Learn how AI+ can free users from repetitive tasks, minimize mental strain, and unlock smarter, more seamless interactions. Take charge and redefine user experiences with AI+—the tool to shape a smarter future.

The modern age calls for a proactive approach: embracing “DIY” (Do It Yourself) techniques powered by AI+ instincts.

AI+ can free users from drudgery, repetitive tasks, and mental over-exertion, empowering you to craft smarter, more efficient experiences. Here’s how you can get started with AI+ solutions:

1. Identify a UX problem and clearly articulate it. (No need to solve it yet!)

2. Ask questions and gather insights. (Curiosity fuels innovation.)

3. Sketch ideas and test their value with users. (Don’t forget input from coders.)

4. Celebrate small wins—they pave the way for bigger breakthroughs.

5. Stay alert to obvious problems and their straightforward solutions.

6. Keep yourself informed—read, discuss, and stay updated on AI+ developments.

7. Share ideas with management, starting small and scaling big.

By analyzing Big Data and leveraging Smart "Semantic" Data to extract meaningful insights, you’ll uncover opportunities to innovate and drive impactful solutions.

Ready to take the next step? Let’s dive deeper into how AI+ can transform your professional practice!

The Joy of AI at Work

Check out these added definitions. Artificial Intelligence (AI). Can your application make “inferences” or understand human speech?

Smart Data uses synonyms from your “ontology” to understand that several words can mean the same thing. Let’s take this further. You can, for example, add “context” to the words like adjacent words, or a special data base that contains the words. That context lets you make a chain of inferences.

The words “car” and “vehicle” have a semantic match. Now add context—in which “car” describes a possession of John Z. Smith destroyed (and lost) by Hurricane Selma and “vehicle” describes a possession of John Z. Smith sold to New Age Car Dealership on the same date and in the same city and state! Perhaps this context suggests that John Z. Smith was defrauding his insurance company by selling something he said had been lost to the weather.

When these types of inferences are done on a grand scale—and with sophistication—data matchups start to happen that reveal previously unknown relationships. This also begins the process of using natural language input for more profound outcomes.

REASONING BY AI APPLICATIONS THE PROCESS OF MATCHING DATA ACROSS A CHAIN OF DATASETS SHOWS RELATIONSHIPS THAT ORDINARY DATA WOULD NEVER SHOW.

When enough resources are unleashed, the process of matching data across a chain of datasets shows relationships that ordinary data would never show. This is often how “reasoning” gets done by AI applications.

This “added intelligence” supports the next step in disambiguating those credit report records we spoke about in our prior newsletter. You can add other data sets to “enrich” the one you are concerned about. You might access public court records, sex offense records, driver’s license or property records to flesh out the profiles of your entity. With enough data, you can see clearly which entity pairs are different or the same

However, as the AI Wikipedia article notes: “As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet.”

Onward With UX/CX Vigor

Let’s finish up our list of cousins to the AI domain.

1. Machine Learning. Machine learning belongs to the field of AI. That is, a machine-like algorithm improves its evaluation powers over time. This demonstrates “intelligence”. It does this by using an algorithm to experiment with different responses to a situation. The machine (a computer program) starts “learning” which responses fall closer to the goal.

A “goal” might be more or larger product purchases by visitors to a retail site (Amazon, anyone?). Or it might be “task completion” by an enterprise end-user, where less time-on-task moves towards the goal of faster, more efficient work.

2. Virtual Reality (VR). Did you buy your goggles yet? (Goggles, not Googles.) Wearing VR goggles to peer into VR-designed apps allows your gaze to move around in the virtual world. Get the right goggles and even your body moves inside virtual space.

Turning your head to the right, shifts the field of view to the right, revealing new parts of the scene. It also can read your gestures.

While this can support medical or military training, the first uses of VR have supported interaction with fictional characters in games of pursuit as well as travel (among other titillations).

Exercise 1: Here’s how Machine Learning could support web-wide repurposing of videos and text for education.

1. If learners enjoy the offering, they proceed. This becomes a criterion by which the machine learning tool decides to use that element again with students who have the same learning style. You, yourself, may do better with audio, video, talking head or pictures, or print-based media or a combination of these. You may do better with smaller chunks compared with larger chunks of knowledge. All these presentation methods fall under “learning style” or “learning skills”.

2. However, teaching the machine learning algorithm over thousands of events and thousands of students is a very (very) tough job. How many students will get upset, waiting for your algorithm to settle down and give consistent results?

