Open-Source GPT v3, v4 Alternatives to Try in 2023

Do you want to unlock the power of natural language processing without relying on hefty GPT-3 models?

If yes, then you’ve come to the right place! This blog post will discuss some open-source alternatives that can help you achieve the same results as GPT-3 without the huge costs and resources required. So let’s explore these tools and see which one is best for you!

Our Top Free GPT-3 Alternative AI models list:

  • OPT by Meta
  • BERT by Google
  • AlexaTM by Amazon
  • GPT-J and GPT-NeoX by EleutherAI
  • Jurassic-1 language model by AI21 labs
  • CodeGen by Salesforce
  • Megatron-Turing NLG by NVIDIA and Microsoft
  • LaMDA by Google
  • GLaM by Google
  • Wu Dao 2.0
  • Chinchilla by DeepMind
  • EleutherAI

Introduction to Popular OpenAI Solution – GPT-3/4

GPT-3 (Generative Pre-trained Transformer 3) is a large, autoregressive language model developed and released by OpenAI. It has been widely praised and adopted by businesses, researchers, and enthusiasts alike as one of the most powerful natural language processing models currently in existence.

Despite its capabilities and popularity, GPT-3 has some drawbacks in terms of cost, data quality and privacy that make it a less than ideal choice for certain applications. Fortunately, there are several open-source alternatives to GPT-3 that provide similar power with fewer of these drawbacks. In this article we will examine some of the key features of GPT-3 and discuss what open-source alternatives can offer to users that may be looking for more flexible and affordable solutions.

The latest version of the GPT model developed by OpenAI is known as GPT-4 (Generative Pretrained Transformer 4), and it represents a major advancement in the field of natural language processing. Built upon the foundation laid by its predecessor, GPT-3, which was released in May 2020 and quickly gained widespread popularity, GPT-4 is a large-scale machine learning model that has been extensively trained on a vast amount of data in order to generate text that is increasingly similar to human language.

Open source alternatives such as Google’s Bidirectional Encoder Representations from Transformers (BERT) and XLNet are two important contenders when considering Turing complete language models as powerful replacements for GPT-3. Both are trained on huge volumes of unlabeled data from online sources to produce meaningful text generation results with superior accuracy compared to traditional approaches. They also offer fine-grained control over pre-training parameters for user specific needs as well as transfer learning capabilities which allow model customization on domain specific tasks. Finally, their open source nature offers flexibility when it comes to pricing structures for users looking for less expensive compute resources or no usage fees at all.

Overview of GPT-3 tool

GPT-3 (Generative Pre-trained Transformer 3) is the third version of OpenAI’s open-source language model. It has been developed by the OpenAI team at large scale on a range of tasks like machine translation, question answering, reading comprehension, and summarization. This AI breakthrough enables applications to predict natural language processing (NLP) with fewer manual steps and better accuracy than previously possible.

GPT-3 can be used to generate text and produce accurate predictions by either learning from few examples or without any training data. This has made it a powerful tool for Natural Language Understanding (NLU), as well as other artificial intelligence applications like optimization or control. The model is built using large datasets in the form of unsupervised learning, where a model learns how to produce answers to questions without requiring any manual input or training data.

The advancements of GPT-3 have been met with interest and praise by many in the research community due to its wide range of capabilities and ability to understand language more holistically and accurately than previously thought possible. However, OpenAI’s open source initiative has sparked debate regarding cloud computing privacy implications associated with its use as well as its potential for misuse in disenfranchising certain languages or communities through biased representations. In response, many have turned to introducing and exploring open-source alternatives to GPT-3 for their Natural Language Processing needs.

Benefits of Open-Source Alternatives to GPT-3

The natural language processing (NLP) industry has been abuzz from the recent commercial release of OpenAI’s Generative Pre-trained Transformer 3 (GPT-3). The massive language model has attracted the attention of both practitioners and enthusiasts alike—due to its potential implications for automation and usability. GPT-3 is an example of a “black box” machine learning model that can be used for many tasks, but its closed source nature limits what users can access.

However, open-source alternatives to GPT-3 are available that offer similar capabilities with the added benefit of being accessible to all. Open source software is freely available, allowing anyone to interrogate its code—allowing transparency and accountability into their processes. Such open source models also provide users with more control over their own data when compared to commercial options.

The advantage of open source software goes beyond mere access; since they are free to modify, they also allow developers to embed important safety measures into their design in order to prevent misuse or abuse of the technology. Additionally, by having multiple versions of a model available at once it allows experts to compare versions and make more informed decisions regarding which model best fits their needs.

Open source alternatives to GPT-3 provide engineers with powerful tools for automation without sacrificing on features or security; allowing them greater freedom and control in developing NLP applications in comparison with closed-source options like GPT-3.

What about ChatGPT?

Q&A in ChatGPT interface

ChatGPT is a chatbot that can answer questions and imitate a dialogue, it’s built on GPT-3 technology. It was announced by OpenAI in November, as a new feature of GPT-3. The chatbot can understand natural language input and generate human-like responses, making it a powerful tool for customer service, personal assistants, and other applications that require natural language processing capabilities. Some experts say that it could replace Google over time.

According to the SimilarWeb portal, its monthly audience is more than 600 million users. And it’s growing by 40% M2M.

How Does ChatGPT Work?

You’ve probably heard of ChatGPT at this point. People use it to do their homework, code frontend web apps, and write scientific papers. Using a language model can feel like magic; a computer understands what you want and gives you the right answer. But under the hood, it’s just code and data.

When you prompt ChatGPT with an instruction, like Write me a poem about cats, it turns that prompt into tokens. Tokens are fragments of text, like write, or poe. Every language model has a different vocabulary of tokens.

Computers can’t directly understand text, so language models turn the tokens into embeddings. Embeddings are similar to Python lists — they look like this [1.1,-1.2,2,.1,...]. Semantically similar tokens are turned into similar lists of numbers.

ChatGPT is a causal language model. This means it takes all of the previous tokens, and tries to predict the next token. It predicts one token at a time. In this way, it’s kind of like autocomplete — it takes all of the text, and tries to predict what comes next.

It makes the prediction by taking the embedding list, and passing it through multiple transformer layers. Transformers are a type of neural network architecture that can find associations between elements in a sequence. They do this using a mechanism called attention. For example, if you’re reading the question Who is Albert Einstein? , and you want to come up with the answer, you’ll mostly pay attention to the words Who and Einstein.

Transformers are trained to identify which words in your prompt to pay attention to in order to generate a response. Training can take thousands of GPUs and several months! During this time, transformers are fed gigabytes of text data so that they can learn the correct associations.

To make a prediction, transformers turn the input embeddings into the correct output embeddings. So you’ll end up with an output embedding like [1.5, -4, -.1.3, .1,...], which you can turn back into a token.

If ChatGPT is only predicting one token at a time, you might wonder how it can come up with entire essays. This is because it’s autoregressive. This means that it predicts a token, then adds it back to the prompt and feeds it back into the model. So the model actually runs once for every token in the output. This is why you see the output of ChatGPT word by word instead of all at once.

ChatGPT stops generating the output when the transformer layers output a special token called a stop token. At this point, you hopefully have a good response to your prompt.

The cool part is that all of this can be done using Python code! PyTorch and Tensorflow are the most commonly used tools for creating language models. If you want to learn more, check out the Zero to GPT series that I’m putting together. This will take you from no deep learning knowledge to training a GPT model.

Popular Open-Source Alternatives to GPT-3

GPT-3 is an artificial intelligence (AI) platform developed by OpenAI and released in May 2020. GPT-3 is the third and largest version of OpenAI’s language model and was trained on a dataset of 45TB of text. This model can be used for a wide range of natural language applications, such as writing, translation, or summarization. However, given its staggering processing power requirements, not all developers are able to use GPT-3 due to its cost or lack of skill necessary to run it.

Fortunately, there are other open-source alternatives that may be suitable for your project. Below are some popular OpenAI GPT-3 competitors:

  • BERT (Bidirectional Encoder Representations from Transformers): BERT is an open-source language representation model developed by Google AI Language research in 2018. It has been pre-trained on more than 40 languages and provides reliable performance across many different tasks like sentiment analysis, question answering, classification etc. It also uses deep learning architectures to process language understanding which makes it suitable for many NLP tasks.
  • XLNet: XLNet is an improvement over the pre-existing Transformer encoder system created by Google AI Researchers in June 2019. XLNet outperforms the state of the art on a variety of natural language understanding tasks such as question answering and document ranking while only requiring significantly less training data than BERT.
  • ELMo (Embeddings from Language Models): ELMo is a deep contextualized word representation that models both shallow semantic features as well as meaning from context using multi layers objective functions over bidirectional language Models (LMs). ELMo was created by Allen Institute for Artificial Intelligence researcher at University of Washington in 2017. It requires significantly less compute compared with other deep learning models like BERT or GPT-3 while still providing reasonable accuracy too on various NLP tasks like text classifications or entity extraction.
  • GPT-Neo (2.7B) – download gpt-neo here GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.

