Sentiment analysis is widely used to gauge public opinion towards products, to analyze customer satisfaction, and to detect trends. With the proliferation of customer reviews, more fine-grained aspect-based sentiment analysis (ABSA) has gained in popularity, as it allows aspects of a product or service to be examined in more detail. To this end, we have launched an ABSA service a while ago and demonstrated how the service can be used to gain insights into the strengths and weaknesses of a product. For performing sentiment analysis on customer reviews (as well as with many other text classification tasks), we face the problem that there are many different categories of reviews such as books, electronics, restaurants, etc. (You only need to have a look at the Departments tab on Amazon to get a feeling for the diversity of these categories.) In Machine Learning and Natural Language Processing, we refer to these different categories as domains; every domain has their unique characteristics.

This event highlights the changes that NVIDIA GPU-accelerated computing has brought to the analytics, machine learning, and deep learning segments. This new computing paradigm is enabling customers to extend the benefits beyond big data with the power of deep learning and accelerated analytics. The leaders from this growing ecosystem of solutions and technologies that are delivering on this promise will lead an active discussion in the keynote and panels.

"Will work still be the place where we integrate individuals into societies?" asks End of Shift -- The Robots Are Taking Over, a new documentary on the future of work by filmmaker Klaus Martens that premiered last week on German and French public television (I make a brief appearance in it as well). It is a rhetorical question, and although not verbalized by the narrator before the end of the film, it is omnipresent from the first scene on and implicitly precludes all interviews and footage that Martens and his crew captured in Germany, France, Japan, and the San Francisco Bay Area. The topic is acute: An oft-cited Oxford study predicted in 2013 that software and robots will eliminate half of the human work force within the next two decades. This year's OECD report comes to a less pessimistic conclusion, emphasizing the heterogeneity of workers' tasks within occupations. It projects that "on average across the 21 OECD countries, 9 percent of jobs are automatable" (e.g. in Germany 12 percent, in France 9 percent, and in the US 9 percent), and low qualified workers will be most affected.

Welcome to the O'Reilly Artificial Intelligence live stream. During one of our scheduled live broadcasts (see schedule below), the live presentation will appear here automatically. Note: All sessions and keynotes from O'Reilly Artificial Intelligence 2016 in New York will be recorded (pending speaker consent) and available in Safari approximately 3 weeks after the conference ends.

Artificial Intelligence and Machine Learning have the potential to radically transform the way that organisations utilise the vast amounts of data that they collect. In this video, Rise has interviewed some leading startups to get their thoughts on the future of AI and ML.

In a world of ubiquitous smart-phone cameras, drones, and Google Street View cars, there's probably never been a more important time to start protecting the identities of people unwittingly captured in photos and videos. But while websites like YouTube have started offering tools to obscure faces and other objects appearing in digital media, researchers have found that those protections can be defeated at an alarming rate thanks to recent advances in artificial intelligence. In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects obscured by blurring, pixelation, and a recently-proposed privacy system called P3 can be successfully identified by a neural network trained on image datasets--in some cases at a more consistent rate than humans. "We argue that humans may no longer be the'gold standard' for extracting information from visual data," the researchers write. "Recent advances in machine learning based on artificial neural networks have led to dramatic improvements in the state of the art for automated image recognition. Trained machine learning models now outperform humans on tasks such as object recognition and determining the geographic location of an image."

The pascal visual object classes (voc) challenge.

The implications of artificial intelligence (AI) for customer service are staggering and truly limitless. But until now, most customer service leaders have been unable to put intelligence in action. They lacked the army of data scientists needed to make sense of the data. And even if they were able to find and hire the data scientists - then what? How to integrate it with their existing customer service technology stack?

BANGALORE: Advancement in robotics, autonomous transport, AI and machine learning could impact more than 5 million people's jobs by 2020, according to World Economic Forum's recent study. 'The Future of Jobs' study states that over 5 million people's jobs are estimated to get impacted by technology advancement across verticals and some 2 million new highly-skilled jobs will be created by 2020. This study is one more indicator of how the wave of automation is expected to sweep across industries. And the global BPO (business process outsourcing) sector is too under its impact with the rise of AI, machine learning and RPA (robotic process automation) technology led automated services. Though, the automation wave is at its early stage, some of the Indian BPO firms led by their CIOs (Chief Information Officers) are already embracing it openly as a way to move forward by adopting new technology and enhancing employees' skills.

Think about the future of automation and AI. The future of automation is here and now. Only, it isn't an edge of the seat sci-fi thriller that you might imagine. Automation is no longer an option in the 21st century enterprise. Across industries, it is already driving efficiency, productivity, agility, adaptability and optimisation.