Before assessing R and Python, I will start with Wolfram Mathematica. You can handle lists and matrices easily, you have all the best mathematical functions, backup of Wolfram Alpha and extremely sophisticated graphics visualizations, that allow you, for instance, to make and visualize an animated gradient descent, animate different weights for a given neural network, choose a specific Machine Learning algorithm and automatically classify your dataset in classes, plot stunning 3D visualizations, make animations and manipulate variables values dynamically at the same time you see the output of your calculation. It has 4.65 Gb size and comes with all libraries integrated. It's a great program when you know the formulae for Machine Learning algorithms, so you can build them from scratch, in a completely customized way. You can also do face recognition, geolocation of objects with 3D plots of map surface, handle cellular automata like any other and develop social networks models with artificial intelligence completely customized.

Understand convolutions (and why they are so much easier to grasp than they seem). Study Convolutional Neural Networks (what they are used for, why we use them, etc.). Review the building blocks of Convolutional Neural Networks, including: Discover common network architecture patterns you can use to design architectures of your own with minimal frustration and headaches. Utilize out-of-the-box CNNs for classification that are pre-trained and ready to be applied to your own images/image datasets (VGG16, VGG19, ResNet50, etc.).

Digital transformation reshapes every aspect of a business, and over recent years this has evolved to become a central component of modern business strategy. There are three fundamental effects of digital innovation: experience and engagement, business innovation, and the secondary effects that result from increased digital capabilities. Over the coming months, as digital technology continues to evolve, I believe organisations will be forced to explore the secondary effects of digital disruption and use technology to reimagine their business and embrace new ways of working in a more efficient, innovative and agile way. Existing digital technology platforms will evolve Digital platforms, interoperable sets of services brought together to create applications, provide the basic building blocks for a [digital] business. There will be growth in each of the five major digital technology platform types to enable the new capabilities and models that characterise today's digital business.

After spending a weekend in Tokyo learning about Artificial Intelligence, my entire perspective changed. I'm a bit embarrassed to admit that I was a sceptic when I walked into the plane for the overnight flight. I went determined to ask probing questions in support of my presupposition that machines can only do what man tells them and that they're void of emotion. Simply defined, AI, as it is commonly referred to, is machine intelligence. According to Russel and Novig, computer scientists known for their contributions to AI, "An ideal'intelligent' machine is a flexible rational agent that perceives its environment and takes actions that maximise its chance of success at some goal."

With the majority of consumers spending significant time on various messaging platforms, brands are turning to these messaging platforms to better interact with consumers. The increase in private messaging between customers and brands is driving companies to turn to chatbots for improved social customer care. The Watson Conversation Service offers a simple, scalable and science-driven solution for developers to build powerful chat bots to address the needs of various brands and companies. As developers leverage Watson Conversation to build cognitive solutions for various, one recurring question is: "How much time should I plan to train my solution" or "How do I know when my model is trained sufficiently well"? While the answer depends greatly on the problem being solved and the data powering the solution, in this blog we offer a common methodology for training the machine learning (ML) models powering your chat bot solution.

Learn what the #Chatbot Craze is all about!

Renewable power sources such as wind now provide about one-third of Germany's electricity. The rows of towering wind turbines and legions of glistening solar panels spread across Germany's landscape are striking emblems of the country's shift to non-nuclear, low-carbon power. But although Germany is the world's poster child for renewable energy, its grids cannot yet cope with the erratic nature of wind and solar power. In June, German meteorologists, engineers and utility firms began to test whether big data and machine learning can make these power sources more grid-friendly. "To operate the grid more efficiently and keep fossil reserves at a minimum, operators need to have a better idea of how much wind and solar power to expect at any given time," says Malte Siefert, a physicist at the Fraunhofer Institute for Wind Energy and Energy System Technology in Kassel, Germany, and a leader on the project, called EWeLiNE.

The hedge fund Numerai is unusually large in staff compared to its rivals, boasting over 7,500 developers. But what's most unusual is that those employees can be entirely anonymous. As TechCrunch reports, the San Francisco startup sends out encrypted trading data to coders that have signed up to work for it. They each develop different machine-learning techniques to make forecasts based on the data, then send predictions back to Numerai. If they're useful, the data scientist gets paid in Bitcoin.

It's not only bud tenders and Uber drivers who are in danger of losing their jobs to automation these days. A small company in Ohio called AI Writer has created a neural net that can churn out the search engine optimization (SEO) filler currently being created by content mills in places like India and the Philippines. And they managed to do it straight out of college on a shoestring budget. AI Writer is a neural net (an AI architecture modeled after the human brain) that is not only capable of teaching itself how to write its own unique internet marketing articles, but those articles are polished enough to fool companies interested in buying that content into thinking it was generated by a human. AI Writer was co-founded by Paul DeMott, who had created a small internet marketing company as a summer job in college and Nick Shah, who had studied neurology for about a year and a half at medical school before dropping out to teach himself about artificial intelligence.

This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems. The hypothesis is that in normal market conditions, premiums are kept at low levels to increase revenues and market share. The traditional approach requires precise figures (point estimates) and so leads to understatement of uncertainty. This keeps a comfort level for us but the hidden risk of underpricing in our premium estimates is hardly given the attention it merits.