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Article
9 min read
Radu Orghidan

Artificial intelligence (AI) has the potential to revolutionise the corporate landscape. Particularly generative AI is attracting substantial attention and investment. Unsurprisingly, competition in the AI field is fierce. Various platforms are available for businesses looking to integrate generative AI, each offering distinct advantages. These include OpenAI, primarily hosted on Microsoft Azure’s public cloud, Google’s Bard, Anthropic’s Claude or open-source AI models like Meta’s LLaMA 2.

 

Besides commercial solutions, platforms offering free model usage, such as Hugging Face, are incredibly valuable, facilitating efficient proof of concept (PoC) creation. Moving towards bespoke AI-centric solutions, some companies prefer a more specialised approach, tailoring generative AI models to their specific needs. This customisation can be achieved on three levels.

 

Firstly, there are copilots, which are collaborative assistants designed to enhance manual efficiency by supporting users with specific tasks. Secondly, intelligent agents can augment processes by taking a more autonomous approach and interacting with users, systems or other agents. Finally, multi-agent systems consist of chains of agents with diverse skills and capabilities, allowing them to achieve more complex objectives.

 

By harnessing the potential of generative AI and utilising these different approaches, businesses can leverage the power of AI to drive innovation and gain a competitive edge in their respective industries.

 

What are some of the major benefits of generative AI?

 

First and foremost, generative AI holds the potential to enhance performance and productivity across a multitude of sectors. Its profound effect on research, as showcased by organisations like Alphafold, is a compelling testament to its vast potential. This demonstrates its capability to address challenges that were deemed nearly insuperable only a few years ago. Looking ahead, companies will have the capacity to utilise independent systems that can autonomously select and implement tools in a sequential order to accomplish a specified task.

 

So, in which ways can generative AI deliver concrete advantages to your business?

 

Information access

 

AI is a game-changer in interpreting structured and unstructured data, enabling straightforward and efficient access to information. For example, generative AI enables information access through its semantic search capability, which focuses on how users express their intention rather than on rigorous keyword matching.

 

This is where vector embeddings come into play. Vector embeddings are a technique used in machine learning and deep learning where words or phrases in a text are mapped to high-dimensional vectors known as word embeddings. These vectors are created so that semantically similar words are close together in the vector space.

 

The connection between generative AI and vector embeddings is deep. While embeddings provide a mathematical and efficient representation of complex data, generative AI leverages these embeddings to search and construct novel and meaningful outputs. Tools such as FAISS (Facebook AI Similarity Search), Pinecone or Chroma allow developers to quickly search for similar multimedia document embeddings. Personalised systems use advanced reasoning and summarisation to improve information access further.

 

Automation

 

AI enables the identification of repetitive tasks, affording users greater time to focus on broader and more impactful projects. An example of such a tool is Advanced Data Analytics, an experimental ChatGPT model that can use Python, handle uploads and downloads and analyse data. It allows users to upload files up to 512 MB and perform complex data analysis simply by asking questions in natural language.

 

The tool helps the user with routine and error-prone data engineering tasks such as cleaning the data, creating visualisations and summarising the data. It provides valuable insights, identifies potential issues and suggests improvements. This gain in efficiency unleashes data scientists’ creativity and drives more value for the organisation.

 

Moving away from text generation, another example is image generation, where AI models can create 2D or 3D assets. They help content generators not only speed up routine graphics work but also find inspiration and a fresh view.

 

Tool innovation

 

AI fosters the development of unique tools, accelerating experimentation and inspiring novel solutions. This snowball effect of fast-paced advancements and discoveries further propels the adoption and integration of AI across industries.

 

A few examples of such innovative solutions and the underlying tools are code generation tools such as Tabnine or GitHub Copilot, image generation models available from Stable Diffusion or Dall-e 3, conversational AI such as Open Assistant and large language models (LLMs) such as OpenAI’s GPT-4 or Meta’s LLaMA.

 

These tools can be tested using free platforms like Hugging Face that enable access to models that can be further extended and customised. They say great power comes with great responsibility, so organisations are incorporating guardrails to prevent random code execution, enforcing the safe use of AI.

 

In conclusion, we can say that AI is no longer a futuristic concept – it is a reality reshaping the business landscape. It can help businesses like yours in many ways – from easing your access to information, smoothing out communication and boosting automation to lighting the spark of tool innovation.

 

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