Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.

This paper proposes an interactive navigation framework by using large language and vision-language models, allowing robots to navigate in environments with traversable obstacles. We utilize the large language model (GPT-3.5) and the open-set Vision-language Model (Grounding DINO) to create an action-aware costmap to perform effective path planning without fine-tuning. With the large models, we can achieve an end-to-end system from textual instructions like "Can you pass through the curtains to deliver medicines to me?", to bounding boxes (e.g., curtains) with action-aware attributes. They can be used to segment LiDAR point clouds into two parts: traversable and untraversable parts, and then an action-aware costmap is constructed for generating a feasible path. The pre-trained large models have great generalization ability and do not require additional annotated data for training, allowing fast deployment in the interactive navigation tasks. We choose to use multiple traversable objects such as curtains and grasses for verification by instructing the robot to traverse them. Besides, traversing curtains in a medical scenario was tested. All experimental results demonstrated the proposed framework's effectiveness and adaptability to diverse environments.

Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.ML Studio is really great and even though you still need your statistic knowledge you can build, test and even deploy a machine learning model without writing a single line of code. It allows for this by offering prebuilt building blocks that can be customized and connected together using a visual interface. To get started, you first need to navigate to Azure ML Studio and sign in with a Microsoft Account. Once registered and signed in, you will see the homepage which provides you with multiple tabs. To create a new experiment you need to navigate to the experiment tab and click on the New button.

In this AI workshop, you are going to build a model to predict the bike demand for a specific hour of a day for the city of Washington. The data is available as sample data in the Azure ML Studio (classic) and is based on the data that has been collected in 2011 and 2012 in Washington. The dataset contains whether information and the number of bikes that have been rented. More information can be found on the UCI Machine Learning repository site: https://archive.ics.uci.edu/ml/datasets/bike We highly recommend you visit that site and investigate what kind of data you have available. Note: this workshop is to get in touch with machine learning.

Now that we have trained our model, we can use our validation set to see how well our model is doing. We can do this by first of making predictions using the Score Model module and then using the Evaluate Model module to get our accuracy and loss metrics. To make predictions on the validation set we connect the trained model to the left input of the Score Model Module and the right output node of the Split data module to the right input of the Score Model Module. When visualizing the output we can see that we have two new columns. The Scored Labels column contains the labels represented by integers of either 0 or 1.

But there are other tools that also claim to make machine learning easier and speed model development. I am wondering how they compare? So, this week, I am taking a look at Amazon SageMaker (SageMaker) and how it compares to Studio. What I found when I looked at SageMaker in comparison to Studio is a significantly different approach to model building. The vendors of each tool would both claim to offer a fully managed service that covers the entire machine learning workflow to build, train, and deploy machine learning models quickly.

But there are other tools that also claim to make machine learning easier and speed model development. I am wondering how they compare? So, this week, I am taking a look at Amazon SageMaker (SageMaker) and how it compares to Studio. What I found when I looked at SageMaker in comparison to Studio is a significantly different approach to model building. The vendors of each tool would both claim to offer a fully managed service that covers the entire machine learning workflow to build, train, and deploy machine learning models quickly.

In this tutorial, you will learn how to employ a simulated dataset from Kaggle to build a machine learning model to both predict and explain whether employees will leave their employer or not and the reason(s) why they may do so. The data comprise a wide range of topics which allow to explain employees' leave behavior in relation with A) organizational factors (department); B) employment relational factors (i.e. This tutorial has the objective to inspire you to explore the possibilities of using machine learning for your own research. You will follow several steps to explore the data and build a machine learning model to predict whether an employee will leave or not, and why. You will build this prediction model with the Azure Machine Learning Studio.

For example, credit card fraud detection looks for unusual purchases. For example a categorical data set for autos could specify year, make, model, and price. For instance, airfare data could be enhanced by days of the week and holidays. See Feature selection and engineering in Azure Machine Learning. An algorithm is also a type of module in Machine Learning Studio. Also referred to as quantitative data.

Anyone who's shopped at Amazon.com knows that you'll always end up buying more than you need. This is because Amazon uses a complex set of algorithms to recommend items based on what you're currently looking at. For example, if you're looking at a particular book on Steve Jobs, Amazon also recommends a list of other titles that other customers who bought this book also bought.). Amazon does this by collecting data about what customers are buying, and using this huge set of data, it's able to make predictions about: The algorithms used by Amazon falls under the domain known as Machine Learning, sometimes also broadly known as Artificial Intelligence (AI). Uber, the ride-sharing giant, uses machine learning for all aspects of its operation. Machine Learning (ML) is a collection of algorithms and techniques used to design systems that learn from data.