in multiple cloud-based locations and accessed by users and devices outside the corporate network. Additionally, legacy DLP systems were not designed to integrate with the wide range of cloud services and infrastructures that are now in use. This makes it difficult, or even impossible, to provide comprehensive protection for data in the cloud. Adding extra technologies to an outdated DLP approach doesn’t make it cloud-ready; it only adds complexity and additional strain on what might be an already-stretched IT department. How modern DLP works To effectively prevent data loss, a DLP system should be integrated and automated to continuously monitor and verify the identity of authorized individuals and devices, their behavior, their collaboration and external data sharing, the applications they’re using and their risks, and many other contextual factors. A modern DLP system performs several critical functions, including the following: Identifies sensitive data wherever it resides and moves, whether it’s data in motion (crossing the Internet, networks, apps, and devices); data at rest (being stored); or data in use (being collaborated on, printed, or faxed). Monitors the data environment to detect who’s accessing data and what they’re doing with it. By monitoring actions, DLP can detect incidents — such as unauthorized sharing of confidential information — that may be in violation of corporate policy and take action to address them. Automatically takes action to enforce policies by, for example, stopping the data flow, encrypting the data, quarantining the confidential information, or unsharing the data on software as a service (SaaS) application. Provides user coaching by automatically notifying users of violations and the reasons behind them, while educating them on safe data-handling practices. Notification also helps to instantly educate users on security policies, reducing the need for incident response teams to manually triage issues. To read more about moving to a modern DLP solution that supports your business goals and protects your company, download Modern Data Loss Prevention (DLP) For Dummies, Netskope Special Edition.

