Information Technology Articles

Article / Updated 12-01-2023

Getting the most out of your unstructured data is an essential task for any organization these days, especially when considering the disparate storage systems, applications, and user locations. So, it’s not an accident that data orchestration is the term that brings everything together. Bringing all your data together shares similarities with conducting an orchestra. Instead of combining the violin, oboe, and cello, this brand of orchestration combines distributed data types from different places, platforms, and locations working as a cohesive entity presented to applications or users anywhere. That’s because historically, accessing high-performance data outside of your computer network was inefficient. Because the storage infrastructure existed in a silo, systems like HPC Parallel (which lets users store and access shared data across multiple networked storage nodes), Enterprise NAS (which allows large-scale storage and access to other networks), and Global Namespace (virtually simplifies network file systems) were limited when it came to sharing. Because each operated independently, the data within each system was siloed making it a problem collaborating with data sets over multiple locations. Collaboration was possible, but too often you lost the ability to have high performance. This Boolean logic decreased potential because having an IT architecture that supported both high performance and collaboration with data sets from different storage silos typically became an either/or decision: You were forced to choose one but never both. What is data orchestration? Data orchestration is the automated process of taking siloed data from multiple data storage systems and locations, combining and organizing it into a single namespace. Then a high-performance file system can place data in the edge service, data center, or cloud service most optimal for the workload. The recent rise of data analytic applications and artificial intelligence (AI) capabilities has accelerated the use of data across different locations and even different organizations. In the next data cycle, organizations will need both high-performance and agility with their data to compete and thrive in a competitive environment. That means data no longer has a 1:1 relationship with the applications and compute environment that generated it. It needs to be used, analyzed, and repurposed with different AI models and alternate workloads, and across a remote, collaborative environment. Hammerspace’s technology makes data available to different foundational models, remote applications, decentralized compute clusters, and remote workers to automate and streamline data-driven development programs, data insights, and business decision making. This capability enables a unified, fast, and efficient global data environment for the entire workflow — from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds. Control of enterprise data services for governance, security, data protection, and compliance can now be implemented globally at a file-granular level across all storage types and locations. Applications and AI models can access data stored in remote locations while using automated orchestration tools to provide high-performance local access when needed for processing. Organizations can grow their talent pools with access to team members no matter where they reside. Decentralizing the data center Data collection has become more prominent, and the traditional system of centralized data management has limitations. Issues of centralized data storage can limit the amount of data available to applications. Then, there are the high infrastructure costs when multiple applications are needed to manage and move data, multiple copies of data are retained in different storage systems, and more headcount is needed to manage the complex, disconnected infrastructure environment. Such setbacks suggest that the data center is no longer the center of data and storage system constraints should no longer define data architectures. Hammerspace specializes in decentralized environments, where data may need to span two or more sites and possibly one or more cloud providers and regions, and/or where a remote workforce needs to collaborate in real time. It enables a global data environment by providing a unified, parallel global file system. Enabling a global data environment Hammerspace completely revolutionizes previously held notions of how unstructured data architectures should be designed, delivering the performance needed across distributed environments to Free workloads from data silos. Eliminate copy proliferation. Provide direct data access through local metadata to applications and users, no matter where the data is stored. This technology allows organizations to take full advantage of the performance capabilities of any server, storage system, and network anywhere in the world. This capability enables a unified, fast, and efficient global data environment for the entire workflow, from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds. The days of enterprises struggling with a siloed, distributed, and inefficient data environment are over. It’s time to start expecting more from data architectures with automated data orchestration. Find out how by downloading Unstructured Data Orchestration For Dummies, Hammerspace Special Edition, here.

