Emerging Tech & AI - Third Edition

Welcome to the Third Edition of?the Emerging Tech & AI Newsletter!

The past two editions can be found here

First Edition

Second Edition

This newsletter's goal is to help you stay up-to-date on the latest trends in emerging technologies. Subscribe to the newsletter today and never miss a beat!

Here's what you can expect in each issue of the Emerging Tech & AI Newsletter:

• A summary of the top AI / emerging technology news from the past week
• Introductory details of a key topic in AI or any other emerging technology (We discuss Retrieval-augmented generation (RAG) ?this week)
• A primer on an emerging technology (We review Homomorphic Encryption this week)
• Examples of how AI tools are being used (We explore AI in Marketing Technology? )

Let's explore the future of technology together!

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Last Week in AI/Emerging Tech

The field of AI is experiencing rapid and continuous progress in various areas. Some of the notable advancements and trends from the last week include:

Big Tech in AI:

1. Amazon One Palm Scanning: ?A neural network learned from images of millions of artificial hands to achieve accuracy higher than scanning two irises.
2. Amazon announced generative AI for SMBs in India.
3. Meta released FACET: Fairness in Computer Vision Evaluation Benchmark.
4. You can now request Meta to stop using personal data to train generative AI.
5. Tesla Launched New $300M AI Cluster for Advanced Computation.
6. Microsoft Infuses AI With Human-Like Reasoning Via an “Algorithm of Thoughts” .
7. Microsoft wants to make an AI backpack.
8. General Motors and Google Cloud are doubling down on their AI partnership .
9. Google introduced new generative AI in India .

Funding & VC Landscape:

1. OpenAI-backed language learning app Speak raised $16M to expand to the US.
2. Martian Lawyers Club raised $2.2M for AI-based game personalization tech.
3. Generative AI startup AI21 Labs landed $155M at a $1.4B valuation.

Other AI News:

1. AI 'nose' predicts smells from molecular structures.
2. India’s first AI school launched in Kerala.
3. Walmart fully embraces generative AI .
4. Call Of Duty used to listen out for hate speech during online matches.

For introductory details, last week we reviewed Healthcare in Metaverse . This week we will explore Retrieval-Augmented Generation (RAG).

Retrieval-Augmented Generation (RAG) - An Introduction

Retrieval-augmented generation (RAG) is a type of language generation model that combines pre-trained parametric and non-parametric memory for language generation. The parametric memory is a large language model (LLM), such as GPT-3, that has been trained on a massive dataset of text and code. The non-parametric memory is a large knowledge base, such as Wikipedia, that has been indexed and stored in a database.

Retrieval Augmented Generation (RAG) can be used to retrieve data from outside a foundation model and augment your prompts by adding the relevant retrieved data in context.

RAG is effective for a variety of tasks, including question-answering, summarization, and creative writing.

Here's an animated example of how a RAG works

Here are some specific examples of how RAG has been used:

• To generate more factual and informative responses to questions. For example, RAG has been used to generate more accurate and comprehensive answers to questions about factual topics, such as the history of the United States or the scientific principles of climate change.
• To create more creative and diverse text formats. For example, RAG has been used to generate different creative text formats, such as poems, code, scripts, musical pieces, emails, letters, etc.
• To improve the performance of other language models. For example, RAG has been used to improve the performance of question-answering models on benchmark datasets.

Here are some specific examples of how companies are using retrieval-augmented generation (RAG):

• Google is using RAG to improve the performance of its search engine. RAG is being used to generate more relevant and informative search results by retrieving and incorporating information from external knowledge bases.
• IBM is using RAG to power its customer service chatbots. RAG is being used to help chatbots provide more accurate and helpful answers to customer questions by retrieving and incorporating information from knowledge bases.
• Salesforce is using RAG to create more personalized sales experiences. RAG is being used to generate personalized sales pitches and recommendations by retrieving and incorporating information about the customer's needs and interests.
• Amazon is using RAG to improve its product recommendations. RAG is being used to generate more relevant product recommendations by retrieving and incorporating information about the customer's purchase history and browsing behaviour.

These are just a few examples of how companies are using RAG. As RAG continues to develop, it is likely to be used in even more applications.

Here are some other potential applications of RAG:

• Financial services: RAG could be used to generate financial reports, create investment strategies, and provide customer support.
• Healthcare: RAG could be used to generate medical reports, diagnose diseases, and develop treatment plans.
• Legal: RAG could be used to generate legal documents, research case law, and provide legal advice.
• Media: RAG could be used to generate news articles, write scripts, and create marketing materials.
• Retail: RAG could be used to generate product descriptions, recommend products to customers, and provide customer service.

