A daylong conference will cover a wide-range of topics related to computational data analysis, from how languages spread to ways of improving the value of crowdsourcing. The Data Science Workshop on Computational Social Science takes place Oct. 20. It's the first of what Dragomir Radev, the A. Bartlett Giamatti Professor of Computer Science, expects will be a regular event. "We decided we should try to cover different areas of data science," said Radev, one of the event's organizers. "We're starting with computational social science first and then switch to other areas in which data science and computer science have made an impact, for example, digital humanities, medicine, finance, etc." Radev said the event is something that likely would not have happened 10 or even five years ago.
After trying out Google's Pixel Buds, I agree with my colleague Karissa Bell when she says the Apple AirPods competitor is the most important gadget the tech company's announced in years. You're either going to think they look cool or look cheap. And while that's important, it's the technology inside that I think will change the way we communicate forever. I'm, of course, talking about their real-time translation feature. I just tried them on today and they're (excuse the language) f**king amazing.
You and I speak a language. Most people speaks at least one language. We've probably not had to think very hard about how we've learnt this language. The jury is still out on this, but we've got some pretty good ideas about how this is done. For Chomsky and others, humans are equipped with an innate ability to learn languages.
Can artificial intelligence replace the human brain?Will it? "Humans were are not built to spend more than two hours looking at a screen or scrolling through excel sheets. Humans are best at being human. Artificial Intelligence will do the rest." Telling words from Jim Stolze, Co-founder of aigency -- an Amsterdam-based company that recruits AI and humans for work.
The Julia programming language is growing fast and its efficiency and speed is now well-known. Even-though I think R is the best language for Data Science, sometimes we just need more. Modelling is an important part of Data Science and sometimes you may need to implement your own algorithms or adapt existing models to your problems. If performance is not essential and the complexity of your problem is small, R alone is enough. However, if you need to run the same model several times on large datasets and available implementations are not suit to your problem, you will need to go beyond R. Fortunately, you can go beyond R in R, which is great because you can do your analysis in R and call complex models from elsewhere.
The world of deep learning is dominated by academics and technology giants pumping thousands of dollars into their research and applications every day. There are so many real-world problems that can be solved by DL that huge corporations aren't solving. There are countless startups trying to solve an array of issues and improve efficiency in countless industries, and many of these fail - not due necessarily to a poor idea or execution, but they are often unfunded and understaffed. The startups with the really extraordinary ideas however, often secure funding from Venture Capitalists, in crowdfunding campaigns, or through awards or grants. The CEOs of these companies are not necessarily AI experts, but are experts in their own industry from artists, to healthcare professionals, scientists, retail managers and many more.
Stolze was addressing reporters in StartUp Village at the Amsterdam Science Park on the sidelines of the first World Summit AI in Amsterdam October 11-12. Heineken and Unilever are big customers, turning to aigency for specific problems; Stolze in turn hooks them up with researchers and even students from the University of Amsterdam. "We apply our own cognitive bias in writing," says Parry Malm, a speaker at the World Summit AI and CEO of Phrasee, a UK-based company whose vision is "to supercharge digital marketing using artificial intelligence." Malm claims Domino's email open rate increased 27% using AI and language optimization.
WeChat, the Chinese messaging app, has apologised for translating "black foreigner" into the N-word. When Ms Jones translated their Chinese response into English using WeChat's translation feature, it read: "The [N-word] is late." It found that in some sentences the phrase "black foreigner" was translated neutrally, but when the phrase was used in a negative context, the app translated it into the N-word. It learns how to use language in context by analysing huge volumes of material, which is why it may choose insulting language to translate negative sentences.
Beside git and shell scripting additional tools are developed to facilitate the development of predictive model in a multi-language environments. In an example that was explained in previous tutorialtarget variable was binary output and logistic regression was used as a training algorithm. We will use the same example from previous blog story, add some Python codes and explain how Feather and DVC can simplify the development process in this combined environment. With tf-idf matrices target is predicted and lasso logistic regression for predicting binary output is used.