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Published: 28/10/2021

Machine Learning vs Machine Discovery. What is Artificial Intelligence Today?

Machine Learning vs Machine Discovery. What is Artificial Intelligence Today?

Yesterday, the participants of the “Data Science After Hours” event had the opportunity to listen to Mateusz Bonecki, CIO at DAC.digital, and his presentation entitled Machine Learning vs Machine Discovery. What is Artificial Intelligence Today?

Machine Learning vs Machine Discovery.

He talked about the sense in which “today’s” artificial intelligence is a continuation of the dethronement of theoretical science by data science announced by Chris Anderson (Wired) over a decade ago.

Intelligent machines follow the data science path: they base their predictions on correlations, not causal relationships. He showed that they go this way even further, because the intelligence of “learning” machines does not consist simply in the acquisition or reproduction of existing knowledge, but in discovering new correlations, thus creating new knowledge.

He presented how the incomprehensibility, inaccessibility or opacity of this knowledge, which is particularly visible, for example, in the case of artificial neural networks, affects not only social life, the economy, legal regulations, intellectual property or science, but also the possible directions of development of AI engineering itself.

You can watch it here:

Data Science after hours

Data Science after hours is a free meetup on data analysis, AI, machine and deep learning, organized by the infoShare Academy programming school. During the second meeting, participants could also listen to the presentation “United Data Forces of OLX – segmenting pros like a pro”, conducted by Tomasz Jamiński, Head of Data Science at OLX Group, Dr. Piotr Sobczyk, Senior Data Scientist at OLX Group and Mikołaj Szal, Data Scientist at OLX Group

They answered the question of whether good coding practices are important in the area of Machine Learning and about what is most important, i.e. the real translation of what this code and the corresponding model do to the customer and product. They told how they joined forces Data Science, UX Research and Product Analytics to deliver behavioral segmentation of sellers and how to use synergies of code, data, and empathy to create new value.

More at: https://infoshareacademy.com/data-science-po-godzinach/

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