If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
It's another graph neural networks survey paper today! Clearly, this covers much of the same territory as we looked at earlier in the week, but when we're lucky enough to get two surveys published in short succession it can add a lot to compare the two different perspectives and sense of what's important. In particular here, Zhou et al., have a different formulation for describing the core GNN problem, and a nice approach to splitting out the various components. Rather than make this a standalone write-up, I'm going to lean heavily on the Graph neural network survey we looked at on Wednesday and try to enrich my understanding starting from there. For this survey, the GNN problem is framed based on the formulation in the original GNN paper, 'The graph neural network model,' Scarselli 2009.
Most self-driving car testing takes place in places like California, Arizona, and Nevada, and there's a reason for that. The sensors these cars rely on to navigate are less reliable in poor weather and other low-visibility conditions. But MIT claims to be developing new tech that could help with that. MIT's experimental sensor reads radiation at sub-terahertz wavelengths, which are between microwave and infrared radiation on the electromagnetic spectrum. That means they can be detected through fog and dust, according to MIT.
The Elements of Statistical Learning - Another valuable statistics text that covers just about everything you might want to know, and then some (it's over 750 pages long). Make sure you get the most updated version of the book from here (as of this writing, that's the 2017 edition). Data Mining and Analysis - This Cambridge University Press text will take you deep into the statistics and algorithms used for various types of data analysis. Do you need books to learn data science?
A machine learning model was used by researchers from the University of Waikato, in New Zealand, to narrow down a massive 8 million tweets to a more manageable 1.2 million in order to look at how te reo Māori is being used in the genre. According to a recent press release, the team focused on 77 Māori loanwords, or te reo Māori words used in an English context, and used them as training data for their machine learning model. Machine learning allows data scientists to provide a computer with a large data set, and teach it to make predictions based on that data. The initial 8 million tweets contained a fair bit of distracting data'noise'. The irrelevant tweets are those that are not used in a New Zealand English context, or were otherwise unrelated.
The duo announced that they would jointly sponsor a prize for AI researchers working to develop ethical, responsible AI tech. The winner will be someone who presented a doctoral thesis on artificial intelligence tech that stands to benefit the common good, The Seattle Times reports. The $6,900 prize and an invitation to Microsoft's office in Seattle is paltry compared to what the two could have offered up, but the contest still represents an increasingly public drive to make sure that AI is developed with the needs of humanity in mind.
The human rights job landscape is changing rapidly. Current and future challenges in combating human rights violations require new skills and tactics. We have compiled a list of 7 free online courses and specializations that will equip you with the knowledge and skills for the human rights jobs of the future. Machine learning and artificial intelligence create new opportunities and challenges for the protection of human rights. Artificial intelligence can help make education, health and economic systems more efficient but also bears the risk to amplify polarization, bias and discrimination against certain groups.