Summary: So Google was one of the first large companies to actually use cloud computing extensively and now, as usual in the English language, there is a move to talk about 'the cloud', dropping the more geeky 'computing' part! Like other buzzwords, take web 2.0 or SaaS (Software as a Service) not everybody has the same […]
So Google was one of the first large companies to actually use cloud computing extensively and now, as usual in the English language, there is a move to talk about 'the cloud', dropping the more geeky 'computing' part! Like other buzzwords, take web 2.0 or SaaS (Software as a Service) not everybody has the same definition. It is highly likely that some people will say that 'could computing' and 'the cloud' are two different things.
Seamless access to hardware and software: the Supercomputer
The main idea with 'the cloud', from an IT perspective, is to seamlessly supply extra resources to providers of IT services and by ricochet to end users of these services. Even though it is not a behemoth word like sustainability that continues to suck in so many different meanings, cloud computing is likely to evolve and cover more and more services. Hey it could even end up being synonymous with the Matrix type concept from the film!
In the case of IT infrastructure, people are interested in outsourcing their hardware needs which can be covered by what is called HaaS (Hardware as a Service). One of the most well known services of this type is Amazon's Elastic Computer Cloud or EC2.
Does this remind you of passive terminals that connect to supercomputer?! It is not a coincidence that equipment like Netbooks are so successful in the current context of more and more key services, instantly available on the internet. Cloud Computing can help provide further such important services to people. The Cloud services currently available and Netbooks are however far more complex than the mainframe and passive terminal model.
HaaS and SaaS
The Cloud services provided can be viewed as building blocks. Both HaaS (Amazon's and Google's server farms) and SaaS type applications (like Google Docs, Salesforce.com and Sliderocket) are purchased without needing to worry about the risks involved in evaluating growing usage. In this sense cloud computing is also referred to as "on demand computing" where you just purchase the system and someone else worries about your evolving needs.
Utility Computing & Server Virtualization
If you are aware of solutions like VMWare that allow you to run several virtual instances of a server on a physical server, then you will quickly see the similarity with cloud computing whereby clusters of servers can provide hundreds of instances of virtual servers. Google's need to 'crunch' huge quantities of data, which requires highly knowledgeable people in cloud computing or grid computing. Like Amazon, Google is now offering the public some of the cloud computing services that used to be restricted to their own projects and internal service requirements.
The ability for companies to tap in to this cloud of both services and hardware (data centers) in the same way you would just turn on the tap or the light, provides powerful and efficient "on demand" services and resources a bit like a utility grid hence the idea of utility computing.
The Cloud is in its infancy but is already proving to be an extremely efficient solution for small to large companies and even the general public though services like Google Aps etc. Could computing is also likely to take on many new meanings along the way
Take a look at the following video where numerous tech celebs explain what Cloud Computing is (audio is a bit shoddy though):
Tim O'Reilly, Dan Farber, Matt Mullenweg, Jay Cross, Brian Solis, Kevin Marks, Steve Gillmor, Jeremy Tanner, Maggie Fox, Tom McGovern, Sam Lawrence, Stowe Boyd, David Tebbutt, Dave McClure, Chris Carfi, Vamshi Krishna and Rod Boothby are asked "what is Could Computing?".
This Intermediate Guide for the ONHT (Objective, Needs, How, Trajectory) Framework transforms you from someone who uses GenAI into someone who thinks with GenAI by adding the missing cognitive functions that current GenAI lacks. The framework works through three critical pillars – Empathy (understanding all stakeholders), Critical Thinking (challenging assumptions), and Human in the Loop (active partnership). Master these patterns and you'll be solving complex problems others can't even approach, becoming indispensable by designing interactions that produce exceptional results rather than just functional outputs.
Stop getting generic AI responses. Learn the four-letter framework that transforms vague requests into precise results. The ONHT framework: Objective (what problem you're solving), Needs (key information that matters), How (the thinking approach), and Trajectory (clear steps to the answer), teaches you to think WITH AI, not through it, turning "analyse customer feedback" into board-ready insights. Real examples show how adding context and structure gets you from Level 1 basics to Level 3 mastery, where AI delivers exactly what you need.
The difference? Knowing how to ask.
GenAI tools are transforming work, but most people get poor results because they don't understand how to communicate with AI built on structured data. This guide is a series of articles that teaches the ONHT framework—a systematic approach to prompting that transforms vague requests into exceptional outputs by focusing on Objectives (what problem), Needs (what information), How (thinking approach), and Trajectory (path to solution). Master this framework and develop an expert mindset grounded in human-in-the-loop thinking, critical analysis, and empathy, and you'll excel with any AI tool, at any company, in any role.
I question the reliability and accuracy of Generative AI (GenAI) in enterprise scenarios, particularly when faced with adversarial questions, highlighting that current Large Language Models (LLMs) may be data-rich but lack in reasoning and causality. I would call for a more balanced approach to AI adoption in cases of assisting users, requiring supervision, and the need for better LLM models that can be trusted, learn, and reason.
