AI Articles

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 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.

Article / Updated 07-05-2023

All that data being collected in manufacturing from IoT devices at unprecedented volume and velocity is driving the fourth industrial revolution. The first industrial revolution was powered by steam. The second was powered by electricity. The third was powered by silicon, which enabled unprecedented computing power. And the fourth industrial revolution is being powered by data. In fact, in the last decade, data has emerged as the new currency that operates across all levels of commerce, right down to the consumer, who pays for the use of “free” social media platforms with their personal data, which those platforms exchange with their clients. The combination of AI and analytics can help manufacturers optimize the use of their IoT data for many applications. Consider three related strategies: proactive replenishment, predictive maintenance, and pervasive visibility. The following figure shows the relationship between the method of controlling costs and the AI technique used to accomplish it. Connected supply chain A supply chain connects a customer to the raw materials required to produce the product. It can be as simple as a single link, such as when the customer stops at a roadside stand to buy tomatoes directly from the farmer. Or it can be very complex and involve dozens of links, such as all the steps between a customer driving a car off the lot back to mining the iron ore or bauxite for the steel or aluminum engine block. In a traditional supply chain, each link operates as a black box, connected to each other by a paper-thin link made up of documents such as purchase orders, shipping manifests, invoices, and the like. Each entity does its own planning, forecasting, and ordering while being blind to conditions on either side of it in the chain. You could think of the links in the traditional supply chain as separate continents, each with its own ecosystem but largely isolated and insulated from the ecosystems of other continents. A connected supply chain brings those continents together like the supercontinent Pangea in the Paleozoic era, forming one interconnected ecosystem of partners, suppliers, and customers. In the connected supply chain, the paper-thin connection of the traditional model is replaced with a digital connection that provides full visibility in all directions. A 2017 McKinsey study suggests that companies that aggressively digitize their supply chains can expect to boost annual growth of earnings by an average of 3.2 percent — the largest increase from digitizing any business area — and annual revenue growth by 2.3 percent. In a recent KRC Research survey of manufacturing executives, 46 percent indicated that big data analytics and IoT are essential for improving supply chain performance. They identified the two areas where big data analytics can have the greatest impact on manufacturing to be improving supply chain performance (32 percent) and enabling real-time decisions and avoiding unplanned downtime (32 percent). The study also identified the top three benefits of big data for manufacturers: Enabling well-informed decisions in real-time (63 percent) Reducing wasted resources (57 percent) Predicting the risks of downtime (56 percent) The connected supply chain enables you to react to changing market needs by sharing insights between every node in the value chain. There are many other applications for AI in manufacturing, some of which I address later, but the three elements of connected supply chain — proactive replenishment, predictive maintenance, and pervasive visibility — provide an intuitive case for using AI to revolutionize your business. Proactive replenishment Optimizing inventory levels while improving customer experience requires the ability to automate much of the replenishment process. Proactive replenishment leverages analytics to monitor inventory consumption and initiate a purchase order on the business network to the supplier to replenish the stock before an out-of-stock situation occurs. The intelligent and connected supply chain provides real-time inventory visibility. In addition to reporting stock levels, it can indicate the condition of each item — such as the temperature at which it is stored — to ensure the quality of those items. Manufacturers can automate the replenishment of parts from the supplier before they are needed in the production process. And, query between suppliers to procure from which has availability, the best rates, and taking into account shipping times required for in-time delivery. Predictive maintenance The ability to predict when a part of a sub-system of a serviceable product is likely to fail and intervene can save a manufacturer millions of dollars, and thus predictive maintenance is a key investment area for the supply chain. Whether that part is within the production process, within the warehousing environment, or part of a connected vehicle, an IoT network automatically monitors and analyzes performance to boost operating capacity and lifespan. This system intelligently decides whether the part needs to be replaced or repaired and can automatically trigger the correct process, typically reducing machine downtime by 30 to 50 percent and increasing machine life by 20 to 40 percent. Studies by the Federal Energy Management Program of the U.S. Department of Energy found that predictive maintenance provides a 30 to 40 percent spending reduction compared to reactive maintenance, and an 8 to 12 percent spending reduction compared to preventative maintenance. Organizations often make incremental progress toward predictive maintenance, starting with monitoring IoT data in the control room and reacting to problematic readings. In the next stage, they run reports to view recommendations for maintenance. In the final stage, they remove the requirement for human interaction to initiate maintenance by automating the creation of repair tickets. Pervasive visibility Pervasive visibility is the ability to see exactly where goods are during their life cycle, providing a view of the current state of all assets across the entire organization and beyond to partners, customers, competitors, and even the impact of the weather on operations and fulfillment. IoT plays a key role in providing that visibility. Current estimates predict that there will be 75 billion connected devices by 2025, ten connected devices for every human on the planet. Most of those devices will be in the manufacturing sector. When you consider that a single device can produce gigabytes of information each minute, the volume and velocity of data within production and operations can easily spiral out of control — or, more often, simply be ignored. While 60 percent of executives that McKinsey surveyed said that IoT data yielded significant insights, 54 percent admitted that they used less than 10 percent of that IoT information. Making sense of that data is where AI comes in. By combining AI and analytics, you can bring together information from a variety of data sources, identify patterns and trends, and provide recommendations for future actions. It provides the basis for new levels of production and business process automation, as well as improved support for employees in their daily roles and in their decision-making. To achieve these gains in the supply chain, AI has the potential to bring together both structured and unstructured data from a wide range of sources, including IoT devices, plant operations, and external partners to identify patterns linking factors such as demand, location, socioeconomic conditions, weather, and political status. This information forms a basis for a new level of supply chain optimization spanning raw materials, logistics, inventory control, and supplier performance and helps anticipate and react to market changes.

