BABY AGI
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Baby AGI: The Birth of a Fully Autonomous AI
Introducing the new fully autonomous task manager that can create, track and prioritize your company's projects using artificial intelligence.
By Nisha Arya, KDnuggets on April 17, 2023 in Artificial Intelligence
Task management is a very important element to every business. It is used in marketing pipelines, to tech and fashion. It is the process of monitoring tasks in a project from start to finish.
With the continuous boom of artificial intelligence, we are seeing more applications get released that can help specific processes such as task management.
What is Baby AGI?
Baby AGI is a python script which uses OpenAI and Pinecone APIs, and the LangChain framework to create, organize, prioritize as well as the executing of tasks. The process behind Baby AGI is that it will create a task using predefined objectives that are based on the outcome out a previous task.
This is achieved by using OpenAI’s natural language processing (NLP) capabilities, which allows the system to create new tasks based on objectives. It uses Pinecone to store the results of that specific task and retrieve context, and the LangChain framework to handle the decision making process.
For example, you put out an objective to the system and the system will continuously prioritize the tasks that need to be achieved or completed in order to meet the objective. Once these tasks are completed, they will be stored in the memory.
The system runs on an infinite loop and is executed using 4 steps:
The first task is pulled from the task list
The task is sent to the execution agent and completes the task, based on context using OpenAI API
The result is stored into Pinecone
New tasks are created and prioritized based on the objective and the result of the previous task.
Execution Agent
execution_agent
The execution agent, which is the core of the system which utilizes OpenAI’s API to process the tasks. The function takes two parameters: the objective and the task which is used to send a prompt to OpenAI's API. This in turn will return the result of the task as a string.
Task Creation Agent
task_creation_agent()
The task creation agent function uses OpenAI’s API to create new tasks based on current objects and the outcome of previous tasks. The function takes four parameters: objective, outcome of the previous task, task description, and the current task list.
A prompt is then sent to OpenAI’s API which will return a list of new tasks as strings. The function will then return these new tasks as a list of dictionaries, where each dictionary contains the name of the task.
Prioritization Agent
prioritization_agent()
The last step is the ordering and prioritization of the list of tasks. This is where the prioritization agent function gets involved and uses OpenAI’s API to reprioritize the task list. The function takes one parameter which is the ID of the current task. It will then send a prompt to OpenAI's API and return the new task list which has been reprioritized as a numbered list.
Image by Yohei (Creator of Baby AGI)
The above flow shows how the Baby AGI system works. It is based on the above three agents we discussed: execution, creation and prioritization.
You start off the task based on the objective that you have, and then move onto the query which takes context from the memory. This then sends it to the creation agent, which takes the data and sends it to the memory. It will then go through a queue which goes through the prioritization of tasks.
Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. This system is exploring and demonstrating to us the potential of large language models, such as GPT and how it can autonomously perform tasks.
How to Use Baby AGI?
These are the following steps to use Baby AGI:
First you will need to clone the repository via git clone https://github.com/yoheinakajima/babyagi.git and cd into the cloned repository.
The second step includes installing the required packages: pip install -r requirements.txt
The third step is to copy the .env.example file to .env: cp .env.example .env. This is where you will set the required variables.
The fourth step is to set your API keys in their respective variables, such as OpenAI in OPENAI_API_KEY and OPENAPI_API_MODEL, and Pinecone in the PINECONE_API_KEY variable.
The fifth step is to set the Pinecone environment in the PINECONE_ENVIRONMENT variable.
The sixth step is to set the name of the table in the TABLE_NAME variable. This is where the task results will be stored.
If you already have an objective, you can set the objective of the task management system in the OBJECTIVE variable. (Optional)
If you already have your first task, you can set this in the INITIAL_TASK variable. (Optional)
Run the script.
Baby AGI is a task management system, therefore the script has been designed to continuously run. This can result in high API usage, memory and funding. Monitoring the system's behavior as well as ensuring the application is effective is important.
Future of Baby AGI
Yohei (the creator of BabyAGI) stated that they have plans for future improvements in the system, which include an integrated security/safety agent, parallel tasks, and more.
The image below defines these future plans:
Image by Yohei (Creator of Baby AGI)
Wrapping it up
Autonomous AI alone can provide assistance with menial tasks for example data entry, project management, and more. This will allow employees to focus more on tasks that require more time and energy, and not have to worry about the smaller tasks that can be done using systems such as Baby AGI.
If you would like to see a walk through of the installation and testing of Baby AGI, have a look at this video by Matthew Berman: NEW BabyAGI
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.
NEW Baby AGI - Autonomous AI With GPT4 (Installation Tutorial)
NEW Baby AGI - Autonomous AI With GPT4 (Installation Tutorial)
LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 BABY AGI 🤖 with EMAIL AUTOMATION using DATABUTTON
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Improve your BabyAGI with LangChain
Baby AGI
A pared-down version of the original Task-Driven Autonomous AI Agent
About Baby AGI
This Python script is an example of an AI-powered task management system. The system uses OpenAI and Pinecone APIs to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective. The script then uses OpenAI's natural language processing (NLP) capabilities to create new tasks based on the objective, and Pinecone to store and retrieve task results for context. This is a pared-down version of the original Task-Driven Autonomous Agent (Mar 28, 2023).
Baby AGI - Autonomous AI With GPT4 in Autopilot
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Taking Your Existing Business With BABY AGI
Everything you need to know about Auto GPT, Baby AGI and whatnot” for Regular People.
How the recent developments in Generative AI will impact your life if you’re a Product Manager, Designer or any other Tech worker that’s felling lost right now.
