10 easy ways to get insight into the needs & pain points of users
User Research is no magic — everyone can do it.
Having studied research at university I didn’t fully grasp the importance of it. I took all that knowledge for granted, and also didn’t quite know where I’d apply it later on in my job. Working as a UX designer today, I learned how important the role of research is in our daily practice, and want to share my knowledge with fellow designers who don’t know how to start making research for UX.
Which information is missing? 🤔
First of all: To know what kind of research you need to do, you need to know which information you are missing to create a kick-ass user experience. Knowing that you can pick a research method and start collecting data to answer all your questions and get the info you need!
Do you need to know how people plan their museum visit? Or who these people are? Or maybe what they dislike about current museum pages? Make sure, you know what you want your research for.
Better done than perfect ⌛
It’s always better to reach out to potential and current users in a quick DIY way than not reaching out because you don’t consider yourself a research expert.
„A surprisingly large number of people claim to practice user-centered design, but fail to ever actually speak with or spend time with users“. User Experience Team of One
It’s always good to talk to people you design things for — no matter how experienced you are as a UX designer. It’s important to remember that other people probably think differently from you, know different things/don’t know the same things as you, and probably use the internet differently.
Open-ended (qualitative): questions that require an individual answer, which is longer than a word. “Why” and “How” questions are great for that. You can ask those open-ended questions in person or in an online survey. Qualitative research is necessary to get to know the scope of the problem, hear different approaches and opinions.
Quantitative: Participants pick one (or several) answers from a range of options. Here you want to find out, which of the 100 things that were mentioned during qualitative research are important for most of your users. The questions should ask “who”, “what”, “when” — find more on how to write great quantitative questions here.
10 DIY research options 🖐️🖐️
In this article, I want to give you a quick overview of easy ways to get some insight into what the people you design for need, want and experience — and how they use your products or products alike.
1. Watch people use the product/page — Observation
If you want to redesign an existing page, a great way to understand its pain points is to watch some people navigating through it. We are not talking about hours and hours of observations in a perfect lab with people who are a 100% fit to the customer profile.
A quick test with a random person during a workshop for only 15 minutes will give astonishing insights — and show your stakeholders, how much needs to get done, and how important research is for a project. This kind of DIY research can be done with your existing product/page/app or a competitor’s page.
Similar to a usability test (point 10), but a little smaller.
2. Get many opinions that you can compare — Online Survey
A quantitative online survey is a great option if you have easy access to potential or current customers/users/visitors online. Only around 1–3% of people who’ll see the survey are going to participate. To get reliable results from the survey, you want to have at least 200 participants.
3. Get some opinions that you can compare — In-person surveys
Often you won’t have access to a big group of people, but still want to get insights into their way of thinking, their needs and current ways to do things. A survey in person can consist of the same questions, like an online questionnaire, so “What”, “When”, “Where”-Questions. The difference is, that you can ask those questions in person — face-to-face, via phone, email or chat.
4. Analyze existing data — Online reviews & groups
Sometimes you can find online reviews about your product or some similar one online, on review pages or Facebook groups. Here you can see what people complain about, what they love about the product/service and what they wish was different.
5. Talk to people who know your customers — Expert interview
Talk to support service, people who are directly in touch with the users/customers and ask them about their experience. What is often misunderstood? What are common questions and pain points? Which are the most common reasons to reach out to the support service? Which information seems unclear?
6. Talk to potential/current customers — Personal interview
Your personal interview of a user/customer can be extremely open and just directed towards understanding their way of thinking and acting — more on that in the book Practical Empathy.
Or you can already have concrete questions you want to ask, for example how they pick a product/service or what are their pain points and wishes for a specific part of their life, etc.
The most important tip for personal interviews is: Listen, give time to think (60sek+), don’t suggest answers or finish their sentences.
7. Qualitative online survey
This method is a little time-consuming but is a great way to find out many individual approaches to something, if you don’t have time, resources or access to meeting the people in person.
In this case, you write a questionnaire consisting of open-ended questions and send it to relevant people. With only 5–6 open questions you will get a great variety of responses, and need to combine similar answers together to know a whole range or different approaches to your question.
8. Use existing data
Understand who is the target audience using Google Analytics Research, Market Research Results, benchmark analysis or other surveys your company/your client’s company has done. You’ll be able to find out the demographic data as well as some behavioral patterns and preferences in that way already.
9. Go where your users are
Talk to people in public places — depending on the product/service you might find your target audience in a coffee shop, grocery store or waiting in line into the opera house. Or ask your friends, colleagues, and acquaintances if they know somebody you are looking for. Get to know what’s driving them, the way they think. Or just tell them you are working on a project in that area, and hear what topic they come up with. Do they give you tips, complain or try to help? That’s already a great insight itself.
10. Usability tests
This type of research is the most commonly mentioned when talking about research for UX designers.
With the help of usability research, you can test if your design solves the user issues by watching people interact with your prototype.
„Usability tests are about watching one person at a time try to use something (whether it’s a Web site, a prototype, or some sketches of a new design) to do typical tasks so you can detect and fix the things that confuse or frustrate them.“ (Don’t Make Me Think, p. 110)
Some overall tips 🌟
Ask about past/current events: Don’t ask generic questions like „What do you eat for dinner usually?“ It’s easier for participants to recall (and for you to get useful data) if you ask specifically ”What did you have for dinner for the past seven days?“
Compensation for interviewees and their time helping you is important!
Explain what’s going to happen before an interview, observation or chit-chat, what you are interested and why
Make it clear to your clients, that investing some thousands for research is essential to create a fantastic user experience. Their ideas for the website/app/service won’t be able to replace talking to their audience directly to make it truly user-centered
I hope this small overview helped you to get the first idea about the research you can do as a UX designer. Let me know if you have some questions and stay tuned as I will write more detailed articles on parts mentioned here.
So your customer-facing teams are inundated with inquiries that are repetitive and mundane, draining employee motivation and resources. You get an idea, “let’s create a chatbot!” You do. But people don’t use it. The experience feels a little worse than a search engine and a little better than a static FAQ page. What went wrong?
In this blog, I uncover some simple tips that can drastically improve your content-retrieving chatbot experience.
First of all, hi! I’m Shilpa and I create conversational user interfaces at SAP Conversational AI.
Over time, I have learned that as bot creators, we need to start honing our instincts when it comes to using chatbots as a solution to our problems.
So lets dive into scenarios where chatbots were preferred by users:
If the perceived amount of time it will take for them to sift through a massive chunk of information far exceeds the value they will receive from the information.
If they know that there are low-stakes tasks they can do quickly. In this instance, they would use a chatbot for something like activating a credit card, but might not want to use one when they want to reverse a large fee on their account (because they know that they will be unable to plead their case without a human being).
The takeaway here is that the more time you spend over the use-cases where your client might need a bot, the better the chances of your clients adopting your chatbot offering.
This leads to my first tip:
1. Ask yourself “Why, Why, Why A Chatbot?“
Knowing when to use a chatbot is essential to the success of your undertaking. A chatbot is a great solution, but it isn’t always the best solution.
