What artificial intelligence can and can´t do
For years, artificial intelligence has fluctuated between the terms “Terminator”, “technology of the future”, or “new superpower”, and “doesn’t work”, “is disappointing”, or “no confidence”.
It is not surprising that there have repeatedly been so-called “AI summers” and “AI winters” over 70 years. More specifically, there have been two AI winters since the 1950s, when AI was being researched and developed: In the early 1970s and late 1980s, the high expectations for the technology were dashed. Investors pulled out, and researchers got less funding.
What changed? The technology got better, we have powerful servers with lots of storage, cloud computing, and there is Big Data. A new opportunity for the potential of artificial intelligence! Yes, we are currently living in an AI summer. AI is again a superpower, the hope of the future.
You can already tell: the expectations are enormous. What is needed for us to use AI successfully? Realistic expectations! How do realistic expectations come? By having enough information.
You will learn what AI can and cannot do in the following article.
What artificial intelligence can do
What is AI? How does AI work? What are the opportunities and dangers of artificial intelligence?
The oldest artificial intelligence:
The history of artificial intelligence begins with a problem: In the 1940s, the British mathematician Alan Turing (1912-1954) was trying to build a “thinking machine”, a computer that could develop intelligence comparable to that of a human being. At the same time, Noam Chomsky (1928- ) was developing a theory of linguistics, which was based on a theoretical framework for machine translation. Both areas were closely interlinked.
Turing devised the famous “Turing Test” of the 1950s to answer the question of whether a machine thinks: A machine is considered to be intelligent if a human being cannot distinguish it from another human being through conversation. The Turing Test has always been controversial.
The AI revolution has just begun and has huge potential to expand. In the next two years, everyone will face the concept of Artificial Intelligence.
Stop.
Are you still reading?
Did you make it this far? Respect. An AI wrote the previous section.
At this point, many thanks to Christopher Ringel, who provided us with his algorithm for this experiment! AI was fed to three to four sentences and was used to write a more advanced post.
Basically, you experienced an AI writing about what artificial intelligence can and can’t do.
What did you think when you read the italicized section? Read it again.
I didn’t know what it was about at the end of the text. I couldn’t quite grasp the connections and essence of the text. It felt like a somewhat disjointed and long lead-in to a topic. And most importantly, there was no answer to the question “What can AI do?”.
At the same time, I was intrigued by the many suggestions and ideas offered by the algorithm on the topic of “what can AI do.” Even though there was no finished text that you could use 1:1, there are some elements that you can build on.
But now back to the question.
The second take: What artificial intelligence can do
We already encounter it constantly in everyday life: Amazon or Netflix suggestions according to one’s interests, navigation systems, Alexa & Co. that understand speech and much more.
All these systems fall under the category of “weak AI”, or a bit nicer in English, “narrow AI”. This definition refers to systems that solve a very specific problem. AI applications are very specialized. That means they can do one thing very well, quickly, and efficiently.
For example, our predictive sales software can accurately forecast customers’ future buying behaviour quickly and efficiently from hundreds of thousands of sales data. However, the software is not capable of composing texts or understanding speech. That would require a different kind of AI algorithm.
Nor can AI systems apply general goals to different situations. For example, a goal-oriented instruction such as “make sure you don’t hurt yourself or anyone else” is not sufficient for autonomous driving. No, every conceivable traffic situation, in different variants, must be recreated for the machine so that it knows how to react.
Artificial intelligence can therefore always be put to good use where there are data sets in which the human can first show the machine how to do it. This is also called “labelling.” The machine then gradually learns from this “pretending, or labelling” and can then apply what it has learned to new data.
How well does that work today?
These are realistic expectations of an AI
In special application areas, AI systems already work extremely well today. In principle, all situations that have routine character.
The AI text above illustrates a prime example of the limitations of artificial intelligence. However, there are still very good AI systems that create flawless standard texts. For example, short stock market reports are sometimes already written and published with AI. Texts with routine character. However, most systems reach their limits when it comes to complicated chains of argumentation and questions.
To keep your expectations of an AI system realistic, you should inform yourself about it beforehand. Here are our top 3 questions you should ask:
1. From what data does AI learn from?
An AI can’t know anything it doesn’t have as a basis. If an image recognition program has only seen dogs and cats, it won’t recognize a tractor.
2. How well is the AI already trained, or what is its hit rate?
No system will always be 100% correct. That is simply the nature of machine learning (a subfield of AI).
In principle, machines are constantly making predictions through mathematics and statistics. This includes predictions about whether a product might appeal, predictions about whether there is a cat in the picture, and predictions about what is the fastest route.
Machines calculate probabilities. The more data and training the systems have, the better they become.
Professional AI systems produce up to 98% accurate results.
3. For what purpose should AI be used?
As I said, AI systems are “expert idiots”. You will not be able to use a single powerful AI tool that coordinates and manages an entire company, for example. Too many different capabilities are needed for that. Today’s AI systems are usually helpers for routine tasks or analysis.
This also applies to AI-based predictive sales analytics software. The software makes reliable sales predictions, such as which customer will soon churn, which price is most appropriate for which product, or where there is cross-selling potential. However, the sales team still has the rudder in their hands and decide for themselves whether and which sales actions follow thereafter.
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What can artificial intelligence (AI) do and what can’t it do? – Conclusion
Today, there are no “Terminator-like” AI systems. Most AI applications have tremendous utility as support tools in everyday work: namely, for routine yet time-consuming tasks.
Our tip is to learn about the rough workings of the various systems and ask the right questions. Only then can you also trust an AI-based software in everyday work and use it correctly.
Overall, we can fully agree with the statement of Dr. Martin Klarmann: “Modern technologies, such as artificial intelligence, are overestimated in the short term and underestimated in the long term.”
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