Goto

Collaborating Authors

agent


smearle/gym-city

#artificialintelligence

The work in this repo was presented and demoed at the 2019 Experimental A.I. in Games (EXAG) workshop, an AIIDE workshop. Feel free to join the conversation surrounding this work via my Twitter, and on r/MachineLearning. The player builds places urban structures on a 2D map. In certain configurations, these structures invite population and vertical development. Reinforcement Learning agents are rewarded as a function of population or other city-wide metrics.


IBM and ServiceNow combine for use of AI to Automate IT

#artificialintelligence

IBM and ServiceNow have expanded their strategic partnership to combine IBM's hybrid cloud software and professional services to ServiceNow's intelligent workflow capabilities and IT service and operations management products. The solution is engineered to help clients realise deeper, AI-driven insights from their data, create a baseline of a typical IT environment, and take succinct recommended actions on outlying behavior to help prevent and fix IT issues at scale. Together, IBM and ServiceNow can help companies free up valuable time and IT resources from maintenance activities, to focus on driving the transformation projects necessary to support the digital demands of their businesses. "AI is one of the biggest forces driving change in the IT industry to the extent that every company is swiftly becoming an AI company," said Arvind Krishna, Chief Executive Officer, IBM. "By partnering with ServiceNow and their market leading Now Platform, clients will be able to use AI to quickly mitigate unforeseen IT incident costs. Watson AIOps with ServiceNow's Now Platform is a powerful new way for clients to use automation to transform their IT operations."


Build a Chatbot with Dialogflow and React Native

#artificialintelligence

Chatbots are a powerful way to provide conversational experiences for any software product. Each conversational experience depends on the implementation of the chatbot to either be a good or poor experience for the end user. The modern day world is living in the technology wave of Artificial Intelligence and bots are a huge part of it. In this tutorial, we are going to build a chatbot application from scratch using Dialogflow and React Native. The main reason to use Google's Dialogflow for this tutorial is that you do not have to go through a hefty signup process by providing your card details, unlike other bot frameworks or similar service providers.


What is Artificial Intelligence and How its work?

#artificialintelligence

Artificial Intelligence ( AI) is a vast branch of computer science that deals with the development of smart machines capable of executing tasks that usually require human intelligence. AI is an interdisciplinary science with different approaches, but in nearly every field of the education field, software industry, developments in machine learning and deep learning are causing a paradigm change. How is artificial intelligence operation? Are robots able to think? Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: "Can machines think?"


What is AI-powered drone mobility support?

#artificialintelligence

Drone connectivity in the sky is an indispensable part of the Internet of Things (IoT): Anywhere, Anytime, Anything. In a recent summer internship project at Ericsson, we explored how Artificial Intelligence (AI) can empower drone mobility support in 5G networks. Our work received the Best Paper Award at the 2020 IEEE Wireless Communications and Networking Conference (WCNC 2020). The award is a recognition of the Ericsson internship program, which offers candidates a chance to learn about the world of work while working on projects that are changing the world of communications. Drones have many applications, ranging from package delivery and surveillance to remote sensing and IoT scenarios.


Microsoft Jericho is an Open Source Framework for Training Machine Learning Models Using…

#artificialintelligence

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Language is one of the hallmarks of human intelligence and one that plays a key role in our learning processes. By using language, we constantly formulate our understanding of a situation of a specific context.


Pinaki Laskar posted on LinkedIn

#artificialintelligence

What are the potentials of deep reinforcement learning? The goal of a #reinforcementlearning agent, interacting with its environment in discrete time steps, is to learn a policy: A x S [0,1], which maximizes the expected cumulative reward R (or minimize a regret function measured as the value of difference between a made decision and the optimal decision). The policy map gives the probability Pr (a/s) of taking action a when in state s. RF learning, approximate dynamic #programming, or neuro-dynamic programming, is modeled as a Markov decision process (MDP). The whole idea is restricted by the standard Anthropomorphic #AI model, the AI system as optimizing a fixed objective, which must be replaced.


Global Big Data Conference

#artificialintelligence

Remember Facebook's automated personal assistant, M, that was released in a bid to compete with Alexa and Siri? After a series of embarrassing mishaps due to poorly trained algorithms, Facebook abruptly pulled the plug. They weren't alone; chatbots are infamous for putting their metaphorical feet in their mouths. While these debacles are tough to watch, the underlying problem is not artificial intelligence (AI) itself. AI succeeds when underpinned with sound strategy and well-trained models.


[D]Why are non-linear approximators such as neural networks unstable for reinforcement learning

#artificialintelligence

As you know, in supervised learning it is important for the data to be iid. In RL the training data is sampled from the state space that the agent chooses to explore, which tends to be highly correlated to the agent's current preferences and a small subset of the total state space. Q learning selects the action with the highest expected reward. So if a1 has an expected reward of 0.49, and a2 has an expected reward of 0.51, a small parameter change can cause the agent to swap from picking a2 100% of the time to picking a1 100% of the time, causing a significant shift in the distribution of data being trained on. At a higher conceptual level, you can think of RL as supervised learning where instead of having clearly defined labels, you'guess' what the label is using a often times noisy reward signal, and the quality of your guess is based on how accurate your policy is.


Optimizing the cost of training AWS DeepRacer reinforcement learning models

#artificialintelligence

AWS DeepRacer is a cloud-based 3D racing simulator, an autonomous 1/18th scale race car driven by reinforcement learning, and a global racing league. Reinforcement learning (RL), an advanced machine learning (ML) technique, enables models to learn complex behaviors without labeled training data and make short-term decisions while optimizing for longer-term goals. But as we humans can attest, learning something well takes time--and time is money. You can build and train a simple "all-wheels-on-track" model in the AWS DeepRacer console in just a couple of hours. However, if you're building complex models involving multiple parameters, a reward function using trigonometry, or generally diving deep into RL, there are steps you can take to optimize the cost of training.