Category: Chat

submitted by /u/maxwell737
[link] [comments]

As pioneers in adopting ChatGPT technology in Malaysia, XIMNET dives in to take a look how far back does Conversational AI go?

Conversational AI has been around for some time, and one of the noteworthy early breakthroughs was when ELIZA, the first chatbot, was constructed in 1966. Pattern matching and substitution methodology were used to explore communication between humans and machines in which both parties did not understand the conversation context.

The next milestone features A.L.I.C.E in 1995, coded using AIML (Artificial Intelligence Markup Language) based on heuristic pattern matching. The Open Source community subsequently gains interest and thus actively contributed to all sorts of research repositories which brings us the vast collection of machine learning models today.

Siri, Google Assistant, Cortana, and Alexa, are the successive technologies rolled out in the 20th century. They are readily accessible via our handy devices and serve as an intelligent personal assistant instead of just simple question-answering based on internet information. NLP, Natural Language Processing, and deep neural networks are the core building blocks of the technology, which allows our machines, appliances, and IOT devices to understand human language at ease. Command execution via voice recognition is the new norm where a simple instruction like “Hey Google, play me some country music!” will easily fire up your Spotify app to your liking.

Rising Star: ChatGPT

A nonprofit American Artificial Intelligence company called OpenAI was created with the common goal of developing artificial intelligence “in the way that is most likely to benefit humanity as a whole,” according to a statement on OpenAI’s website from December 11, 2015.

In November 2022, the public was introduced to ChatGPT, a pre-trained language model that had been fine-tuned on conversational data, and its jaw-dropping capabilities quickly became the talk of the town. The public has been drawn to ChatGPT because of its remarkable capacity to produce natural and compelling responses in a conversational environment, regardless of whether they are AI experts or not. In just 5 days, the AI model has amassed over one million users, prompting people to wonder how ChatGPT can provide such accurate and human-like answers.

Behind the scenes

(A) Large Language Model (LLM)

It all started with a large Language Model (LLM), a type of pre-trained neural network that is designed to understand and generate natural language in a way that is similar to human language. Being one of the largest LLMs available today, ChatGPT consists of over 175 billion parameters which grant it the ability to generate text that is remarkably similar to human writing. These models are engineered to comprehend to process a large corpus of text data to learn the patterns and structures of natural language. By feeding the model a large dataset of text from Wikipedia and Reddit, the model can analyze and learn from the patterns and relationships between the words and phrases in the text. As the model continues to learn and refine its understanding of natural language, it becomes increasingly adept at generating high-quality text outputs.

Training steps like predicting a word in a sentence, be it a next-word prediction or masked language modelling are crucial in shaping a high-accuracy LLM. Both techniques are normally deployed using Long-Short Term Memory (LSTM), which consists of feedback connections, i.e., it is capable of processing the entire sequence of data, apart from single data points such as images. However, the model has its drawbacks which limit the potential of large datasets.

LSTMs have difficulty handling long-term dependencies and struggle to remember information that is many steps removed from the current input.

1. Let’s say we want to train an LSTM to predict the next letter in the sentence “I love to eat pizza”. The LSTM takes in a sequence of letters as input and outputs a probability distribution over the possible next letters. If we only use a small context window (e.g. 2 or 3 letters), the LSTM may struggle to remember important information from earlier in the sentence.
2. For example, if the LSTM only sees the letters “zz” as input, it may have difficulty predicting the next letter “a” because it has forgotten that the sentence began with “I love to eat”

LSTMs have restricted context window sizes. The context window is the set of inputs that the network uses to predict the next output.

1. Let’s say we have a language model that uses an LSTM with a context window size of 3. The model takes in a sequence of three words as input and tries to predict the next word in the sequence.
2. For example, given the input sequence “The cat sat”, the model might predict the next word as “on” if it has learned that the sentence often continues as “The cat sat on the mat”. However, if the full sentence is “The cat sat on the mat in the corner of the room”, the LSTM with a context window size of 3 would only consider “on the mat” as the input and ignore “in the corner of the room”, potentially leading to an incorrect prediction.

To address this, a team at Google Brain introduced transformers in 2017, which significantly improves the ability of LLMs to incorporate meaning, as well as the capacity to handle much larger datasets. Transformers differ from LSTMs in that they can process all input data at the same time. The model can assign varying importance to different parts of the input data in relation to any position of the language sequence, thanks to a self-attention mechanism.

