UX and AI “Artifical Intelligence” how to use machine learning towards a better product experience.

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How I got pulled into this?

My Journey began when I was @ Intuit working on my Patent; Method and system for building and utilizing interactive software system predictive models using biometric data.

Back in my Teradata Days, I gave a presentation on how AI and machine learning can be used towards a better product/customer experience.
Ultimately we want our application to behave like a human, capable of understanding feelings and empathize when needed by providing the customer with a solid solution/experience when needed. Watch Presentation

Empathy in Artificial Intelligence and the real challenge today!

Augmented reality is only believable if that reality is as close to the real reality that you experience as possible. This means that AI Systems have to mimic real human emotions. Only through real human emotions and personal data from you can AI systems augment a reality that you will believe in. With the popularity of social media applications, collecting personal data from you is no longer a problem. However, the real problem lies in modeling real human emotions.

The most difficult part is for AI Systems is to mimic empathy or artificial empathy. I say mimic because AI Systems are not human. AI Systems can learn your behaviors by interacting with you. Then, AI Systems can respond to you in the most “empathetic” way (it determines from its data bank) in situations when empathy is called for. By empathizing with you and interacting with you, the AI System can then gather more behavior characteristics from you. In turn, AI System’s empathetic responses to you will have a more emotional effect on you with each interaction that you have with the AI System.

Bill Mark, president of Information and Computing Services at SRI International, whose AI team invented Siri.

“Some people cry when they’re happy, a lot of people smile when they’re frustrated. So, very simplistic approaches, like thinking that if somebody is smiling they’re happy, are not going to work.” — Information Week

Artificial Empathy is a big area of study for many AI researchers in the coming years. These researchers have to understand the nuances of human emotions, understand the physical and behavioral manifestations of those emotions and then develop mechanisms for the AI system to mimic those behaviors.

AI and Machine Learnings Today!

In recent years, artificial intelligence has emerged from the realm of science fiction and become a very real tool that, depending on who you talk to, will either leave humans jobless and purposeless or improve businesses operations in every industry by taking over mundane and data-heavy processes and leaving human employees with more time and energy to think and work creatively.

For some, “the rise of the robots” is something to fear. Many people view it as a threat to their jobs, a relentless machine that can do everything they do, only faster, better, and without pay or sleep.

But here’s the thing: AI won’t just take jobs, it will create them too. In fact, by the year 2022, AI and machine learning will have displaced 75 million jobs but will have created 133 million as well.

One area that is being revolutionized by improvements in AI is user experience (UX).

What Is User Experience and Why Is it Important?

User experience is the way somebody feels when they interact with a system, such as a website, software, mobile application, or device.

This is a customer-centric era, which means successful companies focus on learning more about their audience’s needs and interests so they can deliver the best possible products, services, and experiences to satisfy those needs.

Tech company Imaginovation reports that almost 80% of people will leave a website that isn’t optimized, citing UX as a highly valuable metric that many companies aren’t measuring.

UX design is all about creating a positive experience for people so they will stay loyal to a brand. For many years, this mission fell to the hands of marketing teams, which performed research and analysis manually.

Thanks to rapid innovation in AI and machine learning technology, companies have powerful new means of delivering a better UX.

How AI Improves UX

In a sense, AI and UX share the same end goal: both are designed to interpret human behavior and anticipate what someone will do next. Predictive analytics is at the foundation of both, and it’s this intersection that creates an opportunity for both companies and customers.

Before we consider real-life examples of how AI is being used to improve the user experience, let’s look at the back end.

What makes it possible for AI to enhance the UX?

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Intelligent Algorithms

The process of creating a better UX comes down to AI algorithms. These smart-systems can process information about site visitors or app users, then apply changes to the model in ways that optimize it for future use. By continually learning and adjusting, the algorithm improves the user experience to offer a more engaging, personalized experience.

Is a master algorithm the solution to our machine learning problems?

Suppose there’s an algorithm that knows what we’re searching for on Google, what we’re buying on Amazon and what we’re listening to on Apple Music or watching on Netflix. It also knows about our recent statuses and shares on Facebook.

Now, this algorithm knows a lot about us and has a better and more complete picture of us.

This powerful “master algorithm” is at the heart of work postulated by Pedro Domingos, author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.

Machine learning has different schools of thought, and each looks at the problem from a different perspective. The symbolists focus more on philosophy, logic, and psychology and view learning as the inverse of deduction. The connectionists focus on physics and neurosciences and believe in the reverse engineering of the brain. The evolutionaries, as the name suggests, draw their conclusions on the basis of genetics and evolutionary biology, whereas the Bayesians focus on statistics and probabilistic inference. And the analogizers depend on extrapolating the similarity judgments by focusing more on psychology and mathematical optimization.