Article / Updated 06-22-2023

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 health care 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. ChatGPT 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. Health care 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 06-09-2023

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.

Cheat Sheet / Updated 06-01-2023

ChatGPT took the world by storm shortly after its debut. If you’re feeling at a loss as to what to do, too excited to decide where to start, or uncertain about how to react to artificial intelligence (AI) entering your space, this cheat sheet will help you make some quick and meaningful headway that you can build on as you go. Read a brief explanation of what ChatGPT is before checking it out either directly online or by connecting with it in one of many software applications. Then learn the keys to mastering ChatGPT: perfecting the prompt and using the right ChatGPT plug-ins. Using ChatGPT will significantly increase your productivity, but you must always fact-check its responses before relying on them. Don’t worry if you make a mistake along the way or find yourself stumped over what to do next. You're not alone. ChatGPT is completely new and mysterious for most people. Keep this cheat sheet handy and dive right in!

Article / Updated 05-26-2023

The first concept that’s important to understand is that artificial intelligence (AI) doesn’t really have anything to do with human intelligence. Yes, some AI is modeled to simulate human intelligence, but that’s what it is: a simulation. When asking "what is artificial intelligence?" notice an interplay between goal seeking, data processing used to achieve that goal, and data acquisition used to better understand the goal. AI technology relies on algorithms to achieve a result that may or may not have anything to do with human goals or methods of achieving those goals. With this in mind, you can categorize AI in four ways: Acting like a human: When a computer acts like a human, it best reflects the Turing test, in which the computer succeeds when differentiation between the computer and a human isn’t possible. This category also reflects what the media would have you believe AI is all about. You see it employed for technologies such as natural language processing, knowledge representation, automated reasoning, and machine learning (all four of which must be present to pass the test). The original Turing Test didn’t include any physical contact. The newer, Total Turing Test does include physical contact in the form of perceptual ability interrogation, which means that the computer must also employ both computer vision and robotics to succeed. Modern techniques include the idea of achieving the goal rather than mimicking humans completely. For example, the Wright Brothers didn’t succeed in creating an airplane by precisely copying the flight of birds; rather, the birds provided ideas that led to aerodynamics that eventually led to human flight. The goal is to fly. Both birds and humans achieve this goal, but they use different approaches. Thinking like a human: When a computer thinks as a human, it performs tasks that require intelligence (as contrasted with rote procedures) from a human to succeed, such as driving a car. To determine whether a program thinks like a human, you must have some method of determining how humans think, which the cognitive modeling approach defines. This model relies on three techniques: Introspection: Detecting and documenting the techniques used to achieve goals by monitoring one’s own thought processes. Psychological testing: Observing a person’s behavior and adding it to a database of similar behaviors from other persons given a similar set of circumstances, goals, resources, and environmental conditions (among other things). Brain imaging: Monitoring brain activity directly through various mechanical means, such as Computerized Axial Tomography (CAT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Magnetoencephalography (MEG). After creating a model, you can write a program that simulates the model. Given the amount of variability among human thought processes and the difficulty of accurately representing these thought processes as part of a program, the results are experimental at best. This category of thinking humanly is often used in psychology and other fields in which modeling the human thought process to create realistic simulations is essential. Thinking rationally: Studying how humans think using some standard enables the creation of guidelines that describe typical human behaviors. A person is considered rational when following these behaviors within certain levels of deviation. A computer that thinks rationally relies on the recorded behaviors to create a guide as to how to interact with an environment based on the data at hand. The goal of this approach is to solve problems logically, when possible. In many cases, this approach would enable the creation of a baseline technique for solving a problem, which would then be modified to actually solve the problem. In other words, the solving of a problem in principle is often different from solving it in practice, but you still need a starting point. Acting rationally: Studying how humans act in given situations under specific constraints enables you to determine which techniques are both efficient and effective. A computer that acts rationally relies on the recorded actions to interact with an environment based on conditions, environmental factors, and existing data. As with rational thought, rational acts depend on a solution in principle, which may not prove useful in practice. However, rational acts do provide a baseline upon which a computer can begin negotiating the successful completion of a goal. Hintze's AI classifications The categories used to define AI offer a way to consider various uses for or ways to apply AI. Some of the systems used to classify AI by type are arbitrary and not distinct. For example, some groups view AI as either strong (generalized intelligence that can adapt to a variety of situations) or weak (specific intelligence