Create Slack Bot Using Python Tutorial with Examples

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.

Chatbots, AI & VOICE Conference 2022 (chatbotconference.com)

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.


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.

Review of the Aeotec Smart Home Hub

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.

Personality NFT

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 👏 !


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.

Review of the Roku Express 4K

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 👏 !


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…

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.


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.

Free Datasets for Self-Driving Cars with Autonomous AI

Self driving cars, also known as autonomous automobiles, can drive themselves with little or no human intervention. Self-driving vehicles have gotten a lot of attention recently, thanks to autonomous AI. In just a few years, artificial intelligence (AI) has gone from being almost ignored to being the most significant R&D expenditure of several corporations worldwide.

autonomous AI company

Self-driving car computer vision engineers use massive amounts of training data from image recognition systems and AI and neural networks to build self-driving car frameworks. The neural networks recognize patterns in the data, then feed them into the AI algorithms. Images from self-driving car cameras are among the data. The neural networks learn to detect traffic lights, trees, checks, pedestrians, road signs, and other elements in any given driving environment.

Many autonomous AI company have begun to produce self driving cars. These businesses put their vehicles through a series of testing to guarantee that they are safe to drive on the road through several autonomous AI technologies like computer vision. To be considered entirely autonomous, a car must travel paths to a predetermined destination without the need for computer vision human interaction through computer vision.

Artificial intelligence (AI) is being made practical in various disciplines thanks to sensor-based technology. LiDAR is a sensor-based technology for autonomous vehicles or self-driving cars. It has become critical for such machines to become aware of their surroundings and drive safely without colliding.

Autonomous cars currently utilize a variety of sensors, including LiDAR, which aids in the detection of things in greater detail. Below is the list of free LiDAR datasets that can be used in self driving cars.

Astyx Dataset HiRes2019

For deep learning-based 3D object recognition, the Astyx Dataset HiRes2019 is a prominent automobile radar dataset. The goal of making this dataset open-source is to make high-resolution radar data available to the research community, supporting and inspiring research on algorithms that use radar sensor data.

Landmarks

This dataset for recognizing man-built and natural landmarks was made public by Google. In 2018, the dataset will be distributed as part of the Kaggle competitions for Landmark Recognition and Landmark Retrieval. It comprises over 2 million photos displaying 30 thousand distinct landmarks from around the world (their geographic distribution is shown below), with several classes 30x more extensive than what is ordinarily accessible in datasets.

Seasonal Ford Multi-AV Dataset

During 2017–18, a fleet of Ford autonomous cars gathered the multi-agent seasonal dataset on various days and times. The vehicles were manually driven on a route in Michigan that encompassed the Detroit Airport, highways, metropolitan centres, a university campus, and a suburban area, among other operating scenarios. Seasonal variations in weather, illumination, construction and traffic conditions in dynamic metropolitan contexts are included in the dataset.

PandaSet

PandaSet combines the best-in-class LiDAR sensors from Hesai with the high-quality data annotation from Scale AI. PandaSet includes data from both a forward-facing LiDAR with image-like resolution (PandarGT) and a mechanical rotating LiDAR (Pandar) (Pandar64). A mix of cuboid and segmentation annotations was used to annotate the gathered data (Scale 3D Sensor Fusion Segmentation).

Level 5

The Level 5 dataset was made public by Lyft, a ride-sharing startup. Level 5 is a large-scale dataset containing raw sensor camera and LiDAR inputs as seen by a fleet of numerous high-end autonomous cars in a particular geographic area. A high-quality, human-labelled 3D bounding box of traffic agents and an underlying HD spatial semantic map are included in the collection.

Conclusion

It is necessary to train the AI model with a large number of annotated images generated by the LiDARs sensor in order for the LiDARs sensors detector to recognize the objects.

LIDAR point cloud segmentation is the most exact way to classify things with an additional property that a perception model can notice for learning.