Article / Updated 12-01-2023

We depend on machines to produce everyday essentials — such as power, food, and medicine — and to support nearly every aspect of society. Thus, machine health is vital to overall manufacturing and business health. Machine health transforms reliability, maintenance, operations, and asset performance management by using artificial intelligence (AI) and Internet of Things (IoT) technologies to improve performance, reduce downtime, and help manufacturers reach Industry 4.0 standards. Transform the way you work In an era of labor shortages and technology innovation, manufacturing needs to move faster toward digitization. Using AI in manufacturing, companies can eliminate repetitive tasks, reduce inefficiencies, and strengthen data-driven decision making. With insights into the real-time condition of their machines, workers can break away from traditional maintenance schedules and manual tasks. This means more time for proactive work and collaboration with other departments, which leads to stronger cross-functional teams. It also leads to further innovations, such as process optimization. When employees have insights into the health of their machines, they can predict their workdays and own their schedules, opening up new opportunities for innovation and engagement. Learn more about transforming the way you work with machine health at www.augury.com/use-cases/business-goal/transform-the-way-you-work. Eliminate unnecessary downtime Unplanned downtime can be expensive. There’s the cost of the repairs themselves, lost production and sales, and reputational damage. Maintenance and reliability teams often have to scramble to diagnose and fix the problem as quickly as possible — often resulting in overtime pay and expedited shipping costs for emergency spare parts. Sudden and catastrophic machine failures can also harm worker morale and jeopardize worker safety. Thus, reducing unnecessary downtime has a significant impact on both your top and bottom lines. Learn more about eliminating unnecessary downtime with machine health at www.augury.com/use-cases/business-goal/eliminate-unnecessary-downtime. Reduce loss, waste, and emissions Reducing waste to improve sustainability has become a top priority for manufacturers for cost-cutting/efficiency purposes, as well as to promote a healthier planet. Healthy machines can run at capacity and with less downtime, leading to less waste and more efficient energy use. An industry study by the Electric Power Research Institute (EPRI) found that optimizing the performance of rotating assets — which account for approximately 54 percent of U.S. industrial electricity consumption — can reduce energy consumption by 12 to 15 percent. Learn more about reducing loss, waste, and emissions with machine health at www.augury.com/use-cases/business-goal/reduce-loss-waste-and-emissions. Maximize yield and capacity Real-time machine health insights allow maintenance teams to adjust shutdown schedules based on the current condition of the machine, its history, and the recommendations of experts. In the longer-term, machine health minimizes unplanned downtime by improving the health of your machines, reduces planned downtime by deferring nonessential maintenance activities, and can increase output on production lines with healthier machines running optimally. Learn more about maximizing yield and capacity with machine health at www.augury.com/use-cases/business-goal/maximize-yield-and-capacity. Optimize asset care When it comes to asset care — from acquiring and storing parts to managing maintenance resources — manufacturers have traditionally taken a more preventive than predictive approach: Machines are serviced on fixed schedules, regardless of whether or not they need maintenance. Many manufacturers keep spare parts for critical machine assets in inventory because calendar-based maintenance dictates when parts should be replaced — whether or not replacement is needed. The alternative is overspending to get parts on short notice and expensive downtime while you wait for the parts to arrive. Whether hoarding parts or paying more for last-minute parts, both methods are inefficient and costly. Machine health empowers you to control how you spend time and money based on the real-time condition of your machine assets. Learn more about optimizing asset care with machine health at www.augury.com/use-cases/business-goal/optimize-asset-care. Start building a winning machine health culture Machine health can transform operations for every manufacturer. Get your free copy of Machine Health For Dummies at https://www.augury.com/machinehealthfordummies/. How much will you save with Augury Machine Health? Use the ROI calculator at www.augury.com/value-calculator/ to see how much time and money you can save with Augury’s Machine Health.

