1. The quirky ways AI researchers gather data to feed their algorithms
Data is the oil that fuels AI development, and it gives us many of the advances we take for granted: YouTube captions, Spotify music recommendations, those creepy ads that follow you around the Internet.
Among the papers on multilingual NLP this year, Microsoft presented one that focused on processing “code-mixed language”—text or speech that switches fluidly between two languages.
The researchers started with Spanglish (Spanish and English), but they lacked enough Spanglish text to train the machine. Read More
2. [1811.01339] Learning to Embed Probabilistic Structures Between Deterministic Chaos and Random Process in a Variational Bayes Predictive-Coding RNN
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3. [1811.01533] Transfer learning for time series classification
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4. Malaria Hero: A web app for faster malaria diagnosis
Malarial parasites, for example, have evolved specialized forms…for uptake into mosquitoes and reproduction in red blood cells. If I built a tool that falsely predicts the majority of cells as infected, clinicians would have to spend as much time confirming predictions of single cell images as they would have counting cells through a microscope. Therefore, I first stared by engineering visually salient features, leveraging a less complex model, and measuring the relevancy of the features to the overall classification. Read More
5. “We don’t just want intelligent apps – we want Skynet…” by Mike Guay
After a high level discussion of scope, the conversation moved to questions about new technologies being applied to enterprise applications. Of particular interest was how the major ERP vendors are investing in AI and Machine Learning – and where and how those technologies were likely to be applied to applications. And how jobs and the required skills in enterprises would change as a result of AI, digital transformation, data science and intelligent applications. Read More
6. Learning Chinese-Specific Encoding for Phonetic Similarity – IBM Research
Although at first glance it may seem that phonetic similarity can only be quantified for audible words, this problem is often present in purely textual spaces. However, many languages, such as Chinese, have a different phonetic structure. The speech sound of a Chinese character is represented by a single syllable in Pinyin, the official Romanization system of Chinese. Read More
7. Robots will build robots in $150 million Chinese factory
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8. 119: How to benefit AI research in Africa; German politician calls for billions in spending to prevent country being left behind; and using deep learning to spot thefts
African AI researchers would like better code switching, maps, to accelerate research:…The research needs of people in Eastern Africa tells us about some of the ways in which AI development will differ in that part of the world…Shopping lists contain a lot of information about a person, and I suspect the same might be true of scientific shopping lists that come from a particular part of the world. “DeepMap and Generation share the deeply-held belief that autonomous vehicles will lead to environmental and social benefits,” said DeepMap’s CEO, James Wu, in a statement.
Spotting thefts and suspicious objects with machine learning:…Applying deep learning to lost object detection: promising, but not yet practical…New research from the University of Twente, Leibniz University, and Zheijiang University shows both the possibility and limitations of today’s deep learning techniques applied to surveillance. Read More
9. The 3Ds of Machine Learning Systems Design
If we are at the start of the fourth industrial we also have the unusual honour of being the first to name our revolution before it’s occurred.
The technology that has driven the revolution in AI is machine learning.
In this post we characterise the process for machine learning systems design, convering some of the issues we face, with the 3D process: Decomposition, Data and Deployment. Read More
10. The Commonality of A.I. and Diversity
How to create more diverse workplaces and how to use artificial intelligence ethically are among the more challenging dilemmas facing business and government. Each task force, composed of about a dozen experts and industry leaders, met for an hour, emerging with some specific guidelines and focus areas that were shared with the conference and could be taken back to companies and other organizations for discussion.
Companies must be aware of and recognize that algorithms are not neutral, but created by humans with biases and beliefs and make every effort to eliminate those biases. Read More
11. Another Use for A.I.: Finding Millions of Unregistered Voters
But Jeff Jonas, a prominent data scientist, is focused on something else: the integrity, updating and expansion of voter rolls. Jonas has played a geeky, behind-the-scenes role in encouraging turnout for the midterm elections on Tuesday. Since its founding in 2012, the nonprofit center has identified 26 million people who are eligible but unregistered to vote, as well as 10 million registered voters who have moved, appear on more than one list or have died. Read More
12. Master any classic video game (with help from an AI algorithm)
If, like me, you spent too much of your youth playing video games, well, at least now you can finally conquer all those games with a little help from artificial intelligence.
Wrap it up: A new Python library provides a way to train a reinforcement-learning algorithm to play just about any old video game.
Game theory: The intersection between games and AI is an interesting one. Read More
13. This robotic cowboy is waving trash bags to keep people from being kicked by cows
A Nebraska cattle facility has turned to a robot to herd its cows—and keep its human workers safe. Employees working in close proximity are in danger of getting kicked or stepped on.
The low-tech robotic solution: A Cargill beef slaughter facility in Nebraska, has turned to a robot to keep more of its workers out of harm’s way. Read More
14. Mything the point: The AI renaissance is simply expensive hardware and PR thrown at an old idea
Comment For the last few years the media has been awash with hyperbole about artificial intelligence (AI) and machine learning technologies. For anyone engaged in cutting-edge hardware in the 1980s, this is puzzling.
With such a torrent of exaggerations and anthropomorphisms being used to describe what are, essentially, dumb and mechanistic systems, now could be a good time for some kind of back-to-basics hardware reality check. Read More
15. AI Healthcare on the NHS gets £50m shot in the arm
The UK government has announced five new “centres of excellence” for digital pathology and imaging, including radiology, using the latest advances in medical AI
The new centres will be based in Leeds, Oxford, Coventry, Glasgow and London, making intelligent image analysis available on the NHS that could potentially lead to better clinical decisions for patients and free up more staff for direct patient care.
The image analysis developed by the team will use AI tools to analyse medical images such as x-rays and microscopic sections from tissue biopsies, revealed UKRI CEP Sir Mark Walport. They will be spearheaded by some of the UK’s leading healthcare companies including GE Healthcare, Siemens, and Philips. Read More