The LiDAR data annotation aids in detecting the road lane and tracking the object with a multi-frame, allowing the self-driving car to recognize the street more precisely and comprehend real-world scenarios.

Several autonomous AI companies like Cogito Tech LLC, Anolytics.AI and others provide high-quality training data for self-driving cars.


Free Datasets for Self-Driving Cars with Autonomous AI was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

SMS Vs Whatsapp — SMS is dying, WhatsApp is taking over

SMS Vs Whatsapp — SMS is dying, WhatsApp is taking over

Last Updated on May 13, 2022

It took almost a century for voice communication to upgrade to text messaging. It took hardly a decade for instant messaging to trump text messaging a.k.a SMS and become the primary mode of communication.

Think about it. Who sends an SMS any more than service providers sending one-time-passwords or the customary birthday wishes. SMS has taken a step back as a communication channel is an understatement. They merely exist as a backup plan when instant messaging is not available, which enjoys a staggering 99.99% uptime.

Why is WhatsApp winning over SMS?

Every now and then a new-age technology comes around and topples the pole position that its predecessor has been occupying. In telecommunication, it is WhatsApp dethroning SMS. There are several reasons why WhatsApp is winning over SMS.

WhatsApp is literally free. No carrier costs attached to instant messaging

You need a working data connection or WiFi connectivity to send and receive messages through WhatsApp. The bandwidth of WhatsApp messaging is minimal and does not cost a bomb in terms of carrier charges.

SMS charges, on the other hand, continue to be exorbitant in most parts of the world. It is only for specific carrier plans that SMS are cheaper or offered as a free utility. When compared side by side, WhatsApp is literally free. The cost factor is what is drawing millions of users to the app and making them send billions of messages on a daily basis.

One account for everything — voice and video calling

Until instant messengers like WhatsApp came over, users had to install multiple apps for multiple purposes. SMS was handled by the default app provided by OEMs (Original Equipment Manufacturers). For video calling one had to resort to Skype, Zoom, Viber, and similar apps.

All those efforts seem like belonging to another time in history. WhatsApp has integrated text messaging, voice calling, video calling, and even sending voice notes all into one chat interface. Also, users do not need multiple accounts to use these features. A single user account created with a phone number is sufficient for video and voice calling. As icing on the cake, there is no need for recharging or maintaining call credits.

Facility to send multimedia attachments

WhatsApp lets users send multimedia attachments just like how email lets you send attachments. One can send images, documents, map location, contacts, audio files, or even snap a picture straight from the app and share it without hopping from one application to another.

Recently, WhatsApp also integrated the option to send and receive money through the app. In other words, WhatsApp is like a Swiss Army knife for the digital world. With the single app, you can do everything from communication to even basic-level collaboration to get work done. It is no surprise that businesses are turning to WhatsApp to maintain and nurture relationships with customers.

Provision for chat backup on a regular basis

One of the downsides of SMS was that every time you swapped sims or even changed phones, all text threads were lost. There was no provision to take a backup, except with the help of the carrier. However, with WhatsApp, there is no such hassle in taking chat backups.

You can schedule chat backups on Whatsapp on a daily, weekly, or monthly basis. The backups are stored in the Google Drive folder chosen by the user. Whenever the WhatsApp account is reset or moved to another phone, the conversations are restored instantly. There is no loss of data compared to SMS where all data is lost without hope of recovery.

Gives message delivery indicators

With SMS, there is no option of knowing whether the message has been delivered to the recipient or it has been lost in transmission. Duplicate messages being delivered or messages getting truncated due to network issues or character limits were rampant when SMS communication was ruling the roost.

WhatsApp eliminated all such ambiguity in text communication by introducing delivery indicators. Although it created some ruckus when it was first introduced, today, it has become accepted as a definite means to determine the deliverability of a message. In fact, delivery indicators are also available for voice messages taking two-way communication to its completion.

