There is a lot of talk of applying artificial intelligence and machine learning to manufacturing.

Fake news and social media posts are such a threat to U.S. security that the Defense Department is launching a project to repel "large-scale, automated disinformation attacks," as the top Republican in Congress blocks efforts to protect the integrity of elections. The Defense Advanced Research Projects Agency wants custom software that can unearth fakes hidden among more than 500,000 stories, photos, videos and audio clips. If successful, the system after four years of trials may expand to detect malicious intent and prevent viral fake news from polarizing society. "A decade ago, today's state of the art would have registered as sci-fi -- that's how fast the improvements have come," said Andrew Grotto at the Center for International Security at Stanford University. "There is no reason to think the pace of innovation will slow any time soon."

"This biometric data is so sensitive that if it is compromised, there is simply no recourse," Shawn Williams, a lawyer for plaintiffs in the class action suit, told Reuters in August. "It's not like a Social Security card or credit card number where you can change the number.

Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning. We study this problem in the context of machine listening (Task 1b of the DCASE 2019 Challenge). We propose a novel approach to learn domain-invariant classifiers in an end-to-end fashion by enforcing equal hidden layer representations for domain-parallel samples, i.e. time-aligned recordings from different recording devices. No classification labels are needed for our domain adaptation (DA) method, which makes the data collection process cheaper.

We present 27 problems encountered in automating the translation of movie/TV show subtitles. We categorize each problem in one of the three categories viz. problems directly related to textual translation, problems related to subtitle creation guidelines, and problems due to adaptability of machine translation (MT) engines. We also present the findings of a translation quality evaluation experiment where we share the frequency of 16 key problems. We show that the systems working at the frontiers of Natural Language Processing do not perform well for subtitles and require some post-processing solutions for redressal of these problems

Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for single-document summarization, yet their outputs are often incoherent and unfaithful to the input. In this paper, we introduce SENECA, a novel System for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. Our framework takes a two-step approach: (1) an entity-aware content selection module first identifies salient sentences from the input, then (2) an abstract generation module conducts cross-sentence information compression and abstraction to generate the final summary, which is trained with rewards to promote coherence, conciseness, and clarity. The two components are further connected using reinforcement learning. Automatic evaluation shows that our model significantly outperforms previous state-of-the-art on ROUGE and our proposed coherence measures on New York Times and CNN/Daily Mail datasets. Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.

I recently gave an informal talk to a class of botany students at Gavilan College. The original topic was nature photography, but I also talked about the data science techniques that I used to create my recently completed photo book, Portraits of Birds: Shoreline Park. The concept for the book was to try to personally take photos of all of the bird species in a particular area, in this case Shoreline at Mountain View Park in Mountain View, California, which I later expanded to include the Palo Alto Baylands. To enumerate the species that have been seen in this area, I turned to two citizen science sites, iNaturalist and eBird, both of which have application programmatic interfaces (APIs). Note that while eBird is specific to birds, iNaturalist contains data on plants and other animals as well.

Using Actus Digital's intelligent, data-driven platform, media companies can automatically tag, organize, and categorize video recordings to enable rapid retrieval of relevant content and clips creation for social media outlets and the web. "In today's media environment, companies are dealing with a massive amount of content and data. How fast they can analyze data, find relevant content, and turn that content into engaging clips is a major differentiator," said Raphael Renous, CTO, Actus Digital. "AI is a game changer for media monitoring, as it opens up an entire new range of workflows and automation options. With our AI media monitoring platform, tagging and clips creation is an instantaneous process based on comprehensive content analysis, and we're excited to bring that unique value prop to our customers."