We know that data retrieval and dispersal is a space where chatbots can really improve the experience of the user, allowing them to directly find the information they are looking for, in a dialogue-driven format. If your data can be configured to meet your clients where they are at, even better!
Here is a case where a chatbot is a great solution:
You have a FAQ document that explains your shipping policies across the world. It is dense and presents exhaustive information which may not be pertinent to all your users. If you create a chatbot that allows your users to define their location in order to display only relevant shipping information — Success! If you create a chatbot that is able to provide accurate shipping information pertaining to your users by using anonymized locational data — Priceless! This is contingent on protecting your users’ identities and respecting their privacy settings, of course.
Here is are cases where chatbots are not the best solution:
A chatbot that retrieves incredibly dense information
A chatbot that displays too many detailed illustrations or gives too many external references
A chatbot that retrieves information which loses either value or comprehensibility, when truncated or summarized
The above would all be better cases for an interactive webpage.
Creating a chatbot is easy with our platform. But, making your chatbot truly invaluable to your clients’ experience takes time. A lot of time. Often, it means picking apart use-cases and finding exactly which kind of dialogue would lead to user satisfaction and retention. The payoff is incredible but it is hard work.
So, ask yourself: “Why am I creating a chatbot? What unique experiences can my chatbot provide over other platforms for the same purpose?”
2. Pre-work: Scoping your solution, understanding the platform and defining your use-cases
Reach out to your clients
Do as much concept validation as possible before embarking on this journey.
If you have existing clients, find out if they would use a chatbot for the tasks you want automated. That is the single best way to avoid wasting resources. If you do not have clients, find related businesses or groups that tackle the same problems you do and find out if they are using a chatbot. If not, inquire about why a chatbot would complete the experiences they are lacking.
Define your user’s journey
I can’t stress this enough. You need to create user journeys for every offeringyou have on your chatbot platform. Evidence suggests that having clarity about every use-case is one of the simplest practices a chatbot creator can undertake to avoid deeper problems later.
For example, let’s say you are offering a card activation solution via chatbot, you should:
Define what the quickest path to card activation looks like for your users.
Ask what your users would need to achieve this task.
Do they need their physical card handy?
Sketch out all the functionalities of the skill.
Would this skill lead to another skill?
Could this skill be connected to another skill?
Where does this skill fit amongst the myriad of skills you are presenting your users with?
Determine the dialogue your users will go through.
What are possible ways that your users will trigger this skill?
Are there shortcuts available and visible?
Do you need graphics? External links? Forwarding/emailing services?
Figure out how you will introduce this functionality.
How will your users know that they can do this via chatbot?
The above is just ONE use case!
Remember the parameters of your chat bubble
It will surprise you how many people do not realize the restrictions and freedoms that come with a specific platform.
With chat windows, you have small real-estate, so the content you are sharing needs to mostly be text-based.
Furthermore, the number of external links provided should be limited. This reduces jarring users as they jump from one portal to the next. In general, keeping people within your chat window for all their needs is a good practice.
Secondly, you are in a dialogue-driven medium, so if you want to just display chunks of data without taking the time to convert them into dialogue, be prepared for reduced engagement. Remember — they have Google for that!
3. Creation: Remembering the beginning, middle, and end of a successful dialogue
The Dialogue Journey
Everything has a beginning, a middle, and an end.
Ensure that you are providing your user with a great first message that tells them:
Who you are.
Why you are here.
How your chat solution can uniquely help them.
What they can do with this chat.
Where they can find other information not available in this chat.
Continuity in Dialogue
When setting up your dialogue-flows, remind yourself that every question comes from something and should lead to something.
Continuity with content will differentiate your chatbot from what’s already out there.
Although your users are using chat to get their information quickly, they also want to have an interaction. The best kind of interactions flows with ease. So remember to prompt your users for possible questions that may naturally flow out of their initial query. This creates context through subject-related prompting or recommendations.
Graceful Exit
When the user achieves his objective, remember to exit the conversation gracefully. The way you exit your conversation should be strategic and genuine.
It is your last moment to make a great impression. Here are a few ways you can exit gracefully:
Ask only ONE feedback question and make it count.
Provide them next steps such as a new offering, or more information.
Perhaps provide a link to a representative if your users’ feedback is negative.
Effective Dialogue Tips
Keep your dialogue first-person. After all, this is the whole point of one-on-one interaction.
Keep the tone conversational. Don’t be too friendly, too formal, too happy, or anything on the extreme ends of the spectrum. Think: “helpful neighbor” when creating the language.
Keep the vocabulary simple, yet impactful: You don’t want to have too many technical or complicated terms because it will inevitably cause frustration. You also don’t want the content so oversimplified that it is insulting to your user. Remember: “helpful neighbor.”
Remind users that your bot is learning, especially if your users indicate that their query wasn’t answered. This is a great “get out of jail free” card.
4. General tips: providing quality, control, creating natural dialogue and establishing context
Ensuring quality
If you are starting with a question and answer knowledge bank, do the work to ensure that each answer is succinct, complete, and leads to an appropriate next question.
Here are other points to think about:
Determining the appropriate next question is the real trick to keeping your users engaged and helping them move through your content easily.
Reminding yourself that people hate scrolling is a good mindset while creating your chatbot, so you can limit the length of your answers.
Unchecked content with errors loses user trust. Always edit your content in a text editor and ensure that your grammar is correct.
Ensure that there are no unknown symbols or accidental changes to your content when importing or exporting it.
Visibility of background processes
The more you tell your users about how you are providing them with a piece of information or a service, the more they empathize with you.
You can do this by telling them that your bot is finding the requested information from a document or that your bot is connecting to a service. When you can’t help your users immediately, explain to them, in simple words, about why this is the case and what they can do next. Creating and leveraging your clients’ or users’ empathy is critical as you collect user feedback to improve your bot. So do it early and often.
Acknowledgment and natural delay
There is always confusion over whether your query was received by a bot.
Remember to play around with states where your bot is finding or accessing information. A trick we like to use is to act like a person would. No matter how quickly information is received or processed, a slight delay is natural in conversation and is less intimidating to the end users.
So whether you use an ellipses animation suggesting that your bot is typing, or a statement like “I’m looking for your answer in my database,” let your users know you’ve received their query and are processing it.
Ask followup questions
If the user poses a question that your bot can’t identify, define how your bot should follow up.
Asking the user to rephrase the question once can help matching, but more than that is annoying.
If the bot isn’t able to match the question, your users may have helped you identify a gap in your content database or new context for a question existing in your database.
Either way, the best way to approach it is to provide possible topics that fall within the context of your users’ queries or to point them to an external source containing the information they are looking for.
5. Reiteration
The chatbot creation journey is an iterative one. Ensure that you continue to improve your bot as you start getting real users’ queries. Ensure that they are receiving what they need and keep refining the experience one use-case at a time.