designed to perform a particular task well). The problem with strong AI is that it doesn’t perform any task well, while weak AI is too specific to perform tasks independently. Even so, just two type classifications won’t do the job even in a general sense. The four classification types promoted by Arend Hintze form a better basis for understanding AI: Reactive machines: The machines you see beating humans at chess or playing on game shows are examples of reactive machines. A reactive machine has no memory or experience upon which to base a decision. Instead, it relies on pure computational power and smart algorithms to recreate every decision every time. This is an example of a weak AI used for a specific purpose. Limited memory: A self-driving car or autonomous robot can’t afford the time to make every decision from scratch. These machines rely on a small amount of memory to provide experiential knowledge of various situations. When the machine sees the same situation, it can rely on experience to reduce reaction time and to provide more resources for making new decisions that haven’t yet been made. This is an example of the current level of strong AI. Theory of mind: A machine that can assess both its required goals and the potential goals of other entities in the same environment has a kind of understanding that is feasible to some extent today, but not in any commercial form. However, for self-driving cars to become truly autonomous, this level of AI must be fully developed. A self-driving car would not only need to know that it must go from one point to another, but also intuit the potentially conflicting goals of drivers around it and react accordingly. Self-awareness: This is the sort of AI that you see in movies. However, it requires technologies that aren’t even remotely possible now because such a machine would have a sense of both self and consciousness. In addition, instead of merely intuiting the goals of others based on environment and other entity reactions, this type of machine would be able to infer the intent of others based on experiential knowledge. Problems defining AI Artificial Intelligence has had several false starts and stops over the years, partly because people don’t really understand what AI is all about, or even what it should accomplish. A major part of the problem is that movies, television shows, and books have all conspired to give false hopes about hat AI could accomplish. In addition, the human tendency to anthropomorphize (give human characteristics to) technology makes it seem as if AI must do more than it can hope to accomplish. Of course, the basis for what you expect from AI is a combination of how you define AI, the technology you have for implementing AI, and the goals you have for AI. Consequently, everyone sees AI differently. Before you can use a term in any meaningful and useful way, you must have a definition for it. After all, if nobody agrees on a meaning, the term has none; it’s just a collection of characters. Defining the idiom (a term whose meaning isn’t clear from the meanings of its constituent elements) is especially important with technical terms that have received more than a little press coverage at various times and in various ways. The term artificial intelligence doesn’t really tell you anything meaningful, which is why there are so many discussions and disagreements about it. Yes, you can argue that what occurs is artificial, not having come from a natural source. However, the intelligence part is, at best, ambiguous. Discerning intelligence People define intelligence in many different ways. However, you can say that intelligence involves certain mental activities composed of the following: Learning: Having the ability to obtain and process new information Reasoning: Being able to manipulate information in various ways Understanding: Considering the result of information manipulation Grasping truths: Determining the validity of the manipulated information Seeing relationships: Divining how validated data interacts with other data Considering meanings: Applying truths to particular situations in a manner consistent with their relationship Separating fact from belief: Determining whether the data is adequately supported by provable sources that can be demonstrated to be consistently valid How does AI work? The list above could easily get quite long, but even this list is relatively prone to interpretation by anyone who accepts it as viable. As you can see from the list, however, intelligence often follows a process that a computer system can mimic as part of a simulation: Set a goal based on needs or wants. Assess the value of any currently known information in support of the goal. Gather additional information that could support the goal. The emphasis here is on information that could support the goal, rather than information that you know will support the goal. Manipulate the data such that it achieves a form consistent with existing information. Define the relationships and truth values between existing and new information. Determine whether the goal is achieved. Modify the goal in light of the new data and its effect on the probability of success. Repeat Steps 2 through 7 as needed until the goal is achieved (found true) or the possibilities for achieving it are exhausted (found false). Even though you can create algorithms and provide access to data in support of this process within a computer, a computer’s capability to achieve intelligence is severely limited. For example, a computer is incapable of understanding anything because it relies on machine processes to manipulate data using pure math in a strictly mechanical fashion. Likewise, computers can’t easily separate truth from mistruth. In fact, no computer can fully implement any of the mental activities described in the list that describes intelligence. As part of deciding what intelligence actually involves, categorizing intelligence is also helpful. Humans don’t use just one type of intelligence, but rather rely on multiple intelligences to perform tasks. Howard Gardner of Harvard has defined a number of these types of intelligence, and knowing