Article / Updated 10-31-2023

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. Don't have time to read the entire article? Jump to the quick read summary. 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. Quick Read Summary Writing effective prompts for ChatGPT is both a craft and a science. A prompt is the phrase or sentence that initiates a response from the AI model. To excel in this skill, consider these essential tips. Crafting an artful prompt: A well-crafted prompt is essential to unlock ChatGPT's potential. Think beyond basic questions and commands. You can assign roles or personas to ChatGPT, set the stage, or address different audiences. Prompt engineering: This skill can be highly valuable. It involves creating prompts that draw out the desired responses from the AI. Prompt engineers often have a background in AI, journalism, or other fields where they've honed their ability to solicit specific information. Thinking in threads: Instead of standalone queries, use prompts as part of a conversation thread. This helps you build on previous outputs and guide the AI's responses toward your desired end. Chaining prompts: Connect prompts sequentially to steer ChatGPT's thought process. This approach can lead to more targeted and refined responses. Be patient and put thought into each prompt. Specificity is key: Detailed prompts lead to more detailed and nuanced responses. Avoid vague or meandering instructions, as ChatGPT mirrors the prompt's clarity. Prompt libraries and tools: Leverage existing resources to improve your prompting skills. There are repositories and tools available, like the Awesome ChatGPT Prompts repository on GitHub and Hugging Face's ChatGPT Prompt Generator. The art of imagination Within reasonable and legal limits, you can instruct ChatGPT for various tasks, from checking homework to creative writing. The only boundary is your imagination. In a world where the demand for skilled prompt writers is increasing, your ability to craft the perfect prompt is a valuable asset. By mastering this art, you can unlock the full potential of ChatGPT and guide its responses to meet your specific needs. Hungry for more? Go back and read the article or check out the book.

Article / Updated 10-30-2023

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. Don't have time to read the entire article? Jump to the quick read summary. 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. Quick Read Summary ChatGPT, a product of OpenAI, represents a groundbreaking advancement in the world of artificial intelligence. Released as a free preview in November 2022, it quickly gained over a million users by January 2023. ChatGPT is a powerful example of generative AI, capable of generating new content instead of just analyzing existing data. This versatile tool is accessible online and is being integrated into various applications like Microsoft Office and Bing search, expanding its utility daily. Users initially engage with ChatGPT for basic tasks like crafting poems, essays, or marketing content. Students use it for homework. But all who use it should be cautious: ChatGPT struggles with riddles and word problems in math. It also has a tendency to make things up. People tend to use ChatGPT to guide or explain something, but its potential goes beyond simple requests. Depending on the quality of your prompt, it can perform a wide range of tasks. Users have leveraged ChatGPT for tasks like conducting interviews with historical figures, recommending color combinations, generating articles, predicting business scenarios, and even diagnosing medical conditions based on patients’ real-world test results. Users can harness ChatGPT’s capabilities for both good and ill, from identifying vulnerabilities in computer systems to creating deepfakes. Therefore, as ChatGPT's popularity soars, concerns about its safety and misuse grow. Legal battles surrounding copyright infringement and accountability for incorrect information continue to emerge. Ethical guidelines and standards are under development by organizations and governments to ensure responsible AI usage. ChatGPT operates differently from search engines and AI assistants. It generates original responses to prompts, making it a valuable tool for diverse tasks. Users must craft prompts effectively to receive meaningful responses, considering factors like context, role assignment, and audience specification. In summary, ChatGPT is a game-changer in AI technology, offering endless possibilities when used responsibly. Its potential for good or harm depends on the user, emphasizing the need for ethical guidelines and responsible AI practices. Hungry for more? Go back and read the article or check out the book.

Article / Updated 08-31-2023

As an IT professional, cybersecurity is the thing most likely to keep you awake at night. You must consider two basic elements as part of your cybersecurity plan: Prevention: The first pillar of cybersecurity is technology that you can deploy to prevent bad actors from penetrating your network and stealing or damaging your data. This technology includes firewalls that block unwelcome access, antivirus programs that detect malicious software, patch management tools that keep your software up to date, and antispam programs that keep suspicious email from reaching your users’ inboxes. The most important part of the prevention pillar is the human firewall. Technology can only go so far in preventing successful cyber attacks. Most successful attacks are the result of users opening email attachments or clicking web links that they should have known were dangerous. Thus, in addition to providing technology to prevent attacks, you also need to make sure your users know how to spot and avoid suspicious email attachments and web links. Recovery: The second pillar of cybersecurity is necessary because the first pillar isn’t always successful. Successful cyber attacks are inevitable, so you need to have technology and plans in place to quickly recover from them when you do.