3. Therefore, the tool must use a pre-learned set of trials to assess the learner’s current knowledge plus learning style and skills. In other words, in earlier research, you figured out some parameters to guide your understanding of student knowledge-acquisition and their success with different learning styles and skills.

4. Then, given the results of these planned trials, your machine learning has a starting point in finding topic-related text and video in the learner’s domain that provide the right-sized steps of knowledge.

5. Those steps can be optimized through machine learning using student success and choice of media and topics to continue as a criterion.

6. By the way, remember our discussion above about AI making “inferences” from context? Here’s a chance to thread together those learning bits and pieces on the web by making sure your learners start with the beginning. Ensure that your learners get the building blocks in the correct order so they can comprehend the next step. AI inference will help your application thread the text from simple to more complex by using semantic understanding of topics.

7. Will the training of your machine still be grueling for thousands of learners, even after the head-start assessment? That’s for you to deal with in detail. Better yet, can you train your tool to identify variables in the media that predict user success? What ratio of “entertainment” to “didactic instruction” do people prefer?

Exercise 2: Here’s how Virtual Reality actually has been proposed as a gamified cyber-security tool.

1. Gamification of hacker-busting is true! I once participated in a contractor UX planning session for responding to a similar government “Request for Proposal” (RFP).

2. The RFP wanted design and coding services for dealing with cyber attacks. The end-users would use their VR to monitor and coordinate a semi-robotic response to large-scale cyber-attacks.

3. Because the VR application would have game-like qualities, the RFP demanded minimal training for the users. Hmm, what about that? Use your DIY AI+ thinking here. Do you buy into that idea?

4. Given the public responses to that unrelated VR/Cybersecurity survey, many folks might agree that gamification is pretty easy for end-users.

5. But, as I told the managers at that meeting, “From my interviews with champion video game players, they put an inordinate amount of time into practice and finding quirks in the program. They find bugs that can be exploited to gain points and stay in the game.”

6. The contractor declined the RFP for the user interface design. Maybe the contractor realized that gamification was not so easy after all. Probably VR could still work for cyber-security, but it still requires expert end-users! They’ll need cyber-combat training, as extensive (if not more) as any top gamer! No easy outs, here.

7. The idea of minimal video game training was definitely an urban legend without merit.

8. By the way, I’m not a gamer. But my “expert knowledge” manifested in 1982 when a buddy and I videotaped video gamers when they gathered in Ottumwa, Iowa for a photo shoot by Life Magazine! You can see some of that footage in two feature films (Chasing Ghosts and King of Kong).

9. Disclosure: I’m just as surprised at that publicity outcome as you! DIY AI+ includes luck playing a supporting role.

Some Final Takes. You’re the Boss Now.

Our theme in this newsletter asks that you become the creator of advanced automation apps—that AI+ stuff.

In this new DIY world order, you no longer wait for ideas from others.

Instead, you identify issues and invent solutions that support enterprise goals. Your training in UX/CX fundamentals qualifies you to imagine a solution that reduces VIMM overloads. (Don’t forget: Visual, Intellectual, Memory and Motor loading.)

Yes, you do have to read and talk to knowledgeable folks about AI, etc. But you do that anyway, right?

As a creator of advanced apps, you now are a manager, with management problems.

Check Out These AI Resources

https://experiments.withgoogle.com/coll ection/ai. This page shows Google experiments in AI. Look around under the “Collections” dropdown for VR and other experiments.

NSA Patent Portfolio for Technology Transfer Program. Look at the Data Science Pages 27-42 for starters. Lots of licensable AI, Smart Data and Machine Learning apps like these:

1. Word Pair Relevancy—allows ranking of docs to a keyword for knowledge discovery

2. Automated Reasoning within and Searching of Documents—supports mapping of legal regulatory documents (and others) to locate content applicable to a given question

3. SAGA: Measuring Similarity between Data Sets—enhances big data analytics in online shopping, social media, law enforcement, etc.

4. Identifying Connected Data in a Relational Database—given a standard relational database, this application helps discover connected data without transforming it.

About HFI

HFI (Human Factors International) is the world's largest company specializing in user experience (UX) design. We’ve been in the business since 1981. Our focus is on helping our clients develop mature and effective UX practices. We provide a complete and seamless suite of advisory, training, certification, methods, standards, and tools to help our clients institutionalize user (customer) centricity. Our vision is to help our clients build long-lasting relationships with their consumers across all touchpoints and leverage the influence that results from engaging digital experiences.