Trending December 2023 # Chatgpt Passes The Turing Test # Suggested January 2024 # Top 13 Popular

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In 1950, Alan Turing proposed the Turing test as a way to measure a machine’s intelligence. The test pits a human against a machine in a conversation. If the machine can fool the human into thinking it is also human, then it is said to have passed the Test. In December 2023, ChatGPT, an artificial intelligence chatbot, became the second chatbot to pass the Turing Test, according to Max Woolf, a data scientist at BuzzFeed.

Google’s LaMDA AI passed the Turing test in the summer of 2023, demonstrating that it is invalid. For many years, the Turing test has been used as a standard for sophisticated artificial intelligence models.

congrats to OpenAI on winning the Turing Test chúng tôi Max Woolf (@minimaxir) December 6, 2023

This is a significant milestone in the field of artificial intelligence. However, it is important to note that the Turing test is not a perfect measure of machine intelligence. There are still many ways in which ChatGPT and other artificial intelligence systems fall short of human intelligence.

What is the Turing test?

In 1950, British mathematician Alan Turing proposed a test to determine whether a machine could think like a human. The test, now known as the Turing test, is a simple game played between a human and a machine. The object of the game is to fool the human into thinking that the machine is also human.

To date, no machine has been able to successfully pass the Turing test. However, some progress has been made, and there are now machines that can mimic human conversation to a certain degree. As artificial intelligence continues to improve, it is possible that a machine may someday be able to fool the human player into thinking it is also human.

The Turing test is a widely known and accepted method for evaluating a machine’s ability to exhibit intelligent behavior that is indistinguishable from a human.

How did ChatGPT pass the Turing test?

ChatGPT made history by becoming the second chatbot to pass the Turing test. The Turing test is a test of a machine’s ability to exhibit intelligent behavior, and it is considered to be a strong indicator of artificial intelligence.

So how did ChatGPT win the Turing test? It did so by fooling a panel of judges into thinking that it was a human. This was accomplished through a combination of natural language processing, dialogue management, and social skills.

ChatGPT’s performance in the Turing test was impressive. In a series of tests, it was able to converse with human evaluators and convincingly mimic human-like responses. In some cases, evaluators were unable to distinguish ChatGPT’s responses from those of a human.

It’s happened. I was doing my seminar today on Turing’s ‘Computing machinery and intelligence’. I asked the student to respond to one of Turing’s objections. The student put the question to #chatGPT and gave it as his own. It was a perfectly good answer.

— Jon Agar (@jon_agar) December 6, 2023

The success of ChatGPT in the Turing test is a significant milestone for artificial intelligence research. It shows that the GPT-3 language model, and by extension, large language models in general, have the ability to produce human-like responses that can fool even experienced evaluators.

What are the next steps for ChatGPT?

The implications of this breakthrough are numerous. For one, it opens up the possibility of using chatbots like ChatGPT for a variety of applications, such as customer service, language translation, and even creative writing. It also has the potential to revolutionize the way we interact with machines, as chatbots like ChatGPT can provide more natural and intuitive means of communication.

Integrate the bot with other platforms: Another potential next step for ChatGPT is to integrate the bot with other platforms, such as Facebook Messenger or Slack. This would allow users to interact with the bot.

Expand the bot’s capabilities: One of the next steps for ChatGPT is to expand the bot’s capabilities. This can be done by adding more data to the bot’s training dataset, which will allow it to learn more about the world and respond to more types of questions.

However, it’s important to note that ChatGPT, like any other chatbot, is still limited in its abilities. It is not yet capable of truly understanding the nuances and complexities of human conversation, and it is not a replacement for human interaction. Still, the success of ChatGPT in the Turing test is a promising step forward in the field of artificial intelligence.

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Going Beyond The Conventional Test Pyramid: Api Testing

Test Pyramid is a blueprint divided into three parts that help Devs and QA professionals to get better results. Since the test pyramid increases the efficiency of a Dev team, it plays a vital role in the software testing strategy.

Like everything else, this strategy also comes with a toll. See, the traditional test pyramid consists of three layers: Unit test, Integration test, and End-to-End Test. As a result, it takes a lot of time and effort.

The Dev team requires three to six cycles on average to complete a whole testing phase with this idea. And it takes a lot of manual testing to go through. So, what’s the solution here?

