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America's Cup: Emirates Team NZ use Artificial Intelligence to find the fastest way

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A few days before racing in the 36th match for the America's Cup, the covers have been lifted on the testing and development process, using Artificial Intelligence employed by Emirates Team New Zealand, and developed in conjunction with one of worlds most prestigious consulting firms McKinsey & Company. While the team's use of simulators has been widely discussed, and one is on display at the America's Cup Village. The team has been working with McKinsey subsidiary Quantum Black to develop a "digital twin" of the team's AC75 that used a process of machine learning to perform many more iterations of a sailing situation than was possible using human crew, and to come up with options that were faster than the crew was currently achieving. AI Bots work particularly well when there is large volume of data. The Bot is programmed to self-learn from its own analysis.


Azure Arc-Enabled Machine Learning Is Now in Preview

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Azure Arc is Microsoft's offering for allowing customers to bring Azure services and management to any infrastructure, including AWS and Google Cloud. This year, during the virtual Ignite conference, the company announced the preview of Azure Arc-enabled machine learning, which extends Azure machine learning capabilities to hybrid and multi-cloud environments. Microsoft launched Azure Arc in November 2019 at their Ignite conference, and the service received support for Kubernetes - announced during the Build conference 2020. Furthermore, the company brought more capabilities to Azure Arc, which they announced at Ignite 2020 with Azure Arc enabled data services. And now, at this year's Ignite, Microsoft continues adding capabilities to the service with Arc-enabled machine learning.


5 Ways Automation And AI Are Transforming Service Desks

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AI has become a game-changer tool in the IT sector. Artificial intelligence and automation have significantly transformed how organizations run their production lines. As AI tools can garner real-time insights, it has facilitated the companies' design and product innovation techniques. When applied correctly, AI and automation can help develop better, faster, and cheaper business techniques. Automation tools can be deployed to automate repetitive tasks, allowing the IT staff to focus on strategic tasks instead of administrative work.


Designing a world where intelligent automation and humans co-exist

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Not surprisingly, the COVID-19 pandemic sparked a permanent shift in how businesses in every industry view artificial intelligence (AI) and automation. In the past, many saw these technologies as a nice-to-have; and therefore, pushed them further out on their roadmaps. Today, companies are realizing how imperative these technologies are as a means to be more productive in an all-digital, work-from-anywhere world. Plus, they're starting to question why employees should be trapped by repetitive processes that hinder their ability to move fast and engage customers with empathy at a time when people need it most. Throughout this past year, my conversations with our customers and other business leaders have shifted from casual inquiries about automation, to the immediate need for more efficient and informed teams.


Understanding the Importance of Digital Twin and its Applications

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Digital twins have become important to business today. By producing a replica of the physical assets of a product or service in an industry, digital twin helps in analyzing the data, lends a platform to check the functioning beforehand so as to develop a solution for any potential problems. The term came into existence when Michael Grieves, then computer engineer at the University of Michigan, wrote about it in 2002 and was named one of Gartner's Top 10 Strategic Technology Trends for 2017. Basically, the digital twin is composed of three components viz., physical entities in the real world, their virtual models and the connected data that tie the two worlds. NASA was the first to leverage the digital twin technology, when Michael mentioned the possibility of creating digital representations of physical systems that had their own entity during a talk with John Vickers, NASA's Director of Technology.


uvipen/Contra-PPO-pytorch

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Here is my python source code for training an agent to play contra nes. By using Proximal Policy Optimization (PPO) algorithm introduced in the paper Proximal Policy Optimization Algorithms paper. For your information, PPO is the algorithm proposed by OpenAI and used for training OpenAI Five, which is the first AI to beat the world champions in an esports game. Specifically, The OpenAI Five dispatched a team of casters and ex-pros with MMR rankings in the 99.95th percentile of Dota 2 players in August 2018. It has been a while since I have released my A3C implementation (A3C code) and PPO implementation (PPO code) for training an agent to play super mario bros.


How innovations in voice have made it an end-to-end commerce channel

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Text-to-speech (TTS) technology isn't exactly new – but the way it's shaping the future certainly is. From smart speakers to voice assistants, TTS is increasingly paramount in day-to-day interactions between brands and end users, leading to enhanced brand experiences and better business outcomes. Up until recently, TTS was confined to a specific use case: voice-enablement of written content to make computers'speak' to those with visual or reading impairments. TTS technology was based on utility and a need to make screen-related content accessible. As such, synthetic speech was traditionally digital-sounding and marred by poor audio quality and speaking style.


Automating Trading and Market Making With Artificial Intelligence

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The goal is to capture information in a market's order books and use that information to predict market movement/direction. That prediction can enable repricing of orders and more efficient market making. Such an approach allows the market maker to provide liquidity whilst making profits at the same time. Market makers are essential to modern markets. They provide the markets with necessary liquidity and make sure the bid/ask spread is reasonably narrow to allow efficient purchasing.


Training Self Driving Cars using Reinforcement Learning

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In this image, there is a robot at position (1, 1), in a maze. That position is the state. The robot has a set of actions that it can perform, move up or move right. The last thing to note is that, the robot will receive a reward whenever it takes an action. The rewards are defined by the programmer, and we'll define the rewards as such.


Council Post: How To Leverage AI, Visualization And IoT To Simplify And Improve Security

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Our new normal has created an even greater need for simplification and very crisp outcomes. Company executives and technology leaders can use artificial intelligence (AI) and internet of things (IoT) technology to enable desired outcomes -- not just for experimental efforts -- and can use visualization to prioritize and communicate the value of security investments. Here are a few examples of how organizations can do that. Understand that cybersecurity complexity is a widespread problem. In the world of security, there is no dearth of point tools.