We have noticed that successful chatbots promote a learning experience for themselves and those using it.
Ask for feedback that will genuinely help you gauge the effectiveness of your content or the tools you are providing.
Strategic feedback integration
Don’t ask your users for feedback after every question. It is frustrating.
Once you get a feeling that your users are comfortable with your chatbot, include strategic feedback prompts that are quick and relevant to their immediate experience. The best way to know what works is to test different feedback collecting strategies before deploying.
Keep it up
Lastly, ensure that all your links and content stays updated, and continuously hone the quality of your content and user experience.
I hope that this blog helps you successfully deploy the “knowledge bot” experiences that will take your business or passion to the next level!
Clap if you found value in this article, so I know that I should continue to write about this topic! If you want to dig deeper or want me to cover another topic, leave a comment below…
Pawel Szymankiewicz is a Senior Product designer at Netguru and co-founder of the coco apps team. Drawing on his expertise in user-centered design, Pawel shares his ultimate guide to designing user-friendly forms people will actually want to complete!
Forms are a key conversion tool—which is why it’s important to master the most effective form design techniques. Frustration should never be a part of a user’s process when filling out a form, and the tips outlined below should help create a more pleasant experience for them.
To start off, I’ll cover the three main components in every form and how to effectively use them. Next, I’ll jump into some best practices for constructing your form from start to finish. Finally, I’ll outline the three types of form layouts to help you decide which format works best for the kind of data you want to collect. Let’s jump in.
The 3 basic form components
Components are the foundation of every form and can be easily compared to a list of ingredients for a Sunday night dinner—you’ll want to choose the best ingredients and serve them well. Let’s cover the three essential components of a form and then jump into the most overlooked nuances around them:
1. Textfields
This is the primary form component that often ends up being over-designed. Save yourself some time and do not reinvent the wheel.
Pick a familiar look: Use one of the two most recognizable textfield shapes: a well-known rectangle or a simple line (made popular by Google’s Material Design). Suggest the expected length of an input by its size.
Describe what you need: Use labels to tell what input is expected. Make sure they are always visible so your form is easy to scan even when it’s filled. Place labels on top of the textfields or as a placeholder that transforms into a floating label while typing. Avoid putting labels on the left or right side of a textfield as it will create a mess once your form starts growing.
Dropdown menus gained their popularity on the web and are here to stay. After all, they do their job pretty well.
Sort dropdown lists alphabetically: Sort answers in alphabetical order to help people find the answer they are looking for quickly.
Place suggested answers on top: If you are sure one (or more) of the answers from the list will be chosen by most people (e.g. based on their current location), put this answer at the top of the dropdown and separate it from the others with a subtle divider.
3. Buttons
The real superstar of every form. Without a button, your form is useless. It’s extremely important to make sure it’s understandable and visible to everyone.
Describe what the action button does: The word “Submit” doesn’t say it all. Make sure the text within your button is as simple and clear as possible. If it’s being used to add products to a shopping cart, label it, “Add to cart”. If it functions to remove unwanted products, label it, “Remove”. Try to describe the action that happens after clicking as concretely as you can.
Use different styles to drive attention: Use colors to suggest whether your button is facilitating a positive or negative action. Different styles can also suggest which action is primary and secondary.
Form construction
Now that you’ve got the basics covered, how are you going to build your form so it’s successful? How is it going to work and interact with your users? Let’s dive into some best practices for constructing your form:
Show advantages of filling the form: If your business depends on people successfully filling out a form, make sure you tell users why it’s worth spending a few minutes of their time on. Carefully choose your words and make sure they indicate the value users will get from filling the form out. Prepare something special—for example, an e-book packed with awesome tips for your target group. In this case, your button’s text can say something like, “Sign Up and Get the FREE e-book!”
Arrange all components under one column: Naturally, we read from top to bottom. Try to place all of the elements under one column—this will make it easier to quickly scan your form without jumping from one side to the other.
Ask only for stuff you really, REALLY need: Keep forms short. Avoid asking for data that isn’t necessary at the moment—you can always gather that later. The shorter your form is, the more likely people are actually going to fill it out.
Explain why you are asking for sensitive data: If you need to ask for some sort of sensitive data that folks might hesitate to share, explain why you are asking for it. Build trust by being transparent about how and why you are going to use this data.
Label fields that are optional: If your form contains some optional textfields, mark them with an “Optional” label instead of placing an asterisk next to them. People generally don’t like asterisks in forms because they remind them of signing complicated contracts and hidden statements.
Group components and list them in logical order: Make it easier to quickly scan your form by inserting a heading on top of the grouped components. Try to follow familiar patterns that people already know from different, common situations.
Don’t use an additional textfield asking users to confirm their password: Instead of making people type their password twice, allow them to peek at it. Even if they don’t and misspell their password at first, they’ll always have a chance (or at least they should) to change it later on.
Show form progress: If your form is rather long, it’s important to show progress so people know where they are and how much of the form is left to complete. This can be done with a simple progress bar or by dividing your form into shorter steps. As long as it gives hope that the end is near, it does the job well.
Save progress: Just as important as showing progress, is actively saving it while the form is being filled. There is no worse scenario than a solid amount of time spent on a form then a lost connection resulting in all of the input data to disappear.
Use icons and images when possible: If you’re asking for something strictly connected with images, besides describing it, show the image as well.
Suggest answers: If your form is open and people are asked to type in different answers that in general are common as Skills or Tags used on Dribbble, suggest answers from your database as they are typed in.
Leave enter/return alone: This key on the keyboard has a very well known role. Don’t make it send the form immediately, especially when it’s long and consists of a couple of text areas. Let the enter be an enter and navigate the cursor to a new line.
Show what went wrong, and remember color is not everything: If there are some validation issues, show what went wrong. That doesn’t mean simply using the color red as an indicator. A nice description ideally supported with advice on how to make things right would be useful as well. You can also try applying a shake animation to poorly filled fields.
Let people know what went right: A simple message that the form was successfully sent to your server is pure gold. No more guessing whether it went through or if some problem occurred during the process.
Types of form layouts
The final stage of designing a form is its presentation. How are you going to sell it visually?
Standard (one page) form:
The most common presentation is a simple one-page form. It consists of all of the components and works great for short and less complicated forms.
Multi-step form:
Multi-step forms are mostly used for check-out processes on e-commerce platforms. People like doing things step-by-step so it’s immediately familiar. By design, it also shows the user’s current progress. This format is best for more complicated, longer forms. It makes them less scary. Just remember to group components well and make sure each step is a closed part of the process.
Conversational form:
Conversational forms are a really nice and natural way of collecting data seamlessly. Their potential still seems to be undiscovered, however, they’ve been around for a while. Asking for one thing at a time gives the impression of a faster form process. While people focus on the conversation, they overlook the actual length of the form.
While forms may not be the most exciting thing to design, we can’t deny their importance and impact. Next time you feel like running away from designing a form, just remember to stop overthinking it, use these tips as general guidelines, and last but not least, think about the people that are going to be filling it out. Keeping them happy will make your business successful!
