The Power of Cloud Computing in AI Technology

Alibaba Reveals Its True Power as an AI Machine

At this Apsara Conference, Alibaba revealed the numbers behind its AI prowess along with a host of new products and solutions in AI.

  1. Apsara AI Platform serves 1 billion global customers.
  2. Apsara AI Platform processes 1 billion images, 1.2 million hours of videos, 0.55 million hours of voice messages, and 500 billion natural language sentences per day.

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Emulating Neural Synapses through AI

Neural synapses in mammal brains are also capable of performing deep learning. New advances in AI and neuroscience aim to emulate, or even surpass, th.
Neural networks are inspired by the biological neurological network system. But do our brains learn the same way as a computer does deep learning? The answer to this question can possibly bring us to a more powerful deep learning model, and, on the other hand, also help us to better understand human intelligence.

Using Better Training Methods for Deep Learning

In the early 2000s, Richards and Lillicrap took Hinton’s classes at the University of Toronto. They were convinced that the deep learning model to some extent accurately reflects the mechanics of the human brain. However, there were several challenges to validating this idea at the time. First of all, it is not yet certain whether deep learning can reach the level of complexity in a human brain. Second, deep learning algorithms typically violate biological facts, which have already been demonstrated by neuroscientists.

Heterogeneous Computing for AI and Big Data — Alibaba Cloud Computing Conference

Alibaba Cloud heterogeneous platform for elastic computing aims to provide high-quality services for organizations to realize scientific and technological innovations.
The heterogeneous computing technology is evolving rapidly in the field of big data and AI in recent years. To cope with this technology revolution, Alibaba Cloud Heterogeneous Computing has made great achievements in both product type and application.

Alibaba Cloud Machine Learning Platform for AI: Online Predictive Deployment for Health Monitoring

This article describes how to use the Online Predictive Deployment feature of Alibaba Cloud’s Machine Learning Platform for AI (PAI) to monitor user health in real time.

Step 1: Model Deployment

Click Deploy in the lower section of the current experiment interface and select Online Predictive Deployment. Select the logistic regression model generated in the heart disease prediction case, as shown in the following screenshot.

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Machine Learning:Heart Disease Prediction

This session introduces how to use Alibaba Cloud Machine Learning Platform For AI to create a heart disease prediction model based on the data collected from heart disease patients.

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FAQ — Machine Learning Platform for AI

How do I upload data?

To upload data on the Machine Learning Platform for AI web interface, make sure that the data is less than 20 MB. To upload data that is greater than 20 MB, you must download the MaxCompute client and then use the tunnel command.

How do I set the algorithm parameters?

To set the algorithm parameters, drag an algorithm component to the canvas and click the component. The corresponding parameters are displayed in the right-side pane.

How do I view experiment results?

If Machine Learning Platform for AI has successfully run a component, it marks the component with a green check. You can right-click a component with a green check to view data or evaluation results.

How do I view and download the model generated from an experiment?

To generate a model, you must first select Setting > General > Auto-generate PMML from the left-side navigation pane. After successfully running an experiment, you can select Model from the left-side navigation pane to check the corresponding model. To view the model parameters, right-click the model. To download a model, right-click the model and select Download PMML.

What is PMML?

PMML is a standard model description file. A PMML file downloaded from Machine Learning Platform for AI can be applied to open-source engines, such as Spark.

Architecture — Machine Learning Platform for AI

The following figure shows the architecture of the Machine Learning Platform for AI.

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