Neural network has shown promising performance on coreference resolution systems that uses mention pair method. With deep neural network, it can learn hidden and deep relations between two mentions. However, there is no work on coreference resolution for Indonesian text that uses this learning technique. The state-of-the-art system for Indonesian text only states the use of lexical and syntactic features can improve the existing coreference resolution system. In this paper, we propose a new coreference resolution system for Indonesian text with mention pair method that uses deep neural network to learn the relations of the two mentions. In addition to lexical and syntactic features, in order to learn the representation of the mentions words and context, we use word embeddings and feed them to Convolutional Neural Network (CNN). Furthermore, we do singleton exclusion using singleton classifier component to prevent singleton mentions entering any entity clusters at the end. Achieving 67.37% without singleton exclusion, 63.27% with trained singleton classifier, and 75.95% with gold singleton classifier on CoNLL average F1 score, our proposed system outperforms the state-of-the-art system.

It is well known that adversarial examples and counterfactual explanations are based on the same mathematical model. However, their relationship has not yet been studied at a conceptual level. The present paper fills this gap. We show that counterfactual reasoning is the common basis of the fields and reliable machine learning their shared goal. Moreover, we illustrate to what extent counterfactual explanations can be regarded as the more general concept than adversarial examples. We introduce the conceptual distinction between feasible and contesting counterfactual explanations and argue that adversarial examples are similar to the latter.

Google users contribute more than 20 million pieces of information on Maps every day – that's more than 200 contributions every second. The uncertainty of traffic can crash the algorithms predicting the best ETA. There is also a chance of new roads and buildings being built all the time. Though Google Maps gets its ETA right most of the time, there is still room for improvement. Researchers at Alphabet-owned DeepMind have partnered with the Google Maps team to improve the accuracy of the real-time ETAs by up to 50% in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C.

The universe is coming into clearer view, thanks to artificial intelligence. Leveraging the deep learning techniques of AI, a research group of astronomers mainly from the National Astronomical Observatory of Japan (NAOJ) have achieved extremely high accuracy for finding and classifying spiral galaxies. The group applied AI to an ultra-wide field-of-view images of the distant Universe observed by the Subaru Telescope and were able to classify as many as 560,00 galaxies from those images, according to the NAOJ. Previously, the amount visible in these images would have been impossible to be counted individually and detected by the human eye and therefore unable to be classified. However, the AI technique allowed the researchers to perform this massive pricing without any human contribution, the NAOJ said.

An Israeli company has developed new artificial-intelligence technology … The company's deep–learning technology initially was developed for the …

I work predominantly in NLP for the last three months at work. It's been a long time I work on the image data. Hence, I decided to build a unique image classifier model as part of my personal project and learning. One thing I am really missing in the current pandemic is traveling. These days I used to see a lot of travel vlogs and travel pictures on Instagram, wondering when we will go back to the normal world.

So you're interested in AI? Then join our online event, TNW2020, where you'll hear how artificial intelligence is transforming industries and businesses. In 2018, a big fan of Nicholas Cage showed us what The Fellowship of the Ring would look like if Cage starred as Frodo, Aragorn, Gimly, and Legolas. The technology he used was deepfake, a type of application that uses artificial intelligence algorithms to manipulate videos. Deepfakes are mostly known for their capability to swap the faces of actors from one video to another. They first appeared in 2018 and quickly rose to fame after they were used to modify adult videos to feature the faces of Hollywood actors and politicians.

We asked GPT-3, OpenAI’s powerful new language generator, to write an essay for us from scratch. The assignment? To convince us robots come in peace

Every day we depend on extraterrestrial devices to send us information about the state of the Earth and surrounding space--currently, there are about 3,000 satellites orbiting the Earth and this number is growing rapidly. Processing and transmitting the wealth of data these devices produce is not a trivial task, given that resources in space such as on-board memory and downlink bandwidth face tight constraints. In the case of satellite images, the data at hand can be extremely large, sometimes as large as 8,000 8,000 pixels. For most practical applications, only part of the great amount of detail encoded in these images is of interest--such as the footprints of buildings, for example--but the current standard approach is to transmit the entire images back to Earth for processing. It seems a more efficient solution would be to process the data on board the spacecraft, arriving at a compressed representation that occupies fewer resources--something that could be achieved using a machine learning model. Unfortunately, running machine learning models tends to be a resource-intensive process even here on Earth. State-of-the-art networks typically consist of many millions of parameters and limited uplink bandwidth makes uploading such large networks to satellites impractical/infeasible. And even if they were pre-loaded prior to launch, they require significant memory bandwidth to fetch weights and compute dot products at runtime.

Autoencoders are an unsupervised learning technique, although they are trained using supervised learning methods. The goal is to minimize reconstruction error based on a loss function, such as the mean squared error. In this post, we will try to detect anomalies in the Johnson & Johnson's historical stock price time series data with an LSTM autoencoder. The data can be downloaded from Yahoo Finance. The time period I selected was from 1985–09–04 to 2020–09–03.