Article / Updated 06-14-2023

Indeed, prompting is both the easy part and the most difficult part of using a generative artificial intelligence (AI) model, like ChatGPT. Difficulties in the complexity of cues and nuances in text-based prompts are why some organizations have a prompt engineering job role. What is a ChatGPT prompt? It's a phrase or sentence that you write in ChatGPT to initiate a response from the AI. ChatGPT responds based on its existing knowledge base. Prompt engineering is the act of crafting an input, which is a deed borne partly of art and partly of logic. And yes, you can do this! However, you might want to practice and polish your prompting skills before you apply for a job. Considering how to prompt ChatGPT, if you have a good command of the subtleties of language and great critical-thinking and problem-solving skills, seasoned with more than a dash of intuitive intelligence, you’ll be amazed at the responses you can tease out of this technology with a single, well-worded prompt. When you prompt ChatGPT, you are embedding the task description in the input (called the prompt) in a natural-language format, rather than entering explicit instructions via computer code. Prompt engineers can be trained AI professionals or people who possess sufficient intuitive intelligence or skills transferrable to crafting the best prompts for ChatGPT (or other generative AI platforms) that solicit the desired outputs. One example of a transferrable skill is a journalist’s ability to tease out the answers they seek in an interview by using direct or indirect methods. Prompt-based learning is a strategy AI engineers use to train large language models. The engineers make the model multipurpose to avoid retraining it for each new language-based task. Currently, the demand for talented writers who know how to write a prompt, or prompt engineers, is very high. However, there is a strong debate as to whether employers should delineate this unique skill as a dedicated job role, a new profession, or a universal skill to be required of most workers, much like typing skills are today. Meanwhile, people are sharing their prompts with other ChatGPT users in several forums. You can see one example on GitHub. How to write a prompt If you enter a basic prompt, you’ll get a bare-bones, encyclopedic-like answer, as shown in the figure below. Do that enough times and you’ll convince yourself that this is just a toy and you can get better results from an internet search engine. This is a typical novice’s mistake and a primary reason why beginners give up before they fully grasp what ChatGPT is and can do. Understand that your previous experience with keywords and search engines does not apply here. To write awesome ChatGPT prompts, you must think of and use ChatGPT in a different way. Think hard about how you’re going to word your prompt. You have many options to consider. You can assign ChatGPT a role or a persona, or several personas and roles if you decide it should respond as a team, as illustrated in the following figure. You can assign yourself a new role or persona as well. Or tell it to address any type of audience, such as a high school graduating class, a surgical team, or attendees at a concert or a technology conference. You can set the stage or situation in great or minimum detail. You can ask a question, give it a command, or require specific behaviors. A prompt, as you can see now, is much more than a question or a command. Your success with ChatGPT hinges on your ability to master crafting a prompt in such a way as to trigger the precise response you seek. Ask yourself these questions as you are writing or evaluating your prompt: Who do you want ChatGPT to be? Where, when, and what is the situation or circumstances you want ChatGPT’s response framed within? Is the question you're entering in the prompt the real question you want it to answer, or were you trying to ask something else? Is the command you're prompting complete enough for ChatGPT to draw from sufficient context to give you a fuller, more complete, and richly nuanced response? And the ultimate question for you to consider: Is your prompt specific and detailed, or vague and meandering? Whichever is the case, that’s what ChatGPT will mirror in its response. ChatGPT’s responses are only as good as your prompt. That’s because the prompt starts a pattern that ChatGPT must then complete. Be intentional and concise about how you present that pattern starter — the prompt. For more details on using ChatGPT, including how to start a chat, reviewing your chat history, and much more, check out my book ChatGPT For Dummies. Thinking in threads Conversations happen when one entity’s expression initiates and influences another entity’s response. Most conversations do not conclude after a simple one-two exchange like this, but rather continue in a flow of responses cued by the interaction with the other participant. The resulting string of messages in a conversation is called a thread. To increase your success with ChatGPT, write prompts as part of a thread rather than as standalone queries. In this way, you'll craft prompts targeted towards the outputs you seek, building one output on another to reach a predetermined end. In other words, you don’t have to pile everything into one prompt. You can write a series of prompts to more precisely direct ChatGPT’s “thought processes.” Basic prompts result in responses that can be too general or vague. When you think in threads, you’re not aiming to craft a series of basic prompts; you’re looking to break down what you seek into prompt blocks that aim ChatGPT’s responses in the direction you want the conversation to go. In effect, you're using serialized prompts to manipulate the content and direction of ChatGPT's response. Does it work all the time? No, of course not. ChatGPT can opt for an entirely different response than expected, repeat an earlier response, or simply hallucinate one. But serialized prompts do work often enough to enable you to keep the conversation targeted and the responses flowing toward the end you seek. You can use this method to shape a single prompt by imagining someone asking for clarification of your thought or question. Write the prompt so that it includes that information, and the AI model will have more of the context it needs to deliver an intelligent and refined answer. ChatGPT will not ask for clarification of your prompt; it will guess at your meaning instead. You’ll typically get better quality responses by clarifying your meaning in the prompt itself at the outset. Chaining prompts and other tips and strategies Here’s a handy list of other tips and refinements to help get you started on the path to mastering the art of the prompt: Plan to spend more time than expected on crafting a prompt. No matter how many times you write prompts, the next one you write won’t be any easier to do. Don’t rush this part. Start by defining the goal. What exactly do you want ChatGPT to deliver? Craft your prompt to push ChatGPT towards that goal; if you know where you want to end up, you’ll be able to craft a prompt that will get you there. Think like a storyteller, not an inquisitor. Give ChatGPT a character or a knowledge level from which it should shape its answer. For example, tell ChatGPT that it's a chemist, an oncologist, a consultant, or any other job role. You can also instruct it to answer as if it were a famous person, such as Churchill, Shakespeare, or Einstein, or a fictional character such as Rocky. Give it a sample of your own writing and instruct ChatGPT to write its answer to your question, or complete the task in the way you would. Remember that any task or thinking exercise (within reason and the law) is fair game and within ChatGPT’s general scope. For example, instruct ChatGPT to check your homework, your kids’ homework, or its own homework. Enter something such as computer code or a text passage in quotation marks and instruct ChatGPT to find errors in it or in the logic behind it. Or skip the homework checking and ask it to help you think instead. Ask it to finish a thought, an exercise, or a mathematical equation that has you stumped. The only limit to what you can ask is your own imagination and whatever few safety rules the AI trainer installed. Be specific. The more details you include in the prompt, the better. Basic prompts lead to basic responses. More specific and concise prompts lead to more detailed responses, more nuanced responses, and better performance in ChatGPT’s responses — and usually well within token limits. Use prompt chains as a way of strategizing. Prompt chaining is a technique used to build chatbots, but we can reimagine it here as a way to develop a strategic plan using combined or serial prompting in ChatGPT. This technique uses multiple prompts to guide ChatGPT through a more complex thought process. You can use multiple prompts as a single input, such as telling ChatGPT it's a team consisting of several members with different roles, all of whom are to answer the one prompt you entered. Or you can use multiple prompts in a sequence in which the output of one becomes the input of the next. In this case, each response builds on the prompt you just entered and the prompts you entered earlier. This type of a prompt chain forms organically, unless you stop ChatGPT from considering earlier prompts in its responses by starting a new chat. Use prompt libraries and tools to improve your prompting. Some examples follows: Check out the Awesome ChatGPT Prompts repository on GitHub at https://github.com/f/awesome-chatgpt-prompts Use a prompt generator to ask ChatGPT to improve your prompt by visiting PromptGenerator. Visit ChatGPT and Bing AI Prompts on GitHub. Use a tool such as Hugging Face’s ChatGPT Prompt Generator. Try specialized prompt templates, such as the curated list for sales and marketing use cases at Tooltester. On GitHub, you can find tons of curated lists in repositories as well as lots of free prompting tools from a variety of sources. Just make sure that you double-check sources, apps, and browser extensions for malware before using or relying on them.

