If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
On today's episode of "The Interview" with The Next Platform, we discuss the role of higher level interfaces to common machine learning and deep learning frameworks, including Caffe. Despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs) according to this episode's guest, Soren Klemm, one of the creators of Python based Barista, which is an open-source graphical high-level interface for the Caffe framework. While Caffe is one of the most popular frameworks for training DNNs, editing prototxt files in order to specify the net architecture and hyper parameters can become a cumbersome and error-prone task. Instead, Barista offers a fully graphical user interface with a graph-based net topology editor. Barista is designed on top of the Caffe infrastructure.
Training and education of the workforce is key to the digital transformation success of many businesses. One tool that has helped foster this is e-learning, especially, as Digital Journal has reported, e-learning is leading the way as businesses shift their training priorities to embrace a digital-first approach. There are many different forms of e-learning, involving a mix of different channels, content, use of video and so on. A shared objective is the importance of flexibility and a blended approach. Advantages to businesses include lower costs, since one training session can be delivered to many people.
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Check out the AI Conference in Beijing, April 10-13, 2018. Hurry--early price ends March 9. Having traveled to China several times over the last few years, I can attest to the strong interest in applications of AI among technologists, business leaders, and policy makers. China is adopting AI tools and technologies at a rapid pace, and since current AI systems rely on large data sets, startups are able to start using AI tools much earlier (a startup in China can quickly have many millions of users). People in the West are curious about the progress of AI research and business models in China. On the flip side, having organized a couple of large conferences in Beijing, I also know that people in China want to hear from AI experts outside their country.
The advertising world loves big, shiny, techy things. Agency and client ears perk right up when they hear about virtual reality kiosks, gadget-filled activations and holograms of dead rock stars. But then there are the tech innovations that sound a bit, or a lot, less sexy. Deep learning is a subset of machine learning that essentially teaches computers to find patterns in sounds, images and other data. And while that may not seem like much fun to your average social marketer or copywriter, the tech giants--the Facebooks, Apples, Googles, Netflixes, Microsofts and Baidus of the world--are investing massive sums of money in it.
From past few decades, our educational system has been slowly adapting itself to the new age of technology. With every day something new is innovating, everyone needs to accept the new change and learn about it. The biggest change that a mankind is right now facing is the new age of technology i.e. As the artificial intelligence is leading its way into the educational system as well it is becoming more important to incorporate the changes in the way the leaning is happening at higher education levels. A recent analysis of artificial intelligence market in U.S educational sector concluded that use of artificial intelligence in this sector has a compound growth rate of 47.5% throughout 2017-2021 forecasted periods.
Throughout the ages, human innovation has been accelerating at a mind-boggling rate. Consider that 1 million years elapsed between the control of fire and the invention of the wheel, but just 5,400 more years until the creation of the Gutenberg press--and a mere 455 additional years before the development of the light bulb. When plotted on a chart to illustrate the human innovation curve, it is clear that there is only one word that can describe the increase in the pace of progress: exponential. However, the innovation curve now is shifting into even higher gear with the proliferation of artificial intelligence (AI). AI grew exponentially in 2017, with no signs of stopping in 2018.
The development of artificial intelligence (AI) has had a huge influence on today's society, as ongoing discussions evaluate the impacts of creating machines and computer systems that can react and perform like humans. These systems can process information in a more cognitive way, making them capable of more human-like functions like learning, decision-making, and visual perception. Hollywood portrayals of hyper-intelligent robots taking over the planet might make artificial intelligence seem intimidating, but there is a lot that can be gained by through these advanced computer systems. Without the element of human error, intelligent machines are capable of unmatched precision and accuracy, and since they don't require fundamental human needs like oxygen or food, they can perform tasks with far fewer limitations. In fact, AI is already popping up everywhere in our daily lives – through social media recommendations, virtual assistants on our smartphones, and even self-driving cars.
China has approved a plan to create a next-generation national laboratory for deep learning. The lab is expected to help China close the gap with Western counterparts in the field of competitive artificial intelligence applications. The National Development and Reform Commission (NDRC) approved plans for a national engineering lab to support the research and development of deep learning technologies.
This is an article I had originally written as part of a stream of work that has now been put on hold indefinitely. I thought it a shame for it to languish in OneNote. Well that is a very good question. To be perfectly frank, not that much has changed of late in the world of Artificial Intelligence (AI) as a whole that should justify all the current excitement. That's not to say that there isn't cool stuff going on; there really is great progress being made… in the world of Machine Learning.