Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural network models to such structured datasets. In the last couple of years, a number of papers re-visited this problem of generalizing neural networks to work on arbitrarily structured graphs (Bruna et al., ICLR 2014; Henaff et al., 2015; Duvenaud et al., NIPS 2015; Li et al., ICLR 2016; Defferrard et al., NIPS 2016; Kipf & Welling, 2016), some of them now achieving very promising results in domains that have previously been dominated by, e.g., kernel-based methods, graph-based regularization techniques and others. In this post, I will give a brief overview of recent developments in this field and point out strengths and drawbacks of various approaches. I wrote a short comment on Ferenc's review here (at the very end of this post).

POSB, one of Singapore's oldest banks and part of the DBS Banking Group, has launched an online virtual assistant, POSB digibank Virtual Assistant. It is powered by the KAI conversational bot/artificial intelligence (AI) platform from a New York-based fintech start-up, Kasisto. POSB's chatbot is available on Facebook Messenger and can answer questions relating to account balances, utility bill payments and fund transfer requests. It will also be rolled out to the WhatsApp and WeChat messaging platforms. Kasisto already supplies it flagship platform to DBS's subsidiary in India, DBS digibank. "We know that our customers are spending time conversing on their favourite mobile messaging apps, and we are immersing ourselves in the customer journey by making it easier and more convenient for them to engage us," explains Jeremy Soo, head of consumer banking group, Singapore, DBS Bank.

Most college students relax over their winter break, eating good food and de-stressing from the previous semester. But when I first talked with Joshua Browder, a Stanford University sophomore, he was busy finishing up schoolwork. I was lucky he had time to talk in the middle of his busy schedule. He's the founder of DoNotPay, a chatbot that helps overturn traffic tickets, and in a few days would be flying to London to meet with government officials about using his technology. Then off to Munich to speak at an international design and innovation conference.

Web users searching for photos and cops looking for suspects in video already benefit from software that understands the content of images. Chris Gibson says it can also make it easier to find treatments for diseases not targeted by existing drugs. "By combining robotics and machine vision, we can work at large scale on hundreds of diseases simultaneously, using a small number of people," says Gibson, who is CEO and cofounder of the 40-person startup Recursion Pharmaceuticals. Recursion uses software to read out the results of high-throughput screening, which automates drug testing in cells. That isn't a new idea, but Recursion uses algorithms that inspect cells at an unusual level of detail.

Voice-controlled assistants are having a moment. But there may be an intriguing wrinkle that their makers have to smooth out: users don't seem to be using many apps. The number of Skills--the Amazon name for apps that operate on its Alexa smart assistant software--available for the company's Echo smart speaker have risen significantly in the past six months, from 950 last May to over 8,000 today. But an analysis of the way people use Alexa and and Google's Assistant platforms shows that third-party apps aren't too well used, nor particularly sticky. The analysis, which was carried out voice software start-up Voice Labs, shows that most app don't get any user reviews, which suggests they're not very popular.

Job titles for data scientists, including details about the simple but powerful classifier used to categorize these job titles. This analysis provides a break down per job category, and granular reports that you can download for free (job titles broken down per company, category and level), as well as NLP (natural language processing) source code. It is based on analyzing connections from multiple LinkedIn profiles - totaling more than 10,000 professionals. The first study was published in June 2013. The table below shows the top job titles in the business analytics category.

Today, with Digitization of everything, 80 percent the data being created is unstructured. Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document's texts processed in financial sectors are examples of unstructured data stored in Big Data. Organizations are turning to natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs. Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact.

Insider Trading is often associated with the illegal activity of trading in shares of ones company based on material non public information. But, insider trading is not always illegal. It is not illegal to own, or buy and sell shares of the company you work for, as long as the transactions are being disclosed publicly in a timely manner and as long as the information that is being used to trade is publicly available. This project focuses legal element of insider trading and its potential impact on short term stock prices. Technical trading schools often tout the relationship between Insider transactions and stock prices.

Influencer marketing tries to leverage a popular person's network of friends and followers. To do that with existing customers, optimization platform Amplero has announced an enhancement to its platform that employs machine learning to power influencer marketing for brands whose customers commonly form networks of users. Called Influencer Optimization, the new approach identifies users with substantial networks of contacts, and then uses machine intelligence to make the least offer that would generate the biggest ripple. Chief Product Officer Matt Fleckenstein outlined a possible use case for me.

I've always had a lot of respect for veterinarians, because they are masters at solving problems based purely on fuzzy symptoms that their patients cannot explain: where it hurts, how long it's been hurting, and what events led up to the problem. Many times the patients don't even know they are sick. Yet a vet is able to make educated guesses with the data they do have, which often results in successful diagnoses and treatments. Wireless local area networks (WLANs) cannot talk, either, which often forces IT administrators to operate like doctors, listening to wireless users describe symptoms in vague terms: "I can't connect." "Sometimes it works, sometimes it doesn't."