Sugandh Dhawan of iamwire.com reports, “New Delhi based SaaS startup, Contify, has launched an enterprise grade competitive intelligence (CI) platform to cater to the large organisations dealing with the job of identifying, sourcing, curating, and disseminating critical business information, across several functions. Founded in 2009 as a content syndication business, Contify is a product company focused in the areas of machine learning, artificial intelligence, and natural language processing. It offers an intelligence platform to enable businesses to monitor their competitors, customers and industries along with critical market variables that impact ones business.” Read more
As the school year gets into full swing, folks might be starting to think about how MOOCs (massive online open courses) can help them on their own educational journeys – whether towards a degree or simply for growing their own knowledge for personal or career reasons. After a meteoric rise, MOOCs such as those offered by Coursera, EdX and Udacity, have taken a few hits. Early results from a study last year by the University of Pennsylvania, for instance, said that MOOC course completion rates average just 4 percent across all courses, and range from 2 to 14 percent depending on the course and measurement of completion. The New York Times reported on some other setbacks here – but also noted that while MOOCs may be reshaped, they’re unlikely to disappear.
Some of that reshaping is underway. Among the efforts is a project announced this summer to take place at Carnegie Mellon University, in a multi-year program funded through a Google Focused Research Award. The announcement says the project will approach the problem from multiple directions, including a data-driven effort that will use machine-learning techniques to personalize the MOOC learning experience.
Daniel Gutierrez reported, “Prelert, the anomaly detection company, today announced the release of an Elasticsearch Connector to help developers quickly and easily deploy its machine learning-based Anomaly Detective® engine on their Elasticsearch ELK (Elasticsearch, Logstash, Kibana) stack. Earlier this year, Prelert released its Engine API enabling developers and power users to leverage its advanced analytics algorithms in their operations monitoring and security architectures. By offering an Elasticsearch Connector, the company further strengthens its commitment to democratizing the use of machine learning technology, providing tools that make it even easier to identify threats and opportunities hidden within massive data sets. Written in Python, the Prelert Elasticsearch Connector source is available on GitHub. This enables developers to apply Prelert’s advanced, machine learning-based analytics to fit the big data needs within their unique environment.”
The article continues with, “Prelert’s Anomaly Detective processes huge volumes of streaming data, automatically learns normal behavior patterns represented by the data and identifies and cross-correlates any anomalies. It routinely processes millions of data points in real-time and identifies performance, security and operational anomalies so they can be acted on before they impact business. The Elasticsearch Connector is the first connector to be officially released by Prelert. Additional connectors to several of the most popular technologies used with big data will be released throughout the coming months.”
Image courtesy Prelert.
A recent report from David Loshin states, “As our world becomes more attuned to the generation, and more importantly, the use of massive amounts of data, information technology (IT) professionals are increasingly looking to new technologies to help focus on deriving value from the velocity of data streaming from a wide variety of data sources. The breadth of the internet and its connective capabilities has enabled the evolution of the internet of things (IoT), a dynamic ecosystem that facilitates the exchange of information among a cohort of devices organized to meet specific business needs. It does this through a growing, yet intricate interconnection of uniquely identifiable computing resources, using the internet’s infrastructure and employing internet protocols. Extending beyond the traditional system-to-system networks, these connected devices span the architectural palette, from traditional computing systems, to specialty embedded computer modules, down to tiny micro-sensors with mobile-networking capabilities.”
Loshin added, “In this paper, geared to the needs of the C-suite, we’ll explore the future of predictive analytics by looking at some potential use cases in which multiple data sets from different types of devices contribute to evolving models that provide value and benefits to hierarchies of vested stakeholders. We’ll also introduce the concept of the “insightful fog,” in which storage models and computing demands are distributed among interconnected devices, facilitating business discoveries that influence improved operations and decisions. We’ll then summarize the key aspects of the intelligent systems that would be able to deliver on the promise of this vision.”
