• John Ennis

5 Ways for Sensory and Consumer Scientists to Prepare for Artificial Intelligence

This article is part of Aigora's "Original Content" series, which consists of our original thoughts at the intersection of consumer science and artificial intelligence.


In 1848, William Carnegie, a Scottish weaver, was unable to find employment as the industrial revolution had destroyed the market in Scotland for hand-produced fabrics. Their family starving, William and his wife Margaret decided to immigrate to America in search of better prospects - in September of that year, they set out for Allegheny, Pennsylvania with their two sons, a five-year-old named Thomas and a thirteen-year-old named Andrew.


Andrew Carnegie and his brother Thomas, three years after arriving in America.

In America, finding the market no better for hand-produced wares, both William and Andrew sought and gained employment at Anchor Cotton Mills. William soon quit, unable to adjust to the new industrial approach to production, and remained unemployed for the remainder of his life and dying - essentially of depression - at age 51. Andrew, on the other hand, rose to the challenge of becoming the family's breadwinner, embraced new industrial approaches, and became one of the wealthiest Americans of all time.


I begin our blog post today with this story not because the fates of either William or Andrew necessarily await us - the social safety net is much stronger now than it was in 1848, and I don't promise that reading the Aigora blog will lead to a net worth in excess of $300 billion - but rather because the contrast in the approaches of the two Carnegies to the first industrial revolution is instructive today as the third industrial revolution is already underway.


There are many perspectives on what defines an industrial revolution, but a perspective that I subscribe to is that, every so often, a new and general-purpose technology (GPT) appears that transforms every aspect of life. In the first industrial revolution, the GPT was the steam engine, which allowed for the harnessing of power away from waterways and without using animals. In the second industrial revolution, the GPT was electricity (historical note: some would argue that the internal combustion engine was also a GPT), which allowed for power to be produced centrally but used locally. Now, in the third industrial revolution, the GPT is automated information processing. Because intelligence can be defined as the ability to accomplish complex goals - and because automated information processing can, in many limited situations, accomplish complex goals - it’s reasonable to say that the GPT of the third industrial revolution is artificial intelligence (AI).


To be clear, we're not yet at a point where machines can perform arbitrarily chosen tasks with human-level ability - an AI that could accomplish such goals would be called Artificial General Intelligence, or AGI, and most experts believe that such technology is still years away. But it's true that the last decade, and especially the last five years, have brought numerous stunning advances for artificial intelligence in areas as disparate as image recognition, natural language processing, and strategic calculation. These advances have led to such applications as countertop vocal assistants, efficient route and schedule planning as used by almost everyone with a smartphone, automated production of news reports, automated stock trading, reduced energy consumption, and - perhaps most surprising - the defeat of the world's top Go player. The reason the defeat of the world Go champion was so significant is that Go has long been considered so subtle in its strategy and astronomical in terms of the number of possible positions that many believed that it would be years before a mechanized approach to Go could match human performance.


So, once we accept that artificial intelligence - in at least a limited form - is in the process of transforming society, the obvious question is how do we best prepare for it? And, since you are reading this blog, we can refine that question to a more specific one, "How should sensory and consumer scientists best prepare for artificial intelligence?" Luckily, helping sensory and consumer scientists prepare for artificial intelligence is the mission of Aigora, so you've come to the right place to have this question answered! Thus, with today's post, we provide the five ways that we believe best help sensory and consumer scientists to prepare for artificial intelligence, leveraging automation to their advantage along the way.


Way 1: Cultivate and participate in a data-aware culture


As we progress through today's post, we're going to move up what we call the "Pyramid of Preparation."


The famous Aigora "Pyramid of Preparation." AI is coming, we must prepare!

At this base of the Pyramid is the widespread adoption of data-scientific tools and workflows that allow team members to easily share not only data but scripts reflecting the work they've performed on the data. While the adoption of processes that originated in computer science may - at first - seem a bit daunting to those from a non-computational background, the need for such processes has driven the production of many tools, such as RStudio, that make the adoption of these processes much less stressful than it would once have been. For further discussion of data science and the benefits it offers sensory and consumer scientists, we recommend our post "5 Reasons Sensory and Consumer Scientists Should Learn (a Little) Data Science."


