Whether they drive themselves or improve the safety of their driver, tomorrow's vehicles will be defined by software. However, it won't be written by developers but by processing data. To prepare for that future, the transportation industry is integrating AI car computers into cars, trucks and shuttles and training them using deep learning in the data center. A benefit of such a software-defined system is that it's capable of handling a wide range of automated driving -- from Level 2 to Level 5. Speaking in Tokyo at the last stop on NVIDIA's seven-city GPU Technology Conference world tour, NVIDIA founder and CEO Jensen Huang demonstrated how the NVIDIA DRIVE platform provides this scalable architecture for autonomous driving. "The future is surely a software defined car," said Huang.

Artificial intelligence has gotten pretty darn smart--at least, at certain tasks. AI has defeated world champions in chess, Go, and now poker. But can artificial intelligence actually think? The answer is complicated, largely because intelligence is complicated. One can be book-smart, street-smart, emotionally gifted, wise, rational, or experienced; it's rare and difficult to be intelligent in all of these ways.

BigDL runs natively on Apache Spark, and because Qubole offers a greatly enhanced and optimized Spark as a service, it makes for a perfect deployment platform. In this Part 1 of a two-part series, you will learn how to get started with distributed Deep Learning library BigDL on Qubole. By the end, you will have BigDL installed on a Spark cluster with a distributed Deep Learning library readily available for you to use in your Deep Learning applications running on Qubole. In Part 2, you will learn how to write a Deep Learning application on Qubole that uses BigDL to identify handwritten digits (0 to 9) using a LeNet-5 (Convolutional Neural Networks) model that you will train and validate using MNIST database. Before we get started, here's some introduction and background on the technologies involved.

In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he'll describe some "tricks of the trade" used in neural network design, and finally, he'll bring the recognition accuracy of his model above 99%. Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.

Financial payments and banking started in a very inefficient and traditional way, which was slow but still acceptable to the customers due to the stage in the information age. There are lucrative but under-utilised banking opportunities and banks in the region need to step up and grasp these opportunities to succeed otherwise its almost a lost game for them. AI technologies such as machine learning, deep learning, prescriptive learning, predictive analytics, virtual agents and natural language understanding technologies are gaining popularity among progressive banks. AI is not a magic push button and it will never be but as a strategy if one adopt it will take them to new height though, particularly for financial institutions, fintechs and banks where data access and security play a critical role. Competition in Fintech world today at its peak, so adoption of new technologies to stay one step ahead of the competition is no brainier. AI in Fintech allows eliminating human error while boosting productivity and increasing bottom line.

An engineer who's learning about using convolutional neural networks for image classification just asked me an interesting question; how does a model know how to recognize objects in different positions in an image? Since this actually requires quite a lot of explanation, I decided to write up my notes here in case they help some other people too. Here's two example images showing the problem that my friend was referring to: If you're trying to recognize all images with the sun shape in them, how do you make sure that the model works even if the sun can be at any position in the image? It's an interesting problem because there are really three stages of enlightenment in how you perceive it: My friend is at the third stage of enlightenment, but is smart enough to realize that there are few accessible explanations of why CNNs cope so well. I don't claim to have any novel insights myself, but over the last few years of working with image models I have picked up some ideas from experience, and heard folklore passed down through the academic family tree, so I want to share what I know.

The internet of things (IoT) promises many advantages - smart cities with integrated transport systems, for instance - but it comes with a significantly increased cybersecurity risk. So how should we be tackling this new threat? Christoph Brandstatter is managing director of the four-star Seehotel, Jagerwirt, in Austria's Alps. His hotel's electronic door locks and other systems were hacked for ransom four times, between December 2016 and January 2017. "We got a ransomware mail which was hidden in a bill from Telekom Austria," says Mr Brandstatter.

TensorFlow is an open-source machine learning software built by Google to train neural networks. TensorFlow's neural networks are expressed in the form of stateful dataflow graphs. Each node in the graph represents the operations performed by neural networks on multi-dimensional arrays. These multi-dimensional arrays are commonly known as "tensors", hence the name TensorFlow. TensorFlow is a deep learning software system.

The medieval art of alchemy was once believed capable of creating gold and even human immortality. The trial-and-error method was however gradually abandoned after pioneers like Issac Newton introduced the science of physics and chemistry in the 1700s. But now, some machine learning researchers are wondering aloud whether today's artificial intelligence research has become a new sort of alchemy. The debate started with Google's Ali Rahimi, winner of the Test-of-Time award at the recent Conference on Neural Information Processing (NIPS). Rahimi put it bluntly in his NIPS presentation: "Machine learning has become alchemy."

You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. John McCarthy, widely recognized as one of the godfathers of AI, defined it as "the science and engineering of making intelligent machines." There are a lot of ways to simulate human intelligence, and some methods are more intelligence than others.