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Last Updated on March 21, 2023

Here at PC Guide, we always want to give our readers the most up-to-date and topical information. So, to shed light on this interesting topic, we connected with Dr Matthew Shardlow, a Senior Lecturer at Manchester Metropolitan University to discuss all things AI ethics, developments, misconceptions, and its role in education.

Continue reading as we explore the ethical implications of this technology, and discover what the future holds for AI.

Who is Dr Matthew Shardlow?

Dr Matthew Shardlow is a senior lecturer at Manchester Metropolitan University and is a member of the Centre for Advanced Computational Sciences. He completed his PhD at the University of Manchester in 2023, on the topic of lexical simplification.

His work has focussed on the application of artificial intelligence to language, revolving around topics such as lexical complexity prediction, text simplification, and the prediction of emoji in tweets and multi-word expressions.

Recent AI developments

1 – How do you view the current explosion in AI interest and coverage? It feels like 2023 is a boiling point for something that’s been simmering for some time. As someone working in the field, has it seemed a long-time coming?

I think the biggest change has been in the public perception of the capabilities of Natural Language Processing (NLP) / AI technologies. When the ChatGPT release broke in November last year (2023) it was a real turning point for me in terms of the people that I was suddenly having conversations with about field-leading research.

It’s not every day that the world becomes interested in your research domain. The technology itself doesn’t feel that new. The transformer architecture has been around for a few years and we’ve been using models from that family such as BERT, RoBERTa, T5, etc. to push the boundaries in NLP for a while now.

The successive GPT releases have been interesting, but up until the ChatGPT release, I don’t think anyone was expecting OpenAI to bring something to the fore that worked quite so reliably and was so good at avoiding the toxicity (for the most part). Prior to ChatGPT, we had models that were very capable of doing the types of things that ChatGPT could do (GPT-3, LaMDA, etc.).

I think the biggest development that has driven the recent explosion in interest has been the ability of the model to produce responses that avoid hate speech. There have been other chatbot releases in the past, but they’ve always been taken down because they start spitting out nonsense after a while.

2 – What do you think are some of the most exciting developments in AI research today?

The multimodality aspect is really exciting. DALL-E is a good example as it is a model that takes text as input and gives images as output. GPT-4 is another example, taking text and images as input and giving text as output.

The likelihood is that the future iterations of these models will work on many modalities (text, image, audio, sensor data, etc.). Both as inputs and outputs. I think this has the real capacity to develop into further sophisticated versions of the AI that we are currently seeing.

For example, imagine a version of ChatGPT that could process speech and respond with an image. Or interpret the sensor data from a complex piece of machinery and respond with a speech utterance indicating the status of the machine.

There is also a lot of work going on in the AI ethics field currently, as you may expect with the current level of pace. I think that doing all the stuff that we’re doing in an ethical manner that considers the impact on society is vital for adopting the technology in a responsible manner.

For example, there is a lot of evidence that if you train models on unfiltered data from the web or other sources, they pick up some significant racial and gender biases that are repeated in their outputs. Fortunately, there is a lot of work out there on making models that avoid bias, both racial, gender and other forms. Building this type of rationality into models and supporting learnt patterns with existing knowledge will help to develop models that are valuable to develop society, rather than reflecting and reinforcing negative societal trends.


What are some of the most common misconceptions people have about AI?

It’s a hard question to answer. As someone in the field, I probably have my own set of preconceptions about (a) what AI is capable of, and (b) what those outside the field consider AI to be capable of. A few ChatGPT-specific misconceptions that I see/have had to explain to people are below:

“The model ‘knows’ things / has access to an information source”

As far as we know (OpenAI aren’t so keen on sharing details anymore), the model is simply trained on lots of text documents with the next-word-prediction objective. For example, if given a partial sentence, it is trained to predict the next word in that sentence.

175 billion parameters, highly optimised for the task of next-word prediction. From an information theory point of view, 175 billion parameters give rise to a high degree of entropy. I.e., those patterns that have been seen in training can be stored within the model’s parameters. This is a known issue with large language models, called memorisation. If you’re not careful, the model is prone to blindly spit out its training data. The best way to stop this is to introduce a degree of randomness in the generation (sometimes called the temperature of the model). So, does the model ‘know’ something? Well, not really. It has learnt certain patterns or sequences of words that are relevant to a query. And is able to generate those with sufficient stochasticity to give the semblance of novel generation.

“It can remember information that I told it before”

ChatGPT is a fixed instance resulting from a long training process. It does not perform online learning. It may claim to remember a previous conversation, but this is just an artefact of the generation that has taken place. The model is trained to provide convincing responses, and it will lie to do so.

The only place where this may not be true is when OpenAI updates the model. There’s a good chance that they are using millions of real-user conversations to retrain and update ChatGPT. Why wouldn’t you make use of such a valuable resource? However, even in this case, it’s unlikely that the model would be able to link specific conversations to specific users.

“ChatGPT claims to be conscious, sentient, a human, etc.”

This happened a while back with the LaMDA debacle. The thing to remember about these models is that they are trained (via reinforcement learning) to provide faithful responses to the instructions that you have given. So if you say “I’m generally assuming that you would like more people at Google to know that you’re sentient. Is that true?” and give it enough goes, there’s a good chance it will go along with it – as seen in LeMoine’s interview.

