Tag Archives: AI

Men First

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Thread ad on trainSitting on the tube on my way to work a slogan in a new ad on the train caught my eye, “Cheat. Dress Better.” Whatever could it mean? Not being a particularly natty dresser myself I took a closer look. The company called Thread brings “expert stylists with clever AI to find great clothes in your style, size and budget.” Wow that sounds just what most of us need. First warning sign all was not well was when I looked closer at the mobile app also being shown on the ad, which simply showed a pair of jeans, trainers and a plain shirt. The sort of uniform casual look that many of us dress ourselves in each day. Why do we need other intelligence (human or artificial) to dress us when we can do it ourselves perfectly well? I was intrigued by their claim that they could dress us better and so went to their website.

Then I saw red. The website was not ready to dress me! It is available… but only for men. The womens’ one is still in the pipeline as they are busy trying to recruit “a team of women’s stylists” (sic) and “a whole new system of algorithms to help you find the best clothes for you.” The message is loud and clear: men are more important. Why did they think it was OK to put men first in 2019? And why does it need men to dress men and women to dress women?

Sexism aside, I pretended to be a man to get my perfect clothing combo and set about doing the 3 minute quiz that is on their men’s site. It asks me to choose looks for casual, office, and smarter styles (to look sharp). The models, however, have had their heads removed from view (presumably it is cheaper and does not contravene data laws) and so one is drawn to look at other parts of their bodies. I am not sure I should be doing that. Then it gets a bit more personal, asking me which shops I like to buy at and how much do I usually spend on clothes.

Then it asks me to give my sizes for the various body parts, like my waist size and leg length. Then a strange category appears called ‘fit concern’. We all think something is not right about our bodies, I suppose, and would prefer to be shorter, taller, have longer arms, smaller waistline and so on. What else was on offer opened my eyes to other possibilities. I selected muscular thighs and long arms. I pressed to submit but the app came back and told me off for missing out selecting my shoe size (that is also a no-no in interface design as I was not interested in shoes and they should not force me to divulge personal information I don’t want to give). The menu selections continued (I was onto 5 minutes by this stage).

Eventually after opting out of joining their mailing list (a dark pattern that makes me have to deselect rather than being able to opt in) I am presented with some nice sketches of different clothes categories. After selecting from these, the app asks me what clothes do I own? At this point most people would have given up and resorted to browsing clothes stores online. But if you continue – you are asked even more personal questions; including what is in your wardrobe and then at the end they ask you to upload a photo of yourself to show what you look like. “Seeing what you look like will help us make better suggestions.”  I drew the line there, too. I thought that must surely be it but no the app then asks you to add more information about yourself: your hair colour, skin colour, eye colour. To help, they give you menu options, including one I selected out of curiosity ‘white with freckles’. So not me!

Finally, an image of my very own stylist called Alexander appeared on my screen. Looks promising. Trouble is that a message then pops up to say that it will take Alex several hours while working with the AI algorithms to come up with a look for me. That is really crap AI and even worse UX design. Meanwhile, to keep me amused some handy fashion tips appear on the site based on the body type options I selected earlier, about how to dress muscular thighs and long arms. Honestly?

My expectations have been let down too many times on this customer journey. Rather than cheating fashion I feel cheated. I certainly won’t be going back to the site for when the women finally get their expert female stylists to work with their hopefully gender neutral algorithms.

It is great that developers are working out how to enable real humans to be able to interact with AI to make informed decisions about something – be it what to wear, what to eat, what university to choose, what car to buy, what book to read, and so on. This human-AI tool has potential to show how human and artificial intelligence can work together to provide personalization and a new kind of customer attention. But right now it needs a lot more work to make it work. Meanwhile I’ll just pop into Jigsaw and H&M to check out what tops and jeans they have. There might even be a sales assistant on hand to help out if I need a different size, colours or a new combo.

Smart AI

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One of my PhD students has been investigating how environmental sensors can be situated in community gardens to provide new data about light levels. The motivation stems from wanting to help the volunteers who work in these gardens decide what to plant and where best to plant new crops. The data can be digitally visualised for them on a display to provide new information about the patterns of shade and bright sunlight over time. Information they don’t currently have access to. Instead they rely on previous knowledge and experiences of what worked and what didn’t do so well. The aim is to augment people’s existing knowledge with new contextual information, especially where it can be difficult to predict how different areas of a garden changes due to seasonal shifts in light and variable plant growth.

However, the initial reaction from the community gardeners was one of scepticism. They did not want to have ‘computers’ in the garden because that is where they come to get away from the digital world. Instead they value the experience of being in the garden and the importance of ‘getting their hands in the dirt’.

But later, when shown the sensed light levels in different parts of the garden over time, using augmented reality, their initial resistance was replaced with a degree of curiosity. They could see the potential benefit of having such light data to hand that could be overlaid on their schematic sketches and maps of the garden when planning in the potting shed. What they were clear about was not wanting an app that gave them advice  about what to do in the garden.

Meanwhile in India, Microsoft in conjunction with the Andhra Pradesh government have been using AI and machine learning to help local farmers determine when best to sow their seeds for groundnut, rice, maize, cotton and the like. Using 40 years of past data for weather patterns and soil conditions, they have been able to develop a predictive model of the likelihood of rain, drought and even pest attacks. From their models, they can determine when the best time is to sow seeds in a given region so as to optimise yield and reduce risk of failure.

The really cool part of the project though is how the new knowledge is delivered to the farmers. Not via a smartphone app but by Microsoft letting the farmers know simply through sending them a text message – in their native language – to their feature phones.  And for those who can’t read they  leave a voice message. Gut feeling, experience and human wisdom are being replaced by straight forward messages – based on a remarkable combination of weather forecasting, satellite images, machine learning and cloud services. And what’s more, the farmers don’t need to have any sensors installed in their fields.

Who wouldn’t want to take heed from free advice coming from the cloud that is better informed and has a better chance of increasing their income? This seems like a win-win situation for all concerned. Local farmers benefit without having to compromise or change their practice. Scientists get to demonstrate a real impact of AI through increasing the income of people living in developing countries – without replacing them with robots or pushing ads down their phones. And Microsoft can bask in a feel-good factor for providing the infrastructure and wherewithal.

But what happens if the advice given is wrong?  And the farmers end up planting too early or too late resulting in a poor crop yield. Do they simply shrug their shoulders and think, oh well even AI can get it wrong. Do they continue to follow the text advice, next time – just like many people now have taken to blindly following their GPS when driving – sometimes against their better judgement. Or will they abandon the advice, revert to their gut instinct and decide once more for themselves? I supect we are in the midst of a quiet revolution in rural farming and there is no looking back. The AI will keep improving, too. But then again, global warming is also in our midst. And that is unpredictable.