(B) GPT

In 2018, openAI released a paper, “Improving Language Understanding by Generative Pre-Training,” — introducing the concept of a Generative Pre-trained Transformer (GPT), which also serves as one of the contributing factors to the significant advancement in the area of transfer learning in the field of natural language processing (NLP). Simply put, GPTs are machine learning models based on the neural network architecture that mimics the human brain. These models are trained on vast amounts of human-generated text data and are capable of performing various tasks such as question generation and answering.

The model later evolved and they released GPT-2, which is a more robust version trained on a corpus of 8 million web pages, comprising 1.5 billion parameters that facilitate text prediction. However, due to their concerns about malicious applications of the powerful technology, they released a much smaller model for researchers to experiment with, as well as a technical paper. Other than next-word prediction, notable use cases include zero-shot learning. As opposed to typical large neural models that require an insane amount of data, a “zero-shot” framework enables measuring a model’s performance having never been trained on the task.

Following two years of parameter adjustments and fine-tuning, GPT-3 was unveiled in May 2020, having been trained on a staggering 45 terabytes of text data, which ultimately translated into 175 billion parameters. It was smarter, faster, and more terrifying than anything we had seen before.

The key success of all GPT models lies within the transformer architecture, which is both encoder (processing the input sequence) and the decoder (generating the output sequence) contain a multi-head self-attention mechanism that enables the model to give different levels of importance to different parts of the sequence in order to understand its meaning and context.

The encoder in a Transformer processes the input sequence and computes key, query, and value vectors. Attention weights are computed using the key and value vectors, while the query vector is used to produce the output. This is done by taking the dot product of the query and key vectors and scaling the result. The output is computed by taking the weighted sum of the value vectors, using the attention weights as the weights. This is then repeated in multiple layers in parallel to learn increasingly complex representations of the input, which brings about the term multi-head attention. Thus, combining the results for a final score allows the Transformer to encode multiple contextual relationships for each word in a sequence.

In spite of all this, since GPTs are trained on large data sets, they do have training data bias reflecting on the generated text. Since it is generative in nature, it has the potential to generate inappropriate content due to a lack of understanding of the true meaning of the context. Limited long-term memory will be one of the drawbacks; unlike humans, they are unable to maintain coherence and consistency in longer pieces of text or over multiple exchanges in a conversation.

(C) ChatGPT

To rectify the shortcomings, OpenAI introduced a twist of including human feedback in the training process to improve the GPT-3 model’s output to match user intent. This technique is called Reinforcement Learning from Human Feedback (RLHF), which is explained in detail in OpenAI’s 2022 paper titled “Training language models to follow instructions with human feedback”.

The figure above summarizes the steps taken by researchers to enhance GPT-3’s ability to follow instructions and accomplish tasks rather than simply predicting the most probable word. To start, a fine-tuning process is carried out, which produces InstructGPT or also known as a supervised fine-tuning model (SFT). This approach uses patterns and structures learned from labeled training data to generate responses. For instance, a chatbot trained on a dataset of medical conversations will generate informative and appropriate responses to medical-related questions based on its supervised policy.

To incentivize a chatbot to produce more suitable and favorable responses, a reward model is necessary. This model takes in a prompt and the chatbot’s responses and outputs a scalar reward based on the desirability of the response. Comparison data is collected by having labelers rank the output they prefer for a given input.

In the last stage, a random prompt is provided to the policy to produce an output, which is then evaluated by the reward model to determine the reward. This reward is then employed to modify the policy using Proximal Policy Optimization (PPO). The Rewards model decides the reward or penalty for each response produced by the chatbot and employs this reward function to steer the learning process, generating relevant, informative, or engaging responses for the user while avoiding producing inappropriate or offensive ones. These processes are then repeated through multiple iterations using Azure AI supercomputing infrastructure which completes the ChatGPT model generation.

How can you use it?

After getting familiar with the architecture behind the tools, how exactly can you implement it in your own applications, for instance, building a chatbot?

Let’s assume that we are using OpenAI’s GPT-3 model, which in their API documentation, is also known as text-davinci-002. The basic steps, including creating an OpenAI API account, setting up an environment to use the API, and programming the chatbot to interact with users.

How about asking ChatGPT to help us with this setup?

import openai
openai.api_key = "YOUR_API_KEY"

def generate_response(prompt):
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0.7,
    )

    message = response.choices[0].text.strip()
    return message

def answer_question(question):
    prompt = f"Q: {question}nA:"
    response = generate_response(prompt)
    return response

while True:
    user_input = input("User: ")
    bot_response = answer_question(user_input)
    print("Bot:", bot_response)

ChatGPT not only generated the code as per my command, but it also provided some explanations and annotations to clarify some sections of the code. This indicates that anyone, whether they are a software developer or a business person, can use ChatGPT’s guidance to build small applications.