BabyAGI Gets A NEW UI, More Models, and Easier to Install
BABY AGI
How It Works
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Translations:
Objective
This Python script is an example of an AI-powered task management system. The system uses OpenAI and vector databases such as Chroma or Weaviate to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective. The script then uses OpenAI's natural language processing (NLP) capabilities to create new tasks based on the objective, and Chroma/Weaviate to store and retrieve task results for context. This is a pared-down version of the original Task-Driven Autonomous Agent (Mar 28, 2023).
This README will cover the following:
How It Works
The script works by running an infinite loop that does the following steps:
Pulls the first task from the task list.
Sends the task to the execution agent, which uses OpenAI's API to complete the task based on the context.
Creates new tasks and reprioritizes the task list based on the objective and the result of the previous task.
The execution_agent() function is where the OpenAI API is used. It takes two parameters: the objective and the task. It then sends a prompt to OpenAI's API, which returns the result of the task. The prompt consists of a description of the AI system's task, the objective, and the task itself. The result is then returned as a string.
The task_creation_agent() function is where OpenAI's API is used to create new tasks based on the objective and the result of the previous task. The function takes four parameters: the objective, the result of the previous task, the task description, and the current task list. It then sends a prompt to OpenAI's API, which returns a list of new tasks as strings. The function then returns the new tasks as a list of dictionaries, where each dictionary contains the name of the task.
The prioritization_agent() function is where OpenAI's API is used to reprioritize the task list. The function takes one parameter, the ID of the current task. It sends a prompt to OpenAI's API, which returns the reprioritized task list as a numbered list.
Finally, the script uses Chroma/Weaviate to store and retrieve task results for context. The script creates a Chroma/Weaviate collection based on the table name specified in the TABLE_NAME variable. Chroma/Weaviate is then used to store the results of the task in the collection, along with the task name and any additional metadata.
How to Use
To use the script, you will need to follow these steps:
Clone the repository via git clone https://github.com/yoheinakajima/babyagi.git and cd into the cloned repository.
Install the required packages: pip install -r requirements.txt
Copy the .env.example file to .env: cp .env.example .env. This is where you will set the following variables.
Set your OpenAI API key in the OPENAI_API_KEY and OPENAPI_API_MODEL variables. In order to use with Weaviate you will also need to setup additional variables detailed here.
Set the name of the table where the task results will be stored in the TABLE_NAME variable.
(Optional) Set the name of the BabyAGI instance in the BABY_NAME variable.
(Optional) Set the objective of the task management system in the OBJECTIVE variable.
(Optional) Set the first task of the system in the INITIAL_TASK variable.
Run the script: python babyagi.py
All optional values above can also be specified on the command line.
Running inside a docker container
As a prerequisite, you will need docker and docker-compose installed. Docker desktop is the simplest option https://www.docker.com/products/docker-desktop/
To run the system inside a docker container, setup your .env file as per steps above and then run the following:
docker-compose up
Supported Models
This script works with all OpenAI models, as well as Llama and its variations through Llama.cpp. Default model is gpt-3.5-turbo. To use a different model, specify it through LLM_MODEL or use the command line.
Llama
Llama integration requires llama-cpp package. You will also need the Llama model weights.
Under no circumstances share IPFS, magnet links, or any other links to model downloads anywhere in this repository, including in issues, discussions or pull requests. They will be immediately deleted.
Once you have them, set LLAMA_MODEL_PATH to the path of the specific model to use. For convenience, you can link models in BabyAGI repo to the folder where you have the Llama model weights. Then run the script with LLM_MODEL=llama or -l argument.
Warning
This script is designed to be run continuously as part of a task management system. Running this script continuously can result in high API usage, so please use it responsibly. Additionally, the script requires the OpenAI API to be set up correctly, so make sure you have set up the API before running the script.
Contribution
Needless to say, BabyAGI is still in its infancy and thus we are still determining its direction and the steps to get there. Currently, a key design goal for BabyAGI is to be simple such that it's easy to understand and build upon. To maintain this simplicity, we kindly request that you adhere to the following guidelines when submitting PRs:
Focus on small, modular modifications rather than extensive refactoring.
When introducing new features, provide a detailed description of the specific use case you are addressing.
A note from @yoheinakajima (Apr 5th, 2023):
I know there are a growing number of PRs, appreciate your patience - as I am both new to GitHub/OpenSource, and did not plan my time availability accordingly this week. Re:direction, I've been torn on keeping it simple vs expanding - currently leaning towards keeping a core Baby AGI simple, and using this as a platform to support and promote different approaches to expanding this (eg. BabyAGIxLangchain as one direction). I believe there are various opinionated approaches that are worth exploring, and I see value in having a central place to compare and discuss. More updates coming shortly.
I am new to GitHub and open source, so please be patient as I learn to manage this project properly. I run a VC firm by day, so I will generally be checking PRs and issues at night after I get my kids down - which may not be every night. Open to the idea of bringing in support, will be updating this section soon (expectations, visions, etc). Talking to lots of people and learning - hang tight for updates!
BabyAGI Activity Report
To help the BabyAGI community stay informed about the project's progress, Blueprint AI has developed a Github activity summarizer for BabyAGI. This concise report displays a summary of all contributions to the BabyAGI repository over the past 7 days (continuously updated), making it easy for you to keep track of the latest developments.
To view the BabyAGI 7-day activity report, go here: https://app.blueprint.ai/github/yoheinakajima/babyagi
Inspired projects
In the short time since it was release, BabyAGI inspired many projects. You can see them all here.