Allows personalization and privacy of profile

Although not necessary for an instant messenger app, WhatsApp gives users the provision to create their personalized profile complete with profile pictures, short bio (with text and emojis). Businesses can even add their alternate contact information as well as map location. The profile personalization can be rightly used as a virtual visiting card when it is time to share contact information.

Further, WhatsApp also gives advanced features for protecting the data privacy of the user. Users can choose to show their profile information only to contacts, or to all. Further, they can also determine who can see their statuses, their display picture, and also their read receipts.

Business-friendly features

SMS marketing has been the staple marketing channel for most businesses. However, it had several downsides that left businesses in the lurch. To begin with, SMS allowed businesses only to send textual content, there was no possibility to share multimedia content. Further, SMS used a phone number that cannot be customized to appear business-like.

Compared to SMS, WhatsApp gives countless business features that make outreach, prospecting, lead generation, and customer support a breeze. It allows businesses to communicate as an entity and not as an individual as is the case with SMS. further, it can be used to send product catalogues, canned responses for FAQs, etc. WhatsApp makes customer communication effortless and interactive compared to SMS which often ends up as a one-way communication channel. It is even possible to create a WhatsApp chatbot with which businesses can deliver a top-notch customer experience.

Why is SMS still used for?

Currently, a small population of businesses is using SMS largely for marketing purposes. Further, it is also used for specific purposes that are best suited. Despite its paucity in features, SMS does assure data security as it cannot be intercepted easily like messages sent across data networks. For that reason, SMS communication is still used for:

  • OTPs — Banks and financial institutions use SMS to deliver one-time passwords forming part of two-factor authentication.
  • Account recovery — Recovery codes for emails and similar accounts are shared via SMS. it ensures that it is the account holder with the same phone number who is requesting for a change in the password or is attempting to recover it.
  • SMS marketing — To send bulk SMSes carrying a business offer, request for sign-ups, demos, walk-ins, etc.

Why ‘NOW’ is the best time to switch to WhatsApp for business

Despite its benefits, does SMS have a place in the future where WhatsApp is headed? Turns out, not much. As mentioned earlier, SMS might be used as an alternative or as a temporary fix when WhatsApp cannot be used (for example, when there is no data connection).

Bonus: How to Get a WhatsApp Business API account in an Easy Way

Real-time communication

Millennials are the social generation. They are always hooked in real-time to one social media channel or another. As a result, they expect communication to be synchronous and happening in real-time. Especially when they are communicating with businesses for inquiries or for post-sales customer support.

WhatsApp fits the bill perfectly as real-time communication is its default offering. Businesses can engage in real-time communication with customers enabling them to dole out information that customers are seeking or in providing assistance in solving customer support queries.

Most popular means of communication

WhatsApp is the leading global mobile messenger with a whopping 2,000 Million monthly active users. Others like Facebook Messenger, WeChat, QQ, Telegram, etc. fall back severely in user numbers.

As the most popular means of communication, WhatsApp has become a must-have for business communication. Also, it does force users to install a new app. Customers can use their personal WhatsApp account to communicate with businesses thus removing all friction that exists in customer communication.

Canned responses for FAQs

One of the difficulties of providing customer support is handling a large volume of repeating queries. FAQs on the website might provide some respite, however, most customers may not bother visiting the website to find the solution.

With WhatsApp, businesses can quickly provide canned responses to frequently asked questions of customers. It gives customers immediate feedback while sparing support agents from having to respond to every basic customer question, expending their valuable time and energy.

Built-in product catalog

WhatsApp can work as a mini-portal where customers can view product catalogues. WhatsApp for business allows businesses to upload their product catalogue complete with images, descriptions, and pricing. This helps drive specific contextual conversations with customers and can also maximize lead generation.

24/7/365 customer support

WhatsApp for business lets you set up auto-replies or away messages that will respond to incoming messages with default replies. Further, WhatsApp chatbots can also be configured to converse with customers like a human agent would using Artificial Intelligence and Machine Learning to understand user sentiments, queries and provide contextually relevant responses. The good news is, they can work round the clock, 24/7/365 without any break. This makes them a perfect companion for businesses that work across multiple time zones.