The full report, “How IT can blend massive connectivity with cognitive computing to enable insights” is available for download for a fee.
Lars Hard of Beta News recently wrote, “Artificial intelligence (AI) has become a bit of a buzzword among technology professionals (and even within the mainstream public) but truthfully, most people do not know how it works or how it is already being integrated within leading enterprise businesses. AI for businesses is today mostly made up of machine learning, wherein algorithms are applied in order to teach systems to learn from data to automate and optimize processes and predict outcomes and gain insights. This simplifies, scales and even introduces new important processes and solutions for complex business problems as machine learning applications learn and improve over time. From medical diagnostics systems, search and recommendation engines, robotics, risk management systems, to security systems, in the future nearly everything connected to the internet will use a form of a machine learning algorithm in order to bring value.” Read more
Tim Beyers of The Motley Fool recently wrote, “For years, International Business Machines has been dabbling with what it calls ‘cognitive computing.’ Now the company that brought you the Watson supercomputer believes it has a chip that can think like the human brain. Called TrueNorth, the chip draws on some 5.4 billion interconnected transistors to form a vast network not unlike the neural networks found in the human brain. That’s a potentially massive breakthrough, especially for Internet-connected mobile devices that encounter new data every second. We’re likely to be years away from mass production of the TrueNorth chip. And even then, experts quoted in this article in The New York Times seem to be split on its potential impact.” Read more
Steve Ranger of Tech Republic reports, “Qwerty [the standard keyboard layout] was a compromise from the start. And as such you’d expect it to be swept away as the technology changed. And yet this odd layout became the standard, used since on billions of devices from typewriters to tablets and PCs. Even as the cold steel of the typewriter was replaced by the cool glass of a touchscreen smartphone, Qwerty has continued to dominate. That is, until now. A number of companies are rethinking the keyboard for the digital age, led by a small UK startup called SwiftKey, so that a mere 150 years after it was first created, the keyboard could finally be made to behave just how the user wants it to.” Read more
Katherine Noyes of Tech News World reports, “The inventors of Apple’s Siri personal assistant have launched an independent effort that could make their first offspring look kind of dumb. Billed by its creators as ‘the global brain,’ Viv aims to radically simplify the world by providing an intelligent interface to everything. ‘They are trying to abstract Siri’s [natural-language processing] interface so you could apply it into other applications and domains,’ Raj Singh, CEO and founder of Tempo AI, told TechNewsWorld. ‘For example, what if I wanted to integrate a Siri-like interface into the Yelp app or the Expedia app?’ Currently, ‘there isn’t a good facility to do this,’ he said. Read more
Will a robot take your job in the future? Given their increasing sophistication, it’s not surprising if the topic is of growing concerns to more people. The Semantic Web Blog has reported, for example, on robots that are learning to do tasks in response to humans’ natural language, and a talking robot on a space journey, covering the gamut from personal assistant to astronaut.
The Pew Research Center released a report last week entitled AI, Robotics and the Future of Jobs. It raises the question of whether advances in robotics and artificial intelligence will displace more jobs than they create by 2025, but the experts the report draws upon for their opinions haven’t reached a consensus on that point yet. Forty-eight percent believe both blue- and white-collar worker jobs are at risk, and that the future will see greater income inequality, more permanent unemployment and greater social disruption as a result. The other 52 percent see a lot of jobs that currently require real people will be taken over by robots or digital agents, as well – but with the happier prospect that humans will figure out new jobs and industries to replace the livings they can no longer make with their own brains or hands.
Aviva Rutkin of New Scientist recently wrote, “Rumours have been circulating that the Chinese search engine is developing a bike that could drive itself through packed city streets. The project isn’t ready to be launched yet but Baidu confirmed it is exploring the idea.The news is intriguing, and not just because self-navigating bikes would be cool. Research into autonomous vehicles is yet another way that Baidu is following Google’s model of pushing at the boundaries of artificial intelligence.” Read more
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