AlchemyLanguage is a collection of APIs that offer text analysis through natural language processing. The AlchemyLanguage APIs can analyze text and help you to understand its sentiment, keywords, entities, high-level concepts and more.

Not every startup CEO can say they were able to grow their business to a point where they were acquired. Even fewer can say they did it twice. But that is exactly the case for AlchemyAPI Founder and CEO Elliot Turner. Turner launched his first startup, MimeStar, a software development company focused on network intrusion detection, while a sophomore in high school. Inc. acquired it by the time he was twenty-one.

IBM Watson has been trained by CaixaBank's foreign-trade specialists to answer questions in Spanish. Your cognitive computing future is here, apparently. Last year, a study by IBM argued that intelligent machines simulating the capabilities of the human brain will fundamentally change a range of industries, including banking. Now, in Spain, IBM Watson, the AI and analytics technology that can interact with humans using natural language and which made a name for itself by winning Jeopardy in 2011, has been trained by CaixaBank's foreign-trade specialists to answer questions and resolve technical problems in Spanish. IBM and CaixaBank describe the new system as the first Watson application in Spanish and the first in Europe's financial sector.

I would like to propose that Watson could solve one of the biggest challenges facing anyone trying to innovate and product their innovation with a US patent - the USPTO first office action. While everyone is working hard and I know the patent office is overloaded, here how three problems I've seen over my years of working that perhaps Watson could address: 1) Speed - it can take 6-12 months to get a first office action 2) Almost any patent application is nowadays first rejected due to obviousness. But the patents cited to create this argument are often taken out of context. To me, these seem like challenges that Watson would be perfectly designed to addressed. And all the literature to be reviewed is, by definition, in the public domain.

This is the direction of the future. Useful AI that can do the research of a thoudand men instantly. It's definitely worth noting that Watson is capable of learning (a point I didn't touch on in this video), so what you see here is the "baby phase" so to speak. I tried to leave out the technical jargon in this video but for those who want to know more, a wiki dump on Watson is below: According to John Rennie, Watson can process 500 gigabytes, the equivalent of a million books, per second. Software Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework.

Cleveland Clinic to use IBM Watson for Genomic Research: Researchers at Cleveland Clinic will use IBM Watson technology in the area of genomic research to help oncologists deliver personalized medicine by uncovering new cancer treatment options for patients. The Lerner Research Institute's Genomic Medicine Institute at Cleveland Clinic plans to evaluate Watson's ability to help oncologists develop more personalized care to patients for a variety of cancers. Clinicians lack the tools and time required to bring DNA-based treatment options to their patients and to do so, they must correlate data from genome sequencing to reams of medical journals, new studies and clinical records. At a time when medical information is doubling every five years, a faster option is needed. This use of Watson aims to find the "needle in the haystack" through identifying patterns in genome sequencing and medical data to unlock insights that will help clinicians bring the promise of genomic medicine to their patients.

Watson has it's visualisation tool called WatsonPaths to show how it has derived answers logically. AI needs large amounts of data and Google, Facebook and Amazon are sitting very pretty in this space. IBM will probably be unable to match either of the 3 – but if it becomes an industry expert – it will mint money in the more expensive and much needed business vertical. To be fair, IBM seems to be transparent on this topic. In this case – they're rolling it out for free!!! Well – upto a point IBM Bluemix services helps with development of a rapid prototype solution.

We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine's ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine’s ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.

Amazon recently announced Amazon Machine Learning, a cloud machine learning solution for Amazon Web Services. Able to pull data effortlessly from RDS, S3 and Redshift, the product could pose a significant threat to Microsoft Azure ML and IBM Watson Analytics. Upon selecting a model, the service asks whether the user would like to holdout data for validation from the training set or to provide holdout data from a different source. Once these selections are made, Amazon ML trains the model on the given dataset. Using the sample dataset of dummy bank customers (5MB in size), training takes roughly 10 minutes.