Each alternative has its own advantages and disadvantages when compared against each other so it’s important to carefully assess which one best fits your project before selecting one for use in your application development process.

Comparison of Open-Source Alternatives to GPT-3

In response to OpenAI’s GPT-3, there have been various efforts to develop open-source large-scale language models. A comparison of the most popular open-source alternatives to GPT-3 is given below.

  • XLNet: XLNet was developed by researchers at Carnegie Mellon University and Google AI Language. It is a Transformer model which uses a number of different training objectives such as auto-regressive, bidirectional and unidirectional mean squared error. XLNet has achieved strong results on language understanding benchmarks such as GLUE and SQuAD.
  • BERT: BERT (Bidirectional Encoder Representations from Transformers) is an open source transformer model initially developed by Google AI in 2018. It has since been applied in many NLP tasks such as question answering, text classification, etc. BERT algorithms achieved impressive results across various NLP tasks such as question answering and natural language inference (QA/NLI). While BERT algorithms are largely effective at transfer learning with pre-trained models, they require very large datasets for adult training which makes them more difficult to replicate than GPT-3’s approach with its 1 trillion parameter pretraining on the web corpus CommonCrawl SQuAD (a collection of questions sourced from Wikipedia).
  • TransformerXL: TransformerXL was developed by researchers at both Huawei Noah’s Ark Lab and Carnegie Mellon University. This open source algorithm aims to extend the current context length of Transformer architecture from 512 tokens to thousand or even millions of tokens allowing it to easily learn cross document or long range dependencies between words even though no datasets currently exist for those types of sequences tasks today. This could be one possible solution for machine translation due to its ability to extract longer phrases than compared to BERT or GPT-3 models which only focuses on local context length of 512 tokens maximum per example text sequence inputted into the model itself.
  • UmbrellaLM: UmbrellaLM was developed by AppliedResearchInc and released under Apache Licensed 2.0 recently in 2021 while leveraging DistilBERT pretraining approaches from HuggingFace Transformers library as well as OpenAI’s GPT2 algorithms for text understanding tasks based off easily fine tuning pretrained weights using extremely small datasets (<50MB) compared against having all models trained from scratch based off traditional large scale TextCorpus datasets (>1GB).

Challenges Associated with Open-Source Alternatives to GPT-3

Given that GPT-3 has been developed by a well-funded organization, open-source alternatives have faced numerous challenges in order to compete. One major challenge is the fact that labelling the training data for these alternatives often involves much more manual effort, whereas GPT-3 was trained on human-written data from sources such as books, Wikipedia, and Reddit.

Another major challenge for open-source alternatives to GPT-3 is scalability. In order to train larger networks and keep up with GPT-3’s performance, more computational power is needed. This can be difficult for a lesser funded organization to acquire as they may not have access to the same resources that OpenAI has at its disposal.

Finally, developing state-of-the art NLP models requires significant human resources – something most open source projects don’t have access to in large enough quantities. While many NLP tasks may be simple enough to be handled by passionate volunteers and interns working part time, there are still certain areas that require highly skilled professionals who may not always be available or willing to contribute their services on an unpaid basis. As a result, only experienced developers with job security can tackle ambitious projects such as creating an alternative to GPT-3 without running into any financial constraints in the long run.

Best Practices for Using Open-Source Alternatives to GPT-3

GPT-3 is a large, state-of-the-art language model released by OpenAI with remarkable performance in many tasks without any labeled training data. Unfortunately, the cost of using GPT-3 models can be prohibitive for many businesses and organizations, making open source alternatives an attractive option. Here are some best practices to consider when using open source alternatives to GPT-3 in your projects:

  1. Select the right model architecture: Before selecting an alternative to GPT-3 as your language model, it is important to assess the different architectures that are available and select one that is suitable for your project. Larger models are not always better, as even mid-sized models can often be more efficient or provide adequate performance for certain applications. Other important factors to consider include how well existing knowledge can be leveraged within your project context, how quickly improvements in accuracy can be expected with additional data, and the difficulty of training on new data or setting hyperparameters.
  2. Consider pre-trained language models: Many open source alternatives come pre-trained on public datasets (e.g., Wikipedia). These can help accelerate projects as no additional training time is needed and they are often suitable for many use cases without modifications. However, they may not offer enough accuracy in specific contexts if fine-tuning them based on specialized data sets is possible and practical – this trade off between time and accuracy should always be weighed up when selecting a model.
  3. Pay attention to documentation and tutorials: When using open source language models it’s important to pay attention to available documentation and tutorials related to the architecture you’ve chosen — this will help you get up to speed quickly with its implementation (i.e., inference) requirements/steps/options which might not be as straightforward as those used by GPT-3 from OpenAI’s API platform.
  4. Document results & collect feedback: Finally, when beginning any ML project it’s important to document results thoroughly — tracking errors or validations for each step including hyperparameter optimization — so that optimizations could be done easily later on; also properly gather user feedback whenever possible as this helps inform decisions around future implementations/improvements of your system’s architecture.

Conclusion: GPT-3/4 open-source alternative

In conclusion, GPT-3/4 is a remarkable language model that has pushed the boundaries of natural language processing. However, not everyone may have access to its commercial version for their project’s requirements. Fortunately, there are several excellent open-source alternatives to GPT-3 which are likewise capable of delivering comparable performance, but at a fraction of the cost and complexity.

These include models such as ELMo, BERT, XLNet and ALBERT. Each model has its own unique strengths and weaknesses which should be considered when selecting the most suitable model for a given task. Additionally, more research will no doubt continue to improve these models as time goes on.

Therefore these open-source language models provide an excellent solution in developing applications that require natural language processing with outstanding performance at a low cost.

Reference Links:

GPT freelance developers are available for hire to utilize this language model for building diverse tools and applications, which provides an opportunity for everyone to create with GPT-3/3,5/4.

Download Apps Like DeepNude or DeepNude Alternatives

Programmers created an application that uses Neural Networks and Machine Learning to remove clothing from the images of women, making them look realistically nude.

That the software is based on pix2pix, an open-source algorithm developed by University of California, Berkeley researchers in 2017.


Pix2pix uses generative adversarial networks (GANs), which work by training an algorithm on a huge dataset of images – in the case of DeepNude, more than 10,000 nude photos of women, the developer said – and then trying to improve against itself. This algorithm is similar to what’s used in deepfake videos, and what self-driving cars use to “imagine” road scenarios.

Although much of the discussion around the potential harms of deepfakes has centered on how this technology can be used to propagate political misinformation and propaganda, it appears that the threat isn’t going away anytime soon. Indeed, that was how deepfakes originally spread – with users on Reddit finding ways to adapt AI research published by academics into creating fake celebrity pornography.

Why DeepNude was created?

Alberto said he was inspired to create DeepNude by ads for gadgets like X-Ray glasses that he saw while browsing magazines from the 1960s and 70s, which he had access to during his childhood. The logo for DeepNude, a man wearing spiral glasses, is an homage to those ads.

“Like everyone, I was fascinated by the idea that they could really exist and this memory remained,” he said. “About two years ago I discovered the potential of Artificial Intelligence (AI) and started studying the basics. When I found out that GAN networks were able to transform a daytime photo into a nighttime one, I realized that it would be possible to transform a dressed photo into a nude one. Eureka! I realized that x-ray glasses are possible! Driven by fun and enthusiasm for that discovery, I did my first tests, obtaining interesting results.”

Alberto said he continued to experiment out of “fun” and curiosity.

Update July 9 2020, 7:55 p.m EST: GitHub removed the DeepNude source code from its website. You can read “Why?” in our post here.

The super power you always wanted!

