I'm new to RNNs and LSTM and would like some direction with a problem I have. I have a data set containing system metrics (like CPU utilization, disk operations, memory use) of an AWS EC2 instance with a total of 7 columns and around 8000 rows. Each row represent 5 minutes of system performance. I want to build a LSTM model to forecast the features for let's say the next half hour based on previous time steps. What would be the best approach for solving a problem like this?

Last month OpenAI rather dramatically withheld the release of its newest language model, GPT-2, because it feared it could be used to automate the mass production of misinformation. The decision also accelerated the AI community's ongoing discussion about how to detect this kind of fake news. In a new experiment, researchers at the MIT-IBM Watson AI Lab and HarvardNLP considered whether the same language models that can write such convincing prose can also spot other model-generated passages. The idea behind this hypothesis is simple: language models produce sentences by predicting the next word in a sequence of text. So if they can easily predict most of the words in a given passage, it's likely it was written by one of their own.

WhatsApp appears to be working on a new feature to help users identify whether an image they receive is legitimate or not. While picking apart update 2.19.73, WABetaInfo discovered a "search by image" function that will let you upload a received image directly to Google to reveal "similar or equal" images on the web. With this info, you should be able to more accurately judge whether the picture is real, or fake news. The feature isn't available yet, and there's no official word on when it will be.

I'm recovering from the hyperventilating hyperbole in the reportage of IBM's labeling of a dataset of facial photographs and making it available to researchers to reduce bias in facial recognition. NBC News went with a headline that read: Facial recognition's'dirty little secret': Millions of online photos scraped without consent. That might merit a "pants on fire" rating if it were in the realm of political reporting. The photos were not "scraped." The NBC story linked to IBM's discussion of its work which, in turn, identified the dataset that it used.

KARACHI: Pakistan has introduced the first-ever business robot journalist, Dante, who writes and publishes a comprehensive report on stocks traded at the Pakistan Stock Exchange (PSX) within a few seconds after the market's closure. "The Pakistan Stock Exchange is closed at 3:30pm (from Monday to Thursday and at 4:30pm on Friday) and it gets the daily report published before it is 3:31pm," said the award-winning tech startup, baseH Technologies, Founder and CEO Anisuddin Sheikh. TheRoboJournalist, also called Dante, had been writing the reports for the past few days, he said at a signing ceremony to get seed money from the Elahi Group of Companies for the project at the National Incubation Centre (NIC) at NED University on Saturday. Simultaneously, the artificial intelligence (AI)-based content writing software develops a video on share trading and gets it uploaded at YouTube, Sheikh said. The robot journalist is capable of doing sports and weather reporting as well.

CHIBA - With the market for business-use unmanned aircraft looking promising in coming years, a large-scale drone expo that kicked off Wednesday showed more companies are eager to get involved with the trend. Companies ranging from the small to the powerful are showing off their business solutions using drones at Japan Drone, an annual exhibition at Makuhari Messe in Chiba that features more than 200 firms and runs until Friday. Telecom giant KDDI Corp. is showcasing its "smart" drone platform connected to KDDI's mobile communication networks across the country, which allows a drone to navigate a wider swath of territory via remote control. "One merit of using our service is that drones can be remote controlled through our communication networks anywhere in Japan, unlike most drones exhibited at this event, which tap Wi-Fi networks with limited coverage," said So Yamazaki, a KDDI official. KDDI will launch the service to corporate customers in June and lists surveillance, inspection, land survey and analysis as the envisioned applications.

Tech Trends is in Austin, Texas to cover the action at the South by Southwest conference. SXSW is a "convergence of the interactive, film and music industries." Over the span of ten days, there are panels, educational sessions, a trade show, special events and more. One particularly unique element of the experience is the intersection of ideas. You can go straight from a concert to a discussion of artificial intelligence.

This paper describes a baseline for the second iteration of the Fact Extraction and VERification shared task (FEVER2.0) which explores the resilience of systems through adversarial evaluation. We present a collection of simple adversarial attacks against systems that participated in the first FEVER shared task. FEVER modeled the assessment of truthfulness of written claims as a joint information retrieval and natural language inference task using evidence from Wikipedia. A large number of participants made use of deep neural networks in their submissions to the shared task. The extent as to whether such models understand language has been the subject of a number of recent investigations and discussion in literature. In this paper, we present a simple method of generating entailment-preserving and entailment-altering perturbations of instances by common patterns within the training data. We find that a number of systems are greatly affected with absolute losses in classification accuracy of up to $29\%$ on the newly perturbed instances. Using these newly generated instances, we construct a sample submission for the FEVER2.0 shared task. Addressing these types of attacks will aid in building more robust fact-checking models, as well as suggest directions to expand the datasets.

As a part of a '1 day in Python' workshop, the capabilities of this versatile language were showcased with cases and demonstrations. We realized the underlying logic of the various data science algorithms through these demonstrations; or, to put it in other words – We got an insight into how computers think! Natural Language Processing (NLP) is concerned with programming computers to process and analyze large amounts of natural language data. These find implementations in: Search engines, Social website feeds, Speech engines and Spam filters. We were given a mixture of words.

As a child, I questioned just about everything. Why was my country different than the ones I saw on television shows and in movies? Why was my gender an obstacle to me becoming a leader someday? Why was educating young girls seen as so threatening to the leaders of my country? My mother would attempt to answer my questions.