People give massive amounts of their personal data to companies every day and these data are used to generate tremendous business values. Some economists and politicians argue that people should be paid for their contributions—but the million-dollar question is: by how much?
The AIhub coffee corner captures the musings of AI experts over a 30-minute conversation. This edition focusses on AI as an inventor. This discussion was prompted by news that an artificial intelligence system was named as the inventor of two ideas in patents filed in the UK, Europe and US last summer.
Monday to Wednesday at AAAI-20 saw a multitude of technical sessions, the exhibition and posters. In addition there were a number of interesting debates, invited talks and panels. Here are some tweets from the final three days of the conference.
If you weren’t able to attend the AAAI20 conference in New York you can catch some of the invited talks and panel sessions via the livestreamed videos. Featured events include Yolande Gil’s presidential address and the Turing Award winners’ session.
The AAAI-20 outstanding paper awards were presented on Tuesday 11th February at the AAAI conference in New York. Awards and honourable mentions were given for: outstanding paper, outstanding student paper and outstanding paper in the special track on AI for social impact. You can read about the award-winning work below.
It was a busy weekend at the 34th Conference on Artificial Intelligence (AAAI). Although the AAAI technical sessions didn’t start in earnest until the Sunday there were numerous workshops on Saturday as well as associated conferences AIES (AI, Ethics and Society) and EAAI (Educational Advances in AI). Here is a selection of tweets from the weekend.
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.
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.
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.
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.
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.