Want to keep up with Pawel? Find him on Dribbble and Twitter.
If you want to improve your writing speed, there are several steps that you can take to improve your efficiency and get assignments done faster. First, you’ll want to do all of your research and organize your thoughts in an outline form. From there, you can set realistic goals for yourself and continue to practice until you improve. If you’re struggling with the act of writing with a pen and paper, you should make sure that you’re comfortable and have the right writing tools. With the right amount of practice and repetition, you can improve your writing speed.
Set realistic goals and deadlines for yourself. If you are newer at writing or aren’t used to writing on a deadline, there’s a good possibility you’ll be slower at writing than someone with more experience. Set goals that make sense for you and are within your ability. If you set a goal and you are stressed or feel like meeting the goal is impossible, set lighter goals for yourself.[1]
Set incrementally higher goals for yourself rather than taking on too much too fast.
If you haven’t written a lot in the past, you won’t be able to write quickly without practice.
For instance, you can set a goal that you’ll have a certain number of pages or words completed within a day. If you’re still working on building speed, daily goals can feel more manageable than more short-term ones (such as hourly goals).
Use a timer to help meet your goals. In order to improve your writing speed, you’ll need a way to measure if you’re improving. Set a timer to the goal that you set for yourself earlier and try to meet your goal within the allotted time. If you don’t have a stopwatch or timer, there are apps that were created specifically for that purpose.[2]
Don’t let the timer stress you out. It’s merely there to remind you of how much time you’re spending on an assignment.
Determine the time of day that you’re most productive. Some people write faster and more efficiently in the morning, while others excel at night. Try writing in both settings and determine which time feels best for you. Then, try to get as much writing done as you can during the times of day when you’re most productive.[3]
Even if you’re a night owl, you may still be more productive in the morning. Write during different parts of the day to see what works best for you.
Write an outline for your assignment. Read the prompt or assignment so you know what you have to write. Perform research and plot out the main points in your essay, paper, or story in an outline form. Determine what you want to include in your writing, and then write 2-3 sentences, or subpoints, within those main points. This will keep your writing precise and on topic, which will reduce the amount you need to delete or edit later.[4]
For example, the main points of your article could be something like, "Describing circuits" and "Powering Electronics." "Describing circuits" could include points like "Explaining a simple circuit" and "Completing a circuit."
Researching things in the middle of writing wastes valuable time.
Include your sources in your outline to save time, especially if you have to make citations later. If you’re using electronic sources, bookmark them on your computer. Include notes in your outline about how you’re going to use your sources and what information you hope to take from them.
Write your first draft quickly and revisit it for edits. Write concisely and accurately, but don’t obsess about spelling or grammar on the first draft. Instead, write your first draft and then come back and edit and review the writing later. This will allow you to get the bulk of the work out of the way and enable you to concentrate on grammar and spelling the second time around.[5]
Wasting time on smaller details can drain time and make the entire writing process much longer.
If you find yourself stuck in a section, move past it and revisit it later with a fresh mind.
Minimize the distractions around you. Distractions like surfing the web, TV, or open chat programs can hinder your efficiency and slow down your writing speed. Find a solitary space where you can write and won’t be distracted by the things going on around you.[6]
Clearing the clutter off your writing desk may also minimize distractions and improve your efficiency.
If you can, put away your phone, tablet, or other devices you might be tempted to use to check social media or browse the web. You can also use productivity apps or extensions (like StayFocused) that temporarily cut off your access to time-wasting websites.
Maintain good posture. Straighten your back and make sure that your feet are resting flat on the floor. Your lower back and hips should be fully supported by the chair that you’re sitting in. Both your knees and your elbows should be bent and you should feel comfortable while you sit. Maintain this posture to reduce fatigue and improve stamina as you write.[7]
Whenever you feel yourself slouching, adjust your posture so you’re sitting correctly.
If your chair is too low or your desk is too high, you may need to purchase new furniture.
Maintaining good posture is also beneficial for your back and hips.
Hold the pen or pencil in a way that’s comfortable for you. The way that you grip your pencil doesn’t have as much of an impact on your writing speed as your comfort does. When writing, make sure that your hand is in a comfortable position and doesn’t cramp or get fatigued as you write. If it does, consider switching the way that you’re holding the pencil or pen to improve your writing speed.[8]
The traditional way to hold a pen or pencil is to support the pen in between your index finger and thumb while the pen rests on your middle finger.
Angling the paper in a different way may also feel more comfortable for you.
Use a pen or pencil that doesn’t require you to press down hard. If you have to press down hard as you write, your hand will get fatigued faster. Find a pen that isn’t so thin that it’s hard to grip, but that isn’t so thick that writing is uncomfortable.[9]
A gel pen may be easier to write with than a ballpoint pen.
A mechanical pencil requires less force to write with than a traditional pencil.
You can purchase a pencil or pen grip to make your writing tool thicker.
Use shorthand if you aren’t writing for an assignment. Methods like Pitman Shorthand and Gregg Shorthand use symbols to represent words, letters, and punctuation. Most of these symbols are quicker and easier to write than letters and words in English and could vastly improve your writing speed. Research and learn how to use either of these shorthand methods online or at a library.[10]
Keep in mind that these shorthand methods aren’t known by everyone and can’t be used for assignments or on tests.
It may take several weeks or even months to master these shorthand methods.
Keep practicing. Practice every day to improve the speed and the appearance of your handwriting. The more that you do it, the quicker and neater your handwriting will become. You can try writing at home, or transcribing notes quickly in the middle of class. Use the things that work and avoid things that slow you down.[11]
If you still aren’t improving after trying everything, talk to a teacher and see if they have any techniques that may help you.
Film making is an art form. For UX designers, it’s a place to find inspiration through stories coming to life. The teamwork and efforts of multi-disciplinary teams coming together for a film offers a revealing perspective into the creative process that can be helpful in understanding design.
Looking back to your own favorite films — why are they so memorable? What is the nature of a compelling story? Here’s some lessons from cinematography to carry over to design.
Credit: Moonlight
1. Choices carry meaning
Colors, like features, follow the changes of the emotions. — Pablo Picasso
While it is a human tendency to look for patterns, things in frame (or out of the frame) play a crucial role in supporting the larger storyline of a film. Color is used to evoke certain emotional shifts or match the tone of the genre. Directors can use palettes to convey tension or suspense, or to highlight an object or person. Subtle framing choices in composition help ensure every shot contributes to the story.
In the same way, UX designers should intentionally craft information architecture that supports customer, organization and business goals, being mindful that such choices (whether intentional or not) steer users toward certain actions and behaviors. The details add up, whether it’s the choices in color, pixels or other imagery to evoke an emotional response.
Lastly, what is obvious to you as a designer may not be obvious to the user, so make sure to incorporate user research to stimulate customer experiences/reactions early and often.
