Category: Chat

Now, if you ask Alexa to use Cortana for some reason you will just hear Alexa says “sorry, the service is no longer available”. The main reason behind this is the removal of the deal that Alexa had made with microphone Cortana a few years ago. Both of them wiped the integration away without making much noise among fans. Officially the climax of this connection was made on September 18 but didn’t get much spotlight.

Cortana and Alexa

Cortana and Alexa tied their connection for Cortana to reach a much wider audience than it had with their own Cortana products. But now, there’s nothing to wonder about the decision behind this disconnection as it’s pretty obvious. However, the importance of the deal always remains iconic as back then Alexa was just a new platform that was providing the ability to run Microsoft programs like Outlook through Cortana. It’s not like they have disconnected themselves from other integration as Alexa can still work on Windows PCs.

However, it’s not the time for Cortana to dissolve because it’s still available in other terms, it’s just done with its smart speaker abilities. You can still use it via Microsoft Outlook to set up any conference or join just by commanding.

Don’t forget to give us your 👏 !

---

Alexa and Cortana end their partnership quietly. was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Conversational AI startup platform Kore.ai crosses $73.5 million series C funding round as Nvidia has added $3.5 million to their funding round. And this investment is indicating the sign of teaming up for the integration of Voice AI technology for customer service centers.

Kore.ai and Nvidia

Kore.ai has made its name by creating a wide virtual assistant platform to build, test and deploy voice and text-based customer service. As soon as their name spreads, the company claims to be a giant tech who has managed more than a billion interactions. However, the company has already funded $73.5 million where an extra of 3.5 million has been added by Nvidia. The investment has been made to tie up with kore.ai and work for advanced conversational Ai projects and platforms.

According to Kore.ai, Nvidia Riva’s specially designed SKD will help to build an advanced technology AI brand voice to enhance customer service and other relations. Riva is the part of expanded conversational AI of Nvidia.

Nvidia’s GPU also makes any Ai much accurate and faster than others to provide a more instant and effective response to customers.

“This relationship will allow us to further optimize the contact center experience for customers and agents thereby improving the speed of business for enterprises, large and small,” Kore CEO Raj Koneru stated.

Nvidia is gradually extending their advanced Ai technological services from allowing cars to park themselves automatically to virtual humans whereas, kore.ai helps to boost enterprise’s business by optimizing customer service experience. Thus the tie-up between these two companies is quite significant.

Don’t forget to give us your 👏 !

---

Nvidia and kore.ai tie-up for Voice AI customer service. was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Chatbots are software tools created to interact with humans through chat. The first chatbots were able to create simple conversations based on a complex system of rules. Using Flask Python Framework and the Kompose Bot, you will be able to build intelligent chatbots.

In this post, we will learn how to add a Kompose chatbot to the Python framework Flask.

Pre-requisites:

You will need a Kommunicate account for deploying the python chatbot.

Kommunicate is a no-code, hybrid chatbot platform that is built over a powerful Conversational AI system.

Also, you will need Python and the Flask framework installed on your system. To read more info about the Flask framework, please follow this link.

We will be using Flask in this tutorial. If you are looking to add Dialogflow chatbot to the Django framework, you can see this tutorial.

Steps to Create a Chatbot using Kompose and Flask Python Framework:

Step1: Login to your Kommunicate dashboard. If you don’t already have a Kommunicate account, you can create one here.Navigate to the Kompose bot builder category and create a new bot by selecting the “Create Bot” button.

Step 2: Navigate to the Kompose Bot Builder, select your bot and click on the “Settings” option present at the top right corner.

Click on the Webhook option present on that page. Here, we need to put the Webhook Name and Webhook URL.

Step3: Create Webhook URL using Python with Flask and enable the webhook server using ngrok

The webhook requires a URL, and it should be an HTTPS protocol. The webhook URL will receive a POST request from the Kompose Bot every time an intent triggers the webhook.

We are using the Python programming language and the Flask framework to create the webhook.

Create a file (for example — app.py). Import all the necessary libraries (ex: os, JSON, flask_ngrok, request) needed for Python. Please check if you have Flask on your system. If not, install it using pip, and here’s the documentation for the same.

To handle all the agent webhook requests, we need to define and add a route/webhook method with a POST request. This URL /webhook will receive a POST request. It executes all the methods inside the method.

After setting up the Python process, let’s use flask ngrok to create a public URL for the webhook and listen to port 5000 (in this example). For Kompose webhook, you will need an HTTPS secured server since the local server (localhost) will not work. You can also use a server and point a domain with HTTPS to that server.

You will get the following URL:

https://85e6-203-189-248-8.ngrok.io/webhook , where the webhook is the POST route for Kompose we mentioned in the Python file.

Step 4: Configure Webhook inside Kompose Settings Page

Copy the URL you created (https://85e6-203-189-248-8.ngrok.io/webhook) — in this example and paste it into the Kompose webhook URL field. Here, I have written the Webhook name as “Test.”

Step 5: Create an intent by clicking on the +Add button under the “Answer” section and “Train the Bot.”

Here, I have created Flask intent and added a training phrase as “What is Flask?”

Step 6: Click on the “Bot Says” option and select the webhook that you created earlier. Here, I have selected the Test webhook created earlier. Now, click on “Train Bot.”

Step 7: Testing

Once the setup is done, you can easily add it to your website or apps using Kommunicate.

& test if the Python chatbot is working.

There you have it, a Python chatbot for your website created using the Flask framework. If you want to read more about how to build a chatbot, you can visit our blog post here.

Originally Published at https://www.kommunicate.io/ on 26/05/2022

---

How to Create a AI Chatbot in Python Framework was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

WhatsApp is unarguably the most popular messaging app which helps people to connect across the globe. It is absolutely fair to say WhatsApp is becoming an integral part of our life. The Platform has more than two billion monthly active users. With this much potential user base, any business cannot afford to ignore WhatsApp. We all are experiencing how beautifully businesses are leveraging platforms to engage with their customers.

Furthermore, many businesses are using a WhatsApp Chatbot to automate various business operations to provide a better experience to users. There are many platforms such as Twilio which allow businesses to integrate the chatbot on WhatsApp.

In this tutorial, we will learn how to connect our WhatsApp account with Twilio sandbox and further how we can send different responses through Twilio. Moreover, when we send any file to the Twilio Whatsapp account then get that file at the server.

Here we learn how we can get different types of responses from the bot such as:

• Text
• Image
• Video
• Document
• Audio

Steps to create a WhatsApp Bot

Step 1: Create a Twilio account by visiting the website.

Step 2: Go to the Twilio WhatsApp website.

