NIPS 2018 (Montreal, Canada), or NeurIPS, as it is called now, is over, and I would like to take the opportunity to dissect one of the papers that received the Best Paper Award at this prestigious conference. The name of the paper is Neural Ordinary Differential Equations (arXiv link) and its authors are affiliated to the famous Vector Institute at the University of Toronto. In this post, I will try to explain some of the main ideas of this paper as well as discuss their potential implications for the future of the field of Deep Learning. Since the paper is quite advanced and touches on concepts such as Ordinary Differential Equations (ODE), Recurrent Neural Networks (RNN) or Normalizing Flows (NF), I suggest that you read up on these terms if you are not familiar with them, since I will not go into details on these. However, I will try to explain the ideas of the paper as intuitively as possible, so that you may get the main concepts without going too much into the technical details. If you are interested, you may read up on these details afterwards in the original paper.

Artificial Intelligent systems are generally defined as computer systems which are to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation. In the context of Infrastructure Asset Management, there are many applications for AI which can replace or augment substantial levels of human effort in order to improve the performance of the Asset or extend its life. As many public organisations are making significant efforts to understand'best practice' asset management, often referring to ISO 550001 for guidance on the The goal of Machine Learning is to learn from data, ingesting a large volume of data a ML algorithm is predominantly focused on a certain task to maximize the performance of machine in performing that task. Artificial Intelligence, on the other hand, is primarily focused on decision making. So while ML allows a system to learn new things from data, and AI attempts to mimic human behavior in a circumstances.

This is because of the fact that the customers can place their queries without any timing or geographical constraints. Grievance solving is one of the significant parts of web-based customer interactions. If you are facing difficulties in meeting the expected response rates of the customers or the increasing quantity of grievances is bothering your customer service team, this article will provide hands-on insight in Chatbot integration to address the issue. Also, buckle up to understand the financial incentives of doing so. Let us start with the basics.

The first revolution of Internet, in 1995, involved connecting personal computers across the globe onto one giant web and make information available at the click of a button. The second revolution, around 2010, saw the emergence of social networks and enabled the human race to become one large family with everyone having the ability to connect with everyone else on issues of common interest. Now the third Internet revolution, led by voice, humanity's most used communication channel, is going to result in a screen-less omnipresent Internet. Presently more than 20 percent of search queries in India are already done by voice. And by 2020, 50 percent of all global searches will be voice searches.

WASHINGTON – The United States said new evidence and analysis of weapons debris recovered from an attack on Saudi oil facilities on Sept. 14 indicates the strike likely came from the north, reinforcing its earlier assessment that Iran was behind the offensive. In an interim report of its investigation -- seen by Reuters ahead of a presentation on Thursday to the United Nations Security Council -- Washington assessed that before hitting its targets, one of the drones traversed a location approximately 200 km (124 miles) to the northwest of the attack site. "This, in combination with the assessed 900 kilometer maximum range of the Unmanned Aerial Vehicle (UAV), indicates with high likelihood that the attack originated north of Abqaiq," the interim report said, referring to the location of one of the Saudi oil facilities that were hit. It added the United States had identified several similarities between the drones used in the raid and an Iranian designed and produced unmanned aircraft known as the IRN-05 UAV. However, the report noted that the analysis of the weapons debris did not definitely reveal the origin of the strike that initially knocked out half of Saudi Arabia's oil production.

NASA's Mars 2020 rover has successfully'passed its driving test' in a major mission milestone that saw it move under its own weight ahead of its launch next year. The rover will leave for Mars in July or August 2020 from the Cape Canaveral Air Force Station in Florida and will travel aboard the new Space Launch System rocket. NASA's robotic vehicle had to demonstrate it could move forwards, backwards and pirouette during the more than 10-hour marathon'driving test' on Tuesday. The next time the Mars 2020 rover drives, it will be rolling over Martian soil. The semi-autonomous vehicle will search for signs of ancient microbial life within the Jezero crater, which contains a dried up lake once filled with water.

From 2009 – 2014, I built one of the first commercially viable robo-advisors, the first deep-learning network that could detect market-moving Congressional legislation, and one of the earliest trading algorithms for illiquid markets. I built those products inside some of the largest financial institutions in the world. And every one of those products was shut down by those banks. Everyone who was part of those teams quit or was fired. But none of the innovations died.

Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects. Our study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.8 9.1 years] from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P 0.05 for all). Subjects with a higher ML score (by Youden's index) had high hazard of suffering events (HR: 10.38, P 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men.

According to McKinsey Global Institude study on impact of AI and automation, transportation and warehousing are one of the most automatable sectors of economy (3rd place), with 60% potential for automation. Predicting the future demands for production and supplies, improving transportation routines, or automating physical inspection and maintenance are some of vast possibilities to use data science in supply chain management. While self-driving cars seem to be a future of transport, we would like to focus on optimizing "here and now", without changing the market – just with smart, data-driven decisions. We would like to focus on a problem of choosing location of warehouse to minimise cost of both freight and warehouse maintenance. It is a complex Data Science problem, that is composed of various independent components that need to be optimised.

The doomsday headlines about automation and job displacement continue to pile up. Nearly half of all jobs are at risk, one report said. Another found that white-collar jobs once thought safe are in the crosshairs. "I fundamentally don't believe that," Mader said of automation displacing huge swaths of workers during a recent speech at Seattle University's Albers School of Business and Economics. "There is so much unstructured work for which you can't actually program the robot to do that work."