From Simple Patterns to Sentience’s Complexity

“In order for AI to be able to overtake a programmers job, implies that the client knows what he wants. We’re safe…”
This job related predicament rises, as I see it, only in a brief moment in a much broader natural course. It is the kind of meme that strives to survive, it probably deserves a definitive NFT minting as of 03.2022 before it will soon fall into oblivion.
I want to start tackling its survival efficiency by talking about the AI and its power as I see it from this stand point: in which in the past decade the AI’s potential reached only its first steps in its infancy with clear signals to world changing capabilities. This goes further down, through refinements (of its rightly chosen) ontology.
Preamble
We live in a world (or better said, this is the way the world works) in which almost everything employs from within some mechanics of refinement and adaptation. A machinery that moves things towards escaping chaos – and this with great energy consumption – but energy that partly comes from within (as the “will” opposed to the “death drive”). This goes from the inherent patterns in nature up to the end spectrum of the human activity(1). The underlying battle between order and entropy(2) on the underlying surface of our particular(3) universe with its laws. And above this, as the layer of simple emerging patterns to the ultimate, macro refinement substrate of sentient manifestations, and on top of it with the layer of symbolic, cultures and abstract thinking. A predictable pattern alright, further on rising within the brain energy patterns, a culmination tip, that leads to the creation of synthetic worlds, artificial sentience, transcendental states of being in the digital universe, as the next steps. A macro, ever growing vertical ontology at work.
Refinement
Within this broader context the refinement produces further deepening of the domains and within AI domain, the current advances allow neural training of some larger than ever data sets, like in the language, vision, with a touch of symbolic, towards incipient meaning. In the context of programming we see first results in training upon some good part of all the human written programming code. And that we are able to put that to work in the business requirements with the programming languages on the real use cases (necessary for a program to have a purpose) for now in the form of AI assisted programming(4).
And further refinement would lead to a more natural way of conceiving programs through language processing of the requirements, from the problems to the actual code generation. And with, again, a further refinement into the symbolic AI with the actual predictable outcome not by only answering the questions, but with solutions offered by AI prior asking the question(5). All that within a domain criteria based on programming/AI ethics, best practices solutions, security, cultural impact, etc.
Symbolic
On the side of symbolic AI at this time there is an upward trend of trying different models of processing, a process in itself that requires further research. At the same time I see that this process is hindered by the fact that the models are still mapping or try mimicking some partial models of the mind, of trying to explain how brain works, and by posing answers to the questions related to consciousness(6).
I am still on the path and researching on my own symbolic model within the essentials, unspoiled concepts advanced through the innovative approach by Ludwig Wittgenstein:

“The reason computers have no understanding of the sentences they process is not that they lack sufficient neuronal complexity, but that they are not, and cannot be, participants in the culture to which the sentences belong. A sentence does not acquire meaning through the correlation, one to one, of its words with objects in the world; it acquires meaning through the use that is made of it in the communal life of human beings.”

There is not only – many would call with yesterday’s standards as a “grim” future – but there is in fact to be reminded that one cannot oppose the refinements because it requires also effort and energy none is possessing enough. Through self cultivated death drive that will only help on the short run…so remember this meme and laugh at its NFT later.

(1) forms of life with language games adaptation, creation activities with continuous refinements of their ontological models, circulating concept cultures.
(2) with simple patterns from which something emerges and with the counter action of opposite forces from nature up to the psyche and symbolic, the death drive.
(3) multiverse theory, in which very briefly explained: the eternal timeless energy waves produces bubbles of universes each with its fundamentals.
(4) copilot software that has the basis all of the github source code.
(5) if we have the right domain question we have the answer, in that the answer is there, it is only that briefly something is obscuring it from view.
(6) on questions related to the knowledge of ourselves, which in fact, are not of scientific nature.

C. Stefan / 24.03.2022

DevOps Skills

The broad picture. Skills to address the “from Code to Infrastructure” paradigm. Bridging ends from code producers to deployment in production – mindset of all involved, get a sense of the process as well do the automation of it and the orchestration and monitoring.