them helps you to relate them to the kinds of tasks that a computer can simulate as intelligence (see the table below for a modified version of these intelligences with additional description). The Kinds of Human Intelligence and How AIs Simulate Them Type Simulation Potential Human Tools Description Visual-spatial Moderate Models, graphics, charts, photographs, drawings, 3-D modeling, video, television, and multimedia Physical-environment intelligence used by people like sailors and architects (among many others). To move at all, humans need to understand their physical environment — that is, its dimensions and characteristics. Every robot or portable computer intelligence requires this capability, but the capability is often difficult to simulate (as with self-driving cars) or less than accurate (as with vacuums that rely as much on bumping as they do on moving intelligently). Bodily-kinesthetic Moderate to High Specialized equipment and real objects Body movements, such as those used by a surgeon or a dancer, require precision and body awareness. Robots commonly use this kind of intelligence to perform repetitive tasks, often with higher precision than humans, but sometimes with less grace. It’s essential to differentiate between human augmentation, such as a surgical device that provides a surgeon with enhanced physical ability, and true independent movement. The former is simply a demonstration of mathematical ability in that it depends on the surgeon for input. Creative None Artistic output, new patterns of thought, inventions, new kinds of musical composition Creativity is the act of developing a new pattern of thought that results in unique output in the form of art, music, and writing. A truly new kind of product is the result of creativity. An AI can simulate existing patterns of thought and even combine them to create what appears to be a unique presentation but is really just a mathematically based version of an existing pattern. In order to create, an AI would need to possess self-awareness, which would require intrapersonal intelligence. Interpersonal Low to Moderate Telephone, audio conferencing, video conferencing, writing, computer conferencing, email Interacting with others occurs at several levels. The goal of this form of intelligence is to obtain, exchange, give, and manipulate information based on the experiences of others. Computers can answer basic questions because of keyword input, not because they understand the question. The intelligence occurs while obtaining information, locating suitable keywords, and then giving information based on those keywords. Cross-referencing terms in a lookup table and then acting on the instructions provided by the table demonstrates logical intelligence, not interpersonal intelligence. Intrapersonal None Books, creative materials, diaries, privacy, and time Looking inward to understand one’s own interests and then setting goals based on those interests is currently a human-only kind of intelligence. As machines, computers have no desires, interests, wants, or creative abilities. An AI processes numeric input using a set of algorithms and provides an output; it isn’t aware of anything that it does, nor does it understand anything that it does. Linguistic (often divided into oral, aural, and written) Low for oral and aural None for written Games, multimedia, books, voice recorders, and spoken words Working with words is an essential tool for communication because spoken and written information exchange is far faster than any other form. This form of intelligence includes understanding oral, aural, and written input, managing the input to develop an answer, and providing an understandable answer as output. In many cases, computers can barely parse input into keywords, can’t actually understand the request at all, and output responses that may not be understandable at all. In humans, oral, aural, and written linguistic intelligence come from different areas of the brain, which means that even with humans, someone who has high written linguistic intelligence may not have similarly high oral linguistic intelligence. Computers don’t currently separate aural and oral linguistic ability — one is simply input and the other output. A computer can’t simulate written linguistic capability because this ability requires creativity. Logical-mathematical High (potentially higher than humans) Logic games, investigations, mysteries, and brain teasers Calculating a result, performing comparisons, exploring patterns, and considering relationships are all areas in which computers currently excel. When you see a computer beat a human on a game show, this is the only form of intelligence that you’re actually seeing, out of seven kinds of intelligence. Yes, you might see small bits of other kinds of intelligence, but this is the focus. Basing an assessment of human-versus-computer intelligence on just one area isn’t a good idea. The reality vs. hype There is a lot of hype about AI out there. If you watch movies such as Her and Ex Machina, you might be led to believe that AI is further along than it is. The problem is that AI is actually in its infancy, and any sort of application like those shown in the movies is the creative output of an overactive imagination. However, the importance of artificial intelligence to the future of technology cannot be overstated. It is already helping people in everyday technologies, and has great potential in everything from customer service to health care, to outer space exploration. The five tribes and the master algorithm You may have heard of something called the singularity, which is responsible for the potential claims presented in the media and movies. The singularity is essentially a master algorithm that encompasses all five tribes of learning used within machine learning. To achieve what these sources are telling you, the machine must be able to learn as a human would — as specified by the seven kinds of intelligence discussed earlier. Here are the five tribes of learning: Symbologists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems. Connectionists: This tribe’s origin is in