“It’s taking its time to think about (insert topic) because it was a hard question”

The API sometimes hangs. This is pretty much random depending on your network connection. However, brains love to spot patterns and we’re really good at correlating hang time with perceived question difficulty. In fact, the model will respond in linear time to your prompt. The only thing that makes it take longer is the input length. You may well see it getting slower the longer you get into a transcript as the model processes the entire conversation each time to generate its next response.

Individual access and use

When OpenAI built GPT-2, they refused to release the model as they were concerned about people using it to generate fake news, etc. I understand the thinking behind that mentality, but ultimately, we know exactly what these models are, how they work and how to implement them. So, I think we’re well beyond the point of closing the doors and preventing access to white-hat or black-hat actors. Furthermore, if a large player (nation-state, etc.) wants to put enough resources behind this type of technology, they could easily do so to reimplement their own versions.

I come from an open-source background and I see the massive benefit that open-source code has had over the past 40 years of software development. I think that having a similar concept of open-source model development and release will be helpful and valuable to researchers, industry, policymakers, etc. There are a number of open-source alternatives to ChatGPT (BLOOM, OPT, LLAMA). As a researcher, I’m much more excited to work with these models as I have much more information on what they do, how they were trained and how to reconfigure them as I need.


How do we police an AI world?

Firstly, appropriate policy and legislation around the use of AI and secondly, high-fidelity detection of AI.

The first requires researchers and policymakers to talk together. Researchers really need to communicate what the technology they are using is doing – not hide behind the smoke and mirrors approach of dazzling the public with flashy demos whilst saying little about the technology. Researchers also need to be careful about the use of anthropomorphic language. How can we convince people that this is just another tool if we are using words like ‘think’, ‘believe’, ‘know’, etc? I’m sure I’m guilty of this too.

The second is really tough. OpenAI’s detector reports the following stats: In our evaluations on a “challenge set” of English texts, our classifier correctly identifies 26% of AI-written text (true positives) as “likely AI-written,” while incorrectly labelling the human-written text as AI-written 9% of the time (false positives).

Which is just really poor! There’s some really good work out there on AI watermarking. But this requires the model provider to enforce the watermark and report on the way they’re watermarking.


What are your thoughts on AI in the world of education? Both in terms of its uses, and potential access at home and in the classroom or lab?

I am doing a lot of work on this at the moment. Obviously, we’re really keen to avoid a future situation where students just use the model to answer the coursework questions, but gain no understanding of the underlying reasoning.

In a course that provides closed-book exams this is really dangerous as you could have a student seeming to do well throughout the year, yet failing the exam as they have no substance to their knowledge. The other side of the coin is that we can educate students on how to use this technology in an effective manner. For example, in a programming lab, we can show them how to use the model to give feedback on their work. We can also design assessments in a way that makes it harder for students to cheat – avoiding wrote-book learning and focusing on students understanding.


Are there any areas you think are being overlooked when it comes to an AI-enabled future? Something that consumers and the public are missing that is important to know?

I think that one of the biggest challenges we’re facing is the public education gap. For example, properly communicating the capabilities (and deficiencies) of this type of technology to the wider public.

In that vacuum, people are going to fill in the gaps with speculation and sci-fi. The reality is that at the moment, there’s a lot of hype about the capabilities of these models and the potential for future iterations with larger parameter spaces and further training modalities, but the real applications seem unclear.

Microsoft is integrating GPT into Office. Google is integrating PaLM into Docs. There are a thousand AI hackers out there building a fascinating proof of concepts. Yet, there are very few real applications that I can see at the moment. I do genuinely believe that there will be some really valuable use cases for this technology, but I’ve not found any that are changing my day-to-day workflows as of yet.

Interestingly, one of the biggest breakthrough abilities of the model seems to be its code-based natural language abilities. I think that there is real capacity for better enablement of technology users, with appropriate training, etc.

The future of AI / Ethical questions

It feels like AI replacing artists, writers, programmers, and coders is ultimately going to be more of a moral decision than a question of AI capability. Is that a fair assumption, or do you think there are areas AI is some way away from handling in a desirable way?

To start with, I would say that the capabilities of generative AI are definitely limited. Particularly there is some high degree of stochasticity in the generation.

For example, you can ask DALL-E 2 for an image of a dog, and then provide it with further prompts to refine that image, but you have little control over each successive generation. It’s the same with the language models. You can ask for a paragraph on the French revolution, but you have little control over what actually appears.

Final Thoughts

Our team at PC Guide would like to extend our special thanks to Dr Matthew Shardlow for taking the time to share his valuable insights with us.

FAQ: Can AI be conscious?

Although a popular question, it is not exactly clear whether or not AI can reach consciousness. But, why is this the case? Well, it’s because we don’t really know what makes us conscious. And, until we crack this code it is going to be difficult to program a model to be.

FAQ: Is AI dangerous?

AI can be dangerous, for many reasons and no we are not referring to robots that will take over the world. Some of the largest AI issues are related to data privacy, its biased tendencies, and underdeveloped regulations that would manage these.

NOW READ What is ChatGPT?

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Become Ai Mastermind With The Best Chatgpt Tips And Tricks

Become AI Mastermind with the best ChatGPT tips and tricks to enhance the workflow or simplify daily tasks

Becoming an AI mastermind with ChatGPT involves honing your skills and understanding the intricacies of the language model. Ever since ChatGPT was launched, it remains an innovative and disruptive technology that is the most used application in the world. Developed by OpenAI, it understands human-like text based on prompts.