Credit: Schindler’s List
2. Help create focus
Images, like words in a dictionary, have no power or value except through their position and relation. — Robert Bresson
A filmmaker uses composition techniques like adjusting the field of view, camera angles, lights and staging to guide a user’s eye to what’s important in a scene. There’s a belief that overwhelming the senses is counterproductive to delivering a story. Therefore, editing is a ruthless but critical part of the process that deletes, re-arranges and filters the film to help give the product its final polish.
In the same way, elements in design need to work together to ensure clarity and simplicity over features. Be candid about what is necessary and what can be omitted, whether it’s a superfluous feature or verbose content. A smooth transition or timely animation can aid in tying together a flow or keeping a user’s attention.
While it’s tempting to neglect your content strategy, it will serve your product well in the long run if you use familiar understandable words that are relevant for your audience, in the UI copy and documentation.
3. Know the rules so you can break them
I’ve found a rut is the consequence of sticking to tried and tested methods that don’t take into account how you or the world has changed. — Twyla Tharp
It’s important to understand why certain methods are used classically so you can know how and when not too “follow the rules”. In film, the 180 degree rule says that the camera should remain on one side of an interaction as a way to keep the audience oriented to characters in a scene so that cuts aren’t jarring or confusing. That statement also hints at when the rule can be violated.
Credit: Stanley Kubrick shifts the camera for a motivated reason in The Shining.
Similarly, in design, there’s a deference for patterns and rules around grid, typography, etc. And while best practices are there for a reason, it’s important to understand that even the good guidelines and rules have their limitations.
For example, David Carson is a notable graphic designer who’s ground-breaking indifference to the grid system demonstrates that breaking of rules can push typography, visual communication and layout to the next level. William Bernbach, the advertising powerhouse known for his campaigns for Volkswagen (Think Small and Lemon), Avis (We Try Harder), and Life Cereal (Mikey Likes It), has said that “the memorable never emerged from a formula”. Rules can be broken, for the right reasons.
Conclusion
There is a natural evolution between art, design and film that makes it inevitable to find parallels. In both great films and UX, you must create focus and clarify what is happening in order to meet your goals. Remember to use all the tools in your arsenal (be it composition, editing, color, imagery, motion, etc.) to not only make the product real for your audience and also for your team throughout the creative process.
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Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience.
When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of 6 human experts and 11% more likely to reduce false-positive exams. Humans and AI achieved similar results when radiologists were able to view prior CT scans.
Risk of cancer two after a screening the model was able to find cancer 9.5% more often but about 5% more likely to reduce false-positive exams compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study.
Detailed in research published today in Nature Medicine, the end-to-end deep learning model can predict whether a patient has lung cancer. It also generates a patient lung cancer malignancy risk score and identifies the location of the malignant tissue in the lungs.
The model will be made available through Google Cloud Healthcare API as Google continues trials and additional tests with partner organizations.
“The AI system uses 3D volumetric deep learning to analyze the full anatomy on chest CT scans as well as patches based on object detection techniques that identify regions with malignant lesions,”Google technical lead Shravya Shetty and product manager Daniel Tse said in a blog post today.
The model was trained using more than 42,000 chest CT screenings images. The CT scans were taken from near 15,000 patients, 578 of whom developed cancer within a year during the 2002 NLST study by the National Institutes of Health (NIH).
Results were then validated with datasets from Northwestern Medicine.
Lung cancer is one of the most common causes of death on Earth, according to World Health Organization data, taking more than 2 million lives annually, killing roughly as many people each year as breast cancer.
The NLST first carried out in 2002 by the National Institutes of Health (NIH) is a low-dose computed tomography (LDCT)screening. A 2015 analysis found that only 2-4% of patients get a LDCT screening today.
“By showing that deep learning can increase specificity without sacrificing sensitivity, we hope to spur more research and conversation around the role AI can play in tipping the cost-benefit scale for cancer screening,” the blog post reads.
Modern entrepreneurship began at the turn of this century with the observation that startups aren’t smaller versions of large companies – large companies at their core execute known business models, while startups search for scalable business models. Lean Methodology consists of three tools designed for entrepreneurs building new ventures:
Customer Development – a process for testing those hypotheses outside the building;
Agile Engineering – to rapidly build minimal viable products to test product/market fit.
These tools tell you how to rapidly find product/market fit inside a market, and how to pivot when your hypotheses are incorrect. However, they don’t help you figure out where to start the search for your new business.
A new tool – the Market Opportunity Navigator – helps do just that. It provides a wide-lens perspective to find different potential market domains for your innovation, before you zoom in and design the business model or test your minimal viable products. This new framework can act as the front-end of Customer Development. It helps figure out the most promising starting position – market domain – for your customer development process. And it helps identify promising Plan B’s and new growth options if you have already embarked on your innovation journey.
Over the years, I have seen many startups and innovation projects perform a painful “re-start” to completely new market domains. With a little more thinking up front these entrepreneurs and innovators could have identified more promising business contexts to play in, and thus avoided this difficult pivot down the road. But while the academic literature is full of papers covering market selection and the literature has some popular books (Blue Ocean Strategy, et al.) there is a lack of easy-to-use tools to do so.
In large companies and government agencies the problem is even more acute. Where do we spend our limited time and resources on our next moves? While the Innovation Pipeline tells us how to go to from sourcing to delivery how do we prioritize our choices? The Market Opportunity Navigator is a useful adjunct to the curation and prioritization steps.
In three simple steps the Market Opportunity Navigator can help you:
Identify a portfolio of market opportunities stemming from your technology or unique abilities
Reveal the most attractive domain(s) by evaluating the potential and challenges of each option
Prioritize market opportunities smartly to set the boundaries for your lean experimentations
I asked Sharon and Marc to summarize why market selection is important and describe an example of how to use it.
Different Playgrounds mean different Rules of the Game There are many ways in which you may have identified a market for your business. Some of you may have identified a market need based on your own experience, or you may have been approached by potential customers, or if you are corporate innovator you may have applied an innovative solution to an existing target market. Yet, are you sure that this is the best opportunity? Could there be greener pastures (larger markets, more profitable markets, etc.) out there for commercializing your technology or unique abilities?
Taking the time to reveal the most promising market – the best starting position – before you engage in a focused customer development process is critical, because market domains vary in their value creation potential, competitive landscape, regulatory regime and risks associated with launching new products. In fact, by not asking “Where to Play” innovators risk choosing an inferior playground – one that does not allow the project to prosper. Beyond the possible loss of revenues, this early decision may be difficult to change, or even irreversible: it influences how you develop your technology going forward, raise money, write patents, recruit employees and pick a brand name. If re-start in another target market is required, such a pivot is painful, costly, and sometimes even impossible.
Finding the best starting position is a learning process that takes time and bandwidth – two scarce resources. So instead of taking a deliberate step back to understand their portfolio of opportunities, entrepreneurs and innovators often just start running. They make a bet and engage in customer development experiments – adopting “local” pivots in a relatively fixed context, until a scalable business model is (hopefully) revealed. This can be a big bet! The search for product/market fit and for a scalable, promising business model should therefore begin with uncovering and understanding the different market contexts in which you can play. In fact, by adopting a wider lens, the search process shifts from 2D (finding a product-market fit) to 3D (finding multiple product-market fits in different market contexts).