neuroscience, and the group relies on backpropagation to solve problems. Evolutionaries: The evolutionaries tribe originates in evolutionary biology, relying on genetic programming to solve problems. Bayesians: This tribe’s origin is in statistics and relies on probabilistic inference to solve problems. Analogizers: The origin of this tribe is in psychology. The group relies on kernel machines to solve problems. The ultimate goal of machine learning is to combine the technologies and strategies embraced by the five tribes to create a single algorithm (the master algorithm) that can learn anything. Of course, achieving that goal is a long way off. Even so, scientists such as Pedro Domingos at the University of Washington are currently working toward that goal. To make things even less clear, the five tribes may not be able to provide enough information to actually solve the problem of human intelligence, so creating master algorithms for all five tribes may still not yield the singularity. At this point, you should be amazed at just how much people don’t know about how they think or why they think in a certain manner. Any rumors you hear about AI taking over the world or becoming superior to people are just plain false. Considering sources of hype There are many sources of AI hype. Quite a bit of the hype comes from the media and is presented by people who have no idea of what AI is all about, except perhaps from a sci-fi novel they read once. So, it’s not just movies or television that cause problems with AI hype; it’s all sorts of other media sources as well. You can often find news reports presenting AI as being able to do something that it can’t possibly do because the reporter doesn’t understand the technology. Oddly enough, many news services now use AI to at least start articles for reporters. Some products should be tested a lot more before being placed on the market. The “2020 in Review: 10 AI Failures” article at SyncedReview.com discusses ten products hyped by their developer but which fell flat on their faces. Some of these failures are huge and reflect badly on the ability of AI to perform tasks as a whole. However, something to consider with a few of these failures is that people may have interfered with the device using the AI. Obviously, testing procedures need to start considering the possibility of people purposely tampering with the AI as a potential source of errors. Until that happens, the AI will fail to perform as expected because people will continue to fiddle with the software in an attempt to cause it to fail in a humorous manner. Another cause of problems comes from asking the wrong person about AI. Not every scientist, no matter how smart, knows enough about AI to provide a competent opinion about the technology and the direction it will take in the future. Asking a biologist about the future of AI in general is akin to asking your dentist to perform brain surgery — it simply isn’t a good idea. Yet, many stories appear with people like these as the information source. To discover the future direction of AI, it’s best to ask a computer scientist or data scientist with a strong background in AI research. Understanding user overestimation Because of hype (and sometimes laziness or fatigue), users continually overestimate the ability of AI to perform tasks. For example, a Tesla owner was recently found sleeping in his car while the car zoomed along the highway at 90 mph. However, even with the user significantly overestimating the ability of the technology to drive a car, it does apparently work well enough (at least, for this driver) to avoid a complete failure. However, you need not be speeding down a highway at 90 mph to encounter user overestimation. Robot vacuums can also fail to meet expectations, usually because users believe they can just plug in the device and then never think about vacuuming again. After all, movies portray the devices working precisely in this manner. The article “How to Solve the Most Annoying Robot Vacuum Cleaner Problems” at RobotsInMyHome.com discusses troubleshooting techniques for various robotic vacuums for a good reason — the robots still need human intervention. The point is that most robots need human intervention at some point because they simply lack the knowledge to go it alone. What is AI technology? Artificial intelligence is a sub-discipline of computer science that works by combining large amounts of data with fast, iterative algorithms with the goal of enabling computers to solve complex problems and complete complex tasks. To see AI at work, you need to have some sort of computing system, an application that contains the required software, and a knowledge base. For artificial intelligence, the computers could be anything with a chip inside; in fact, a smartphone does just as well as a desktop computer for some applications. Of course, if you’re Amazon and you want to provide advice on a particular person’s next buying decision, the smartphone won’t do — you need a really big computing system for that application. The size of the computing system is directly proportional to the amount of work you expect the AI to perform. The application can also vary in size, complexity, and even location. For example, if you’re a business and want to analyze client data to determine how best to make a sales pitch, you might rely on a server-based application to perform the task. On the other hand, if you’re a customer and want to find products on Amazon to go with your current purchase items, the application doesn’t even reside on your computer; you access it through a web-based application located on Amazon’s servers. The knowledge base varies in location and size as well. The more complex the data, the more you can obtain from it, but the more you need to manipulate it as well. You get no free lunch when it comes to knowledge management. The interplay between location and time is also important. A network connection affords you access to a large knowledge base online but costs you in time because of the latency of network connections. However, localized databases, while fast, tend to lack details in many cases.

