Friday, December 13, 2024

Microsoft 365 Copilot permits customers to create their very own autonomous brokers

Microsoft is continuous to enhance generative AI throughout Home windows with new updates to Microsoft 365 Copilot.

The corporate has introduced that the power for customers to create their very own autonomous brokers in Copilot Studio is shifting from non-public to public preview subsequent month.

Brokers may be triggered by particular occasions and act on their very own, relatively than being activated by a dialog. As an example, when an e-mail arrives, an agent may be activated to lookup the sender’s particulars and account, see earlier communications, test stock, ask the sender their preferences, after which take the required actions to shut a ticket. 

“Consider brokers as the brand new apps for an AI-powered world. Each group may have a constellation of brokers — starting from easy prompt-and-response to totally autonomous. They’ll work on behalf of a person, crew or perform to execute and orchestrate enterprise processes. Copilot is the way you’ll work together with these brokers, they usually’ll do every thing from accelerating lead technology and processing gross sales orders to automating your provide chain,” Microsoft wrote in its announcement

The corporate shared some examples of consumers which have already constructed their very own autonomous brokers, together with Pets at Residence, who created an agent to compile circumstances for human evaluate; McKinsey & Firm, who created an agent to hurry up shopper onboarding; and Thomson Reuters, who created an agent to hurry up its authorized due diligence course of.

Microsoft can also be releasing ten new autonomous brokers in Dynamics 365, which is its enterprise useful resource planning (ERP) and buyer relationship administration (CRM) software program.

New brokers spans gross sales, service, finance, and provide chain use circumstances, and embody examples like:

  • Gross sales Qualification Agent, which researches leads, prioritizes alternatives, and guides buyer outreach
  • Provider Communications Agent, which tracks provider efficiency to detect and reply to delays
  • Buyer Intent and Buyer Data Administration Brokers, which study from customer support representatives methods to resolve buyer points and add articles to an organization’s information base. 

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TensorFlow Lite for R: A Quick Start Guide? When you’re ready to unleash the power of machine learning in your R projects, TensorFlow Lite is an excellent choice. This guide will walk you through the steps to get started with TensorFlow Lite in R. First things first, make sure you have the necessary packages installed. You’ll need tensorflow and rtensorflowlite. If not, you can install them using the following commands: install.packages(“tensorflow”) install.packages(“rtensorflowlite”) Now that you’re all set, let’s create a simple TensorFlow model in R. “`R library(tensorflow) model <- tf$sequential( tf$layer$dense(units = 10, activation="relu", input_shape=c(784)), tf$layer$dense(units = 10, activation="softmax") ) %>% compile(optimizer = “adam”, loss = “categorical_crossentropy”, metrics = c(“accuracy”)) “` In this example, we’re creating a simple neural network with two hidden layers. The first layer has 10 units and uses the ReLU activation function, while the second layer has 10 units and uses the softmax activation function. Once you’ve created your model, you can train it using the following code: “`R model %>% fit(X_train, y_train, epochs = 5) “` In this example, we’re training our model for 5 epochs using the X_train and y_train data. Finally, let’s convert our trained model to TensorFlow Lite using the following code: “`R library(rtensorflowlite) model_tflite <- tf$convert_to_tflite(model) ``` And that's it! You've successfully converted your trained TensorFlow model to TensorFlow Lite.
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