This story has appeared in "The Machinist" magazine May 2019 issue. Artificial Intelligence (AI) in Manufacturing domain is pretty much where Digitization was about 10–15 years ago. Very few people are actively doing anything with it, few folks have it on presentation slides and most people want to go with it but don't know where to start. This is not just the story in India but also a global one. As manufacturing companies are starting to see value from digitization, AI brings the promise to increase that value exponentially.

Aerospace & Defense Industry to See Greatest Impact from Artificial Intelligence Compared to Other Key Emerging Technologies, Accenture Report Finds Study underscores the need for reskilling in the sector for future competitiveness NEW YORK; June 13, 2019 – The aerospace and defense (A&D) industry will be more affected by artificial intelligence (AI) than by any other major emerging technology over the next three years, according to Aerospace & Defense Technology Vision 2019, the annual report from Accenture (NYSE: ACN) that predicts key technology trends likely to redefine business. The study also underscores the growing importance of reskilling programs as a competitive lever. AI, comprising technologies that range from machine learning to natural language processing, enables machines to sense, comprehend, act and learn in order to extend human capabilities. One-third (33%) of A&D executives surveyed cited AI as the technology that will have the greatest impact on their organization over the next three years -- more than quantum computing, distributed ledger or extended reality. In fact, two-thirds (67%) of A&D executives said they have either adopted AI within their business or are piloting the technology.

Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating. A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.

Facebook will not remove a deepfake video of CEO Mark Zuckerberg falsely portraying him claiming to control the future thanks to stolen data - similar to the platform's refusal to take down a doctored video of Speaker of the House Nancy Pelosi in May. The video created by Bill Posters and Daniel Howe in partnership with advertising company Canny is hashtagged '#deepfake,' a term that refers to video or audio with virtually undetectable edits made to portray something very different than the original media source. In the edited video which also includes a CBSN logo, Zuckerberg is made to appear to say, 'Imagine this for a second: One man, with total control of billions of people's stolen data, all their secrets, their lives, their futures. I owe it all to Spectre. Spectre showed me that whoever controls the data, controls the future.'

Adobe researchers have developed an AI tool that could make spotting'deepfakes' a whole lot easier. The tool is able to detect edits to images, such as those that would potentially go unnoticed to the naked eye, especially in doctored deepfake videos. It comes as deepfake videos, which use deep learning to digitally splice fake audio onto the mouth of someone talking, continue to be on the rise. Adobe researchers have developed an AI tool that could make it easier to spot'deepfakes'. Deepfakes are so named because they utilise deep learning, a form of artificial intelligence, to create fake videos. They are made by feeding a computer an algorithm, or set of instructions, as well as lots of images and audio of the target person.

E3's wide variety of games in development includes creations that offer players an alternative from the typical action-adventures and online battles. LOS ANGELES -- Hundreds of video games are headed toward TVs, console systems, computer displays and mobile devices in the coming months. Of course, that means impending releases from longtime favorite franchises such as "The Legend of Zelda,""Star Wars" and "Call of Duty" and new takes on beloved characters including "Marvel's Avengers." But the breadth of games in development includes many creations that will offer players an alternative from the typical wave of action-adventures and online battles. Here's a quartet of quirky, offbeat treats uncovered from the array on display at the Electronic Entertainment Expo, which wrapped up here earlier this week.

Catastrophic forgetting of connectionist neural networks is caused by the global sharing of parameters among all training examples. In this study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example. At one extreme, if each input example uses a disjoint set of parameters, there is no sharing of parameters thus no catastrophic forgetting. At the other extreme, if the parameters are the same for every example, it reduces to the conventional neural network. We then introduce a clipped version of maxout networks which lies in the middle, i.e. parameters are shared partially among examples. Based on the parameter sharing analysis, we can locate a limited set of examples that are interfered when learning a new example. We propose to perform rehearsal on this set to prevent forgetting, which is termed as conditional rehearsal. Finally, we demonstrate the effectiveness of the proposed method in an online non-stationary setup, where updates are made after each new example and the distribution of the received example shifts over time.

Automatic text generation has received much attention owing to rapid development of deep neural networks. In general, text generation systems based on statistical language model will not consider anthropomorphic characteristics, which results in machine-like generated texts. To fill the gap, we propose a conditional language generation model with Big Five Personality (BFP) feature vectors as input context, which writes human-like short texts. The short text generator consists of a layer of long short memory network (LSTM), where a BFP feature vector is concatenated as one part of input for each cell. To enable supervised training generation model, a text classification model based convolution neural network (CNN) has been used to prepare BFP-tagged Chinese micro-blog corpora. Validated by a BFP linguistic computational model, our generated Chinese short texts exhibit discriminative personality styles, which are also syntactically correct and semantically smooth with appropriate emoticons. With combination of natural language generation with psychological linguistics, our proposed BFP-dependent text generation model can be widely used for individualization in machine translation, image caption, dialogue generation and so on.

This paper presents a novel, syllable-structured Chinese lyrics generation model given a piece of original melody. Most previously reported lyrics generation models fail to include the relationship between lyrics and melody. In this work, we propose to interpret lyrics-melody alignments as syllable structural information and use a multi-channel sequence-to-sequence model with considering both phrasal structures and semantics. Two different RNN encoders are applied, one of which is for encoding syllable structures while the other for semantic encoding with contextual sentences or input keywords. Moreover, a large Chinese lyrics corpus for model training is leveraged. With automatic and human evaluations, results demonstrate the effectiveness of our proposed lyrics generation model. To the best of our knowledge, there is few previous reports on lyrics generation considering both music and linguistic perspectives.

Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/