aster.cloud aster.cloud
  • /
  • Platforms
    • Public Cloud
    • On-Premise
    • Hybrid Cloud
    • Data
  • Architecture
    • Design
    • Solutions
    • Enterprise
  • Engineering
    • Automation
    • Software Engineering
    • Project Management
    • DevOps
  • Programming
    • Learning
  • Tools
  • About
  • /
  • Platforms
    • Public Cloud
    • On-Premise
    • Hybrid Cloud
    • Data
  • Architecture
    • Design
    • Solutions
    • Enterprise
  • Engineering
    • Automation
    • Software Engineering
    • Project Management
    • DevOps
  • Programming
    • Learning
  • Tools
  • About
aster.cloud aster.cloud
  • /
  • Platforms
    • Public Cloud
    • On-Premise
    • Hybrid Cloud
    • Data
  • Architecture
    • Design
    • Solutions
    • Enterprise
  • Engineering
    • Automation
    • Software Engineering
    • Project Management
    • DevOps
  • Programming
    • Learning
  • Tools
  • About
AI | Crystal emerging from bluish fog watercolors and silver
  • Technology

The Emergence of Intelligence – How AI Self-Assembles Through Complexity

  • Dean Marc
  • July 5, 2023
  • 2 minute read

The creation of AI, at its core, is a process of emergence through complexity. It is not “born” in a biological sense, but rather it’s built and trained through layers of algorithms and data. The concept of AI “emerging” refers to the phenomenon that as the complexity of an AI system increases, new properties and capabilities can manifest that were not explicitly programmed into the system. This is often seen in machine learning and deep learning systems, where the AI can learn from data and improve over time, exhibiting behaviours that may seem to “emerge” organically from the learning process.

Similar to the lifecycle of a typical software product or hardware infrastructure, the development of an AI system also follows a lifecycle, sometimes referred to as the AI development lifecycle or AI project lifecycle. This lifecycle typically outlines the sequential stages involved in the development, deployment, and maintenance of an AI system.


Partner with aster.cloud
for your next big idea.
Let us know here.



From our partners:

CITI.IO :: Business. Institutions. Society. Global Political Economy.
CYBERPOGO.COM :: For the Arts, Sciences, and Technology.
DADAHACKS.COM :: Parenting For The Rest Of Us.
ZEDISTA.COM :: Entertainment. Sports. Culture. Escape.
TAKUMAKU.COM :: For The Hearth And Home.
ASTER.CLOUD :: From The Cloud And Beyond.
LIWAIWAI.COM :: Intelligence, Inside and Outside.
GLOBALCLOUDPLATFORMS.COM :: For The World's Computing Needs.
FIREGULAMAN.COM :: For The Fire In The Belly Of The Coder.
ASTERCASTER.COM :: Supra Astra. Beyond The Stars.
BARTDAY.COM :: Prosperity For Everyone.

1. Problem Definition: The first step in the AI lifecycle is defining the problem that needs to be solved. This includes understanding business goals, defining specific objectives for the AI system, and identifying key performance indicators (KPIs) to measure the success of the AI system.

2. Data Collection: AI systems require data to learn from. This step involves gathering relevant data that the AI system will use to train. This could involve data creation, data augmentation, or collecting data from different sources.

3. Data Preparation: The collected data is cleaned and organised. This might involve dealing with missing or inconsistent data, normalisation, and other forms of preprocessing to make the data suitable for training an AI model.

Read More  3 Microsoft Azure AI Product Features That Accelerate Language Learning

4. Model Selection & Training: In this stage, an appropriate AI model is chosen based on the problem at hand. The model is then trained using the prepared data. This involves tuning parameters, selecting features, and iteratively refining the model.

5. Evaluation: After training, the model’s performance is evaluated. This involves testing the model on unseen data and measuring its performance using pre-defined KPIs.

6. Deployment: If the model’s performance is satisfactory, it is deployed into the real-world environment where it begins to make predictions or decisions based on new data.