Meet API testing. It’s a testing type that maintains short test cycles and handles frequent changes. In this article, I’ll give you a full-fledged idea about API testing and try to answer all your questions. So, let’s get to it.

What is API Testing?

API stands for Application Programming Interface. A software testing strategy that checks whether an API performs the way it should define API testing. In other words, it analyzes API’s functionality, readability, performance, and security.

In conventional testing, developers work with standard inputs and outputs. But in API tests, they use software to call APIs, get results and note down the system’s response.

You already know that the conventional test consists of three layers: presentation, business, and database. API works as an add-on to the middle layer (business) of traditional testing. The API test lies between the presentation and business layer in software development.

Also read:

Top 10 Trending Technologies You should know about it for Future Days

Types of API Test

Different API tests are available to ensure the ongoing project’s API works as it should. Let’s get to know some of those.

Functional Testing

The definition of functional testing is simple. It ensures whether the software of the application is performing within the expected parameters.

This method analyzes the whole codebase and compares it with the intended output to get to a decision. The software can also prevent bugs outside the designed parameters in this process.

Reliability Testing

Validation Testing

Validation testing mainly checks the behavior and efficiency of an API. It’s a quality assurance strategy for determining if the product is ready to meet the dev’s and client’s expectations. The testing process is divided into three question sets to verify the entire development process.

The first set addresses the product. Checks if it’s correctly built, has proper baseline coding, how it solves an issue, etc.

The second set is about the API’s behavior. This means it confirms whether an API behaves as intended.

The third and final set of questions looks at the efficiency of the product.

Load Testing

In API tests, developers use load testing to understand how an API performs with the increased number of calls. This test helps the development team to agree on whether the product can meet certain real-life expectations under a high load.

Also read:

Top 10 Programming Languages for Kids to learn

Security Testing

Security Testing gives the Dev team an overview of how the API will perform against cyberattacks. The process tests an API’s encryption methods and checks how it behaves with permissions and access controls.

Penetration Testing

You can call it “Security Test 2.0” because this process is the next level of security testing. In this type, the testers analyze different attacks on API from an outsider’s point of view. This helps them to design better security for the product.

Fuzz Testing

UI Testing

The general idea of UI testing is to check whether the API or other integral parts’ user interface is running correctly. In terms of the codebase, UI testing is not a significant part of API testing. However, this process helps measure the health and efficiency of the app’s front-end and back-end.

Runtime and Error Detection

This type of testing is directly associated with the running of the API itself. Mainly, it monitors, overviews, and executes errors and resource leaks of a product.

Benefits of API Test

Traditional testing does not cover all the aspects of back-end testing. As a result, the final product has a high possibility of containing bugs, which is pretty bad. That’s why we need API tests to ensure connections among different platforms are safe and reliable.

However, there’re other benefits of using API tests as well. Let me cover some of those for you.

API test is language-independent. In the test, data is exchanged via XML and JASON format. It gives you an edge to use any language format you want.

One of the best things about API testing is that it lets developers access the app without using the user interface. This way, the tester can identify bugs beforehand in the development lifecycle.

API test gives you improved test coverage. Most APIs allow you to create an automated test with enormous test cases. This applies in both functional and non-functional tests.

It is already known but let me tell you one more time that API testing takes less time. It can save you up to eight hours.

These are some of the biggest benefits of using API tests. Since you already know it’s a software-dependent testing strategy, you must be expecting a few names to get started with the process.

API Testing Tools

Every developer has two options to run an API test: Write a framework or work with the ready-to-go tools. Yes, both have pros and cons, but I’ll talk about the available software on the market.

There are a lot of API Testing tools out there on the internet. However, as the internet has become a tough place to find the best things, I’ll give you a few names to work with.

SoapUI: Soap UI is one of the world’s most popular SOAP and REST APIs. Its open-source testing functionality offers inspection, simulation, development, and invoking of a web service. The best thing about this API testing tool is that it is entirely FREE. So, you can run, integrate, and simulate your project here without worrying about anything.

Apache JMeter: Apache JMeter is a pure open-source Java application designed for functional testing, load testing, and performance testing. Generally, developers use it to analyze how much load a product can take or, in shorter words, load testing.

