Prof. Dr. Ulrich Anders


2020-06-06

AI (Artificial Intelligence) Beyond Buzz Words

ai tensorflow

There is a lot of buzz around AI (Artificial Intelligence). People speak about AI and suggest all kind of areas or problems to which AI will be applied to in the future. However, is this realistic? Marketing campaigns add to this believe, because many tools or systems now claim to have AI. But what does that mean? Where does the AI come in?

Personally I believe that AI will help us solve a lot of problems that we humans otherwise could not find a solution to. The reason for that is basically the Moravec’s paradox. This paradox simply says that (1) the computer is good at things, we are not good at, e.g. massive computations or repetitive tasks. If further says that (2) this is also true vice versa, that we are good at what the computer is not good at, e.g. creative problem-solving. In terms of pattern recognition at the moment it is probably a draw and it depends on the area of application. So AI will definitely have a place in areas that need, require or enable massive computations.

But what is AI? AI can be seen as a set of methodologies, that perform all kinds of cognitive tasks and then make appropriate decisions based on them. There are many solutions in the area of AI but a very prominent one is a neural network. You can see an example of a neural network in the picture above. This is a screenshot from a neural network that was built with Tensorflow. Tensorflow is open source software and can be found on GitHub. That means the software can be downloaded by everybody for building neural networks. Tensorflow is one of the most prominent AI solutions in the world and often also the basis for other project such as natural language processing. But of course there are other renowned toolkits for building neural networks such as PyTorch.

It is easy to spot that the neural network above looks similar to a regression model from statistics with some exogeneous and some endogeneous variables — only with many more layers in between. In my PhD thesis from 1997 about Statistical Neural Networks I have, indeed, shown, that neural networks are similar to nonlinear nonparametric regression models.

So is the application also similar? Just from looking at the neural network above you can easily understand the following points:

  1. In order for a neural network to give an answer to a difficult question you need to exactly specify this question. Otherwise the neural network cannot produce a fitting answer.

  2. You need to train the network for producing an answer. Train means to determine the strength of the connections between the individual neurons. For this you need to have a lot of data that fit to the question. It is obvious, the more difficult the question and the more complex the network is (i.e. the more layers and neurons it has) the more data you need. Also the data need to be of good quality and cleaned for outliers or missing values. Otherwise the result may be distorted.

  3. Building and dimensioning the neural network for the question is a lot of work. Finding, cleaning and storing the data is also a lot of work. It requires a lot of expertise, technical knowledge and IT infrastructure.

At the moment artificial intelligence can only answer the specific questions it has been trained for. Artificial intelligence therefore will not replace human intelligence any time soon when it comes down to understanding, emotional intelligence, critical thinking or problem-solving.

But two more things are also clear: (1) AI can or will only be applied to questions that have massive amounts of relevant data. Neural networks have beaten experts in games such as backgammon because it is easy to produce any amount of training, test and validation data. The same is true for all kinds of pattern recognition. (2) AI will with a high priority be applied in areas that do have large scale effects so that an efficiency gain can justify the high investments necessary in knowledge, skills, data and IT-infrastructure.

So, if you come across the buzz word AI in ads or in marketing campaings or if you wonder whether a certain application of AI is reasonable you may want to consider the following points:

  • Which question should an artificial intelligence or a neural network exactly answer?
  • Is this a widespread question and is the answer widely applicable? Will the answer differ depending on the domain (country, language, segment, legal or regulatory issues, etc.)
  • Where does the necessary data come from? If the answer is different per domain, is there enough domain specific data?
  • Is it realistic to get this data?
  • In which database are the data going to be administered? Is it mainly historical data or is it a frequent flow of data?
  • Who collects the data and where is it collected?
  • Who controls and programs the artificial intelligence?
  • Who monitors the artificial intelligence and carries out the quality assurance?
  • Does the necessary knowledge exist?
  • Can the inputs, costs and efforts be justified by the expected output, income or gains?
  • Is it realistic that the efforts will be made or will the knowledge rather be applied in a different area?

© Prof. Dr. Ulrich Anders

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