Mizuho Financial Group Inc. plans to change its personnel policy to allow employees to hold side jobs by the end of next March, the first such move by a mega-bank. By helping employees seek new challenges and opportunities, Mizuho hopes they will use the experience and ideas they acquire outside of the group to create new financial services at Mizuho, President Tatsufumi Sakai said in a recent interview. "I want everyone to refine their skills through a diverse range of challenges so that they can all be specialists in their fields," he said. The 59-year-old chief executive hopes new job opportunities will encourage employees to make use of their experiences in startups, manufacturing companies and other industries to create new business ideas for Mizuho. The initiative is expected to be introduced in the second half of fiscal 2019, after rules on labor and information management are worked out.

Apple's latest next version of its iPhone software, iOS 13, will speed up phones, the company has claimed, saying apps will launch up to twice as fast. "Nothing is more important to our iPhone users than performance," said Craig Federighi, Apple's senior vice president of software engineering, while announcing the new software on Monday at the firm's annual developer conference in San Jose, California. Federighi said app updates in iOS 13 would be up to 50% smaller with 60% fewer updates and the new system would speed up other tasks, too, such as Face ID facial recognition unlocking the iPhone XS 30% faster. The big crowd pleaser, however, appeared to be the introduction of Apple's system-wide "dark mode", which turns the user interface from black text on a white background to white text on a black background, as has become popular in a variety of apps. Dark mode reduces eye strain in low light conditions and saves battery power on top-end smartphones because black pixels on OLED screens – such as those on the iPhone X and XS – consume no power.

The intent of this glossary is to provide clear definitions of the technical terms specific to deep artificial neural networks. It is a work in progress. An activation, or activation function, for a neural network is defined as the mapping of the input to the output via a non-linear transform function at each "node", which is simply a locus of computation within the net. Each layer in a neural net consists of many nodes, and the number of nodes in a layer is known as its width. Activation algorithms are the gates that determine, at each node in the net, whether and to what extent to transmit the signal the node has received from the previous layer. A combination of weights (coefficients) and biases work on the input data from the previous layer to determine whether that signal surpasses a given treshhold and is deemed significant. Those weights and biases are slowly updated as the neural net minimizes its error; i.e. the level of nodes' activation change in the course of learning. These activation functions allow neural networks to make complex boundary decisions for features at various levels of abstraction. Adadelta is an updater, or learning algorithm, related to gradient descent. Unlike SGD, which applies the same learning rate to all parameters of the network, Adadelta adapts the learning rate per parameter. Adagrad, short for adaptive gradient, is an updater or learning algorithm that adjust the learning rate for each parameter in the net by monitoring the squared gradients in the course of learning. It is a substitute for SGD, and can be useful when processing sparse data. Affine is a fancy word for a fully connected layer in a neural network. "Fully connected" means that all the nodes of one layer connect to all the nodes of the subsequent layer. A restricted Boltzmann machine, for example, is a fully connected layer. Convolutional networks use affine layers interspersed with both their namesake convolutional layers (which create feature maps based on convolutions) and downsampling layers, which throw out a lot of data and only keep the maximum value. "Affine" derives from the Latin affinis, which means bordering or connected with. Each connection, in an affine layer, is a passage whereby input is multiplied by a weight and added to a bias before it accumulates with all other inputs at a given node, the sum of which is then passed through an activation function: e.g.

Organizations are unlocking the business value of next-generation technologies that are on the rise. Join us in this European Business AI & Robotics event to discuss the latest developments of emerging technologies like advanced analytics, automation and more that are transforming the way we run business. Meet new business partners at our unique B2B matchmaking and peer-to-peer networking session that will gather top-level decision makers and influential players responsible for technology developments across all functions all together under one roof.

Welcome to Episode 4 of the AI Blockchain Alliance with executives from Magic Leap, Office Depot & Modernizing Medicine EOS Miami, the founder of AI Blockchain Alliance, will be participating in the AI focused Ted Talk at Boca Raton Innovation Campus (where the IBM PC was invented!). AI is revolutionizing the way companies engage with customers, compete with each other, and grow in the marketplace. But the line between computer software and AI systems is opaque. And can machines really demonstrate intelligence? Learn how AI is fueling powerful ways to help companies get closer to their customers and share knowledge in an informative, effective and personalized exchange.

