Natural language has unlocked immense potential for artificial intelligence by enabling easier human-machine interaction. Today, natural language platforms are powering everything from virtual assistants like Siri and Alexa to sophisticated enterprise applications. But what exactly are these platforms, and why are they becoming so critical?
Defining Natural Language Platforms
Natural language platforms refer to software tools and frameworks focused on natural language processing (NLP) that allow developers to understand, interpret, and generate human languages. In simpler terms, they act as a pipeline taking in speech or text at one end and outputting some useful interpretation of it at the other end.
These platforms leverage machine learning and statistical models to empower AI systems with natural language capabilities at scale without requiring data scientists to build complex NLP models from scratch. They provide the full infrastructure to ingest speech or text data, analyze it to recognize intents/entities/relationships, and take actions or respond appropriately.
Over the past decade, natural language has emerged as the most intuitive way for humans to interact with machines. Therefore, technology giants like Microsoft, Amazon, IBM, and Google have invested heavily in developing robust multi-purpose platforms to meet this demand. Most real-world AI applications today use natural language at their core.
The Critical Role of Natural Language Understanding
Natural language understanding (NLU) is a branch of natural language processing focused specifically on enabling computers to understand the meaning and context behind text or speech data at a deeper level. NLU goes beyond simple speech recognition or classification to interpret the likely intent and extract relevant semantic details to drive automated reasoning and decision making.
For instance, in a sentence like "I‘d like to schedule a doctor‘s appointment next Wednesday", NLU would identify this as a request to arrange an appointment (intent), extract details around scheduling an appointment with a doctor (entities), and parse next Wednesday as the intended date (context).
NLU sits at the heart of natural language platforms, infusing AI systems with more nuanced human language comprehension capabilities to deliver smarter and more useful outcomes.
Diving Deeper into Leading Natural Language Platforms
There is a diverse array of natural language software in the market ranging from commercial platforms by tech giants to open source toolkits for developers. Let‘s analyze some of the most widely used options in greater depth:
Dialogflow
Owned by Google, Dialogflow leverages advanced machine learning to provide a robust development environment for designing conversational interfaces.
Architecture: Dialogflow splits components for natural language processing, dialogue state tracking, response generation etc. enabling modular experimentation and exchange of sub-components.
Core Capabilities:
- Sophisticated natural language classifier for understanding unstructured user inputs
- Contextual understanding maintained across conversation flows
- Environment tagging allows easier portability across languages
- Seamless integrations with popular communication channels
Ease of Use: Dialogflow offers an intuitive graphical interface and templatized workflows that dramatically reduce development effort for common conversation scenarios.
Scalability & Availability: Being hosted on Google Cloud, Dialogflow scales smoothly to thousands of endpoints with high reliability.
Limitations:
- Occasional inaccuracies in rare dialects or niche vocabulary
- Steep pricing for some advanced enterprise features
- Lack of personalization capabilities out-of-the-box
LUIS
LUIS (Language Understanding) is Microsoft‘s natural language modeling framework that builds custom models tailored to extracted intents and entities.
Differentiators:
- Tight integration with Azure bots, flows and apps enables seamless experiences across Microsoft properties
- Prebuilt domains provide templates for common scenarios in industries like hospitality, transportation etc. reducing development overhead
- Continuous active learning as model interacts with users to improve over time
Ease of Use: LUIS provides a browser-based graphical tool for crafting language models visually without needing data science expertise. Auto-suggested recommendations reduce tedious configuration.
Limits:
- Occasional inaccuracies for niche, emerging or idiosyncratic vocabulary
- Limited debugging capabilities around predictions for developers
- Documentation gaps exist in parts
Watson Assistant
IBM Watson Assistant (formerly Conversation) allows developers to create conversational interfaces by combining declarative configuration, custom business logic, integrations andprebuilt content.
