Machine learning is a subset of artificial intelligence that enables computers to learn without being programmed.

They can take many forms, ranging from everyday explanations for questions such as why one likes Star Wars, to sophisticated formalization in the philosophy of science (Salmon, 2006), to simply highlighting features in recent work on interpretable machine learning (Ribeiro et al., 2016). Although everyday explanations are mostly encoded in natural language, natural language explanations remain understudied in NLP, partly due to a lack of appropriate datasets and problem formulations. To address these challenges, we leverage /r/ChangeMyView, a community dedicated to sharing counterarguments to controversial views on Reddit, to build a sizable dataset of naturally-occurring explanations. Specifically, in /r/ChangeMyView, an original poster (OP) first delineates the rationales for a (controversial) opinion (e.g., in Table 1, "most hit music artists today are bad musicians"). Members of /r/ChangeMyVieware invited to provide counterarguments. If a counterargument changes the OP's view, the OP awards a to indicate the change and is required to explain why the counterargument is persuasive . In this work, we refer to what is being explained, including both the original post and the persuasive comment, as the explanandum.

The team found that the machine learning correctly identified bird species 84% of the time.

What kind of feelings spring to mind when you hear the words "Artificial Intelligence"? Are they positive or negative? Considering how AI is represented in the media, chances are that those feelings are somewhat negative. There are plenty of examples to support those feelings. Even the positive-sounding stories about "efficiency" and "productivity" mask a rather uncomfortable question for most people reading them that basically boils down to, "Will the robots take my job?" It's easy to focus on the negative stories, but it's important to balance this by recognising that, as humans, we're all susceptible to hard-wired cognitive biases that skew our sense of risk.

A University of Alberta artificial intelligence expert is behind a project meant to provide isolated seniors with companionship. Osmar Zaïane is the project head for the Automated Nursing Program, an in-development chatbot designed to simulate dynamic conversation and provide social fulfilment for elders experiencing loneliness. We don't have enough nursing homes for everybody and not everyone wants to go to nursing homes," Zaïane said. "Often they lose their partner in life, so they live at home, alone, and their families are far away. The project differs from popular chatbots like Siri and Alex, which are task-oriented, meaning they respond to inputs to perform functions like playing a song or turning on the lights.

I've been thing about this since I saw the book Humanize in 2012. The book was written by Jamie Notter and Maddie Grant who run Human Workplaces. About that same time, I started blogging and tweeting using the #humanize hash tag. I thought I had started doing that before I saw their book, but memory is such an unreliable thing their book probably informed my writing. Either way, the start of talking about HR being more human for me was 2012.

The explosion in unstructured data, such as images, videos, sound records, and text, requires an effective solution for computer vision, voice recognition, and natural language processing. How to extract value from unstructured data poses as a big challenge for many enterprises. AI, especially deep learning, has been proved as an effective solution. Vectorization of data features enables people to perform content-based search on unstructured data. For example, you can perform content-based image retrieval, including facial recognition and object detection, etc.

It can take years of birdwatching experience to tell one species from the next.