Step 3: Connect Twilio with WhatsApp.

Then we need to go to WhatsApp and send the message to the given number as described in shown above screenshot. When we send the message, it appears as shown in the below screen.

On the Twilio sandbox, we will also get a note of congratulations.

Step 4: Install some libraries.

$ pip install flask 
$ pip install twilio

Step 5: Create a flask app.

Add below code below to create a simple flask app and run it.

from flask import Flask 
app = Flask(__name__) 

@app.route("/")
def wa_hello(): 
   return "Hello, World!" if __name__ == "__main__": 

app.run(debug=True)

After running the code we will find the localhost link in our terminal such as http://127.0.0.1:5000/ and when we visit that link, we can see the “Hello, World!” message there.

Step 6: NGROK setup.

First download ngrok in the local system.

Go to the Ngrok and type the command “ngrok http 5000” after running this command you will get the links.

From that Ngrok links, copy the HTTPS link and paste it to your browser. We can see the response is the same as we got for the local host.

Step 7: Twilio connection.

Copy the below code and prepare the python script.

from flask import Flask, request 
from twilio.twiml.messaging_response import MessagingResponse 

app = Flask(__name__) 

@app.route("/wa")
 def wa_hello():
   return "Hello, World!" 
@app.route("/wasms", methods=['POST'])
 def wa_sms_reply():
   """Respond to incoming calls with a simple text message.""" 

   # Fetch the message 
   msg = request.form.get('Body').lower() 

   # Reading the message from the whatsapp      
   print("msg-->",msg) 
   resp = MessagingResponse() 
   reply=resp.message() 
   # Create reply 
   if msg == "hi": 
    reply.body("hello!") 
   return str(resp) 

if __name__ == "__main__": app.run(debug=True)

With this code, we are reading the message sent by the user to the Twilio number and if it reads the “hi” message then it responds with the “hello!” message.

Now we need to run the above code script. For the link which we will get from the Ngrok, we need to copy that link and paste it to the Twilio account.

To paste the Ngrok link into the Twilio account, go to the website.

Paste your link in the given section with the endpoint that you have decided. In this section we are adding ‘/wasms’ with Ngrok URL, the endpoint we are using for the flask app.

And then scroll down and click on save.

Now go to WhatsApp and message the Twilio bot with a “hi” message.

The bot will respond with the “hello” message.

Step 8: Send different forms of files through the bot.

We can also get the different files such as images, videos, audio, and document files through our chatbot. This can be done by sharing the file URL in the media section.

By running the following code we can also get files through the bot.

from flask import Flask, request 
from twilio.twiml.messaging_response import MessagingResponse

app = Flask(__name__)

@app.route("/wa")
 def wa_hello():
   return "Hello, World!" 
@app.route("/wasms", methods=['POST'])
 def wa_sms_reply():
    """Respond to incoming calls with a simple text message.""" 

    # Fetch the message 
    msg = request.form.get('Body').lower() 

    # Reading the message from the whatsapp 
    print("msg-->",msg) 
    resp = MessagingResponse() 
    reply=resp.message() 

    # Create reply 
    # Text response 
    if msg == "hi": reply.body("hello!") # Image response elif msg       == "image":    
  reply.media('https://raw.githubusercontent.com/fbsamples/original-coast-clothing/main/public/styles/male-work.jpg',caption="jj ccp")
   
   # Audio response 
   elif msg == "audio":   reply.media('http://www.largesound.com/ashborytour/sound/brobob.mp3') 
   # Video response 
   elif msg == "video": reply.media('https://www.appsloveworld.com/wp-content/uploads/2018/10/640.mp4') 

   # File response
   elif msg == "file":  reply.media('http://www.africau.edu/images/default/sample.pdf'
) 
   # resp = MessagingResponse() 
   # resp.message("You said: {}".format(msg)) 

  else: 
  reply.body("from you") return str(resp) 

if __name__ == "__main__": 
 app.run(debug=True)

In the media section, we can pass the URL of the file and in the body section, we are passing the text that we want to send.

It will try to read the message sent by the user and when it encounters a specific message then it will respond to it with the reply that we had fed to it.

We need to type an image and the bot will respond with the image. Similarly, we can do with other forms of files.

Step 9: Upload an image and get it at the backend.

First, we will upload an image to the bot.

And see what type of JSON we are getting at the backend.

From this response, we will try to get the URL from the file and the extension for the file and save it to our computer.

For that, we need to add the following code to our previous code.

from flask import Flask, request 
from twilio.twiml.messaging_response import MessagingResponse

app = Flask(__name__)

@app.route("/wa")
 def wa_hello():
   return "Hello, World!" 
@app.route("/wasms", methods=['POST'])
 def wa_sms_reply():
    """Respond to incoming calls with a simple text message."""
   # Fetch the message 
   Fetch_msg= request.form print("Fetch_msg-->",Fetch_msg) 
   try: 
    # Storing the file that user send to the Twilio whatsapp number in our computer 
    msg_url=request.form.get('MediaUrl0') 
    # Getting the URL of the file 
    print("msg_url-->",msg_url)    
    msg_ext=request.form.get('MediaContentType0') 

    # Getting the extension for the file 
    print("msg_ext-->",msg_ext) 
    ext = msg_ext.split('/')[-1]
    print("ext-->",ext)
    if msg_url != None: 
     json_path = requests.get(msg_url) 
     filename = msg_url.split('/')[-1]  
     open(filename+"."+ext, 'wb').write(json_path.content) 

   # Storing the file except: print("no url-->>") 
   msg =  request.form.get('Body').lower() 

   # Reading the messsage from the whatsapp 
   print("msg-->",msg) 
   reply=resp.message()
   # Create reply
   # Text response
   if msg == "hi":
    reply.body("hello!") 

    # Image response 
   elif msg == "image":      reply.media('https://raw.githubusercontent.com/fbsamples/original-coast-clothing/main/public/styles/male-work.jpg',caption="jj ccp") 

  # Audio response     
  elif msg == "audio":     reply.media('http://www.largesound.com/ashborytour/sound/brobob.mp3')       
     
 # Video response
 elif msg == "video": 
  reply.media('https://www.appsloveworld.com/wp-content/uploads/2018/10/640.mp4')         

  # Document response     
  elif msg == "file":           reply.media('http://www.africau.edu/images/default/sample.pdf') 
  else: 
    reply.body("from you")

  return str(resp)   

if __name__ == "__main__": 
  app.run(debug=True)

After getting the URL from the response we will save the URL in another variable and then we will also extract the extension for the file which we have uploaded. Then we are trying to get the name for the file on which we will be saving the file on our computer. For that, we are getting the file name through the URL which we have got in response. After getting all the information we are saving the file with the help of the file name and the extension for the file.

