:speech_balloon: Easy way to create conversation chats

🎯🗯 Dataset generation for AI chatbots, NLP tasks, named entity recognition or text classification models using a simple DSL!

Building a Simple Chatbot from Scratch in Python (using NLTK)

Simple chatbot UI for the Web with JSON scripting 👋🤖🤙

Simple chatbot implementation with PyTorch.

A simple, clean, easy to modify Slack chatbot

Your Trusty Memory-enabled AI Companion - Simple RAG chatbot optimized for local LLMs | 12 Languages Supported | OpenAI API Compatible

A simple, choice-driven chatbot framework for your website written in just over 100 lines of vanilla JavaScript (and some CSS)

A simple interface for working with intents and chatbots.

A simple CLI chatbot that demonstrates the integration of the Model Context Protocol (MCP).

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Learn how to use Watson Assistant and Watson Discovery. This application demonstrates a simple abstraction of a chatbot interacting with a Cloudant NoSQL database, using a Slack UI.

Built a simple chatbot from a sequence-to-sequence model with TensorFlow.

Simple chatbot exercise using only JavaScript, HTML, CSS

A simple API to integrate chatbots written in Javascript with WhatsApp Web :speech_balloon::calling: (Store hotfix https://github.com/Zibri/WhatsAppWebApi/blob/master/zstore.js)

This package makes it simple to start building a chatbot in PHP. Give me 10 minutes of your time and I will give you a chatbot starter setup.

A simple chatbot ui template made with react

Lightweight, simple embedded Open WebUI widget, allowing you to easily implement chatbot capabilities and RAG workflows into your existing tools, apps and webpages!

Simple Docker Compose to load gpt4all (Llama.cpp) as an API and chatbot-ui for the web interface. This mimics OpenAI's ChatGPT but as a local instance (offline).

