Artificial Intelligence scenarios which can you use with us now

 Do not be afraid from AI. Today the boost of generative AI create a big tension of taking the advantage and not to miss the opportunity to surf the wave. Important is to stay calm and focused and use it right way and with right experts. The AI, ML, generative AI are really hammer but you do not want to use hammer on flea and on other side have it when it is needed.

CCW are experts for helping companies using cloud computing to maximize their potential. Important part of future IT systems and usage scenarios of cloud is machine learning and artificial inteligence. CCW is partner of AWS, Oracle Cloud Infrastructure and Microsoft Azure cloud. In this article we will focus on leveraging AI services from these clouds. It is the fastest , cheapest and most efficient way to use Artificial Intelligence and we can guide you and make it easy for you. We mention some scenarios which bring immediately benefit for company and can be implemented quick and easy. 

Language Services

Natural Language Understanding (NLU) is used to extract information from documents, emails, chats and customer communication. Based on the model used the language can be detected, customer sentiment and opinions, text classification. All these information can be used to orchestrate business processes such as prioritize emails for routing, automatic closure of claims, service requests.

The language AI can do following tasks :

• Analyze sentiment and mine opinions
• detect language
• classify text and extract information
• Personally Identifiable Information (PII) detection
• Document summarization
• Conversation summarization

Azure AI Language Studio

Azure Example of sentiment analysis using Azure AI Language Studio using Azure AI Language service is analysing customer complaint about visiting telco shop in the shopping mall.

 

 {
    "documents": [
        {
            "id": "id__13312",
            "sentiment": "mixed",
            "confidenceScores": {
                "positive": 0.17,
                "neutral": 0.27,
                "negative": 0.56
            },
            "sentences": [
                {
                    "sentiment": "positive",
                    "confidenceScores": {
                        "positive": 0.65,
                        "neutral": 0.34,
                        "negative": 0
                    },
                    "offset": 0,
                    "length": 72,
                    "text": "Dobrý deň, Chcem reklamovať Vašu službu vo Vašej predajni v OC Central. "
                },
                {
                    "sentiment": "negative",
                    "confidenceScores": {
                        "positive": 0.03,
                        "neutral": 0.35,
                        "negative": 0.62
                    },
                    "offset": 72,
                    "length": 75,
                    "text": "Chcel som si kúpiť paušál, no v obchode nebol nikto ochotný sa mi venovať. "
                },
                {
                    "sentiment": "negative",
                    "confidenceScores": {
                        "positive": 0,
                        "neutral": 0.38,
                        "negative": 0.62
                    },
                    "offset": 147,
                    "length": 102,
                    "text": "Keď som prišiel po radu, povedali mi, že vaše systémy sú mimo prevádzky a mal by som prísť ďalší deň. "
                },
                {
                    "sentiment": "negative",
                    "confidenceScores": {
                        "positive": 0,
                        "neutral": 0,
                        "negative": 1
                    },
                    "offset": 249,
                    "length": 22,
                    "text": "som velmi nespokojny. "
                },
                {
                    "sentiment": "neutral",
                    "confidenceScores": {
                        "positive": 0.16,
                        "neutral": 0.84,
                        "negative": 0
                    },
                    "offset": 271,
                    "length": 23,
                    "text": "s pozdravom Jozef Novák"
                }
            ],
            "warnings": []
        }
    ],
    "errors": [],
    "modelVersion": "2022-11-01"
}
 

AWS Amazon Comprehend

AWS Example of  Personally Identifiable Information (PII) analysis mode in AWS Comprehend service

API call

{
    "Text": "Good day,nI want to complain about your service at your store in OC Central. I wanted to buy a flat rate, but there was no one in the store willing to attend to me. When I came for advice I was told your systems were down and I should come another day. I am very dissatisfied.nSincerely,nJozef Novák",
    "LanguageCode": "en"
} 

Result

 { "Entities": [ { "Score": 0.9995710253715515, "Type": "ADDRESS", "BeginOffset": 65, "EndOffset": 75 }, { "Score": 0.9999931454658508, "Type": "NAME", "BeginOffset": 288, "EndOffset": 299 } ] 
} 

 Oracle Cloud Infrastructure AI Service – Language

OCI Example of complex analysis using language detection, text classification, named entity recognition, key phrase extraction, sentiment analysis.

 

AI driven Chatbot

 Oracle Digital Assistant

 Oracle Digital Assistant delivers a complete AI platform to create conversational experiences for business applications through text, chat, and voice interfaces.

The platform is so called Conversational AI  which has following features :

• Generative AI features
• Nature language understanding and machine learning
• AI-powered voice
• Analytics and insights
• conversational designer
• dialogue and domain trainer
• native multilingual support
• multichannel support
• automated bot-to-agent transfer

 Microsoft Azure OpenAI Conversational AI

AI-generated bots, questions and answers and contact center solutions to streamline and improve customer service. 

The GPT-35-Turbo and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, meaning they accepted a prompt string and returned a completion to append to the prompt. However, the GPT-35-Turbo and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format, and return a completion that represents a model-written message in the chat. While this format was designed specifically for multi-turn conversations, you'll find it can also work well for non-chat scenarios too.

In Azure OpenAI there are two different options for interacting with these type of models:

• Chat Completion API.
• Completion API with Chat Markup Language (ChatML).