Article / Updated 06-09-2023

If statistics has been described as the science of deriving insights from data, then what’s the difference between a statistician and a data scientist? Good question! While many tasks in data science require a fair bit of statistical know how, the scope and breadth of a data scientist’s knowledge and skill base is distinct from those of a statistician. The core distinctions are outlined below. Subject matter expertise: One of the core features of data scientists is that they offer a sophisticated degree of expertise in the area to which they apply their analytical methods. Data scientists need this so that they’re able to truly understand the implications and applications of the data insights they generate. A data scientist should have enough subject matter expertise to be able to identify the significance of their findings and independently decide how to proceed in the analysis. In contrast, statisticians usually have an incredibly deep knowledge of statistics, but very little expertise in the subject matters to which they apply statistical methods. Most of the time, statisticians are required to consult with external subject matter experts to truly get a firm grasp on the significance of their findings, and to be able to decide the best way to move forward in an analysis. Mathematical and machine learning approaches: Statisticians rely mostly on statistical methods and processes when deriving insights from data. In contrast, data scientists are required to pull from a wide variety of techniques to derive data insights. These include statistical methods, but also include approaches that are not based in statistics — like those found in mathematics, clustering, classification, and non-statistical machine learning approaches. Seeing the importance of statistical know-how You don't need to go out and get a degree in statistics to practice data science, but you should at least get familiar with some of the more fundamental methods that are used in statistical data analysis. These include: Linear regression: Linear regression is useful for modeling the relationships between a dependent variable and one or several independent variables. The purpose of linear regression is to discover (and quantify the strength of) important correlations between dependent and independent variables. Time-series analysis: Time series analysis involves analyzing a collection of data on attribute values over time, in order to predict future instances of the measure based on the past observational data. Monte Carlo simulations: The Monte Carlo method is a simulation technique you can use to test hypotheses, to generate parameter estimates, to predict scenario outcomes, and to validate models. The method is powerful because it can be used to very quickly simulate anywhere from 1 to 10,000 (or more) simulation samples for any processes you are trying to evaluate. Statistics for spatial data: One fundamental and important property of spatial data is that it’s not random. It’s spatially dependent and autocorrelated. When modeling spatial data, avoid statistical methods that assume your data is random. Kriging and krige are two statistical methods that you can use to model spatial data. These methods enable you to produce predictive surfaces for entire study areas based on sets of known points in geographic space. Working with clustering, classification, and machine learning methods Machine learning is the application of computational algorithms to learn from (or deduce patterns in) raw datasets. Clustering is a particular type of machine learning —unsupervised machine learning, to be precise, meaning that the algorithms must learn from unlabeled data, and as such, they must use inferential methods to discover correlations. Classification, on the other hand, is called supervised machine learning, meaning that the algorithms learn from labeled data. The following descriptions introduce some of the more basic clustering and classification approaches: k-means clustering: You generally deploy k-means algorithms to subdivide data points of a dataset into clusters based on nearest mean values. To determine the optimal division of your data points into clusters, such that the distance between points in each cluster is minimized, you can use k-means clustering. Nearest neighbor algorithms: The purpose of a nearest neighbor analysis is to search for and locate either a nearest point in space or a nearest numerical value, depending on the attribute you use for the basis of comparison. Kernel density estimation: An alternative way to identify clusters in your data is to use a density smoothing function. Kernel density estimation (KDE) works by placing a kernel a weighting function that is useful for quantifying density — on each data point in the data set, and then summing the kernels to generate a kernel density estimate for the overall region. Keeping mathematical methods in the mix Lots gets said about the value of statistics in the practice of data science, but applied mathematical methods are seldom mentioned. To be frank, mathematics is the basis of all quantitative analyses. Its importance should not be understated. The two following mathematical methods are particularly useful in data science. Multi-criteria decision making (MCDM): MCDM is a mathematical decision modeling approach that you can use when you have several criteria or alternatives that you must simultaneously evaluate when making a decision. Markov chains: A Markov chain is a mathematical method that chains together a series of randomly generated variables that represent the present state in order to model how changes in present state variables affect future states.