Collaborate with internal management teams involved in the DevOps process and stay familiar with the objectives, roadmap, blocking issues and other project areas.
Have the skills to mentor and advise team members on the best ways to deliver code, what tools to use when coding and how to test the latest features.

The target. Fast provisioning: be able to setup new machines fast. Good monitoring: to be quickly able to diagnose failures and trace them down. Quickly rollback to a previous version of the microservice. Rapid app deployment through fully automated pipelines. Create the Devops mindset / culture.

DevOps engineers need to know how to use and understand the roles of the following types of tools:
1. Version control: GitHub, GitLab
2. Continuous Integration servers: code coming in repository server and triggers build and doc: Jenkins, GitLab CI, Atlassian Bamboo, Circle CI, GitHub Actions
3. Configuration management: Software Configuration Management SCM Tools: Configuration management occurs when a configuration platform is used to automate, monitor, design and manage otherwise manual configuration processes. System-wide changes take place across servers and networks, storage, applications, and other managed systems: Puppet, Ansible, Chef
4. Deployment automation: Ansible Tower, Bamboo
5. Containers: containerd, Docker, Artifactory
6. Infrastructure Orchestration: automating the provisioning of the infrastructure services needed to support an app moving into production – in the right order, is orchestration: Terraform, Ansible (also Config. Management Tool), Chef, Kubernetes
7. Monitoring and analytics: Prometheus, Datadog, Splunk
8. Testing and Cloud Quality tools: a test automation platform uses scripts to automate the whole process of software testing. Identify the tests that need to be automated. Research and analyze the automation tools that meet your automation needs and budget. Based on the requirements, shortlist two most suitable tools. Do a pilot for two best tools and select the better one. Discuss the chosen automation tools with other stakeholders, explain the choice, and get their approval. Proceed to test automation
Tools: Kobiton, Eggplant, TestProject, LambdaTest
9. Network protocols from layers 4 to 7, nginx, caching, Service Mesh.
10. Programming skills with Java, Shell, Python, JS, Ruby…

Also:
Monitoring production environments
Performance measurements
Security
Cloud administration
Get proper alerts when something is wrong or unavailable
Help resolve problems either through online support or technical troubleshooting

Web3 & DeFi space

World Wide Web3
Brief history from simple showcase to owning the Internet:
https://www.youtube.com/watch?v=AFb5gosfKJY
https://www.youtube.com/watch?v=B0ZJMdSvAj8

Blockchain, Smart Contracts, NFT
https://www.youtube.com/watch?v=JPGNvKy6DTA
https://www.youtube.com/watch?v=22O6a87-GcQ
Impact:
https://youtu.be/0u6cho7YpQQ?t=560

DAO (Decentralized Autonomous Organization)
https://www.youtube.com/watch?v=pA6CGuXEKtQ

Owning your data
https://www.youtube.com/watch?v=P2n5HY-eidU
https://www.youtube.com/watch?v=kqnxZ_IByKE

Business Models
https://www.youtube.com/watch?v=AI1N6dY8vSQ
https://www.youtube.com/watch?v=-n5084IfnxU

Gaming
https://www.youtube.com/watch?v=BdtrE4TLaeE
Metaverse
https://www.youtube.com/watch?v=ExUovs0n4bA

Social Money
https://www.youtube.com/watch?v=Ka7QZM6zx40

DeFi
The space of future finances. The departure from the standard centralized (banks) finances model to the digital counterpart, decentralized. The new money model enabling the fuel that will drive the Web3.

Metaverse

  • The metaverse encompasses immersive environments, often (but not always) using virtual- or augmented-reality technology.
  • The metaverse is “always on” and exists in real time.
  • The metaverse spans the virtual and physical worlds, as well as multiple platforms.
  • The metaverse is powered by a fully functioning virtual economy, often (but not always) built on cryptocurrency and digital goods and assets, including nonfungible tokens (NFTs).
  • The metaverse enables people to have virtual identities, presence, and “agency,” including peer-to-peer interactions, transactions, user-generated content, and “world-building.”