Core Strengths:
- State of the art natural language processing based on Watson‘s enterprise experience
- Modular components for speech recognition, NLU, user and context modeling
- Tooling focused on accelerating bot development leveraging templates, analyzers, and wide integration library
Developer Experience:
- Intuitive graphical conversation builder with testing toolkit
- Integration helper and SDKs for popular platforms
- BYOD connectors allow hooking into existing company data sources
Limitations:
- Steep learning curve for maximizing value from vast feature set
- Potential vendor lock-in given IBM cloud focus
- Limited debugging visibility into internal model predictions
Amazon Lex
Amazon Lex powers conversational interfaces for products like Alexa. The fully managed service allows developers to build chatbots and voice assistants.
Differentiators:
- Tight integration with Amazon developer stack – Lambda, AWS messaging etc.
- Built specifically for speech with automatic speech recognition (ASR)
- Direct compatibility with all Alexa enabled devices at global scale with minimal additional effort
- Affordable and easy to prototype complex voice forward applications
Ease of Use:
- Wizard based console walks through steps of creating bots
- Templates and blueprints available for common use cases
- Integration with Amazon Kendra to incorporate enterprise knowledge bases
Limits:
- NLU capability lags behind some other vendors
- American English focus for the most comprehensive experience
- Limited debugging and explainability into NLU predictions
Rasa Open Source
As an open-source alternative, Rasa focuses on delivering enterprise-grade NLU and conversation management accessible to a wider community of developers.
Key Differentiators:
- Open source NLU and framework code enables complete customization aligned to project needs
- Modular architecture allows swapping out components like NLU engines
- Lightweight and customizable runtimes for easier scaling
- Transparent model predictions and explanations around bot behavior
Ease of Use: The open source toolkit equips both chatbot developers and AI/ML experts with accessible tooling through sample code, tutorials, plugins and active forums.
Limits:
- Open source license requires more hands-on effort to integrate, maintain and support
- Specialized expertise still required for customization tasks
- Limited prebuilt small talk and vertical capabilities out-of-the-box
Evaluating Platforms on Key Criteria
Here is an in-depth comparative analysis across some major evaluation parameters enterprises should consider while selecting a natural language platform:
1. Accuracy & Prediction Capabilities
Platforms were measured on out-of-the-box accuracy across language understanding tasks like intents, entities, sentiment analysis etc. across diverse datasets spanning industry vocabularies and user phrasing patterns.
Watson Assistant and Dialogflow achieve top benchmarks on key accuracy KPIs based on their use of cutting edge techniques like transformers, customer interaction learning and semantic clustering. However, niche open vocabulary leaves room for improvement across platforms.
2. Development Experience
Ease of use for developers was compared across dimensions like availability of templatized workflows, ready templates for common scenarios, integration libraries etc. Debugging capabilities and tooling to analyze model behaviors were also evaluated.
Dialogflow streamlines creation of sophisticated bots without needing extensive coding through its visual builder and testing tools. Frameworks like Rasa enable modular experimentation and customization for AI/ML experts. Debugging and interpetability remains a gap across most platforms currently.
3. Scalability
Scalability analysis looked at tested workloads across concurrent users, endpoints, request volumes etc. out-of-the-box as well as sizing limits. Architectural flexibility to scale through configurations vs needing code changes was reviewed.
Cloud-native platforms like Lex and Dialogflow achieved the highest scalability marks by leveraging their cloud infrastructure. Customization complexity rises for niche scaling requirements.
4. Enterprise Suitability
Suitability factors like security certifications, compliance support, retirement policies for models/data etc. were checked given enterprise preferences. Handling of availability, data regulations when operating across global public cloud infrastructure was considered.
Watson Assistant led with the most comprehensive enterprise support across security, data privacy, retention policies etc. Compliance coverage varies across other players.
5. Total Cost of Ownership
TCO models accounted for license costs based on workload assumptions for conversational bots, customization needs and add-on capabilities required. Effort estimates around developer productivity and manpower overheads were included.
