It seems Amazon may be putting profits before customers. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers. Programmers involved with the search algorithm are said to have opposed the change, as Amazon's principles stress they'work to earn and keep customer trust'. The changes were cited by sources familiar with the situation, who claimed Amazon's product-search system was changed last year. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers Prior to the switch, algorithms would first show products that were bestsellers or relevant to what customers were looking to purchase.

Democratic presidential candidate Rep. Tusli Gabbard, D-Hawaii took President Trump to task over his response to attacks on Saudi oil fields, likening his wait-and-see approach to being "Saudi Arabia's b----." Trump tweeted earlier Sunday that the U.S. was "waiting to hear from the Kingdom as to who they believe was the cause of the attack, and under what terms we would proceed!" "Trump awaits instructions from his Saudi masters," Gabbard tweeted in response. "Having our country act as Saudi Arabia's b---- is not'America First.'" The attack on the world's largest oil processing facility and a nearby oil field in Saudi Arabia were hit by drone attacks early Saturday by Iranian-backed Yemeni rebels. GABBARD WARNS THAT DNC DEBATE QUALIFICATIONS COULD MAKE VOTERS THINK'THE FIX IS ALREADY IN' Democratic presidential candidate U.S. Rep. Tulsi Gabbard, D-Hawaii, speaks during the New Hampshire state Democratic Party convention, Saturday, Sept. 7, 2019, in Manchester, NH. (AP) Gabbard made the same off-color remark last year after Trump stood by Saudi Arabia amid backlash for the murder of writer Jamal Khashoggi.

This post was co-written with Baptiste Rocca. This old saying expresses pretty well the underlying idea that rules the very powerful "ensemble methods" in machine learning. Roughly, ensemble learning methods, that often trust the top rankings of many machine learning competitions (including Kaggle's competitions), are based on the hypothesis that combining multiple models together can often produce a much more powerful model. The purpose of this post is to introduce various notions of ensemble learning. We will give the reader some necessary keys to well understand and use related methods and be able to design adapted solutions when needed.

SHANGHAI, China, Sept. 16, 2019 (GLOBE NEWSWIRE) -- On August 29th, with the theme of "Intelligent Connectivity, Infinite Possibilities", the 2019 World Artificial Intelligence Conference (WAIC), co-sponsored by the National Development and Reform Commission, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, National Internet Information Office, Chinese Academy of Sciences, Chinese Academy of Engineering and Shanghai Municipal People's Government, was solemnly held in Shanghai. More than 500 top universities, international organizations and the world's most influential scientists, entrepreneurs and investors in the field of artificial intelligence gathered in Shanghai. Turing Award winners Raj Reddy and Manuel Blum, former Dean of the School of Computer Science at CMU & Chief AI Scientist of Squirrel AI Learning Tom Mitchell, Nobel Prize winner George Smoot, "Father of Machine Learning", Finn E. Kydland, Swiss AI Lab IDSIA Scientific Director Jürgen Schmidhuber Co-founder and CEO of Tesla Elon Musk, Chairman of the Board of Directors and CEO of Tencent Pony (Huateng) Ma, Co-chairman of the United Nations High-level Group on Digital Cooperation Jack Ma etc., delivered brilliant speeches and conversations respectively. In the top-leader conversation session, Elon Musk, Co-founder and CEO of Tesla, conducted an in-depth conversation with Jack Ma, Co-chairman of the United Nations High-level Group on Digital Cooperation. When it comes to education, Musk said, "The lecture is the worst because it's too slow. It's hard to make fewer mistakes for us in predicting the future, but you have to try first, and then to adjust it according to the errors you have predicted before."

Big data and data science are concepts often heard together. It is believed that nowadays there are large amounts of data and that data science can draw valuable insights from all these terabytes of information. However, in a practical scenario, you will often have limited data to solve a problem. Gathering a big dataset can be prohibitively expensive or simply impossible (e.g., only having records from a certain time period when doing time series analysis). As a result, there is often no choice but to work with a small dataset, trying to get as accurate predictions as possible.

Hewlett Packard Enterprise Co. today is expanding its reach into artificial intelligence development with a software platform that supports the full lifecycle of machine learning model construction and deployment using the self-contained software environments called containers. HPE ML Ops provides for the rapid rollout of machine learning workloads across on-premises, public cloud and hybrid cloud environments. The idea is to enable development teams to employ processes similar to those used in DevOps, the rapid application-building technique that that involves frequent code releases and constant refinement. The result is reductions in model deployment times from months to days, HPE said. The company is attacking a common problem with machine learning projects, which is a lack of resources and operational processes to deploy them.

The recent rise of insurtechs has spurred modernization in the fundamentals of insurance, including policy creation, underwriting, and claims management. Insurtechs, such as Hippo and Lemonade, have implemented AI and data analytics to provide quick and accurate quotes to their customers while also decreasing their own operating costs. Such innovation has disrupted the traditional insurance industry and has helped alter underlying business models of legacy companies, forcing them to invest heavily in insurtech technology to remain competitive. Hippo, which promotes savings of up to 25% on home insurance, offers customer quotes in less than 60 seconds and also provides complimentary smart home sensor kits to all policyholders. These kits include a two-sensor smart home monitoring system and premium discounts for helping to prevent common risks from fire, water damage, and break-ins.

But do you know how to leverage AI within your SEO strategy? Let's see what holds the future of SEO and how it impacts industries. AI is developing at a steady pace and the impact of it is visible in all industries. It has been popular particularly last year when industries really saw the potential of artificial intelligence. The general opinion switched when it comes to AI – while everyone used to think that AI will take away jobs from people, now they realize that it will rather complement the jobs and make them more interesting.

AI technologies have well and truly reformed information systems by making them far more adaptive to humans while significantly improving the interaction between humans and computer systems. With this, AI in insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors. From smart chatbots that offer quick customer service round the clock to the array of machine learning technologies that spruce up the functioning of any workplace through its automation power, the expanding potential of artificial intelligence in insurance is already being used in many ways. With increased awareness and resources about the game-changing influence of AI in the Insurance industry, the initial hesitations and shallow discomfort around its implementation are now fading quickly as it begins to trust in the calibre and numerous opportunities brought forward by Artificial Intelligence and Machine Learning. The only question that remains is – how far can we push its capabilities?

When Peter Cushing turned to face the camera in Rogue One, Star Wars fans were as excited as they were confused. After all, the actor had died more than 20 years earlier, and yet, there was no mistaking him. For a major Hollywood movie, this is a clever trick. But not everyone is trying to entertain us, and you don't need a million-dollar budget to deceive. "You take the face of one person and put it on the body of another," said Jeff Smith, associate director at the National Center for Media Forensics at the University of Colorado Denver.