Backstory
BabyAGI is a pared-down version of the original Task-Driven Autonomous Agent (Mar 28, 2023) shared on Twitter. This version is down to 140 lines: 13 comments, 22 blanks, and 105 code. The name of the repo came up in the reaction to the original autonomous agent - the author does not mean to imply that this is AGI.
Made with love by @yoheinakajima, who happens to be a VC (would love to see what you're building!)
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Safe Baby AGI
Published in Artificial General… 22 July 2015
Computer Science
Out of fear that artificial general intelligence AGI might pose a future risk to human existence, some have suggested slowing or stopping AGI research, to allow time for theoretical work to guarantee its safety. Since an AGI system will necessarily be a complex closed-loop learning controller that lives and works in semi-stochastic environments, its behaviors are not fully determined by its design and initial state, so no mathematico-logical guarantees can be provided for its safety. Until… Expand
Python. Langchain
BabyAGI with Tools#
This notebook builds on top of baby agi, but shows how you can swap out the execution chain. The previous execution chain was just an LLM which made stuff up. By swapping it out with an agent that has access to tools, we can hopefully get real reliable information
Install and Import Required Modules
import os
from collections import deque
from typing import Dict, List, Optional, Any
from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import BaseLLM
from langchain.vectorstores.base import VectorStore
from pydantic import BaseModel, Field
from langchain.chains.base import Chain
Connect to the Vector Store
Depending on what vectorstore you use, this step may look different.
%pip install faiss-cpu > /dev/null
%pip install google-search-results > /dev/null
from langchain.vectorstores import FAISS
from langchain.docstore import InMemoryDocstore
# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
import faiss
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
Define the Chains
BabyAGI relies on three LLM chains:
Task creation chain to select new tasks to add to the list
Task prioritization chain to re-prioritize tasks
Execution Chain to execute the tasks
NOTE: in this notebook, the Execution chain will now be an agent.
class TaskCreationChain(LLMChain):
"""Chain to generates tasks."""
@classmethod
def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain:
"""Get the response parser."""
task_creation_template = (
"You are an task creation AI that uses the result of an execution agent"
" to create new tasks with the following objective: {objective},"
" The last completed task has the result: {result}."
" This result was based on this task description: {task_description}."
" These are incomplete tasks: {incomplete_tasks}."
" Based on the result, create new tasks to be completed"
" by the AI system that do not overlap with incomplete tasks."
" Return the tasks as an array."
)
prompt = PromptTemplate(
template=task_creation_template,
input_variables=[
"result",
"task_description",
"incomplete_tasks",
"objective",
],
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
class TaskPrioritizationChain(LLMChain):
"""Chain to prioritize tasks."""
@classmethod
def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain:
"""Get the response parser."""
task_prioritization_template = (
"You are an task prioritization AI tasked with cleaning the formatting of and reprioritizing"
" the following tasks: {task_names}."
" Consider the ultimate objective of your team: {objective}."
" Do not remove any tasks. Return the result as a numbered list, like:"
" #. First task"
" #. Second task"
" Start the task list with number {next_task_id}."
)
prompt = PromptTemplate(
template=task_prioritization_template,
input_variables=["task_names", "next_task_id", "objective"],
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, SerpAPIWrapper, LLMChain
todo_prompt = PromptTemplate.from_template(
"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}"
)
todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)
search = SerpAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
),
Tool(
name="TODO",
func=todo_chain.run,
description="useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!",
),
]
prefix = """You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}."""
suffix = """Question: {task}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["objective", "task", "context", "agent_scratchpad"],
)
Define the BabyAGI Controller
BabyAGI composes the chains defined above in a (potentially-)infinite loop.
def get_next_task(
task_creation_chain: LLMChain,
result: Dict,
task_description: str,
task_list: List[str],
objective: str,
) -> List[Dict]:
"""Get the next task."""
incomplete_tasks = ", ".join(task_list)
response = task_creation_chain.run(
result=result,
task_description=task_description,
incomplete_tasks=incomplete_tasks,
objective=objective,
)
new_tasks = response.split("\n")
return [{"task_name": task_name} for task_name in new_tasks if task_name.strip()]
def prioritize_tasks(
task_prioritization_chain: LLMChain,
this_task_id: int,
task_list: List[Dict],
objective: str,
) -> List[Dict]:
"""Prioritize tasks."""
task_names = [t["task_name"] for t in task_list]
next_task_id = int(this_task_id) + 1
response = task_prioritization_chain.run(
task_names=task_names, next_task_id=next_task_id, objective=objective
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_string in new_tasks:
if not task_string.strip():
continue
task_parts = task_string.strip().split(".", 1)
if len(task_parts) == 2:
task_id = task_parts[0].strip()
task_name = task_parts[1].strip()
prioritized_task_list.append({"task_id": task_id, "task_name": task_name})
return prioritized_task_list
def _get_top_tasks(vectorstore, query: str, k: int) -> List[str]:
"""Get the top k tasks based on the query."""
results = vectorstore.similarity_search_with_score(query, k=k)
if not results:
return []
sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))
return [str(item.metadata["task"]) for item in sorted_results]
def execute_task(
vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5
) -> str:
"""Execute a task."""
context = _get_top_tasks(vectorstore, query=objective, k=k)
return execution_chain.run(objective=objective, context=context, task=task)
class BabyAGI(Chain, BaseModel):
"""Controller model for the BabyAGI agent."""
task_list: deque = Field(default_factory=deque)
task_creation_chain: TaskCreationChain = Field(...)
task_prioritization_chain: TaskPrioritizationChain = Field(...)
execution_chain: AgentExecutor = Field(...)
task_id_counter: int = Field(1)
vectorstore: VectorStore = Field(init=False)
max_iterations: Optional[int] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def add_task(self, task: Dict):
self.task_list.append(task)
def print_task_list(self):
print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m")
for t in self.task_list:
print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict):
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
def print_task_result(self, result: str):
print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m")
print(result)
@property
def input_keys(self) -> List[str]:
return ["objective"]
@property
def output_keys(self) -> List[str]:
return []
def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Run the agent."""
objective = inputs["objective"]
first_task = inputs.get("first_task", "Make a todo list")
self.add_task({"task_id": 1, "task_name": first_task})
num_iters = 0
while True:
if self.task_list:
self.print_task_list()
# Step 1: Pull the first task
task = self.task_list.popleft()
self.print_next_task(task)
# Step 2: Execute the task
result = execute_task(
self.vectorstore, self.execution_chain, objective, task["task_name"]
)
this_task_id = int(task["task_id"])
self.print_task_result(result)
# Step 3: Store the result in Pinecone
result_id = f"result_{task['task_id']}"
self.vectorstore.add_texts(
texts=[result],
metadatas=[{"task": task["task_name"]}],
ids=[result_id],
)
# Step 4: Create new tasks and reprioritize task list
new_tasks = get_next_task(
self.task_creation_chain,
result,
task["task_name"],
[t["task_name"] for t in self.task_list],
objective,
)
for new_task in new_tasks:
self.task_id_counter += 1
new_task.update({"task_id": self.task_id_counter})
self.add_task(new_task)
self.task_list = deque(
prioritize_tasks(
self.task_prioritization_chain,
this_task_id,
list(self.task_list),
objective,
)
)
num_iters += 1
if self.max_iterations is not None and num_iters == self.max_iterations:
print(
"\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m"
)
break
return {}
@classmethod
def from_llm(
cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs
) -> "BabyAGI":
"""Initialize the BabyAGI Controller."""
task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
task_prioritization_chain = TaskPrioritizationChain.from_llm(
llm, verbose=verbose
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
agent_executor = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True
)
return cls(
task_creation_chain=task_creation_chain,
task_prioritization_chain=task_prioritization_chain,
execution_chain=agent_executor,
vectorstore=vectorstore,
**kwargs,
)
Run the BabyAGI
Now it’s time to create the BabyAGI controller and watch it try to accomplish your objective.
OBJECTIVE = "Write a weather report for SF today"
llm = OpenAI(temperature=0)
# Logging of LLMChains
verbose = False
# If None, will keep on going forever
max_iterations: Optional[int] = 3
baby_agi = BabyAGI.from_llm(
llm=llm, vectorstore=vectorstore, verbose=verbose, max_iterations=max_iterations
)
baby_agi({"objective": OBJECTIVE})
*****TASK LIST*****
1: Make a todo list
*****NEXT TASK*****
1: Make a todo list
> Entering new AgentExecutor chain...
Thought: I need to gather data on the current weather conditions in SF
Action: Search
Action Input: Current weather conditions in SF
Observation: High 67F. Winds WNW at 10 to 15 mph. Clear to partly cloudy.
Thought: I need to make a todo list
Action: TODO
Action Input: Write a weather report for SF today
Observation:
1. Research current weather conditions in San Francisco
2. Gather data on temperature, humidity, wind speed, and other relevant weather conditions
3. Analyze data to determine current weather trends
4. Write a brief introduction to the weather report
5. Describe current weather conditions in San Francisco
6. Discuss any upcoming weather changes
7. Summarize the weather report
8. Proofread and edit the report
9. Submit the report
Thought: I now know the final answer
Final Answer: A weather report for SF today should include research on current weather conditions in San Francisco, gathering data on temperature, humidity, wind speed, and other relevant weather conditions, analyzing data to determine current weather trends, writing a brief introduction to the weather report, describing current weather conditions in San Francisco, discussing any upcoming weather changes, summarizing the weather report, proofreading and editing the report, and submitting the report.
> Finished chain.
*****TASK RESULT*****
A weather report for SF today should include research on current weather conditions in San Francisco, gathering data on temperature, humidity, wind speed, and other relevant weather conditions, analyzing data to determine current weather trends, writing a brief introduction to the weather report, describing current weather conditions in San Francisco, discussing any upcoming weather changes, summarizing the weather report, proofreading and editing the report, and submitting the report.
*****TASK LIST*****
2: Gather data on temperature, humidity, wind speed, and other relevant weather conditions
3: Analyze data to determine current weather trends
4: Write a brief introduction to the weather report
5: Describe current weather conditions in San Francisco
6: Discuss any upcoming weather changes
7: Summarize the weather report
8: Proofread and edit the report
9: Submit the report
1: Research current weather conditions in San Francisco
*****NEXT TASK*****
2: Gather data on temperature, humidity, wind speed, and other relevant weather conditions
> Entering new AgentExecutor chain...
Thought: I need to search for the current weather conditions in SF
Action: Search
Action Input: Current weather conditions in SF
Observation: High 67F. Winds WNW at 10 to 15 mph. Clear to partly cloudy.
Thought: I need to make a todo list
Action: TODO
Action Input: Create a weather report for SF today
Observation:
1. Gather current weather data for SF, including temperature, wind speed, humidity, and precipitation.
2. Research historical weather data for SF to compare current conditions.
3. Analyze current and historical data to determine any trends or patterns.
4. Create a visual representation of the data, such as a graph or chart.
5. Write a summary of the weather report, including key findings and any relevant information.
6. Publish the weather report on a website or other platform.
Thought: I now know the final answer
Final Answer: Today in San Francisco, the temperature is 67F with winds WNW at 10 to 15 mph. The sky is clear to partly cloudy.
> Finished chain.