Quantitative Usability Testing

Due to the sheer capacity for data processing, AI is a powerful asset to have for any form of testing. AI professionals can use machine learning-based systems to track and evaluate a vast array of crucial UX metrics, such as:

  • Device users are on when they visit a website
  • Location of users
  • Session time
  • Session length
  • Pages visited
  • Categories/products viewed
  • Bounce rates
  • Exit pages
  • User flow

Together, these key metrics help analysts develop a clear picture of user behaviors and interests, which is beneficial when it comes to experimenting with new ideas and running usability tests.

Eliminates Bias in Testing

Another benefit of AI for UX is that it eliminates any one-sided approaches to A/B testing. It’s not always easy to remain impartial with split testing, and quite often, man-made reasoning can influence results. By comparison, AI relies solely on hard data, adopting a binary approach that reflects the actual results.

Deep Learning

With deep learning, an AI system can analyze hundreds or even thousands of different design variations and then generate alternatives. This can be applied in many facets of UX, such as creating buttons on a website, or graphics in an app. By pairing this deep learning technology with behavioral data insights, AI can improve UX in ways that are more likely to connect with users.

6 Ways Companies Improve UX with AI

AI professionals can help companies unlock the full potential of data so that businesses can learn more about their customers, and in turn, offer a better user experience.

Here are six ways we can use AI to improve UX.

To successfully define personality as it relates to communication, designers will now have to combine four different types of behavioral data:

  • Gestural Data. The way we would identify conversational tone via face and hand motions.
  • Physiological Data. The way we would measure heart rate, blood pressure, and skin temperature.
  • Facial Recognition. The way we would verify a person, and interpret their emotions and expressions.
  • Deep Learning. The way we would understand speech, and how language is used.
  • Energy / Thermal Dynamics ( Aura ) Sensory/Readings ( not possible today ) we could be sitting silently, not providing any of the data above, but our energy could be totally otherwise. We cry out of happiness, and etc.. Through quantum computing and certain UV sensory readings via a special thermodynamic microscopic camera, we are able to capture any living being’s thermal dynamics or so-called “ Aura readings “ it will truly expose your inner feeling or so-called. overall energy Combined with the other methods above AI can truly understand if the person is crying because they are angry or happy, their Aura readings will be able to confirm that data.

Emotion AI

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Kantar Webcast — Building an Emotionally Intelligent Brand: Emotion AI for Advertising

By 2022, your personal device will know more about your emotional state than your own family.

While AI robots aren’t capable of displaying genuine emotion like humans, emotion AI is an exciting new field that is capable of learning from people — and influencing them.

This technology involves:

  • Data analysis
  • Machine learning algorithms
  • Facial recognition software

Together, these aspects analyze various emotional responses to different elements of a brand’s website, products, and marketing content. The AI categorizes the responses into groups, such as happy, angry, or sad.

Businesses can leverage these insights to optimize their marketing strategies by identifying designs and content that show more promise for engagement.

Another example of emotion AI in action is the emotional car software from Affectiva, which is capable of detecting when people are angry or unfocused on the road. With this, the onboard AI can assume control of the car or stop it completely to prevent any accidents.

Complex Data Analysis

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Traditionally, UX teams look to heat maps and split testing when they are trying to boost user engagement. As it can collect a lot more data and analyze it quicker, AI will inevitably take over.

This offers excellent potential for UX teams, as they can use deep learning technology to track and analyze large data sets. Already, many e-commerce businesses harness the power of AI to improve UX in this way, learning more from user behavior to tailor the site design accordingly.

Before AI, this complex data analysis wasn’t possible. Now, businesses can react in real-time to provide a continually improving user experience.

The Turkish-based company Segmentify specializes in this area, helping businesses drill down into their consumer data to enhance e-commerce personalization. Indian retail brand Goto enlisted the help of Segmentify to boost their average order values by 33%.

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While much of the user experience focuses on a company’s website or landing pages, the UX starts from the first moment a brand engages a new prospect.

Unfortunately, it’s incredibly tough for a business to grab the attention of users online these days. Just consider these stats:

  • Intense competition — Consumers are exposed to thousands of brand messages every day. This competition has crushed organic reach on social media to less than 2%. (Curatti)
  • Shorter attention spans — Studies have found that digital technology has caused human attention span to drop to just eight seconds — less than that of a goldfish. (Medical Daily)
  • People are less receptive to ads — Many people use software to block online ads, with 70% of StopAd users blocking an average of 200 ads every day on desktop alone. (StopAd)

Now, marketers need to be more creative and imaginative with their strategies. It’s the only way to stand out from the pack, and it’s the genuinely original efforts that captivate most consumers.

Here’s where AI excels.

Social media platforms are a treasure trove for marketers, offering up tons of information about consumer interests and preferences. With this information, companies can improve UX with AI.

Every time a user likes a page shares a photo, makes a comment, or displays interest in products online, the AI system gathers the data. Over time, with more data, it can segment audiences into smaller, more defined groups, until it offers marketing and advertising on a one-to-one level.

