Luminoso, the company that turns unstructured text data into business critical insights, announced that it has promoted Henning Smith to Chief Technology Officer. "As the demand for Luminoso's text analytics applications have surged in recent years, our engineering and infrastructure teams have ensured our applications are exceeding customers' needs," said Adam Carte, CEO of Luminoso. "Henning Smith has been a stellar leader of our engineers during his tenure at Luminoso. As we continue to grow Luminoso globally, we're confident that Henning will ensure our customers continue to enjoy best-in-class text analytics solutions through both cloud and on-premise deployments." Henning has more than twenty years of experience leading and developing engineering teams in distributed locations, and managing global software development projects from inception through delivery.

Growing up in Seattle, I was exposed to tech at a pretty young age. Most of my friends' parents worked for Microsoft. I spent a lot of my free time working on little coding projects, and even started my own business developing Video game mods in high school. Movies like 2001: A Space Odyssey captured my imagination, and gave me a sense that AI was going to be an important part of the future, even if it seemed distant at the time. But I really wanted to get involved. In my Senior year of High School, I took an AI summer course at Stanford.

The Tech Tribune staff has compiled the very best tech startups in Cambridge, Massachusetts. Additionally, all companies must be independent (un-acquired), privately owned, at most 10 years old, and have received at least one round of funding in order to qualify. Looking for a badge to celebrate your awesome accomplishment? "Cambridge Mobile Telematics (CMT) pioneered telematics for usage-based and behavior-based programs making roads and drivers safer around the world. Founded in 2010 by two MIT professors, CMT's accomplished team of expert scientists and experienced entrepreneurs developed DriveWell, an advanced mobile-sensing and big data platform delivering an end-to-end smartphone telematics solution. DriveWell provides valuable feedback to users, helping them to improve driving performance and become more aware of unsafe behaviors. DriveWell is the first telematics platform in the industry to provide both traditional vehicle-centric, usage-based-insurance (UBI) and driver-centric, behavior-based insurance (BBI) solutions. Through the DriveWell program, CMT's partners can easily measure mileage, time of day, roadways and risky driving behaviors – giving them a complete picture of every trip and allowing them to segment high-risk vs low-risk customers easily."

A US company claims to already know the nominations and winner of Best Picture for next months annual Academy Awards – aka the Oscars, by using artificial intelligence. The Massachusetts based start-up, Luminoso, unveiled its list (see below) almost two weeks before voting for the list of nominees officially closes (January 24) – and more than a month before the awards takes place at the Dolby Theatre in Hollywood (February 26). The firm generated the results by first pulling together over 84,000 reviews written by movie goers (not critics) which have been published on the IMDB website over the past four years (2013-2016) . It then used its Natural Language Processing software, 'Luminoso Analytics', to analyze the text and identify correlations between topics discussed in the reviews and the eventual Oscar nominees and winners. It found that certain terms, including "narrative," "cinematography," "plot," "visuals," "stunning," "experience," and "masterpiece," were more prevalent in reviews of moves that later went on to be nominated and/or win the Oscars.

I have been telling people for a while that ConceptNet is a valuable source of information for semantic vectors, or "word embeddings" as they've been called since the neural-net people showed up in 2013 and renamed everything. Let's call them "word vectors", even though they can represent phrases too. The idea is to compute a vector space where similar vectors represent words or phrases with similar meanings. In particular, I've been pointing to results showing that our precomputed vectors, ConceptNet Numberbatch, are the state of the art in multiple languages. Now we've verified this by participating in SemEval 2017 Task 2, "Multilingual and Cross-lingual Semantic Word Similarity", and winning in a landslide. SemEval is a long-running evaluation of computational semantics.

Deep learning involves'training' a computational system to'understand' natural language, so inferring complex meaning rather than just understanding the surface meaning. The computer is then quizzed on the information and goes back to learn from its mistakes. These multiple layers help systems analyse and make decisions about data more independently, "Such as whether or not an email is spam, to use a simple example", explains Rob Speer, chief science officer at Luminoso, a Massachusetts-based text analysis and artificial intelligence company. "For many companies, a major reason to turn to deep learning over machine learning is that there are fewer steps of human intervention required to train the system before it can work with data." This significantly cuts staff effort and reduces the burden on the poor workies.

Can the language used in movie reviews hold the tea leaves revealing the winners of the Academy Awards? That's the hypothesis of Luminoso Technologies, an artificial-intelligence startup that specializes in natural-language processing, which has already declared the likely best-picture winner of the 2017 Academy Awards before the nominations are even out: Pablo Larraín's biopic "Jackie," starring Natalie Portman as Jacqueline Bouvier Kennedy. Here's the methodology: The company analyzed user movie reviews for 2013-15 in IMDb, focusing on the 50 most popular movies of each year, to see if there was a correlation behind the concepts that appeared in their language and the eventual Oscar nominees that year. Luminoso's software found certain specific concepts -- such as "cinematography," "masterpiece," "stunning," "visuals" and "experience" -- were highly correlated with films that received nominations. Concepts like "narrative" had less correlation with Oscar nods, and a few (like "CGI" and "horror") had negative correlation.

A company that successfully predicted that Donald Trump would become the next US president using artificial intelligence, claims to already know the nominations and winner of Best Picture for next months annual Academy Awards – aka The Oscars. The Massachusetts based start-up, Luminoso, unveiled its list (see below) almost two weeks before voting for the list of nominees officially closes (January 24) – and more than a month before the awards takes place at the Dolby Theatre in Hollywood (February 26). The firm pulled together over 84,000 reviews written by movie goers (not critics) which have been published on the IMDB website over the past four year (2013-2016) . It then used its Natural Language Processing software, 'Luminoso Analytics', to analyze the text and identify correlations between topics discussed in the reviews and the eventual Oscar nominees and winners. It found that certain terms, including "narrative," "cinematography," "plot," "visuals," "stunning," "experience," and "masterpiece," were more prevalent in reviews of moves that later went on to be nominated and/or win the Oscars.

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