There Are 157 New Emojis. These Ones Are Actually Useful.

Slate

I haven't even gotten around to using the last batch yet! On Wednesday, the Unicode Consortium, the organization responsible for the standardization of emojis, added 157 new ones to the emoji keyboard, bringing the total number of emojis to 2,823, which is frankly more emojis than any human can keep track of.


All models are wrong - Wikipedia, the free encyclopedia

#artificialintelligence

"All models are wrong" is a common aphorism in statistics. It is generally attributed to the statistician George Box. The first record of Box saying "all models are wrong" is in a 1976 paper published in the Journal of the American Statistical Association.[1] The paragraph containing the aphorism is below. Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration.


Searching Twitter: Separating the Tweet from the Chaff

AAAI Conferences

Within the millions of digital communications posted in online social networks, there is undoubtedly some valuable and useful information. Although a large portion of social media content is considered to be babble, research shows that people share useful links, provide recommendations to friends, answer questions, and solve problems. In this paper, we report on a qualitative investigation into the different factors that make tweets ‘useful’ and ‘not useful’ for a set of common search tasks. The investigation found 16 features that help make a tweet useful, noting that useful tweets often showed 2 or 3 of these features. ‘Not useful’ tweets, however, typically had only one of 17 clear and striking features. Further, we saw that these features can be weighted as according to different types of search tasks. Our results contribute a novel framework for extracting useful information from real-time streams of social-media content that will be used in the design of a future retrieval system.


Patterns for Research in Machine Learning

#artificialintelligence

Here I list a handful of code patterns that I wish I was more aware of when I started my PhD. Each on its own may seem pointless, but collectively they go a long way towards making the typical research workflow more efficient. And an efficient workflow makes it just that little bit easier to ask the research questions that matter.


Future of AI in Agriculture Learnitude Technologies

#artificialintelligence

Agriculture has been facing major challenges like lack of irrigation, change in temperature, groundwater density, food wastage, cold storage, and much more. The technology will be useful in helping farmers in high yielding and having a better seasonal crop at regular interval. In this digital transformation age, technology companies across the world have been developing the best solutions based on agriculture technology (AgTech) to enhance production. Digital transformation and technology adoption have brought many radical changes in many sectors including agriculture. It is speculated that the implementation of Artificial Intelligence (AI) in agriculture will transform the sector.