{"id":3523,"date":"2017-02-15T06:33:09","date_gmt":"2017-02-15T06:33:09","guid":{"rendered":"https:\/\/www.rsoftsolution.com\/blog\/gamalon-leverages-the-work-of-an-18th-century-reverend-to-organize-unstructured-enterprise-data.html"},"modified":"2017-02-15T06:33:09","modified_gmt":"2017-02-15T06:33:09","slug":"gamalon-leverages-the-work-of-an-18th-century-reverend-to-organize-unstructured-enterprise-data","status":"publish","type":"post","link":"https:\/\/www.rbsitsoftwaresolution.in\/blog\/gamalon-leverages-the-work-of-an-18th-century-reverend-to-organize-unstructured-enterprise-data.html","title":{"rendered":"Gamalon leverages the work of an 18th century reverend to organize unstructured enterprise data"},"content":{"rendered":"


\n<\/p>\n

It\u2019s hard to fathom that the work of\u00a0Reverend Thomas Bayes is still coming back to drive cutting edge advancements in AI, but that\u2019s exactly what\u2019s happening. DARPA-backed\u00a0Gamalon<\/a> is the latest carrier of the Bayesian baton, launching today with a solution to help enterprises better manage their gnarly unstructured data.<\/p>\n

The world of enterprise is full of unstructured data. This includes product codes, SKUs, and text from sources not formally cataloged in spreadsheets. Organization opens doors for businesses to extract new insights from existing\u00a0resources and processes.<\/p>\n

Gamalon is releasing two products today for\u00a0AWS, Azure and Google Cloud customers to help them with this problem. The first, Structure, converts paragraphs into structured data. The\u00a0second, Match, duplicates and links these data rows.<\/p>\n

The underlying technology powering these\u00a0solutions differs from many typical machine learning approaches in the way it approaches\u00a0prior knowledge. One way to think about this sort of\u00a0Bayesian framework\u00a0is in the context of a medical diagnosis.<\/p>\n

Let\u2019s say someone asks a doctor what they make of their cough. The doctor contemplates and decides that the person could either have a cold or lung cancer. After all, people suffering from both typically exhibit\u00a0a cough. The missing information however is that very few people walk around with lung cancer while many more have colds.<\/p>\n

Bayesian frameworks let us take that extra dimension of information into account and update it as new data is created to build models of the world \u2014 an ideal way to think about drawing\u00a0conclusions with\u00a0data. An oversimplified deep learning model might\u00a0just use the symptom data of thousands of hospital patients and try to extrapolate the given ailment. The reality is that the two approaches aren\u2019t quite this opposed<\/a>, but the metaphor gets the idea across.<\/p>\n

\"Founder<\/p>\n

Founder Ben Vigoda<\/p>\n

The result for Gamalon is a system that promises developers a clearer view of how models work. In contrast, deep learning models give us conclusions about data without much detail on what drives the analysis. Even still, both approaches have their ideal use cases \u2014 but historically the later has been given a lot more attention.<\/p>\n

According to the company\u2019s founder\u00a0Ben Vigoda, Gamalon is writing\u00a0neural networks as probabilistic programs, building sub-routines within neural nets to combine them with other trained models.<\/p>\n

Collections of models can be easily combined to produce better results. This modularity enables a lot of problems to be solved with less data. The company\u00a0is capitalizing on all of this by equipping computers to build models by themselves, a\u00a0differentiating factor with respect to startups like Geometric Intelligence<\/a>. Ideally humans and machines can work hand-in-hand. Fortunately for the humans, this ultimately places more value on domain knowledge and less value on pure mathematical prowess.<\/p>\n

With the competitive advantage figured out,\u00a0Gamalon next turned its head to commercialization. The startup trained a version of its framework on enterprise data and gave it a home in the cloud. Beta customers can use the system self-service and\u00a0Gamalon will offer some professional services if necessary. Typical early customers have been e-commerce and manufacturing businesses that have massive amounts of unstructured data originating from a wide variety of places.<\/p>\n

\u201cUnderstanding unstructured data is a problem for 90 percent of enterprise companies,\u201d asserted Aydin Senkut, a partner at Felicis Ventures. \u201cA ton of audit money and human time is wasted looking for anomalies that a program\u00a0could learn to find.\u201d<\/p>\n

To date, Felicis Ventures<\/a>, Boston Seed Capital<\/a> and Rivas Capital\u00a0<\/a>have lined up alongside angels like Adam D\u2019Angelo, Andy Bechtolsheim, Steve Blank, Ivan Chong and Georges Harik to pour $4.45 million into the company. This comes on top of $7.7 million in government RD contracts from DARPA<\/a> for a total of $12.15 million in financing.<\/p>\n

<\/p>\n

Featured Image: Paul Taylor\/Getty Images<\/small><\/p>\n

Article source: http:\/\/feedproxy.google.com\/~r\/techcrunchIt\/~3\/AaTmwEFETiA\/<\/p>","protected":false},"excerpt":{"rendered":"

It\u2019s hard to fathom that the work of\u00a0Reverend Thomas Bayes is still coming back to drive cutting edge advancements in AI, but that\u2019s exactly what\u2019s happening. DARPA-backed\u00a0Gamalon is the latest <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[19],"tags":[25],"class_list":["post-3523","post","type-post","status-publish","format-standard","hentry","category-enterprise-software","tag-enterprise"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t