Article / Updated 06-09-2023

After you install TensorFlow, you're ready to start creating and executing applications. This section walks through the process of running an application that prints a simple message. Exploring the example code You can download this example code from the "Downloads" link on Wiley.com. The archive’s name is tf_dummies.zip, and if you decompress it, you see that it contains folders named after chapters (ch2, ch3, and so on). Each chapter folder contains one or more Python files (*.py). In each case, you can execute the module by changing to the directory and running python or python3 followed by the filename. For example, if you have Python 2 installed, you can execute the code in simple_math.py by changing to the ch3 directory and entering the following command: python simple_math.py Feel free to use this example code in professional products, academic work, and morally questionable experiments. But do not use any of this code to program evil robots! Launching Hello TensorFlow! Programming books have a long tradition of introducing their topic with a simple example that prints a welcoming message. If you open the ch2 directory in this book’s example code, you find a module named hello_tensorflow.py. This listing presents the code. Hello TensorFlow! """A simple TensorFlow application""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf # Create tensor msg = tf.string_join(["Hello ", "TensorFlow!"]) # Launch session with tf.Session() as sess: print(sess.run(msg)) This code performs three important tasks: Creates a Tensor named msg that contains two string elements. Creates a Session named sess and makes it the default session. Launches the new Session and prints its result. Running the code is simple. Open a command line and change to the ch2 directory in this book's example code. Then, if you’re using Python 2, you can execute the following command: python hello_tensorflow.py If you’re using Python 3, you can run the module with the following command: python3 hello_tensorflow.py As the Python interpreter does its magic, you should see the following message: b'Hello TensorFlow' The welcome message is straightforward, but the application’s code probably isn’t as clear. A Tensor instance is an n-dimensional array that contains numeric or string data. Tensors play a central role in TensorFlow development. A Session serves as the environment in which TensorFlow operations can be executed. All TensorFlow operations, from addition to optimization, must be executed through a session.