After running the code we can find that the image that we send to the bot is saved to our computer.

We can also try another file because for that file also we are getting the URL and through the URL we are saving the file to our computer.

Here any message is detected which is not defined then it will respond with the “from you” message.

By following the above tutorial, we have learned that with the help of Twilio sandbox connect and WhatsApp how we can get text messages, images, videos, audios, and files from the Chatbot. Then, how we can upload the different types of files that we need to send to the chatbot, to store them on our computer. You can find the full integration code in our Github Repository.

Hope you liked our tutorial! You can try it on your own and if you face any difficulty then do let us know in the comment.

Want to learn how to build a chatbot for other platforms? Well, you can follow our Chatbot development tutorial to build Telegram Bot, Slack Bot, and Discord Bot.

Originally published at Create WhatsApp Bot with Twilio Using Python Tutorial with Examples on April 19, 2022.

---

Create WhatsApp Bot with Twilio Using Python Tutorial with Examples was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction

Slack is a useful tool for remote teams to interact more quickly and keep documents in one location. Slackbot is helpful for creating automatic messages for many purposes.

Here in this tutorial, we will learn how we can create a bot in slack and add it to our channel and get the text response from the bot, and further how we can get an image, video, audio, or file through the bot.

In addition to that, we will also learn how to save the file sent by the user in the channel to the bot on the server-side. Here we will also learn how we can get customized responses from the bot such as buttons and polls.

Here we learn how we can get different types of responses from the bot such as:

• Text
• Image
• Video
• Document
• Audio
• Buttons
• Poll

Steps to create a Slack Bot

Step 1: Open slack API console. ( https://api.slack.com/).

Step 2: Click on “Your apps”.

Step 3: Click on “Create New App”.

Step 4: Click on “From scratch “.

Step 5: Now give your app name and select workspace then click on the “Create App” button.

Step 6: Click on the “App Home” button and click on “Review Scopes to Add”.

Step 7: After clicking on the “Review Scope to Add” button, scroll down and find the Scope section. Then click on the “Add an OAuth Scopes” Button and add “chat: write” as shown in the below image.

Step 8: Now click on “Install to Workspace” and press on “Allow” to generate an OAuth token.

Step 9: Now create a python file (bot.py) and copy the above “OAuth Token” and paste it into the .py file as shown below image.

SLACK_TOKEN="<Your Token>"

Step 10: Now we install slack client python packages using “pip install slackclient” in our virtual environment.

Step 11: Now we create the channel in slack and add our app to it. To open your slack account go to the channel bar and click on the “+” sign. Then click on “Create a new channel”.

Step 12: Now type your channel name and click on the “Create” Button.

Step 13: Now Add your app to the channel by just typing “/invite @Your_App_Name” (use the app name that you want to connect with the channel) in channel chat.

Step 1 4: Now import the slack module and follow the code shown in the image below. Using this code we can send a “Hello” message to our “#test” channel. Also, we will discuss how to create a channel in the next step.

import slack
 
SLACK_TOKEN="<Your Token>"
 
client = slack.WebClient(token=SLACK_TOKEN)
client.chat_postMessage(channel='#justtest',text='Hello')

Step 15: Now we will see how we can respond to the “Hi” message from the user with a “Hello” message from the bot.

First off, all go to the Slack Developer Console in that go to event subscription and Enable Events.

Step 16: Now install some packages.

$ pip install flask 
$ pip install slackeventsapi

Step 17: Now just create a simple flask app like shown below.

import slack
from flask import Flask
from slackeventsapi import SlackEventAdapter
 
# This is slack token
SLACK_TOKEN="<Your Token>"
 
app = Flask(__name__)
 
client = slack.WebClient(token=SLACK_TOKEN)
client.chat_postMessage(channel='#justtest',text='Hello World!')
 
 
if __name__ == "__main__":
    app.run(debug=True)

The channel name and channel=’#justtest’ string should be matched otherwise, it will not work.

Step 18: Now again go to the Slack Developer Console. In that go to “Basic Information” in the left panel, then scroll down to “Signing Secret” copy it, and add it to the code.

Step 19: Now after adding Signing Secret to our code we need to pass it to the slack event adapter.

import slack
from flask import Flask
from slackeventsapi import SlackEventAdapter
 
SLACK_TOKEN="<Your Token>"
SIGNING_SECRET="<Your SIGNING SECRET>"
 
app = Flask(__name__)
slack_event_adapter = SlackEventAdapter(SIGNING_SECRET, '/slack/events', app)
 
client = slack.WebClient(token=SLACK_TOKEN)
client.chat_postMessage(channel='#justtest',text='Hello World!')
 
 
if __name__ == "__main__":
    app.run(debug=True)

Step 20: Now we need to run this code and we will notice that now we will have a webserver running on http://127.0.0.1:5000/.

Now we will need NGROK for running that program and get the public IP address by running the code “ngrok http 5000” on NGROK and then we will get a public IP address.

Now we need to again go to the Slack Developer Console, then go to Event Subscription and paste Ngrok URL with the endpoint that we define in the code.

Step 21: After that scroll down and go to the “Subscribe to bot events” then go to “Add Bot User Events” and then add a message. channels to it.

After that click on Save Changes.

Step 22: Now go to “OAuths and Permission” and scroll down to “Scopes” there you will see that a scope name “channels: history” is added, if not then add it to the “Scopes”.

Now Just Scroll up to “OAuth Tokens” & “Redirect URLs”. Click on Reinstall App and then click on Allow.

Step 23: Now we need to create a route, create a function that can handle all these events.

Here in the code, we can get the event information about what that event was about like the message that was sent in the text and all of that.

Channel_id will return the channel id in which the message was sent.

User_id will return the id of the user who sends the message.

Text will give us the text which was written by the user.

Then below is the code to check the message when the user says “hi” and the slack bot responds with “Hello”.

import slack
from flask import Flask
from slackeventsapi import SlackEventAdapter
 
SLACK_TOKEN="<Your Token>"
SIGNING_SECRET="<Your SIGNING SECRET>"
 
app = Flask(__name__)
slack_event_adapter = SlackEventAdapter(SIGNING_SECRET, '/slack/events', app)
 
client = slack.WebClient(token=SLACK_TOKEN)
 
@ slack_event_adapter.on('message')
def message(payload):
    print(payload)
    event = payload.get('event', {})
    channel_id = event.get('channel')
    user_id = event.get('user')
    text = event.get('text')
 
    if text == "hi":
        client.chat_postMessage(channel=channel_id,text="Hello")
 
if __name__ == "__main__":
    app.run(debug=True)

After running the code when we type “hi” in our channel, we will get the reply “Hello” from the bot.

