​AI Tech Circle​

Welcome to your weekly AI Newsletter from ​AITechCircle​!

This newsletter has become an essential resource for me and many others in the AI community. It is packed with practical insights that will immediately boost your work or business.

Dive into this week’s updates, and take a moment to share them with a friend or colleague who could gain from these valuable insights! AITechCircle​

Today at a Glance:

• The Platform for SaaS to LLMs for SaaS – The second part, Buy (Shapers)
• Generative AI Use cases in the Health Care Industry, medical documentation with AI-powered transcription.
• AI Weekly news and updates covering newly released LLMs
• Courses and events to attend

Extending SaaS with Oracle’s Generative AI Managed Services

During the last week, we had a topic, ‘Starting the AI Implementation Journey – Adopt,‘ the first step into your journey of adopting AI, Generative AI, into the organization. What is the appropriate route to follow? Each organization has unique requirements and situations, so to the max, you can have specific patterns that can help you to drive the intake into your organization; eventually, you will be looking into the best approach for your organization.

The three-part series covers the three stages of the AI Implementation Journey.

1. Adopt (Takers): Leverage AI features embedded in existing SaaS applications, focusing on ease and low costs.
2. Buy (Shapers): Expand AI capabilities using a Generative AI portfolio, aiming to extend functionalities.
3. Build (Makers): Develop and train custom Generative AI models, focusing on business value and governance

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The first part was already covered during last week’s edition, and I will cover the second phase this week.

Buy (Shapers):

This phase of the AI adoption journey is ideal for organizations ready to expand their capabilities without diving into the complexity of custom model development. You will utilize AI to shape and extend existing systems, making AI integration more straightforward and scalable.

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Let’s have a look at the case of where you are using Oracle’s SaaS application.

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Oracle’s managed Generative AI service enables Fusion Applications users to access AI-powered extensions that adapt to their needs, offering quick-to-implement, customizable, low-risk solutions.

This managed AI model extends SaaS applications similarly to traditional SaaS extensions, and the term was coined as PaaS for SaaS.

It allows organizations to leverage powerful AI capabilities without overhauling their tech stacks or IT landscape. Organizations can use Oracle’s Generative AI service to enhance their applications without significant disruption, achieving smoother integration and seamless scaling.

The conventional architecture for SaaS extended with the Platform services is outlined below:

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Now, let’s move forward to the Generative AI era. We’ll explore how to add to your existing Application ecosystem by integrating large language model (LLM) capabilities.

Models available from Cohere and Meta for OCI Generative AI include:

• Cohere Command R: Designed for scalable, efficient retrieval-augmented generation (RAG), Command R improves throughput, reduces latency, and supports a larger context window. It performs well across 10 languages.
• Cohere Command R+: Built on Command R, this model offers deeper language understanding for specialized tasks like long-form content creation, summarization, and industry-specific language generation.
• Cohere Embed: English and multilingual embedding models that convert text into vector embeddings. “Light” versions offer smaller, faster processing for English-only applications.
• Meta Llama 3: A versatile, open-source model with enhanced reasoning, code generation, and instruction-following capabilities. OCI offers the 70B model, supporting fine-tuning via LoRA.

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The above architecture components will help you embrace the most innovative technology in your ecosystem with less disruption and more value for organizations.

This will enable you to avoid the race to drive adoption from only the use case point of view.

In this approach, your tech stack is set, and there are plenty of use cases that you can start piloting in the organization.

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I’d love to hear your insights and experiences regarding the route you’re taking on your AI journey.

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Could you share your feedback on how you plan to implement Generative AI into your organization?

Weekly News & Updates…

Last week’s AI breakthroughs marked another leap forward in the tech revolution.

