Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But... securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous architectural and coordination work to minimize the volatility of the cloud environment and leverage the security features of the cloud to the benefit of the CICD pipeline.
It's never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat.
We're releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room from where it was placed during training. EPG trains agents to have a prior notion of what constitutes making progress on a novel task. Rather than encoding prior knowledge through a learned policy network, EPG encodes it as a learned loss function. Agents are then able to use this loss function, defined as a temporal-convolutional neural network, to learn quickly on a novel task.
Next week, I'll be at Think 2018 in Las Vegas. As I share this, several of my clients are deciding if they should make larger investments in data science and machine learning over the next 6-18 months. I believe data science and machine learning are the dynamic duo that are going to transform how we do business and how we lead our lives. I'll talk about machine learning after I attend the IBM Think 2018 conference. As an external technology advisor, I'm paid to help my clients better understand where they should invest, sometimes how much, and, in several cases, who should lead their pilot programs.
Artificial Intelligence (AI) is everywhere these days. It's simultaneously heralded as both the greatest thing since sliced bread -- freeing us from driving cars, diagnosing diseases better, and so on -- and the worst thing imaginable-- displacing millions of jobs, and a step towards the inevitable AI domination of humans. Lost in this hyperbole are the many simple, yet effective, enabling innovations that AI makes possible. Just like we rely on machines in the physical world to excavate holes for buildings or transport people or cargo long distances, we increasingly rely on machine algorithms such as machine learning (ML) models in the online, networked world. These innovations enable us to keep our email from overflowing with spam and to index and catalog enormous volumes of text for simple and fast retrieval, along with a wide range of other efficiencies.
Suprema ID is taking the opportunity of next week's ID4Africa 2018 exhibition to launch new fingerprint scanner solutions featuring liveness detection based on machine learning technology. The RealScan-D is a dual finger enrollment scanner with FBI IAFIS Appendix F certification, while the RealScan-G1 is a FAP30 enrollment scanner boasting of IP54-rated water and dust resistance. Both devices are compact and portable, and both feature a wide platen facilitating the easy collection of detailed fingerprints. Most importantly, according to Suprema, both devices feature machine learning Live Finger Detection technology designed to identify synthetic materials used in spoofing such as clay, silicon, paper, film, and rubber. In a statement, Suprema CEO Bogun Park suggested that his firm's new technology meets a market trend, explaining, "We are experiencing increasing demand for anti-spoofing technology on our live scanning devices."
In this post, we reproduce the recent Uber paper "Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning", which amazingly showed that simple genetic algorithms sometimes performed better than apparently advanced reinforcement learning algorithms on well studied problems such as Atari games. We will ourselves reach state of the art performance on Frostbite, a game that had stumped reinforcement learning algorithms for years before Uber finally solved it with this paper. We will also learn about the dark art of training neural networks using genetic algorithms. In a way this could be considered part 3 of my deep reinforcement learning, but I think this article can also stand alone. Note that unlike these previous tutorials, this post will be using PyTorch instead of Keras, mainly because this is what I personally have switched to, but also because PyTorch does happen to be more suited for this particular use case.