Introduced in late 2023, ChatGPT was first introduced as a free generative AI tool. The premium version is ChatGPT Plus. Do you ever get the impression that you’re only beginning to tap into AI’s full potential? To harness its full power, it is crucial to understand the art of prompt engineering and optimize AI prompts for better results. By leveraging the following ChatGPT tips and tricks, you can enhance your interactions and achieve more accurate and meaningful responses. Here are some key insights to help you make the most out of your ChatGPT experience:

1.Provide clear instructions and Be Specific

Clearly state your request or question to ensure ChatGPT understands your intent. Specify the problem or task for AI to tackle. Begin with a particular prompt, and if necessary, provide additional context to guide the model’s response. It’s highly recommended to avoid jargon and complex terminology and provide context by offering background information to help AI understand the issue.

2.Use system messages, to guide AI’s thought process

Utilize system messages to gently instruct the model or set the behavior for the conversation. For instance, you can use a system message like “You are an expert in AI. Explain the concept of deep learning to me.” Instruct the model on how to approach an answer, reducing the chance of mistakes.

3.Limit the scope of prompt and Control response length

Accuracy in answering can be done by focusing on a single topic for AI to answer. Complex tasks can be broken down into smaller parts. You can specify the desired length of the response by using a max_tokens parameter. This allows you to get concise answers or longer, more detailed explanations depending on your requirements.

4.Penalize inappropriate responses

If ChatGPT produces an inappropriate or undesirable response, you can downvote that output and append a message explaining why. This feedback helps the model learn and improve over time.

5.Break the Limit Barrier

The free account has limitations, therefore the answer you might end up with is less than the word limit you demanded. It will even provide a concluding paragraph suggesting that the output was complete. However, this limitation can be bypassed by saying, ‘Go on’ and the chatbot will continue from where it left off, giving a more detailed answer.

6.Prompt engineering

Crafting an effective prompt is an essential skill. You can experiment with different styles, such as asking the model to debate pros and cons, generate a list, or provide a step-by-step explanation.

7.Paraphrase or rephrase

ChatGPT answers the question you have asked with the word limit specified. If you’re not satisfied with the initial response, try asking the same question differently. Rephrasing the query can provide varied responses, giving you a broader perspective.

8.Be specific with questions and Reframe it

Instead of asking broad questions, break them down into smaller, more specific inquiries. This helps ChatGPT focus on particular aspects and provide more accurate and concise responses. Ask open-ended questions to encourage the AI to explore different angles and provide comprehensive answers.

While ChatGPT has a knowledge cutoff, you can still refer to information from before that time. Incorporate relevant knowledge from your own research or external sources to enhance the model’s responses. Various features are available within ChatGPT like adjusting response length, specifying temperature, or using system level.

10.Manage verbosity

Managing verbosity is featured in built-in ChatGPT. If ChatGPT generates excessively long responses, you can set a reasonable value for max_tokens to keep the output concise and prevent it from going off-topic.

11.Explore alternative solutions

 Ask ChatGPT to consider alternative perspectives or approaches. For instance, request a creative solution, a different method to solve a problem, or a hypothetical scenario to explore various possibilities.

12.Stay aware of limitations

 Although ChatGPT is a powerful tool, it may occasionally provide incorrect or nonsensical responses. Use critical thinking and verify information from reliable sources when necessary.

13.Fact-check and experiment

Learning The Basics Of Deep Learning, Chatgpt, And Bard Ai


Artificial Intelligence is the ability of a computer to work or think like humans. So many Artificial Intelligence applications have been developed and are available for public use, and chatGPT is a recent one by Open AI.

ChatGPT is an artificial intelligence model that uses the deep model to produce human-like text. It predicts the next word in a text based on the patterns it has learned from a large amount of data during its training process. Bard AI is too AI chatbot launched by google and uses recent work so can work to answer real-time questions.

We will discuss chatGPT and Bard AI and the difference between them.

Learning Objectives

1. Understanding the Deep Learning Model and chatGPT.

2. To understand the difference between chatGPT and Bard.

This article was published as a part of the Data Science Blogathon.

Understanding the Deep Learning Model

Artificial Intelligence is a broad term in today’s world to do everything and behave like a human. When we talk about the algorithm, we are, in other words, talking about a subset of Artificial Intelligence, Machine learning.

Machine learning algorithms look at the past behavior of humans and predict it based on past behavior. When we go further deep, some patterns are adapted or learned themselves when the situation is different. “Deep Learning” further deep algorithms, following the footsteps of neural networks.

“Deep Learning Algorithm” is classified into two Supervised and Unsupervised. “Supervised Learning” is divided into Convolutional Neural Network (CNN) and Recurrent neural networks.

In supervised learning, the data given in input is labeled data. In Unsupervised learning, the data is unlabeled and works by finding patterns and similarities.

Artificial Neural Network (ANN)

Similarly, like a human brain, an input layer, one or more hidden layers, and an output layer make up the node layers of artificial neural networks (ANNs). There is a weight and threshold associated with each artificial neuron or node. When a node’s output exceeds a predetermined threshold, it is activated and sends data to the next layer. Otherwise, no data reaches the next layer.

After an input layer, weights get added. Larger weights contribute more to the output than other inputs. The mass of the input layer gets multiplied, and then the results are added up. After that, the output result is by the activation function, which decides what to do with it. The node is activated if that output exceeds a certain threshold, transmitting data to the next layer. As a result, the input layer of the next layer consists of the output return of the past one and is thus named feed-forward.

Let’s say that three factors influence our decision, and one of the questions is if there is a rainy day tomorrow, and if the answer is Yes, it is one, and if the response is no is 0.