Academic research published in Management Science investigated 85 VC-backed startups and offered a conclusion that seems obvious in hindsight: “look before you leap.” The big idea was that experienced entrepreneurs tend to generate a portfolio of market opportunities before deciding where to play, thereby laying the ground for significant performance benefits. In other words, understanding your arena of opportunities is a key asset for entrepreneurs and innovators.
Identifying your Arena and Choosing Where to Play The Market Opportunity Navigator provides a visual framework to discover, compare and prioritize different market domains and business contexts. It helps you to think about your arena, rather than your industry – a key mindset shift in today’s competitive landscape.
The Navigator walks you through a three-step process that helps you to make a more informed choice. It does so in a friendly, intuitive manner, with a visual design board and 3 worksheets to guide the process.
You can download the Navigator and its worksheets here.
Putting it all together: A Superset of Tools Mapping out your market opportunities to understand your most promising starting position generates valuable insights for your innovation journey. In short, the big-picture view provided by the Navigator helps you zoom-out to understand “where to play,” while the detailed views of the lean approach and the Business Model and Value Proposition Canvases help you zoom-in and understand in detail “how to play.” Together, they create a superset of tools that supports you in an iterative learning process until you find a scalable, promising business.
Having a market opportunity portfolio to draw from offers an additional benefit. By having gamed out multiple markets, you can bake agility into the DNA of your venture – a key component in the Lean methodology. It allows you to carefully select and keep open backup and growth options. If a “re-start” is eventually required, it will be less painful and less costly.
Let’s take a look at an example from the startup world to see how the Market Opportunity Navigator works.
We Can Fly Anywhere – but Where Do We Go First? Flyability develops drones to inspect difficult-to-access locations. In theory, they can custom-build their drone to perform different jobs in completely different markets: industrial inspection, search and rescue, entertainment or surveillance – to name but a few. Each of these markets varies significantly in its business context and in its promise for growth. Furthermore, each market would require its own customer development process to reveal a scalable, repeatable business model – clearly a demanding process that is difficult to run simultaneously in multiple domains.
So how did Flyability find its best starting position – the initial market domain where the founders should engage in detailed customer discovery and build their business? They used the Navigator and its three worksheets to guide their process.
Worksheet 1: Generate your market opportunity set The founders’ first idea was to use the drone for observing critical disasters, like the reactor meltdowns in Fukushima, Japan. Yet, by going through the first step of the Navigator, the team began to uncover alternative markets where their drone could add value for customers. Among others, they considered drone-based inspection of boilers in thermal power plants, the inspection of oil & gas storage tanks, and intelligence-gathering by police forces. Overall, five market domains seemed interesting and required further evaluation.
Worksheet 2: Evaluate market opportunity attractiveness Using the second step of the Navigator, the team systematically examined the potential of each market and its unique challenges. This allowed Flyability to map out their options and visually compare their attractiveness. Gradually, it became clear that thermal power plants were a “gold mine” option worth playing in. They could now use the Business Model Canvas and the lean experimentation processes to design and validate a scalable business model within this market.
Worksheet 3: Design your agile focus strategy Once the founders chose their primary market, they could leverage alternatives to create a more agile company by mitigating risk and avoiding locking-in. Specifically, using the third step of the Navigator, the founders designed a small portfolio of backup and growth options that they would keep open. This foresight laid the ground to early key decisions that have long-term consequences, like how they developed their drone or chose their brand name. In addition, it helped them clearly define which options they would place in storage for now (as focusing is about saying no more than anything else).
By employing the Market Opportunity Navigator, Flyability has not only figured out “Where to Play” it has mapped out an interesting growth path that is appealing to investors. To get a better sense of this process, you can view Flyability’s Navigator below, or read the full case study by clicking here.
Insights for VCs, Tech Transfer Officers, and Social Entrepreneurs Identifying your arena of opportunities is not only key for startups and established firms, but for anyone dealing with technology commercialization. For VC’s, the macro-level perspective shows the market opportunities that can be addressed by a startup and lays out a clear monetization process over time. It also offers a portfolio perspective when screening initial or successive investments. If you are working for a Tech Transfer Office, a wide-lens perspective is essential for assessing the value of an invention, and for figuring out in which hands you should put it. Furthermore, if you are trying to address a social problem, the Navigator helps ensure you identify a market that allows you to generate an economic bottom-line in addition to your social impact.
Lessons Learned
Lean Startup tools offer the details of “how to play,” while the Market Opportunity Navigator helps you to zoom-out to understand “where to play”
There are multiple “starting positions” for your customer discovery journey
Each starting point has different challenges to overcome
What would be your most valuable domain?
The Market Opportunity Navigator is an easy-to-apply framework for this process
Worksheets and supporting material can be downloaded at www.wheretoplay.co
from Steve Blank https://steveblank.com/2019/05/07/how-to-stop-playing-target-market-roulette-a-new-addition-to-the-lean-toolset/
AI in Five, Fifty and Five Hundred Years — Part Two — Fifty Years
Check out part one of this series for what the next five to fifteen years looks like in AI. In part two we get super sci-fi and see if our crystal ball can reach 50 years into the future.
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“Any sufficiently advanced technology is indistinguishable from magic.”
— Arthur C Clark
Welcome to 2069.
Intelligence infuses every aspect of our daily lives.
Once dumb objects have woken up. Your shirt is babbling away with your shades and having a conversation with your girlfriend’s pearl earrings when she’s traveling to give a talk in Brazil.
Everything from our houses, to weapons, to planes, trains and automobiles, to roads, clothes, jewelry, headphones, glasses, and eye contacts are wild with thoughts.
The dynamic new algorithms that pushed us past deep learning and powered the fourth wave of the intelligence revolution sprang from world wide efforts to map every single neuron and connection in the human brain. Eventually the processors and biotechnology caught up with our ambitions and scientists succeeded beyond our wildest expectations.
Like the human genome project before it, the brain mapping race around the globe took decades and cost billions. But the breakthroughs drove the price down dramatically and a few decades later you could map the human mind for a fraction of world reserve currency Bitcoin and back it up to distributed, decentralized storage running on foglets.
It got so cheap that everyone from the local handy man to the most powerful CEOs on Earth, started snapshotting/backing up their mind at different stages in their life in the hopes of being recreated later with clones or virtually.
What did neuromapping have to do with AI?
Data scientists didn’t discover the secret universal learning algorithms at the heart of the human mind but they could watch them in real time as brain simulations ran on the neurochips inspired by the projects.
We didn’t discover how to learn. We hacked learning.
Deep learning, symbolic reasoning and genetic algorithms looked primitive by comparison to the rapid learning systems of the 2040s to the 2060s.