Article / Updated 08-31-2023

Security techniques and technology — physical security, user account security, server security, and locking down your servers — are child’s play compared with the most difficult job of network security: securing your network’s users. All the best-laid security plans are for naught if your users write down their passwords on sticky notes and post them on their computers and click every link that shows up in their email. The key to securing your network users is to empower your users to realize that they’re an important part of your company’s cybersecurity plan, and then show them what they can do to become an effective human firewall. This necessarily involves training, and of course IT training is usually the most dreaded type of training there is. So, do your best to make the training fun and engaging rather than dull and boring. If training isn’t your thing, search the web. You’ll find plenty of inexpensive options for online cybersecurity training, ranging from simple and short videos to full-length online courses. You’ll also need to establish a written cybersecurity policy and stick to it. Have a meeting with everyone to go over the security policy to make sure that everyone understands the rules. Also, make sure to have consequences when violations occur. Here are some suggestions for some basic security rules you can incorporate into your security policy: Never write down your password or give it to someone else. Accounts should not be shared. Never use someone else’s account to access a resource that you can’t access under your own account. If you need access to some network resource that isn’t available to you, you should formally request access under your own account. Likewise, never give your account information to a co-worker so that he or she can access a needed resource. Your co-worker should instead formally request access under his or her own account. Don’t install any software or hardware on your computer — especially wireless access devices or modems — without first obtaining permission. Don’t enable file and printer sharing on workstations without first getting permission. Never attempt to disable or bypass the network’s security features.

Article / Updated 07-27-2023

In growth, you use testing methods to optimize your web design and messaging so that it performs at its absolute best with the audiences to which it's targeted. Although testing and web analytics methods are both intended to optimize performance, testing goes one layer deeper than web analytics. You use web analytics to get a general idea about the interests of your channel audiences and how well your marketing efforts are paying off over time. After you have this information, you can then go in deeper to test variations on live visitors in order to gain empirical evidence about what designs and messaging your visitors actually prefer. Testing tactics can help you optimize your website design or brand messaging for increased conversions in all layers of the funnel. Testing is also useful when optimizing your landing pages for user activations and revenue conversions. Checking out common types of testing in growth When you use data insights to increase growth for e-commerce businesses, you're likely to run into the three following testing tactics: A/B split testing, multivariate testing, and mouse-click heat map analytics. An A/B split test is an optimization tactic you can use to split variations of your website or brand messaging between sets of live audiences in order to gauge responses and decide which of the two variations performs best. A/B split testing is the simplest testing method you can use for website or messaging optimization. Multivariate testing is, in many ways, similar to the multivariate regression analysis that I discuss in Chapter 5. Like multivariate regression analysis, multivariate testing allows you to uncover relationships, correlations, and causations between variables and outcomes. In the case of multivariate testing, you're testing several conversion factors simultaneously over an extended period in order to uncover which factors are responsible for increased conversions. Multivariate testing is more complicated than A/B split testing, but it usually provides quicker and more powerful results. Lastly, you can use mouse-click heat map analytics to see how visitors are responding to your design and messaging choices. In this type of testing, you use the mouse-click heat map to help you make optimal website design and messaging choices to ensure that you're doing everything you can to keep your visitors focused and converting. Landing pages are meant to offer visitors little to no options, except to convert or to exit the page. Because a visitor has so few options on what he can do on a landing page, you don't really need to use multivariate testing or website mouse-click heat maps. Simple A/B split tests suffice. Data scientists working in growth hacking should be familiar with (and know how to derive insight from) the following testing applications: Webtrends: Offers a conversion-optimization feature that includes functionality for A/B split testing and multivariate testing. Optimizely: A popular product among the growth-hacking community. You can use Optimizely for multipage funnel testing, A/B split testing, and multivariate testing, among other things. Visual Website Optimizer: An excellent tool for A/B split testing and multivariate testing. Testing for acquisitions Acquisitions testing provides feedback on how well your content performs with prospective users in your assorted channels. You can use acquisitions testing to help compare your message's performance in each channel, helping you optimize your messaging on a per-channel basis. If you want to optimize the performance of your brand's published images, you can use acquisition testing to compare image performance across your channels as well. Lastly, if you want to increase your acquisitions through increases in user referrals, use testing to help optimize your referrals messaging for the referrals channels. Acquisition testing can help you begin to understand the specific preferences of prospective users on a channel-by-channel basis. You can use A/B split testing to improve your acquisitions in the following ways: Social messaging optimization: After you use social analytics to deduce the general interests and preferences of users in each of your social channels, you can then further optimize your brand messaging along those channels by using A/B split testing to compare your headlines and social media messaging within each channel. Brand image and messaging optimization: Compare and optimize the respective performances of images along each of your social channels. Optimized referral messaging: Test the effectiveness of your email messaging at converting new user referrals. Testing for activations Activation testing provides feedback on how well your website and its content perform in converting acquired users to active users. The results of activation testing can help you optimize your website and landing pages for maximum sign-ups and subscriptions. Here's how you'd use testing methods to optimize user activation growth: Website conversion optimization: Make sure your website is optimized for user activation conversions. You can use A/B split testing, multivariate testing, or a mouse-click heat map data visualization to help you optimize your website design. Landing pages: If your landing page has a simple call to action that prompts guests to subscribe to your email list, you can use A/B split testing for simple design optimization of this page and the call-to-action messaging. Testing for retentions Retentions testing provides feedback on how well your blog post and email headlines are performing among your base of activated users. If you want to optimize your headlines so that active users want to continue active engagements with your brand, test the performance of your user-retention tactics. Here's how you can use testing methods to optimize user retention growth: Headline optimization: Use A/B split testing to optimize the headlines of your blog posts and email marketing messages. Test different headline varieties within your different channels, and then use the varieties that perform the best. Email open rates and RSS view rates are ideal metrics to track the performance of each headline variation. Conversion rate optimization: Use A/B split testing on the messaging within your emails to decide which messaging variety more effectively gets your activated users to engage with your brand. The more effective your email messaging is at getting activated users to take a desired action, the greater your user retention rates. Testing for revenue growth Revenue testing gauges the performance of revenue-generating landing pages, e-commerce pages, and brand messaging. Revenue testing methods can help you optimize your landing and e-commerce pages for sales conversions. Here's how you can use testing methods to optimize revenue growth: Website conversion optimization: You can use A/B split testing, multivariate testing, or a mouse-click heat map data visualization to help optimize your sales page and shopping cart design for revenue-generating conversions. Landing page optimization: If you have a landing page with a simple call to action that prompts guests to make a purchase, you can use A/B split testing for design optimization.

