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) …
SINGAPORE, 22 February 2017 -- In our increasingly digital world, new and emerging innovations are set to disrupt the way people live, work and play. According to youth across the Asia Pacific region, the most exciting technologies expected to have the largest impact on their future lives will be artificial intelligence (AI), virtual/mixed/augmented reality (VR/MR/AR), and Internet of Things (IoT), based on survey findings released today by Microsoft. In the Microsoft Asia Digital Future Survey, 1,400 youth were polled across 14 markets across the Asia Pacific region, comprising Australia, China, Hong Kong, India, Indonesia, Japan, Korea, Malaysia, New Zealand, Philippines, Singapore, Taiwan, Thailand and Vietnam. Artificial intelligence (AI) is ranked as the top technology that youth expect to have the biggest impact on their lives. In recent years, the confluence of power devices, cloud and data has enabled bold visions on how AI can be an integrated part of our digital future.
Microsoft announced today that its Visual Studio integrated development environment is getting a new set of tools aimed at easing the process of building AI systems. Visual Studio Tools for AI is a package that's designed to provide developers with built-in support for creating applications with a wide variety of machine learning frameworks, like Caffe2, TensorFlow, CNTK, and MXNet. Once users have coded up models inside Visual Studio, the AI tools make it easier for them to send that code off to Microsoft's Azure cloud platform for training and deployment. Launching these tools brings a host of advanced capabilities to developers in a point-and-click format that would have previously required the use of a command line interface. It should make building AI systems more accessible for a class of developers that haven't been able to use Visual Studio's rich development environment to its full potential for that purpose.
Dr. Hugh Martin, principal lecturer in agricultural science at the Royal Agricultural University, looks at how the IoT revolution is helping usher in a new age of farming Industry 4.0 is a well-known idea. Perhaps less well-known is Agriculture 4.0. Martin identifies three previous revolutions in agriculture – dating back to the introduction of one of the original pieces of farming technology in 1730 in the form of Jethro Tull's seed drill. Broadly, these three revolutions can be defined as; the introduction of mechanisation, the use of mineral fertilisers, and the industrialisation of production processes. Now, Martin believes, connectivity and data management are set to unleash the next stage.
Summary: Reinforcement Learning (RL) is likely to be the next big push in artificial intelligence. It's the core technique for robotics, smart IoT, game play, and many other emerging areas. But the concept of modeling in RL is very different from our statistical techniques and deep learning. In this two part series we'll take a look at the basics of RL models, how they're built and used. In the next part, we'll address some of the complexities that make development a challenge.
Users of R have long been deprived of the opportunity to join the deep learning movement while remaining within the same programming language. With the release of MXNet, the situation began to change, but the frequent updates to the original documentation and changes that break backward compatibility still limit the popularity of this library. TensorFlow, Theano, CNTK) combined with detailed documentation and a lot of examples looks much more attractive. This article presents a solution to the problem of segmenting images in Carvana Image Masking Challenge, in which you want to learn how to separate cars photographed from 16 different angles will be dismantled. The neural network part is fully implemented on Keras, image processing is answered by magick (interface to ImageMagick), and parallel processing is provided by parallel doParallel foreach (Windows) or parallel doMC foreach (Linux).
Currently I'm searching for a Reinforcement Learning toolkit for autonomous driving to test the influence of several safety aspects during learning as a reward function. So far I have tested OpenAI Gym with the "Neon racer" environment, which does not provide those information. Are there any other toolkits you would suggest me for this purpose?
Imaging in three dimensions rather than two offers numerous advantages for machines working in the factories of the future by granting them a whole new perspective to view the world. Combined with embedded processing and deep learning, this new perspective could soon allow robots to navigate and work in factories autonomously by enabling them to detect and interact with objects, anticipate human movements and understand given gesture commands. Certain challenges must first be overcome to unlock this promising potential, however, such as ensuring standardisation across large sensing ecosystems and increasing widespread understanding of what 3D vision can do within industry. Three-dimensional imaging can be achieved by a variety of formats, each using different mechanics to capture depth information. Imaging firm Framos was recently announced as a supplier of Intel's RealSense stereovision technology, which uses two cameras and a special purpose ASIC processor to calculate a 3D point cloud from the data of the two perspectives.
For as long as artificial intelligence and machine learning tools have been moving into the workforce, there have been rumblings of robots taking over the work of people, and the impact that could have on their career prospects. However, new studies undertaken by global professional services brand Genpact of 5,000 respondents in the United Kingdom, United States, and Australia, shows that the level of concern among the workers themselves is not very high. Roughly twenty percent of those surveyed in the UK felt that their jobs were threatened by AI, with only six percent feeling this strongly. But, although they did not feel overly cautious about their own prospects, they saw the potential disadvantages for the next generation of workers, with over fifty percent responding there was a threat to their children's careers, and over eighty percent stating that new skills will be needed for those workers in order to succeed in an AI advanced environment. The reason for this caution can be found in the training, or lack thereof, in the use of AI.
The technology is a form of deep-learning artificial intelligence software developed to fit onto mobile computer chips. This allows artificial intelligence to be used in a range of devices, from smartphones to industrial robots. This portability would enable devices to operate independent of the Internet while using artificial intelligence that performs equivalent to tethered neural networks. With this, a hosting chip embedded in a smartphone could run a speech-activated virtual assistant and undertake other intelligent features, such as controlling data usage. Other applications include operating drones and surveillance cameras in remote areas.
Who would have thought that the stories around self-driven cars could actually come true, so much so that machine learning algorithms can enable computers to communicate with humans, drive cars, play games and do things human cannot do. Machine Learning with its mathematical algorithms and scientific innovations have become a huge part of our lives. For example, when Google auto-corrects a misspelled word, it applies probability algorithm, an action performed using Machine Learning, which compares the database of the previous searches done by millions of other users and predicts the word we intend to use. With the ever-increasing knowledge in science and technology, machine learning is not far behind to be the new switchboard for Higher Education, personalising education at all levels. It reads and identifies the data patterns to inform algorithms that can make data-driven predictions and decisions.