Starter licenses are free or low-cost for initial experimentation across platforms. Organizations should model TCO in-depth based on growth and integration complexity.
| Platform | Accuracy & Prediction | Development Experience | Scalability | Enterprise Suitability | Total Cost |
|---|---|---|---|---|---|
| Dialogflow | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | $$$ |
| LUIS | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | ⭐⭐ | $$ |
| Watson Assistant | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | $$$ |
| Lex | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | $$ |
| Rasa Open Source | ⭐⭐⭐ | ⭐⭐ | ⭐⭐ | ⭐ | $ |
Table: Comparative analysis of leading natural language software platforms across key parameters. 5 stars denotes highest rating.
Based on their stated enterprise priorities, organizations should dive deeper into vendor evaluation kits, POC test cases and consult analyst mind maps covering relative strengths.
Emerging Innovation Areas
Rapid advancements in AI algorithms, availability of data and compute power is expanding the scope of natural language platforms:
Multimodal Understanding: Combining language, images, sensors is enabling richer meaning for contextual understanding and reasoning – like clarifying ambiguities.
Explainability & Debugging: Model interpretability techniques help debug bottlenecks and build trust by explaining predictions. DARPA recently launched an NLP program focused on explainable models.
Knowledge Access: Structured, unstructured data is being linked to conversations leveraging graph databases and metadata enrichment for consistent omnichannel experiences.
Industry-Specific Capabilities: Prebuilt classifiers tuned on industry terminology, workflows and patterns are accelerating adoption in verticals like healthcare, financial services.
Semantic Synthesis: NLP advances are being applied to transform unstructured data into structured knowledge by analyzing patterns and semantics.
Growth Trends and Market Outlook
As per leading research firms, global spend on AI is estimated to reach $500 billion by 2024 based on increased enterprise adoption of advanced ML capabilities spanning computer vision, NLP, predictive analytics etc.
NLP is expected to be the fastest growing category here – as reflected in the chart below. Ability to extract insights and patterns from 80% of the world‘s unstructured data and derive action from it creates immense value.
What‘s Driving Demand?
- Maturing capabilities around contextual understanding and reasoning
- Demand for seamless conversational interfaces and virtual assistants
- Applications in customer engagement, sales & marketing
- Growing use cases across verticals – contract analysis, clinical documentation etc.
- Expanding multi-lingual, speech and vision use cases
- Continuous data generation from IoT, wearables, robotics etc.
As per IDC, the conversational AI market supporting interactive chatbots and intelligent assistants is projected to reach $15.7 billion by 2027 reflecting an explosive CAGR of 23.14% stimulated by adoption across banking, healthcare and retail industries.
Best Practices for Platform Selection
Here are some tips enterprises should consider during vendor evaluation cycles:
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Understand long term product vision & roadmap given pace of innovation. Build relationships with product leaders.
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Focus on industry & domain capabilities like pre-built vocabularies, workflows beyond just accuracy benchmarks.
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Leverage trial licenses for in-depth POC experimentation aligned to real-world scenarios.
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Quantify TCO spanning licensing, developer productivity, extensibility etc. beyond just sticker prices.
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Evaluate ease of integration & migration with existing environments to avoid lock-ins.
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Request commitments around model explainability & debugging support to build comfort.
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Account for compliance, global support & security requirements like data sovereignty, availability etc. based on footprint.
In summary, having a clear view of current and future initiatives, institutionalizing evaluation as adoption progresses and confirming responsiveness on enterprise expectations is advised.
The Future of Natural Language is Exciting!
We are nearing a stage where interactive AI leveraging natural language as the primary interface will be commonplace. As expectations around coherent, meaningful interactions increase, natural language understanding will continue to play a pivotal role.
Advancements in contextual understanding, personalized experiences, interpretability and multi-lingual support will expand the scope of how natural language platforms can deliver value. Exciting times lie ahead as speech and language technologies become integral to application development!
I hope this comprehensive guide equipped you with a deeper understanding of natural language platforms, how they compare and ways to select solutions aligned to your needs. Do reach out for any additional questions!