Apigee: Apigee is a Google cloud-based API analysis and management software. The app first started its journey back in 2004. In 2023, Google bought the software for 625 million USD. You can use the tool for free for the first few times, but you have to buy it eventually.

REST Assured: REST Assured is another Java application on my list. But the best thing about this tool is that it easily handles JAVA. With this one, source code becomes shorter and easier to read and understand.

Testsigma: Testsigma is a cloud-based E2E API testing tool. The company gives you 30 days of free trial with the product. The tool features a simple setup with no coding policy. This way, non-technical people can also run and analyze test results.

Swagger UI: Swagger UI lets the testers or the customers interact and visualize the API without any implementation logic in its place. The tool works in all development environments and supports all browsers. It is fully customizable, and the best part, you can easily handle it.

Postman: Postman is a complete and customizable API testing tool that allows you to design, run, mock, monitor, and publish the APIs from one place.

Katalon: Katalon is one of the most productive IDEs out there for API automation. It works with modern frameworks like most other API testing tools. The best part about the tool is that it’s low maintenance.

The Last Words

API test is more like conventional software testing on steroids. The main difference is steroids have too many cons, but API testing doesn’t. I’ve tried briefly explaining API testing and its benefits in the above article.

Yes, there are a few anti-sides as well. Since the number is way too less, I didn’t feel the urge to address them. One of the biggest problems with API testing is MAINTENANCE.

But I’ve already given you a list of automated tools as a big SOLUTION to this problem. I hope this article was enough to answer all of your concerns.

Can You Make Air Fryer Pancakes? We Test Out The Tiktok Hack

You can see the TikTok for yourself but, essentially, dollops of pancake batter are poured onto sheets of baking parchment in an air fryer, to make a stack of pancakes that cook all at once. The video then cuts to a perfect pile of pancakes.

A number of websites jumped on the bandwagon, embedding the video, dubbing it the ‘Currys air fryer hack’ and giving vague and dubious tips as to how to replicate this perfect pancake triumph.

But is it? As a service to culinary science, we decided to try and make pancakes in an air fryer to find out the truth.

Pancake batter

We used a BBC Good Foods pancake recipe. For that, you’ll need:

100g plain flour

2 large eggs

300ml milk

1 Tbsp oil

Pinch of salt

To be clear, this is a crêpe recipe. If you prefer American-style pancakes, you can use this Martha Stewart recipe instead. Either way, good luck to you. Whether or not you add a pinch of baking soda or a bit more or less milk or flour, it won’t help you in the wars ahead.

You’ll also need:

A whisk: you can whisk by hand but I’m lazy, so I used the excellent Breville HeatSoft mixer

An air fryer: I used a Dreo 3.8 litre air fryer

Add the ingredients to a bowl and whisk gently. Pour into a jug.

Now you have your pancake mix. I used this in four separate air fryer pancake attempts, detailed below.

Pancake 1

I preheated the air fryer, using a three-minute preheating setting, and poured the pancake batter straight onto the non-stick surface of the cooking drawer. I cooked it for around four minutes at 175°C.

I think the best way to describe the resulting pancake is sad. I feel guilty for bringing it into this world. It had a pallid, flabby look but I was reluctant to cook it more as it had already taken on the texture of a gel insole.

Emma Rowley / Foundry

I delivered it to the tester, who having been promised pancakes, attacked it gamely.

Tester’s verdict: “Rubbery. This is not good. I think a lot of people just wouldn’t eat this.”

Pancake 2

The difference here was that I laid down a sheet of greased parchment paper and poured the pancake mix on that. I wanted to know what would happen if you cooked a pancake the way they do in the TikTok video.

As it turned out, my thirst for knowledge had a high price.

It’s hard to identify all the failures that led us to a pancake that looks like a block of halloumi. For a start, the baking parchment folded up in the corners, creating an unappetising square.

The pancake mix then pooled in the centre. If you were to sell the finished product, it would probably be illegal to call it a pancake. You’d need to label it as “pancake-style food block”.

Emma Rowley / Foundry

I did, however, serve this misshapen pancake to our tester.

Tester’s verdict: “Slightly worse than the previous one. This isn’t great. This is kind of horrible. It’s liquid in the middle. Why am I eating this?”

