Derrick Harris of GigaOM reports, “Twitter has acquired a stealthy computer vision startup called Madbits, which was founded by former New York University researchers. Clément Farabet and Louis-Alexandre Etezad-Heydari. Farabet is a protégé of Facebook AI Lab director and New York University professor Yann LeCun, while Etezad-Heydari was advised by Larry Maloney and Eero Simoncelli.” Read more
Aviva Rutkin of New Scientist recently wrote, “Rumours have been circulating that the Chinese search engine is developing a bike that could drive itself through packed city streets. The project isn’t ready to be launched yet but Baidu confirmed it is exploring the idea.The news is intriguing, and not just because self-navigating bikes would be cool. Research into autonomous vehicles is yet another way that Baidu is following Google’s model of pushing at the boundaries of artificial intelligence.” Read more
Suzanne Kattau of Silicon Angle reports, “IBM and the United Services Automobile Association (USAA), a financial services provider for the military community, today announced they have teamed up to offer IBM’s Watson Engagement Advisor in a pilot program to assist USAA members. USAA provides insurance, banking, investments, retirement products and advice to 10.4 million current and former members of the U.S. military and their families. Named after IBM founder Thomas J. Watson, IBM Watson uses natural language processing and analytics, and can process information similar to the way people think. This helps organizations to quickly analyze, understand and respond to vast amounts of Big Data. IBM’s Watson Engagement Advisor analyzed USAA’s business data and now understands more than 3,000 documents on topics exclusive to military transitions.” Read more
Senzari Unveils MusicGraph.ai at the GraphLab Conference 2014, Showcasing the Music Industry’s First Graph Analytics and Intelligence Engine
SAN FRANCISCO–(BUSINESS WIRE)–Today Senzari® introduced MusicGraph.ai, the first web-based graph analytics and intelligence engine for the music industry at the GraphLab Conference 2014, the annual gathering of leading data scientists and machine learning experts. MusicGraph.ai will serve as the primary dashboard for MusicGraph®, where API clients will be able to view detailed reports on their API usage and manage their account. More importantly, through this dashboard, they will also be able to access a comprehensive library of algorithms to extract even more value from the world’s most extensive repository of music data.
“We believe that in MusicGraph’s first iteration we democratized music data access, and now, with MusicGraph.ai, we are democratizing music knowledge. Most impressively, our team was able to make this leap possible in just months, not years.” Read more
AlphaSense’s Advanced Linguistics Search Engine Could Buy Back Time For Financial Analysts To Do More In-Depth Research
When Raj Neervannan, CTO and co-founder of financial search engine company AlphaSense, thinks about search, he thinks about it “as a killer app that is only growing…..People want answers, not noise. They want to ask more intelligent questions and get to the next level of computer-aided intelligence.”
For AlphaSense’s customers – analysts at large investment firms and banks or any other industry, as well as one-person shops – that means search needs to get them out of ferreting through piles of research docs for the nuggets of information they really need. Neervannan knows the pain of trying to interpret a CEO’s commentary to understand what he or she was really saying when making the point that numbers were going down when referring to inventory turns. (Jack Kokko, former analyst at Morgan Stanley, is AlphaSense’s other co-founder.)
“You are essentially digging through sets of documents [using keyword search], finding locations of terms, pulling them in piece by piece and constructing a case as to what the company’s inventory turn was really like – what other companies’ similar information was, how that matches up. You have to do quantitative analysis and benchmarks, and it can take weeks,” he says.
Terence Tse, Mark Esposito, and Olaf Groth of the Harvard Business Review write, “While we are surrounded by a wave of new disruptive technologies and apps, HR still hasn’t improved how it evaluates the prospective workforce. Traditional hiring processes that revolve around CVs are no longer sufficient – they don’t pinpoint the right qualities demanded of leaders today, and their dated criteria obscures many talented individuals from even hitting the radar. There is nothing inherently wrong with resumes – they highlight applicants’ past achievements and experience. But while CVs are good at showcasing formal skills, they’re not very useful for identifying values and behavior.” Read more
Dave Lloyd of ClickZ recently wrote, “2014 has been heralded the year of content marketing. At the same time, we’re optimizing our search marketing practices for the semantic search environment. Together, there’s a need to merge the two different objectives into a unified strategy. From a search marketing perspective, it makes sense to integrate content marketing and semantic search optimization practices. The introduction of Hummingbird has taught us to deploy search optimization strategies that contextualize queries. Digital marketing with content, on the other hand, is deployed to drive traffic and engage prospects. You can see where the two might combine to form a natural single-track strategy, right? Read more
Schemaless structured document search system SIREn (Semantic Information Retrieval ENgine) has posted some impressive benchmarks for a demonstration it did of its prowess in searching complex nested documents. A blog here discusses the test, which indexed a collection of about 44,000 U.S. patent grant documents, with an average of 1,822 nested objects per doc, comparing Lucene’s Blockjoin capability to SIREn.
The finding for the test dataset: “Blockjoin required 3,077MB to create facets over the three chosen fields and had a query time of 90.96ms. SIREn on the other hand required just 126 MB with a query time of 8.36ms. Blockjoin required 2442% more memory while being 10.88 times slower!”
SIREn, which was launched into its own website and community as part of SindiceTech’s relaunch (see our story here), attributes the results to its use of a fundamentally different conceptual model from the Blockjoin approach. In-depth tech details of the test are discussed here. There it also is explained that while the focus of the document is Lucene/Solr, the results are identically applicable to ElasticSearch which, under the hood, uses Lucene’s Blockjoin to support nested documents.
The Semantic Web Blog also checked in with SindiceTech CEO Giovanni Tummarello to get a further read on how SIREn has evolved since the relaunch to enable such results, and in other respects.