Affordable customer communication

Setting up a WhatsApp account for business communication doesn’t cost a bomb. It does not require any additional app development or integrations. Of course, you can add to the app’s functionality through integrations, however, to perform basic functions these are not essential. As a result, the cost involved in using WhatsApp for customer support or lead generation is minimal.

In a nutshell

By now you must have understood that the days of SMS are over and WhatsApp is here to stay. Even the competition from other instant messaging apps has not affected WhatsApp as it continues to grow in user base as the default instant messenger.

For businesses, WhatsApp offers a simple and intuitive mode of communication. It does not require a new app or any additional investment. It offers maximum value for the time and resources spent.

Why ‘NOW’ is the best time to switch to WhatsApp for business.

At Kommunicate, we are envisioning a world-beating customer support solution to empower the new era of customer support. We would love to have you on board to have a first-hand experience of Kommunicate. You can signup here and start delighting your customers right away.

Originally published at https://www.kommunicate.io on March 4, 2022.


SMS Vs Whatsapp — SMS is dying, WhatsApp is taking over was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

What are the Natural Language Processing Challenges, and How to Fix?

They say ‘Action speaks louder than Words’. Yet, in some cases, words (precisely deciphered) can determine the entire course of action relevant to highly intelligent machines and models. This approach to making the words more meaningful to the machines is NLP or Natural Language Processing.

For the unversed, NLP is a subfield of Artificial Intelligence capable of breaking down human language and feeding the tenets of the same to the intelligent models. NLP, paired with NLU (Natural Language Understanding) and NLG (Natural Language Generation), aims at developing highly intelligent and proactive search engines, grammar checkers, translates, voice assistants, and more.

Simply put, NLP breaks down the language complexities, presents the same to machines as data sets to take reference from, and also extracts the intent and context to develop them further. Yet, implementing them comes with its share of challenges.

What is NLP: From a Startup’s Perspective?

It is hard for humans to learn a new language, let alone machines. However, if we need machines to help us out across the day, they need to understand and respond to the human-type of parlance. Natural Language Processing makes it easy by breaking down the human language into machine-understandable bits, used to train models to perfection.

Also, NLP has support from NLU, which aims at breaking down the words and sentences from a contextual point of view. Finally, there is NLG to help machines respond by generating their own version of human language for two-way communication.

Startups planning to design and develop chatbots, voice assistants, and other interactive tools need to rely on NLP services and solutions to develop the machines with accurate language and intent deciphering capabilities.

NLP Challenges to Consider

Words can have different meanings. Slangs can be harder to put out contextual. And certain languages are just hard to feed in, owing to the lack of resources. Despite being one of the more sought-after technologies, NLP comes with the following rooted and implementation AI challenges.

Lack of Context for Homographs, Homophones, and Homonyms

A ‘Bat’ can be a sporting tool and even a tree-hanging, winged mammal. Despite the spelling being the same, they differ when meaning and context are concerned. Similarly, ‘There’ and ‘Their’ sound the same yet have different spellings and meanings to them.

Even humans at times find it hard to understand the subtle differences in usage. Therefore, despite NLP being considered one of the more reliable options to train machines in the language-specific domain, words with similar spellings, sounds, and pronunciations can throw the context off rather significantly.

Ambiguity

If you think mere words can be confusing, here are is an ambiguous sentence with unclear interpretations.

“I snapped a kid in the mall with my camera”- If the spoken to, it can be the case that the machine gets confused as to whether the kid was snapped using the camera or when the kid was snapped, he had your camera.

This form of confusion or ambiguity is quite common if you rely on non-credible NLP solutions. As far as categorization is concerned, ambiguities can be segregated as Syntactic (meaning-based), Lexical (word-based), and Semantic (context-based).