Cheat Sheet / Updated 07-24-2023

Blockchain technology is much more than just another way to store data. It's a radical new method of storing validated data and transaction information in an indelible, trusted repository. Blockchain has the potential to disrupt business as we know it, and in the process, provide a rich new source of behavioral data. Data analysts have long found valuable insights from historical data, and blockchain can expose new and reliable data to drive business strategy. To best leverage the value that blockchain data offers, become familiar with blockchain technology and how it stores data, and learn how to extract and analyze this data.

Article / Updated 07-24-2023

In 2008, Bitcoin was the only blockchain implementation. At that time, Bitcoin and blockchain were synonymous. Now hundreds of different blockchain implementations exist. Each new blockchain implementation emerges to address a particular need and each one is unique. However, blockchains tend to share many features with other blockchains. Before examining blockchain applications and data, it helps to look at their similarities. Check out this article to learn how blockchains work. Categorizing blockchain implementations One of the most common ways to evaluate blockchains is to consider the underlying data visibility, that is, who can see and access the blockchain data. And just as important, who can participate in the decision (consensus) to add new blocks to the blockchain? The three primary blockchain models are public, private, and hybrid. Opening blockchain to everyone Nakamoto’s original blockchain proposal described a public blockchain. After all, blockchain technology is all about providing trusted transactions among untrusted participants. Sharing a ledger of transactions among nodes in a public network provides a classic untrusted network. If anyone can join the network, you have no criteria on which to base your trust. It’s almost like throwing s $20 bill out your window and trusting that only the person you intend to pick it up will do so. Public blockchain implementations, including Bitcoin and Ethereum, depend on a consensus algorithm that makes it hard to mine blocks but easy to validate them. PoW is the most common consensus algorithm in use today for public blockchains, but that may change. Ethereum is in the process of transitioning to the Proof of Stake (PoS) consensus algorithm, which requires less computation and depends on how much blockchain currency a node holds. The idea is that a node with more blockchain currency would be affected negatively if it participates in unethical behavior. The higher the stake you have in something, the greater the chance that you’ll care about its integrity. Because public blockchains are open to anyone (anyone can become a node on the network), no permission is needed to join. For this reason, a public blockchain is also called a permissionless blockchain. Public (permissionless) blockchains are most often used for new apps that interact with the public in general. A public blockchain is like a retail store, in that anyone can walk into the store and shop. Limiting blockchain access The opposite of a public blockchain is a private blockchain, such as Hyperledger Fabric. In a private blockchain, also called a permissioned blockchain, the entity that owns and controls the blockchain grants and revokes access to the blockchain data. Because most enterprises manage sensitive or private data, private blockchains are commonly used because they can limit access to that data. The blockchain data is still transparent and readily available but is subject to the owning entity’s access requirements. Some have argued that private blockchains violate data transparency, the original intent of blockchain technology. Although private blockchains can limit data access (and go against the philosophy of the original blockchain in Bitcoin), limited transparency also allows enterprises to consider blockchain technology for new apps in a private environment. Without the private blockchain option, the technology likely would never be considered for most enterprise applications. Combining the best of both worlds A classic blockchain use case is a supply chain app, which manages a product from its production all the way through its consumption. The beginning of the supply chain is when a product is manufactured, harvested, caught, or otherwise provisioned to send to an eventual customer. The supply chain app then tracks and manages each transfer of ownership as the product makes its way to the physical location where the consumer purchases it. Supply chain apps manage product movement, process payment at each stage in the movement lifecycle, and create an audit trail that can be used to investigate the actions of each owner along the supply chain. Blockchain technology is well suited to support the transfer of ownership