Kik is one of the most popular chat apps among teens with 275M MAUs and 40% of those are in the 13–24 year old demographic. In April, Kik launched its own bot store with 16 launch partners including Sephora, H&M, Vine, the Weather Channel, and Funny or Die. Using Kik’s bots currently feel like using the internet in 1994, very rough around the edges and limited functionality / usefulness. However, we’ll see how their API and bots progress over time, Kik’s popularity among an attractive demographic might convince some brands to invest in the platform.
There is no one right answer to this question, as the best solution will depend upon the specifics of your scenario and how the user would reasonably expect the bot to respond. However, as your conversation complexity increases dialogs become harder to manage. For complex branchings situations, it may be easier to create your own flow of control logic to keep track of your user's conversation.
As discussed earlier here also, each sentence is broken down into different words and each word then is used as input for the neural networks. The weighted connections are then calculated by different iterations through the training data thousands of times. Each time improving the weights to making it accurate. The trained data of neural network is a comparable algorithm more and less code. When there is a comparably small sample, where the training sentences have 200 different words and 20 classes, then that would be a matrix of 200×20. But this matrix size increases by n times more gradually and can cause a huge number of errors. In this kind of situations, processing speed should be considerably high.
On the other hand, early adoption can be somewhat of a curse. In 2011, many companies and individuals, myself included, invested a lot of time and money into Google+, dubbed to be bigger than Facebook at the time. They acquired over 10 million new users within the first two weeks of launch and things were looking positive. Many companies doubled-down on growing a community within the platform, hopeful of using it as a new and growing acquisition channel, but things didn't exactly pan out that way.
Ursprünglich rein textbasiert, haben sich Chatbots durch immer stärker werdende Spracherkennung und Sprachsynthese weiterentwickelt und bieten neben reinen Textdialogen auch vollständig gesprochene Dialoge oder einen Mix aus beidem an. Zusätzlich können auch weitere Medien genutzt werden, beispielsweise Bilder und Videos. Gerade mit der starken Nutzung von mobilen Endgeräten (Smartphones, Wearables) wird diese Möglichkeit der Nutzung von Chatbots weiter zunehmen (Stand: Nov. 2016). Mit fortschreitender Verbesserung sind Chatbots dabei nicht nur auf wenige eingegrenzte Themenbereiche (Wettervorhersage, Nachrichten usw.) begrenzt, sondern ermöglichen erweiterte Dialoge und Dienstleistungen für den Nutzer. Diese entwickeln sich so zu Intelligenten Persönlichen Assistenten.
The main challenge is in teaching a chatbot to understand the language of your customers. In every business, customers express themselves differently and each group of a target audience speaks its own way. The language is influenced by advertising campaigns on the market, the political situation in the country, releases of new services and products from Google, Apple and Pepsi among others. The way people speak depends on their city, mood, weather and moon phase. An important role in the communication of the business with customers may have the release of the film Star Wars, for example. That’s why training a chatbot to understand correctly everything the user types requires a lot of efforts.
The bot (which also offers users the opportunity to chat with your friendly neighborhood Spiderman) isn’t a true conversational agent, in the sense that the bot’s responses are currently a little limited; this isn’t a truly “freestyle” chatbot. For example, in the conversation above, the bot didn’t recognize the reply as a valid response – kind of a bummer if you’re hoping for an immersive experience.
Build a bot directly from one of the top messaging apps themselves. By building a bot in Telegram, you can easily run a bot in the application itself. The company recently open-sourced their chatbot code, making it easy for third-parties to integrate and create bots of their own. Their Telegram API, which they also built, can send customized notifications, news, reminders, or alerts. Integrate the API with other popular apps such as YouTube and Github for a unique customer experience.
There are obvious revenue opportunities around subscriptions, advertising and commerce. If bots are designed to save you time that you’d normally spend on mundane tasks or interactions, it’s possible they’ll seem valuable enough to justify a subscription fee. If bots start to replace some of the functions that you’d normally use a search engine like Google for, it’s easy to imagine some sort of advertising component. Or if bots help you shop, the bot-maker could arrange for a commission.
One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities.