Engaging with everydAI: How to move AI from imperfect algorithms to perfect user interactions
The Engaging with EverydAI webinar and Everyday AI research are now available. Access them using the links below:
The Engaging with EverydAI webinar
Download the Everyday AI research
View the Everyday AI research insights
Q&As from the Engaging with EverydAI webinar
Downloadable AI project tools
Making Use of AI
Human interactions with AI devices can sometimes be a cause of frustration, but what’s really behind this failure, and how can AI services give a better experience?
In only trying to improve their algorithm, many AI-empowered systems are keeping their focus too narrow. For many users, the next step isn’t data being crunched more effectively but a need to be able to guide their device. A recognition that a user's state of mind can change the response they expect to receive. Past choices alone can’t always be relied upon to generate the right answer in every situation.
Everyday AI is a research collaboration between Alice Labs and the Centre for Consumer Society Research, University of Helsinki, in partnership with Reaktor.
Engaging with EverydAI webinar
The Engaging with EverydAI webinar took place on 5th May at 9 am CET. You can watch back the full research presentation and panel discussion to discover:
- What the challenges are that leave users feeling helpless with AI services
- Why one size doesn’t fit all for AI recommendations, even when looking at an individual level
- The different roles users adopt when interacting with AI
- What steps AI services can take to serve their users better
- Why it's time to move beyond the pure algorithm and allow the human touch
Watch the webinar:
Download the full Everyday AI research
Documenting everyday experiences with two algorithmically-empowered services, recommender systems and digital assistants, the Everyday AI research is a vital read for anyone working with AI-systems or looking into their application.
Downloadable tools to assist your AI projects
There are many challenges to getting an AI project off the ground, or refining one already underway.
To assist with this process, Reaktor is making available a series of exclusive downloadable guides over the coming weeks put together by our expert team.
8 key things business leaders should know about AI
Understanding the impact AI have from a business perspective is a crucial step in ensuring it is sufficiently resourced, in terms of time, money and skills. This checklist of essentials to consider brings together the vital elements and justifications to include when calculating the value and impact of a project and organising its resourcing.
Tips for improving the interaction of your AI-based system
How do you design to minimise moments of frustration for users when they interact with your AI-system? Lead Designer (interaction) at Reaktor, Karri-Pekka Laakso, has put together eight actionable tips to make it a better experience.
7 rules to understand the scope and requirements of AI and data
What expectations and limits should you have of AI? What preparation do you need to make for the data you collect for your AI system? Can the model and the data give you all the answers? All these and more are answered in this downloadable set of rules.
Answered: Your questions from the Engaging with EverydAI webinar
The Everyday AI research presentation and panel discussion prompted many interesting questions from the audience watching.
Time constraints meant we were unable to answer them all during the webinar. However, we collated the questions and put them to our panel after we went offline. Here are their responses gathered together.
Engaging With EverydAI Insights
You are also invited to read further insight summaries and opinions into the full research.
What happens when AI makes a mistake?
Alexa, why don’t you understand what I want rather than what I say? AI systems are on a constant learning curve to algorithmically improve their replies. Users are generally appreciative of how they respond, but what about when AI gets it wrong? Maybe it keeps mixing up two places with the same name. Or recommending you watch more movies because you watched something for work or study reasons. How can this be solved?
Why humans are complicated in a way AI may never understand
Can AI tell when you’re in a silly mood? Or a sad one? What about when you want to experiment with a new content genre but need a form of curated guidance? A user can ask the same question in two different moods and expect different answers. How can AI services respond to this need?
Is design the key to the next step forward for AI?
It’s tempting to see the algorithm as the continuing cure to all AI problems, but it overlooks the importance of how users interface with a service. What happens when a user can see a way to progress a query but the AI can’t? Without the tools to steer, is that a failing in AI or design? Is the next step in AI going to be design-led?