Article / Updated 06-09-2023

Blockchain technology alone cannot provide rich analytics results. For all that blockchain is, it can’t magically provide more data than other technologies. Before selecting blockchain technology for any new development or analytics project, clearly justify why such a decision makes sense. If you already depend on blockchain technology to store data, the decision to use that data for analysis is a lot easier to justify. Here, you examine some reasons why blockchain-supported analytics may allow you to leverage your data in interesting ways. Leveraging newly accessible decentralized tools to analyze blockchain data You’ll want to learn how to manually access and analyze blockchain data. But, it's also important to understand how to exercise granular control over your data throughout the analytics process, higher-level tools make the task easier. The growing number of decentralized data analytics solutions means more opportunities to build analytics models with less effort. Third-party tools may reduce the amount of control you have over the models you deploy, but they can dramatically increase analytics productivity. The following list of blockchain analytics solutions is not exhaustive and is likely to change rapidly. Take a few minutes to conduct your own internet search for blockchain analytics tools. You’ll likely find even more software and services: Endor: A blockchain-based AI prediction platform that has the goal of making the technology accessible to organizations of all sizes. Endor is both a blockchain analytics protocol and a prediction engine that integrates on-chain and off-chain data for analysis. Crystal: A blockchain analytics platform that integrates with the Bitcoin and Ethereum blockchains and focuses on cryptocurrency transaction analytics. Different Crystal products cater to small organizations, enterprises, and law enforcement agencies. OXT: The most focused of the three products listed, OXT is an analytics and visualization explorer tool for the Bitcoin blockchain. Although OXT doesn’t provide analytics support for a variety of blockchains, it attempts to provide a wide range of analytics options for Bitcoin. Monetizing blockchain data Today’s economy is driven by data, and the amount of data being collected about individuals and their behavior is staggering. Think of the last time you accessed your favorite shopping site. Chances are, you saw an ad that you found relevant. Those targeted ads seem to be getting better and better at figuring out what would interest you. The capability to align ads with user preferences depends on an analytics engine acquiring enough data about the user to reliably predict products or services of interest. Blockchain data can represent the next logical phase of data’s value to the enterprise. As more and more consumers realize the value of their personal data, interest is growing in the capability to control that data. Consumers now want to control how their data is being used and demand incentives or compensation for the use of their data. Blockchain technology can provide a central point of presence for personal data and the ability for the data’s owner to authorize access to that data. Removing personal data from common central data stores, such as Google and Facebook, has the potential to revolutionize marketing and advertising. Smaller organizations could access valuable marketing information by asking permission from the data owner as opposed to the large data aggregators. Circumventing big players such as Google and Facebook could reduce marketing costs and allow incentives to flow directly to individuals. There is a long way to go to move away from current personal data usage practices, but blockchain technology makes it possible. This process may be accelerated by emerging regulations that protect individual rights to control private data. For example, the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) both strengthen an individual’s ability to control access to, and use of, their personal data. Exchanging and integrating blockchain data effectively Much of the value of blockchain data is in its capability to relate to off-chain data. Most blockchain apps refer to some data stored in off-chain repositories. It doesn’t make sense to store every type of data in a blockchain. Reference data, which is commonly data that gets updated to reflect changing conditions, may not be good candidates for storing in a blockchain. Blockchain technology excels at recording value transfers between owners. All applications define and maintain additional information that supports and provides details for transactions but doesn’t directly participate in transactions. Such information, such as product description or customer notes, may make more sense to store in an off-chain repository. Any time blockchain apps rely on on-chain and off-chain data, integration methods become a concern. Even if your app uses only on-chain data, it is likely that analytics models will integrate with off-chain data. For example, owners in blockchain environments are identified by addresses. These addresses have no context external to the blockchain. Any association between an address and a real-world identity is likely stored in an off-chain repository. Another example of the need for off-chain data is when analyzing aircraft safety trends. Perhaps your analysis correlates blockchain-based incident and accident data with weather conditions. Although each blockchain transaction contains a timestamp, you’d have to consult an external weather database to determine prevailing weather conditions at the time of the transaction. Many examples of the need to integrate off-chain data with on-chain transactions exist. Part of the data acquisition phase of any analytics project is to identify data sources and access methods. In a blockchain analytics project, that process means identifying off-chain data you need to satisfy the goals of your project and how to get that data. Want to learn more? Check out our Blockchain Data Analytics Cheat Sheet.