Errors relevant to Speed and Text

Machines relying on semantic feed cannot be trained if the speech and text bits are erroneous. This issue is analogous to the involvement of misused or even misspelled words, which can make the model act up over time. Even though evolved grammar correction tools are good enough to weed out sentence-specific mistakes, the training data needs to be error-free to facilitate accurate development in the first place.

Inability to Fit in Slangs and Colloquialisms

Even if the NLP services try and scale beyond ambiguities, errors, and homonyms, fitting in slags or culture-specific verbatim isn’t easy. There are words that lack standard dictionary references but might still be relevant to a specific audience set. If you plan to design a custom AI-powered voice assistant or model, it is important to fit in relevant references to make the resource perceptive enough.

One example would be a ‘Big Bang Theory-specific ‘chatbot that understands ‘Buzzinga’ and even responds to the same.

Apathy towards Vertical-Specific Lingo

Like the culture-specific parlance, certain businesses use highly technical and vertical-specific terminologies that might not agree with a standard NLP-powered model. Therefore, if you plan on developing field-specific modes with speech recognition capabilities, the process of entity extraction, training, and data procurement needs to be highly curated and specific.

Lack of Usable Data

NLP hinges on the concepts of sentimental and linguistic analysis of the language, followed by data procurement, cleansing, labeling, and training. Yet, some languages do not have a lot of usable data or historical context for the NLP solutions to work around with.

Lack of R&D

NLP implementation isn’t one-dimensional. Instead, it requires assistive technologies like neural networking and deep learning to evolve into something path-breaking. Adding customized algorithms to specific NLP implementations is a great way to design custom models-a hack that is often shot down due to the lack of adequate research and development tools.

Scale Above These Problems, Today: How to Choose the Right Vendor?

From fixing ambiguity to errors to issues with data collection, it is important to have the right vendor at your disposal to train and develop the envisioned NLP Model. And while several factors need to be considered, here are some of the more desirable features to consider while connecting:

  • Sizable, domain-specific database (audio, speech, and video), regardless of the language.
  • Capability to implement Part-of-Speech tagging for cutting out ambiguities.
  • Support for custom assistive technologies like Mulingual Sentence Embeddings to improve the quality of interpretation.
  • Seamless data annotation to label data sets as per the requirements.
  • Multi-lingual database with off-the-shelf picks to work with.

Vendors offering most or even some of these features can be considered for designing your NLP models.

Wrap-Up

Needless to say, NLP has evolved into one of the more widely accepted and hailed Artificial Intelligence-powered technologies. If you are into specifics, the NLP market is expected to grow by almost 1400% by 2025, as compared to that in 2017. As per expectations and extrapolations, the NLP market will be valued at almost 43 billion by the end of 2025 — Statista

Despite the benefits, Natural Language Processing comes with a few limitations-something that you can address upon connecting with a reliable AI vendor.

Vatsal Ghiya, founder of Shaip, is an entrepreneur with more than 20 years of experience in healthcare AI software and services.

Originally published at https://thinkml.ai on June 1, 2022.


What are the Natural Language Processing Challenges, and How to Fix? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Re-imagining Customer Service with Conversational AI

In an era of post-pandemic changes, organizations face challenges in creating exceptional service experiences for their customers. The pandemic has pressed the greater urge among industries for the creation of human-tech symbiosis to make the personalized customer experience as flexible as possible. So, what’s the next frontier in evolving employee-customer relationships?

The Answer lies in Conversational AI.

You might think voice interfaces are nothing new, but with the technology evolution, these voice interfaces have become better listeners and conversationalists. According to PwC, “ “around 27% of customers were unable to comprehend whether they interacted with a bot or human agents. And Conversational AI chatbot has made this possible.

Conversational AI uses technologies like Machine Learning and NLP to make human-machine communication possible. These AI technologies recognize the pattern in text and speech and translate their meaning across human and computer languages.