Step 24: Get an image through the bot.

Now to get the image through the bot we need to store the image at the specific path which we want to send to the user and give that path of the image in the file section.

Now by adding the following code to the above code we can get an image through the bot.

if text == "hi":
    client.chat_postMessage(channel=channel_id, text="Hello")
 
if text == "image":
    try:
        response = client.files_upload(
            file='/home/pragnakalpdev23/mysite/slack_file_display/download (2).jpg',
            initial_comment='This is a sample Image',
            channels=channel_id
        )
    except SlackApiError as e:
        # You will get a SlackApiError if "ok" is False
        assert e.response["ok"] is False
        # str like 'invalid_auth', 'channel_not_found'
        assert e.response["error"]
        print(f"Got an error: {e.response['error']}")

Here we can also send the caption with the text so to send the text with the image we need to pass the text in the “initial_comment”.

After running the code when we type “image” in our channel, we will get a reply from the bot with an image that we have passed.

Step 25: Get avideo from the bot.

To get the video from the bot we need to do the similar thing as we have done in the image and we also need to specify the path where the video is stored.

First, we will need to import the following library to our code.

from slack.errors import SlackApiError

Now add the below code.

if text == "video":
    try:
        response = client.files_upload(
            file='/home/pragnakalpdev23/mysite/slack_file_display/sample-mp4-file-small.mp4',
            # initial_comment='This is a sample video',
            channels=channel_id
        )
    except SlackApiError as e:
        # You will get a SlackApiError if "ok" is False
        assert e.response["ok"] is False
        # str like 'invalid_auth', 'channel_not_found'
        assert e.response["error"]
        print(f"Got an error: {e.response['error']}")

Here also get the caption with the video by passing the caption in “initial_comment”.

After running the code, if we type “video” in our channel then we will get a reply from the bot with the video that we have passed.

Step 26: Get audio and document through the bot.

In a similar way, we can get the audio and document through the bot by passing the file for audio and video.

Here also we add a caption with audio and document that we are sending by passing the text in the “initial_comment”.

if text == "file":
    try:
        response = client.files_upload(
            file='/home/pragnakalpdev23/mysite/slack_file_display/sample.pdf',
            # initial_comment='This is a sample file',
            channels=channel_id
        )
    except SlackApiError as e:
        # You will get a SlackApiError if "ok" is False
        assert e.response["ok"] is False
        # str like 'invalid_auth', 'channel_not_found'
        assert e.response["error"]
        print(f"Got an error: {e.response['error']}")
 
if text == "audio":
    try:
        response = client.files_upload(
            file='/home/pragnakalpdev23/mysite/slack_file_display/file_example_MP3_700KB.mp3',
            # initial_comment='This is a sample audio',
            channels=channel_id
        )
    except SlackApiError as e:
        # You will get a SlackApiError if "ok" is False
        assert e.response["ok"] is False
        # str like 'invalid_auth', 'channel_not_found'
        assert e.response["error"]
        print(f"Got an error: {e.response['error']}")

Now run the code, when we type “audio” in our channel then we will get the reply from the bot with audio that we have passed and when we type “file” in our channel then we will get the reply from the bot with a file that we have passed.

Step 27: Get Images with different methods.

We can also get images by passing the image in different formats. Go to the website for the Slack Block Builder. There we can see a screen like this.

Here on the Left side scroll to the image and click on “no title”. After that, you will see the block of code which is generated. So copy that block of code.

Now merge the below code with the previous code and run it.

if text == "img":
    message_to_send = {"channel": channel_id, "blocks":  [
        {
            "type": "image",
            "image_url": "https://i1.wp.com/thetempest.co/wp-content/uploads/2017/08/The-wise-words-of-Michael-Scott-Imgur-2.jpg?w=1024&ssl=1",
            "alt_text": "inspiration"
        }
    ]}
 
 
try:
    return client.chat_postMessage(**message_to_send)
except:
    print("No hi found")

After running the code when we type “img” in our channel, we will get the reply from the bot with an image which URL we have passed in the URL section.

Step 28: To get the radio button.

Now to get the radio button from the bot we need to again go to the website.

Now again scroll down to the “Input” and select “radio buttons” and then copy that payload of code.

Now we need to add the below code in our previous code of the “blocks” section.

if text == "radiobtn":
    message_to_send = {"channel": channel_id, "blocks": [
        {
            "type": "input",
            "element": {
                    "type": "radio_buttons",
                    "options": [
                            {
                                "text": {
                                    "type": "plain_text",
                                    "text": "*this is plain_text text*",
                                            "emoji": True
                                },
                                "value": "value-0"
                            },
                        {
                                "text": {
                                    "type": "plain_text",
                                    "text": "*this is plain_text text*",
                                            "emoji": True
                                },
                                "value": "value-1"
                                },
                        {
                                "text": {
                                    "type": "plain_text",
                                    "text": "*this is plain_text text*",
                                            "emoji": True
                                },
                                "value": "value-2"
                                }
                    ],
                "action_id": "radio_buttons-action"
            },
            "label": {
                "type": "plain_text",
                "text": "Label",
                "emoji": True
            }
        }
    ]}
 
try:
    return client.chat_postMessage(**message_to_send)
except:
    print("No hi found")

After running the code when we type “radiobtn” in our channel then we will get a reply from the bot with a radio button.

We can also add more responses and combine many responses together. To get the response we just need to create the payload from the website, copy the payload and paste it in the “blocks” section as we have done in the above code.

Step 29: Get an image at the server-side if we send the image to the bot.

First, send the image to the bot.

Now check at the terminal we will get the JSON response as shown in the below screenshot when we send an image to the bot.

Now we need to get the file name, URL for the file, and user id for the image that we have uploaded by adding the given code in our previous code.

We will get the response like the following in our terminal.

try:
    img_name = payload['event']['files'][0]['name']
    print("img_name:-->", img_name)
    img_url = payload['event']['files'][0]['url_private']
    print("img_url:-->", img_url)
    user_n = payload['event']['files'][0]['user']
    print("user_n:-->", user_n)
    file_name = img_url.split('/')[-1]
    print("file_name:-->", file_name)
except:
    print("not found 1-->>")

Now we have saved the image that we send by passing the URL and saving the file in our current directory at our server-side.