Article / Updated 06-07-2023

To share Excel 2019 workbooks from your OneDrive, you follow these steps: Open the workbook file you want to share in Excel 2019 and then click the Share button at the far right of the row with the Ribbon. If you’ve not yet saved the workbook on your OneDrive, a Share dialog box appears inviting you to upload the workbook file to OneDrive. Once you have clicked the OneDrive button and the file is uploaded to the cloud, the Share dialog box changes into the Send Link dialog box (similar to the one shown in the figure) where you specify the people with whom to share the file. Begin typing the name or e-mail address of the first person with whom you want to share the workbook in the text box with the insertion point. When Excel finds a match to the person’s name in your Outlook address book or verifies the e-mail address you entered, click the button below this text box to add this recipient. (Optional) Click the Anyone with This Link Can Edit drop-down button to open the Link Settings dialog box where you can modify the people for whom the link works, deny editing privileges to those with whom you share the file, and/or set an expiration date after which the link is no longer operational before clicking the Apply button. By default, Excel 2019 creates a sharing link that enables anyone who can access the workbook file online access to the file even when they are not logged into Office 365 or OneDrive. To restrict access to only coworkers in your company who are logged into Office 365, click the People in <organization> option (where organization is the name of your company as in People in Mind Over Media, the name of my company). To restrict the file sharing to only those to whom you’ve given prior access to the workbook file or its folder on your SharePoint site, click the People with Existing Access option. To create a sharing link that only particular people can use, click the Specific People option before you click the Apply button. Then, in the Send Link dialog box, click the ellipsis (…) to the right of the Send Link title and click Manage Access on the drop-menu to open the Permissions dialog box where you select the names of the people with whom to share the workbook file before you click the back arrow button to return to the Send Link dialog box. By default, Excel allows the people with whom you share your workbooks to make editing changes to the workbook that are automatically saved on your OneDrive. If you want to restrict your recipients to reviewing the data without being able to make changes, be sure to click the Allow Editing check box to remove its check mark before you click Apply. If you wish to set an expiration date after which the sharing link is no longer operational, click the Set Expiration Date button to open the pop-up calendar where you select an expiration date by clicking it in the calendar. After selecting the expiration date, click somewhere in the dialog box to close the pop-up calendar and enter the date in the Link Settings dialog box. (Optional) Click the Add a Message text box and type any personal message that you want to incorporate as part of the e-mail with the generic invitation to share the file.By default, Excel creates a generic invitation. After adding all the recipients with whom you wish to share the workbook file in this manner, click the Send button in the Send Link pane.As soon as you click this Share button, Excel e-mails the invitation to share the workbook to each of the recipients. All the people with whom you share a workbook receive an e-mail message containing a hyperlink to the workbook on your OneDrive. When they follow this link (and sign into the site if this is required), a copy of the workbook opens on a new page in their default web browser using the Excel Online web app. If you’ve given the user permission to edit the file, the web app contains an Edit Workbook drop-down button. When the coworkers with whom you’ve shared the workbook click this button in Excel Online, they have a choice between choosing the Edit in Excel or Edit in Excel Online option from its drop-down menu. When the user chooses Edit in Excel, the workbook is downloaded and opened in his version of Excel. When the user chooses Edit in Excel Online, the browser opens the workbook in a new version of the Excel Online, containing Home, Insert, Data, Review, and View tabs, each with a more limited set of command options than Excel 2019, which you can use in making any necessary changes and which are automatically saved to workbook on the OneDrive when you close Excel Online. While sharing a workbook with the default Anyone Can Edit option, all changes made by the people with whom you’ve shared the workbook are automatically saved by the AutoSave feature. If you happen to have the workbook open in Excel 2019 on your computer with the same worksheet displayed, their editing changes automatically appear in your worksheet (in as close to real time as the speed of your Internet access provides). Likewise, all the editing changes that you make to the workbook in Excel 2019 are automatically updated in their workbooks in Excel Online. Microsoft refers to this process as co-authoring. If a questionable editing change appears in your worksheet when co-authoring with a coworker, add a comment to the cell containing the edit-in-question (Review-->New Comment) that communicates your reservations about the change they made. A small balloon then appears above the cell where you made the comment in the user’s worksheet in Excel Online. When the coworker clicks this balloon, Excel Online displays the text of your comment calling into question their edit in a Comments task pane. They can then reply to your reservations by typing their explanation for the change in the same comment in this task pane and then updating it in your workbook by clicking the Post button or they can just go ahead and make any necessary updates reflecting your reservations directly in the worksheet in Excel Online.

