Artificial Intelligence (AI) is and has been on people’s minds for a long time. Advertising agencies and marketing experts talk about:
- What does Artificial Intelligence mean for marketing agencies?
- How Artificial Intelligence Is Revolutionizing the Digital Marketing…
Such titles promise much more than most of the blog or webpage entries deliver.
One of the criticisms of AI is that such systems are unable to ace an eighth-grade science exam. The main reason being that current AI systems:
“…[cannot go] beyond surface text to a deeper understanding of the meaning underlying each question, then use reasoning to find the appropriate answer.” (p. 63)
Schoenick, Carissa, Clark, Peter, Tafjord, Oyvind, Turney, Peter, and Etzioni, Oren. (September 2017). Moving beyond the Turing Test with the Allen AI Science Challenge. Commun. ACM 60(9), p. 60-64. DOI: https://doi.org/10.1145/3122814
Check out the video at the bottom of this post !
Read the rest of this blog entry to:
- define what an expert system is;
- show why Pinterest’s updates are based on an imperfect AI system;
- illustrate the challenges of using AI to augment marketing;
- watch an interesting video about AI and learning science further below; and
- ask you for your feedback, input and opinions – join the discussion.
This entry is part of our series of posts on AI. To stay tuned and get the latest updates, including on AI and marketing, sign up for our newsletter.
This project is part of our White Paper project for the Competence Circle Technology, Innovation and Management #ccTIM from the German Marketing Association (Deutscher Marketing Verband).
This post continues our discussion entitled, What is marketing automation?
In the 1980s, we were all interested in Decision Support System(s) (DSS) and expert systems. The use of AI garnered a lot of interest from the business press.
Using AI became easier, at least in theory, thanks to the rapidly decreasing costs of calculating or doing the arithmetic for ever larger data sets. This made it feasible to use many mathematical operations to gain insights into user and customer behaviour.
At the same time, AI systems represented the risk of amplifying implicit bias contained in the data sets they were trained on. In turn, some systems can make wrongful inferences or judgments about users. Below we attempt to define what an expert system is.
[su_box title=”Defining an expert system” box_color=”#86bac5″ radius=”9″ class=”aligncenter max-width: 700px”]
An expert system uses specialised knowledge and expertise from a human expert in a particular problem area and converts it into software code. With the help of such code, the expert system can emulate the decision-making ability of a human expert. It allows the system to perform at a level of competence that is better than that of non-expert humans.
Expert systems are part of a general category of computer applications known as artificial intelligence.
Expert systems can be used to diagnose patients, to put together a system that identifies fake news, and so on. Difficulties can arise when interpreting results produced by “black box” systems whose workings are often hard to analyse.
Edward Feigenbaum is seen as the father of expert systems.
See also definition by Encyclopaedia Britannica.[/su_box]
Of course, in cases where decisions can be clearly defined with one or even many algorithms (i.e. mathematical operations), we expect expert systems, and thus computers, to take over most of the tasks currently done by humans.
For an expert system to work well, two things are paramount:
- its rules and algorithms need to work properly, and
- the rules and decisions made need to be the right ones.
Hence, expert systems are often downgraded to represent expert support systems, which support humans in making better decisions. We define expert support systems below.
[su_box title=”Defining an expert support system” box_color=”#86bac5″ radius=”9″ class=”aligncenter max-width: 700px”]
An expert support system helps people solve problems. Like an expert system it allows the system to perform at a level of competence that is better than that of non-expert humans.
For instance, with Legalos, the user of the expert support system enters relevant information. The expert support system then uses this information and generates a template, for example a contract between a company and its cloud services provider. Here, the expert support system can provide the entrepreneur with several types of standard contracts very quickly. In turn, this helps keep a company’s legal costs down.
See also: Luconi, Fred L., Malone, Thomas, W. & Scott Morten, Michael, S. (December 1984). Expert systems and expert support systems: The next challenge for management. Boston: MIT working paper #122, Slong wp #1630-85. Retrieved 2018-06-12 from http://dspace.mit.edu/bitstream/handle/1721.1/47478/expertsystemsexp00luco.pdf
In general, an expert system must acquire knowledge from experts. Such insights are then applied to a large set of probability-based rules to make a decision.
By contrast, an expert support system still requires the human user to weigh some of the factors and then arrive at a decision.
2. Pinterest updates – more noise
Many companies use such technology. For instance, Pinterest and Instagram use similar AI to figure out what Pins you should check out on Pinterest or which Instagramers you should follow. Twitter operates the same way, and so does Facebook (see your newsfeed) or LinkedIn (whom you should connect with).
