The 34th AAAI Conference on Artificial Intelligence (AAAI-20), held in New York, started yesterday (Friday 7 February) and runs until Wednesday 12 February. Our Managing Editor, Lucy Smith, will be attending, covering the conference and meeting researchers.

By Jean Frederic Isingizwe Nturambirwe, Stellenbosch University and Umezuruike Linus Opara, Stellenbosch University

Modern farming has evolved by adopting technical advances such as machines for ploughing and harvesting, controlled irrigation, fertilisers, pesticides, crop breeding and genetics research. These have helped farmers to produce large crops of a good quality in a fairly predictable way.

But there’s still progress to be made in getting the best possible yields from different kinds of soils. And big losses still occur – especially during and after harvest – where monitoring and handling of produce isn’t done well. The industry needs smart and precise solutions and these are becoming available through new technology.

What’s hot on arXiv? Here are the most tweeted papers that were uploaded onto arXiv during January 2020.

Results are powered by Arxiv Sanity Preserver.

By Laura Smith and Marvin Zhang

One of the most important markers of intelligence is the ability to learn by watching others. Humans are particularly good at this, often being able to learn tasks by observing other humans. This is possible because we are not simply copying the actions that other humans take. Rather, we first imagine ourselves performing the task, and this provides a starting point for further practicing the task in the real world.

Happy new year 2020! Below are the tweets about AI we have collected this month.

By Larry Medsker

AI Policy Matters is a regular column in AI Matters featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog.

The Machine Learning for Health workshop at NeurIPS 2019 brought together machine learning researchers, clinicians, and healthcare data experts. With the theme “what makes machine learning in medicine different?” the aim was to elucidate the obstacles that make the development of machine learning models for healthcare uniquely challenging.

By Michael Janner

Reinforcement learning systems can make decisions in one of two ways. In the model-based approach, a system uses a predictive model of the world to ask questions of the form “what will happen if I do x?” to choose the best x¹. In the alternative model-free approach, the modeling step is bypassed altogether in favor of learning a control policy directly. Although in practice the line between these two techniques can become blurred, as a coarse guide it is useful for dividing up the space of algorithmic possibilities.

Is our autonomy affected by interacting with intelligent machines designed to persuade us? That’s what researchers at the University of Bristol attempted to find out through an analysis of the interaction between intelligent software agents and human users.