CB Insights revealed the AI 100 winners during The Innovation Summit in Santa Barbara, a gathering of top executives and investors to explore the industries of the future. The CB Insights research team selected the winners based on a combination of data submitted by the companies, responses to interview questions and the company's Mosaic Score. Mosaic is an algorithm built with funding from the National Science Foundation that gives predictive intelligence into the health of private companies. "At Ayasdi, we see clearly that intelligent applications enabled by AI and big data will be as transformative for knowledge-based processes as the industrial revolution was for mechanical processes," said Ayasdi CEO Gurjeet Singh. "Intelligent applications running on our platform are already becoming a fundamental part of our customer's operations with breakthrough results.
Can you tell us a bit about MindMeld? MindMeld is a leading Conversational AI company, now offering Deep-Domain Conversational AI. The company has pioneered the AI technology behind the emerging generation of intelligent voice and chat assistants and currently powers advanced conversational experiences used by some of the world's largest media companies, government agencies, automotive manufacturers, and global retailers. MindMeld's customers and investors include Google, Samsung, Intel, Telefonica, Liberty Global, IDG, USAA, Uniqlo, Spotify, In-Q-Tel and others. What does Deep-Domain Conversational AI mean?
When IBM began constructing its Cloud Video portfolio earlier this year through the acquisitions of Clearleap and Ustream, it was always envisaged that at some stage, its Watson AI assets would be brought into play to bolster its video analytics capabilities with cognitive computing. An early example of the application of this technique to video included IBM's use of experimental Watson APIs to create a "cognitive movie trailer." The system learned from previous horror trailers what was likely to have made them effective, and then identified relevant scenes in an un-released movie that could improve response. IBM also partnered this year with the US Open to convert commentary to text with greater accuracy by having Watson learn tennis terminology and player names before the tournament. The recently announced slew of Watson-inspired video applications indicates that promise has now become a reality.
Amazon, Google, Facebook, IBM, and Microsoft have announced they are forming a non-for-profit organisation to educate the public about artificial intelligence (AI) technologies, as well as alleviate anxieties around its application. The collective, which includes Google's AI subsidiary DeepMind, also plans to develop best practices on the challenges and opportunities within the field of AI. The organisation, called Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI), will address legal and ethical challenges that AI presents, encourage public discourse, and identify opportunities to use AI to bring improvements to society. The organisation does not intend to be a regulatory body, with a statement saying it does "not intend to lobby government or other policymaking bodies." Members of the Partnership on AI will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology.
In my earlier articles, I had discussed about about application of Big data for gathering Insights on green revolution and witnessed about a research work on supply chain management using big data analytics on agriculture. Incrementally, got an opportunity to implement data science methodology (a game theory approach) to make the results of SCM as an incentive compatible one. However, in this article I am trying to discuss about a large scale digital image processing obtained using time-series photographs of agricultural fields and sensor data for parameters, that should be done parallely with the help of Big Data Analytics such that the result of this work can facilitate SCM process exponentially. We are focusing on using deep learning and machine learning techniques for identifying patterns for making predictins and decision making on large-scale stored / near real-time data sets. By this, we can identify the crop type, quality, maturity period for harvesting, early identification of bugs and diseases, soil quality attributes, early identification of need for soil nourishments etc., on a larger farms.
Inspired by the development of semantic technologies in recent years, in statistical analysis field the traditional methodology of designing, publishing and consuming statistical datasets is evolving to so-called "Linked Statistical Data" by associating semantics with dimensions, attributes and observation values based on Linked Data design principles. The representation of datasets is no longer a combination of magic words and numbers. Everything is becoming meaningful when URIs replace their positions as dereferencable resources, which further establishes the relations between resources implicitly and automatically. Different datasets are no longer isolated and all datasets share a globally, uniquely and uniformly defined structure. At this point, it is time to start building data-oriented applications and services with the traditional statistical computing languages such as R, while benefiting from the omnipotent semantic power of the SPARQL query language.
BERLIN stands for Behavioural Event Reconstruction Linguistic Interface for Narratives. I introduced BERLIN a few blogs ago - in my "final blog." Theoretically after one's final blog, no further blogs are forthcoming. However, I am now posting bonus blogs reflecting aspects of the same closing subject. Today, I will be elaborating on BERLIN's syntax and how its searches are facilitated.
Welcome to the roundup of the best new Android applications, games, and live wallpapers that went live in the Play Store or were spotted by us in the previous 2 weeks or so. This week's roundup is brought to you by Playtime Internet Radio from HandyApps. This useful all-in-one radio manager allows users to search for specific songs or shows streaming on thousands of live channels all across the Internet, or use the more conventional recommendation engine for a more random experience. Local FM radio stations can be streamed through the app, and there are no charges or subscriptions. Playtime is also compatible with Chromecast, and it's recently been given a visual overhaul.
When you go to the grocery store, you see that items of a similar nature are displayed nearby to each other. When you organize the clothes in your closet, you put similar items together (e.g. Every personal organizing tip on the web to save you from your clutter suggests some sort of grouping of similar items together. Even we don't notice it, we are involved in grouping similar objects together in every aspect of our life. This is called clustering in machine learning, so in this post I will provide an overview of data mining clustering methods.
What if the apps on your phone knew where you were, what you were doing, what's nearby, and even what the weather was like outside, and then combined this information to react intelligently to your current situation? Would that be creepy or amazing? We will soon find out, it seems. At this week's Google I/O conference, the company introduced new tools for app developers that will allow them to create applications that customize themselves to a user's current context. For example, a streaming music application could display an energetic playlist when you plug in your headphones and start jogging.