DeepNude twitter

Best Apps Like DeepNude (Free Tools Included)

Deepnude Online Generator to Make Nude Pictures and Download [TOP 15 List]

  1. v2.0 – a new AI online service that can produce the most realistic fake images on the market. Premium users do not have time limits and un-blurred image.
  2. – an easy-to-use online deepnude app enabling users to create their own deepfake porn fast and seamlessly. Quick generate faceswap videos, photos, and GIFs
  3. DeepNudeTo – deep fake nude maker tool has 10 free uncensored photos with Watermarks. ? Paid Plans start from $10 in Bitcoins.
  4. – is the best app in 2020 but now has a boring captcha.
  5. SukebeZone – paid tool from OpenDreamnet AI development team. Multiple photos and gallery but have only Paid plans for $9.99 per 50 photos.
  6. Deepnude Online website app (aka Nudify) – Undress any photo using the power of AI algorithms (no download software), try it out for free! ✔️ Without Watermarks.
  7. DnGG – HQ but without Paid Plan you get only Blurred Photo. ⛔ It doesn’t work Now.
  8. DeepNude Telegram Bots – has an easy user interface for quick work! Check it All!
  9. – Official Website. ⛔ Doesn’t work Now.
  10. – quick but has too many ads.
  11. – is worked tool but used an old version of the Deepnude ML model.
  12. – new project. ⛔ It Doesn’t work Now.
  13. – They manually create Fake Nudes of Girls You Know for $45 per photo!?
  14. DreamTime – Desktop tool. Try it, but first, you need to learn the basics of programming.
  15. DeepNude – X-Ray Image – New project. ⛔ It Doesn’t work Now.

Check our Comparison Table of popular Deepnude Online Generators here.

Don’t waste your time with deepnude variants. We have already tested all these tools and formed our own impression. And you can Support us!

Other 3 Fun Apps Like DeepNude

Links to DeepNude APK files broken. Many of these duplicates can harm your devices and can steal your bank account information. Better if you can take a look at apps like DeepNude to make sure you use them safely. We dived deep into the Internet and found you some fun and very similar applications that are still available.

#1. Nomao Camera Xray App

Best Deep Nude Alternative

Nomao camera application isn’t only a swap for the cameras, it can likewise supplant the display on your portable. So it is best to state Namao is an option in contrast to Android’s default camera and exhibition. Additionally, you should comprehend seeing shrouded objects or bare camera isn’t Nomao’s camera highlights. 

#2. Xray Scanner Prank

XRay Scanner Prank is designed for fun only, it’s a prank app.

To use XRay Scanner, open the app and place the phone on desired body part to scan. The scanner will scan the body part and it will analyse and show you the X-Ray. 

XRay Body Scanner Prank will only show a simulated version of X-Ray of a body but not real one.

#3. Full Body Doctor Simulator

The camera is used for the most realistic effect!
Scan people and learn how the skeleton looks like!
Play with friends in the airport guard or subway, scanning people at the entrance and exit!
Or feel like a doctor jokingly scanning patients! Play and have fun!
Earn experience points and unlock different parts of the body!

Yes, I know that there’s nothing better than DeepNude now.

DeepNude App for Android (APK) – Doesn’t Exist! Don’t Download It!

To be honest, there was never a DeepNude APK released by the developer. So the users who are searching to download DeepNude APK or DeepNude App for Android, stop searching it.

Buy DeepNude App Software

Last summer, the popularity of this app has grown and many references appeared on the Internet. After the official site was closed, the program began to be searched in other places. On some resources, they offer to buy an application or its clone. We don’t recommend this. 99% chance that there is a Fake!!!

What the DeepNude Website looked like …

Why Did DeepNude App Shut Down?

There are several reasons why DeepNude developer has shut it down. It’s for society. And it’s just because he feels the app can be used in the wrong way by many users. Now, this doesn’t make me understand one thing that if he knows this problem might arise in the future. Why did he develop this app beforehand?

Stop searching for DeepNude APK version for Android and save yourself from falling into a trap.

DeepNude App in News

  • “A deepfake bot is being used to “undress” underage girls”. MIT Technology Review. Source –
  • Arianne Cohen (May 13, 2020). “Horrifying DeepNude app, which undressed women, is replaced by an evil twin”. Fast Company. Source –
  • Samantha Cole (June 27, 2019). “This Horrifying App Undresses a Photo of Any Woman With a Single Click”. Vice. Source –
  • James Vincent (June 27, 2019). “New AI deepfake app creates nude images of women in seconds”. The Verge. Source –
  • Sigal Samuel (June 27, 2019). “A guy made a deepfake app to turn photos of women into nudes. It didn’t go well.”. Vox Media. Source –
  • Taylor Telford (June 29, 2019). “The world is not yet ready for DeepNude”. The Washington Post. Source -

What skills do you need to make Similar Software?

The core of the pix2pix framework is written with Python. To customize this algorithm for yourself, you need Python knowledge. Python is currently among the TOP 3 Image Recognition Programming Languages. The AI-based app was built for OS Windows 10 and Linux OS, and could utilize GPUs as well as CPU cores to generate its fake nude images of women from submitted clothed pictures.

How to install DeepNude lib from Github?

Check our Guide here.

DeepNude is a ML library for Windows that can be installed via Github. Download the zip folder, extract it and run DeepNude-master.exe. This will install the DeepNude software on your computer or server.

To install from GitHub, open a command prompt and navigate to the directory with the extracted files. This needs to be done from an administrative account on your interface and not as a user account. Once there, type: git clone –depth=1 –branch=master –single-branch –recursive


Download any of these DeepNude alternatives Now!

I hope you enjoyed our article about Alternative DeepNude. It will be cool if you find other similar apps. Leave a comment and we can update our list.

2023 – Time for new hope!

Become a part of the new version NOW!

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DALL-E: Creating Images from Text – Try Online

Craiyon to try for free online

Open AI Company shared the results of their research, which I can not call anything other than magic – DALL · E, a new neuron, a continuation of the GPT-3 idea on transformers, but this time for generating images from text.

Generative Pre-trained Transformer 3 is an autoregressive language model released in 2020 that uses deep learning to produce human-like text. Initial release date: June 11, 2020. Number of parameters: 175 billion

I often write here about fantasy, they say, I fed Harry Potter to the neuron and received illustrations of all the scenes of the book – it seems this is no longer a fantasy, but they still don’t give me anything to dig deeper.

DALL · E neuron with 12 billion parameters, trained in picture-text pairs, its tasks:

  • Synthesize pictures by text description
  • Draw pictures with a part at the input, taking into account the text description

Open AI has already teased some things in this area before, and finally it has reached such a level that the jaw is falling off, look at the examples that I have attached, at the top of the text that was given at the input.

I’m sure she won’t be allowed to play for everyone yet.
I foresee this research will greatly affect many areas and industries, as the applications are endless.

Your Own AI Designer in 1 click

A fiction that has already become a reality. You tell the computer (in words!) what you want to see in a picture, and it draws it.

One more small step for GPT-3 and a huge leap for the design industry. You describe a photo/illustration in words and voila (so far 256×256). In short, there will be more good design around in the next 10-15 years. Designer’s work will become much more interesting (a lot of routine will go away).

This is the client’s paradise: “play with fonts”, “enlarge the logo”, “highlight in red” – all this is readily done by a mentally balanced machine.
On the other hand, unusual illustrations for children’s books, for example, are also welcome.

What profession to master in order not to be left behind in 10 years?

Read here our Free Guide how to use Dall-e tool.

Midjourney AI art generator from text

You’ve definitely seen these impressive illustrations on Twitter, Reddit and popular tech tabloids.

What is MidJourney? 

MidJourney can transform any imagination into art from text. Some AI-generated art might look a little sloppy. The resulting arts from MidJourney are truly amazing. They’re not only original, but also some of the most breathtaking.

Official website:

It’s the new alternative for Dall-e tool.
Dall-E and Midjourney are the Future of AI Art Generation.

This text-to-image idea is fascinating to us so we applied to be part of the tool’s beta . It was so fun to play with! We will show you some of the artworks that caught our eye in this article. Next, we’ll show you keywords that we’ve tried or learned from other people’s experiments. These will hopefully inspire you and provide more detailed instructions to help you create artwork.

Top Tweets of arts were created by a computer

It’s good idea to get draft for new NFT for free!

Midjourney AI on Reddit – Hot threads

Main subreddit –

Midjourney – best AI online free art generator in 2022

We’ve all been there, staring at a blank canvas with no idea where to start. At Midjourney, we know how hard it can be to find inspiration and get those creative juices flowing. That’s why we created the Midjourney AI online free art generator that automatically generates ideas for you! Simply pick your genre, choose a size and watch as the AI work.

MidJourney’s Collection of Materials

MidJourney’s Quick Start Guide is a good resource for beginners. After some experiments you may start to understand what keywords do. So do we! We can’t resist sharing our discoveries. We’ll also share with you the keywords that we have used and the amazing results.

