Table 1.
Properties of various state-of-the-art chatbots
| Chatbot name | Developer | Technology | Input output | Auto learn | Approach | Limitations |
|---|---|---|---|---|---|---|
| ELIZA | Joseph Weizen-baum | Rule-based NLP | Text | No | Pattern matching template scripts based response | Logical reasoning and responses |
| ALICE | Richard Wallace | Rule-based AI ML | Text | No | Pattern Matching input Template Matching output | Personality modelling and reasoning ability |
| Mitsku | Steve Worswick Mitsku | Rule-based AI ML and NLP | Text | No | NLP based heuristic search | Large training data and dialogue management |
| Alexa | Amazon | Python, Java, Node, JS | Voice | Yes | Generative Model | Open access echo conversation, cloud |
| Watson | IBM | NLP with Deep QA, Apache, UIMA | Text, voice | Yes | Retrieval based model | Data structures process, learning time, maintenance cost |
| LUIS | Microsoft | AI, ML and NLG | Text, voice | Yes | Meaning and information extraction from user | Usability platform and non-linkable medium |
| Google Now/assistant | AI, DNN, NLP, NLU, naïve algorithm | Text, voice | Yes | Search by pattern matching for mobile | Net dependency and mobile limitations | |
| Dialogue flow | AI, DL, NLP, cloud | Text, voice, image | Yes | Voice and text exchanges using ML and NLP | Limited web hooks and integrations of manual works | |
| Amazon lex | Amazon | DL, NLP, ASR | Text, voice | Yes | ASR for converting speech to text and NLU to recognize the intent of the text | Complex web integration and less deployment channels. No multilingual supports and critical in entities mapping |
| SIRI | Apple | AI, NLP, objective C | Text, voice, image | Yes | Learning based | Lack of emotional engagement with users |