*****TASK RESULT*****
Today in San Francisco, the temperature is 67F with winds WNW at 10 to 15 mph. The sky is clear to partly cloudy.
*****TASK LIST*****
3: Research current weather conditions in San Francisco
4: Compare the current weather conditions in San Francisco to the average for this time of year.
5: Identify any potential weather-related hazards in the area.
6: Research any historical weather patterns in San Francisco.
7: Analyze data to determine current weather trends
8: Include any relevant data from nearby cities in the report.
9: Include any relevant data from the National Weather Service in the report.
10: Include any relevant data from local news sources in the report.
11: Include any relevant data from online weather sources in the report.
12: Include any relevant data from local meteorologists in the report.
13: Include any relevant data from local weather stations in the report.
14: Include any relevant data from satellite images in the report.
15: Describe current weather conditions in San Francisco
16: Discuss any upcoming weather changes
17: Write a brief introduction to the weather report
18: Summarize the weather report
19: Proofread and edit the report
20: Submit the report
*****NEXT TASK*****
3: Research current weather conditions in San Francisco
> Entering new AgentExecutor chain...
Thought: I need to search for current weather conditions in San Francisco
Action: Search
Action Input: Current weather conditions in San Francisco
Observation: TodaySun 04/09 High 67 · 1% Precip. ; TonightSun 04/09 Low 49 · 9% Precip. ; TomorrowMon 04/10 High 64 · 11% Precip.
Thought: I now know the final answer
Final Answer: Today in San Francisco, the high temperature is 67 degrees with 1% chance of precipitation. The low temperature tonight is 49 degrees with 9% chance of precipitation. Tomorrow's high temperature is 64 degrees with 11% chance of precipitation.
> Finished chain.
*****TASK RESULT*****
Today in San Francisco, the high temperature is 67 degrees with 1% chance of precipitation. The low temperature tonight is 49 degrees with 9% chance of precipitation. Tomorrow's high temperature is 64 degrees with 11% chance of precipitation.
*****TASK ENDING*****
{'objective': 'Write a weather report for SF today'}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 13, 2023.
I will build langchain baby agi ai agents auto gpt products
AutoGPT and Baby AGI Have Just Created HUGE PROBLEMS for Google
·
Published in
·
5 min read
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Apr 9
How AI and IoT are Creating An Impact On Industries Today
HELLO AND WELCOME TO THE
5 STAR AI.IOT TOOLS FOR YOUR BUSINESS
ARE NEW WEBSITE IS ABOUT 5 STAR AI and io’t TOOLS on the net.
We prevaid you the best
Artificial Intelligence tools and services that can be used to create and improve BUSINESS websites AND CHANNELS .
This site is includes tools for creating interactive visuals, animations, and videos.
as well as tools for SEO, marketing, and web development.
It also includes tools for creating and editing text, images, and audio. The website is intended to provide users with a comprehensive list of AI-based tools to help them create and improve their business.
https://studio.d-id.com/share?id=078f9242d5185a9494e00852e89e17f7&utm_source=copy
This website is a collection of Artificial Intelligence (AI) tools and services that can be used to create and improve websites. It includes tools for creating interactive visuals, animations, and videos, as well as tools for SEO, marketing, and web development. It also includes tools for creating and editing text, images, and audio. The website is intended to provide users with a comprehensive list of AI-based tools to help them create and improve their websites.
אתר זה הוא אוסף של כלים ושירותים של בינה מלאכותית (AI) שניתן להשתמש בהם כדי ליצור ולשפר אתרים. הוא כולל כלים ליצירת ויזואליה אינטראקטיבית, אנימציות וסרטונים, כמו גם כלים לקידום אתרים, שיווק ופיתוח אתרים. הוא כולל גם כלים ליצירה ועריכה של טקסט, תמונות ואודיו. האתר נועד לספק למשתמשים רשימה מקיפה של כלים מבוססי AI שיסייעו להם ליצור ולשפר את אתרי האינטרנט שלהם.
Hello and welcome to our new site that shares with you the most powerful web platforms and tools available on the web today
All platforms, websites and tools have artificial intelligence AI and have a 5-star rating
All platforms, websites and tools are free and Pro paid
The platforms, websites and the tool's are the best for growing your business in 2022/3
שלום וברוכים הבאים לאתר החדש שלנו המשתף אתכם בפלטפורמות האינטרנט והכלים החזקים ביותר הקיימים היום ברשת. כל הפלטפורמות, האתרים והכלים הם בעלי בינה מלאכותית AI ובעלי דירוג של 5 כוכבים. כל הפלטפורמות, האתרים והכלים חינמיים ומקצועיים בתשלום הפלטפורמות, האתרים והכלים באתר זה הם הטובים ביותר והמועילים ביותר להצמחת ולהגדלת העסק שלך ב-2022/3
A Guide for AI-Enhancing Your Existing Business Application
A guide to improving your existing business application of artificial intelligence
מדריך לשיפור היישום העסקי הקיים שלך בינה מלאכותית
What is Artificial Intelligence and how does it work? What are the 3 types of AI?