However, it is important to note that ChatGPT’s responses may not always be precise, especially in the context of code implementation. Although it has extensive knowledge of various programming languages, it can still make errors or generate incomplete or incorrect code. While ChatGPT can handle many tasks, it may not be an expert in any particular field. Therefore, it is crucial to validate the information provided by ChatGPT before relying on it. The best approach is to still ask follow-up questions or provide a detailed prompt to generate a response that better fits your use case.

What’s next?

As ChatGPT continue to make headlines ever since its grand launch in November 2022, we have seen people asking whether ChatGPT will dominate or even monopolize the entire chatbot industry due to its impressive language processing capabilities and ability to generate human-like responses.

Integrating ChatGPT into any business model is not a straightforward solution. When businesses deploy a chatbot, they typically expect it to be able to communicate with customers on their behalf. Additionally, the training data used to build the language models are only up to date as of 2021, so any new developments or changes after that may not be incorporated into the knowledge base. This raises the question of how to ensure that the chatbot powered by GPT-3 has access to the most current information relevant to the organization. While fine-tuning the model with custom data sets is an option, it’s important to keep in mind that OpenAI invested significant resources and computing power to create multiple versions of the model, and the responses generated by the model still do not meet expectations at 100% accuracy. How tolerable are the businesses when it comes to handling the unpredictable nature of generative models like GPT?

While there is no definitive answer, it’s clear that current chatbot solutions will need to elevate their offerings in order to meet evolving market demands. In the coming months, we can expect to see various chatbot providers formulate new strategies and introduce new features to ensure that their products remain competitive against ChatGPT in the industry. Instead of frowning upon whether AI will take over our jobs, let’s embrace the changes and focus on leveraging them to improve efficiency and create new opportunities for growth and innovation.

XIMNET is a digital solutions provider with two decades of track records specialising in web application development, AI Chatbot and system integration.

XIMNET is launching a brand new way of building AI Chatbot with XYAN. Join our waitlist to get early access today.

---

ChatGPT: The Technicalities behind the Rising Star of Conversational AI was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Ever since its conception, chatbots have been leveraged by industries across the globe to serve a wide variety of use cases. From enabling simple conversations to handling helpdesk support to facilitating purchases, chatbots have come a long way.

If you were to put it in numbers, research shows that a whopping 1.4 billion people use chatbots today.

Without question, the chatbot presence in the healthcare industry has been booming. In fact, if things continue at this pace, the healthcare chatbot industry will reach $967.7 million by 2027.

There are several interesting applications for healthcare chatbots. If you’re curious to know more, simply give our article on the top use cases of healthcare chatbots a whirl.

It is also important to pause and wonder how chatbots and conversational AI-powered systems are able to effortlessly converse with humans. That too in a language that is simple and easy for us to comprehend.

This is where Natural Language Processing (NLP) makes its entrance.

In order to understand in detail how you can build and execute healthcare chatbots for different use cases, it is critical to understand how to create such chatbots. And this is what we intend to cover in this article.

Let’s start with the most important question.

What is NLP?

Natural language — the language that humans use to communicate with each other.

Programming language- the language that a human uses to enable a computer system to understand its intent. Python, Java, C++, C, etc., are all examples of programming languages.

Imagine a situation where you can communicate with machines and computers without having to use such programming languages. Easy and seamless. Right?

Fortunately, you don’t have to put in a lot of effort trying to imagine such a situation because NLP makes this possible.

Natural language processing is a computational program that converts both spoken and written forms of natural language into inputs or codes that the computer is able to make sense of.

NLP-powered chatbots are capable of understanding the intent behind conversations and then creating contextual and relevant responses for users.

With NLP, you can train your chatbots through multiple conversations and content examples. This, in turn, allows your healthcare chatbots to gain access to a wider pool of data to learn from, equipping it to predict what kind of questions users are likely to ask and how to frame due responses.

Interesting. Right?

We hope that you now have a better understanding of natural language processing and its role in creating artificial intelligence systems. Let’s now move on to more details.

How do healthcare chatbots using NLP work?

A chatbot that is built using NLP has five key steps in how it works to convert natural language text or speech into code. Let’s explore each of these steps and what it entails.

1. Tokenization

This is the process by which you can break entire sentences into either word. The name of this process is word tokenization or sentences — whose name is sentence tokenization. This is a method of data processing.

Extract the tokens from sentences, and use them to prepare a vocabulary, which is simply a collection of unique tokens. These tokens help the AI system to understand the context of a conversation.

2. Normalizing

Imagine that you are texting your colleague. Naturally, different people have a tendency to misspell certain words, use short forms, and enter certain words in uppercase letters and others in lowercase. Essentially, there is a lot of randomness to the way different people text.