RAG allows businesses to achieve customized solutions while maintaining data relevance and optimizing costs. By adopting RAG, companies can use the reasoning capabilities of LLMs, utilizing their existing models to process and generate responses based on new data. RAG facilitates periodic data updates without the need for fine-tuning, thereby streamlining the integration of LLMs into businesses. Enabling an LLM to access custom data involves the following steps:

For our next section, we will review Homomorphic Encryption

A Primer on Homomorphic Encryption

Homomorphic encryption is a type of encryption that allows computations to be performed on encrypted data without decrypting it first. This means that data can be kept secure while still being able to be analyzed.

Homomorphic encryption comes in various forms, each with its own properties and use cases. The main types of homomorphic encryption include:

• Partially Homomorphic Encryption (PHE)
• Somewhat Homomorphic Encryption (SHE)
• Fully Homomorphic Encryption (FHE)
• Leveled Homomorphic Encryption
• Ring-LWE-based Homomorphic Encryption
• Lattice-based Homomorphic Encryption

Homomorphic encryption has numerous potential applications, especially in scenarios where data privacy is of utmost importance. Some of these applications include:

1. Secure Cloud Computing: Users can perform computations on their sensitive data stored in the cloud without revealing the data's contents to the cloud provider.
2. Secure Data Sharing: Multiple parties can perform collaborative computations on encrypted data without revealing their inputs to each other.
3. Privacy-Preserving Machine Learning: It allows for machine learning models to be trained on encrypted data, preserving the privacy of the data source.
4. Secure Data Analysis: Data analytics and processing can be performed on encrypted data, ensuring the privacy of sensitive information.

However, it's worth noting that homomorphic encryption comes with some trade-offs, such as increased computational complexity and slower performance compared to traditional non-encrypted computations. Researchers and practitioners are continually working on improving the efficiency and usability of homomorphic encryption techniques to make them more practical for real-world applications.

Want to know more? Let us know in the comments and we will write a detailed blog at Technologia

For our next section, let's review how is AI being used in Marketing Technology

AI in Marketing Technology

Martech, short for "marketing technology," refers to the use of technology and software platforms to support and enhance marketing efforts and campaigns. Artificial Intelligence (AI) is being increasingly utilized in marketing to enhance various aspects of campaigns, customer engagement, and data-driven decision-making. Here are some ways AI is being used in marketing:

1. Personalization: AI algorithms analyze vast amounts of customer data to create personalized experiences. This includes tailoring content, product recommendations, and marketing messages to individual preferences and behaviours, resulting in higher conversion rates and customer satisfaction.
2. Predictive Analytics: AI-powered predictive analytics can forecast future trends and customer behaviours based on historical data. Marketers use these insights to make data-driven decisions, optimize marketing strategies, and allocate resources more effectively.
3. Content Generation: AI-powered tools can automate content creation, such as generating product descriptions, news articles, and social media posts. This can save time and resources for marketers while maintaining quality.
4. Email Marketing: AI can optimize email marketing campaigns by analyzing user behaviour, segmenting email lists, and sending personalized content at the right times. This leads to improved open and click-through rates.
5. Ad Targeting: AI algorithms can analyze user data and behaviour to target ads more precisely. Programmatic advertising uses AI to automate ad buying, making it more efficient and cost-effective.
6. Customer Segmentation: AI can segment customers into distinct groups based on their behaviour, preferences, and demographics. This enables marketers to create targeted campaigns that resonate with specific audiences.
7. Social Media Management: AI tools can analyze social media data to identify trends, sentiment, and engagement patterns. Marketers use this information to refine their social media strategies and content.
8. Marketing Automation: AI-driven marketing automation platforms can automate repetitive tasks, such as lead nurturing, drip email campaigns, and social media posting. This improves efficiency and frees up time for more strategic activities.
9. A/B Testing and Optimization: AI can accelerate A/B testing by quickly analyzing data and determining which variations of marketing content perform best. This allows marketers to make real-time adjustments to improve results.
10. Fraud Detection: AI can identify and prevent ad fraud by analyzing click patterns and user behavior to distinguish between legitimate and fraudulent clicks or impressions.
11. Content Curation: AI algorithms can curate and recommend relevant content to users, keeping them engaged and informed, which is particularly useful in content marketing.

Want to know more? Let us know in the comments and we will write a detailed blog at Technologia

References:

1. Retrieval Augmented Generation using Azure Machine Learning prompt flow
2. Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing models
3. RAG - Amazon

What content would you be interested to see in future editions? Do let us know in the comments!

Disclosure: Some content in RAG, Homomorphic Encryption, and AI in Marketing Technology sections was taken from Google Bard and Chat GPT.

Thanks for reading. See you next week!