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) …
Organizations already have plenty to worry about in terms of data protection, but a new type of cyberattack could prove much more damaging and harder to remediate. A destruction of service (DeOS) attack has the potential to destroy the data backups and safety nets organizations rely on to restore their systems and data following an attack, according to Cisco. DeOS attacks are a more dangerous version of distributed denial of service (DDoS), which employs botnets to overload the target organization's servers with traffic until they can no longer handle the extra load. DDoS attacks last hours or days, after which a company can resume normal operations. This is one of the many new security risks that are emerging with the Internet of Things (IoT).
– Big data helps to make strategy for future and understand user behaviors. In 1959, Arther Samuel gave very simple definition of Machine Learning as "a Field of study that gives computer the ability to learn without being explicitly programmed". Now almost after 58 years from then we still have not progressed much beyond this definition if we compare the progress we made in other areas from same time. Machine Learning and Deep Learning) is not so new, have you heard of accepting selfie as authentication for your shopping bill payment, Siri on your iPhone etc. A Decentralized Autonomous Organization (DAO) is a process that manifests these characteristics.
The insurance industry – like many elements within Financial Services (FS) – has come under intense pressure over the past decade or so. The fintech revolution has meant that smaller and more agile startups are able to offer a variety of new services to consumers and businesses. These services are not only more interactive and based on the latest technologies, but they are also services that bigger insurance firms cannot easily offer. This increased competition from newer market entrants is a growing problem for more established insurance providers. A 2016 PwC survey revealed that 65 per cent of insurance chief executives see new market entrants as a threat to growth, while 69 per cent of insurance chiefs were concerned about the speed of technological change in their industry.
Lab41 is currently in the midst of Project Hermes, an exploration of different recommender systems in order to build up some intuition (and of course, hard data) about how these algorithms can be used to solve data, code, and expert discovery problems in a number of large organizations. Anna's post gives a great overview of recommenders which you should check out if you haven't already. The ideal way to tackle this problem would be to go to each organization, find the data they have, and use it to build a recommender system. But this isn't feasible for multiple reasons: it doesn't scale because there are far more large organizations than there are members of Lab41, and of course most of these organizations would be hesitant to share their data with outsiders. Instead, we need a more general solution that anyone can apply as a guideline.
Summary: This is the second in our chatbot series. Here we explore Natural Language Understanding (NLU), the front end of all chatbots. We'll discuss the programming necessary to build rules based chatbots and then look at the use of deep learning algorithms that are the basis for AI enabled chatbots. In our last article which was the first in this series about chatbots we covered the basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. In this installment we'll explore in more depth how Natural Language Understanding (NLU) based on deep neural net RNN/LSTMs enables both rules based and AI chatbots.
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.
When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you're like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don't ease the choice. In this article for Statsbot, I will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you'll find the structured overview of the main features of described algorithms.
Light forms the global backbone of information transmission yet is rarely used for information transformation. Digital optical logic faces fundamental physical challenges1. Many analog approaches have been researched2,3,4, but analog optical co-processors have faced major economic challenges. Optical systems have never achieved competitive manufacturability, nor have they satisfied a sufficiently general processing demand better than digital electronic contemporaries. Incipient changes in the supply and demand for photonics have the potential to spark a resurgence in optical information processing.
Discussions about machine learning's impact on radiology might begin with image interpretation, but that's only the tip of the iceberg. When it comes to realizing the technology's full potential, it's like Bachman Turner Overdrive sang many years ago: You ain't seen nothing yet. The authors of a new analysis published in the Journal of the American College of Radiology wrote at length about the many applications of machine learning. "Machine learning has the potential to solve many challenges that currently exist in radiology beyond image interpretation," wrote lead author Paras Lakhani, MD, department of radiology at Thomas Jefferson University Hospital in Philadelphia, and colleagues. "One of the reasons there is great excitement in radiology today is the access to digital Big Data.
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.