Now, extrapolate this randomness to how people communicate with chatbots. Unless the system is able to get rid of such randomness, it won’t be able to provide sensible inputs to the machine for a clear and crisp interpretation of a user’s conversation. Normalization refers to the process in NLP by which such randomness, errors, and irrelevant words are eliminated or converted to their ‘normal’ version.

For instance:

Input: cn i book an apptmnt with my dr 2day?

Output after normalization: Can I book an appointment with my doctor today?

3. Recognizing entities

Now that a sentence has been broken down (tokenized) and normalized, the system proceeds to understand the different entities in the sentence.

Entities are nothing but categories to which different words belong. Some examples of entities include Name, Location, Organization, etc. Recognizing entities allows the chatbot to understand the subject of the conversation.

For instance, take the sentence — Mary works at Mt. Sinai Medical Hospital, North Dakota.

In this example, the chatbot would recognize Mary as a name, Mt. Sinai Medical Hospital as an organization, and North Dakota as a location.

Check out our guide on Intents vs. Entities to learn more.

4. Dependency parsing

In natural language processing, dependency parsing refers to the process by which the chatbot identifies the dependencies between different phrases in a sentence. It is based on the assumption that every phrase or linguistic unit in a sentence has a dependency on each other, thereby determining the correct grammatical structure of a sentence.

5. Generation

This is the final step in NLP, wherein the chatbot puts together all the information obtained in the previous four steps and then decides the most accurate response that should be given to the user.

Why should you consider building an NLP-based healthcare chatbot?

One of the most important things to understand about NLP is that not every chatbot can be built using NLP. However, for the healthcare industry, NLP-based chatbots are a surefire way to increase patient engagement. This is because only NLP-based healthcare chatbots can truly understand the intent in patient communication and formulate relevant responses. This is in stark contrast to systems that simply process inputs and use default responses.

You can continually train your NLP-based healthcare chatbots to provide streamlined, tailored responses. This is especially important if you plan to leverage healthcare chatbots in your patient engagement and communication strategy.

As demand for healthcare systems grows, the only way to cut down overhead costs and increase the efficiency of patient engagement is to deploy conversational AI-powered chatbots built using NLP to act as the first point of contact between your patient and healthcare practice.

Create a healthcare chatbot using NLP.

Building your own healthcare chatbot using NLP is a relatively complex process depending on which route you choose. Healthcare chatbots can be developed either with assistance from third-party vendors or you can opt for custom development.

Here’s what this means

Custom DIY Development

In this method of developing healthcare chatbots, you rely heavily on either your own coding skills or that of your tech team.

In order for it to work, you need to have the expert knowledge to build and develop NLP- powered healthcare chatbots. These chatbots must perfectly align with what your healthcare business needs.

Of course, the biggest advantage of this method is the fact that you can customize it to the extent you prefer. However, when you consider factors like time and cost, it may be wiser to consider a third-party vendor.

Building your healthcare chatbot using third-party bot builders

In case you don’t want to take the DIY development route for your healthcare chatbot using NLP, you can always opt for building chatbot solutions with third-party vendors.

For instance, Kommunicate, a customer support automation software, enables users to build NLP-powered healthcare chatbots that are not only customized to their business requirements but also can be built with ease. Their NLP-based codeless bot builder uses a simple drag-and-drop method to build your own conversational AI-powered healthcare chatbot in minutes.

You can check it out here.

Their powerful NLP chatbot builder provides a simple and intuitive interface on a powerful conversational AI system for you to build your healthcare chatbot with ease. In fact, you can build a bot using Kommunicate in just five easy steps. Here’s how:

1. Getting started

First, you need to sign in to Kommunicate using your email ID. Once you are logged in, open the dashboard and then navigate to ‘Bots.’ Click ‘Create A Bot,’ and that will take you to Kompose, Kommunicate’s bot builder.

Click ‘Create A Bot’ to start building your bot.

2. Build your bot

Choose from readily available templates to start with or build your bot from scratch customized to your requirements.

Once you choose your template, you can then go ahead and choose your bot’s name and avatar and set the default language you want your bot to communicate in. You can also choose to enable the ‘Automatic bot to human handoff,’ which allows the bot to seamlessly hand off the conversation to a human agent if it does not recognize the user query.

3. Compose the Welcome message

Once you’ve set up your bot, it’s time to compose the welcome message. The welcome message is basically how your bot greets a user. You can add both images and buttons with your welcome message to make the message more interactive.

