1.

Think collaboratively, not competitively

systems are no longer deaf or blind, infact over the past five years alone the accuracy of this intelligence has increased to an average of 95% when compared to the human eye. Computers can now see, percieve and make decisions based on a statistical judgements. We are very familair with autonomous cars or lawnmowers so take Pinterests visual search as an example; Users are able to search the site by taking a picture with their phone, and with the use of AI, the software will recognise the object and find search results — literally an AI lens in your pocket.

This interesting thing is that AI is now less likely to be pre-programmed but embedded into technology in order to continuously learn behaviours, meaning that they are able to personalise decisions based on what it is learning. The sweet spot for this type of machine learning, is when these decisions do not need to be perfect and can work with humans. Let people do what they do best and let machines do what people do worst (Humans take pictures, machines find answers) because in order for us to build trust in the of AI we must feel reassured, included and informed.

2.

for interaction

I mean it’s very likely conversational AI is the future of interface design, the way in which we are already interacting with AI devices in the home is a huge hint. Just think of the last time Alexa or Google answered your questions, these technologies are already being embedded in our homes so whats to stop even more sensors reading and responding to our needs?

The importance of this evolution is in the interaction with these devices. We need to feel in control, comfortable and as if these devices are helping us be our best selves. Yes, Alexa may tell me the weather but will it wake me up ten minutes earlier if it’s raining? No, not yet.

This is because conversational AI is the next step yet to be realised, the moment a device replies “I don’t understand, teach me” is the moment it has been though. It means that these devices will no longer need to be pre-programmed because they can be programmed through conversation, by the user, at a moments notice. These machines will be defined through their sensory input so what will it be?

This increased flexibility in interaction is one of the biggest drivers in innovation — imagine entering a supermarket and being directed to the isle you needed the most — these interactions with products and product systems are able to define the future of interaction and experience design as they will be embedded within products that learn.

3.

Be transparent, be inclusive

Technology fails when it is not inclusive and AI has the potential to fail on a grand scale. The recent GDPR act has perhaps put this at the forefront of our minds but lets not bash it’s importance because of a barrage of emails. Data privacy is one of AI’s key contraints, without our data (small or big) it’s decision making ability becomes limited. We should not under estimate the transparency needed when it comes to AI using a users data as data is becoming more and more valuable to organisations who want to make money.

To avoid this we should be cautious of misleading others through design. It’s no longer ok to ask if a user can share data with you in order to track their location as it is unclear why you need it. You should provide transparency and include users in the decision by giving them more background — you could use a phrase like “We need you to share your data so we can track your sleeping pattern and help you sleep better” — something that would offer the suer value in exchange for data.



Source link https://uxplanet.org/the-impact-of-ai-on--design-in--c62e4c5e4242?source=rss—-819cc2aaeee0—4

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