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) …
Cognitive computing is the game-changing technology that could be the answer to marketers' and sellers' prayers. It could also be one of the most disruptive forces their functions face. Armed with insights about customers at every touchpoint, professionals using cognitive computing are able to create and deliver the personalized, intuitive experiences customers expect. But are Chief Marketing Officers (CMOs) and heads of sales ready to make the cognitive leap? Our study explores the extent to which these executives are embracing cognitive technologies today, the challenges they face and the lessons they can learn from outperforming companies that are already applying cognitive solutions and driving a cognitive-enabled vision for their business.
Practical machine learning development has advanced at a remarkable pace. This is reflected by not only a rise in actual products based on, or offering, machine learning capabilities but also a rise in new development frameworks and methodologies, most of which are backed by open-source projects. In fact, developers and researchers beginning a new project can be easily overwhelmed by the choice of frameworks offered out there. These new tools vary considerably -- and striking a balance between keeping up with new trends and ensuring project stability and reliability can be hard. The list below describes five of the most popular open-source machine learning frameworks, what they offer, and what use cases they can best be applied to.
As enterprises look to deploy distributed ledgers, the industry's largest IT providers have launched blockchain-as-a-service (BaaS), offering a way to test the nascent technology without the cost or risk of deploying it in-house. The BaaS offerings could help companies who don't want to build out new infrastructure or try to find in-house developers, which are in hot demand. "The thing to be thinking about is that we're still in the early innings of this blockchain wave," said Bill Fearnley Jr., IDC's research director for Worldwide Blockchain Strategies. "There are very few people with multiple years of deep, hands-on experience." While heavily hyped, blockchain technology – which gained its initial notoriety from bitcoin cryptocurrency – has the potential to offer a new paradigm for the way information is shared; tech vendors and companies are rushing to figure out how they can use the distributed ledger technology to save time and admin costs.
Mindfire, a new foundation with the goal of "decoding the mind" to help develop true artificial intelligence (AI) is launching November 17th in Zurich, Switzerland. Futurism spoke with the founder of Starmind and president of the foundation, Pascal Kaufmann to learn more about its goals and the path to reach them. "We cannot achieve True AI until we understand actual intelligence. Intelligence has evolved as a means of nature to successfully guide us through an ever-changing environment. This gave rise to behavior, emotions, and consciousness.
Since version 1.2, Google dropped GPU support on macOS from TensorFlow. As of today, the last Mac that integrated an nVidia GPU was released in 2014. Only their latest operating system, macOS High Sierra, supports external GPUs via Thunderbolt 3.1 Who doesn't have the money to get one of the latest MacBook Pro, plus an external GPU enclosure, plus a GPU, has to purchase an old MacPro and fit a GPU in there. Any way you see it, it's quite a niche market. There's another community that Google forgot.
But consider an artificially intelligent gun or computer that can think for itself. Thursday, we looked at the dicey ethical issues of autonomous computers, machines and robots. In Isaac Asimov's sci-fi classic: I, Robot, robots were always required to obey three laws. A robot must obey human orders except where such orders would conflict with the First Law. A robot must protect its own existence so it does not conflict with the First or Second Laws.
Needless to say, IoT is one of the most talked about technologies in 2017. According to Statista, the global IoT market is forecast to be valued at more than 1.7 trillion U.S. dollars. "What According To You Is The Most Exciting IoT Trend To Watch For In 2018?" I think the most exciting IoT trend to watch out for in 2018 is the use of Blockchain technology to accelerate transactions, ensure trust, and reduce costs. The Internet of Things, (IoT), is such and exciting yet complex ecosystem.
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Includes: 40.5 hours on-demand video 20 Articles 2 Supplemental Resources Full lifetime access Access on mobile and TV Certificate of Completion Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
It can be hard to prepare data when you're just getting started with deep learning. Long Short-Term Memory, or LSTM, recurrent neural networks expect three-dimensional input in the Keras Python deep learning library. If you have a long sequence of thousands of observations in your time series data, you must split your time series into samples and then reshape it for your LSTM model. In this tutorial, you will discover exactly how to prepare your univariate time series data for an LSTM model in Python with Keras. How to Prepare Univariate Time Series Data for Long Short-Term Memory Networks Photo by Miguel Mendez, some rights reserved.
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.