MidJourney can create artworks for commercial use.

These License details have been posted by MidJourney in the Discord channel #rule. There you can find the License details.

If you are not a Paid Member, you can use the Assets under the Creative Commons Noncommercial, 4.0 Attribution International License.

You can freely use any Assets created by Paid Members. The Assets can be used to copy, modify and merge them, as well as publish, distribute and sell copies. You can use or resell the Assets in any manner that is related to blockchain technology. Midjourney may charge 20% of any revenue generated by Assets over $20,000 per month.

For complete details please see our terms of service.

MidJourney has been tested with many prompt ideas. It seems like it still needs to be able to create vectors and graphics precisely. We will continue to wait to see.

How much cost Midjourney AI generated art?


Can I cancel my subscription plan?

You are free to cancel your subscription at any time but the cancellation will be effective at the end of the current billing cycle. If you change your mind, you can un-cancel your plan before the end of the cycle.

What is unlimited personal use?

We”re going to try to let you make as many images as you want, but if you go crazy we might have to tell you to “relax”. Subscriptions are intended for a single user.

Complete Guide to DALL-E

A lot of people are asking for the link to the DALL-E Mini site. Ive put it below.

Why DALL-E is the Best AI Generating Tool for Marketing Needs

dall e creating images from text

DALL-E is the best AI image creating tool for marketing needs or only for fun. It offers a range of features that can help you create images from text. You can use this software to generate images for your website, blog or social media posts.

It takes just a few minutes to install and start using DALL-E. All you need to do is upload an image or enter the text and hit generate. The software will automatically create an image that matches the text you have entered.

This software is free and easy to use, which makes it the perfect choice for marketers who are looking for an AI generating tool that they can use with ease.

Best DALL-E from Twitter and Reddit topic:


  1. Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H. (2016). “Generative adversarial text to image synthesis”. In ICML 2016. 
  2. Reed, S., Akata, Z., Mohan, S., Tenka, S., Schiele, B., Lee, H. (2016). “Learning what and where to draw”. In NIPS 2016. 
  3. Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang X., Metaxas, D. (2016). “StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks”. In ICCY 2017. 
  4. Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D. (2017). “StackGAN++: realistic image synthesis with stacked generative adversarial networks”. In IEEE TPAMI 2018. 
  5. Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X. (2017). “AttnGAN: Fine-grained text to image generation with attentional generative adversarial networks
  6. Li, W., Zhang, P., Zhang, L., Huang, Q., He, X., Lyu, S., Gao, J. (2019). “Object-driven text-to-image synthesis via adversarial training”. In CVPR 2019. 
  7. Koh, J. Y., Baldridge, J., Lee, H., Yang, Y. (2020). “Text-to-image generation grounded by fine-grained user attention”. In WACV 2021. 
  8. Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., Yosinski, J. (2016). “Plug & play generative networks: conditional iterative generation of images in latent space
  9. Cho, J., Lu, J., Schwen, D., Hajishirzi, H., Kembhavi, A. (2020). “X-LXMERT: Paint, caption, and answer questions with multi-modal transformers”. EMNLP 2020. 
  10. Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint (2013). 
  11. Rezende, Danilo Jimenez, Shakir Mohamed, and Daan Wierstra. “Stochastic backpropagation and approximate inference in deep generative models.” arXiv preprint (2014). 
  12. Jang, E., Gu, S., Poole, B. (2016). “Categorical reparametrization with Gumbel-softmax”. 
  13. Maddison, C., Mnih, A., Teh, Y. W. (2016). “The Concrete distribution: a continuous relaxation of discrete random variables”. 
  14. van den Oord, A., Vinyals, O., Kavukcuoglu, K. (2017). “Neural discrete representation learning”. 
  15. Razavi, A., van der Oord, A., Vinyals, O. (2019). “Generating diverse high-fidelity images with VQ-VAE-2”. 
  16. Andreas, J., Klein, D., Levine, S. (2017). “Learning with Latent Language”. 
  17. Smolensky, P. (1990). “Tensor product variable binding and the representation of symbolic structures in connectionist systems”. 
  18. Plate, T. (1995). “Holographic reduced representations: convolution algebra for compositional distributed representations”. 
  19. Gayler, R. (1998). “Multiplicative binding, representation operators & analogy”. 
  20. Kanerva, P. (1997). “Fully distributed representations”. 


FaceMagic App Review: Swap Faces

Have you ever been sick of posting the same old selfies on Instagram? Do you want to shake things up a little bit? FaceMagic is here to blow you away. FaceMagic is an AI-based face swap app powered by deep fake technology that lets you swap your face on gifs, videos, photos, and whatnot.

The rapid growth of AI has produced insight in many industries. It is making games, video games, movies, and travel more interesting and accessible. This app is a new way to think about taking selfies and photographs in general – you take the creative process forward by yourself. Make a meme, make your friends dance, or replace yourself in iconic TV shows and movies. 

Introduction: What is the FaceMagic App?

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FaceMagic is a photo editor app that lets you swap faces with one another. You can choose from a range of pre-existing faces or import your own.

The FaceMagic app is an easy way to change your appearance, add makeup, remove wrinkles and make other adjustments to your face. It is available for free on both Android and iOS devices.

How Does the FaceMagic App Work?

FaceMagic is a photo editing software that is designed to make your face look like it has been edited.

FaceMagic is a photo editing software that can help you create the perfect selfie. It can remove any blemishes, wrinkles, or acne from your skin and make you look like you have flawless skin.

Why are People So Interested in the FaceMagic App?

FaceMagic is a popular, free app for mobile devices that allows people to swap faces between two photos. It has been downloaded by more than 50 million users and has been featured on the Today Show, Good Morning America, and Time Magazine.

The app is so popular because it allows people to make funny and creative edits of their selfies. The app also provides an easy way to edit group photos when someone in the photo is not available or has already left the event.

The Best Way to Use the Face Magic App!

Face Magic App is a new app that lets you swap your face with someone else’s. It also lets you do some other cool things like adding cool filters and overlaying text on top of the photo.

The app has become very popular in the past few months, with over 4 million downloads. It is now one of the most downloaded apps in both the Apple Store and Google Play store!

The best way to use Face Magic App is to have fun with it!

5 FaceMagic App Hacks to Create Amazing Photos Without Photoshop

keywords: face swap app, face swap photo editor, swap faces app, photo editing app

Face swap app is a photo editing app that allows you to swap faces in photos. It’s an amazing and fun way to share your favorite photos with friends and family.

It can also be used for practical reasons like changing your profile picture on social media platforms. You could also use it for commercial purposes like adding your favorite celebrity’s face to a product advertisement or creating a meme from an existing photo.

FaceMagic is one of the most popular face swap apps available in the market today. It has more than 100 million downloads worldwide and offers more than 200 different types of effects, filters, and frames.

What is Reface App?

Reface is the top-rated AI face swap app. Reface app is also advanced, fun and well-known worldwide.

A photo is worth a thousand words. Our technology is giving you a whole new way to express yourself on social media with the Reface app. It allows you to swap your face with someone else’s in the photo and share it with your friends or use it as your profile picture. You can also take selfies, edit them and swap faces before posting them on social media.

FaceMagic App Review – A Photo Editor That Lets You Swap Faces Like Magic

FaceMagic is a photo editor that lets you swap faces like magic. The app takes a photo of your face and then you can choose from different celebrities to place over top of it. This app is perfect for those who are looking for a way to change their face in order to make themselves look more attractive or just plain silly.

FaceMagic is an app that provides users with the ability to swap faces with celebrities in order to make themselves look better, more attractive, or just plain silly.

Conclusion: The Ultimate Guide to Using the Face Magic Photo Editor

In this guide, we have talked about the importance of using Face Magic Photo Editor. We have also discussed some of the best features of this photo editing app and how you can use it to edit your photos.

We hope that you find this guide useful and informative.

AI Nudifier Online: How it works?

Nudifier Online is a free online photo editor that allows you to remove unwanted clothing from any photo. It is a great alternative to Photoshop for those who don’t have the budget for it or are not skilled enough in Photoshop.

The main difference between Nudifier Online and Photoshop is that Nudifier Online can only remove clothing while Photoshop can edit the entire image.

Introduction: What is the AI Nudifier & Why It is Beneficial

Nudifier is a free AI-powered app that automatically applies the best effects to your photos. It also provides you with a range of filters, presets, and effects to choose from.

Nudifier takes all the hassle out of photo editing for you and all you have to do is pick a filter or effect that suits your needs.

