Collaborating Authors


Anomaly Detection through Reinforcement Learning


As Artificial Intelligence is becoming a mainstream and easily available commercial technology, both organizations and criminals are trying to take full advantage of it. In particular, there are predictions by cyber security experts that going forward, the world will witness many AI-powered cyber attacks1. This mandates the development of more sophisticated cyber defense systems using autonomous agents which are capable of generating and executing effective policies against such attacks, without human feedback in the loop. In this series of blog posts, we plan to write about such next generation cyber defense systems. One effective approach of detecting many types of cyber threats is to treat it as an anomaly detection problem and use machine learning or signature-based approaches to build detection systems.

How to Make AIs Target Objects with Unity ML Agents


We often hear in the news about this thing called "machine learning" and how computers are "learning" to perform certain tasks. From the examples we see, it almost seems like magic when a computer creates perfect landscapes from thin air or makes a painting talk. But what is often overlooked, and what we want to cover in this tutorial, is that machine learning can be used in video game creation as well. In other words, we can use machine learning to make better and more interesting video games by training our AIs to perform certain tasks automatically with machine learning algorithms. This tutorial will show you how we can use Unity ML agents to make an AI target and find a game object. More specifically, we'll be looking at how to customize the training process to create an AI with a very specific proficiency in this task. Through this, you will get to see just how much potential machine learning has when it comes to making AI for video games. So, without further ado, let's get started and learn how to code powerful AIs with the power of Unity and machine learning combined!

Army advances learning capabilities of drone swarms


Army researchers developed a reinforcement learning approach that will allow swarms of unmanned aerial and ground vehicles to optimally accomplish various missions while minimizing performance uncertainty.Swarming is a method of operations where multiple autonomous systems act as a cohesive unit by actively coordinating their actions.Army researchers said future multi-domain battles will require swarms of dynamically coupled, coordinated heterogeneous mobile platforms to overmatch enemy capabilities and threats targeting U.S. forces.The Army is looking to swarming technology to be able to execute time-consuming or dangerous tasks, said Dr. Jemin George of the U.S. Army Combat Capabilities Development Command's Army Research Laboratory."Finding optimal guidance policies for these swarming vehicles in real-time is a key requirement for enhancing warfighters' tactical situational awareness, allowing the U.S. Army to dominate in a contested environment," George said.Reinforcement learning ...

Maintaining the illusion of reality: transfer in RL by keeping agents in the DARC


Reinforcement learning (RL) is often touted as a promising approach for costly and risk-sensitive applications, yet practicing and learning in those domains directly is expensive. It costs time (e.g., OpenAI's Dota2 project used 10,000 years of experience), it costs money (e.g., "inexpensive" robotic arms used in research typically cost 10,000 to 30,000 dollars), and it could even be dangerous to humans. How can an intelligent agent learn to solve tasks in environments in which it cannot practice? For many tasks, such as assistive robotics and self-driving cars, we may have access to a different practice area, which we will call the source domain. While the source domain has different dynamics than the target domain, experience in the source domain is much cheaper to collect.

The Future of Chatbots and Conversational AI in 2020


The Golden age of conversational artificial intelligence (AI) is here. Conversational bots or simply put chatbots have applications that range from customer-facing AI assistants, support chatbots, skill chatbots, assistant bots, and transactional bots. The interest of business in this segment is rampant with the huge investments in this emerging technology by governments, healthcare institutions, manufacturing enterprises and so on. Conversational Chatbots are leveraging the power of conversational AI to improve their customer experience, and thereby increase the shareholder returns. Technology grows with time, and it just gets better and better day by day changing on an almost daily basis and often it becomes difficult for enterprises to keep up with this pace of change!

Has OpenAI Surpassed DeepMind?


OpenAI's GPT-3 is the talk of the town, and the media is giving it all the attention. Many analysts are even comparing it to AGI because of its practical applicability. Initially disclosed in a research paper in May, GPT-3 is the next version of GPT-2 and is 100x larger than it. It is far more competent than its forerunner due to the number of parameters it is trained on, which is 175 billion for GPT-3 versus 1.5 billion for GPT-2. After the successful launch of GPT-3, other AI companies seem to have been overshadowed.

How Technology is Shaping the Real Estate Industry


Advancements in technology are shaping the way real estate agents and homeowners navigate the home selling and buying process. In today's modern world, real estate professionals rely on sophisticated data to drive decisions, assess home value, and find ideal buyers. Keeping up with the times can be difficult, but the technology that's now available to agents is an exciting and convenient shift. Here are a few ways new technology is shaping the real estate industry and how agents can gain a competitive advantage by staying informed on the latest trends. Artificial intelligence (AI) is revolutionizing the real estate industry. While still a relatively new trend, AI is here to stay.

Customization and automation are key to customer journeys over the phone


Two words that seem to be at odds with each other, especially if you look at them within the context of phone channels in customer relations! It's somewhat counterintuitive, particularly when you factor in the roles they play in brand's strategies regarding customer relations optimization. To begin with, let's define what these two terms mean in the field of customer relations: What's the new, shared ambition bringing these two extremes together? Indeed, properly automated, tailor-made customer journeys via phone channels will no longer be viewed, wrongly, as a necessary evil by some customers, nor considered a costly proposition by most brands. They will be perceived as an integral element praised both for its efficiency and its personal touch.

"What's that? Reinforcement Learning in the Real-world?"


Reinforcement Learning offers a distinctive way of solving the Machine Learning puzzle. It's sequential decision-making ability, and suitability to tasks requiring a trade-off between immediate and long-term returns are some components that make it desirable in settings where supervised-learning or unsupervised learning approaches would, in comparison, not fit as well. By having agents start with zero knowledge then learn qualitatively good behaviour through interaction with the environment, it's almost fair to say Reinforcement Learning (RL) is the closest thing we have to Artificial General Intelligence yet. We can see RL being used in robotics control, treatment design in healthcare, among others; but why aren't we boasting of many RL agents being scaled up to real-world production systems? There's a reason why games, like Atari, are such nice RL benchmarks -- they let us care only about maximizing the score and not worry about designing a reward function.

What Is Machine Learning? Why It Matters for Your Business?


Machine learning and Artificial intelligence are the new buzz words that are being thrown around more than any other trending technology today. It is starting to reshape how we think about building products. It's time we understood what it is and why it matters. Machine Learning: (ML) is an area of computational science that enables machines (computers) to undertake tasks without being explicitly programmed. The idea behind machine learning is that by training computers to analyze and interpret existing data from prior human interactions, machines are able to find patterns and structures in data.