1. Bringing developer choice to Github Copilot with Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and OpenAI’s o1-preview link​
2. SmolLM v2 pushes the state-of-the-art performances of LLMs under 2B parameters with three sizes: 135M, 360M, and 1.7B parameters. link​
3. MBZUAI has released Nanda, the world’s most advanced open-source Hindi large language model (LLM), developed by the University’s Institute of Foundation Models (IFM) in partnership with Inception (a G42 company) and Cerebras Systems. Llama-3-Nanda-10B-Chat (or Nanda for short) is a 10 billion parameter pre-trained and instruction-tuned bilingual large language model for both Hindi and English, trained on a dataset containing 65 billion Hindi tokens. The model is based on transformer-based decoder-only (LLaMA-3) architecture. link​
4. STORM is a research preview from the Stanford Open Virtual Assistant Lab link​
5. Google’s Learn About: conversational learning companion, link​

The Cloud: the backbone of the AI revolution

• Oracle AI Infrastructure helps accelerate assessment times of explosion damage on urban terrains link​
• The Three Computer Solution: Powering the Next Wave of AI Robotics link​

Gen AI Use Case of the Week:

Streamline medical documentation with AI-powered transcription. Large Language Models (LLMs) can transform doctor-patient conversations into accurate, structured medical records in real-time, reducing manual workload, minimizing errors, and enhancing patient care efficiency.

To Read the detailed use case, link here:

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Chief AI Officer (CAIO) Corner:

The role of Chief Artificial Intelligence Officer (CAIO) has become increasingly vital across various sectors, including government and private enterprises. From this week onward, we will have the CAIO corner covering specific actions, learning the latest news, what is happening, and how this role is leading the AI revolution.

Let’s start this week with what responsibilities they have.

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Favorite Tip Of The Week:

Here’s my favorite resource of the week.

​Deloitte’s recent analysis highlights the transformative potential of generative AI across various industries. The report indicates that nearly 80% of business and IT leaders anticipate significant industry changes due to generative AI within the next three years. Private investments in this technology have surged, rising from approximately $3 billion in 2022 to $25 billion in 2023, with projections reaching $40 billion in 2024 and over $150 billion by 2027. This rapid adoption underscores the urgency for organizations to integrate generative AI into their operations to enhance productivity and maintain a competitive edge. Deloitte has outlined Four futures of generative AI in the enterprise: Scenario planning for strategic resilience and adaptability.

Source: Four futures of generative AI in the enterprise: Scenario planning for strategic resilience and adaptability

Potential of AI

• ​Gartner Predicts 40% of Generative AI Solutions Will Be Multimodal By 2027. “Multimodal GenAI is one of two technologies identified in the 2024 Gartner Hype Cycle for Generative AI, where early adoption has potential to lead to notable competitive advantage and time-to-market benefits. Along with open-source large language models (LLMs), both technologies have high impact potential on organizations within the next five years.”

Things to Know…

The European Union’s Artificial Intelligence Act (AI Act) establishes a comprehensive legal framework to regulate AI technologies to ensure safety, fundamental rights, and ethical standards. It classifies AI systems into unacceptable, high, limited, and minimal.

risk categories, imposing obligations accordingly. High-risk applications, such as those in critical infrastructure and law enforcement, face stringent requirements, while certain AI practices are prohibited. The Act also mandates transparency for AI-generated content and introduces governance structures for enforcement. link​

The Opportunity…

Podcast:

• This week’s Open Tech Talks episode 148 is “Strategic AI Adoption for Businesses with Nick Jain.” The CEO, Idea Scale

Apple | Spotify | Amazon Music ​

Courses to attend:

• ​Introducing Multimodal Llama 3.2 from Deep Learning

Events:

• ​Future of AI, 6-7 November 2024, London, UK
• ​9th Middle East Enterprise AI & Analytics Forum, Nov 7, 2024, Dubai, UAE
• ​Global AI Show, Dec 12, 13, 2024, Dubai, UAE

Tech and Tools…

• ​Docling parses documents and exports them to the desired format with ease and speed
• ​kotaemon : An open-source, clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind

Data Sets…

• The datasets are crucial for any AI or ML project, and lacking a reliable dataset presents significant challenges. I found an article about synthetic datasets detailing their benefits and creation methods. You can read “Synthetic data: save money, time and carbon with open source.”​

Other Technology News

Want to stay updated on the latest information in the field of Information Technology? Here’s what you should know:

• Why multi-agent AI tackles complexities LLMs can’t, as reported by VentureBeat​
• Quantum Machines and Nvidia use machine learning to get closer to an error-corrected quantum computer, as reported by TechCrunch​

Join a mini email course on Generative AI …

​Introduction to Generative AI for Newbies​

Earlier week’s Post:

And that’s a wrap!

Thank you, as always, for taking the time to read.

I’d love to hear your thoughts. Hit reply and let me know what you find most valuable this week! Your feedback means a lot.

Until next week,

Kashif Manzoor