Nudifier FAQs Answered

Nudifier is an AI-powered tool that can transform any image into a nude version. It is a free online service that has gained popularity in recent years.

Nudifier has been designed to be used by anyone and everyone, as long as they are over 18 years old. However, it may not be legal in some countries to use nudifier for this purpose.

The legality of nudifying images online is still unclear.

Some countries have banned the usage of this AI-powered tool, while others have not yet taken any action on it.

How to Use AI Nudifer?

Nudifier is an AI-powered app that uses machine learning to detect nudity in images.

Nudifier’s algorithm can detect nudity in images. It can also identify the parts of the body that are naked and blur them out.

Nudifier is available for free on Android and iOS devices and can be found by Browser on the Website

Step 1: Go to website.

Step 2: Upload photo.

Step 3: Waiting up to 5 min.

Step 4: Check to Result or just Fail.

Other Deepnude AI App we reviewed in this post.

Is Nudify Online AI tool Legal?

Nudify is a free online AI tool that can help you to create an image of your favorite celebrity nude. It is a free online application that uses artificial intelligence to generate images of celebrities nude. The AI software has been designed to detect the face and body of the celebrity and then generates a photorealistic representation of their naked body.

The legality of Nudify is not clear, but it does not seem like it is illegal or against any laws or regulations because it does not require any personal information from the user.

Nudify is not Spotify!

Conclusion: Why AI Nudifiers are the Future of Content Creation

The last section I want to talk about is the conclusion. This is where you summarize what you have talked about in the previous sections, and make a call to action for your readers. Your conclusion should be short and sweet, and it should remind your readers of all the points that were made in the paper.

AI in Dating: How Smart Technologies Are Used in the Online Dating World

Everybody knows that artificial intelligence has found its way to most industries. It makes complex processes easier and never gets tired. It can predict behavioral patterns and even helps with reading emotions. The list of ways in which AI helps is long, let’s try to group the most important points. We’ll focus on one of the first industries that started using artificial intelligence. The two most important factors of online dating are safety and efficiency. AI improved both. Let’s see how.

AI on Guard for Relevant Matches

We’ll start with how AI makes dating sites more efficient. Online dating has come a long way since its beginning. That means singles now can choose platforms that fit them the best. Nowadays, along with general dating sites, there are many niche online platforms that target specific groups of people looking for a specific type of relationship. So, straight people join sites for straight people while gay men meet each other on gay dating sites. In turn, lesbian women have a bunch of platforms, but most still pick the best of all lesbian dating sites available today. They do so because they know that joining a platform only for lesbians makes their chances of getting dates much better. Lesbian women are sure that every other member on the site is their potential partner. That alone was a breakthrough in the online dating industry. Niche sites are much more effective than general sites despite having smaller communities. It’s easier to connect people who have something in common than those who don’t. That was the whole purpose of specialized sites.

And then AI took that one step further. Niche and general sites started using artificial intelligence to become even more effective. Because AI matchmaking is faster than the human brain since it can process more data in less time. Users no longer have to spend a lot of time manually searching for relevant matches (of course, this possibility is still present on dating sites). After filling in your details and indicating your desired preferences, the matchmaking algorithm will offer you a variety of suitable potential matches. And you no longer need to choose among all site users, but only among those who contain a set of qualities that you are looking for in a partner, and who may like you based on their request and your profile. But there is something even more. Not only does artificial intelligence connect better matches based on the provided data, but it also learns what each member prefers.


For example, when a single woman joins a lesbian dating site to find her perfect girlfriend, she browses profiles, stops to read interesting descriptions, zooms to check out profile photos, sends messages to some girls, etc. All that time, AI notices (and remembers) what members made this single woman stop browsing. It usually turns out that all the lesbian users who grab one’s attention have much in common. Smart intelligence technology collects this data to make offers more relevant to each user, thus improving the dating experience on the site.

How AI Improves User Experience

Artificial intelligence makes dating sites more effective because it improves user experience. It’s easier to explain how that’s possible on the example of Facebook and its other companies. Do you know how you always see more content related to that one post you checked out? Thank AI for that. Stop to check out comments on a post related to COVID. You’ll start getting a lot more posts like that in your feed. It’s like that on every social media. AI thinks that people stop to read things they’re interested in. On most dating sites that manifest in presenting better matches in less time. 

That is, if a single woman is looking for a woman on a lesbian dating site, for example, browsing ladies from her local area, then the site will offer her exactly the local lesbians in whom she is interested. Yes, users still have to spend time on the site to give AI enough info. However, if a woman spends a couple of hours looking for her potential lesbian girlfriend nearby, the AI on the site will pick that up. At the same time, if another woman spends her time looking for someone like our first woman, the AI will remember that too. Then, those two lesbian women will be more likely to see each other on the site’s features. You can look at AI on dating sites as some sort of cupid. It watches over you, learns who you are, and tries to help you reach your goal.

How AI Secures Users

We explained how AI makes online dating sites more efficient. Now let us tell you something about safety. We won’t touch any technology that’s still not in use on dating sites, such as AI emotion detectors. We’ll mention how AI currently helps people stay safe while looking for dates.

As you know, AI processes a tremendous amount of information every second and never gets tired. In other words, it watches over the whole site to prevent issues before they happen. If some attacker wants to steal the personal data of one of the users, he will not be able to do so. Because AI will see errors in the code before he does and fix them. 

Also, based on AI, anti-spam and anti-fraud systems are being developed and implemented. These systems filter users for “suspicious activity” or block users for trigger words and images contained in posts, profiles, and the like. This reduces the risk of users facing insults, racism, sexism and other negative factors of online communication platforms. Therefore, the positive experience on dating sites for black dating or lesbian dating will only grow every year. Aren’t we living in the best era for being single?

What is ShortlyAI? Best AI Writer for Stories alternatives

ShortlyAI is a new AI writer for stories. It is the best AI writing tool I have tried so far.

The idea behind ShortlyAI is to help writers with their creativity, not replace it. With this AI writing assistant, writers can use their own creativity to come up with story ideas and structure them in a way that makes sense for what they are trying to say.

ShortlyAI has an easy-to-use interface that lets you build your story idea with just a few clicks. The interface also has all the features you would expect from any good writing software, including templates and shortcuts to make your job easier.

Free ShortlyAI Alternative and Best AI Writer for Stories

Recently, the AI writing assistant named ShortlyAI has made an appearance. The following article is about this AI writing assistant and compares it with some of its best competitors.

ShortlyAI is a new-born AI writer that delivers good quality. It’s free to use, which makes it an attractive alternative for companies that want to try out the AI writing assistant before they start paying for it.

How to build own AI Application like ShortlyAI?

First, we need to understand what is AI and how it can be applied in various industries. We then know that AI is not a single “thing” but a set of technologies and techniques that can be used to solve problems. AI provides the computational power for intelligent behavior and the ability to learn from experience and execute tasks with precision.

With this in mind, we start our research on which type of AI would be most appropriate for our app idea. For example, if we want to make an app like ShortlyAI (in which you write your message and it generates a personalized GIF), we would need an “AI Writer.”

We then need to figure out how long it will take us to build this application. If we want to develop it by ourselves, we will have to

How to find good AI web developers?

Finding a talented and good AI web developer is one of the most difficult tasks in the modern internet era. It’s not just about finding someone who can code, but also about finding someone who understands the importance of AI for the future of our industries.

However, some things you can do to find a skilled AI web developer are: visiting online forums where developers are discussing technology, searching for an AI web developer on LinkedIn, or looking for an AI web developer on Google.

How to work Nude Filters and Porn Detector Software?

The following instructions might be helpful for those of you who have never worked with the program:

  1. Download the software (either by clicking the link below or on the right side of this page).
  2. Install it to a folder.
  3. Open the folder and run the executable file to start the program.
  4. Enter your email address, then click “Register.”
  5. Enter your password and click “Create Password.”
  6. Click

How to Boost Your Productivity Using AI

What is AI?

Technology has had a huge impact on our society and the way we do things. It has also improved how machines work and the services they offer through Artificial Intelligence (AI). Generally, AI describes a task that is performed by a machine that would previously require human intelligence. 

AI is defined as machines that respond to stimulation that is consistent with traditional responses from humans. AI makes it possible for machines to learn from experience and adjust to new inputs. That is possible since it uses technology that uses a large amount of data and recognizes data patterns.