4. Setup questions and answers

The next step is to add phrases that your user is most likely to ask and how the bot responds to them. The bot builder offers suggestions, but you can create your own as well. The best part is that since the bots are NLP-powered, they are capable of recognizing intent for similar phrases as well. The more phrases you add, the more amount of data for your bot to learn from and the higher the accuracy.

5. Test your chatbot

Your chatbot is almost ready! Now all you have to do is test it.

In the chatbot preview section, you will find an option to ‘Test Chatbot.’ This will take you to a new page for a demo.

The chatbot will then display the welcome message, buttons, text, etc., as you set it up and then continue to provide responses as per the phrases you have added to the bot.

Conclusion

Healthcare chatbots are here to stay. What we see with chatbots in healthcare today is simply a small fraction of what the future holds.

These conversational AI-powered systems will continue to play a crucial role in interacting with patients. Some of their other applications include answering medical queries, collecting patient records, and more. And with the rapid advancements in NLP, it is inevitable that going forward, healthcare chatbots will tackle much more sophisticated use cases.

If you’d like to learn more about medical chatbots, their use cases, and how they are built, check out our latest article here.

For Original Content Click here

---

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

Artificial Intelligence and Influencer Marketing — Rise of the Virtual Influencers

Today, data, analytics, and algorithms are the cornerstone of modern marketing. Within the marketing industry, influencer marketing has become a glamorous yet effective way to improve brand awareness and increase revenue.

According to statista.com, “The popularity of influencer marketing worldwide is growing at such a high speed that the global market size is expected to reach 22.2 billion U.S. dollars by 2025”

Influencer marketing is the process through which a company works with an online influencer to promote one of its goods or services.

Artificial Intelligence (AI) has changed the game for influencer marketing.

• Artificial intelligence tools can assist you in avoiding influencer fraud by thoroughly examining an influencer’s profile and identifying any red flags.
• Marketers can assess whether the followers of an influencer fit the profile of their target audience using AI and machine learning.
• AI can search through millions of topics each day to discover which ones are being discussed by actual people

Chatbots as Influencers — Virtual Influencers

Bot Libre chatbots, through its diverse and dynamic features , are able to respond to a large group of people, in a small amount of time and in a more human way. This makes them an incredible asset to business marketing.

The idea of an influencer chatbot is not new, as 2021 reports shared that an influencer chatbot created for the US brand CoverGirl, which was hosted on the messenger app was able to bring in 14 times better conversion rates. Other influencer bots developed include Lil Miquela an AI-generated influencer who counts Prada as one of her clients, and Knox Frost, who was a major influencer bot for the World Health Organisation (WHO) during the height of the pandemic.

Bot Libre is a free, open-source platform where especially small and medium-sized companies can create their own chatbots and AI solutions suitable for the web, mobile apps, social media, gaming, and the metaverse.

Here are some benefits of building your own bot influencer with Bot Libre:

• Chatbots are active 24/7 and unaffected by time zone
• Boost customer service quality
• Able to reach multiple users
• Chatbots can effectively collect, analyze and properly use customer data
• Chatbots can be easily customized to fit the audience
• Bots can quickly and comprehensively customize content based on online engagement.

The data speaks for itself, influencer marketing is one of the top contributors to a business ROI, and with chatbots and AI, influencer marketing can become more targeted, scalable, and affordable.

If you are interested in building your own influencer marketing solution with the use of AI, send an email to sales@botlibre.com.

Learned something? Please give us a clap below and share!

---

Artificial Intelligence and Influencer Marketing — Rise of the Virtual Influencers was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Artificial Intelligence & The Retail Industry — Dynamic and Inclusive

Consumers are constantly seeking a more diverse and dynamic shopping experience that offers personalized products and services, and while the internet has already made this more possible, there is still much to be accomplished and enjoyed.

This is where artificial intelligence (AI) comes in. Global AI revenue is expected to increase from $643.7 million in 2016 to more than $36.8 billion in 2025, according to marketing research firm Tractica. Therefore making AI solutions part of your business retail strategy promises to be advantageous to client and revenue growth.

Bot Libre, through its open source and business platform, offers expert and personalized artificial intelligence solutions to various small and medium-sized companies in the retail industry.

Some interesting Bot Libre AI features include:

Easy 10-Click Building Process (little to no programming involved)

Speech / 3D / Metaverse Avatars

Multilingual

Social Media Integration

Consultation

Personalized Development Services

Benefits

Improved Efficiency

AI can be a gamechanger in optimizing the operations of a retail business as the software can act as a sales assistant, shorten cashier lines for customers by accepting money pay, refill inventory through real-time stock monitoring, and create digital storefronts and personalized virtual spaces to shop and try items.

