What is cleverbots name

The future of artificial intelligence remains a topic of hot debate among programmers. But why ask Frankenstein when his monster is ready to spill the beans?

I decided to interview a few chatbots about their experiences, the mysteries of the universe, and the process of learning. And then, like I do with all my friends, I rated their humanity and intelligence on a point scale. Which, in my experience, works decently well in human-human dialogue as well. Although ALICE is a three-time winner of the Loebner Prize, an award bequeathed annually upon particularly humanoid conversational robots, it has never managed to pass the Turing test.

Alright, not terrible so far. Except, you know, for the human sandwich part. She will process user conversation for keywords, search those keywords, and match them to a topic that will guide her response.

The program chooses how to respond to you fuzzily, and contextually, the whole of your conversation being compared to the millions that have taken place before. Many people say there is no bot - that it is connecting people together, live. The AI can seem human because it says things real people do say, but it is always software, imitating people.

Updates to the software have been mostly behind the scenes. In , Cleverbot was upgraded to use GPU serving techniques. The developers of Cleverbot are attempting to build a new version using machine learning techniques. A significant part of the engine behind Cleverbot and an API for accessing it has been made available to developers in the form of Cleverscript.

A service for directly accessing Cleverbot has been made available to developers in the form of Cleverbot. An app that uses the Cleverscript engine to play a game of 20 Questions, has been launched under the name Clevernator. Unlike other such games, the player asks the questions and it is the role of the AI to understand, and answer factually.

An app that allows owners to create and talk to their own small Cleverbot-like AI has been launched, called Cleverme! In early , a Twitch stream of two Google Home devices modified to talk to each other using Cleverbot.

Anonymous Not logged in Create account Log in. Hand W iki. The two methods roughly agree on results. In September the top 10 languages were as follows:.

This only reflects data collected in one month. It is difficult to analyse the language content of the entire data set. Most language detection routines recommend inputting tens or hundreds of words. We can detect the language over a whole conversation, though many users switch between languages.

Often a conversation starts in English and changes to Spanish or German when the user realises that Cleverbot speaks their language. Based on informal language detection measurements, we suspect the full data set roughly follows the proportions above but with more English.

These ranges are akin to alphabets. For instance, the Unicode numbers from 32 to represent Latin-based characters used for writing most European languages. As of October , the active database contains the following Unicode range distribution.

In this table, the Greek is particularly interesting because Cleverbot only became fully UTF-8 aware in late Greek was learned virtually from scratch using the learning mechanism described above and now has nearly 40, rows. Russian, Japanase and Korean data was imported from separate language-specific versions of Cleverbot at around the same time and has since grown significantly.

Demographically all three websites are most popular in the age group according to Google Analytics September We suspect that under 18s also make up a significant proportion of our visitors as well though this is not measured in Analytics:.

We mentioned that Cleverbot actively uses less data than it could due to technical practicalities. There are also social and moral considersations. Cleverbot employs many filtering rules and patterns to determine which rows should make up the active database. The rules favour longer non-repetitive lines and conversations, and largely ban swearing and explicit sexual references so as to prevent Cleverbot from engaging in such activities.

Much of the filtering consists of matches against manually curated lists of strings, which are more completely specified in English than most other languages. With this in mind, we can present some general statistics about the Cleverbot data, with figures actual and estimated as of 2nd December Much of our active data was collected before we started storing all unfiltered data, and as a result only 6. On that basis the total overall number of lines can be said to be million.

We have now introduced the Cleverbot data set, including how the data is collected and a brief statistical breakdown. Now we will address how suitable this data is for machine learning. Thus far all statistics have referred to conversational interactions: the bot says something and the user replies.

Each row in the database represents one of these interactions. Some people swear at Cleverbot or try to chat it up, change the topic every line or type complete nonsense. But all of those are still valid human responses in the current conversational context. A very small percent may be other bots chatting with Cleverbot or Cleverbot chatting against itself , but we have various rules in our top level servers to prevent that kind of usage or learning. Usually it follows the flow of the conversation and sometimes it is strikingly good, but it can also suddenly change topics and may come across as having a poor factual memory — it forgets names and preferences.

We have run most of our own machine learning test on the data using the second method above, because it effectively doubles the size of the data set, and is much easier to work with. In we started using modern machine learning techniques to build a new, more intelligent conversational AI. We began with unsupervised learning techniques to build a model that could capture the natural structure of our data at a line and conversation level.

We were inspired by the word-level vector relationships that word2vec reveals. Impressively, these can be plotted, vividly showing the word-level data structures:. As described in the introduction, going from word vectors to line vectors the composition problem is an open machine learning challenge. We hoped that with sheer quantity of data, we could more-or-less bypass the issue and analyse the relationships between lines directly, without first splitting them into words.

This turned out to be impractical. There are far too many unique lines. Instead we used a simple composition approach, followed by clustering, to reduce the number of unique lines, and then analysed the clustered lines. All these stages are unsupervised.

Our experiment was as follows:. We aimed to build an efficient model of Cleverbot data in order to encode line level relationships.

To this end, we implemented the following pipeline:. We have tested on 2 million, 20 million and 50 million lines 1, 10, and 25 million interactions treating both the bot and user side of the conversation equally. We first extract the bot and user columns from the raw log files, lower case them, and remove punctuation. We save the output to a long text file, with each line of conversation on one line of the file. We run the resulting text file through word2vec to turn all the words into dimensional vectors.

We use relatively low dimensions to allow the following stages to run faster. We use skip-grams with a context window of 12 which usually encompasses the whole line as they are on average 3 words long.

Then we cluster those summed and normalised lines.