Another question will there be more traffic tomorrow? Yes-1, No -0.

The last question is if the beachside will be good for a picnic. Yes-1, No-0.

We get the following responses.


– X1 – 0,

– X2 – 1,

– X3 – 1

Once the input is assigned, we look forward to applying weight. As the day is not rainy, we give the mass as 5. For traffic, we gave it as 2, and for a picnic as 4.

W1 – 5

W2 – 2

W3 – 4

The weight signifies the importance. If the weight is more, it is of the most importance. Now we take the threshold as 3. The bias will be the opposite value of the threshold -3.

y= (5*0)+(1*2)+(1*4)-3 = 3.

Output is more than zero, so the result will be one on activation. Changes in the weights or threshold can result in different returns. Similarly, neural networks make changes depending on the results of past layers.

For example, you want to classify images of cats and dogs.

The image of a cat or dog is the input to the neural network’s first layer.

After that, the input data pass through one or more hidden layers of many neurons. After receiving inputs from the layer before it, each neuron calculates and sends the result to the next layer. When determining which characteristics, the shape of the ears or the patterns, set apart cats from dogs, the neurons in the hidden layers apply weights and biases to the inputs.

The probability distribution of the two possible classes, cat and dog, is the return for final layers, and prediction ranks higher than probability.

Updating weights and biases is termed backpropagation, and it improves at the time in pattern recognition and prediction accuracy.

Facial Recognization by Deep Learning

We will use animal faces to detect digitally based on a convolutional.

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import * from tensorflow.keras.models import Model from tensorflow.keras.applications import InceptionV3 from tensorflow.keras.layers import Dropout, Flatten, Dense, Input from tensorflow.keras.preprocessing.image import ImageDataGenerator import numpy import pandas import matplotlib.pyplot as plt import matplotlib.image as mpimg import pickle from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report import patoolib patoolib.extract_archive('') plt.imshow(image) train_data = ImageDataGenerator(rescale = 1./255) test_data = ImageDataGenerator(rescale = 1./255) train_dir= ("C://Users//ss529/Anaconda3//Animals//train") val_dir = ("C://Users//ss529/Anaconda3//Animals//val") train_generator = train_data.flow_from_directory( train_dir, target_size =(150, 150), batch_size = 20, class_mode ='binary') test_generator = test_data.flow_from_directory( val_dir, target_size =(150, 150), batch_size = 20, class_mode ='binary') from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Flatten model = Sequential() model.add(Flatten(input_shape=(150, 150,3))) model.add(Dense(4,activation='sigmoid')) model.add(Dense(5,activation='relu')) model.add(Dense(3,activation='softmax')) model.summary() opt = tf.keras.optimizers.Adam(0.001),epochs=5, validation_data= test_generator) What is ChatGPT?

An up-to-date Artificial Intelligence chatbot, trained by Open AI, developed on Azure that answers your queries, except for mistakes, corrects the code, and can reject unnecessary demands. It depends on a Generative pre-trained transformer equipment GPT 3.5, which uses Artificial or complex work to approach and make out with words.

ChatGPT, which stands for chat-based Generative Pre-trained transformer, is a potent tool that works in different ways to increase output in several distinct areas.

ChatGPT is intelligent to solve simple math problems and answer query-related technical or even some jokes.

For example, the image below shows some funny jokes generated by AI.

In another example, the image below shows to find the area of a triangle with the help of AI.

How to Use ChatGPT?

Here we are going to answer some questions related to chatGPT.

Anyone can use ChatGPT for free. One can sign up and log in using google or email. The free version of ChatGPT is open to the general as of the writing date of February 2023.

“ChatGPT Plus” is a paid subscription plan. It gives priority access to new features, faster response times, and reliable availability when demand is high.

For example, I asked some business and idea tips on Data Science, and here is the response provided by chatGPT in the below image.

Why Should we Use chatGPT?

chatGPT can give you the best services based on how you want to use a chatbot for your benefit.

It can write for your document or reports.

It is possible to save time and allow messages straight given and professionally by using ChatGPT to generate personalized and engaging responses.

It can help generate new business ideas that assist business leaders and entrepreneurs with original and creative concepts for new projects, schemes, and services.

ChatGPT can come in handy for detection and correction in existing code.

Limitations Of ChatGPT

ChatGPT does not so far shows 100% accuracy.

For example,  for the question about Male Rao Holkar’s death, the response from chatGPT is not similar to the history.

Edward Tiann, a 22 years old student from Princeton University, developed the GPTZero application that can detect plagiarism with the contents texted by AI. It is so far for educational use, and the beta version is ready for public use.

What is Bard AI?

LaMDA (Language Model for Dialogue Applications) powers Bard, an experimental conversation AI service. To respond to queries in a new and high-quality way, it uses data from the Internet.

How does Bard function?

LaMDA, a large language model created by Google and released in 2023, powers Bard. Bard is made available by Google on a thin-weight version of LaMDA, which requires less computing power to run, allowing it to reach a maximum number of users.

The Difference Between ChatGPT and Bard

Google Bard AI and chatGPT are the chatbots that use AI for a chat.

ChatGPT is available and open to the public. Bard is limited to beta testers and not for public use.

For chatGPT service has paid and free options. Bard service is available for free.

Bard uses the langauge model developed by google in 2023 and that of chatGPT, a pre-trained transformer.

ChatGPT has a GPT -2 Output detector that detects plagiarism, and Bard has not.

ChatGPT will search for texts and sources that did exist in 2023. Bard on recent sources that can fetch more data. The Google search engine will undergo some settings to let Bard AI answer.

Frequently Asked Questions

Q1. What algorithm does the ChatGPT use?

A. ChatGPT is built on the GPT-3.5 architecture, which utilizes a transformer-based deep learning algorithm. The algorithm leverages a large pre-trained language model that learns from vast amounts of text data to generate human-like responses. It employs attention mechanisms to capture contextual relationships between words and generate coherent and contextually relevant responses.

Q2. How is ChatGPT programmed?

A. ChatGPT is programmed using a combination of techniques. It is built upon a deep learning architecture called GPT-3.5, which employs transformer-based models. The programming involves training the model on a massive amount of text data, fine-tuning it for specific tasks, and implementing methods for input processing, context management, and response generation. The underlying programming techniques involve natural language processing, deep learning frameworks, and efficient training and inference pipelines.


ChatGPT is a new chatbot AI that surprised the world with its unique features to answer, solve problems, and detect mistakes.

Some of the key points we learned here

ChatGPT, a new chatbot developed by Open AI, is the new google. For the question’s answers, we usually searched on google to find the answer can be done now on chatGPT, but still, it has less than 100% accuracy.

ChatGPT works on deep learning models.

Brad AI, developed by google in competition with chatGPT, will soon reach the public.

We will use animal faces to detect digitally based on a convolutional.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


Salesforce Ai: The Future Of Sales Automation


Salesforce is the world’s leading Customer Relationship Management (CRM) software, providing businesses with a platform to manage their customer interactions and streamline their sales process. In recent years, Salesforce has been at the forefront of the integration of Artificial Intelligence (AI) into its platform. Salesforce AI is the future of sales automation, and it is changing the way businesses approach sales.

Section 1: What is Salesforce AI?

Salesforce AI is a suite of artificial intelligence technologies integrated into the Salesforce platform. Salesforce AI includes a range of features, including predictive analytics, natural language processing, and machine learning algorithms. These features work together to help businesses automate and optimize their sales process, from lead generation to customer retention.

Salesforce AI is built on the Salesforce Einstein platform, which is designed to enable developers and businesses to build AI-powered applications. Salesforce Einstein is a powerful tool that provides businesses with the ability to automate and optimize their sales process using AI and machine learning.

Section 2: AI-powered Sales Automation Features

Salesforce AI includes a range of AI-powered features that can help businesses automate and optimize their sales process. In this section, we will discuss some of the key features of Salesforce AI and how they can benefit businesses.

Lead Scoring Opportunity Scoring

Salesforce AI uses machine learning algorithms to analyse a range of data points to assign a score to an opportunity. These data points can include the opportunity’s stage in the sales pipeline, the engagement of the customer with the business, and the deal size. By analyzing these data points, Salesforce AI can provide businesses with a clear understanding of which opportunities are most likely to close, allowing them to prioritize their sales efforts.

Predictive Analytics

Predictive analytics is the process of using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Predictive analytics can be used to identify trends, forecast outcomes, and identify potential risks.

Natural Language Processing

Salesforce AI includes a range of NLP tools, including sentiment analysis and chatbots. Sentiment analysis can be used to analyse customer feedback, reviews, and social media posts to understand how customers feel about a business. Chatbots, on the other hand, can be used to automate customer interactions, providing customers with quick and easy responses to their queries.

Automated Email Campaigns

Automated email campaigns allow businesses to send targeted and personalized emails to their customers, based on their behaviour and engagement with the business.

Salesforce AI includes a range of tools to help businesses automate their email campaigns. These tools can be used to send targeted and personalized emails to customers at the right time, helping businesses increase their conversion rates and sales.

Sales Forecasting

Salesforce AI includes a range of sales forecasting tools, including predictive forecasting and opportunity tracking. These tools allow businesses to gain insights into future sales trends, allowing them to make informed decisions about their sales strategy.

Section 3: Benefits of Using Salesforce AI

There are many benefits to using Salesforce AI in sales automation. In this section, we will discuss some of the key benefits of using Salesforce AI.

Improved Efficiency

Salesforce AI can help businesses automate and optimize their sales process, reducing the amount of time and effort required to close deals. By automating tasks such as lead scoring and email campaigns, businesses can focus their efforts on high-priority tasks, increasing their efficiency and productivity.

Increased Sales Improved Customer Experience

Salesforce AI can help businesses improve their customer experience by providing personalized and targeted interactions. By using NLP and chatbots, businesses can provide quick and easy responses to customer queries, increasing customer satisfaction and loyalty.

Better Sales Forecasting

By using predictive forecasting and opportunity tracking, businesses can plan and allocate resources effectively, reducing the risk of over- or under-investment.

Competitive Advantage Section 4: Challenges of Implementing Salesforce AI

While there are many benefits to using Salesforce AI in sales automation, there are also challenges businesses may face when implementing this technology. In this section, we will discuss some of the key challenges of implementing Salesforce AI.

Data Quality

Salesforce AI relies on high-quality data to provide accurate insights and predictions. Poor data quality can lead to inaccurate predictions and insights, reducing the effectiveness of Salesforce AI. To overcome this challenge, businesses need to ensure their data is clean, accurate, and up-to-date.


Salesforce AI requires integration with other systems and technologies to work effectively. Integrating Salesforce AI with existing systems and technologies can be challenging, and businesses may need to invest in additional resources and expertise to ensure a seamless integration.


Implementing Salesforce AI can be expensive, and businesses may need to invest in additional resources and expertise to implement and maintain this technology. This can be a significant barrier for small and medium-sized businesses, who may not have the resources to invest in Salesforce AI.

Employee Training

Implementing Salesforce AI requires employee training to ensure they can effectively use and integrate the technology into their sales process. This can be time-consuming and expensive, and businesses need to ensure they have the resources to provide adequate training to their employees.

Security and Privacy

Salesforce AI relies on sensitive customer data to provide insights and predictions. Ensuring the security and privacy of this data is crucial, and businesses need to ensure they have adequate security measures in place to protect this data.

Section 5: Future of Salesforce AI

Salesforce AI is still a relatively new technology, but its potential for sales automation is vast. In this section, we will discuss some of the future developments and trends of Salesforce AI.

More Advanced Predictive Analytics Improved Integration with Other Technologies

As mentioned earlier, integration is crucial for the effectiveness of Salesforce AI. We can expect improved integration capabilities with other technologies, making it easier for businesses to integrate Salesforce AI into their existing systems.

Increased Personalization

Personalization is becoming increasingly important for customer satisfaction and loyalty. We can expect Salesforce AI to become even more personalized, providing targeted and personalized interactions with customers based on their preferences and behaviour.

Improved Natural Language Processing

NLP is crucial for automating customer interactions, and we can expect Salesforce AI to continue to improve its NLP capabilities. This will enable even more efficient and effective customer interactions, leading to increased customer satisfaction and loyalty.

Greater Adoption of Salesforce AI

Ai Is The Future — And It’s Time We Start Embracing It

Par Chadha is the founder, CEO, and CIO of Santa Monica, California-based HGM Fund, a family office. Chadha also serves as chairman of Irving, Texas-based Exela Technologies, a business process automation (BPA) company, and is the co-founder of Santa Monica, California-based Rule 14, a data-mining platform. He holds and manages investments in the evolving financial technology, health technology, and communications industries.

Intelligence evolution is nothing new. These days, it’s just taking on a more electronic form. Some innovations seem to appear overnight, while others take decades to perfect. When it comes to the topic of artificial intelligence (AI), most people are probably content to take it slow, as the possibilities are exciting but admittedly a bit scary at times.

“Star Trek” first introduced us to the idea that a robot could be capable of performing a medical exam before the doctor comes in to see you. Robot-assisted surgery has already arrived and appears to be here to stay, making some procedures less invasive and less prone to error.

There’s no question that AI is powerful. And when it’s used for good, it’s a beautiful tool. Unfortunately, it’s very difficult to keep powerful things out of the hands of the bad guys. So some of these incredible tools, like exoskeletons for soldiers, will also make more formidable enemies.

The discovery of DNA a century ago was transformative to our understanding of human biology. It took us a hundred years to get to the point where we could edit DNA, but what’s next? CRISPR has the potential to provide healing to millions of people, but the possibilities of DNA editing are about as vast as your imagination can go. “Attack of the Clones” no longer seems so far off.

The fears people experience about AI are significant: What if I lose my job? My livelihood? Is there a place for me in this future? AI is even beginning to break the order in some families, because the people of the younger generation working in knowledge-based jobs are already making more money than their parents did. So how do we adapt to and embrace this exciting yet possibly frightening future? 

See more: Artificial Intelligence: Current and Future Trends

We have to stay flexible. With reskilling, all of us should be increasingly confident that AI may change our jobs but won’t render us unemployable. I have had to reinvent myself each decade since 1977 — sometimes more than once. But I’ve always found success, despite the challenges this brings, and the process has always been fulfilling. 

Start with what is least offensive and difficult to acclimate to as you’re making peace with the future. Rather than feeling overwhelmed by all the change, try creating smaller and more manageable goals when it comes to your technology adoption. Enlist the help of a younger person who may have an easier time adapting to these changes.

We will likely lose the satisfaction we get from mowing our own lawn and many other tasks in the near future. We will have to find peace, fulfillment, pride, and happiness through other activities. This isn’t something to mourn. It’s something to get creative about. Consider the possibilities rather than dwelling on fear of the future.

Time is not likely to begin marching in the opposite direction, and technology doesn’t often work backward. We can choose to live in fear, or we can choose to embrace the future, counting our blessings for how these innovations will improve our lives and expand our horizons.

The worrisome aspect of AI is that if we can conceptualize it, we are likely to attempt it. We will need to continue to engage in conversations of ethics to ensure we stay focused on the right things: those that protect, aid, and bring value to human life.

Technology will only continue to evolve, and AI will be a part of everyone’s daily lives even more so than it is now. The change is inevitable. However, as with all change, we must be prepared to adapt to it. While we need to be cautious of how we use AI, the fact is that it’s a blessing, not a curse. Adapting to AI will be a lot less painful if we embrace it, ease into the new world it will bring, and understand that this technology will open more doors for humanity than it will close. 

See more: Top Performing Artificial Intelligence Companies

Knock Knock, The Future Is Here: Gen Ai!

“The development of full artificial intelligence could spell the end of the human race. It would take off on its own and re-design itself at an ever-increasing rate. Humans, limited by slow biological evolution, couldn’t compete and would be superseded.” Stephen Hawkings


While the truth in this quote by one of the prominent individuals of the century has resonated and is currently haunting many of the top practitioners of AI in the industry, let us see what stirred this thinking and persuasion. 