and maintain an indelible record of each step in the process. Many supply chains are complex and consist of multiple organizations. In such cases, data suffers as it is exported from one participant, transmitted to the next participant, and then imported into their data system. A single blockchain would simplify the export/transport/import cycle and auditing. An additional benefit of blockchain technology in supply chain apps is the ease with which a product’s provenance (a trace of owners back to its origin) is readily available. Many of today’s supply chains are made up of several enterprises that enter into agreements to work together for mutual benefit. Although the participants in a supply chain are business partners, they do not fully trust one another. A blockchain can provide the level of transactional and data trust that the enterprises need. The best solution is a semi-private blockchain – that is, the blockchain is public for supply chain participants but not to anyone else. This type of blockchain (one that is owned by a group of entities) is called a hybrid, or consortium, blockchain. The participants jointly own the blockchain and agree on policies to govern access. Describing basic blockchain type features Each type of blockchain has specific strengths and weaknesses. Which one to use depends on the goals and target environment. You have to know why you need blockchain and what you expect to get from it before you can make an informed decision as to what type of blockchain would be best. The best solution for one organization may not be the best solution for another. The table below shows how blockchain types compare and why you might choose one over the other. Differences in Types of Blockchain Feature Public Private Hybrid Permission Permissionless Permissioned (limited to organization members) Permissioned (limited to consortium members) Consensus PoW, PoS, and so on Authorized participants Varies; can use any method Performance Slow (due to consensus) Fast (relatively) Generally fast Identity Virtually anonymous Validated identity Validated identity The primary differences between each type of blockchain are the consensus algorithm used and whether participants are known or anonymous. These two concepts are related. An unknown (and therefore completely untrusted) participant will require an environment with a more rigorous consensus algorithm. On the other hand, if you know the transaction participants, you can use a less rigorous consensus algorithm. Contrasting popular enterprise blockchain implementations Dozens of blockchain implementations are available today, and soon there will be hundreds. Each new blockchain implementation targets a specific market and offers unique features. There isn’t room in this article to cover even a fair number of blockchain implementations, but you should be aware of some of the most popular. Remember that you’ll be learning about blockchain analytics in this book. Although organizations of all sizes are starting to leverage the power of analytics, enterprises were early adopters and have the most mature approach to extracting value from data. The What Matrix website provides a comprehensive comparison of top enterprise blockchains. Visit whatmatrix.com for up-to-date blockchain information. Following are the top enterprise blockchain implementations and some of their strengths and weaknesses (ranking is based on the What Matrix website): Hyperledger Fabric: The flagship blockchain implementation from the Linux Foundation. Hyperledger is an open-source project backed by a diverse consortium of large corporations. Hyperledger’s modular-based architecture and rich support make it the highest rated enterprise blockchain. VeChain: Currently more popular that Hyperledger, having the highest number of enterprise use cases among products reviewed by What Matrix. VeChain includes support for two native cryptocurrencies and states that its focus is on efficient enterprise collaboration. Ripple Transaction Protocol: A blockchain that focuses on financial markets. Instead of appealing to general use cases, Ripple caters to organizations that want to implement financial transaction blockchain apps. Ripple was the first commercially available blockchain focused on financial solutions. Ethereum: The most popular general-purpose, public blockchain implementation. Although Ethereum is not technically an enterprise solution, it's in use in multiple proof of concept projects. The preceding list is just a brief overview of a small sample of blockchain implementations. If you’re just beginning to learn about blockchain technology in general, start out with Ethereum, which is one of the easier blockchain implementations to learn. After that, you can progress to another blockchain that may be better aligned with your organization. Want to learn more? Check out our Blockchain Data Analytic Cheat Sheet.