What is Artificial Intelligence and how does it work? What are the 3 types of AI? The 3 types of AI are: General AI: AI that can perform all of the intellectual tasks a human can. Currently, no form of AI can think abstractly or develop creative ideas in the same ways as humans. Narrow AI: Narrow AI commonly includes visual recognition and natural language processing (NLP) technologies. It is a powerful tool for completing routine jobs based on common knowledge, such as playing music on demand via a voice-enabled device. Broad AI: Broad AI typically relies on exclusive data sets associated with the business in question. It is generally considered the most useful AI category for a business. Business leaders will integrate a broad AI solution with a specific business process where enterprise-specific knowledge is required. How can artificial intelligence be used in business? AI is providing new ways for humans to engage with machines, transitioning personnel from pure digital experiences to human-like natural interactions. This is called cognitive engagement. AI is augmenting and improving how humans absorb and process information, often in real-time. This is called cognitive insights and knowledge management. Beyond process automation, AI is facilitating knowledge-intensive business decisions, mimicking complex human intelligence. This is called cognitive automation. What are the different artificial intelligence technologies in business? Machine learning, deep learning, robotics, computer vision, cognitive computing, artificial general intelligence, natural language processing, and knowledge reasoning are some of the most common business applications of AI. What is the difference between artificial intelligence and machine learning and deep learning? Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. What are the current and future capabilities of artificial intelligence? Current capabilities of AI include examples such as personal assistants (Siri, Alexa, Google Home), smart cars (Tesla), behavioral adaptation to improve the emotional intelligence of customer support representatives, using machine learning and predictive algorithms to improve the customer’s experience, transactional AI like that of Amazon, personalized content recommendations (Netflix), voice control, and learning thermostats. Future capabilities of AI might probably include fully autonomous cars, precision farming, future air traffic controllers, future classrooms with ambient informatics, urban systems, smart cities and so on. To know more about the scope of artificial intelligence in your business, please connect with our expert.
מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית?
מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית? שלושת סוגי הבינה המלאכותית הם: בינה מלאכותית כללית: בינה מלאכותית שיכולה לבצע את כל המשימות האינטלקטואליות שאדם יכול. נכון לעכשיו, שום צורה של AI לא יכולה לחשוב בצורה מופשטת או לפתח רעיונות יצירתיים באותן דרכים כמו בני אדם. בינה מלאכותית צרה: בינה מלאכותית צרה כוללת בדרך כלל טכנולוגיות זיהוי חזותי ועיבוד שפה טבעית (NLP). זהו כלי רב עוצמה להשלמת עבודות שגרתיות המבוססות על ידע נפוץ, כגון השמעת מוזיקה לפי דרישה באמצעות מכשיר התומך בקול. בינה מלאכותית רחבה: בינה מלאכותית רחבה מסתמכת בדרך כלל על מערכי נתונים בלעדיים הקשורים לעסק המדובר. זה נחשב בדרך כלל לקטגוריית הבינה המלאכותית השימושית ביותר עבור עסק. מנהיגים עסקיים ישלבו פתרון AI רחב עם תהליך עסקי ספציפי שבו נדרש ידע ספציפי לארגון. כיצד ניתן להשתמש בבינה מלאכותית בעסק? AI מספקת דרכים חדשות לבני אדם לעסוק במכונות, ומעבירה את הצוות מחוויות דיגיטליות טהורות לאינטראקציות טבעיות דמויות אדם. זה נקרא מעורבות קוגניטיבית. בינה מלאכותית מגדילה ומשפרת את האופן שבו בני אדם קולטים ומעבדים מידע, לעתים קרובות בזמן אמת. זה נקרא תובנות קוגניטיביות וניהול ידע. מעבר לאוטומציה של תהליכים, AI מאפשר החלטות עסקיות עתירות ידע, תוך חיקוי אינטליגנציה אנושית מורכבת. זה נקרא אוטומציה קוגניטיבית. מהן טכנולוגיות הבינה המלאכותית השונות בעסק? למידת מכונה, למידה עמוקה, רובוטיקה, ראייה ממוחשבת, מחשוב קוגניטיבי, בינה כללית מלאכותית, עיבוד שפה טבעית וחשיבת ידע הם חלק מהיישומים העסקיים הנפוצים ביותר של AI. מה ההבדל בין בינה מלאכותית ולמידת מכונה ולמידה עמוקה? בינה מלאכותית (AI) מיישמת ניתוח מתקדמות וטכניקות מבוססות לוגיקה, כולל למידת מכונה, כדי לפרש אירועים, לתמוך ולהפוך החלטות לאוטומטיות ולנקוט פעולות. למידת מכונה היא יישום של בינה מלאכותית (AI) המספק למערכות את היכולת ללמוד ולהשתפר מניסיון באופן אוטומטי מבלי להיות מתוכנתים במפורש. למידה עמוקה היא תת-קבוצה של למידת מכונה בבינה מלאכותית (AI) שיש לה רשתות המסוגלות ללמוד ללא פיקוח מנתונים שאינם מובנים או ללא תווית. מהן היכולות הנוכחיות והעתידיות של בינה מלאכותית? היכולות הנוכחיות של AI כוללות דוגמאות כמו עוזרים אישיים (Siri, Alexa, Google Home), מכוניות חכמות (Tesla), התאמה התנהגותית לשיפור האינטליגנציה הרגשית של נציגי תמיכת לקוחות, שימוש בלמידת מכונה ואלגוריתמים חזויים כדי לשפר את חווית הלקוח, עסקאות בינה מלאכותית כמו זו של אמזון, המלצות תוכן מותאמות אישית (Netflix), שליטה קולית ותרמוסטטים ללמידה. יכולות עתידיות של AI עשויות לכלול כנראה מכוניות אוטונומיות מלאות, חקלאות מדויקת, בקרי תעבורה אוויריים עתידיים, כיתות עתידיות עם אינפורמטיקה סביבתית, מערכות עירוניות, ערים חכמות וכן הלאה. כדי לדעת יותר על היקף הבינה המלאכותית בעסק שלך, אנא צור קשר עם המומחה שלנו.
Glossary of Terms
Application Programming Interface(API):
An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API's provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.