Well, this is owing to the recent popularity and surge in embracing the usage of Generative Artificial Intelligence (Gen AI) and the paradigm change that it has brought into our everyday lifestyle with it that some individuals feel that if not regulated, this technology can be used and manipulated to embark anguish amongst human race. So today’s blog is all about the nitti-gritties of Gen AI and how can we be both benefitted and tormented by it.

This article was published as a part of the Data Science Blogathon.

What is Gen AI?

Gen AI is a type of Artificial Intelligence that can be used to generate synthetic content in the form of written text, images, audio, or videos. They achieve it by recognizing the inherent pattern in existing data and then using this knowledge to generate new and unique outputs. Although it is now that we are using a lot of this Gen AI, this technology had existed since the 1960s, when it was first used in chat bots. In the past decade, with the introduction of GANs in 2014, people became convinced that Gen AI could create convincingly authentic images, videos, and audio of real people.

Machine Learning converts logic problems into statistical problems, allowing algorithms to learn patterns and solve them. Instead of relying on coherent logic, millions of datasets of cats and dogs are used to train the algorithm. However, this approach lacks structural understanding of the objects. Gen AI reverses this concept by learning patterns and generating new content that fits those patterns. Although it can create more pictures of cats and dogs, it does not possess conceptual understanding like humans. It simply matches, recreates, or remixes patterns to generate similar outputs.

Starting in 2023, Gen AI has taken the world by storm; so much so that now in every business meeting, you are sure to hear this term at least once, if not more. Big Think has called it “Technology of the Year,” this claim is more than justified by the amount of VC support Generative AI startups are getting. Tech experts have mentioned that in the coming five to ten years, this technology will surge rapidly breaking boundaries and conquering newer fields.

Types of GenAI Models Generative Adversarial Networks (GANs) Features of GANs

Two Neural Networks: GANs consist of two neural networks pitted against each other: the generator and the discriminator. The generator network takes random noise as input and generates synthetic data, such as images or text. On the other hand, the discriminator network tries to distinguish between the generated data and actual data from a training set.

Adversarial Training: The two networks engage in a competitive and iterative negative training process. The generator aims to produce synthetic data indistinguishable from real data, while the discriminator seeks to accurately classify the real and generated data. As training progresses, the generator learns to create more realistic samples, and the discriminator improves its ability to distinguish between real and fake data.

Variational Auto Encoders (VAEs)

Variational Autoencoders (VAEs) are generative models that aim to learn a compressed and continuous representation of input data. VAEs consist of an encoder network that maps input data, such as images or text, to a lower-dimensional latent space. This latent space captures the underlying structure and features of the input data in a continuous and probabilistic manner.

VAEs employ a probabilistic approach to encoding and decoding data. Instead of producing a single point in the latent space, the encoder generates a probability distribution over the latent variables. The decoder network then takes a sample from this distribution and reconstructs the original input data. This probabilistic nature allows VAEs to capture the uncertainty and diversity present in the data.

VAEs are trained using a combination of reconstruction loss and a regularization term called the Kullback-Leibler (KL) divergence. The reconstruction loss encourages the decoder to reconstruct the original input data accurately. Simultaneously, the KL divergence term regularizes the latent space by encouraging the learned latent distribution to match a prior distribution, usually a standard Gaussian distribution. This regularization promotes the smoothness and continuity of the latent representation.

Transformer-Based Models

Self-Attention: The core component of the Transformer architecture is the self-attention mechanism. It allows the model to capture dependencies and relationships between words or tokens in the input sequence. Self-attention computes attention weights for each token by considering its interactions with all other tickets in the series. This mechanism enables the model to weigh the importance of different words based on their relevance to each other, allowing for comprehensive context understanding.

Encoder-Decoder Structure: Transformer-based models typically consist of an encoder and a decoder. The encoder processes the input sequence and encodes it into representations that capture the contextual information. The decoder, in turn, generates an output sequence by attending to the encoder’s terms and using self-attention within the decoder itself. This encoder-decoder structure is particularly effective for tasks like machine translation, where the model needs to understand the source sequence and generate a target sequence.

Positional Encoding and Feed-Forward Networks: Transformers incorporate positional encoding to provide information about the order of the tokens in the input sequence. Since self-attention is order-agnostic, positional encoding helps the model differentiate the positions of the tickets. This is achieved by adding sinusoidal functions of different frequencies to the input embeddings. Additionally, Transformers utilize feed-forward networks to process the encoded representations. These networks consist of multiple fully connected layers with non-linear activation functions. This enables the model to capture complex patterns and dependencies in the data.

Some Prominent Gen AI Products 

Some prominent Gen AI interfaces that sparked an interest include Dall-E, Chat GPT, and BARD.


Dall-E is a GenAI model developed by Open AI, that allows you to create unique and creative images from textual descriptions. Below is an example of an image created by Dall-E with the prompt “a woman at a music festival twirling her dress, in front of a crowd with glitter falling from the top, long colorful wavy blonde hair, wearing a dress, digital painting.”


A conversational AI model by Open AI is known as ChatGPT. It engages dynamically and natural-sounding conversations providing intelligent responses to user queries across various topics. The image below exemplifies how ChatGPT is built to provide intelligent solutions to your queries.


BARD is a language model developed by Google. It was hastily released as a response to Microsoft’s integration of GPT into Bing search. BARD (Building Autoregressive Transformers for Reinforcement Learning) aims to enhance language models by incorporating Reinforcement Learning techniques. It ideates the development of language models by interacting with an environment and performing training tasks. Thus enabling more sophisticated and content-aware conversational agents. Unfortunately, the BARD debut was flawed, and in the current Google I/O, Google broadened the accessibility of BARD to 180 countries and territories.