Twitter has just announced it has picked up London-based Fabula AI. The deep learning startup has been developing technology to try to identify online disinformation by looking at patterns in how fake stuff vs genuine news spreads online -- making it an obvious fit for the rumor-riled social network. Social media giants remain under increasing political pressure to get a handle on online disinformation to ensure that manipulative messages don't, for example, get a free pass to fiddle with democratic processes. Twitter says the acquisition of Fabula will help it build out its internal machine learning capabilities -- writing that the UK startup's "world-class team of machine learning researchers" will feed an internal research group it's building out, led by Sandeep Pandey, its head of ML/AI engineering. This research group will focus on "a few key strategic areas such as natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning" -- now with Fabula co-founder and chief scientist, Michael Bronstein, as a leading light within it.

Reinforcement learning is currently one of the hottest topics in machine learning. For a recent conference we attended (the awesome Data Festival in Munich), we've developed a reinforcement learning model that learns to play Super Mario Bros on NES so that visitors, that come to our booth, can compete against the agent in terms of level completion time. The promotion was a great success and people enjoyed the „human vs. machine" competition. There was only one contestant who was able to beat the AI by taking a secret shortcut, that the AI wasn't aware of. Also, developing the model in Python was a lot of fun. So, I decided to write a blog post about it that covers some of the fundamental concepts of reinforcement learning as well as the actual implementation of our Super Mario agent in TensorFlow (beware, I've used TensorFlow 1.13.1, TensorFlow 2.0 was not released at the time of writing this article). Most machine learning models have an explicit connection between inputs and outputs that does not change during training time. Therefore, it can be difficult to model or predict systems, where the inputs or targets themselves depend on previous predictions. However, often, the world around the model updates itself with every prediction made. What sounds quite abstract is actually a very common situation in the real world: autonomous driving, machine control, process automation etc. -- in many situations, decisions that are made by models have an impact on their surroundings and consequently on the next actions to be taken. Classical supervised learning approaches can only be used to a limited extend in such kinds of situations. To solve the latter, machine learning models are needed that are able to cope with time-dependent variation of inputs and outputs that are interdependent. This is where reinforcement learning comes into play. In reinforcement learning, the model (called agent) interacts with its environment by choosing from a set of possible actions (action space) in each state of the environment that cause either positive or negative rewards from the environment. Think of rewards as an abstract concept of signalizing that the action taken was good or bad. Thereby, the reward issued by the environment can be immediate or delayed into the future. By learning from the combination of environment states, actions and corresponding rewards (so called transitions), the agent tries to reach an optimal set of decision rules (the policy) that maximize the total reward gathered by the agent in each state. In reinforcement learning we often use a learning concept called Q-learning. Q-learning is based on so called Q-values, that help the agent determining the optimal action, given the current state of the environment. Q-values are „discounted" future rewards, that our agent collects during training by taking actions and moving through the different states of the environment.

The Government Artificial Intelligence (AI) Readiness Index, compiled by Oxford Insights and the International Development Research Centre, ranks the governments of 194 nations according to how prepared they are to utilise AI in the provision of public services. According to global consulting firm PriceWaterhouseCooper, AI technologies are forecast to add an additional $15.7 trillion to the global economy by 2030, with $6.6 trillion to come from an increase in productivity and $9.1 trillion from consumption-side effects. The score that Oxford Insights provides for each country comprises of 11 input metrics grouped under four high-level topics: governance; infrastructure and data; skills and education; and government public services. On a global level, the top ranking countries (and their scores) were: Singapore (9.186), The likes of India (7.515) and China (7.37) were ranked 17th and 20th respectively.

"AI art", or more precisely art created with neural networks, has recently started to receive broad media coverage in newspapers (New York Times), magazines (The Atlantic), and countless blogs. Combined with the ongoing general "AI hype" and multiple recent museum and gallery exhibitions, this coverage has produced the impression of a new star rising in the art world: that of machine-generated art. It has also led to the popularization of an ever-growing list of philosophical questions surrounding the use of computers for the creation of art. This brief article provides a pragmatic evaluation of the new genre of AI art from the perspective of art history. It attempts to show that most of the philosophical questions commonly cited as unique issues of AI art have been addressed before with respect to previous iterations of generative art starting in the late 1950s. In other words: while AI art has certainly produced novel and interesting works, from an art historical perspective it is not the revolution as which it is portrayed.

Objects made with 3-D printing can be lighter, stronger, and more complex than those produced through traditional manufacturing methods. But several technical challenges must be overcome before 3-D printing transforms the production of most devices. Commercially available printers generally offer only high speed, high precision, or high-quality materials. Rarely do they offer all three, limiting their usefulness as a manufacturing tool. Today, 3-D printing is used mainly for prototyping and low-volume production of specialized parts.