One such example is the outdoor clothing retailer North Face. It has incorporated the IBM Watson technology to create unique shopping experiences, which are personalized for every client online. This customized retail experience increases engagement, which in turn boosts customer satisfaction and sales.

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Chatbots are arguably the most popular way businesses can improve user experience with artificial intelligence. These virtual assistants are commonplace now, taking customer support to new levels through natural language processing.

This subfield of artificial intelligence uses machine learning data to learn more about customer interactions, and it derives meaning from text input or speech. Contrary to what you may expect, rather than losing the human touch, companies that use AI chatbots note increases in customer service metrics. Capgemini reports that 75% of businesses using chatbots report at least a 10% boost in customer satisfaction.

Not only can chatbots work 24/7, but they are also immune to frustration and stress from unruly customers. It’s unlikely that robots will completely replace humans in customer service, but it’s already clear we can massively improve UX with AI chatbots.

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Automation has become an integral aspect of marketing in the past decade and continues to be rolled out in almost every industry. It has the power to save people time, money, and effort, which is all great news for companies that want to outsource menial, repetitive tasks.

But what about the user experience?

When Tesla trialed its autonomous vehicle in 2018, a devastating crash was a significant blow for the company’s reputation.

A mishap is to be expected during innovation, but even this tragedy doesn’t undermine the raw potential of automation for improving UX.

In terms of using automation to enhance the user experience, Amazon is among the very best. The retail giant attributes 35% of its revenue to personalized product recommendations, which are constantly updated and optimized to engage every individual user. This high-level automation allows companies to maximize their returns with every customer and increases the chance of turning first-time visitors into lifelong brand advocates.

Will AI Eliminate the Need for UX Designers?

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With everything said so far, it may seem that AI professionals will soon wipe out the need for UX designers. However, this isn’t going to happen anytime soon.

As brilliant as AI technology is now, it still lacks certain traits and capabilities that only a human can offer. Customer service may be better with chatbots, but people are still needed to manage them. Similarly, emotion AI may be amazing but without humans to make sense of the data, it can’t be put to good use.

When we consider the user experience, AI excels at learning as machines learn more from experience and data. However, it can’t handle complex design tasks. Instead, it’s best employed when testing ideas and collecting behavioral data.

Netflix uses AI to improve UX in this way, as its system tracks engagement data for different graphic layouts and program banners. This content localization helps the company determine the most compelling imagery to use for its range of TV shows and movies.

The founder of Sandstorm Design, Sandy Marsico, explains that AI is only a tool, not a replacement for human analysis.

“AI won’t figure out the problems we need to solve — AI helps us have a deeper understanding of our user so we can tailor our content and messaging to anticipate motivations and behaviors.”

UX is still the priority, and while AI adds powerful insights, it can’t tell the whole story.

AI Improves UX by Creating Stronger Human Connections

When you think about all the ways we can improve UX with AI, you’ll realize they all lead to a shared end goal:

AI creates deeper human connections with people.

The technology makes it possible to improve social listening, data analysis, and facial recognition so that companies can get to know their customers on a much more intimate level.

Ironic as it may seem, the reality is that as the world embraces machine learning and artificial intelligence, companies can offer more personalization, which humanizes brands with a relatable face and voice that resonates with their audience.

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Smart home assistants like Alexa continue to evolve each year, not only to become more useful in terms of voice-activated applications but also to respond in more human ways. Similarly, the Concept-I car from Toyota is marketed not as a car, but as a partner. The onboard AI improves UX by connecting with the family on a personal level.

While all of this may seem like it’s bordering on the plot of a sci-fi movie, the truth is that AI enhances the UX in ways that no other technology or marketing strategy can.

As a result, people are more willing to engage. That’s great news for consumers and for the companies and AI professionals that are trying to serve them.

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Is artificial intelligence setting tech development on a dangerous path? Are we going to become slaves to the machines, or is AI the gateway to the ultimate progress of mankind?

Finally, we’re still mastering natural language when it comes to interacting with devices.

For some reason, when faced with a machine, humans talk like a machine.

When we talk to Amazon’s Echo, we usually say: “Alexa, [wait for response indicator] “what’s the weather today?”

But, when we talk to an actual human near us, we tend to say things like “Hey, what’s it like outside?” No name. No pause. No time. All context is assumed. Interacting with machines is unavoidable, so we need to design them to act and react in a more human-like way — give it unique personalities — ones which complement our own personality and can adapt to our emotions.

When the Mini Cooper car was reintroduced in 2002, one of the most delightful brand experiences was the voice interface.

Drivers were able to pick a gender and an accent for how the car’s navigation system would communicate with them. Although the voices were all programmed to give the exact same responses, there was something magical about picking one, about identifying the personality of a passenger that we wanted to join us on our journey. It was a great start, and it’s good to know that empathetic experience designers are still the ones in the driver’s seat.

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