Technology: A tool or an agent?

Last week, the CEO of Google, Sundar Pichai, presented in a quite impressive  keynote the capacities of their new digital assistant Google Duplex. They showed how their assistant was able to make calls to book appointments or make restaurant reservations while carrying out quite naturally sounding conversations. In fact, it was able to understand and react to ambiguity and could deal with information that was not quite what it originally asked for – Quite a leap forward from the very limited interactions and tasks that for example Apple’s Siri used to be able to perform.

Sparked by this presentation, this article juxtaposes the most dominating two views on the position of technology currently exposed by the main technology providers:

One view is that technology mainly serves as a tool for people, as sort of a “bicycle of the mind” enhancing human capabilities. This view is notably demonstrated by the philosophies of Apple and Microsoft. Both these companies come from a background of being the main drivers of personal computing and come from the same era in the 1970’s.

The other view is that technology is supposed to act as an agent, that is by carrying out tasks for you independently. This view is attributed to Google and Facebook – companies that are much younger than Apple and Microsoft from the internet era. In fact, this is exactly what the new abilities of Duplex should show you: It is able to carry out tedious tasks, such as making appointments, for you instead of you. In this second scope, you could also place the autonomously driving cars.

One of the main implications of these different view points is their different ethical setup. While in the tool model, the users – the ones literally using the tools – are always in control and therefore assume responsibility of the actions of operating these tools. On the other hand, the question of responsibility becomes less clear in the second model when technology has its own agency. If this technological agent, say your autonomous digital assistant or your autonomous car does something that goes against your intentions? Who is at fault? You, the technology provider, or maybe the tech agent itself? What if it actually acted upon something that you wanted but maybe only subconsciously?

Therefore, defining the exact scope, the precise intentions and possible means of the actions performed by such an agent seems crucial. In my opinion, having these three things transparent is what we as users should demand from all our “agent providers” aka Google and Facebook.

Repost: Can an algorithm beat VOGUE in predicting fashion trends?

What is Fashion’s next big trend is a three digit billion dollar question. It is however not a question, that, personally, I thought I wouldn’t have much to contribute to, given that I work in a technology company, where the biggest trend seems to be goofy meme t-shirts. So, naturally, our approach to fashion trends is completely non-traditional and does not rely on our own fashion expertise (which is probably a good thing).

Traditionally, trend scouting starts by closely following high fashion designers and fashion shows, which may be picked up later by the mainstream fashion industry.

Other trend sources can come from films and TV series, influences from YouTube and the blogosphere. To determine exactly what will sell in the next seasons used to be a question only industry experts, with decades of experience, could determine. Then, it was more of a guessing game, when a trend wave would pick up and how long it would last before slowly ebbing out or being completely consumed by another trend.

At Lokad, our data scientists have developed algorithms to revolutionize this trend scouting process, allowing for large scale reliable and accurate sales predictions based on prior seasons’ sales history. Furthermore, while trend-scouting may be the holy grail of purchasing decisions for fashion organisation, identifying statistical noise is equally important and usually completely overlooked. Excluding noise in demand, that is observed “links” between products that are happening at random without any particular trend connection, can boost the effectiveness of any buying decisions.

To find out what actually drives sales, that is what attributes of a product make customers buy a product is somewhat elusive. Defining a product for now is more of an art than a science. At Lokad, our quantitative approach allows us to study precisely what has triggered sales, may it be a fancy product name, a combination of price, value of material or may be a certain visible accessory, which made a customer choose one product over other similar products.

In particular, Lokad can leverage similarities between products to predict sales quantities of products that have never been sold anywhere by comparing it to sales of other products in the past. To look for similarities between products, Lokad analyses not only product attributes such as materials or colours, but also envisions leveraging the selling history of customers.

Identifying early trend adopters within a customer base allows Lokad to obtain a next season “preview”: for example, imagine you have that one very fashionable colleague who always wears a certain colour before everyone else does. With big data crunching tools, Lokad can identify this colleague and through their current shopping habits can pick up what many others will want to buy next season.

However, in the end, there is no way around a team of fashion experts who can distinguish the mega trends from the “ordinary” ones. However, with Lokad, this expert group can leverage their insights on a much higher scale with high statistically sound computational power – minimizing costs for any brand or market place. This is what we refer to as augmented human intelligence, putting the enablement of the expert group as the ultimate goal of our work.


This article was first published on LinkedIn on October 14, 2017. 

Disclaimer: Katharina currently works as a supply chain scientist at Lokad.