For that, we need to add the following code to our previous code.

try:
    img_name = payload['event']['files'][0]['name']
    print("img_name:-->", img_name)
    img_url = payload['event']['files'][0]['url_private']
    print("img_url:-->", img_url)
    user_n = payload['event']['files'][0]['user']
    print("user_n:-->", user_n)
    file_name = img_url.split('/')[-1]
    print("file_name:-->", file_name)
    try:
        json_path = requests.get(img_url)
    except:
        print("nnnn mm ")
    if user_n != "<Your Bot User Id>":
        with open(file_name, "wb") as f:
            f.write(json_path.content)
except:
    print("not found 1-->>")

Here in the code, we are specifying the user id of the bot because when any file such as an image, video, etc is being sent by the bot then also we are getting a similar type of JSON at the backend which we get when a user sends any file. So we only want the files that have been sent by the user not by the bot to be stored on our server-side. Therefore we are specifying the user id for the bot and if the user id is not equal to the bot user id then only the file would be saved.

After adding the code and running it we can see that our image will be saved on our server-side.

Step 30: Get video at the server-side if we send video to the bot.

First, send the video to the bot.

We will get the following JSON at the backend.

Similarly, we need to extract the file name, URL, and user id. Then run the code in the similar way we get in the video at our server-side.

Similar way we can also get audio and file on our server-side.

We have implemented the code to get the image, video, and audio from the file and also the same on the server-side if we send the file to the bot. In this Github Repository, you can find the full code implementation with all functionalities that we discussed in the above tutorial.

With the tutorial, we learned about the creation of the slack bot and getting the response through the bot such as text, image, video, audio, and file. Moreover, when a user sends any type of file to the channel bot we can get it saved at the server-side in our computer.

Try to build Slack Bot by yourself with the tutorial. Please comment below if you will face any difficulty while following the tutorial. You can also implement such a bot on the Telegram channel. You may do this by following our Telegram Bot instructions given in the tutorial.

Originally published at Create Slack Bot Using Python Tutorial With Examples on April 13, 2022.

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Create Slack Bot Using Python Tutorial with Examples was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

“Chatbots?” – “Damit habe ich nur schlechte Erfahrungen gemacht!”

Solche Aussagen treten mit dem Aufschwung der Technologie und dem einfachen Zugang häufiger auf. Experten aus dem Bereich wissen aber: es geht auch anders. Denn tatsächlich können 80 % aller Kundenanfragen mit Hilfe von gut designten Bots gelöst werden (Accenture). Durch unsere jahrelange Erfahrung haben wir 20 wertvolle und einfach umzusetzende Tipps gesammelt, welche die Benutzerfreundlichkeit und von Voice und Chatbots immens verbessern:

 

1. Gib deinen Nutzer die Möglichkeit sowohl über Buttons als auch die Freitexteingabe mit deinem Chatbot zu kommunizieren. So kann der Nutzer sein Anliegen am besten eingeben und entsprechend frei formulieren.
2. Führe vor deinem Go Live einen User Acceptance Test durch. So findest du heraus, ob der Bot die Bedürfnisse deiner Zielgruppe wirklich erfüllt.
3. Wende die “One-Breath-Technique” bei der Ausarbeitung von Voice Bot Messages an. Wenn du die Antwort laut aussprechen kannst, ohne einen Atemzug zu nehmen, hat die Antwort die optimale Länge. 
4. Mach deinen Bot sympathischer, in dem er emphatisch auf die Fragen und Antworten deiner Nutzer reagiert. Je nach Anwendungsbereich und eigener Unternehmenskultur kannst du deinen Bot frech oder besonders seriös wirken lassen.
5. Vermeide zu komplexe und ineffiziente Software Integrationen bei Voice Bots. Sie können zu einer zeitlichen Verzögerung der Antwort führen, was beim Nutzer schnell zu Frust und einem vorzeitigen Abbruch führen kann.
6. “Wie bitte?” Manchmal versteht man nicht alles sofort. Dein Voice Bot muss daher in der Lage sein, eine Antwort für die Nutzer zu wiederholen.
7. Ein Chatbot ist wie ein Baby. Er muss erst lernen. Trainiere deinen Bot vor allem direkt nach dem Go live. Gerade am Anfang müssen sich Nutzer und Bot erst einmal einspielen. Es kommen Kundenanfragen rein mit denen eventuell nicht gerechnet wurde. Die Antwort hierauf kann der Bot nicht eigenständig nachtrainieren, weshalb hier die Mithilfe des Menschen gefordert ist.
8. Verwende verschiedene Varianten bei der Fehlermeldung oder anderen häufig verwendeten Antworten deines Bots. So wird die Unterhaltung natürlicher für die Nutzer sein.
9. Nutze die Startnachricht, um deine Nutzer zu begrüßen und ihnen zu erklären, was dein Bot kann und was nicht. So verhinderst du falsche Erwartungen.
10. Sei ehrlich und sage deinen Nutzern, dass sie sich mit einem virtuellen Assistenten und nicht mit einem echten Menschen unterhalten. Falls diese Tatsache verschleiert wird und der Nutzer es herausfindet fühlt er sich eventuell hintergangen  und ist frustriert (Dieses Phänomen nennt man auch Uncanny Valley).
11. Reduziere die Entwicklungszeit von Bots und konzentriere dich eher auf das Trainieren und Optimieren nach dem Go Live. So kannst du deinen Bot individuell an die ersten Konversationen mit deinen Kunden anpassen und fehlende Intents nachpflegen.
12. Prüfe regelmäßig, ob noch alle Funktionen deines Bots einwandfrei laufen. Am leichtesten geht das mit automatisierten Tests. Wenn Konversationen  ins Stocken kommen ist die Wahrscheinlichkeit, dass Nutzer abspringen, sehr hoch.
13. Definiere KPIs und Ziele, die du mit deinem Bot Projekt erreichen möchtest. Gängige KPIs sind Metriken wie die Rate der Fehlernachricht oder die Rate von Human Handovers. 
14. Erstelle Personas für die Nutzer deines Bots. Mit ihnen kannst du die Konversationen bestmöglich an die Bedürfnisse deiner Nutzer anpassen.
15. Wenn du deine FAQs für deinen Bot übernehmen möchtest, musst du sie an das neue Format anpassen. Adaptiere sie so, dass die Informationen wie in einem echten Gespräch vermittelt werden.
16. Benutze echte Kundenanfragen aus der Vergangenheit um Inhalte für deinen Bot zu erstellen. So stellst du sicher, dass der Bot die Anliegen deiner Nutzer auch beantworten kann.
17. Wenn Chatbots mal keine Antwort parat haben, sollten sie die Möglichkeit bieten an einen echten Mitarbeiter nahtlos weiterzuleiten. Das erhöht die Kundenzufriedenheit und das Vertrauen. Den Grund für den Handover sollte man sich im Gesprächsverlauf genauer anschauen und den entsprechenden Inhalt nachpflegen, um die Konversation mit dem Bot zu optimieren.
18. Nutze die Testmethode “Wizard of Oz” noch vor dem Start der Chatbot Entwicklung, um dein Konzept der Conversation Map an echten Nutzern zu validieren. Hierbei lässt unter Ersparnis von Zeit und finanziellen Mitteln herausfinden, ob einzelne Funktionalitäten überhaupt genutzt werden und notwendig sind.
19. Baue in den Chatbot Feedback Fragen wie “War diese Information hilfreich?” ein. Damit kannst du direkt im Gespräch die  Kundenzufriedenheit messen. 
20. Du kannst mit Bots entweder bestehende Kommunikation automatisieren oder neue Kommunikationswege schaffen. Letzteres verlangt ein Vermarktungskonzept. Überlege dir frühzeitig wie deine Nutzer auf den Voice oder Chatbot aufmerksam gemacht werden können. Soll dies in einer groß angelegten Marketingkampagne durchgesetzt werden oder nur als defensiver Hinweis auf der Unternehmenswebsite.