Article / Updated 05-25-2023

You can hardly avoid hearing about artificial intelligence (AI) today. You see AI in the movies, in the news, in books, and online. It's been in the news a lot lately, with all of the frenzy surrounding ChatGPT (see more about that below). AI is part of robots, self-driving (SD) cars, drones, medical systems, online shopping sites, and all sorts of other technologies that affect your daily life in so many ways. Some people have come to trust AIs so much, that they fall asleep while their self-driving cars take them to their destination — illegally, of course. Many pundits are burying you in information (and disinformation) about AI, too. Some see AI as cute and fuzzy; others see it as a potential mass murderer of the human race. The problem with being so loaded down with information in so many ways is that you struggle to separate what’s real from what is simply the product of an overactive imagination. Just how far can you trust your AI, anyway? Much of the hype about AI originates from the excessive and unrealistic expectations of scientists, entrepreneurs, and businesspersons. This article helps you understand some of the history of artificial intelligence and evolution of AI. The ChatGPT controversy The latest media storm around AI came in early January 2023, when OpenAI's free preview of its ChatGPT chatbot (released in November 2022) reached 100 million users. OpenAI then released a subscription service called ChatGPT Plus, and an upgraded version of its product, ChatGPT-4, in March 2023. A chatbot is a computer program designed to simulate human conversation. ChatGPT (GPT stands for generative pretrained transformer) is a particularly powerful chatbot able to produce natural, human-like writing through its use of 570GB of data from the Internet. Representing one of the latest achievements in the development of artificial intelligence, ChatGPT can answer questions and write articles, poems, emails, and research papers; it can also write programming code, translate languages, and perform other tasks related to language. ChatGPT's possible real-world uses include: Customer service Ecommerce Research Education and training Computer code writing and debugging Scheduling and booking Entertainment Health care information and assistance However, while many people are excited about the possibilities for ChatGPT and other similar technologies being developed, there are plenty of concerns about how it can be used in bad ways, too — for example, to cheat in school by having it write essays and research papers. It’s difficult to discern whether a piece of writing has been generated by ChatGPT or a human. In addition, the technology is far from perfect; the text it produces is often inaccurate and biased, and therefore, can spread false and even harmful information. AI can, and is, serving us well in many ways, but it’s important to understand its limitations. AI will never be able to engage in certain essential activities and tasks, and won’t be able to do other ones until far into the future. For example, while it can produce a piece of music with the data you’ve entered and in the style of a particular musician, say Beethoven, it cannot actually create anything. AI doesn’t have an imagination or original ideas. The history of AI, starting with Dartmouth Looking at artificial intelligence history begins with the earliest computers, which were just that: computing devices. They mimicked the human ability to manipulate symbols in order to perform basic math tasks, such as addition. Logical reasoning later added the capability to perform mathematical reasoning through comparisons (such as determining whether one value is greater than another value). However, for artificial intelligence evolution, humans still needed to define the algorithm used to perform the computation, provide the required data in the right format, and then interpret the result. During the summer of 1956, various scientists attended a workshop held on the Dartmouth College campus in Hanover, New Hampshire, to do something more. They predicted that machines that could reason as effectively as humans would require, at most, a generation to come about. They were wrong. Only now have we realized machines that can perform mathematical and logical reasoning as effectively as a human (which means that computers must master at least six more intelligences before reaching anything even close to human intelligence). The stated problem with the Dartmouth College and other endeavors of the time relates to hardware — the processing capability to perform calculations quickly enough to create a simulation. However, that’s not really the whole problem. Yes, hardware does figure in to the picture, but you can’t simulate processes that you don’t understand. Even so, the reason that AI is somewhat effective today is that the hardware has finally become powerful enough to support the required number of calculations. The biggest problem with these early attempts (and still a considerable problem today) is that we don’t understand how humans reason well enough to create a simulation of any sort — assuming that a direction simulation is even possible. Consider the issues surrounding the accomplishment of manned flight by the Wright brothers. They succeeded not by simulating birds, but rather by understanding the processes that birds use, thereby creating the field of aerodynamics. Consequently, when someone says that the next big AI innovation is right around the corner and yet no concrete dissertation exists of the processes involved, the innovation is anything but right around the corner. Continuing with expert systems Expert systems first appeared in the 1970s and again in the 1980s as an attempt to reduce the computational requirements posed by AI using the knowledge of experts. A number of expert system representations appeared, including: Rule based: These use "if … then" statements to base decisions on rules of thumb. Frame based: These use databases organized into related hierarchies of generic information called frames. Logic based: These rely on set theory to establish relationships). The advent of expert systems is important in artificial intelligence background because they present the first truly useful and successful implementations of AI. You still see expert systems in use today, although they aren’t called that any longer. For example, the spelling and grammar checkers in your application are kinds of expert systems. The grammar checker, especially, is strongly rule based. It pays to look around to see other places where expert systems may still see practical use in everyday applications. A problem with expert systems is that they can be hard to create and maintain. Early users had to learn specialized programming languages, such as List Processing (LisP) or Prolog. Some vendors saw an opportunity to put expert systems in the hands of less experienced or novice programmers. However, the products they used generally provided extremely limited functionality in using small knowledge bases. In the 1990s, the phrase expert system began to disappear. The idea