Informed Customer Targeting

Retail artificial intelligence technologies can provide customers with a tailored shopping experience. Technologies like fingerprint and face recognition can recognize returning clients and keep track of their preferences. They can therefore suggest products and create campaigns for them.

Inclusivity

AI solutions, particularly through the metaverse, will provide increased accessibility to persons with a disability, allowing them to have a safer and more enjoyable shopping experience. It might also make accessibility features (such as sound direction and level, contrast, caption size, and menu navigation) customizable.

With AI, the possibilities are endless. If you are an AI, chatbot and metaverse enthusiast looking to build your business in the retail industry. Then send us an email for a free discovery call and an explainer on our development services.

Email: sales@botlibre.com

Learned something? Please give us a clap below and share!

---

Artificial Intelligence & The Retail Industry — Dynamic and Inclusive was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

From writing amazing job descriptions to scheduling interviews, personalized communications, and many more to add, today’s hiring software is brimming with unique features to streamline and simplify the recruitment process.

Though, with an untold number of solutions available in the market, picking up the right hiring software that caters to your business needs can be a real challenge.

Especially finding and hiring the best talent nowadays without ATS can make the entire recruitment process forlorn. But, purchasing an ATS (Applicant Tracking System) is one big step in the right direction to hiring the best employees for your organization.

After all, these solutions offer advantages, most of which are beneficial for hiring and crafting an excellent candidate’s experience. A handful of the advantages are listed here:

• Minimized time and cost-to-hire
• Maximized efficiencies
• Streamlined communication
• Improved quality of hire

Therefore, it’s rightly said, “The right ATS can exceptionally improve your hiring process”.

But how do you choose a recruiting solution that fits your business needs and also consider the candidate’s and hiring manager’s experience?

I understand that many ATS share the same functionalities and features, so it’s important for you to pick an ATS that goes beyond just storing candidate information.

Ready to learn more? Let’s look at the 8 features your applicant tracking system should have.

8 Must-have technical features to look for in a hiring software

Centralisation — All at one place

Swapping between endless spreadsheets, career sites, job portals, job descriptions, and outreach, managing candidates is a labor-oriented task — the leg work to be done before the interview stage.

This is one of the reasons why recruiters and HRs are opting for applicant tracking systems — to centralize their efforts.

Centralization allows you to keep everything related to the candidate in one place — making it a lot easier to create precise workflows which consist of everything from candidate profiles, interviews, profiles, communication, feedback, and all other essential details as organized.

Analytics, reporting, and dashboard

When almost everything about recruiting is fast-paced, staying proactive when sourcing and hiring the right talent is a must.

But, without the important insights needed to make informed decisions, you could bear high costs with minimum ROI.

This is where data comes in the big picture.

ATS reporting and analytics features help hire teams better understand their talent pipeline while focusing on diversity recruiting and tracking hiring progress — all in one place. Besides, you can access a dashboard to collect, store, and present data when strategizing recruitment or making any amendments to your hiring process.

Candidate sourcing

It’s never enough to post job openings on job portals or solely depend on LinkedIn to manage applications. There are numerous online places to source candidates from.

All you need to do is use an applicant tracking system (ATS) to help you enrich the candidate pipeline so applicants from different sources flow into one place. Also, a prospective ATS should allow you to store applicant information, quickly search for reference candidates, create applicant profiles, and automate outreach.

Video recording

Video recording is an unskippable element in recruiting nowadays.

So, be sure that your potential ATS can add another layer to your hiring strategy with the ability to record short videos. This feature will allow you to expedite communications with your candidates by having them reply to your screening questions. Also, it will enable them to connect more directly with the hiring team, eventually saving time.

CRM (Candidate Relationship Management)

The probability of coming across a candidate you want to handhold is high, but you don’t have a relevant job opening at the moment. In these kinds of scenarios, having a candidate relationship management (CRM) solution lets you leverage the candidate down the road when the right time comes.

Furthermore, a CRM helps you nurture meaningful relationships with potential candidates through your ATS, which could add to your culture.

A handful of features you must consider is the ability to tag, vet, and search your candidate database. In addition, check if you can create workflows that allow you to view past outreach and help you stay connected with active candidates.

Automation and customization

To make sure workflows ease the hiring burden on your talent team, customization, and automation are quick fixes. For instance, automated interview scheduling, collecting candidate feedback, personalizing outreach, setting up meetings, and much more stuff with merely a few clicks.

DEI (Diversity, Equity, and Inclusion) reporting

There’s no doubt organizations across the globe are getting more concerned about diversity recruiting; this eventually outlines the fact that hiring teams need access to deeper data that helps them nurture progress with DEI.