GENARATED BY NISHEN Mtroid whatsApp bot 🪀 Command:`setup `✨️ Description:` edit bot settings `⚠️️ Warn `🪀 Command:` install <br> `✨️ Description:` Install external plugins. <br> `⚠️️ Warn:` Get plugins only from https://t.me/AlphaXplugin. `🪀 Command:` plugin<br> `✨️ Description:` Shows the plugins you have installed. `🪀 Command:` remove<br> `✨️ Description:` Removes the plugin. `🪀 Command:` admin<br> `✨️ Description:` Admin menu. `🪀 Command:` ban <br> `✨️ Description:` Ban someone in the group. Reply to message or tag a person to use command. `🪀 Command:` gname <br> `✨️ Description:` Change group name. `🪀 Command:` gdesc<br> `✨️ Description:` Change group discription. `🪀 Command:` dis <br> `✨️ Description:`  Disappearing message on/off. <br> `💡 Example:` .dis on/off `🪀 Command:` reset<br> `✨️ Description:` Reset group invitation link. `🪀 Command:` gpp<br> `✨️ Description:` Set group profile picture `🪀 Command:` add<br> `✨️ Description:` Adds someone to the group. `🪀 Command:` promote <br> `✨️ Description:` Makes any person an admin. `🪀 Command:` demote <br> `✨️ Description:` Takes the authority of any admin. `🪀 Command:` mute <br> `✨️ Description:` Mute the group chat. Only the admins can send a message. ⌨️ Example: .mute & .mute 5m etc `🪀 Command:` unmute <br> `✨️ Description:` Unmute the group chat. Anyone can send a message. `🪀 Command:` invite <br> `✨️ Description:` Provides the group's invitation link. `🪀 Command:` afk <br> `✨️ Description:` It makes you AFK - Away From Keyboard. `🪀 Command:` art pack<br> `✨️ Description:` Beautifull artpack with more than 100 messages. `🪀 Command:` aspm <br> `✨️ Description:` This command for any emergency situation about any kind of WhatsApp SPAM in Group `🪀 Command:` alag <br> `✨️ Description:` This command for any emergency situation about any kind of WhatsApp SPAM in Chat `🪀 Command:` linkblock <br> `✨️ Description:` Activates the block link tool. <br> `💡 Example:` .linkblock on / off `🪀 Command:` CrAsH<br> `✨️ Description:` send BUG VIRUS to group. `🪀 Command:` CrAsH high<br> `✨️ Description:` send BUG VIRUS to group untill you stop. `🪀 Command:` -carbon `🪀 Command:` clear<br> `✨️ Description:` Clears all the messages from the chat. `🪀 Command:` qr <br> `✨️ Description:` To create an qr code from the word you give. `🪀 Command:` bcode <br> `✨️ Description:` To create an barcode from the word you give. `🪀 Command:` compliment<br> `✨️ Description:` It sends complimentry sentenses. `🪀 Command:` toaudio<br> `✨️ Description:` Converts video to sound. `🪀 Command:` toimage<br> `✨️ Description:` Converts the sticker to a photo. `🪀 Command:` tovideo<br> `✨️ Description:` Converts animated stickers to video. `🪀 Command:` deepai<br> `✨️ Description:` Runs the most powerful artificial intelligence tools using artificial neural networks. `🪀 Command:` details<br> `✨️ Description:` Displays metadata data of group or person. `🪀 Command:` dict <br> `✨️ Description:` Use it as a dictionary. Eg: .dict enUS;lead For supporting languages send •.lngcode• `🪀 Command:` dst<br> `✨️ Description:` Download status you repled. `🪀 Command:` emedia<br> `✨️ Description:` It is a plugin with more than 25 media tools. `🪀 Command:` emoji <br> `✨️ Description:` You can get Emoji as image. `🪀 Command:` print <br> `✨️ Description:` Prints the inside of the file on the server. `🪀 Command:` bashmedia <br> `✨️ Description:` Sends audio, video and photos inside the server. <br> `💡 Example:` video.mp4 && media/gif/pic.mp4 `🪀 Command:` addserver<br> `✨️ Description:` Uploads image, audio or video to the server. `🪀 Command:` term <br> `✨️ Description:` Allows to run the command on the server's shell. `🪀 Command:` mediainfo<br> `✨️ Description:` Shows the technical information of the replied video. `🪀 Command:` pmsend <br> `✨️ Description:` Sends a private message to the replied person. `🪀 Command:` pmttssend <br> `✨️ Description:` Sends a private voice message to the respondent. `🪀 Command:` ffmpeg <br> `✨️ Description:` Applies the desired ffmpeg filter to the video. ⌨️ Example: .ffmpeg fade=in:0:30 `🪀 Command:` filter <br> `✨️ Description:` It adds a filter. If someone writes your filter, it send the answer. If you just write .filter, it show's your filter list. `🪀 Command:` stop <br> `✨️ Description:` Stops the filter you added previously. `🪀 Command:` bgmlist<br> `✨️ Description:` Bgm List. `🪀 Command:` github <br> `✨️ Description:` It Send Github