Article / Updated 07-10-2023

They say if it ain’t broke, don’t fix it, but anyone with high-value assets, whether a fleet of bucket trucks or drilling rigs, knows preventive maintenance is much more effective than performing repairs reactively. Servicing equipment before it fails reduces costly downtime and extends its lifespan, thus stretching your resources as far as possible. This concept certainly isn’t new. Routine equipment checks and preventive maintenance in general have been the responsibility of every maintenance department for decades. But here’s the good part. You can use AI and Internet of Things (IoT) sensors to go beyond preventive maintenance to implement predictive maintenance. Preventive maintenance prevents failures with inspections and services performed at predetermined intervals. Predictive maintenance uses large volumes of data and advanced analytics to anticipate the likelihood of failure based on the history and current status of a specific piece of equipment and recommends service before it happens. How do you like the sound of that? It’s the sound of asset performance optimization. This figure traces the evolution of this concept. Spying on Your Machines Asset performance optimization (APO) collects the digital output from IoT-enabled equipment and associated processes, analyzes the data, tracks performance, and makes recommendations regarding maintenance. APO allows you to forecast future needs and perform predictive maintenance before immediate actions are needed. Although some machines run continuously with little need for maintenance, others require much more care and attention to operate at their best level. Determining which equipment needs more frequent servicing can be time-consuming and tedious. Often maintenance guidelines rely heavily on guesswork. Time frames for tune-ups tend to be little more than suggestions, based on information such as shop manuals and a recommendation from the lead mechanics rather than hard data, such as metrics from the performance history of each piece of equipment, including downtime and previous failures. APO, on the other hand, analyzes both structured and unstructured data, such as field notes, to add context for equipment readings and deliver more precise recommendations. Using IoT devices, APO systems gather data from sensors, EIM systems, and external sources. It then uses AI to acquire, merge, manage, and analyze this information, presenting the results using real-time dashboards that can be shared and reviewed quickly for at-a-glance updates. Throughout the lifespan of the equipment, through regular use and maintenance, the system continues to learn and improve its insights over time. Fixing It Before It Breaks APO allows you to take a strategic approach to predictive maintenance by focusing on what will make the greatest difference to your operation. Unforeseen equipment malfunctions and downtime cause disruptions, which can ultimately jeopardize project timelines, customer satisfaction, and business revenue. These are benefits of APO: Smoother, more predictable operations: Equipment issues are addressed preemptively instead of after a disruption occurs, leading to greater overall operational efficiency. Implementing APO can help companies achieve a 70-percent elimination of unanticipated equipment failure. Reduced downtime: Preventive maintenance typically reduces machine downtime by 30 to 50 percent. Boosted productivity: In addition to reducing downtime, predictive maintenance allows you to become more strategic in scheduling maintenance. This also can uncover any routine maintenance tasks that can be performed at less frequent intervals. Lowered costs: APO can reduce the time required to plan maintenance by 20 to 50 percent, increase equipment uptime and availability by 10 to 20 percent, and reduce overall maintenance costs by 5 to 10 percent, according to a Deloitte study. Increased customer satisfaction: Assets nearing failures can sacrifice production quality, cause service outages, and create other circumstances that ultimately affect the customer. By preventing these issues from happening, APO helps companies achieve and maintain better customer satisfaction. Improved safety outcomes: Equipment malfunctions can result in serious injury, but often companies don’t know a system is about to fail until it’s too late. PwC reports that APO and predictive maintenance can improve maintenance safety by up to 14 percent. Reduced