I have written several articles on Artificial Neural Networks, but they were just random articles on random concepts. This series of articles will give you a detailed idea of Artificial Neural Networks and concepts related to it. The resources and references to all the contents will be mentioned at the end of the series so you can study all of the concepts in depth. So, let's start with a very basic question, "What is AI and what are Artificial Neural Networks?" In this very first article in this series I will try to answer this basic questions and then we will go ahead in depth in further articles.

Artificial intelligence (AI) technologies advanced rapidly over the past several years. Governments around the world responded by developing AI strategies. France released its national AI strategy in March 2018, emphasising research funds, ethical issues, and inequality. China stated a goal of being the top AI country by 2030. The EU, Canada, Japan, the Obama administration, the Trump administration, and many others have put forth their own plans (Sutton 2018).

There are two critical issues that will affect security over the next 20 years, artificial intelligence and bio-engineering (including nanotechnology). AI is defined as an algorithm capable of being trained to identify live data and create activity based on that data. AI also has the capability to educate itself and to mature based on associations to which it has been exposed. In short, it is code that writes itself. The Singularity is defined as that time in the future when the capabilities of humans and those of technology are equal.

Pretty much every tech startup that boasts its use of artificial intelligence is actually focused on an ultra-specific problem. Visual effects company Digital Domain has an AI algorithm that automates and enhances video editing; healthcare startup Babylon developed an AI chatbot to answer the constant barrage of patient questions. They do what they're supposed to. But they'll never lead to something more, something bigger. No matter how great these AI systems sound, no startup will ever stumble upon artificial general intelligence (AGI), which is essentially a complete, human-like AI system that has become truly intelligent.

More than a dozen human rights groups have sent a letter to Google urging the company not to offer censored internet search in China, amid reports it is planning to again begin offering the service in the giant Asian market. The joint letter dated Tuesday calls on CEO Sundar Pichai to explain what Google is doing to safeguard users from the Chinese government's censorship and surveillance. It describes the censored search engine app, codenamed "Dragonfly", as representing "an alarming capitulation by Google on human rights. "The Chinese government extensively violates the rights to freedom of expression and privacy; by accommodating the Chinese authorities' repression of dissent, Google would be actively participating in those violations for millions of internet users in China," said the letter That follows a letter earlier this month signed by more than a thousand Google employees protesting the company's secretive plan to build a search engine that would comply with Chinese ...

Chinese AI startups raised $5 billion in venture capital funding during 2017 -- a half-billion more than their U.S. counterparts -- indicating investor confidence in the technologies, business strategies, and market potential of Chinese startups, according to research by market advisory firm ABI Research. In global terms, investment values for AI startups increased 150 percent -- from $4 billion to $10.7 billion -- between 2016 and 2017. U.S. startups raised $4.4 billion from 155 investments, while Chinese startups netted $4.9 billion from 19 investments. "The bullish sentiment shared among Chinese investors is a clear sign that China is going all-in in artificial intelligence," said Lian Jye Su, a principal analyst at ABI. "The government of China is setting clear policy guidelines for the future development of AI, and startups are responding with cutting edge AI technologies across many industries." ABI found that AI startups in Europe have diversified interests across different industries and verticals, mainly for use cases such as cybersecurity, digital ID, public safety, healthcare, and IoT.

Artificial intelligence (AI) startups in China raised nearly $5 billion in venture capital (VC) funding in 2017, overtaking their US counterparts in yet another part of the AI race, according to a Monday report from ABI Research. Overall investments in AI startups increased 150% globally year-over-year in 2017, growing from $4 billion in 2016 to $10.7 billion last year, the report found. AI startups in the US raised $4.4 billion from 155 investments. Meanwhile, Chinese startups raised $4.9 billion from only 19 investments, indicating a focus on mature AI applications with strong commercial viability and successful use cases, the report noted. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) The Chinese government has also made AI a priority, laying out a major policy plan calling for the nation to become the world's primary AI innovation center by 2030, attracting more investment in AI startups, the report noted.

Researchers are developing software that will help military robots be more independent. The new software aims to ensure that a robot can always get itself back up after a fall, no matter what its orientation. The ability for robots to get back on their own feet means soldiers wouldn't have to risk their own lives to come to the aid of autonomous systems. Army researchers are developing new software that aims to ensure that a robot can always get itself back up after a fall, no matter what its orientation. The latest effort from the US Army Research Lab and Johns Hopkins University Applied Physics Lab builds on feedback from soldiers at an Army training course.

Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis. For Chinese, previous researches identify EDUs just through discriminating the functions of punctuations. In this paper, we argue that Chinese EDUs may not end at the punctuation positions and should follow the definition of EDU in RST-DT. With this definition, we conduct Chinese discourse segmentation with the help of English labeled data. Using discourse commonality between English and Chinese, we design an adversarial neural network framework to extract common language-independent features and language-specific features which are useful for discourse segmentation, when there is no or only a small scale of Chinese labeled data available. Experiments on discourse segmentation demonstrate that our models can leverage common features from bilingual data, and learn efficient Chinese-specific features from a small amount of Chinese labeled data, outperforming the baseline models.

We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users' preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/