Swinburne University of Technology

Amir Aryani

Integrating Large Language Models (LLMs) such as GPT into organizations’ data workflows is a complex process with various challenges. These obstacles include but are not limited to technical, operational, ethical, and legal dimensions, each presenting hurdles that organisations must navigate to harness the full potential of LLMs effectively.

What are the challenges in integrating LLMs into organisations' data workflows?

I asked Generative AI Models about their context window. Their response was intriguing. The context window for a large language model (LLM) like OpenAI’s GPT refers to the maximum amount of text the model can consider at any one time when generating a response. This includes both the prompt provided by the user and the model’s generated text.

What is your Context Window?

Authors: Hui Yin, Amir Aryani  As we discussed in our previous article “A Brief Introduction to Retrieval Augmented Generation (RAG)”, RAG is an artificial intelligence framework that incorporates the latest reliable external knowledge and aims to improve the quality of responses generated by pre-trained language models (PLM). Initially, it was designed to improve the performance of knowledge-intensive NLP tasks (Lewis et al., 2020). As

Navigating the Present: Exploring Practical Horizons of Retrieval-Augmented Generation (RAG)

At the time of writing this post (November 19, 2023), there are 401,014 models and transformers listed on Huggingface.co. This number is a testament to the success and impact of the open-source model in the development of AI technologies. However, the challenge at hand is understanding the available models and keeping up to date with the latest developments.

Estimated time: 12 Years to Review all 400k Models on Hugging Face

Prompt engineering is a technique used to effectively communicate with large language models (LLM) like GPT-3 or GPT-4 and get the desired output. Here there are applications of 8 distinct prompt techniques to interact with Mistral AI and find out how we can prompt effectively to learn about Foundation Models.
<strong>
 Zero-Shot Learning
</strong>
: This involves giving the AI a task without any prior examples.

8 Types of Prompt Engineering

There is a plethora of articles and predictions about the future development of AI. Reading through them suggests three main areas of high-impact changes that would transform our daily lives: Healthcare, Automation, and Manufacturing. The impact of AI in these industries is profound and multifaceted, with each sector experiencing unique transformations. Some of these ideas are described in this NVIDIA article by Cliff Edwards.

Revolutionizing Wellness and Wheels: AI’s Transformative Role in Health and Automotive Industries

Procrastination, a common adversary in our daily lives, often leads to a backlog of tasks and ambitions. It’s a silent thief, stealthily stealing opportunities and time. You might have a stack of books on your bookshelf, longing to be read. Perhaps there’s a new language you’ve wanted to learn, or piles of paperwork cluttering your office that you’ve been meaning to organize.

Maximizing Productivity in 10-Minute Bursts

Authors: Hui Yin , Amir Aryani  With the increasing application of large language models in various scenarios, people realize that these models are not omnipotent. When generating dialogues (Shuster et al., 2021), the models often produce hallucinations, leading to inaccurate answers.

A Brief Introduction to Retrieval Augmented Generation (RAG)

Stories by Amir Aryani on Medium

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*Hm2MG0A0IJ7r9BRM07vhxA.png" />
<figcaption>DALL-E (Dec 2023)</figcaption>
</figure>

There is a plethora of articles and predictions about the future
development of AI. Reading through them suggests three main areas of
high-impact changes that would transform our daily lives: Healthcare,
Automation, and Manufacturing. The impact of AI in these industries is
profound and multifaceted, with each sector experiencing unique
transformations. Some of these ideas are described in this
[NVIDIA](https://blogs.nvidia.com/blog/2024-ai-predictions/) article by
[Cliff Edwards](https://blogs.nvidia.com/blog/author/cliffedwards/).
Some transformations are already underway, while others are expected to
emerge within the next five to ten years.

### Healthcare

1.  **Diagnostics and Patient Care**: AI algorithms can excel in
    analysing complex medical data, leading to more accurate and quicker
    diagnoses. For example, we are already benefiting from analysing
    medical imaging for detecting diseases like cancer with a higher
    degree of precision.
2.  **Personalised Medicine**: AI can help in tailoring treatments to
    individual patient needs based on their genetic makeup and specific
    health profiles, moving away from a one-size-fits-all approach to
    healthcare.
3.  **Operational Efficiency**: AI-driven systems can streamline
    hospital operations, from patient triage to resource allocation,
    thereby reducing wait times and improving patient care.
4.  **Drug Discovery and Development**: AI accelerates the process of
    drug discovery by identifying potential drug candidates and
    predicting their effectiveness and side effects more rapidly than
    traditional methods.

### Automotive

1.  **Self-Driving Technology**: Self-driving cars are already here,
    albeit in testing mode.
2.  **Enhanced Safety Features**: AI contributes to advanced
    driver-assistance systems (ADAS), improving vehicle safety by
    alerting drivers to potential hazards and even taking control to
    avoid accidents.

### Manufacturing

1.  **Automation of Production Lines**: AI-driven robots and machines
    are increasingly used in manufacturing for tasks that require
    precision, speed, and consistency. This automation boosts
    productivity and quality while reducing costs.
2.  **Predictive Maintenance**: AI systems analyse data from
    manufacturing equipment to predict when maintenance is needed,
    thereby preventing costly downtime and prolonging the life of the
    machinery.
3.  **Supply Chain Optimization:** AI helps in forecasting demand,
    optimizing inventory levels, and enhancing logistics planning,
    leading to more efficient supply chains.

While predicting the future based on opinion articles isn't exactly
scientific, some of these transformations are already underway. There is
ample evidence suggesting that, unless significantly interrupted, they
will change how we use healthcare, cars, and manufacturing processes.

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