Artificial Intelligence

Forging Ahead in the New AI Economy

"Forge Ahead" photo of arrow on pavementWe are seeing the beginning of the new artificial intelligence economy. This has many parallels to the infrastructure-as-a-service wave led by Amazon Web Services (AWS), which provided the world with access to highly-scalable compute capacity. AI technologies are being exposed as core infrastructure via the cloud, enabling companies to build smarter applications and services.

If you think you aren’t already a part of the AI economy, think again. Most of us are already participating through our interaction with popular applications and services. For example, Google Maps uses AI technology to better understand Street View images to give more accurate directions; and both Siri and Google Now use a combination of speech recognition, language understanding, and predictive modeling to act as digital personal assistants.

So the big question is: why now? Historically, AI technologies have been limited by a lack of data, insufficient compute capability, and poor algorithms. We’re now witnessing the convergence of three major forces: ready access to massive data, highly scalable on-demand compute capability, and a number of core algorithmic breakthroughs that enable us to better train robust AI systems. This is a perfect storm that has resulted in significant advances in computers’ ability to understand text, images, video, and speech. Read more

Artificial Intelligence Enhances Video Games

Video Game player taking action.A recent announcement on EurekAlert! states: “Researchers from North Carolina State University have developed artificial intelligence (AI) software that is significantly better than any previous technology at predicting what goal a player is trying to achieve in a video game. The advance holds promise for helping game developers design new ways of improving the gameplay experience for players.”We developed this software for use in educational gaming, but it has applications for all video game developers,” says Dr. James Lester, a professor of computer science at NC State and senior author of a paper on the work. “This is a key step in developing player-adaptive games that can respond to player actions to improve the gaming experience, either for entertainment or – in our case – for education.” The researchers used “deep learning” to develop the AI software. Deep learning describes a family of machine learning techniques that can extrapolate patterns from large collections of data and make predictions. Deep learning has been actively investigated in various research domains such as computer vision and natural language processing in both academia and industry.”

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Baidu Takes on Artificial Intelligence

Baidu logoDaniel Sparks of The Motley Fool reported, “”Chinese companies are starting to dream,” said early investor in Baidu (NASDAQ: BIDU  ) and managing partner at GGV Capital Jixun Foo. Foo’s proclamation was made in an in-depth article by MIT Technology Review, which examined the Chinese search giant’s new effort to change the world with artificial intelligence. The company’s new AI lab does, indeed, accompany some lofty aspirations — ones big enough to hopefully help Baidu become a global Internet powerhouse and to compete with the likes of Google in increasingly important emerging markets where the default search engine hasn’t yet taken the throne. But what are the implications for investors? Fortunately, Baidu’s growing infatuation with AI looks like it could give birth to winning strategies that could build sustainable value over the long haul.”

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Introducing RoboBrain: An Online Brain for Robots

robobrainDaniela Hernandez of Wired recently wrote, “If you walk into the computer science building at Stanford University, Mobi is standing in the lobby, encased in glass. He looks a bit like a garbage can, with a rod for a neck and a camera for eyes. He was one of several robots developed at Stanford in the 1980s to study how machines might learn to navigate their environment—a stepping stone toward intelligent robots that could live and work alongside humans. He worked, but not especially well. The best he could do was follow a path along a wall. Like so many other robots, his ‘brain’ was on the small side. Now, just down the hall from Mobi, scientists led by roboticist Ashutosh Saxena are taking this mission several steps further. They’re working to build machines that can see, hear, comprehend natural language (both written and spoken), and develop an understanding of the world around them, in much the same way that people do. Read more

Cortana Enhances Predictive Capabilities

cortana304x200Jason Mick recently blogged, “While iOS 8 should make Apple, Inc.’s (AAPL) Siri substantially smarter, Microsoft Corp.’s (MSFT) Windows Phone voice-controlled assistant Cortana currently enjoys a nice lead in natural language processing and the ability to interface with multiple apps to perform useful functions.

Cortana is a commercial product, but it’s also a bit of lab experiment for the folks at Microsoft.  During the 2014 FIFA World Cup, Microsoft showed off its increasingly sophisticated prediction algorithms, which correctly guessed 15 out of 16 winners in the knockout round stage.  Its sole mistake was picking Brazil to beat the Netherlands (whoops) for third place in the consolation match.”

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Nervana Systems Raises $3.3M for Deep Learning

nervanaDerrick Harris of GigaOM reports, “Nervana Systems, a San Diego-based startup building a specialized system for deep learning applications, has raised a $3.3 million series A round of venture capital. Draper Fisher Jurvetson led the round, which also included Allen & Co., AME Ventures and Fuel Capital. Nervana launched in April with a $600,00 seed round. The idea behind the company is that deep learning — the advanced type of machine learning that is presently revolutionizing fields such as computer vision and text analysis — could really benefit from hardware designed specifically for the types of neural networks on which it’s based and the amount of data they often need to crunch.” Read more

The Future of Intelligent Virtual Assistants? Consolidation

Photo of Nova SpivackSerial entrepreneur and thought leader Nova Spivack recently wrote for Gigaom, “When we talk about the future of artificial intelligence (AI), the discussion often focuses on the advancements and capabilities of the technology, or even the risks and opportunities inherent in the potential cultural implications. What we frequently overlook, however, is the future of AI as a business. IBM Watson’s recent acquisition and deployment of Cognea signals an important shift in the AI and intelligent virtual assistant (IVA) market, and offers an indication of both of the potentials of AI as a business and the areas where the market still needs development. The AI business is about to be transformed by consolidation. Consolidation carries real risks, but it is generally a sign of technological maturation. And it’s about time, as AI is no longer simply a side project, or an R&D euphemism. AI is finally center stage.”

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Contify Relies on NLP, AI, and Machine Learning for new Competitive Intelligence Platform

contifySugandh 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

Making Progress On MOOCs

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Image courtesy palphy/Flickr

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 CourseraEdX 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.

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Prelert’s Elasticsearch Equipped with Anomaly Detection

Prelert logoDaniel 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.”

Read more here.

Image courtesy Prelert.

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