that expert systems were a failure did appear, but the reality is that expert systems were simply so successful that they became ingrained in the applications that they were designed to support. Using the example of a word processor, at one time you needed to buy a separate grammar checking application, such as RightWriter. However, word processors now have grammar checkers built in because they proved so useful (if not always accurate). Overcoming the AI winters The term AI winter refers to a period of reduced funding in the development of AI. In general, AI has followed a path on which proponents overstate what is possible, inducing people with no technology knowledge at all, but lots of money, to make investments. A period of criticism then follows when AI fails to meet expectations, and finally, the reduction in funding occurs. A number of these cycles have occurred over the years — all of them devastating to true progress. AI is currently in a new hype phase because of machine learning, a technology that helps computers learn from data. Having a computer learn from data means not depending on a human programmer to set operations (tasks), but rather deriving them directly from examples that show how the computer should behave. ' Machine learning is like educating a baby by showing it how to behave through example. This technology has pitfalls because the computer can learn how to do things incorrectly through careless teaching. At this time, the most successful solution is deep learning, which is a technology that strives to imitate the human brain. Deep learning is possible because of the availability of powerful computers, smarter algorithms, large datasets produced by the digitalization of our society, and huge investments from businesses such as Google, Facebook, Amazon, and others that take advantage of this AI renaissance for their own businesses. People are saying that the AI winter is over because of deep learning, and that’s true for now. However, when you look around at the ways in which people are viewing AI, you can easily figure out that another criticism phase will eventually occur unless proponents tone the rhetoric down. A brief artificial intelligence timeline 1942: First electronic digital computer built by John Vincent Atanasoff and Clifford Berry at Iowa State University 1950: Alan Turing paper “Computing Machinery and Intelligence;” his proposal later became “The Turing Test,” which measured machine AI 1958: Perceptron computer, built by Cornell University Professor Frank Rosenblatt, regarded as first artificial neural network 1966: First “chatterbox” (later shortened to chatbot) — created by Joseph Weizenbaum, a German-American computer scientist — uses natural language processing to converse with humans 1971: First commercial microprocessor by Intel 1988: Jabberwacky, a chatbot created by British computer scientist Rollo Carpenter, provides interesting and entertaining conversation to humans 1990s: Early days of the Internet 1992: TD-Gammon, developed by Gerald Tesauro, of IBM; an artificial neural network trained by temporal-difference learning to play high-level backgammon 1997: IBM's Deep Blue chess computer defeats Russian chess grandmaster Garry Kasparov; Windows releases a speech recognition software, developed by Dragon Systems 2012: AlexNet, a convolutional neural network architecture, primarily designed by Alex Krizhevsky, a Ukrainian-born, Canadian computer scientist 2020: OpenAI beta tests GPT-3, which uses deep learning to create code, poetry, and other language and writing tasks; it's the first such chatbot that can create content almost indistinguishable from human-created content 2023: In January, OpenAI releases a free preview of its ChatGPT-3 to the public, and in March releases the upgrade ChatGPT-4 AI in our everyday lives You’re using AI in some way today; in fact, you probably rely on AI in many different ways — you just don’t notice it because it’s so mundane. A smart thermostat for your home may not sound very exciting, but it’s an incredibly practical use for a technology that has some people running for the hills in terror. As the development of AI has continued, there are now really cool uses for AI. For example, you may not know there is a medical monitoring device that can actually predict when you might have a heart problem, but such a device exists. AI powers drones, drives cars, and makes all sorts of robots possible. You see AI used today in all sorts of space applications, and the evolution of artificial intelligence figures prominently in all the space adventures humans will have tomorrow. The potential uses for AI number in the millions — all safely out of sight even when they’re quite dramatic in nature. Here are some of the ways in which you might see AI used: Fraud detection: You get a call from your credit card company asking whether you made a particular purchase. The credit card company isn’t being nosy; it’s simply alerting you to the fact that someone else could be making a purchase using your card. The AI embedded within the credit card company’s code detected an unfamiliar spending pattern and alerted someone to it. Resource scheduling: Many organizations need to schedule the use of resources efficiently. For example, a hospital may have to determine where to put a patient based on the patient’s needs, availability of skilled experts, and the amount of time the doctor expects the patient to be in the hospital. Complex analysis: Humans often need help with complex analysis because there are literally too many factors to consider. For example, the same set of symptoms could indicate more than one problem. A doctor or other expert might need help making a diagnosis in a timely manner to save a patient’s life. Automation: Any form of automation can benefit from the addition of AI to handle unexpected changes or events. A problem with some types of automation today is that an unexpected event, such as an object in the wrong place, can actually cause the automation to stop. Adding AI to the automation can allow the automation to handle unexpected events and continue as if nothing happened. Customer service: The customer service line you call today may not even have a human behind it. The automation is good enough to follow scripts and use various resources to handle the vast majority of your questions. With good voice inflection (provided by AI as well), you may not even be able to tell that you’re talking with a computer. Safety systems: Many of the safety systems found in machines of various sorts today rely on AI to take over the vehicle in a time of crisis. For example, many automatic braking systems (ABS) rely on AI to stop the car based on all the inputs that a vehicle can provide, such as the direction of a skid. Computerized ABS is actually relatively old at 40 years from a technology perspective. Machine efficiency: AI can help control a machine in such a manner as to obtain maximum efficiency. The AI controls the use of resources so that the system doesn’t overshoot speed or other goals. Every ounce of power is used precisely as needed to provide the desired services.