User Data. <br> `💡 Example:` .github WhatsApp `🪀 Command:` welcome<br> `✨️ Description:` It sets the welcome message. If you leave it blank it shows the welcome message. `🪀 Command:` goodbye<br> `✨️ Description:` Sets the goodbye message. If you leave blank, it show's the goodbye message. `🪀 Command:` help<br> `✨️ Description:` Gives information about using the bot from the Help menu. `🪀 Command:` varset <br> `✨️ Description:` Changes the text of modules like alive, afk etc.. `🪀 Command:` restart<br> `✨️ Description:` Restart bot. `🪀 Command:` poweroff<br> `✨️ Description:` Shutdown bot. `🪀 Command:` dyno<br> `✨️ Description:` Check heroku dyno usage `🪀 Command:` setvar <br> `✨️ Description:` Set heroku config var `🪀 Command:` delvar <br> `✨️ Description:` Delete heroku config var `🪀 Command:` getvar <br> `✨️ Description:` Get heroku config var `🪀 Command:` hpmod <br> `✨️ Description:` To get mod apps info. `🪀 Command:` insult<br> `✨️ Description:` It gives random insults. `🪀 Command:` locate<br> `✨️ Description:` It send your location. <br> `⚠️️ Warn:` Please open your location before using command! `🪀 Command:` logmsg<br> `✨️ Description:` Saves the message you reply to your private number. <br> `⚠️️ Warn:` Does not support animated stickers! `🪀 Command:` logomaker<br> `✨️ Description:` Shows logomaker tools with unlimited access. `🪀 Command:` meme <br> `✨️ Description:` Photo memes you replied to. `🪀 Command:` movie <br> `✨️ Description:` Shows movie info. `🪀 Command:` neko<br> `✨️ Description:` Replied messages will be added to nekobin.com. `🪀 Command:` song <br> `✨️ Description:` Uploads the song you wrote. `🪀 Command:` video <br> `✨️ Description:` Downloads video from YouTube. `🪀 Command:` fb <br> `✨️ Description:` Download video from facebook. `🪀 Command:` tiktok <br> `✨️ Description:` Download tiktok video. `🪀 Command:` notes<br> `✨️ Description:` Shows all your existing notes. `🪀 Command:` save <br> `✨️ Description:` Reply a message and type .save or just use .save <Your note> without replying `🪀 Command:` deleteNotes<br> `✨️ Description:` Deletes *all* your saved notes. `🪀 Command:` ocr <br> `✨️ Description:` Reads the text on the photo you have replied. `🪀 Command:` pinimg <br> `✨️ Description:` Downloas images from Pinterest. `🪀 Command:` playst <br> `✨️ Description:` Get app details from play store. `🪀 Command:` profile<br> `✨️ Description:` Profile menu. `🪀 Command:` getpp<br> `✨️ Description:` Get pofile picture. `🪀 Command:` setbio <br> `✨️ Description:` Set your about. `🪀 Command:` getbio<br> `✨️ Description:` Get user about. `🪀 Command:` archive<br> `✨️ Description:` Archive chat. `🪀 Command:` unarchive<br> `✨️ Description:` Unarchive chat. `🪀 Command:` pin<br> `✨️ Description:` Archive chat. `🪀 Command:` unpin<br> `✨️ Description:` Unarchive chat. `🪀 Command:` pp<br> `✨️ Description:` Makes the profile photo what photo you reply. `🪀 Command:` kickme<br> `✨️ Description:` It kicks you from the group you are using it in. `🪀 Command:` block <br> `✨️ Description:` Block user. `🪀 Command:` unblock <br> `✨️ Description:` Unblock user. `🪀 Command:` jid <br> `✨️ Description:` Giving user's JID. `🪀 Command:` rdmore <br> `✨️ Description:` Add readmore to your message >> Use # to get readmore. `🪀 Command:` removebg <br> `✨️ Description:` Removes the background of the photos. `🪀 Command:` report <br> `✨️ Description:` Sends reports to group admins. `🪀 Command:` roll<br> `✨️ Description:` Roll dice randomly. `🪀 Command:` scam <br> `✨️ Description:` Creates 5 minutes of fake actions. `🪀 Command:` scan <br> `✨️ Description:` Checks whether the entered number is registered on WhatApp. `🪀 Command:` trt<br> `✨️ Description:` It translates with Google Translate. You must reply any message. <br> `💡 Example:` .trt en si (From English to Sinhala) `🪀 Command:` antilink <br> `✨️ Description:` Activates the Antilink tool. <br> `💡 Example:` .antilink on / off `🪀 Command:` autobio <br> `✨️ Description:` Add live clock to your bio! <br> `💡 Example:` .autobio on / off `🪀 Command:` detectlang<br> `✨️ Description:` Guess the language of the replied message. `🪀 Command:` currency `🪀 Command:` tts <br> `✨️ Description:` It converts text to sound. `🪀 Command:` music <br> `✨️ Description:` Uploads the song you wrote. `🪀 Command:` smp3 <br> `✨️ Description:` Get song as a mp3 documet file `🪀 Command:` mp4 <br> `✨️ Description:` Downloads video from YouTube. `🪀 