Schon die Beachtung nur einiger dieser Tipps, wird schnell für mehr Akzeptanz der Bots sorgen und die Nutzerzufriedenheit deutlich steigern. Man sollte allerdings vor allem die Zeit nach dem Go Live dauerhaft in Betreuung und Optimierung investieren. Kundenbedürfnisse ändern sich stetig und das Wissen des Bots muss gepflegt und erweitert werden. Denn vor allem bei der Technologie von Conversational AI bedeutet Stillstand Rückschritt. 

Lade dir hier die passende Infografik mit einer Übersicht aller 20 Tipps herunter. 

Seid ihr in eurer Bot-Entwicklung auf Fragen und Schwierigkeiten gestoßen? Dann schreibt uns einen Kommentar unten im Kommentarbereich und wir geben euch für euer Anliegen einen individuellen Tipp! 

20 Tipps zur Entwicklung & Optimierung von Voice & Chatbots

User Experience

Juni 20, 2022

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by Michelle Skodowski

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0 Comments12 Minutes

Akzeptanz von Chatbots – Wollen meine Kunden überhaupt einen virtuellen Assistenten benutzen?

Use Cases,User Experience,Best Practices

Mai 31, 2022

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by Michelle Skodowski

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0 Comments7 Minutes

Wie virtuelle Assistenten dem Einkauf helfen können Preise bei Lieferanten anzufragen

Use Cases,Einkauf

Februar 9, 2022

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by Michelle Skodowski

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0 Comments11 Minutes

Der Beitrag 20 Tipps zur Entwicklung & Optimierung von Voice & Chatbots erschien zuerst auf BOTfriends.

Samsung’s SmartThings was the subject of a widespread rumor that it was about to be closed down entirely. It turns out that this was just partially right.

Samsung decided to keep making software but no longer make hardware under its own brand, so production of the Hub was moved to Aeotec, and we now have the Aeotec Smart Home Hub.

The Aeotec Smart Home Hub is essentially the same as the SmartThings V3 Hub in that it can work with Z-Wave and Zigbee devices and is controlled by the SmartThings software.

Is it worthwhile to upgrade if you already have a hub, and what about if you’re fresh to the platform? In our full Aeotec Smart Home Hub review, I’ll answer all of your questions.

Design and Installation of the Aeotec Smart Home Hub

The only difference between the Aeotec Smart Home Hub and the Samsung SmartThings Hub V3 is the branding.

That’s since both products are identical; the only difference is that the hub is now manufactured by Aeotec rather than Samsung.

Two crucial modules are hidden inside the case: Z-Wave and Zigbee. You’re essentially wrapped for a huge proportion of standalone sensors, lights, and other smart devices with these two options.

Although you can link via Wi-Fi, the Smart Home Hub has an Ethernet port on the back. I prefer Ethernet for reliability, but Wi-Fi is a fine replacement if you don’t have access to your router via a physical cable.

This model lacks a battery, and the SmartThings V2 hub was the only one with one. Is that a problem? Only if you plan to utilize SmartThings as the foundation for your home security system.

I wouldn’t bother because it’s too difficult to establish, and a devoted warning, like the Ring Alarm, is far more convenient.

The Hub doesn’t really need battery backup unless it’s being used as an alarm: if the power goes out, everything it’s controlling goes out of power as well.

You append the Hub to your account using the SmartThings app once it’s been powered up. The Hub must be selected as an Aeotec model rather than a Samsung model, but the setup is otherwise identical to the V3 Hub. The installation process is guided by a short setup wizard.

Features of the Aeotec Smart Home Hub

The Aeotec smart home hub, like other intelligent home hubs, doesn’t do much. That is, it serves as a hub for Zigbee and Z-Wave devices, but the SmartThings platform is responsible for the intelligence.

Numerous devices are connected via cloud accounts, so you don’t even require a hub to use SmartThings. If you’re a Yale Conexis L1 smart lock, for example, you will need a hub because it connects to SmartThings via the optional Z-Wave module; however, if you have a newer Yale Linus lock, you will not need a hub because it connects to SmartThings via the web.

In fact, most of the well-known brands that support SmartThings do so through the cloud. Nest (cameras, doorbells, and thermostats), Ring (cameras and doorbells), and Arlo are all examples of this.

Everything is controlled through the app, in which you can assemble devices into rooms and regulate each product as expected: lock and unlock smart locks, switch lighting fixtures and modify their color, and so on.

So, what’s the big deal about the hub? Z-Wave and Zigbee devices, on the other hand, are ideal for sensors and remote controls because they can run for years on a single set of batteries, providing the essentials for a smart home.

There are a plethora of SmartThings-compatible devices available, with Aeotec producing a large number of them. You can create some very intelligent rules using sensors. For example, if motion isn’t detected in a room for a certain amount of time, you can turn your Tint lights and Sonos player off automatically. A door sensor, for example, can turn on a light.

Upgrade to WebCORE and you’ll be able to enact much more sophisticated routines, including ones that are limited by time of day and even device status. It’s not a tool for the inexperienced, but it’s difficult to imagine another intelligent home platform that offers such sophisticated automation.

SmartThings has two significant drawbacks.

The first is that device support is not universal, which applies to every other smart platform. If you want to accomplish something, you’ll almost certainly use one or more platforms (IFTTT, HomeKit via Homebridge, and so on).