Cryptocurrency ASIC makers out of China were the first to build the radical new chips that changed the way we processed intelligence, taking their fabrication and design skills to the next level, dominating the next wave of computing. Quantum computing breakthroughs from World War III used for encryption during the war, lead to a fifth wave that made even the neurochips look underpowered, until the older chips made a comeback by using wetware made from stem cells grown into artificial neurons.
Now, everything is alive.
Our smart bandages monitor our wounds and tell us when to change the dressing or whether the infection is healing or getting worse. Tiny patches in the bandages react to infection by releasing antibiotics.
Pill bottles remind us to take our medicine or warn us not to take it if we wake up groggy from a night of drinking and try to take it twice.
Self-Driving trucks in the movie Logan, an amazing and subtle take on the future.
Self-driving trucks with no cab for humans race along the highway at 200 miles per hour on special roads across China and the US. They’re nothing but stripped down flatbeds with wheels, carrying standard smart shipping containers. The containers know every single object inside of them, the objects scanned by nanosats and tracked in space forever on the AR/IoT web. As soon as dishes come off the industrial 3D printer at the autofactory, they’re tracked right into your house.
When the dishes break they call for a replacement themselves.
The containers arrive on self-piloting boats. When pirates try to hijack the containers, the boats go to evasive maneuvers and the containers screech and call for the coast guard or the navy.
A shipment of the latest and most fashionable black jackets hit your house via drone delivery. When you go to Berghain in Berlin to dance the day away, your jacket adjusts its threads as the temperature changes or loosen the threads as it gets hotter. No more wearing layers as you move from hot to cold in the ice and snow of winter to the warmth of a self-driving car.
Of course, if you’re at Berghain you just take it off as soon as you get inside anyway and the robot coat check system of floating hooks scans your iris ID and messages a claim code to your info-necklace.
Our eye glasses and contacts and shades show us meta-information about the world around us, from the contact information of people we know, to details of historical landmarks, to warnings of high crime areas to avoid, to the location of friends and family who have given us permission to see where they are during certain times of the day.
An AI personal assistant that lives in your necklace, projects as a hologram, and splits time between your ear buds and your augmented reality contacts, processing half of its routines on distributed foglet platforms, does all kinds of tasks that used to take hours. It bugs you to get up in the morning, sets meetings, finds a nice bottle of wine on the way to a party, crafts a killer workout to get you into summer shape, suggests the latest fashions and finds the perfect restaurant to take a date, while telling you what to say on that date to help you seal the deal with a night of passion.
Kids used to have to use their imagination with their toys. Remember army men and action figures? You had to pose them yourself and give them voices and stage epic war sagas with them in the backyard.
Today’s kids don’t want a teddy bear that just sits there slack-jawed, staring at the wall. Teddy bears now walk and talk and get kids to beg their parents to buy things. And action figures that don’t run and leap and charge the backyard battlefield screaming are worthless except to collectors who pine for simpler times when the mind’s eye had a lot more work to do.
Students don’t write in a diary, they confess to one that talks back and gives them advice about their boy troubles and being popular in school.
Judy Jetson talks to Di-Di, her diary.
Bikes have wobble detection and bizarre alien frames that look strange but were designed by machines to make them more stable. You could probably peddle them if you wanted to, but if you get tired the AI coupled with millimeter precise GPS takes over and drives for you.
Multi-material 3D printersand molecular printers in factories call out when they’re breaking down and order maintenance. Robots swap the printers when they break and feed them more bars of metal and wood and rubber.
Solar roadway in China. Image courtesy of Pick My Solar.
Old roads are repaired by drones with fast hardening concrete. Smart roads, made of industrial glass solar panels display messages and let the city grid know when they need repairs.
Fire suppression systems don’t just put out fires in houses, they direct families where to run and what rooms to avoid, all while keeping them calm with a soothing voice and calling autonomous fire trucks.
But it all started with self-driving cars.
Autonomy and the Brave New World
By the 2020’s self-driving everything had swept across the planet.
Still humans were under the delusion that, like Rain Main, they’re very, very good drivers. Humans have the great ability to fool themselves that way.
Early on, every fiery self-driving car crash made the news. Angry people pointed out how dangerous they were and some reactionary governments banned them.
Those governments paid a heavy price just like the ones that banned Uber in favor of cabs that everyone hated. You can’t hold back the tides of time. Eventually, the death toll from human drivers looked absurd next to countries with rapidly growing populations of autonomous cars and citizens everywhere demanded their governments revoke the bans.
After a decade no self-driving car crashes made the news because humans killed so many more people with their inability to pay perfect attention to ever changing road conditions.
By 2029 the death toll plummeted due to advanced monitoring cameras that could spot crashes in seconds and send paramedics screeching into the night to save drivers. But it wasn’t just the cameras. The cars themselves could contact the city grid and tell them what happened, reading data from their black box and signaling traffic control to reroute cars and send help.
By 2039, the cameras had spread from superhighways to backroads and they called out to autonomous paramedics that rushed the scene with super-human precision, the bots prying victims from collapsed frames and triaging wounds with next-wave antibiotics and material that expanded to fill the gash and stop sepsis in 15 seconds flat.
Steering wheels, once required by law, disappeared all together.
The inside of cars were redesigned as mini-apartments on wheels, with tables and chairs facing each other.
Drinking and driving became a good thing.
Older generations who swore that people would always want to hold the wheel and feel the road rumbling beneath their feet had passed on into the great beyond. Younger generations couldn’t understand what all the fuss was about when they’d rather be chatting, working or putting their feet up as they read a book on their glasses or augmented eyes.
By 2049 humans driving was all but illegal without a special permit and barely anyone died on the road.
Human driven cars became a booming niche market, the way records did after the rise of CDs and Napster and Apple Music and Spotify.
But it wasn’t all good.
Those self-driving cars that saved so many lives also cost a billion lives.
Fast Food Nation and The Winds of World War III
The doomsayers were right.
AI did kill off jobs faster than we could replace them.
They were wrong too but that would take time and a disaster to set the world to rights once more.
By 2030, a lethal combination of rising authoritarianism, democratic decay, automated 3D and molecular printer driven factories and self-driving cars created the perfect storm.
Twenty years ago I wrote a short story called In the Cracks of the Machine, where fast food bots caused a labor crisis that spiraled out of control. I did miss self-driving cars though. (Nobody is perfect.)
3.8 million jobs vaporized overnight as the fast food industry replaced people with machines.
It started with janitors. Why pay someone minimum wage when you can spend $10,000 and get a machine that cleans perfectly, never gets tired, only needs maintenance once a year, and lasts fifty years?
Pretty soon burger flippers, fry cooks and everyone else was out. The franchises that resisted went out of business.
Governments didn’t react fast enough. When they tried to pass laws to keep people in jobs, they only accelerated the collapse as the fast food chains that couldn’t use the loopholes went bankrupt.
People stopped buying cars when sharing economy firms could get one to most people in a big city in five seconds at less for satoshis on the bit-USD. Economies dependent on food service jobs and cranking out parts of the global supply chain of vehicles crashed hard.