So, ensure your applicant tracking system can deliver these insights.

Recruitment marketing

Often, there is a big myth about recruitment marketing that it only consists of what your company does on social media. But the reality is totally different.

The real hiring landscape is that recruitment marketing should sheath every candidate’s touchpoint with your company. Every recruitment process stage should align with your recruitment marketing, including career pages, job portals, nurture relationships, and job descriptions.

Finally…

If you are taking one thing away from here, remember that the easiest way to choose the best ATS is not to have a strategy. Instead, consider the abovementioned features, which will help you stay ahead of the curve and head towards success.

If you are perplexed, experts are here to address your needs thoroughly.

---

Must-Have Technical Features of a Hiring Software was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

submitted by /u/That1DegenerateGuy
[link] [comments]

ChatGPT, Bard, and other AI showcases: how Conversational AI platforms have adopted new technologies.

On November 30, 2022, OpenAI, a San Francisco-based AI research and deployment firm, introduced ChatGPT as a research preview. Within just five days of its launch, ChatGPT achieved the remarkable feat of attracting 1 million users, which was confirmed by OpenAI’s founder, Sam Altman, via Twitter. OpenAI’s success and increasing value can be partly attributed to its partnership with Microsoft. The tech giant invested $1 billion in the company in 2019, and has plans to invest another $10 billion in the nearest future.

The technology behind ChatGPT isn`t new and is called Generative AI, but with the success of the chatbot, it has attracted even more attention. Generative AI is a branch of AI that generates various types of data such as audio, images, text, code, and more, using existing data as inspiration and creating new outputs.

Evaluate your Conversational AI solution against our Chatbot Analysis Framework

ChatGPT, the latest language model from the GPT-3 series, has set new standards in the AI industry. Using only 570 GB of textual data from the web, it has trained a large comprehensive language model that represents a significant advancement in the field. ChatGPT is considered to be the largest language model ever created, with 175 billion ML parameters.

OpenAI made the API of GPT-3 available to the public on November 18, 2021, so every business has had an opportunity to use this technology and integrate this Generative AI model. However, it was only after the launch of the ChatGPT showcase and everyone’s testing and trying their own use cases within it, that the world started to hear about OpenAI`s technology.

Check out Top Chatbot Analytics Tools

Benefits of integrating ChatGPT technologies with Conversational AI platforms or service providers

What does ChatGPT and this Generative AI technology mean for Conversational AI platforms or service providers? Is it a new market competitor, substitutor, or maybe assistant? We decided to ask ChatGPT what its own thoughts on that are.

To summarize, GPT-3 technology can enhance the functionality of Conversational AI platforms and provide:

• More accurate and human-like responses.
• Improved chatbot language understanding.
• Better chatbot personalization through fine-tuning for specific domains or industries.
• Multilingual chatbot.
• Automation of simple support tasks, freeing up customer service agents to focus on more complex issues.

The integration of ChatGPT itself into a conversational AI platform can significantly improve its accuracy, fluency, versatility, and user experience. To enable all the range of benefits of ChatGPT for Conversational AI platforms, providers need to integrate the technology via API, which is not available as an open source solution, but companies could submit a request using OpenAI API Waitlist, yet API for GPT-3 technology itself is available via the link.

What Conversational AI platform has already adopted ChatGPT-inspired technology?

According to ChatGPT’s answer, OpenAI’s GPT (Generative Pretrained Transformer) technology, of which ChatGPT is a variant, has been adopted by several conversational AI platforms. Some notable examples include:

• Replika AI: A personal AI companion that learns to communicate with its user.
• Haptik: A conversational AI platform for customer service and engagement.
• Virtual Personnel: A virtual customer service agent powered by AI.
• BotStar: A conversational AI platform for businesses to build and deploy AI-powered chatbots.
• Botpress: An open-source conversational AI platform for building and deploying bots.

These are just a few examples of using the technology that stands behind ChatGPT, and it`s popularity in the Conversational AI market is growing rapidly as more companies recognize the benefits it can bring to their platforms.

Intercom, an Irish customer service platform, has integrated OpenAI’s GPT-3.5 technology (which was the basis for ChatGPT) into its Inbox and Articles products. Currently, 100 customers are testing the new features within the limited Beta versions. The new features include ‘Composer AI’ which helps support agents write customer responses, ‘Conversation summarization’ which summarizes customer conversations for efficient handover between agents, and ‘Article generator AI’ that generates a full article version from a summary provided by the authors. The company plans to make these features more widely available this year.

