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- 2024-12-22
2024-12-22
LangChain state of AI in 2024, Meta Video WaterMarking

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LangChain - state of AI in 2024
Meta VideoSeal - verifying video authenticity
One line of Code to Load HuggingFace Dataset
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Bringing insights into the latest trends and breakthroughs in AI
LangChain
State of AI in 2024
Synopsis
LangChain's "State of AI 2024" report provides an in-depth analysis of the current artificial intelligence landscape, announcing that 30k users per month have been signing up for LangSmith. The report underscores how these developments are reshaping user experiences, driving innovation, and setting the stage for future breakthroughs in AI technology.
Core Observations
Top 10 AI providers: OpenAI, Ollama, Anthropic, Azure OpenAI, and Groc, …
Top 10 Vector Store Providers : Chrome, Faiss, PineCone, PgVector (PostGres), Qdrant, …
Usage: Python and Javascript are two main users, with 85% python.
AIAgents: this is ranked #1 focus in AI right now.
LLM as a Judge: They ranked the top 10 metric.
Broader Context
The insights presented in LangChain's "State of AI 2024" reflect the dynamic and rapidly evolving nature of AI this year 🤯. A key note is the growing influence of Javascript - 15% and growing. The data provided for top providers of AI models and vector store match sentiments of the developer community and are recorded with log traces in LangGraph. LangChain claims that LangGraph is the standard logging and tracing tool for AI apps in the market right now.
Meta
Video Watermarking for AI Integrity
Synopsis
Meta has introduced VideoSeal, a groundbreaking technology for video watermarking that ensures the authenticity and traceability of AI-generated videos. This innovation addresses concerns about misuse and misinformation in AI-generated media, offering a secure way to identify and verify content origins.
Core Observations
VideoSeal embeds invisible verifiable watermarks into their videos.
Fraud Protection: crucial in an era where deepfake incidents have quadrupled, now accounting for 7% of global fraud cases.
Resilience to Manipulation: The tool's watermarks remain detectable even after common video edits such as blurring, cropping, or compression.
Open-Source Accessibility: under the MIT license, the code is complete with research papers, training code, and inference code.
Broader Context
The proliferation of AI-generated content and sophisticated video editing tools has made it both important and challenging to moderate digital platforms. VideoSeal addresses these challenges by embedding imperceptible signals into videos, allowing for identification. Basically, online news will have a tool to verify its authenticity, so, we will now find it hard to give Jackie Chain Trump’s hairstyle.
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⚙️ BUILDERS BYTES ⚙️
Informing builders of latest technologies and how to use them
TimeScale - PostGres
Load HuggingFace Dataset - 1 line of Code
What will you learn today?
In a few lines, you can load a HuggingFace dataset into your PostGres database. With support like PGAI that can be set up in a single SQL line, this is extremely powerful.
Key Takeaways
Timescale: Timescale plugin PGAI auto loads datasets into SQL. Learn more about their embeddings here (video).
HuggingFace Support: built in native support for huggingFace. Nothing more to say really, just start pulling data.
Simple: added a third point because we seem to like to think in 3s. It’s so simple a 3rd point isn’t needed, so I wanted to make a point by saying that.
First, let’s get set up
# start the docker container
docker run -d --name pgai -p 5432:5432 \
-v pg-data:/home/postgres/pgdata/data \
-e POSTGRES_PASSWORD=password timescale/timescaledb-ha:pg17
# install PGAI in database
docker exec -it pgai psql -c "CREATE EXTENSION ai CASCADE;"
# go into the container
docker exec -it pgai psql
Now, let’s create a table from your dataset
select ai.load_dataset('rajpurkar/squad', table_name => 'squad');
Ya, that’s it! You just loaded a training set into your database in a single line of code.
SQuAD Training set
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
Check out our repo here with demos released every newsletter.
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