How people behave at home is fundamentally different than how they behave at the grocery store, at work or at the nightclub. We dress appropriately, act appropriately and experience differently based on certain assumptions and knowledge about the unwritten rules that are attached to these spaces.

COVID-19 has created a new normal where our homes now are also our office, our doctors’ clinic, our movie theatre and our bank. What are the implications of this? How should we be designing these separate AI systems in a way that they share, but do not share too much?

How spaces are used and utilized have changed because of the onset of the pandemic and AI developers and designers must reassess their assumptions. With the lines blurred between home life and work life, a great deal of thought must now be put into AIX design ensuring end-users are part of the process in defining how AI not only understands, but also acts to provide optimal user experiences based on their environment.

What needs to be considered as both AI-enabled services evolve is where will the line drawn between a home and work AI offering. Will they be mutually separate entities or somehow merge and contain the ability to communicate with each other? It may be a dilemma for some and an opportunity for others, but worth discussing now as we re-establish commonly held assumptions about our spaces.

David Foster, Head of Lyft Transit, Bikes and Scooters, believes Interoperability is key.

“AI is typically evolving today in isolated islands – most of our cars aren’t talking directly to our home and certainly aren’t talking to our home without our explicit inputs to that AI in the vehicle. I think ultimately that will happen, because we have to assume that AI is going to operate in a heterogeneous world with a lot of non AI-driven consumers or other devices or vehicles … so interoperability is going to be key. The ability to be adaptive and predictive and context aware is also going to be key. “People might be nervous about AI, talking unaided let’s say from our car to our home or to our work because of concerns around security or identity or intent. But using AI so that I can ask my car to turn on the lights or the heat, my house on the way home … I’m showing intent and I’m showing context. I think many people would be comfortable with that type of approach today.” As we design new artificial intelligence experiences for end-users, it is going to be evermore important to ensure we consider our environment and the contextual realities that we assume as humans within different spaces. But even more important will be the challenge of codifying our very human understanding of the world based on our sense of place and the behaviours and especially the information that we share between those spaces. When our work AI and home AI converge with our entertainment AI and our healthcare AI, there are some very serious implications about how data is shared across the systems, when it is shared and for what purpose. Developers and policymakers will need to consider the firewalls and handshakes that will need to happen, and end-users should be part of this process every step of the way.

A principal theme in Level 3 of the AIX Framework revolves around understanding. AI, at this point, understands the patterns and principles across systems to meet predefined missions.
Of note, AI shares learning outcomes to achieve a broader mission. Whatever that mission is. It could mean interpreting the mood of an individual at one end of the spectrum or helping society as a whole grow and learn at the other.

An example of a value defined as a purpose for AI occurred recently when EU consumer group Euroconsumers, published a white paper on how AI can be leveraged by consumers to accelerate Europe’s sustainability agenda.

Two key recommendations came from the report:

AI driven tools and complementary technologies can help power the sustainability transition in different industries including household utilities, food, mobility and retail

There is currently a significant lack of trust and satisfaction in the consumer AI experience.

Companies developing consumer-facing AI services for the green and digital transition have a perfect opportunity to help people achieve their sustainability goals and demonstrate they can deliver on trustworthy AI at the same time.

There was also a stark warning for all AI developers: Trust is paramount.
“Without trust from the consumer, AI will not be able to achieve its true potential,” said spokesperson Marco Pierani. “It would only be detrimental. More now than ever, tech companies should maximize their efforts to create AI that would not only improve the lives of consumers, but society as a whole.”

For Alex Zafiroglu, Deputy Director at the 3A Institute, purpose speaks to the underlying problem that the AI is solving for the end-user. This purpose must align with our goals and this is the context that needs to underpin the AI’s understanding and functioning. “We need to have a usage roadmaps or experience roadmaps, particularly for thinking about an AI experience framework, such as the [AIX Framework]. And in that case, you need to think very critically about those humans, who are at the far end of the solution who are basically what the industry would call end-users. You also need to think about the value that is being generated by the application of artificial intelligence solutions in a particular context, because context is incredibly important. You need to think about, to what ends are you building solutions, both for those end-users and for your direct customer.”

Mita Yun, the co-founder and CEO of Zoetic AI, makers of the emotive companion robot Kiki, suggested in a recent blog that when we think of Alexa having a personality, we’ll describe it as her responding to certain things in a certain way – what she does or doesn’t do based on a question or request.
“That’s because Alexa doesn’t really have a personality. Almost every single AI or voice-based product does not have one. When The Food Network discusses how Rachael Ray’s “personality” will come through the voice of Alexa, what they mean is that they’re adding expressions in her voice.