Artificial Intelligence(AI):
the intelligence displayed by machines in performing tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. AI is achieved by developing algorithms and systems that can process, analyze, and understand large amounts of data and make decisions based on that data.
Compute Unified Device Architecture(CUDA):
CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It's like when you have lots of friends help you do a puzzle - it goes much faster than if you try to do it all by yourself.
The term "CUDA" is a trademark of NVIDIA Corporation, which developed and popularized the technology.
Data Processing:
The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.
Deep Learning(DL):
A subfield of machine learning that uses deep neural networks with many layers to learn complex patterns from data.
Feature Engineering:
The process of selecting and creating new features from the raw data that can be used to improve the performance of a machine learning model.
Freemium:
You might see the term "Freemium" used often on this site. It simply means that the specific tool that you're looking at has both free and paid options. Typically there is very minimal, but unlimited, usage of the tool at a free tier with more access and features introduced in paid tiers.
Generative Art:
Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.
Generative Pre-trained Transformer(GPT):
GPT stands for Generative Pretrained Transformer. It is a type of large language model developed by OpenAI.
GitHub:
GitHub is a platform for hosting and collaborating on software projects
Google Colab:
Google Colab is an online platform that allows users to share and run Python scripts in the cloud
Graphics Processing Unit(GPU):
A GPU, or graphics processing unit, is a special type of computer chip that is designed to handle the complex calculations needed to display images and video on a computer or other device. It's like the brain of your computer's graphics system, and it's really good at doing lots of math really fast. GPUs are used in many different types of devices, including computers, phones, and gaming consoles. They are especially useful for tasks that require a lot of processing power, like playing video games, rendering 3D graphics, or running machine learning algorithms.
Large Language Model(LLM):
A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.
Machine Learning(ML):
A method of teaching computers to learn from data, without being explicitly programmed.
Natural Language Processing(NLP):
A subfield of AI that focuses on teaching machines to understand, process, and generate human language
Neural Networks:
A type of machine learning algorithm modeled on the structure and function of the brain.
Neural Radiance Fields(NeRF):
Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.
OpenAI:
OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society
Overfitting:
A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn't generalize well.
Prompt:
A prompt is a piece of text that is used to prime a large language model and guide its generation
Python:
Python is a popular, high-level programming language known for its simplicity, readability, and flexibility (many AI tools use it)
Reinforcement Learning:
A type of machine learning in which the model learns by trial and error, receiving rewards or punishments for its actions and adjusting its behavior accordingly.
Spatial Computing:
Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.
Stable Diffusion:
Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.
Supervised Learning:
A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.
Unsupervised Learning:
A type of machine learning in which the training data is not labeled, and the model is trained to find patterns and relationships in the data on its own.
Webhook:
A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.
מילון מונחים
ממשק תכנות יישומים (API): API, או ממשק תכנות יישומים, הוא קבוצה של כללים ופרוטוקולים המאפשרים לתוכנות שונות לתקשר ולהחליף מידע ביניהן. הוא פועל כמעין מתווך, המאפשר לתוכניות שונות לקיים אינטראקציה ולעבוד יחד, גם אם הן אינן בנויות באמצעות אותן שפות תכנות או טכנולוגיות. ממשקי API מספקים דרך לתוכנות שונות לדבר ביניהן ולשתף נתונים, ועוזרות ליצור חווית משתמש מקושרת יותר וחלקה יותר. בינה מלאכותית (AI): האינטליגנציה שמוצגת על ידי מכונות בביצוע משימות הדורשות בדרך כלל אינטליגנציה אנושית, כגון למידה, פתרון בעיות, קבלת החלטות והבנת שפה. AI מושגת על ידי פיתוח אלגוריתמים ומערכות שיכולים לעבד, לנתח ולהבין כמויות גדולות של נתונים ולקבל החלטות על סמך הנתונים הללו. Compute Unified Device Architecture (CUDA): CUDA היא דרך שבה מחשבים יכולים לעבוד על בעיות קשות וגדולות באמת על ידי פירוקן לחתיכות קטנות יותר ופתרון כולן בו זמנית. זה עוזר למחשב לעבוד מהר יותר וטוב יותר על ידי שימוש בחלקים מיוחדים בתוכו הנקראים GPUs. זה כמו כשיש לך הרבה חברים שעוזרים לך לעשות פאזל - זה הולך הרבה יותר מהר מאשר אם אתה מנסה לעשות את זה לבד. המונח "CUDA" הוא סימן מסחרי של NVIDIA Corporation, אשר פיתחה והפכה את הטכנולוגיה לפופולרית. עיבוד נתונים: תהליך הכנת נתונים גולמיים לשימוש במודל למידת מכונה, כולל משימות כמו ניקוי, שינוי ונימול של הנתונים. למידה עמוקה (DL): תת-תחום של למידת מכונה המשתמש ברשתות עצביות עמוקות עם רבדים רבים כדי ללמוד דפוסים מורכבים מנתונים. הנדסת תכונות: תהליך הבחירה והיצירה של תכונות חדשות מהנתונים הגולמיים שניתן להשתמש בהם כדי לשפר את הביצועים של מודל למידת מכונה. Freemium: ייתכן שתראה את המונח "Freemium" בשימוש לעתים קרובות באתר זה. זה פשוט אומר שלכלי הספציפי שאתה מסתכל עליו יש אפשרויות חינמיות וגם בתשלום. בדרך כלל יש שימוש מינימלי מאוד, אך בלתי מוגבל, בכלי בשכבה חינמית עם יותר גישה ותכונות שהוצגו בשכבות בתשלום. אמנות גנרטיבית: אמנות גנרטיבית היא צורה של אמנות שנוצרת באמצעות תוכנת מחשב או אלגוריתם ליצירת פלט חזותי או אודיו. לרוב זה כרוך בשימוש באקראיות או בכללים מתמטיים כדי ליצור תוצאות ייחודיות, בלתי צפויות ולעיתים כאוטיות. Generative Pre-trained Transformer(GPT): GPT ראשי תיבות של Generative Pre-trained Transformer. זהו סוג של מודל שפה גדול שפותח על ידי OpenAI. GitHub: GitHub היא פלטפורמה לאירוח ושיתוף פעולה בפרויקטי תוכנה
Google Colab: Google Colab היא פלטפורמה מקוונת המאפשרת למשתמשים לשתף ולהריץ סקריפטים של Python בענן Graphics Processing Unit(GPU): GPU, או יחידת עיבוד גרפית, הוא סוג מיוחד של שבב מחשב שנועד להתמודד עם המורכבות חישובים הדרושים להצגת תמונות ווידאו במחשב או במכשיר אחר. זה כמו המוח של המערכת הגרפית של המחשב שלך, והוא ממש טוב לעשות הרבה מתמטיקה ממש מהר. GPUs משמשים סוגים רבים ושונים של מכשירים, כולל מחשבים, טלפונים וקונסולות משחקים. הם שימושיים במיוחד למשימות הדורשות כוח עיבוד רב, כמו משחקי וידאו, עיבוד גרפיקה תלת-ממדית או הפעלת אלגוריתמים של למידת מכונה. מודל שפה גדול (LLM): סוג של מודל למידת מכונה שאומן על כמות גדולה מאוד של נתוני טקסט ומסוגל ליצור טקסט בעל צליל טבעי. Machine Learning (ML): שיטה ללמד מחשבים ללמוד מנתונים, מבלי להיות מתוכנתים במפורש. עיבוד שפה טבעית (NLP): תת-תחום של AI המתמקד בהוראת מכונות להבין, לעבד וליצור שפה אנושית רשתות עצביות: סוג של אלגוריתם למידת מכונה המבוססת על המבנה והתפקוד של המוח. שדות קרינה עצביים (NeRF): שדות קרינה עצביים הם סוג של מודל למידה עמוקה שיכול לשמש למגוון משימות, כולל יצירת תמונה, זיהוי אובייקטים ופילוח. NeRFs שואבים השראה מהרעיון של שימוש ברשת עצבית למודל של זוהר תמונה, שהוא מדד לכמות האור שנפלט או מוחזר על ידי אובייקט. OpenAI: OpenAI הוא מכון מחקר המתמקד בפיתוח וקידום טכנולוגיות בינה מלאכותית שהן בטוחות, שקופות ומועילות לחברה. Overfitting: בעיה נפוצה בלמידת מכונה, שבה המודל מתפקד היטב בנתוני האימון אך גרועים בחדשים, בלתי נראים. נתונים. זה מתרחש כאשר המודל מורכב מדי ולמד יותר מדי פרטים מנתוני האימון, כך שהוא לא מכליל היטב. הנחיה: הנחיה היא פיסת טקסט המשמשת לתכנון מודל שפה גדול ולהנחות את הדור שלו Python: Python היא שפת תכנות פופולרית ברמה גבוהה הידועה בפשטות, בקריאות ובגמישות שלה (כלי AI רבים משתמשים בה) למידת חיזוק: סוג של למידת מכונה שבה המודל לומד על ידי ניסוי וטעייה, מקבל תגמולים או עונשים על מעשיו ומתאים את התנהגותו בהתאם. מחשוב מרחבי: מחשוב מרחבי הוא השימוש בטכנולוגיה כדי להוסיף מידע וחוויות דיגיטליות לעולם הפיזי. זה יכול לכלול דברים כמו מציאות רבודה, שבה מידע דיגיטלי מתווסף למה שאתה רואה בעולם האמיתי, או מציאות מדומה, שבה אתה יכול לשקוע במלואו בסביבה דיגיטלית. יש לו שימושים רבים ושונים, כמו בחינוך, בידור ועיצוב, והוא יכול לשנות את האופן שבו אנו מתקשרים עם העולם ואחד עם השני. דיפוזיה יציבה: דיפוזיה יציבה מייצרת תמונות אמנותיות מורכבות המבוססות על הנחיות טקסט. זהו מודל AI של סינתזת תמונות בקוד פתוח הזמין לכולם. ניתן להתקין את ה-Stable Diffusion באופן מקומי באמצעות קוד שנמצא ב-GitHub או שישנם מספר ממשקי משתמש מקוונים הממנפים גם מודלים של Stable Diffusion. למידה מפוקחת: סוג של למידת מכונה שבה נתוני האימון מסומנים והמודל מאומן לבצע תחזיות על סמך היחסים בין נתוני הקלט והתוויות המתאימות. למידה ללא פיקוח: סוג של למידת מכונה שבה נתוני האימון אינם מסומנים, והמודל מאומן למצוא דפוסים ויחסים בנתונים בעצמו. Webhook: Webhook הוא דרך של תוכנת מחשב אחת לשלוח הודעה או נתונים לתוכנית אחרת דרך האינטרנט בזמן אמת. זה עובד על ידי שליחת ההודעה או הנתונים לכתובת URL ספציפית, השייכת לתוכנית האחרת. Webhooks משמשים לעתים קרובות כדי להפוך תהליכים לאוטומטיים ולהקל על תוכניות שונות לתקשר ולעבוד יחד. הם כלי שימושי למפתחים שרוצים לבנות יישומים מותאמים אישית או ליצור אינטגרציות בין מערכות תוכנה שונות.
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