This article is a technical blog compiled by AI Research and originally titled.
The year in AI/ML advances: 2018roundup
Author Xavier Amatriain
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AI/machine learning 2018 annual progress roundup
For me, it has become a routine to summarize the progress of machine learning at this time of the year (e.g. my answer on Quora last year). As usual, this summary is bound to be biased by my own interests and concerns, but I've tried to make it as wide-ranging as possible. Please note that the following is my blog response on Quora.
If I needed to summarize in a few lines the main highlights of machine learning in 2018, these would be the ones I might mention.
The cooling off of the artificial intelligence hype and fear-mongering.
More attention focused on specific issues, such as equity, interpretability or causality.
Deep learning has played out and is applied in practice to more than just image classification (especially natural language processing).
the battle for artificial intelligence frameworks is heating up and if you want to be a celebrity, you better release some of your own.
Let's explore them in more detail.
If 2017 may have been the peak of fear-mongering and AI hype, 2018 seems to be the year we start to calm down a bit. Admittedly, some people have continued to preach their fears about AI, but they may have been too busy with other issues to make this an important part of their agenda. Meanwhile, publishers and other media outlets seem to have calmed down, arguing that while self-driving cars and similar technologies are coming our way, they won't be born right away. Nonetheless, there are still some who are defending the bad idea that we should regulate AI instead of focusing on regulating its outcomes.
But happily, this year the focus seems to have shifted to more specific issues that can be addressed. For example, there is a lot of discussion about fairness and there are many conferences on the topic (see FATML or ACM FAT) and even some online courses from Google.