7. Monitoring and Maintenance: After deployment, the AI system needs to be continuously monitored to ensure it is performing as expected. The system may require updates, retraining with new data, or even a complete redesign if the problem scope changes or if the model performance degrades over time.

8. Retirement: If an AI system is no longer needed, or if a better solution has been developed, the AI system is retired. This includes taking care of any data that the system was using or generated.

Ethics and privacy considerations should also be part of the entire lifecycle, from initial problem definition and data collection to deployment and retirement specifically for this context. It is important to ensure that AI systems are developed and used in a way that respects user privacy, minimises bias, and promotes fairness and transparency .


For enquiries, product placements, sponsorships, and collaborations, connect with us at [email protected]. We'd love to hear from you!

Our humans need coffee too! Your support is highly appreciated, thank you!

Dean Marc

Part of the more nomadic tribe of humanity, Dean believes a boat anchored ashore, while safe, is a tragedy, as this denies the boat its purpose. Dean normally works as a strategist, advisor, operator, mentor, coder, and janitor for several technology companies, open-source communities, and startups. Otherwise, he's on a hunt for some good bean or leaf to enjoy a good read on some newly (re)discovered city or walking roads less taken with his little one.

Related Topics
  • AI
  • Artificial Intelligence
  • Machine Learning
  • ML
You May Also Like
View Post
  • Computing
  • Multi-Cloud
  • Technology

How to create an AWS free tier account

  • July 10, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

How to configure multiple AWS CLI authentication credentials

  • July 10, 2025
View Post
  • Technology

Formula E accelerates its work with Google Cloud Storage and Google Workspace

  • July 9, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

What is database as a service (DBaaS)?

  • July 7, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

The cloud’s role in PQC migration

  • July 7, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

Hybrid cloud has hit the mainstream – but firms are still confused about costs

  • July 7, 2025
View Post
  • Technology

Building secure, scalable AI in the cloud with Microsoft Azure

  • July 5, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

Turns out OpenAI is the customer behind Oracle’s mysterious $30 billion cloud deal

  • July 3, 2025

Stay Connected!
LATEST
  • How to create an AWS free tier account
    • July 10, 2025
  • How to configure multiple AWS CLI authentication credentials
    • July 10, 2025
  • 3
    Formula E accelerates its work with Google Cloud Storage and Google Workspace
    • July 9, 2025
  • What is database as a service (DBaaS)?
    • July 7, 2025
  • The cloud’s role in PQC migration
    • July 7, 2025
  • 6
    Hybrid cloud has hit the mainstream – but firms are still confused about costs
    • July 7, 2025
  • 7
    Building secure, scalable AI in the cloud with Microsoft Azure
    • July 5, 2025
  • 8
    Turns out OpenAI is the customer behind Oracle’s mysterious $30 billion cloud deal
    • July 3, 2025
  • aster-cloud-erp-bill_of_materials_2 9
    What is an SBOM (software bill of materials)?
    • July 2, 2025
  • aster-cloud-sms-pexels-tim-samuel-6697306 10
    Send SMS texts with Amazon’s SNS simple notification service
    • July 1, 2025
about
Hello World!

We are aster.cloud. We’re created by programmers for programmers.

Our site aims to provide guides, programming tips, reviews, and interesting materials for tech people and those who want to learn in general.

We would like to hear from you.

If you have any feedback, enquiries, or sponsorship request, kindly reach out to us at:

[email protected]
Most Popular
  • 1
    A looming hyperscaler exodus? UK IT leaders are thinking of ditching US cloud providers – here’s why
    • June 26, 2025
  • Genome 2
    AlphaGenome: AI for better understanding the genome
    • June 25, 2025
  • aster-cloud-website-pexels-goumbik-574069 3
    Host a static website on AWS with Amazon S3 and Route 53
    • June 27, 2025
  • Camping 4
    The Summer Adventures : Camping Essentials
    • June 27, 2025
  • 6 edge monitoring best practices in the cloud
    • June 25, 2025
  • /
  • Technology
  • Tools
  • About
  • Contact Us

Input your search keywords and press Enter.