Article / Updated 06-07-2023

If you’ve been paying attention to the tech world recently, you’ve likely heard a number of new terms being thrown around — virtual reality, augmented reality, extended reality, and mixed reality — and you may have wondered what they mean. To help make sense of it all, here’s a breakdown of each of those terms, how they’re alike, and how they’re different. Virtual reality (VR): A computer-simulated reality that simulates a fully artificial environment that does not physically exist. Users within VR are closed off from the real world. Consumer VR executions typically consist of a headset and some sort of controller. Augmented reality (AR): A way of viewing the real world in which your view of the real world is “augmented” by computer-generated input, such as still images, audio, or video. AR differs from VR in that AR augments (adds to) a real-world scene, instead of creating something from scratch. AR headsets aren’t quite commonplace yet, but you may have an AR device in your pocket: Newer generations of both iOS and Android devices have been enabled with AR capabilities. Mixed reality (MR): MR may take your view of the real world and integrate computer-generated content that can interact with that view of the real world. Or it may take a fully digital environment and connect it to real-world objects. In this way, MR can sometimes function similarly to VR and sometimes function similarly to AR. You’ll often hear the terms being used interchangeably, which can be confusing. Here’s a quick glance at the differences. In MR, you may have a view of the real world, and a digital basketball may appear to bounce off the real world floor and walls, or a digital rocket ship may appear to land on your coffee table. This is AR-based MR, and you’ll often just hear these experiences referred to as AR. In other MR instances, you may only see a completely digital environment with no view of the real world, but that digital environment is connected to real-world objects around you. In your virtual world, real-world tables or chairs may digitally appear as rocks or trees. Real-world office walls may appear as moss-covered cave walls. This is VR-based MR, sometimes called augmented virtuality. Mixed reality is gaining traction in the industry, especially AR-based mixed reality. Remember that it is not uncommon for the terms augmented reality and mixed reality to be used synonymously. Extended reality (XR): The umbrella term used for all these technologies. It can cover everything from VR to MR to AR technologies. People sometimes use the term virtual reality to refer to all of the above, but the correct umbrella term is extended reality.

Article / Updated 06-07-2023

Excel can help you make all sorts of calculations. Here's a selection of Excel's statistical worksheet functions. Each one returns a value into a selected cell. Check out these functions for central tendency and variability. Function What it calculates AVERAGE Mean of a set of numbers AVERAGEIF Mean of a set of numbers that meet a condition AVERAGEIFS Mean of a set of numbers that meet one or more conditions HARMEAN Harmonic mean of a set of positive numbers GEOMEAN Geometric mean of a set of positive numbers MODE.SNGL Mode of a set of numbers MEDIAN Median of a set of numbers VAR.P Variance of a set of numbers considered to be a population VAR.S Variance of a set of numbers considered to be a sample STDEV.P Standard deviation of a set of numbers considered to be a population STDEV.S Standard deviation of a set of numbers considered to be a sample STANDARDIZE A standard score based on a given mean and standard deviation These handy functions for relative standing can also be very useful. Function What it calculates RANK.EQ Rank of a number in a set of numbers. If more than one number has the same rank, it returns the top rank of those numbers. RANK.AVG Rank of a number in a set of numbers. If more than one number has the same rank, it returns their average. PERCENTRANK.INC Rank of a number in a set of numbers, expressed as a percent of the numbers it's greater than or equal to. PERCENTRANT.EXC Rank of a number in a set of numbers, expressed as a percent of the numbers it's greater than. PERCENTILE.INC The indicated percentile in a set of numbers, in terms of "greater than or equal to." PERCENTILE.EXC The indicated percentile in a set of numbers, in terms of "greater than." QUARTILE.INC The 1st, 2nd, 3rd, or 4th quartile of a set of numbers, in terms of "greater than or equal to." QUARTILE.EXC The 1st, 2nd, 3rd, or 4th quartile of a set of numbers, in terms of "greater than." These functions for correlation and regression are also good ones to know. Function What it Calculates CORREL Correlation coefficient between two sets of numbers PEARSON Same as CORREL. (Go figure!) RSQ Coefficient of determination between two sets of numbers (square of the correlation coefficient) SLOPE Slope of a regression line through two sets of numbers INTERCEPT Intercept of a regression line through two sets of numbers STEYX Standard error of estimate for a regression line through two sets of numbers