Then again, when it comes to personality and context, there is an argument to be made here that it is not necessarily about personality as much as it is personalization. In that regard, there are questions to ponder, key among them what do developers of AI systems and applications need to think about as the technology starts to allow for much more personal, real-time engagement?

Just as humans interact through non-verbal cues, how can AI know and respond appropriately if we are annoyed, angry, sad, etc? How should it behave differently for different personalities? Rather than a home AI, a car AI, etc. should the future be more concentrated around personal AI? What are the dangers of that?
This is all about the context of people’s unique personality and using tried-and-true AIX Design methodologies that allow AI to take into consideration different people’s attitudes, beliefs and everything else that make up a personality.

There are a multitude of questions that must be answered such as how will an AI system serve one person differently than another and how can it understand that people have moods? How does the feedback loop shape our personality? What personality should we show if it is a point of feedback?

Feedback loops, writes Natalie Fletcher in this blog from AI company Clarifai, “ensure that AI results do not stagnate. This also has a significant advantage in that this data used to train new versions of the model is of the same real-world distribution that the customer cares about predicting over. Without them, AI will choose the path of least resistance, even when that path is wrong, causing its performance to deteriorate. By incorporating a feed loop, you can reinforce your models’ training and keep them improving over time.”

Certainly what must also be avoided is a repeat of the Cambridge Analytica-Facebook scandal, which used behavioural models based on the OCEAN model – an acronym that stands for openness, conscientiousness, extroversion, agreeableness and neuroticism that was first developed in the 1980s to assess an individual’s personality.

To that end, what worries David Foster, are AI-based systems that are controlling critical things and are thrown into the market before they are fully either developed or regulated in some sense or another. “I do still think that the human brain is a marvellous piece of computing equipment. And we do not quite fully understand all of the calculations that we are subconsciously making as we go about the world today. Therefore, how do we model those so that AI can make equivalently good decisions. And how do we model the decisions where there is no good answer to the problem?” And since everyone’s personality is different, a dose of reality is also needed both for now and in the future via this insightful observation from Alex Zafiroglu: “As an anthropologist and as someone that has worked in advanced R&D technology company and worked on product teams, I would say, no, you’re never going to get a solution that works for all people at all times.”

While still a long way away from being truly contextual, a good example of AI understanding context occurred last year when Amazon introduced Sidewalk, its vision of what it called a “neighbourhood network” designed to make a person’s devices work better both inside their home and beyond the front door.

In the future, Sidewalk is meant to support a range of experiences from using Sidewalk-enabled devices to help find pets or valuables, to smart security and lighting, to diagnostics for appliances and tools.

There will no doubt be many iterations of Sidewalk and other services like it, and through it all developers, in particular, need to think about not only creating a cool new AI application, but considering the contextual layers required in creating the optimal AI experience for end-users (while tip-toeing the potential ethical minefield).

As it pertains to how end-users will interface with AI, IBM in a posting on its Web site entitled How conversation (with context) will usher in the AI future, points out that in the “past few years, advances in artificial intelligence have captured the public imagination and led to widespread acceptance of AI-infused assistants. But this accelerating pace of innovation comes with increased uncertainty about where the technology is headed and how it will impact society.

“The consensus is that within three to five years, advances in AI will make the conversational capabilities of computers vastly more sophisticated, paving the way for a sea change in computing. And the key lies in helping machines master one critical element for effective conversation – context.”

Context can be defined in many ways, but at the end of the day it is about ensuring that AI can read all of the signs be it the need to understand its environment or behave in appropriate ways depending on the situation. We humans read contextual cues all the time but lump it under what we call ‘common sense’. However, for AI systems and products, common sense isn’t very common at all.

Charles Lee Isbell Jr., Dean of Computing at Georgia Institute of Technology in his interview for this report was asked the question ‘do you believe it’s possible to teach machines common sense and the assumptions we’ve built up as humans?’ “The answer is yes. We can build this and in fact, we must build these kinds of notions. And I think the way we do it is around story. We tell ourselves stories about how something is supposed to happen, and we build up these kinds of data structures and experiences that allow us to generalize from one to the other. What does it have to do with built in assumptions? “Well, there’s a whole bunch of assumptions built into that. The assumption is that other people are like you. And that they are doing the things like you are and that you can predict what they are going to do, because you know what they are going to do. You are fundamentally building this idea that the things I am interacting with are like me and they have the same desires and drives and the same physical limitations. And so on. Now this is a problem. “If you actually don’t have anything in common physically or psychologically or whatever, because you may not even have a fundamental language upon which you can agree about what’s happening, common sense no longer works.” In the digital age, they say that content is king. If so, as we move into a new era of developing systems and products for AI experience, it could be argued that context is queen … and we know who’s the real power behind the throne.