Give A.I. Long Boring Jobs

Though some believe AI will take over their jobs, some are happy with this technology in the workplace. The reason being AI helps in creating a more diverse work environment, and it will do long, boring, and dangerous jobs. Thus, this will give humans ample time to continue being humans.

The use of AI has a huge impact in various sectors from healthcare, education, manufacturing, politics, and many more. Since AI can infiltrate almost any industry, it should be trained to handle boring tasks. By doing this, humans will be in a position to handle higher-level tasks.

Tools for Better Productivity on an AI Basis

AI machines are known to offer efficiency and can be used by businesses to improve efficiency. But for the tools to work, people need to learn how to make use of the AI Learning tools to improve performance. Learn about the tools that can save time and help to increase productivity.

  • Neptune: This is a lightweight but powerful metadata store for MLOps. The tools give you a centralized location you can use to display your metadata. By doing so, you can easily track the learning experience and results of your machine. The tool is flexible, and it is easy for you to integrate it with other machine learning frameworks.
  • Scikit-Learn: This is an open library source with a wide collection of tools to build machine learning models and solve statistical modeling problems. Using this tool will be easy for you to train your database on any desired algorithm. Thus, this will save you from the frustration of building your model from scratch.
  • Tensor flow: With this tool, you can build, train, and deploy models fast and easily. It comes with a comprehensive collection of tools and resources that can build ML-powered addition, their applications. This tool will be easy for you to build and deploy deep learning models in different environments.

Audio To Text Converter That Will Help You Work Faster

Transcribing audios can be a tedious task in your workplace. But with AI, that does not have to be the case. As long as you select the right tools, they will convert your audio to text and save you the time you used to do it manually. Here is a look at some tools you can use. This is web software that you can use to transcribe your videos automatically. Audext is affordable and fast. Some features you will get when you use this software are:

  • Speaker Identification
  • Built-in editor
  • Various audio formats
  • Timestamps
  • Voice recognition The software will offer you accuracy as well as perfect transcription each time. The system will keep your data safe and private. Some features you will get when you use this software are:

  • Sync files stored in the cloud
  • Can add speaker labels and timestamp
  • Import already done transcription for no charges With this software, you can record audio from your phone or browser and then get it to convert it then and there. With otter, you will get automatic transcription, and it is easy for you to group and add members to it. Some features you will get from this software are:

  • Searching and jumping to the keywords within the transcript
  • Can speed up, slow down, or jump the audio
  • Can train the software to recognize certain voices for fast referencing in the feature

Future of AI

AI is working all around us by impacting how we live our lives, search engines, and dating prospects. It is hard to imagine AI getting any better. According to research, Ai will continue to drive massive innovation that will help in fueling many industries. In addition, it will have the potential to create many new sectors for growth. Thus, this will lead to the creation of more jobs.


Whether we fight it or not, AI is here to stay. For that reason, companies and industries should stop fighting this technology and start embracing it. The best way of doing this is by being aware of it and adapting it to the new technology.

Google AI Hub: what, why, how

Artificial intelligence (AI) and machine learning (ML) increasingly seem to be indispensable tools that developers need to be able to handle. There are many ways these tools can be put to use, applied to applications and products. In research and academia, the subject has been around for 70 years or so — more or less the same time span which separates the birth of computers and information technology from the present day. However the popularity of this field has fluctuated considerably in the last few decades, experiencing dark times (the infamous ‘AI Winter’) and golden eras, such as the present (a phase that does not seem destined to end any time soon).

Why you may need artificial intelligence?

The immediate impact on everyday lives of Artificial Intelligence and similar technologies has never been as popular and widely (if not wildly) acknowledged as in the present day. Every CEO wants their company to use it, produce it, develop it — and every developer wants to join the party. Of course, there is nothing wrong with that: on the contrary, for an entrepreneur it is a natural impulse to exploit state of the art technologies in order to keep pace with competitors and to try to take a forward step before them. It is also perfectly natural for a developer to be intrigued, at the very least, by an impressive and pervasive technology that, although still rather intricate from the theoretical point of view, is largely accessible in terms of both tools and programming systems.

Even if you don’t want to learn Python, R or Scala (though you should!) and prefer to stick to the Java and C# you probably use in your daily work, ready to use libraries and frameworks will be found within your favourite computer language. If readers will permit a personal digression, my first experiences with AI were in BASIC(!) and my first professional project in the field (being paid to deliver an AI product) some twenty years ago was in C: at the time I had to do most of the work ‘by hand’, due to a lack of standardised libraries (or indeed any libraries at all) suited to my purpose.

Today, things are simpler for developers in this respect: one can learn a library or framework for an already-familiar language, or learn the foundations of an easier interactive language, such as Python or R, and start using de facto the standard libraries such as TensorFlow that are available for many mainstream languages (even for Javascript).

In short, it is a natural and healthy instinct for a developer to be interested in participating in and delivering AI projects. The easiest introduction involves finding tutorials, explanations, or introductions written by other developers, and downloading open source tools. Such tools (Jupyter notebooks, for example) are usually easy to install and easy to use for those who are just starting to code and to solve problems using AI methods.

Of course, where both CEOs and developers (whose salaries are paid by CEOs) want to work with AI, it is obvious that the team’s joint efforts will result in the delivery of AI products or solutions to sell to customers.

However, it is precisely at this point that things become difficult: while a single developer may create a Jupyter notebook that brilliantly solves some regression, prediction or generation problem, to transform that solitary effort into a standard delivery pipeline is very difficult — often, it may be better to restart from scratch.

On the one hand, projects — collective efforts performed by teams — are what leads to delivery; on the other hand, an enterprising solution needs to satisfy business requirements — the first goal of any profitable project. In other words, first the business case, next the technology required to efficiently satisfy that need.

Developers playing with Pytorch late at night may produce interesting prototypes, which may suggest ways to solve a problem or need experienced by the company but creating a new product on the strength of that idea alone is another matter entirely. A production pipeline with delivery of an AI-based product, made for a specific purpose as its goal is needed, and will need to be managed properly. Artificial Intelligence project management is another interesting issue but will be dealt with elsewhere.

What is Google AI Hub?

The time has now come to introduce our main character, Google AI Hub: at first glance, this is just a repository of tools able to provide the individual parts of the pipeline mentioned above. It is also an ecosystem of plugins and goes as far as supplying end-to-end pipelines to support the delivery of an AI product, at different levels of abstractions, according to the resources available to produce it.

In fact, AI Hub is more than a repository, providing different assets for different goals: for example, it can be used to learn ML algorithms, or to use built artefacts available either in the public domain via Google, or shared as plug-ins within your organisation. Alternatively, one can use AI Hub to share one’s own models, code and more with peers in the same organisation — a hub that facilitates collaboration on AI projects by means of reuse, sharing and factoring.Let’s begin by finding something useful just to play with — something ready to use. Visit the site homepage on which assets are classified in categories in a menu of the left hand side. Choose the ‘Notebook’ category for this example:

This offers a list of notebooks provided by Google. For our current purposes, we could open the first and start using it.

Once we access the asset — in this case a Notebook — we can open it in Colab to explore and exploit. This is a simple asset exploitation of course, but Google-provided notebooks are great; well documented and easy to use, they’re a good way to learn by doing.

Among the available assets we find datasets, services (API, for example, which may be called on by your application to use built-in functionalities, or to train your model via transfer learning, etc.), trained models, TensorFlow modules, virtual machine images, and Kubeflow pipelines. All these assets occur somewhere in the development process of an AI application. The importance of Kubeflow pipelines — an interesting way to embed AI models inside an application — should be particularly stressed, but more on that later.

How to benefit from Google AI Hub

In this introductory note we cannot give a general overview of all the tools available on the Google AI Hub dashboard (the platform itself provides several tutorials on how to start using each tool and resource it makes available). In place of this, we offer some hints on the task of deploying a scalable ML application through the hub.

An important initial note about using AI Hub for practice is that you will need a Google Cloud Platform account. Starter accounts that are essentially free of charge are available, but you’ll need to provide bank account details. It’s probably best to operate inside an organisation account instead — typically one belonging to your company: organisations have the ability to use and share assets via the Hub. For example, if you work in R&D you can share prototypes with your colleagues working on architecture, delivery or another aspect of the product.

The dashboard of the platform allows management of projects using assets from the hub. A project may start as a simple Jupyter notebook, for which you can choose not only the language (Python 2/3, R, …) but also the computational sizing (e.g. if you need some kind of GPU to properly run it, etc.) and other parameters. All of these factors determine the cost of the service needed to run the notebook.