Recently, I got just such an update (see image below), suggesting that I go and check out 18 pins I should be interested in, based on my board #MCLago.
3. When expert systems fail to augment marketing
As you can see in the image above, whatever criteria Pinterest used to determine what pins might be of interest to me, ‘common sense’ was not programmed into this decision-making process. How it concluded that I wanted to meet single men is a mystery to me.
Why I should care about Lipitor – a prescription drug – is unclear. Yes, I do post medical stuff, but primarily about minimally-invasive endoluminal or endolumenal surgery, because of my work with Lumendi Ltd.
On the upper left in the above screenshot you can see some people in a photo. The program concluded this from one of my recent pins. I had recently posted something – with video – about a Syrian refugee (the picture shows the trainee with her co-workers and bosses). So the thought was I would like another one. Well, here a deeper understanding of the meaning underlying the item I pinned would have allowed Pinterest’s expert system to find a picture in a similar realm.
Instead, it inferred that I would be interested in “Who’s In and Who’s Out for the Next Season of Nashville“. Seems a little ridiculous.
Basically, an expert system needs to be able to do more than do simple math. Moreover, predictions are not enough to automate the decision-making process or task with the help of AI (see Agrawal, Gans & Goldfarb, Spring 2017). Below, we list the six key things an expert system must be able to handle to get AI to deliver the most value.
Agrawal, Aja, Gans, Joshua S. & Goldfarb, Avi (Spring 2017). What to expect from artificial intelligence. MIT Sloan Management Review, 58(3), pp. 23-26. Retrieved 2018-06-12 from https://sloanreview.mit.edu/x/58311
[su_box title=”Expert system for automation: Performing these 6 actions is a must” box_color=”#86bac5″ title_color=”#ffffff” radius=”5″ width=”px 700″ ]
An expert system not only executes tasks efficiently, but more importantly, gets a few things right, such as:
1. Data analysis: What kind of photos or status updates does this individual post?
2. Prediction: What action would the recipient take and / or would this potentially be of interest to the customer?
3. Judgment: Yes, this status update / photo is of interest to the user / customer.
4. Action: Include photos of interest and mail out newsletter to subscriber, user or customer.
5. Key Performance Indicator (KPI): The recipient has clicked on several of those 18 suggested pins. This expert system did better than average.
6. Quality of service: The pins the client clicked on provided content that represents added value for this user.
Unless the expert system we use can do the above, marketing activities are more likely hampered than augmented.
4. Ultimate test: Does this content answer the question I am asking?
As pointed out above, whether the user clicked on several suggested pins is one possible KPI. For instance, I clicked on more pins than could be expected. Nevertheless, ultimately it is not the clicks on pins recommended to me by Pinterest that matter. Instead, the ultimate criteria for a user is whether those pins provide information that represents added value.
As the above shows, somebody is spreading her opinions regarding KPIs. We all know that the life cycle of a client is important, but if you are running a start-up, this could be of lesser importance than getting new clients who can help you pay the rent.
Strategising your sales revenue approach is interesting, but not something that everybody needs to do. Treating your clients respectfully and providing a service that they feel is worth the money they paid you most certainly helps. When it comes to revenues, that applies regardless if you track it with a spreadsheet or do it on a piece of paper.
5. What is your opinion?
The verdict is simple. The expert system that Pinterest uses to serve me weekly or more with an email of suggested pins does not do a good job. The recommendations it makes indicate that the AI system lacks a deeper understanding of the meaning underlying each pin I uploaded. In turn, it cannot source pins that might interest me.
But do not be fooled, neither Twitter nor Instagram do better with these things. Developing a well-functioning expert system takes a lot of work and testing.
However, the fact that expert systems do make errors was already pointed out by researchers in the 1990s:
Williams, Joseph (1990). When expert systems are wrong. In Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems (SIGBDP ’90), p 661-690. DOI: https://doi.org/10.1145/97709.97761
On reviewing the challenges and benefits of expert systems and neural networks, things do not appear to have become easier in 2014, even though the benefits can be substantial (e.g., https://link.springer.com/article/10.1007/s10916-014-0110-5).
What I would love to know is what you think about these issues in 2018 (#ccTIM will continue updating you on this subject):
- Do you think AI (artificial intelligence) will revolutionise marketing? Please explain why or why not.
- Do you have examples of great expert systems, for instance in marketing, management or production?
The author declares that he had no conflict of interest with respect to the content, authorship or publication of this blog entry (i.e. to the best of my knowledge, I got neither a freebie from any of the aforementioned companies, nor are they our clients).
Check out this video, worth watching – see quote at the beginning for reference to research paper that is source for video below.