It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems. Demonstration-based preference inference (DemoPI) is a promising approach to mitigate this problem. Understanding the behaviours and values of energy customers is an example of a scenario where preference inference can be used to gain insights into the values of energy customers with multiple objectives, e.g. cost and comfort. In this work, we applied the state-of-art DemoPI method, i.e., the dynamic weight-based preference inference (DWPI) algorithm in a multi-objective residential energy consumption setting to infer preferences from energy consumption demonstrations by simulated users following a rule-based approach. According to our experimental results, the DWPI model achieves accurate demonstration-based preference inferring in three scenarios. These advancements enhance the usability and effectiveness of multi-objective reinforcement learning (MORL) in energy management, enabling more intuitive and user-friendly preference specifications, and opening the door for DWPI to be applied in real-world settings.

As mentioned, self optimising algorithms based on AI and neural networks are simply not yet suitable for every machine builder. For most machine builders (and customers) the focus is on creating a smooth and fast running machine with a high OEE. In addition, touching the PLC software on optimised equipment and machines is considered a no-go for most, as their motto probably is: 'never touch a running system'. However, a realistic step for every machine builder is to learn from your machines. New machines are like a new car, they have failures in the beginning and need to be optimised.

After the article in Nature about the open-source of AlphaFold v2.0 on GitHub by DeepMind, many in the scientific and research community have wanted to try out DeepMind's AlphaFold implementation firsthand. With compute resources through Amazon Elastic Compute Cloud (Amazon EC2) with Nvidia GPU, you can quickly get AlphaFold running and try it out yourself. In this post, I provide you with step-by-step instructions on how to install AlphaFold on an EC2 instance with Nvidia GPU. The process starts with a Deep Learning Amazon Machine Image (DLAMI). After installation, we run predictions using the AlphaFold model with CASP14 samples on the instance.

We can easy to set up a training environment from a notebook with "click" for elastic of CPUs/GPUs * Connectivity and easy to deploy – AWS SageMaker is AWS managed service and it easy to integrate with other AWS services inside of a private network. Which also impact to big data solution, ETL processed with data can be processing inside of a private network and reduce cost for the transfer. AWS managed service will help to reduce the resource we need to create.

One year after the nascent COVID-19 pandemic set off fears of economic devastation and mass unemployment on an unprecedented scale, many industries – particularly hospitality, travel, and arts and entertainment – are still grappling with a grim reality. But contrary to many expectations, hiring has bounced back – or even accelerated – in many key sectors, including healthcare, logistics, and high-tech. For recruiters working in industries that are in hiring mode, the pandemic has triggered a deluge of applications as the unemployed seek out new positions and freelancers search for more stable work. On the one hand, this has forced recruiters to sift through reams of applications. On the other, while finding qualified candidates remains a critical challenge, the sheer volume of applications coming in means that it's easier than it was before the pandemic: A Criteria survey released in October revealed that 68% of recruiters felt it was challenging to find high-quality candidates in 2020, compared to 87% who said the same in 2019.

Artificial intelligence has its spread everywhere these days and has been impacting every walk of life in the past decade. These days when we are surrounded by tons of gadgets and devices, there is an increasing role that artificial intelligence has to play. People have feared artificial intelligence right since its inception. Movies have in the past portrayed AI as a demonic character. Many people have equated AI with a major threat to human existence. So where does all this fuss about AI lead to?