risk of litigation and penalties: With fewer breakdowns and disruptions to service, organizations can minimize their risk of costly fines, lawsuits, and subsequent reputational damage. Ultimately, in any industry with high-value equipment, or even large numbers of low-cost assets, APO pays off. It directs technicians’ efforts to the machines that need attention the most, instead of performing inspections or maintenance on the equipment that doesn’t need it. This leads to predictable and seamless operations, improved uptime, increased revenue opportunities, and greater customer satisfaction. Learning from the Future APO solutions allow you to enhance your operations by making your machines smarter and sharing that intelligence with your workforce, thereby maximizing the value of both your human teams and your mechanical equipment. As the age of AI advances, the companies that thrive will be those that find the best ways to harness data for improved operational performance. Data collection APO continuously collects data on mechanical performance from IoT sensors in virtually any type of device or machine, ranging from hydraulic brake system sensors on a train to temperature monitors inside industrial and medical refrigerators holding sensitive products. The system collects numerous data points from the sensors, blending them with other sources, and analyzes the results. For example, in the case of a hydraulic brake system, APO compares current data to historical performance records, including failure reports, to deliver predictive maintenance insights. When further data inputs are blended with this specific brake data, even richer and more accurate recommendations can be delivered. Additional input samples from internal and third-party sources can be blended to provide context and greater insight; these types of input can be invaluable: Weather data Maintenance recommendations from manuals Supplier quality data Historical brake maintenance schedules and failure rates Passenger travel analysis Heavy or unusual usage data Using this comprehensive blend of data, you can implement an APO solution to recognize patterns and perform an in-depth analysis in multiple applications, from manufacturing plants to utilities and even healthcare. The data can include metrics such as temperature, movement, light exposure, and more collected from IoT sensors on fleets, plants, pipelines, medical imaging equipment, jets, grids, and any other Internet-enabled device. Analysis AI uses big data analytics and natural-language processing to derive key data points from structured data as well as unstructured content such as field notes and equipment manuals. You can use AI to analyze this information and relevant historical data to identify patterns and generate questions that help engineers, maintenance supervisors, production managers, and other key personnel make informed and timely decisions. APO solutions use these patterns to make predictive conclusions that help you answer questions in various areas, such as these: Timing: Am I performing inspections at appropriate intervals? Would shortening the intervals improve overall uptime? Or could I afford to lengthen the intervals to reduce resource expenditure? Quality: Could defective components be slipping through my inspections and leading to downtime? If so, how can I improve the inspection process to prevent this issue moving forward? Design: Can my design be modified to reduce future failures? For example, a predictive conclusion formed by the patterns observed in the case of the train brake system might indicate the need for shorter inspection intervals. This is where humans come in and leverage all of these valuable findings to improve their business. Putting insights to use After patterns are identified and their related questions are answered, the predictive conclusions provided by APO solutions can then be implemented. For example, train maintenance workers can schedule inspections more frequently to check for a key component in the hydraulic brake system that has shown a tendency to fail. Or perhaps the APO solution discovers that a defective component in the train needs attention. Field engineers can use a digital model of the train to determine a repair strategy. If the part cannot be repaired, the APO solution can trigger a replacement part order through the supply chain network, using automation to streamline the process of getting the train back up and running.