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?".
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.
I discuss my experience with chatbots, contrasting older rules-based systems with newer GenAI (General Artificial Intelligence) chatbots. We cannot dismiss the creative capabilities of GenAI-based chatbots, but these systems lack reliability, especially in customer-facing applications, and improvements in the way AI is structured could lead to a "software renaissance," potentially reducing society's technical debt.
The article discusses the contrasting debate on how AI safety is and should be managed, its impact on technical debt, and its societal implications.
It notes the Center for AI Safety's call for a worldwide focus on the risks of AI, and Meredith Whittaker's criticism that such warnings preserve the status quo, strengthening tech giants' dominance. The piece also highlights AI's potential to decrease societal and technical debt by making software production cheaper, simpler, and resulting in far more innovation. It provides examples of cost-effective open-source models that perform well and emphasizes the rapid pace of AI innovation. Last, the article emphasises the need for adaptive legislation to match the pace of AI innovation, empowering suitable government entities for oversight, defining appropriate scopes for legislation and regulation, addressing ethical issues and biases in AI, and promoting public engagement in AI regulatory decisions.
Japan has made its ruling on the situation between Content creators and Businesses. Japanese companies that use AI have the freedom to use content for training purposes without the burden of copyright laws. This news about the copyright laws in Japan reported over at Technomancers is seen as Businesses: 1 / Content Creators: 0 The […]