Article / Updated 05-09-2023

Artificial intelligence (AI) is great at automation, which can make it ideal for tasks in health care. It never deviates from the procedure, never gets tired, and never makes mistakes as long as the initial procedure is correct. Unlike humans, AI never needs a vacation or a break or even an eight-hour day (not that many in the medical profession have that, either). Consequently, the same AI that interacts with a patient for breakfast will do so for lunch and dinner as well. So, at the outset, AI has some significant advantages if viewed solely on the bases of consistency, accuracy, and longevity. Working with medical records The major way in which an AI helps in medicine is medical records. In the past, everyone used paper records to store patient data. Each patient might also have a blackboard that medical personnel use to record information daily during a hospital stay. Various charts contain patient data, and the doctor might also have notes. Having all these sources of information in so many different places made it hard to keep track of the patient in any significant way. Using an AI, along with a computer database, helps make information accessible, consistent, and reliable. Products such as Google Deepmind Health enable personnel to mine the patient information to see patterns in data that aren’t obvious. Doctors don’t necessarily interact with records in the same way that everyone else does. The use of products such as IBM’s WatsonPaths helps doctors interact with patient data of all sorts in new ways to make better diagnostic decisions about patient health. You can see a video on how this product works. Medicine is about a team approach, with many people of varying specialties working together. However, anyone who watches the process for a while soon realizes that these people don’t communicate among themselves sufficiently because they’re all quite busy treating patients. Products such as CloudMedX take all the input from the all parties involved and performs risk analysis on it. The result is that the software can help locate potentially problematic areas that could reduce the likelihood of a good patient outcome. In other words, this product does some of the talking that the various stakeholders would likely do if they weren’t submerged in patient care. Predicting the future Some truly amazing predictive software based on medical records includes CareSkore, which actually uses algorithms to determine the likelihood of a patient’s requiring readmission into the hospital after a stay. By performing this task, hospital staff can review reasons for potential readmission and address them before the patient leaves the hospital, making readmission less likely. Along with this strategy, Zephyr Health helps doctors evaluate various therapies and choose those most likely to result in a positive outcome — again reducing the risk that a patient will require readmission to the hospital. This video tells you more about Zephyr Health. In some respects, your genetics form a map of what will happen to you in the future. Consequently, knowing about your genetics can increase your understanding of your strengths and weaknesses, helping you to live a better life. Deep Genomics is discovering how mutations in your genetics affect you as a person. Mutations need not always produce a negative result; some mutations actually make people better, so knowing about mutations can be a positive experience, too. Check out this video for more details. Making procedures safer Doctors need lots of data to make good decisions. However, with data being spread out all over the place, doctors who lack the ability to analyze that disparate data quickly often make imperfect decisions. To make procedures safer, a doctor needs not only access to the data but also some means of organizing and analyzing it in a manner reflecting the doctor’s specialty. One such product is Oncora Medical, which collects and organizes medical records for radiation oncologists. As a result, is these doctors can deliver the right amount of radiation to just the right locations to obtain a better result with a lower potential for unanticipated side effects. Doctors also have trouble obtaining necessary information because the machines they use tend to be expensive and huge. An innovator named Jonathan Rothberg has decided to change all that by using the Butterfly Network. Imagine an iPhone-sized device that can perform both an MRI and an ultrasound. The picture on the website is nothing short of amazing. Creating better medications Everyone complains about the price of medications today. Yes, medications can do amazing things for people, but they cost so much that some people end up mortgaging homes to obtain them. Part of the problem is that testing takes a lot of time. Performing a tissue analysis to observe the effects of a new drug can take up to a year. Fortunately, products such as 3Scan can greatly reduce the time required to obtain the same tissue analysis to as little as one day. Of course, better still would be the drug company having a better idea of which drugs are likely to work and which aren’t before investing any money in research. Atomwise uses a huge database of molecular structures to perform analyses on which molecules will answer a particular need. In 2015, researchers used Atomwise to create medications that would make Ebola less likely to infect others. The analysis that would have taken human researchers months or possibly years to perform took Atomwise just one day to complete. Imagine this scenario in the midst of a potentially global epidemic. If Atomwise can perform the analysis required to render the virus or bacteria noncontagious in one day, the potential epidemic could be curtailed before becoming widespread. Drug companies also produce a huge number of drugs. The reason for this impressive productivity, besides profitability, is that every person is just a little different. A drug that performs well and produces no side effects on one person might not perform well at all and could even harm a different person. Turbine enables drug companies to perform drug simulations so that the drug companies can locate the drugs most likely to work with a particular person’s body. Turbine’s current emphasis is on cancer treatments, but it’s easy to see how this same approach could work in many other areas. Medications can take many forms. Some people think they come only in pill or shot form, yet your body produces a wide range of medications in the form of microbiomes. Your body actually contains ten times as many microbes as it does human cells, and many of these microbes are essential for life; you’d quickly die without them. Whole Biome is using a variety of methods to make these microbiomes work better for you so that you don’t necessarily need a pill or a shot to cure something. Check out this video for additional information. Some companies have yet to realize their potential, but they’re likely to do so eventually. One such company is Recursion Pharmaceuticals, which employs automation to explore ways to use known drugs, bioactive drugs, and pharmaceuticals that didn’t previously make the grade to solve new problems. The company has had some success in helping to solve rare genetic diseases, and it has a goal of curing 100 diseases in the next ten years (obviously, an extremely high goal to reach).