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When something unexpected happens, it is our intuition to ask questions and try to understand why it has happened. If we can determine the reason of that unexpected event, it may be possible for us to prevent such an outcome next time. However, the ways we, humans try to understand, and reason things are sometimes superstitious, and we cannot explain what is really going on. Correlation which can only state that an event happened around the same time as another event can’t provide reason too. To know in depth what is the real reason behind an event, it is a necessity to look closer at causality. We need to look closer at how information flows between two events. It is the information flow that shows that there is a causal relation between them. General causality is necessary to identify the causes.
Mathematical models have so far been able to work two causes at most. Therefore, the models that are created for general causality have been very restricted. Now, with an artificial leap forward, developers have created the first capable model for general causality which would work without the provided time-sequenced data and it would be able to identify multiple causal connections. The model’s name is Multivariate Additive Noise Model, MANM in short.
Two researchers, Prof. Tshilidzi Marwala from the University of Johannesburg and Dr. Pramod Kumar Parida from National Institute of Technology Rourkela have created a new model and tested the model on simulations and real-world datasets. Findings are published in the journal, Neural Networks.
People can sometimes think there is a connection between two events because of superstition. For example, in some cultures, seeing a black cat and connecting this with something bad happened afterward. However, from an artificial intelligence point of view, we state that there are no causal links between the cat and what happened afterwards. The cat was merely seen just before the second event but there is no correlation with what happened later.
A good example to understand how this new program works is looking at the example of financial situations. For example, a household that may be in debt. Such a financial situation can cause acute restrictions on the household, eventually becoming a void like situation from which there is no likely escape of. But do the people of that household understand the causal connections between what happens to them?
One of the researchers, Dr. Pramod Kumar Parida said that “The reasons of long-time continuous household debt are a good example to what the new model is capable of doing. At a household level, questions are: did the household lose some or all its income? Are some or all members of that household spend more than their income? Has something bad happened to household members that forcing them to spend hugely, as an example, medical concerns? Are they using their savings and investments, which have now finished? Are all of these things happening, if so, which are the more dominantly causes the debt? If enough information about the household’s financial transactions are provided, all given with date and time information, the causal connections between household’s income, spending, savings, investments, and debt are possible for someone to figure out.”
Parida continues, 'What are the reasons people in a city, or a region are struggling financially? Why are they not capable of getting out of debt? Now, it is no longer possible for professionals to figure this out from available data. Particularly, if we want the causal connections on household’s income, spending, savings and debt for the city or region, rather than professionals’ insights. Here, causality theory fails to answer, because the financial transaction data of the households in the city or region will be unfinished. Also, date and time information are missing from collected data. Financial endeavor in the low, middle- and high-income households are likely very varied, so we would want to see the different causes from the analysis.”
He continues by saying, 'With this model, we can recognize multiple essential driving forces causing the household debt. In this model, these factors are called the independent parent causal connections. We can also see which causal connections are more dominant than the others. Through a repeated second trial with the data, the minor driving factors are detected, and those minor factors are called the independent child causal connections. In this way, it is possible to identify a possible pecking order of the causal connections.'
Artificial intelligence is very promising in health care due to being able the efficiently and effectively analyze data from apps, smartphones, and other wearable technology. Bots could very well replace doctors. And bots or automated programs are highly likely to play a very important role in finding cures to some of the hardest diseases and conditions. Therefore, AI is rapidly moving into healthcare systems, led by tech giants and emerging startups.
“A lot of excitement around these tools,” said Lynda Chin, vice chancellor, and chief innovation officer at the University of Texas and continued “but technology alone is unlikely to translate into wide-scale health benefits. Data from sources are different than medical records and it is difficult to access them due to privacy and other regulations. Integration of data in health care delivery where doctors may not know how to use new systems.”
We, humans, are generally good at relational reasoning. Relational reasoning is a kind of thinking that uses logic to connect and compare, places, sequences, and other entities. Relational reasoning is an important part of higher thought. It is what is difficult for artificial intelligence to master. There are two main types of AI, namely, statistical and symbolic, those have been slow to develop similar capabilities in relational reasoning. Statistical AI, or “machine learning” as it is called is great at pattern recognition but not using logic. On the other hand, symbolic AI can make reasonings on relationships between entities with using predetermined rules, but it lacks learning on the go. However, researchers at Google’s DeepMind division have developed a new algorithm to overcome relational reasoning problem and it has already had edge over humans at a complicated image perception simulation. The DeepMind division had also tried its neural network on a language-based task.
A new technique is promising for a way to make a connection in between and understanding relational reasonings with an artificial neural network. Like the way our neurons are work in the brain, neural nets make connections of tiny algorithms that work in cooperation to find patterns in data. They can have specialized architectures for processing images, languages and games. In this case, the network is forced to discover the relationships that exist between entities.
By this paper, we wanted to show how the world could be changed if we could solve this the long-term problem at once and forever, we assumed that it can be done by AI, which will be able to distinguish the difference between correlation and causation even in the hardest examples. By understanding the basics finally with extending our knowledge about AI we can write the reasoning machine that had one possibility to solve those problems for us wit very high efficiency, we stated some important facts that should have AI to do that.
Furthermore, this could solve the most important problems for humanity. We provided many health, psychological and statistical examples. The world we live now in will be completely different from what it is right now because we don’t know too many things that could help us to build the world around us, because of many reasons, such as too big statistical data, not a full understanding of causation, mistakes in making conclusions etc.
That’s what we wanted to accomplish to find the way to change something, so we could live better, our descendants too. AI is a very powerful tool, that could be used for understanding many things and causation & correlation is one of the most difficult and broad examples.
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