Singapore and Switzerland suffered as lending and investment dried up. The African miracle that had seen a dozen countries in the massive continent take over the low cost manufacturing of goods from China came to a grinding halt. The economic contagion quickly spread to Russia and the EU, sweeping up Germany, France, Italy before taking down whole swaths of China and the US.
Like so many times in our history, with people out of work, they get angry and they mass in the streets. Collations form and extremist groups multiply like cancer cells. Soon young men are battling it out in the streets because they don’t have any other outlet for their energies.
Unlike the wars of the past, when World War III hit, no country on the planet was spared.
It started as stealth cyberwar that quickly erupted into all out assaults on the electrical grid and communications infrastructures, leaving people in the dark and cut off from the wider world.
In weeks, the superpowers of the world declared open war.
AI played a thousand different roles in the rising conflict.
Bullets rarely missed as smart rounds curved around corners and slammed targets with shark like killer instinct.
Micro-drone swarms stalked cities, their tiny mind’s eye filled with ever changing targets. If they didn’t get their man, they recharged and went out again endlessly every night until finally they found their target, hitting it like a dart and exploding.
And AIs directed the war.
Like AlphaGo decades before dominating the best Go masters in the world with ease, the secretive quantum computing AIs fought billions of virtual battles in perpetual simulation, mastering ways to crush the enemy, from all out total war, to guerrilla war, to complex counter-insurgency tactics big and small.
They delivered comprehensive big picture strategy analysis and directed battles better than any general, sending troops to fight and die on the hills and the beaches. They sent men and machines to kill, the generals increasingly relying on them to win the war, rather than human instinct.
Special Forces landed in stealth jets and their personal battle assistant choreographed their every step as they crept into houses at zero dark thirty, killing high value targets before they even got out of bed. The soldiers were centaurs, their minds threaded with delicate neural laces, giving them direct access to real-time AI, juicing up their response times and their nervous systems, as their nanothreaded augmented muscles moved at super-human speed up the stairs.
But while the human toll was high, for every human that died, ten bots died.
A dozen different variants of robotic soldier joined the fight, from skeletal pack animals carrying heavy loads, to autonomous jets, to self-driving tanks to full scale robotic soldiers that fought and died besides their brothers and sisters in arms.
In the end, 10 billion machines and 1.5 billion souls perished when the last shot was finally fired and the world stepped back to see the horrors it had wrought.
The Phoenix and the Ashes
But like all wars before it, it was not the end of life as we know it.
Humanity rose from the ashes.
It was the war to end all wars but what followed was an profound era of peace.
People came home and never wanted to fight again. They wanted to settle down and have children and spend time laughing and drinking with friends and shaking off the wounds and scars of the past.
War accelerates technology.
World War II started with propeller planes but five years later jet engines streaked across the sky, not in time to win the war, but in time to change the way people traveled the world after the war.
Many of the technologies that mushroomed during World War III, like quantum computing, augmented reality, digital ledgers, crypto-voting platforms, meshnet encrypted communication, molecular printing, payment systems, universal learning algorithms reverse engineered from simulating human brains, and advanced medical diagnostics swept into the civilian world.
AIs that once directed heavily bearded spec-ops soldiers to their prey, now chat with children about how to overcome bullies and make new friends.
Battle AIs were repurposed to run entire economies, ordering goods and services before people even knew they wanted them and directing factories to speed up or slow down.
Robotic soldiers were scrapped or redeployed as police or to plant trees to help the ecosystem recover from the devastating effects of all out war. The drones took to the scorched fields and the skies and rapidly planted trees and flowers and food and stimulated them with ultraviolet light and a perfect PH balance of food and nutrients designed to rebuild the broken soil.
Automated, self-contained hospital pods revolutionized paramedics, bringing the hospital to the patient.
And the death toll had a silver lining.
When we don’t solve our problems, nature solves them in the most brutal and ugly ways. With less people needing jobs, the job market took off into a booming era of unprecedented expansion.
Broken windows are a fallacy in day to day economics. But sometimes a lot of broken windows can reboot the entire world economy.
Either way, the economy boomed at a fevered pitch in the 2040s and 2050s.
AI created an explosion of new jobs the likes of which the world had never seen. First came the destruction but then came the creation. New jobs we couldn’t imagine before the old ones disappeared started to flourish.
Turns out the janitor bots needed a lot more maintenance then their creators thought and a whole industry spawned to clean them and upgrade them.
Car modifiers and after-market upgrades to modular vehicles erupted all over the world, creating new economic powerhouses.
AI psychologists traced down problems when complex systems went haywire.
Genetic designers, helped with centaur AIs, created brand new foods and organisms and antibiotics and medicines.
A new generation of children shrugged off debt and didn’t bother owning homes or cars or big TVs. They embraced a just-in-time sharing economy with AIs directing many of their day to day actions in a way that would have terrified the suburban middle class of generations past. They moved from place to place, their computers inside their heads as they traveled the world and worked remotely.
More and more people had grown up with AI and thought nothing of enhancing their intelligence with a neural lace or augmented eyes that could see with superhuman perfection. People worked side by side with AI, as politicians and big companies and decentralized autonomous organizations (DAOs) learned how to balance the needs of man and machine.
The Forest Grown Wild
In just fifty years we saw the mustard seeds of the dawn of the Age of Intelligence grow into a wild and uncontrolled forest.
The universal power of AI mirrored the demonic aspects of the human mind and the better angels of our nature too.
It helped us lose our minds and kill each other, as we love to do periodically, and it repaired those minds with AI derived psychotherapy techniques like targeted electrotherapy and pleasure center stimulation implants. When that didn’t work the machines designed happy pills that made the Prozac of the past look like Gummy Bears.
It augmented our health care and automated entire economies.
Roads got rightfully repaired, tracked by drones and cameras that never slept. And people stopped dying on those roads on the way to work.
Our clothes and handbags woke up. Our smart phones faded into our necklaces and contacts and augmented eyeballs.
The very streets and sidewalks beneath our feet got wise.
And for a time people returned to balance and prosperity in the eternal cycle of life and death, boom and bust, happiness and sadness.
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In the next article, we’ll go well beyond the next fifty years and take a look at the world in five hundred years, a world beyond all imagination.
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If you liked this article come see me talk on AI in Five, Fifty, and Five Hundred Years at Rise of AI in Berlin, May 16 2019 and get 25% off tickets!
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I’ve got a new podcast, The Daily PostHuman, covering crypto, AI, tech, the future, history, society and more! Check it out for expanded coverage of my most famous articles and ideas. Get on the RSS feed and never miss an episode and stay tuned for some very special guests in the next few months!
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A bit about me: I’m an author, engineer, pro-blogger, podcaster and public speaker.
You can check out my latest novel, an epic Chinese sci-fi civil war sagawhere China throws off the chains of communism and becomes the world’s first direct democracy, running a highly advanced, artificially intelligent decentralized app platform with no leaders.
https://www.invisionapp.com/inside-design/6-tips-for-remote-user-testing/