Needless to say, you can edit and run your notebook on the cloud platform as you would in your local environment: you’ll find all the main tools already available for whichever language and framework you chose; for example, TensorFlow is already installed in the Python environments, and you can ‘Pip’ whatever additional packages you need.

It is also easy to pull and push your notebooks from and to Git repositories, or to containerize your notebook in order to install specific libraries and acquire the level of customization your code requires to run properly.

At a certain point (probably at the start!) you’ll need to handle a dataset, perhaps to train your model or to fine tune a pre-trained model. AI Hub provides a section on datasets that is not simply a bookmark or repository but allows for labelling data. This is a practical need in many projects, and the lack of a dataset appropriate for your supervised model is a frequent issue when trying to build a product based on ML models.

In this section of the hub you can add a dataset for which you can specify the kind of data and its source, upload data and specify a label set which provides the complete list (to the best of your knowledge) of labels of your data. This is not only for recording purposes: in fact you can also add a set of instructions and rules according to which human labellers may attach labels to the elements of your dataset. This feature allows you to specify the requirements of a labelling activity to be performed by someone paid to do it on your behalf.

However, labelling data is not an easy task and is subject to ambiguities (people do this task instead of a machine for some very good reasons!) so one may need to refine instructions and initially provide a limited trial dataset on which to assess both the quality of labelling and the level of description actually required in the instructions. Since this is a crucial step in training a ML model, real life projects will require people to manage this activity by collaborating closely with the developers to get a useful, and as unbiased as possible, dataset on which to train the ML model.

‘Jobs’ is another interesting feature from the AI platform. Used to train models, you may define these using standard built-in algorithms or your own algorithm, according to your model’s needs. In most cases algorithms built in the platform will suffice for training purposes.

Up to this point we have talked about models, datasets (and the interesting labelling feature) and training jobs: these tasks form the bulk of an AI developer’s day-to-day work, whether on their local systems or on the shared tools provided by their organisations.

A complete, end-to-end ML pipeline is somewhat more complicated, however, requiring at least the following steps

  • Data ingestion to encapsulate data sourcing and persistence: this should be an independent process for each dataset needed, and is a typical job;
  • Data preparation: to extract, transform and select features which increase efficiency and should not deteriorate performances;
  • Data segregation, to split datasets into the parts needed for different purposes, for example: training set and validation set, as required by different validation strategies.
  • Model training on training datasets, which may be parallelized using either datasets or models (most applications put different models to work).
  • Model assessment on validation datasets, when performance measurements are also taken.
  • Model deployment: the model could be programmed in a framework which is not the native framework of the application (e.g. R for modelling, C# for production code) so that deployment may demand containerization, service exposition, wrapping, etc.
  • Model use in the production environment with new data.
  • Model maintenance — mostly performance measurement and monitoring, to correct and recalibrate the model if needed.

In this ‘model lifecycle’, the final step, i.e., the integration with the application which needs the model, is typically not covered by AI frameworks and hence is the most problematic step for a developer team, yet the most important step for the business.

The ecosystem which AI Hub embraces to achieve these results is based on Kubeflow (in turn based on Kubernetes), which is essentially used as the infrastructure for deploying containerized models in different clusters, and as the basic tool to access scalable solutions.

A possible lifecycle could be as follows (for more information on this specific tool check this link).

  1. Set up the system in a development environment, for example on premises e.g., on your laptop.
  2. Use the same tools that work for large cloud infrastructures in the development environment, particularly in designs based on decoupled microservices etc.
  3. Deploy the same solution to a production environment (on premises or cloud cluster) and scale it according to real need.

Kubeflow began as the way Google ran Tensorflow internally, using a specific pipeline designed to let TensorFlow jobs run on Kubernetes.

A final word on sharing: as we have said, all these tasks cannot be accomplished by a single developer alone, unless they are experimenting by themselves: in production environments a team of developers, analysts and architects usually cooperate to deliver the project. Developers in particular cooperate, and sharing is an essential part of cooperation.

Assets uploaded or configured on AI Hub can be shared in different ways:

  • simply add a colleague by using their email address, much as in other Google tools when sharing documents, etc.
  • share with a Google group
  • share with the entire organisation to which one belongs.

Moreover, different profiles may be assigned to people we are sharing with, essentially a read only profile and an edit profile.

All in all, although it is not always easy to use and is subject to several constraints, Google AI Hub is a complex tool which may be used to deploy and scale ML applications or ML models to integrate into business applications, within a uniform framework. It is difficult to say if this will become the standard of ML deployment but it certainly traces a roadmap toward a flexible engineering of the ML model lifecycle.

RefaceAI: How to build Affordable deepfake App

In 2019, the Ukrainian IT-company Neocortext (current RefaceAI) released the Doublicat mobile app (now Reface), with which the user can replace the face on the gif with his own. Six months later, the application was already changing faces to video, and by August the number of its installations exceeded 20 million.

About RefaceAI

Category: Entertainment
Initial Release Date: Dec 23, 2019
Size:84.4 mb
Company HQ:Ukraine
Content Rating:Rated 12+

According to the analytics service App Annie as of August 15, Reface is among the ten most popular apps on iOS in 15 countries, and on Android – in 19.

source: sensortower

How the user interacts with the service?

To insert their face into a GIF or video, the user takes a photo in the application and selects a template, for example, a fragment from a movie. After that, the algorithm changes its appearance in a few seconds. The result can be downloaded immediately or shared on social networks.

How does the technology work and where is it used?

Usually, when creating a deepfake, it takes a lot of time to train a neural network. At the same time, a separate network must be trained for each person.

However, RefaceAI has created a universal Artificial neural network to replace any human face, thanks to which a deepfake is obtained in seconds. The developers have trained the network on millions of images from open libraries (the company does not disclose the name), so it can change faces in both photos and videos.

Having received the user’s photo, the network “translates” it into face embeddings – an anonymized set of numbers. According to it, the machine determines the facial features and transfers them to the template.

Deepfake turns out to be more realistic thanks to machine learning, including a GAN-type neural network – its peculiarity is that it includes two networks that train each other. In the case of Reface, for example, they “adjust” the color of the user’s face to the lighting of the original video or picture.

Startup Success Story

RefaceAI, the company behind Reface, was founded in 2011 by:

  • Roman Mogilny – CEO.
  • Oles Petriv – Technical Director.
  • Yaroslav Boyko – Chief Operating Officer.

Before the face-swapping app, entrepreneurs had been involved in various projects for seven years: developing websites, collaborating with post-production studios for Hollywood films, where machine learning technologies were needed. For example, they converted a video from 2D to 3D format.

In 2018, the company came up with the idea to create an app that would replace faces in photos. At that time, RefaceAI employed six people.

RefaceAI has raised 1 round. This was a Pre-Seed round raised on Dec 5, 2019. Adventures Lab has invested in the startup between $300,000 and $500,000. &

In March 2019, Elon Musk posted photos on Twitter with his face instead of Dwayne Johnson’s. The image featured the Reflect watermark. Due to this publication, application traffic has grown tenfold, entrepreneurs noticed.

By September, the co-founders realized that simply changing faces in photos was not enough. At that time, product manager Ivan Altsibeev joined the team. He will suggest switching to gifs. The idea turned into a Doublicat app. It was presented at Product Hunt in January 2020.

According to Mogilny, Petriva and Boyko, Doublicat received about 600 mentions in the media, including Forbes and Mashable.

post on Forbes

Six months later, the company added face-to-video to the app and renamed Doublicat to Reface. With the new feature, the service has grown in popularity, with Britney Spears, Snoop Dogg and other celebrities sharing their videos.

Reface currently has 20 million installs and continues to grow. How quickly, the company does not specify. Her spokesperson added that 65% of users share content created in the app.

The basic version of Reface is free. The company receives income from advertising and paid subscriptions, where you can turn off the watermark: 199 rubles per week, 299 rubles per month and 1990 rubles per year. The company does not disclose the total revenue from the service.

To replace faces in photos, developers use images with open licenses, and for gifs they partner with sites like Tenor.

tenor website screen

In the case of the video, the company adheres to the advice of lawyers:

  • Content falls under US copyright fair use and therefore does not require licensing.
  • Limits the length of the videos, their quality and the rest of the content.

If the copyright holder wants to exclude their materials from the application, Reface App will remove them.

What’s next?

In an interview Mogilny, Petriv and Boyko explained that the popularity of such applications is usually short-lived, so they use mechanics to retain users.

According to entrepreneurs, Reface will move forward not only the appearance of new content, but also its localization – so that the user can insert his face into a clip with a popular star in the country.

