Apple GPT

Today, it's a big day for Apple AI space.

Apple is developing artificial intelligence tools to challenge OpenAI, Google and others, according to a new report from Bloomberg’s Mark Gurman. The tech giant has created a chatbot that some engineers are internally referring to as “Apple GPT.” 

The report says Apple has built its own framework, codenamed “Ajax” to create large language models, which are AI-based systems that power offerings like Open AI’s ChatGPT and Google’s Bard. 

Apple has yet to determine a strategy for releasing the technology to consumers but is reportedly aiming to make a significant AI-related announcement next year.

Vector db usage

As generative Artificial Intelligence is surging in recent times, vector databases are in high demands in this era.  It is represented in the industry newsletter: TheAiEdge.io

The purpose of vector db is to index the data with vectors that relate to that data.  Hierarchical Navigable Small World (HNSW) is one of the most efficient ways to build indexes for vector databases.  It builds a similarity graph and traverse that graph to find the nodes, which are the closest to a query vector.

Counterfactual variants

Another interesting research paper on "Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks." 

Ref: https://arxiv.org/abs/2307.02477

It briefs the various language models to show impressive performance on a wide variety of tasks.  It covers transferrable reasoning skills by introducing counterfactual variants of familiar tasks. Some of use cases like drawing & music tasks, are attached.

It has been observed a consistent and substantial performance degradation on these counterfactual tasks, across LMs (GPT-4, GPT-3.5, Claude, PaLM-2). 

LLMs Survey

This week, read an interesting research paper on LLM - "A Survey of Large Language Models"

It reviewed the recent progress of Large Language Models (LLMs) with size larger than 10B.

This research covers 4 important aspects of LLMs

1. Pre-training
2. Adaptation tuning
3. Utilization
4. Evaluation

More details are available at

Paper ref: https://paperswithcode.com/paper/a-survey-of-large-language-models

Github code ref: https://github.com/RUCAIBox/LLMSurvey

AI Landscape

Artificial Intelligence is an emerging technology across the world. 

Recently, I came across Top-50 platform/frameworks from Twitter feed.  They are foundational aspects of Generative AI application landscape.  

Being software engineer, it is essential to be aware of these disturptive technologies.

LangChain

In a nutshell, LangChain connects the industry popular Large Language Models such as OpenAI and HuggingFace Hub, to few external sources like external files, Database, Google, Wikipedia and Wolfram. 

It provides abstractions (chains and agents) and tools (prompt templates, memory, document loaders, output parsers) 

The core objective of Langchain, is to orchestrate LLM workflow/pipeline by interfacing between text input and output. 

Ref: https://python.langchain.com/