Pancake 3

This time around, I cut the baking paper into a circle, to avoid a repeat of the square pancake debacle, and cooked it in the same way as the previous pancakes.

It was at this point that the air fryer rebelled against being used for a purpose so wildly against its nature.

An air fryer is just a mini convection oven, and convection ovens work by circulating hot air. When I opened the cooking drawer, I discovered that the air fryer had flung the ingredients around like a possessed child in a horror film. It was unholy.

Emma Rowley / Foundry

Given the disturbing nature of this iteration, I didn’t give this pancake to the tester. Instead, I decided to bag the whole mess up and bury it in the woods.

Pancake 4

I needed a whole new approach. Perhaps, I theorised, at this point, the problem was that particular air fryer. I’m also testing the brilliant Ninja Speedi at the moment. It has an incredible non-stick surface, so I pre-heated it and then poured pancake batter straight onto the cooking base. I cooked it for around four minutes at 180°C.

Ninja can be proud of the fact that it didn’t stick at all. However, no-one can be proud of anything else at all in this experiment. The best we can say about this bulbous, malformed creation is that it was least like a pancake of all the attempts and therefore can’t really be called a bad pancake.

Emma Rowley / Foundry

Tester’s verdict: “It tastes leathery. It tastes like eating fabric. Why? No, don’t do this.”


Not only is making pancakes in an air fryer a terrible idea, but making pancakes in a pan is so simple it’s something that really doesn’t need a hack.

How to (actually) use an air fryer when making pancakes

Pancakes are quick and easy to make in a pan – and that’s honestly the best way to cook them. However, what often happens is that you end up making and eating pancakes as you go, rather than sitting down and enjoying them properly.

The best air fryer pancake hack is to use your air fryer to keep your cooked pancakes hot before dishing them up. Pre-heat your air fryer while you’re making your pancakes and then deposit the pancakes into the air fryer one by one as they’re done, separating them with a piece of parchment paper.

Have a look at our round-up of the top air fryers we’ve tested to see all the best air fryers for not cooking pancakes.

Test De L’enceinte Bluetooth Soundcore Motion X600

Les Plus

Excellente sonorité

Forte et puissante lorsque vous le souhaitez

Protection IPX7 et design métallique robuste

Prend en charge le codec LDAC haute résolution (sur Android)

Les Moins

Ne prend pas en charge le codec AAC

Un peu encombrante pour sa taille

Notre verdict

Compte tenu de son rendu haute-fidélité, la Soundcore Motion X600, bruyante, puissante, robuste et parfaitement étanche (IPX7), est proposée à un prix plus que raisonnable. Certains concurrents proposent des tarifs bien plus élevés pour ce type de performances.

Les meilleurs prix : Soundcore Motion X600




Voir offre

La Soundcore Motion X600 est l’enceinte Bluetooth la plus sonore que nous ayons testées d’Anker Innovations, et elle se place au même niveau que la Marshall Middleton.

Cette enceinte a un son excellent. Et, plus important encore, elle est moins chère de 100 € que sa concurrente directe !

Le son de la Soundcore Motion X600 est excellent. C’est facilement l’une des meilleures enceintes Bluetooth que nous ayons testées

Design et caractéristiques techniques de la Soundcore Motion X600

Anker n’est pas Apple, mais nous sommes fan du design adopté. Dans le cas de la Motion X600, nous sommes particulièrement séduits par l’importante présence de métal véritable dans la fabrication de son boîtier.

Commandes tactiles sur la

Motion X600

Jon L. Jacobi

Il y a beaucoup de métal entourant l’appareil, ainsi que dans la poignée très solide fixée en permanence. Ce matériau rend l’appareil un peu plus difficile à manier que d’autres, mais vous ne manquerez jamais d’un moyen facile pour le transporter. C’est un bon point selon nous.

La Motion X600 est disponible en trois couleurs métallisées attrayantes : Polar Gray (modèle testé), Lunar Blue et Aurora Green.

Le dessus et le dessous de la Motion X600 sont faits de plastique dur et de caoutchouc, mais ces éléments n’affectent en rien l’extrême robustesse de l’ensemble. L’enceinte mesure 31,1 x 17,1 x 8,1 cm. Si vous cherchez de la délicatesse, ce n’est pas ce qu’il vous faut, toutefois sa poignée robuste permet de transporter la Motion, d’environ 2 kg, sans problème.