Read Also: Call Center Automation using AI-Powered Chatbot

Kore.AI, a leading Conversational AI platform for optimized customer and employee experiences, has provided a comprehensive answer on how technology utilized in the ChatGPT bot Compliment Conversational AI Platforms. Features such as automatic intent recognition, and slot and entity identification are integrated with models like Open AI to provide advanced capabilities such as automatic answers to FAQs, improved human-bot interactions, and faster dialog development. In addition, large language models (LLMs) can be used by Conversational AI Platforms to generate initial prompts, messages, and sample conversations, saving a significant amount of time and providing an excellent starting point for conversation designers to refine responses.

Download checklist to evaluate Conversational AI Platforms: Conversational AI Platforms Checklist

LivePerson also adds ChatGPT’s Generative AI model to the customer service bot platform. LivePerson plans to integrate LLMs into its Conversational Cloud platform. The generative AI will also be incorporated into the company’s Conversation Assist feature to keep the chatbots up-to-date. In addition, LivePerson will use generative AI in its behind-the-scenes tools to provide businesses with conversation summaries, form filling, and customer information updates during and after conversations with the chatbots. The collected data will help improve future AI systems.

Cognigy, a global leader in Conversational AI, went through all the stages of grief and evolved from denial to acceptance in a month, when in December they highlighted Showstoppers for a pureplay ChatGPT bot in Customer Service, and then in January presented a product Demo: with LLM-assisted bot building. Basically, the ideas behind Cognigy’s release is to assess the drawbacks of both Conversational AI and ChatGPT, and combine the most advantageous facets of the two.

That is why businesses are looking for ChatGPT alternatives, and here is the list of the most popular ones.

ChatGPT Conversational AI Alternatives businesses can elect

There exist several alternatives to ChatGPT Generative AI model in the Conversational AI industry that businesses can choose from. Some of the prominent ones include:

• Google’s LaMDA (Language Model for Dialogue Applications), a large language model trained on a diverse range of internet text and capable of generating human-like responses to various types of questions and prompts.
• Facebook’s Blender, a large-scale, multi-turn, multi-domain conversational AI model, pre-trained on a diverse range of internet text.
• IBM Watson Assistant, a conversational AI platform that enables businesses to build conversational experiences for customers across any channel or device.

On February 6, 2023, Google unveiled the ChatGPT rival Bard — an experimental conversational AI service powered by LaMDA (Language Model for Dialogue Applications). What’s unique about this technology? Its approach is a bit different from the OpenAI since it is trained in dialogue data and focuses on three key parameters — Safety, Quality, and Groundedness. ChatGpt, however, is based on three models — code-DaVinci-002, text-DaVinci-002 (it was trained by humans who were checking if the answer was correct), and an additional base model to understand codes. With a large number of parameters, LaMDA excels in generating responses based on freely accessible conversation data. It can handle various customer service and marketing automation tasks, and its future looks promising with the support of platforms like Amazon.

As of now, Google is concentrating on tuning its solution to provide secure service, and it has announced to start the first real business testing in a month, so only after that may we have the opportunity to see the real feedback from the enterprises.

Gleb Dobzhanskiy, VP of Engineering at Master of Code: “We are already working on adding ChatGPT-like functionality to the existing bots of our clients. So, we do not need to throw away old flow-based bots and replace them with a new generating-AI base one. We can augment existing bots with the GPT-3 based flows on a custom fine-tuned GPT-3 model. This model can keep the focus on business-case-specific knowledge and not try to answer every question as generic ChatGPT does.”

Master of Code, as a Conversational AI solution provider and certified delivery partner of LivePerson, has extensive expertise Conversational AI platforms integration with third-party systems to collect information about customers in order to provide a personalized omnichannel experience. OpenAI`s solution could be one of the systems integrated into the conversational flow, starting with questions from the customer’s standpoint and ending with replies derived from Conversational AI. And here at Master of Code, we can share our expertise in Conversational AI development, building conversational solutions, both voice and text, within Conversation Design best practices, and integrating new technologies into ready-made systems.

While working on each project, we collaborate with stakeholders to assess the feasibility of deploying Conversational AI solutions. This involves selecting the appropriate technology, and determining the data sources and the necessary integrations, with the goal being delivering the best user experience. Undoubtedly, OpenAI or Google may be one of the technologies in this stack since, at the end of the day, our main objective is to improve efficiency and address customer issues promptly and accurately.

Want to learn more? Master of Code designs, builds and launches exceptional mobile, web, and conversational experiences.
Contact Us

---

ChatGPT, Bard, and other AI showcases: how Conversational AI platforms have adopted new… was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

submitted by /u/Taldoran
[link] [comments]

submitted by /u/followshubhu
[link] [comments]