Natural Language Processing seems to have become somewhat of a commodity in recent years. More than a few companies have sprung up that offer basic NLP capabilities through a cloud API. If you'd like to know whether a text carries a positive or negative message, or what people or companies it mentions, you can just send it to one of these black boxes, and receive the answer in less than a second. Superficially, all these NLP APIs look more or less the same. Textrazor, AlchemyAPI, Aylien, MeaningCloud and Lexalytics all offer similar services (named entity recognition, sentiment analysis, keyword extraction, topic identification, etc.), and do so through similar interfaces.

Panasonic just created a new 10 million budget for its new corporate shopping list in the areas of Artificial Intelligence (AI) and Machine Learning Technologies (MLT). The Japanese technology giant is looking at these two streams as the future beacon of change and development, for its handset business unit which currently faces an immensely competitive market in the country. The 10 million budget has been set aside for either creating joint-ventures with other enterprises, or perhaps even acquiring a few smaller ones. Pankaj Rana, Panasonic's head of mobility division in India, South-Asia, Middle-East, and Africa commented, "The budget is in tune of 10 million to start with and as we see progress on this front and things go in right direction, then there will be no constraint on the budget part. We can spend as high as possible. Some part of this budget has been generated from the India business, while some portion has been allocated from Japan."

Machine learning is great and it does some amazing things, but even though we refer to the techniques as "neural networks" the way these systems learn is different from the way people learn. The biggest difference is that these algorithms/systems have insatiable appetites for clean data. You have to present one of these systems with huge numbers of pictures of kittens before it has any hope of labeling kittens reliably. As opposed to a child, who can be shown three pictures of kittens, and who at that point would probably perform as well as the exhaustively trained neural net. In all fairness, if we examine what these (deep) neural nets are learning we can see that the contest is not really fair.

The handset vendor has already set aside an initial corpus of 10 million for the development of this technologyRisk-prediction tool for diabetes patients. Read more ... » through a merger and acquisition or a jointToyota Goes To Silicon Valley, Enters Artificial Intelligence & Robotics Industry. "The budget is in tune of 10 million to start with, and as we see progress on this front and things go in right direction, then there will be no constraint on the budget part. We can spend as high as possible. Some part of this budget has been generated from the India business, while some portion has been allocated from Japan," Pankaj Rana, head of mobility division, India, South Asia, Middle East and Africa at Panasonic, told ET. "Our team would be traveling to Silicon Valley soon. We will have new products ready with AI in 9-12 months. In the last three months, we have finalized whatSingapore-based adtech startup wants to revolutionize multiscreen conversations. Read more ... » we will do and budgets have been allocated from Panasonic Japan and Panasonic India. Now we have to find a partner and start working on timeline, while understandingHow machine learning will take off in the cloud. Read more ... » the market," he said.

NEW DELHI: Panasonic India is scouting for companies to acquire in the next 6-9 months to develop artificial intelligence (AI) and Machine learning technologies, which it wants to integrate with its future smartphones to differentiate from rival vendors in the crowded yet fast growing market. The handset vendor has already set aside an initial corpus of 10 million for the development of this technology through a merger and acquisition or a joint venture. "The budget is in tune of 10 million to start with, and as we see progress on this front and things go in right direction, then there will be no constraint on the budget part. We can spend as high as possible. Some part of this budget has been generated from the India business, while some portion has been allocated from Japan," Pankaj Rana, head of mobility division, India, South Asia, Middle East and Africa at Panasonic, told ET. "Our team would be traveling to Silicon Valley soon. We will have new products ready with AI in 9-12 months. In the last three months, we have finalized what we will do and budgets have been allocated from Panasonic Japan and Panasonic India. Now we have to find a partner and start working on timeline, while understanding the market," he said.

NEW DELHI: Panasonic India is scouting for companies to acquire in 6-9 months to develop artificial intelligence (AI) and Machine learning technologies, which it wants to integrate with its future smartphones to differentiate from rival vendors in the crowded yet fast growing market. The handset vendor has already set aside an initial corpus of 10 million for the development of this technology through a merger and acquisition or a joint venture. "The budget is in tune of 10 million to start with, and as we see progress on this front and things go in right direction, then there will be no constraint on the budget part. We can spend as high as possible. Some part of this budget has been generated from the India business, while some portion has been allocated from Japan," Pankaj Rana, head of mobility division, India, South Asia, Middle East and Africa at Panasonic, told ET. "Our team would be traveling to Silicon Valley soon. We will have new products ready with AI in 9-12 months. In the last three months, we have finalized what we will do and budgets have already been allocated from Panasonic Japan and Panasonic India. Now we have to find partner and start executive on timeline, while understanding the market," he said.

At WebRTC Argentina we covered a number of topics, including an introduction to WebRTC with coding demonstrations, demos of WebRTC use cases, and design/UX for WebRTC. We concluded the mini-conference with this talk, where we wanted to stretch people's minds a little bit about WebRTC. WebRTC is not simply about 1-1 video chats or business conferencing tools. WebRTC can be one part of larger technological change in our society, and in this talk we give updates on WebRTC in the short term as well as one vision for how WebRTC and Artificial Intelligence may intersect in the future. You can see the complete presentation below, and we've included the transcript below the video.

Here are upcoming meetings and conferences, for July 2016 and beyond. Save 10% with the KDNUGGETS registration code. Aug 29 - Sep 1, Image Processing, Computer Vision and Machine Learning based on Optimization and PDE. Use code CDOINSUR to save 10% on registration. Sep 23, MLconf Atlanta Machine Learning Conference - mention "KDNuggets" and save 18%.

JUDY WOODRUFF: Today, the Obama administration revealed new information that sheds light on the reality of modern warfare, the number of civilians accidentally killed in U.S. airstrikes. JOHN YANG: Today's release is the first time the White House has said how many terrorists and innocent civilians it believes have been killed by airstrikes, including by drones. Between 2009 and 2015, the administration says it launched 473 airstrikes in Pakistan, Yemen and Africa. It estimates that as many as 2,581 combatants, and as many as 116 noncombatants were killed. Now, these numbers do not include airstrikes in Iraq, Afghanistan or Syria, what the administration calls areas of active hostilities. A new executive order has also been issued, with the aim of decreasing the number of civilian deaths.

This article is an interview with computational linguist Jason Baldridge. It's for anyone who's interested in, or needs to know about, natural language processing (NLP). Jason and NLP go way back. He joined the University of Texas linguistics faculty in 2005 and, a few years back, helped build a text-analytics system for social-media agency Converseon. Jason's Austin start-up, People Pattern, applies NLP and machine learning for social-audience insights; he co-founded the company in 2013 and serves as chief scientist.