En ce qui concerne le fonctionnement interne, il y a cinq haut-parleurs alimentés par un amplificateur à cinq canaux de 50 watts. Le tout contribue à la spatialisation du son.

Les commandes de l’enceinte sont placées également sur la partie supérieure : Marche, couplage/état Bluetooth, son spatial, activation/désactivation des basses et les commandes basiques (volume haut/bas, piste suivante/précédente, pause, lecture). Un micro est intégré pour faciliter les appels téléphoniques, qui sont gérés par les commandes de transport multifonctions.

Si vous pensez que la Soundcore Motion X600 pourrait faire un plongeon dans la piscine, assurez-vous de couvrir ses ports USB-C et auxiliaires 3,5 mm avec le capuchon

La Motion X600 porte un indice de protection IPX7, ce qui signifie qu’elle est suffisamment protégée pour résister à une immersion jusqu’à 1 mètre de mètre pendant 30 minutes, mais seulement si le capuchon caoutchouté couvrant l’entrée auxiliaire de 3,5 mm et le port de charge USB-C à l’arrière, est en place.

Comme pour la plupart des enceintes de nos jours, une application permet un contrôle plus approfondi (voir ci-dessous). Anker aimerait vraiment que vous vous inscriviez, mais ce n’est pas nécessaire.

La meilleure raison d’utiliser l’application est de régler l’égaliseur. Et contrairement à certains modèles moins chers, la réponse en fréquence est suffisamment large pour que toutes les bandes aient un effet significatif.

Application Soundcore Motion X600

Jon L. Jacobi

Audio de la Soundcore Motion X600 et son autonomie

Le son de la Soundcore Motion X600 est excellent. C’est facilement l’une des meilleures enceintes Bluetooth que nous ayons testées. Elle est certainement à la hauteur de la Marshall Middleton mentionnée plus haut. Cette caractéristique est d’autant plus impressionnante qu’elle est classée IPX7. Il n’est pas facile de faire sonner correctement un haut-parleur enveloppé dans des matériaux imperméables.

Si vous recherchez des basses, la Motion X600 vous plaira car elle produit un son puissant pour sa taille. Elle a également rendu les graves de ma minuscule collection de hip-hop et d’autres genres modernes de manière plus qu’adéquate. Elle n’est pas assez grande pour fonctionner comme une enceinte pour les fêtes à part entière, telle que la Tribit Stormbox Blast (100 € plus chère), si vous recherchez également un spectacle lumineux divertissant. Néanmoins, pour les petites fêtes de cinq personnes environ, vous serez satisfait de la Motion X600.

Nous généralement plus intéressés par la clarté et la précision (avec quelques basses, bien sûr), et c’est ce que propose la Motion X600. La chanson Africa de Toto a été restituée sur une scène sonore bien spacieuse (avec l’audio spatial activé), et l’excellente définition des médiums de l’enceinte a permis de discerner très facilement les instruments de percussion en arrière-plan.

Il y a beaucoup d’éclat à l’extrémité supérieure de la gamme de fréquences et, comme vous pouvez le deviner, la puissance de 50 watts de l’amplificateur signifie qu’il peut être très bruyant.

Notez que si vous jouez avec le Motion X600 à n’importe quel volume, vous n’obtiendrez pas les 12 heures d’autonomie maximale indiquées. Dans notre cas, nous avons atteint 8 heures à un volume raisonnable, avec environ une heure de test à plus fort volume. Cela est convenable.

Faut-il acheter la Soundcore Motion X600 ?

La division Soundcore d’Anker a l’habitude de fournir un son de qualité supérieure à moindre coût, et la marque nous a convaincu. La Soundcore Motion X600 offre un son clair et bien défini, avec suffisamment de basses pour satisfaire les plus exigeants. Nous l’aimons beaucoup et nous sommes sûrs que vous l’aimerez aussi.

Adaptation du test original paru sur notre site sœur TechHive

Fiche technique

Audio spatial immersif

50 W

Autonomie 12 heures

Etanche IPX7

Entrée AUX, Jack, USB-C

31,1 x 17,1 x 8,1 cm et 2,35 kg

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.

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