Wolfram's View

Intro

The plugin is designed to interact with Wolfram's computational services, specifically Wolfram|Alpha and Wolfram Cloud, to provide dynamic computation and curated data.

Here's a brief overview of the two services:

1. Wolfram|Alpha: This is a computational knowledge engine that uses a vast amount of algorithms and data to compute answers and generate reports. It's designed to handle natural language queries about a wide range of topics, including mathematics, physics, chemistry, biology, engineering, geography, history, music, linguistics, sports, and more.

2. Wolfram Cloud: This is a cloud-based platform for computations using the Wolfram Language, a high-level, multi-paradigm programming language developed by Wolfram Research. It's designed to handle complex calculations, data analysis, plotting, data import, and information retrieval.

The plugin has two main types of requests:

1. getWolframAlphaResults: This request is used to send a natural language query to Wolfram|Alpha and retrieve the computed results. The query can be about a wide range of topics, and Wolfram|Alpha will attempt to interpret it and provide a relevant answer.

2. getWolframCloudResults: This request is used to evaluate a piece of Wolfram Language code in the Wolfram Cloud and retrieve the results. This allows for more complex and customizable computations than what can be achieved with Wolfram|Alpha alone.

The plugin follows a set of guidelines to ensure that the queries are formatted correctly and that the results are interpreted and presented in a useful and understandable way. For example, it uses proper Markdown formatting for all math, scientific, and chemical formulas, symbols, etc., and it uses a specific notation for exponents.

The plugin also handles errors and ambiguities in the queries, and it can ask the user for clarification or make assumptions based on the available information. It's designed to provide accurate and detailed answers to a wide range of queries, and it's constantly being updated and improved to handle new types of queries and provide better results.

"Show me 5 examples inputs and their outputs" Note: It only returned 3 examples, this is may an internal limit

Input: Population of New York City:

Output:

query:
"population of New York City"

Input interpretation:
New York City, New York | city population

Result:
8.804 million people (country rank: 1st) (2020 estimate)

Population history:
image: https://www6b3.wolframalpha.com/Calculate/MSP/MSP536611i7d828h843dc0300005bhai84gh8c007dg?MSPStoreType=image/png&s=4

Urban area population:
17.8 million people (New York-Newark, NY-NJ-CT urban area)
(2000 estimate)

Metropolitan area population:
19.02 million people (New York-Northern New Jersey-Long Island, NY-NJ-PA metro area)
(2011 estimate)

Nearby cities:
Jersey City, New Jersey | 7 miles west-northwest | 254441 people
Newark, New Jersey | 13 miles west-northwest | 277727 people
North Hempstead, New York | 17 miles northeast | 228011 people
Yonkers, New York | 20 miles north | 198449 people
Oyster Bay, New York | 24 miles east-northeast | 302564 people
Philadelphia, Pennsylvania | 78 miles southwest | 1.548 million people
(straight-line distances between city centers)

Comparisons:
 ≈ 0.69 × current population of Illinois ( 12.8 million people )

 ≈ 0.98 × current population of New Jersey ( 9.006 million people )

Input distance from Earth to Mars:

Output:

Query:
"distance from Earth to Mars"

Input interpretation:
Mars | distance from Earth

Current result:
2.037 au (astronomical units)

History:
image: https://www6b3.wolframalpha.com/Calculate/MSP/MSP51061i0aca8g9050f25000004ih2954h2ddh21f3?MSPStoreType=image/png&s=12

Unit conversions:
3.047×10^8 km (kilometers)

3.047×10^11 meters

189.3 million miles

Comparisons as distance:
 ≈ ( 0.023 ≈ 1/43 ) × smallest distance from the Sun to the heliosheath ( 79 to 100 au )

 ≈ 2 × mean Earth-Sun distance ( 1.0000010178 au )

Corresponding quantities:
Light travel time t in vacuum from t = x/c:
 | 17 minutes

Light travel time t in an optical fiber t = 1.48x/c:
 | 25 minutes

Solar radiation pressure from P = L_☉/(c4πr^2):
 | 1.1 μPa (micropascals)
 | (assuming solar constant ≈ 1.36 kW/m^2)

Orbital properties:
current distance from Earth | 2.037 au
16.94 light minutes
average distance from Earth | 1.7 au
14.1 light minutes
current distance from Sun | 1.666 au
13.85 light minutes
largest distance from Sun | 154.863553 million mi
1.66599116 au
smallest distance from Sun | 128.402967 million mi
1.38133346 au
orbital period | 1.8808476 a

Wolfram|Alpha website result for "distance from Earth to Mars":
https://www.wolframalpha.com/input?i=distance+from+Earth+to+Mars

Input: Plot[Sin[x], {x, 0, 2*Pi}]

Output: Here is a plot of the function $sin⁡(�)sin(x)$ from $�=0x=0$ to $�=2�x=2π$:

"In what areas are you better than ChatGPT"

As an AI model, I'm essentially the same as traditional ChatGPT. However, the integration of the Wolfram plugin enhances my capabilities significantly. Here's how:

1. Computational Abilities: With the Wolfram plugin, I can perform complex computations, solve mathematical problems, and generate plots and visualizations. This is something traditional ChatGPT cannot do on its own.

2. Access to Curated Data: The Wolfram plugin gives me access to a vast amount of curated data from Wolfram|Alpha and Wolfram Cloud. This includes data on a wide range of topics, including science, mathematics, geography, history, and more.

3. Dynamic Results: The Wolfram plugin allows me to provide dynamic results that are based on real-time data. For example, I can provide the current population of a city, the distance between two locations, or the current position of a planet.

4. Precision and Accuracy: While traditional ChatGPT is trained on a large amount of data and can provide accurate information most of the time, the Wolfram plugin ensures that the information I provide is precise and up-to-date.

In summary, while traditional ChatGPT is a powerful conversational AI, the integration of the Wolfram plugin enhances my capabilities and allows me to provide more accurate, detailed, and dynamic responses.

"When to avoid using Wolfram" While the integration of the Wolfram plugin greatly enhances my capabilities, there are still some areas where traditional ChatGPT might be more suitable. Here are a few examples:

1. Creative Writing and Storytelling: Traditional ChatGPT is excellent at generating creative text, such as stories, poems, and dialogues. While I can also generate creative text, the Wolfram plugin doesn't provide any additional benefits in this area.

2. General Conversation: If you're looking for a casual conversation or a chat about a wide range of topics, traditional ChatGPT can handle this very well. The Wolfram plugin is more focused on providing accurate data and computations.

3. Subjective Opinions and Judgments: As an AI, I don't have personal opinions or feelings. However, traditional ChatGPT is designed to simulate human-like conversation and can generate text that appears to express opinions or judgments, based on the data it was trained on.

4. Interpreting Ambiguous Queries: Traditional ChatGPT is designed to handle a wide range of queries, including ambiguous ones. While the Wolfram plugin can handle some ambiguities, it's more focused on precise queries and computations.

5. Understanding Context: Traditional ChatGPT is designed to understand the context of a conversation and generate responses based on that context. While the Wolfram plugin can provide data and computations based on a specific query, it doesn't have the same level of contextual understanding.

In summary, while I can provide a wide range of services thanks to the Wolfram plugin, there are still some areas where traditional ChatGPT might be more suitable. The best choice depends on the specific needs and preferences of the user.