For example, I have SmartThings running through Homebridge so that when my Yale Conexis L1 unlocks using HomeKit, it automatically disables my Ring Alarm.

Although third-party Ring assimilation for SmartThings exists, it is difficult to set up, so I’m forced to use two platforms to accomplish the same task.

Second, Samsung has harmed Amazon’s Alexa skills. You could choose which gadgets are available to Alexa in an older version of SmartThings; you can’t in the current version, so it’s easy to get multiple copies.

If you connect Hue lights to SmartThings, they’ll show up twice in Alexa: once under the Tint Skill and once under SmartThings Skill.

Our comprehensive guide to Smart Things explains everything you need to know about the system and what it can and can’t do. All that needs to be said is that the Aeotec Smart Home Hub is a solid foundation for Zigbee and Z-Wave devices.

Should you upgrade your Aeotec Smart Home Hub?

Should you upgrade your existing SmartThings Hub? This is a more tough problem for holders of an existing SmartThings Hub. If you have a SmartThings Hub V1, the solution is clear: yes, because the V1 hub is no longer functional.

If you already have a SmartThings V2 or V3 hub, it’s unlikely that you’ll want to upgrade because the Aeotec Smart Home Hub doesn’t add any new features or functionality.

That isn’t to say that support for the V2 and V3 hubs won’t be phased out in the future, but you won’t acquire anything by switching for the time being. I’d only suggest upgrading if your current hub is giving you trouble.

The problem for V1 hub owners is that there is no easy method to switch to the Aeotec Smart Home Hub. Instead, you must manually uninstall all of your existing devices and migrate them one by one to the new hub — for more information, see our guide to moving to the Aeotec Smart Home Hub.

There is at least a program for V2 and V3 users that tries to accomplish the job automatically, however, I was awaiting Samsung support to grant me access to it to check if it works at the time of writing. The upgrade process is clearly not as straightforward as it should be.

Should you buy the Aeotec Smart Home Hub if you don’t already have one?

SmartThings is still one of the top smart home automation platforms, despite losing a few features and having a worse Alexa Skill since switching from the Classic app to the new one. The Aeotec Smart Home Hub is worth buying, as it always is, depending on what you want to do with it.

In many cases, the Hub isn’t required at all because most new technology has its own hub or connects via Wi-Fi, allowing you to add items to your SmartThings account through the cloud.

There’s also the fact that rival systems can utilise detectors from your current smart home kit, so you don’t have to buy Zigbee or Z-Wave devices separately.

Use Amazon Alexa Routines, for instance, and you may use Hue motion sensors or Ring Alarm sensors to control them. HomeKit can also employ Hue motion sensors, whereas Homebridge vastly enhances your options.

In the end, it comes down to hardware preference, and some gadgets operate best when connected to the Aeotec Smart Home Hub.

This hub is a terrific method to employ a broader variety of sensors and controls, or if you have specific smart locks, and SmartThings is a strong platform.

PROS

• Support — Z-Wave and Zigbee
• SmartThings integration is flawless.
• Wi-Fi and Ethernet connectivity

CONS

• It’s a pain to get to this hub.
• Hardware support for SmartThings is limited.

Don’t forget to give us your 👏 !

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Review of the Aeotec Smart Home Hub was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

What’s not to appreciate about a low-cost, high-spec vehicle? A low-cost streamer that gets the fundamentals right, including access to all of the major streaming services.

While Amazon, Apple, and Google all have their own streaming boxes, they also bind you to their respective companies. If you don’t like the notion of that, the Roku Express 4K might be a better option.

This little streaming box is not just one of the cheapest Ultra HD streaming boxes at $34.99, but it also supports all of the mainstreaming providers, has HDR (although not Dolby Vision), and Google Cast and AirPlay 2 capabilities.

On the other hand, this model comes with a rather cheap IR remote control, a laborious signup process, and the streamer has one of the most boring user interfaces available.

However, it gets the essentials right, so if you’re looking for a low-cost 4K streaming solution, there’s nothing better.

Design of the Roku Express 4K

The Roku Express 4K is unlike most compact streaming devices in that it does not plug into the back of the TV.

Because this model contains an IR remote, it must be put in direct line of sight, thus it’s tucked away in a little box that you can place in front of your TV (or on your TV if you use the bundled sticky pad).

There are only two connectors on the back: an HDMI output and a Micro-USB power port, which can also be utilised with an Ethernet adaptor if the dual-band Wi-Fi isn’t sufficient.

The Roku Streaming Stick+ or Express+ come with a basic IR remote; if you want Bluetooth and voice control, you’ll have to pay a little more.

The remote control is about the appropriate size for me, though it’s not nearly as comfortable to hold and use as the Alexa remote on the Fire TV Stick 4K, the Apple TV 4K’s touch remote, or the Chromecast with Google TV’s remote.

The Roku control has slightly squishy buttons and a shoddy feel to it. Controls differ by country as well. While all locations feature four shortcut buttons, the shortcuts vary by region: in the United Kingdom, Netflix, Spotify, Apple TV+, and Rakuten TV; in the United States, Netflix, Disney+, Apple TV+, and Hulu. At the very least, that’s one button I’ll never use.

The IR remote also lacks volume and TV power buttons, which are only found on the Roku Express 4K+.

Setup for the Roku Express 4K

The Roku Express 4K is really easy to set up. To get your streaming box linked to the internet and your Roku account, simply plug it into your TV (a short 70cm HDMI cable is included), connect the power, and follow the on-screen instructions.

You’ll need to establish a Roku account using your phone or laptop if you don’t already have one. It’s a tedious procedure that requires you to add a payment method to your account in case you want to utilise any of the premium Roku channels (hint: you probably won’t).

Once you’ve logged in, you may choose which Channels (apps) you want to use, and they’ll be downloaded and installed immediately. Each app requires sign-in, and the method varies by app. Even so, you only have to complete this task once.

Features of the Roku Express 4K

If you’ve used any other streaming device, you’re undoubtedly used to interfaces that provide shortcuts to the most recent video and highlight what’s available, so Roku’s UI will come as a surprise.

It has to be the most boring user interface out there, with only shortcuts to the programmes you’ve loaded and nothing else.

Netflix, Apple TV+, Disney+, Amazon Prime Video, Hulu, and HBO Max are among the major streaming services offered.

Apps vary by region, but you should be able to find what you’re looking for in each. In the United Kingdom, for example, all of the major catch-up channels are available.

You may also add a lot of different Channels to Roku.