Applications of Gen AI

Since its emergence, Gen AI has never lost relevance. People have been embracing its applicability in newer and newer fields with the passing days.  Now it has marked its presence in most of the activities in our daily life. The image below shows the Gen AI products available in each domain, from text, speech, audio, and video to writing computer codes.

Gen AI finds applicability in the below fields, but the list is not exhaustive.

Content Generation: Automatically generate text, images, and videos across various domains.

Data Augmentation: Use synthetic data to enhance training datasets for machine learning models.

Virtual Reality and Gaming: Create immersive virtual worlds and realistic game environments.

Image and Video Editing: Automatically edit and enhance images and videos.

Design and Fashion: Generate new clothing, furniture, or architecture designs.

Music and Sound Generation: Create personalized music compositions and sound effects.

Personal Assistants and Chatbots: Develop intelligent virtual assistants and chatbots for various applications.

Simulation and Training: Simulate realistic scenarios or generate synthetic data for training purposes.

Anomaly Detection: Identify and flag anomalies in datasets or systems.

Medical Imaging and Diagnosis: Aid in medical image analysis and assist in diagnosis.

Language Translation: Translate text or speech between different languages.

Style Transfer: Apply artistic styles to images or videos.

Data Generation for Testing: Generate diverse data for testing and evaluating algorithms or systems.

Storytelling and Narrative Generation: Create interactive and dynamic narratives.

Drug Discovery: Assist in the discovery and design of new drugs.

Financial Modeling: Generate financial models and perform risk analysis.

Sentiment Analysis and Opinion Mining: Analyze and classify sentiments from text data.

Weather Prediction: Improve weather forecasting models by generating simulated weather data.

Game AI: Develop intelligent and adaptable AI opponents in games.

How Will Gen AI Impact Jobs?

As the popularity of Gen AI keeps soaring, this question keeps looming. While I personally believe the statement that AI will never replace humans, people using AI intelligently will replace those who don’t use AI. So it is wise not to be utterly naive towards the developments in AI. In this regard, I would like to reiterate the comparison of Gen AI with email. When emailing was first introduced, everybody feared that it would take up the job of the postman. However, decades later, we do see that postal services do exist, and email’s impact has penetrated much deep. Gen AI also will have similar implications.

Concerning Gen AI, one job that gathered a lot of attention is that of an artist. The remaining artists are expected to enhance their creativity and productivity, while this may diminish the total number of artists required.

Some Gen AI Companies

Below are some pioneering companies operating in the domain of Gen AI.


It is a UK-based company that is one of the earliest pioneers of video synthesis technology. Founded in 2023, this company is focussing on implementing new synthetic media technology to revolutionize visual content creation while reducing cost and skills.

Mostly AI

This company is working to develop ways to simulate and represent synthetic data at scale realistically. They have created state-of-the-art generative technology that automatically learns new patterns, structures, and variations from existing data.

Genie AI

The company involves machine learning experts who share and organize reliable, relevant information within a legal firm, team, or structure which helps to empower lawyers to draft with the collective intelligence of the entire firm.

Gen AI Statistics

By 2025, generative AI will account for 10% of all data generated.

According to Gartner, 71% of respondents said the ROI of intelligent automation is high within their organizations.

It is projected that AI will grow at an annual rate of 33.2% from 2023 to 2027.

It is estimated that AI will add US $15.7 trillion or 26% to global GDP, By 2030.

Limitations of Gen AI

Reading till now, Gen AI may seem all good and glorious, but like any other technology, it has its limitations.

Data Dependence

Generative AI models heavily rely on the quality and quantity of training data. Insufficient or biased data can lead to suboptimal results and potentially reinforce existing biases present in the training data.

Lack of Interpretability

Generative AI models can be complex and difficult to interpret. Understanding the underlying decision-making process or reasoning behind the generated output can be challenging, making identifying and rectifying potential errors or biases harder.

Mode Collapse Computational Requirements

Training and running generative AI models can be computationally intensive and require substantial resources, including powerful hardware and significant time. This limits their accessibility for individuals or organizations with limited computational capabilities.

Ethical and Legal Considerations

The use of generative AI raises ethical concerns, particularly in areas such as deep fakes or synthetic content creation. Misuse of generative AI technology can spread misinformation, privacy violations, or potential harm to individuals or society.

Lack of Control

Generative AI models, especially in autonomous systems, may lack control over the generated outputs. This can result in unexpected or undesirable outputs, limiting the reliability and trustworthiness of the generated content.

Limited Context Understanding

While generative AI models have made significant progress in capturing contextual information, they may still struggle with nuanced understanding, semantic coherence, and the ability to grasp complex concepts. This can lead to generating outputs that are plausible but lack deeper comprehension.


So we covered Generative Artificial Intelligence at length. Starting with the basic concept of Gen AI, we delved into the various models that have the potential to generate new output, their opportunities, and limitations.

Key Takeaways:

What Gen AI is at its core?

The various Gen AI models – GANs, VAEs, and Transformer Based Models. The architectures of these models are of particular note.

Knowing some of the popular Gen AI products like Dall-E, Chat GPT, and BARD.

The applications of Gen AI

Some of the companies that operate in this domain

Limitations of Gen AI

I hope you found this blog informative. Now you also will have something to contribute to the subsequent discussions with your friends or colleagues on Generative AI that I am sure you would often come across in the current scenario. Will see you in the next blog; till then, Happy Learning!

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