In Part One, we explore the different approaches to AI deployment, cloud, on-premise, and edge computing, highlighting the key benefits and challenges of each.

In Part 1, we explored the fundamental differences between cloud-based AI and self-hosted AI solutions, outlining the key pros and cons of each approach. Now, in Part 2, we dive deeper into the practical considerations when choosing between these options and provide insights on how to make an informed decision for your business.

Artificial Intelligence is a transformative tool for businesses, but deploying AI solutions requires careful consideration of infrastructure, cost, and long-term strategy. One of the most critical decisions is choosing between cloud-based AI and self-hosted AI. Each approach offers distinct advantages and drawbacks, depending on your business needs, technical expertise, and budget.

Artificial intelligence is no longer a futuristic concept, it is an essential tool that businesses can leverage for efficiency, insight and competitive advantage.

However, building an AI capability requires investment into infrastructure, expertise and strategy. Depending on the type of AI you want to deploy, your business will need to make different considerations.

Artificial Intelligence offers businesses powerful tools to enhance products and services, but not every AI implementation delivers real value. For SMEs, the key is to determine whether AI genuinely improves outcomes, rather than adopting it for the sake of innovation. Below is a practical checklist to help you assess whether AI is the right fit for your business.

The rise of AI in business began with the widespread adoption of ChatGPT, developed from language translation algorithms. Almost immediately, every conceivable application of Large Language Models (LLMs) was soon identified, leading to the rapid emergence of targeted AI services designed to monetise ChatGPT and other LLMs.

AI is expected to revitalise the metaverse concept by providing more efficient ways to generate meaningful content. Whilst creative industries have valid concerns about evolving interpretations of copyright, only large organisations are likely to have the resources to defend their intellectual property within the emerging legal framework.

When Facebook rebranded as Meta in June 2022, it anticipated rapid growth in virtual and augmented reality. However, the structure of the market has not evolved as Meta had envisioned.

Annual financial reports for heritage organisations suggest that digital engagement is scarcely recognised as a revenue opportunity by management boards. Primarily, revenue comes from membership fees, in-person visits, café and gift shop sales, and benefactors.

The graph shows the positions of three types of corporate AI solutions, namely Chat GPT, DALL E and Neuralangelo. These three products are contemporary examples of Large Language Models, Generative AI and 3D image reconstruction, respectively.

DeepSeek is the most publicised result of these efforts, but other forms of neural networks have achieved even greater reductions in processing. We will discuss a case where elimination of uncertainty within the numerical method results in better performance, and how that has been achieved.

In our last blog post we introduced the concept of experts, which play an important role within Large Language Models.

Experts are one method of reducing the complexity of training and using an LLM translator. In daily life human experts have much greater confidence in their conclusions than those who lack experience, they avoid endless trawling through data by using their knowledge of a subject.

During the evolution of automatic translators, it was realised that the chance of a word occurring in a sentence was highly dependent on the structure of the sentence. A sentence that contains the word ‘tomato’ is much more likely to include ‘greenhouse’ than ‘particle accelerator’.

There has been much discussion about DeepSeek in the media and the step change in share prices of OpenAI and NVIDIA that it caused. It is our intention in this short article to provide a history to the creation of the DeepSeek product and surmise how it will impact the wider AI landscape.

As AI technology continues to evolve, so too will the legal frameworks governing its use in creative industries.

As artificial intelligence becomes an integral part of creative industries, small and medium enterprises (SMEs) must navigate a complex copyright landscape. While fully AI-generated content generally lacks copyright protection, businesses can take steps to ensure their AI-assisted creations remain legally protected. In this article, we explore practical strategies for safeguarding intellectual property in an AI-driven world.

As artificial intelligence continues to transform creative industries, many small and medium enterprises (SMEs) are asking a crucial question: who owns the rights to AI-generated content? The answer isn’t straightforward, as copyright laws in the USA, EU, and UK are still evolving to address this challenge. This article explores the current legal landscape and what it means for SMEs using AI-powered tools.

At the forefront of innovation, businesses are continuously searching for ways to harness the power of AI to stay competitive. As a leader in AI for 3D reconstruction, Aralia Systems recognises the unique challenges and opportunities that come with adopting AI in the creative industries.