Learning a little data science is a good idea, plus it's fun!

Way 2: Automate routine activities, especially report preparation


Moving up the Pyramid of Preparation, we arrive at the second layer, which is seeking to automate as many routine tasks as possible. The first step towards this end is to record whenever you find yourself doing tasks more than once. There is a principle in data science that if you find yourself copying and pasting text, you should think about creating a function instead - you can apply the same principle to your regular life. Once you find yourself doing a task more than once, it's time to write out the process behind the task. As W. Edwards Deming said,


"If you can't describe what you are doing as a process, you don't know what you're doing."

For example, as I've been writing these blog posts, I've found that many of the steps are the same from week to week. Consequently, I've created a cloud document that describes the process involved in creating new blog content in 26 steps (at time of writing). With these steps listed, I've been able to refine my blog writing process itself, with the benefit that I inherit any improvement to the process in all my future blog writing. Specifically, I can ask myself whether some steps can be streamlined, automated, delegated, or eliminated altogether. For example, Step 3 for me used to be "Proofread." I then found I could use the service Grammarly to automate this step. Also, I've found that it's more efficient to conduct some steps in a different order, while other steps have been reduced or dropped as unnecessary.


The benefits of working on my process don't stop with myself, however. When I hire someone to help me with these blog posts, I'll be able to share the cloud document, so that my new employee can use what I've learned, get updates in real time should I improve the process, and even make suggestions to improve the process. Finally, because this post is on the topic of AI preparation, it's worth noting that we can use AI itself to document processes, as cloud documents can be accessed via smartphone, enabling the native speech-to-text ability of most smartphones to speak steps of a process directly into the document.


If you're not looking to use existing technology to improve your processes, you're at a disadvantage professionally. On the other hand, by leveraging new technologies to improve your processes, new levels of productivity are just waiting for you. For example, for just one example of a process that is now largely automatable and which consumes resources for many sensory and consumer scientists, we recommend our post "5 Tools to Help Sensory and Consumer Scientists Automate Beautiful Reports."


Automation of routine processes is your friend, freeing resources for more creative work.

Way 3: Incorporate the use of advanced computational tools into regular practice


At the third layer of the Pyramid of Preparation, we find "Use of advanced computational tools for sensory and consumer science." This topic is vast by itself, so we will simply state here that advances in computing an algorithmic power allow sensory and consumer scientists to pose and answer questions that might never have been considered only just a few years ago. For more on this topic, we recommend our post "5 Computational Advances that are Helping Sensory and Consumer Scientists Answer New Questions."


Answer new questions with advanced computational tools.

Way 4: Stay aware of new applications of artificial intelligence


At the top of the Pyramid, we find "AI awareness." What this top layer means is that many of the cutting edge applications of AI require data sets that are either larger or better organized than those to which most sensory or consumer scientists have access. As algorithms improve - and as sensory and consumer science departments adopt data-aware cultures, as mentioned in Way 1 - this limitation will gradually be removed but, for now, the best thing that sensory and consumer scientists can do is to simply keep their eyes open as to what are the relevant developments at the intersection of consumer science and AI. To help with this goal, every Friday we release a new post in the series, "Eye on AI." Also, for a list of breakthroughs that AI offers sensory and consumer scientists, some of which are ready for implementation now using chatbots or AI-powered speakers, we recommend our post "5 AI-Powered Breakthroughs That Will Help Sensory and Consumer Scientists Scale New Heights."


Awareness is the first step towards change.

Way 5: Never stop learning


The fifth way was originally going to be to look for opportunities to combine the prior four ways - for example, to automate reports that include advanced computational tools or to combine speech-to-text technology into one's report preparation. However, as I wrote this post, I realized that the fifth way is a "meta" way. Expressly, we must accept that the days of people receiving an education in their twenties and then working the rest of their lives from that education are over - we must now all dedicate time every single day to learning new things. The good news here is that it's the best time in the history of the world to learn new things. For the first time in human history, knowledge is truly abundant, and there are close to no limits on the information and even the technology available for the interested learner. In fact, the biggest challenge in today's information society is choosing what to spend our limited time learning. Fortunately, Aigora exists to help sensory and consumer scientists with that choice, and we hope that blog posts like this are helping you with your choices.


Ask and ye shall receive.

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