How NotebookLM Works:

Unlike general-purpose chatbots that draw on vast, public datasets, NotebookLM focuses on your uploaded files. You can add PDFs, Google Docs, or link directly to specific websites. NotebookLM then creates a personalized AI model based on this information, allowing for more focused and relevant responses. This personalized approach is a key differentiator, ensuring that the AI’s understanding is grounded in your specific research materials.

AI-powered coding assistants have become increasingly popular, promising to boost developer productivity and streamline the coding process. Tools like GitHub Copilot and Cursor offer impressive capabilities, generating code snippets, suggesting completions, and even creating entire functions based on prompts. However, alongside these benefits come potential pitfalls that developers need to be aware of, as highlighted in recent discussions on the Cursor forum.

The Allure of AI Assistance:

The appeal of AI coding assistants is undeniable. They can:

Prompt engineering is hard. If you’re from a programming background you may find it very odd that all of a sudden you’re trying to get a computer to do something by bribing it (“I’ll give you a 25% tip”), encouring it (“You’re a leading expert on how to prompt”) and plain just nagging it (“Do not”).

Let’s be honest, prompt engineering can feel like a dark art. You spend hours tweaking words, adding clauses, and praying to the AI gods for a decent output. It’s tedious, time-consuming, and often feels more like trial-and-error than actual engineering. If you’re tired of wrestling with prompts, I have good news: DSPy is here to change the game.

Large language models (LLMs) have become synonymous with cutting-edge AI, capable of generating realistic text, translating languages, and writing different kinds of creative content. But what if you could leverage this power on your own machine, with complete privacy and control?

Running LLMs locally might seem daunting, but it’s becoming increasingly accessible. Here’s a breakdown of why you might consider it, and how it’s easier than you think:

The Allure of Local LLMs

Artificial intelligence (AI) is rapidly transforming our world, but it comes with a hidden cost: carbon emissions.

According to a recent study by the Allen Institute for AI, training a single large language model can produce up to 550 tons of carbon dioxide, equivalent to the emissions of five cars over their lifetime.

This is because AI training requires massive amounts of computing power, which in turn relies on electricity generated by fossil fuels.

Here is an example of how to use BigQuery ML on a public dataset to create a logistic regression model to predict whether a user will click on an ad:

# Import the BigQuery ML library
from google.cloud import bigquery
from google.cloud.bigquery import Model

# Get the dataset and table
dataset = bigquery.Dataset("bigquery-public-data.samples.churn")
table = dataset.table("churn")

# Create a model
model = Model('my_model',
              model_type='logistic_regression',
              input_label_column='churn',
              input_features_columns=['tenure', 'contract', 'monthly_charges'])

# Train the model
model.train(table)

# Make a prediction
prediction = model.predict(STRUCT(tenure=12, contract='month-to-month', monthly_charges=100))

# Print the prediction
print(prediction)

This code will first create a logistic regression model named my_model. The model will be trained on a public dataset called bigquery-public-data.samples.churn. The churn dataset contains data about customer churn, with the churn column indicating whether a customer has churned. The tenure, contract, and monthly_charges columns are the input features columns.

BigQuery ML and Vertex AI Generative AI (GenAI) are both machine learning (ML) services that can be used to build and deploy ML models. However, there are some key differences between the two services.

• BigQuery ML: BigQuery ML is a fully managed ML service that allows you to build and deploy ML models without having to manage any infrastructure. BigQuery ML uses the same machine learning algorithms as Vertex AI, but it does not offer the same level of flexibility or control.
• Vertex AI Generative AI: Vertex AI Generative AI is a managed ML service that offers a wider range of generative AI models than BigQuery ML. Vertex AI Generative AI also offers more flexibility and control over the ML model training process.

If you are looking for a fully managed ML service that is easy to use, then BigQuery ML is a good option. If you need more flexibility and control over the ML model training process, then Vertex AI Generative AI is a better option.