Many are quite a niche, the majority is absolute nonsense, and they primarily serve as clutter that you must sift through in order to find the actual streaming services you seek.

The search feature works across all of your installed apps, allowing you to locate what you’re looking for fast without having to launch numerous apps.

It’s a nuisance to type in anything with the IR remote, but you can get the Roku app for your phone.

This device not only replicates the classic remote but also has voice search and the ability to text using your phone’s keyboard. Voice search works well and is faster than using the on-screen keypad to find something to watch.

Voice control is also available through Amazon Alexa, Google Assistant, and Apple Siri. Amazon Alexa and Google Assistant both allow you to play/pause and search, obviating the need for the Roku voice remote.

Controlling Siri is a little more difficult. Siri wouldn’t let me operate my Roku Express 4K with my HomePod Mini; I had to use my iPhone instead. Even then, Apple prefers to take control, utilising AirPlay to transfer the requested content to the Express 4K rather than relying on the player’s own search.

The Roku player is also shown in the Home app, with an on/off switch. This, however, only functions if the Express 4K is displaying something over AirPlay, not if you’ve launched an app and content directly. Still, having Siri integration is a fantastic thing to have.

It’s also wonderful to have AirPlay 2 and Google Cast support, which allows you to stream material from your phone or even mirror it to your TV. Roku Express 4K: 4K and HDR Streaming

There’s 4K support and HDR, which is great, but just HDR and HDR10+ are available, not Dolby Vision. Does it make a difference? Yes, of course. If you have a Dolby Vision TV, the absence of compatibility here means you won’t be able to obtain the same visual quality as you would with an alternative streamer like the Chromecast with Google TV.

If you may not have a Dolby Vision TV, any streaming platform on any device, streaming at a maximum of 60 frames per second, would suffice. Of course, quality varies depending on the content and app.

Every app supports Dolby Atmos sound, so if you have a compatible device, like the Sonos Arc, you’ll get the finest audio quality out of the Roku Express 4K.

4K Roku Express

The Roku Express 4K is by far the most affordable streaming device, and it hits the majority of the right notes. It comes with all of the major apps you could desire, as well as Dolby Atmos audio and 4K HDR video. However, the remote isn’t great, and the lack of Dolby Vision may turn off some viewers. Still, at this price, it’s difficult to disagree with the total package, and if pricing is your only concern, this is a terrific streamer.

PROS

• Compact
• A wide range of apps is available.
• Support for Dolby Atmos
• HDR 4K

CONS

• The best option is not to use remote control.
• There is no Dolby Vision.
• The user interface is simple.

Don’t forget to give us your 👏 !

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Review of the Roku Express 4K was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How is Computer Vision changing the Insurance Sector for Good- Top 5 Use Cases that take Center-Stage? — TechyWorld+

Artificial Intelligence makes machines smarter, period! Yet, the way they do it is as different and intriguing as the concerned vertical. For instance, the likes of Natural Language Processing come in handy if you were to design and develop witty chatbots and digital assistants. Similarly, if you want to make the insurance sector more transparent and accommodative toward the users, Computer Vision is the AI subdomain that you must focus on.

Today, we are going to discuss the latter: the Insurance Sector, to be exact, and how Computer Vision is increasingly streamlining it with just the right set of innovations. For the unversed, Computer Vision is one of the few AI and Machine Learning applications that allow computing devices to understand and preempt scenarios based on visual inputs.

And if you want a heads-up as to which field has already been making the use of this technology, take a cue from autonomous driving and intelligent vehicles that can now better identify pedestrians, signals, and even driver emotions with well-trained Computer Vision models.

Top Computer Vision Use Cases that are Relevant to the Insurance Sector

The insurance sector, till 2019, was plagued with procedural challenges. Piling paperwork was an issue, and so was the element of bias that kept surfacing every time a claim was processed. Implementation of Artificial Intelligence eventually made life easier for the insured and even the insured.

​​However, the enhancements weren’t limited to automation and transparent claim processing. Here are some of the top use cases that have taken the insurance sector by a storm and might help the entire industry turn up the heat in the years to come:

The insurers are progressively relying on potent computer vision models to identify the growing number of frauds in the concerned sector. Computer Vision coupled with NLP empowers machines to scan and weed out fake images and invalid documents, thereby minimizing the instances of unscrupulous claims.

At present, fraud detection using Computer Vision is still an assistive use case as the alerts are sent over to humans for final evaluation. In time, we can see the models taking up the reins and making decisions independently.

‘How much claim to process’ happens to be one of the most important questions for the insurers to answer. Managing claims is a step-pronged approach involving damage estimation, claim registration, back-and-forth reporting, payments, etc. As there is a lot of paperwork involved, human errors are common.

Computer vision, paired with natural language processing, can streamline the entire process by identifying the exact nature and extent of damages and processing claims accordingly.

Here is a sub-domain of claims management that requires explicitly trained computer vision models to work. The process involves the insured uploading the images of the damaged vehicle online and the extensively trained model evaluating the same to cross-check the extent and veracity of the claim.

This tool is expected to save time for the insured and insurer by automated assessments and releasing the cleared amount instantly. However, high-quality training data needs to be fed into the models for systems like these to work.

Do you know anything about ‘Builders’ Insurance’? It is a risk insurance plan considered by builders that cover the under-construction buildings, construction material on-site, and even the employees.

However, the insurance companies, besides providing the insurance, also have Computer Vision-trained surveillance setups installed at specific locations to analyze the threat quotient of the concerned area whilst minimizing accidents.

These surveillance setups can visually identify if the builder is adhering to all the safety standards like necessary equipment for employees, safe-proofing the entire site, executing processes in the desired and safe way, and chances of mishaps if any.

When insurance-relevant customer support is concerned, AI models trained with quality data and annotation datasets can be a handful. Models trained in computer vision can interact with customers better, either as chatbots or as assistants, to understand even the most specific concerns. Therefore, visually trained models are absolutely necessary if insurance companies want to improve their customer experience.

Wrap-Up

AI technology is adequately disruptive and is arguably one of the best technologies that insurance companies can rely on to maximize output by minimizing frauds and misplaced claims. Even the insured can experience quicker claim settlements with intelligent machines working alongside humans to positively impact the insurance sector.

And while we build on these use-cases in 2022, it is a matter of time before AI, Machine Learning, Computer Vision, and NLP join hands to make this space more proactive by targeting some of the other complex bottlenecks in insurance, including premium assessment and still high settlement times.

Originally published at https://techyworld.co.uk on May 19, 2022.

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How is Computer Vision changing the Insurance Sector for Good- Top 5 Use Cases that take… was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.