Command:` yt <br> `✨️ Description:` It searchs on YouTube. `🪀 Command:` wiki <br> `✨️ Description:` Searches query on Wikipedia. `🪀 Command:` img <br> `✨️ Description:` Searches for related pics on Google. `🪀 Command:` lyric <br> `✨️ Description:` Finds the lyrics of the song. `🪀 Command:` covid <br> `✨️ Description:` Shows the daily and overall covid table of more than 15 countries. `🪀 Command:` ss <br> `✨️ Description:` Takes a screenshot from the page in the given link. `🪀 Command:` simi <br> `✨️ Description:` Are you bored? ... Fool around with SimSimi. ... World first popular Chatbot for daily conversation. `🪀 Command:` spdf <br> `✨️ Description:` Site to pdf file. `🪀 Command:` insta <br> `✨️ Description:` Downloads videos or photos from Instagram. `🪀 Command:` animesay <br> `✨️ Description:` It writes the text inside the banner the anime girl is holding `🪀 Command:` changesay <br> `✨️ Description:` Turns the text into the change my mind poster. `🪀 Command:` trumpsay <br> `✨️ Description:` Converts the text to Trump's tweet. `🪀 Command:` audio spam<br> `✨️ Description:` Sends the replied audio as spam. `🪀 Command:` foto spam<br> `✨️ Description:` Sends the replied photo as spam. `🪀 Command:` sticker spam<br> `✨️ Description:` Convert the replied photo or video to sticker and send it as spam. `🪀 Command:` vid spam `🪀 Command:` killspam<br> `✨️ Description:` Stops spam command. `🪀 Command:` spam <br> `✨️ Description:` It spam until you stop it. ⌨️ Example: .spam test `🪀 Command:` spotify <br> `✨️ Description:` Get music details from spotify. `🪀 Command:` st<br> `✨️ Description:` It converts your replied photo or video to sticker. `🪀 Command:` sweather<br> `✨️ Description:` Gives you the weekly interpretations of space weather observations provided by the Space Weather Research Center (SWRC) for a p. `🪀 Command:` alive <br> `✨️ Description:` Does bot work? `🪀 Command:` sysd<br> `✨️ Description:` Shows the system properties. `🪀 Command:` tagadmin `🪀 Command:` tg <br> `✨️ Description:` Tags everyone in the group. `🪀 Command:` pmall<br> `✨️ Description:` Sends the replied message to all members in the group. `🪀 Command:` tblend <br> `✨️ Description:` Applies the selected TBlend effect to videos. `🪀 Command:` link<br> `✨️ Description:` The image you reply to uploads to telegra.ph and provides its link. `🪀 Command:` unvoice<br> `✨️ Description:` Converts audio to sound recording. `🪀 Command:` up<br> `✨️ Description:` Checks the update your bot. `🪀 Command:` up now<br> `✨️ Description:` It makes updates. `🪀 Command:` voicy<br> `✨️ Description:` It converts audio to text. `🪀 Command:` wp<br> `✨️ Description:` It sends high resolution wallpapers. `🪀 Command:` wame <br> `✨️ Description:` Get a link to the user chat. `🪀 Command:` weather <br> `✨️ Description:` Shows the weather. `🪀 Command:` speedtest <br> `✨️ Description:` Measures Download and Upload speed. <br> `💡 Example:` speedtest user // speedtest server `🪀 Command:` ping<br> `✨️ Description:` Measures your ping. `🪀 Command:` short <br> `✨️ Description:` Shorten the long link. `🪀 Command:` calc <br> `✨️ Description:` Performs simple math operations. `🪀 Command:` xapi<br> `✨️ Description:` Xteam API key info. `🪀 Command:` joke<br> `✨️ Description:` Send random jokes. `🪀 Command:` quote<br> `✨️ Description:` Send random quotes.

A simple and clear example for implement a chatbot with Bedrock (Claude, Nova and DeepSeek) + LangChain + Streamlit.

A simple python chatbot for Facebook messenger

Simple ChatBot introducing NLP and Machine Learning for Classification of Sentences

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

A Simple ChatBot Widget

A simple Chatbot App using AIML

This repository holds files for the simple chatbot wrote in TensorFlow 1.4, with attention mechanism and bucketing.

This project is a simple yet effective chatbot designed to help users find Google job openings that best match their profiles. It was developed as part of a Google-sponsored hackathon in Brazil, where it proudly earned 5th place among the finalists. Note: All content is in Portuguese, as the competition was held locally.

A simple web-based chatbot application that utilizes a cleaned dataset to provide responses. This project combines a Python backend with a frontend built using HTML, CSS, and JavaScript.

Simple html ollama chatbot that is easy to install. Simply copy the html file on your computer and run it.