Artificial intelligence (AI) still can't see the future, but a new algorithm may come close: Using nothing but written movie summaries, the AI can consistently tell which films will play well--or rottenly--to critics and audiences. If the model can be further refined, it could one day help producers predict whether a movie will be a flop at the box office, before it's even made. To test several models, researchers used plot summaries of 42,306 movies from all over the world, many collected from Wikipedia. The models broke up the summaries by sentence and used something called sentiment analysis to analyze each one. Sentences considered "positive," such as "Thor loves his hammer," would receive a rating closer to one.

A company's most valuable asset is its human capital. In fact, people skills were ranked as the third most important force that will affect enterprises in the next two years by more than 12,800 C-level executives who participated in the most recent IBM Global C-suite Study. In addition, only half of the 2,100 CHRO participants said they currently have the people skills and resources to execute their business strategies. As Gina Dellabarca, General Manager of Human Resources for Westpac New Zealand, says in the C-suite Study report: "Our most important priority in HR is finding talent for the future, not just for now. We're focused on the formidable challenge of attracting, developing, and retaining employees with skills we haven't yet determined."

While the purpose of most fake news is misinformation and political propaganda, our team sees it as a new type of myth that is created by people in the age of internet identities and artificial intelligence. Seeking insights on the fear and desire hidden underneath these modified or generated stories, we use machine learning methods to generate fake articles and present them in the form of an online news blog. This paper aims to share the details of our pipeline and the techniques used for full generation of fake news, from dataset collection to presentation as a media art project on the internet. Keywords: Fake news, Article generation, LSTM, RNN, Language model, Machine learning, AI, Media art, Internet art, Web, Blog, Human-AI Co-Creation Introduction Is fake news a new type of myth that people are creating in the age of internet and artificial intelligence? K. Shu et al says fake news can have many definitions, and one narrow definition is "a news article that is intentionally and verifiably false."

Classical object detection frameworks lack of utilizing objects' surrounding information. In this article, we introduce the graph convolutional networks (GCN) into the object detection, and propose a new framework called OD-GCN (object detection with graph convolutional network). It utilizes the category relationship to improve the detection precision. We set up a knowledge graph to reflect the co-exist relationships among objects. GCN plays the role of post-processing to adjust the output of base object detection models. It is a flexible framework that any pre-trained object detection models can be used as the base model. In the experiments, we try several popular base detection models, OD-GCN always improve mAP by 1-5 pp in COCO dataset. In addition, visualized analysis reveals the benchmark improvement is quite logical in human's opinion.

Hello I want to create a neural network for the game Ult. It is my first neural network that I will create. I want to have 90 inputs to the layer(81 representing the sub boards and 9 the global boards, -1 for occupied by O, 0 for empty and 1 for occupied by X). I want to include one or two hidden layers with 40 nodes each(Sigmoid function). The output layer has 1 output node ranging from [-1,1] representing 1 that X will win and -1 that O will win.

A global Industrial Artificial Intelligence market report has come to the stands.

The rise of many technologies can be traced back through a progression of major breakthroughs and incremental improvements. When we look back, the steps of these progressions often seem logical. Music formats, for example, gradually evolved from analogue to digital formats, moving from vinyl records, to cassettes, to CDs, to Mp3s and now streaming services. In some cases, it can seem obvious in hindsight how one innovation would naturally lead to the next. But when we try to examine how today's technologies will progress, the long-term direction is far from obvious.