As per a report, “Around 57% of customers agree that Conversational AI bots can deliver a large return on investment for minimal effort.

On top of that Conversational AI constantly learns from previous experiences using machine learning datasets to offer real-time insight and excellent customer service. Also, Conversational AI not only manually understands and responds to our queries but also can be connected to other AI technologies such as search and vision to fast-track the process.

Technologies Inclusion In Conversational AI

1. Natural Language Processing

Although machines are quick and efficient, they are not experts in understanding human context, speech, or intent. But, Natural Language Processing with its capabilities such as text modeling, text categorization, text clustering, information extraction, and entity recognition understands the intent behind the text and speech and extracts meaning from the natural sentence to offer a personalized experience to customers.

2. Intent Detection

In general, intent defines what action should be triggered based on conversational inputs. And it becomes more typical when you have to find intent from voice instead of text due to our natural proclivity for telling stories. Using a combination of machine learning & NLP capabilities, the Conversational AI bot automatically associates words or expressions with a particular intent. And to make it one step further, intent detection can be combined with data extraction to identify specific datasets such as locations, dates, companies, names and etc.

3. Speech-to-Text

Once an information input is received from a human, machines can’t translate speech into text. To make the sense of what is spoken and asked, enterprises can leverage OCR technology integrated with a Conversational AI bot. By converting speech-to-text, Conversational AI bot enable enterprises to deliver better response for every interaction.

4. Value Extraction

Every company is data, but if your enterprises can’t make out what’s relevant, it’s challenging to scale. Value extraction is the model through which AI agents extract the relevant information from customer queries and store them against the relevant chatbot. Conversational AI reduces the manual effort by automating the slot labeling techniques and identifying the key specification from the user’s spoken instructions to a voice assistant autonomously. For example- a banking assistant has to extract the cash value and recipient in order to make a transaction on the user’s behalf.

5. Text-to-Speech

Manually processed conversion of text into speech require constant manual effort and cost to Text-to-speech process in Conversational AI involves the translation of the text into speech automatically. By using a pre-defined workflow with text records and identification Conversational AI bot can offer a faster response to natural text and speech.

How Conversational AI can be used for customer service?

Needless to say, Conversational AI is the best asset to offer a personalized and real-time customer experience. From resolving queries on time to understating user intent autonomously, Conversational AI has a lot to offer on its plate to customers. Conversation AI use cases for customer service include:

You might have heard of the statement that “Data is the new oil”. And enterprises have a huge amount of data collected from customer transaction history, past interactions, transcripted calls, and chat sessions. This invaluable information can be leveraged to create a hyper-personalized customer experience and quick service ticket resolution. Conversational AI monitors all the conversations that happened with customers and makes data of their requirements, and creates a powerful ecosystem for predicting customers’ next actions.

Regardless of industries, support agents need a vast amount of information to process resolution and answer FAQs. Conversational AI could be a great help in assisting the agents. By monitoring the conversations agents are having with customers, Conversational AI can extract the information and customer intent and transfer the required information to support agents at the appropriate time. This saves both customer and support agents time. That’s how Conversational AI offers superior customer experience, lowered support costs, and high employee satisfaction.

Conversational AI bots can be easily integrated with reservation and booking systems to fully automate the reservation process. This automated reduce the manual effort required in processing booking and reservation and sending confirmation to the customer. Also, this Conversational AI chatbot can offer a personalized experience to customers by learning from the customer’s past interactions.

Conclusion

No matter the type of organization you rule, small or big, customer service plays a key role in scaling the business and getting higher ROI. And Conversational AI is a helping hand not only to optimize the customer service game but also to rule in the competitive market. The Conversational AI moment is now, and it’s high time to paddle for better customer service with AI capabilities.

Author Bio

Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Linkedin: https://www.linkedin.com/in/vatsal-ghiya-4191855/ Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.

Originally published at https://technewsgather.com on May 30, 2022.


Re-imagining Customer Service with Conversational AI was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.