Since 2018, RefaceAI has grown to 40 employees. She is currently conducting closed beta testing of the Reface Studio web platform. With its help, creators of entertainment content will be able to insert faces into any video. In the future, the company plans to replace bodies as well.

As conceived by the founders, the new service will work in the b2b segment as well: it will be useful for creative agencies, filmmakers and computer game developers.

One of the problems that Reface Studio can face is using the service to create fake news and replace the faces of famous people. To prevent possible harm to the public, developers will apply two approaches:

  1. You cannot use the service anonymously.
  2. The video created in the “studio” will have an invisible mark that the project was created using Reface Studio.

App Monetization

Top In-App Purchases from AppStore

  • Weekly $2.99
  • Annual Plus $27.99
  • Monthly $4.99
  • Annual $27.99

Original post

Latest Technology Trends That Influence Future of Businesses

Every year Future Today Institute publishes a report about latest technology trends, which will affect different sectors of the economy. In 2019 the number of innovations, mentioned in the forecast, was almost doubled if to compare to the previous year. Technologies evolve faster than ever and so that your business keeps pace with the times, you should pay attention to the latest technology trends. What innovations in IT sphere will be in demand in 2020 and how to apply the emerging technology trends for business development?

Artificial Intelligence

Stanford University’s Scientists believe that by 2030, cities will use Artificial Intelligence (AI) to ensure the safety of the population. According to forecast, the AI will help to prevent crimes and even act as an assistant in court proceedings.

Solutions based on AI have already formed the basis of many innovative ideas and technology trends: from smart house to the face recognition and even emotions simulation. Elements of artificial intelligence — machine and deep learning are now widely used in robotics. The Future Today Institute researchers assume — soon artificial intelligence will become a part of almost all technology inventions.

Voice and visual product search

According to the recent Gartner’s forecast, by 2021 leading trading companies will implement visual and voice product search to their platforms. Using AI technology, large online stores will be able to understand desires and interests of their consumers. Analysts predict that as a result of this innovation, marketplaces’ revenue will increase by 30%.

Indeed individual approach to the customer

Real-time learning — emerging trend in information technology, based on AI conception. Using this technology large online stores, for example, will be able to display the products individually for each user, depending on his/her behavior on the website. This innovative technology will help to adjust the website’s model using a continuous flow of transactions data in real time.

Transport Industry Innovations

While Tesla actively develops its self-driving cars, Amazon has, for the first time, demonstrated the successful drone delivery in a public place. It is quite obvious that emerging technology trends will soon change the habitual attitude to transportation and logistics.

Logistics Automation

ARC Advisory Group analysts believe that delivery automation is quite a topical issue not only for carriers but also for large online stores. Steve Banker, one of the ARC Advisory Group directors, notes — when retail trades increase from 9 to 30 percents large companies face the question — whether they are ready to be flexible enough in goods delivery.

The demand for web-based TMS and SCM solutions (system supply chain management) will grow in the coming years. Cloud services for the logistics automation will become a trend, which helps to optimize the process of goods delivery not only for 3PL-providers, large online stores will also benefit from this solutions.

Self-driving cars

Self-driving cars — another technology trend (based on the concept of artificial intelligence), which the biggest companies have a bet. Tesla already allows drivers to engage the autopilot in their cars, provided that they will follow the road. Uber has also recently launched about a hundred Autonomous cars on the streets of Pittsburgh. However, innovation meanwhile is only in a testing stage — Uber’s specialist is in the car and constantly oversees the work of the algorithm and security.

According to the Oliver Wyman’s forecast, by the 2030 half of the cars will be equipped with an autopilot feature. In a note to Forbes Oliver Wyman’s analysts even speculate about how insurance companies will assess risks, considering the growing number of self-driving cars. It is obvious that autonomous cars will soon become quite commonplace.

Mobile trends:


Gartner experts have also predicted that by the 2021 year the largest enterprises will spend on chatbots much more than on mobile applications development, and the chatbots market share will be close to $3.5 billion. As we may see from the latest technology trends, chatbots will acquire human traits — firstly they will learn how to communicate (as Siri is already doing), and then to recognize and simulate human emotions.

We are getting into the so-called «post-application era» — virtual assistants, that are not tied to specific mobile programs will get leading positions. Experts believe that in a couple of years chatbots will penetrate all spheres of communication.

Internet of Х

In 1990 John Romkey created the world’s first Internet-thing, by connecting his toaster to the network. Since then, the number of devices managed via the Internet has grown considerably and formed the so-called Internet of Things or IoT. Now in there are about 9 billion «smart» objects that are managed via the Internet: mobile devices, industrial equipment, the smart house elements, etc. According to McKinsey, the number of smart devices will grow and will soon reach a trillion. Gartner experts recommend that businesses already, to start investing in IoT solutions: «Every supplier must, at the very least, make plans to implement IoT technology into its products, for both consumer and business buyers.»

In parallel with the increasing number of smart devices, LPWA is developing (Low-Power Wide-area network, that is an alternative to Wi-Fi). LPWA will allow the signal to travel great distances and overcome obstacles. The LPWA evolution, along with the increasing number of smart devices, will push the development of the IoT. Internet of Things will evolve to the Internet of X, the concept that can be applied to almost everything — assures Future Today Institute. As an example, scientists cite a startup Consumer Physics — using the application you can scan your food and get information about a number of calories it contains.

Education in the pocket

New innovations in technology appear almost every day and to stay on top of it, professionals in all fields will need to periodically update their knowledge. Considering the modern pace of life, online education is gaining momentum. Moreover, in order to rich its consumers, education should be as accessible as possible, so the futurists suggest a boom in educational mobile applications.

Big Data

The analysis of large amounts of data is already widely used by various companies. With the growing amount of information and the AI evolution, Big Data will develop and will be adapted to be applicable to even more spheres of life. Big Data will be one of the Online Marketing tools. This technology will allow to quickly process huge amounts of data about purchasing behavior. More about the technology we will write in the next article.

Experience design

User experience helps to highlight the company among competitors. Design based on user experience will continue its development together with the latest technologies. Traditional interfaces will transform in voice or even neural (the so-called «brain-computer»). The only thing that will stay unchanged — the main goal of a quality user experience is to make technological innovations convenient and user-friendly.

Smart Gadgets, Smart Homes, And Smart Interior Design

Smart home concept infographic concept technology system air conditioning and security lighting fire alarm vector set.

Important Benefits Of Artificial Intelligence

This blog will help us to know about how an Artificial Intelligence helping us in many ways.

Did you realize Artificial Intelligence may bring about a whopping $15.7 trillion into the international market by 2030!? In addition to economical positive aspects, AI is additionally responsible to generating our own lives less difficult. This short article is about how important Of Artificial Intelligence can assist you to comprehend just how Artificial Intelligence is affecting most of domain names of their entire life and also in the last profiting to humankind.

Here I will be talking about some important benefits of Artificial Intelligence in the following domains:

  1. In Automation
  2. In Productivity
  3. In Decision Making
  4. In Solving Complex Problems
  5. In Economy
  6. In Managing Repetitive Tasks
  7. In Personalization
  8. In Global Defense
  9. In Disaster Management
  10. In Lifestyle

To get in-depth knowledge of Artificial Intelligence you can enroll for live Artificial Intelligence Training by Edunbox with 24/7 support and lifetime access.

In Automation

Artificial Intelligence may be used to automate whatever that develops from tasks which demand extreme labour to this practice of recruiting.

You will find a variety of AI-based software which may be utilized to automate the recruiting procedure. These tools help to free the staff members from dull manual responsibilities and permit them to concentrate on complex responsibilities such as decision making and strategizing.

In Automation

A good instance of the could be that the AI recruiter MYA. This Application concentrates on automating dull regions of the recruiting process like monitoring sourcing and screening.

Mya is trained by utilizing Advanced level Machine Learning calculations and also using Natural Language Processing (NLP) to choose upon details which include up, within a conversation. Mya is additionally responsible for making candidate profiles and perform analytics and finally shortlist the deserving applicants.

In Productivity

Artificial Intelligence Is now a requirement in the entire enterprise world. It really is getting used to handle highly specialized tasks which want maximum work along with time.

Did you understand that 64 percent of most companies or businesses rely on AI-based software to elevated productivity and growth?

                                                                               In Productivity

A good illustration of this kind of application could be your legal robot. I predict it that the Harvey Spectre of this digital planet.

This bot utilizes machine learning methods or techniques such as deep learning along with Natural Language Processing to comprehend and examine valid records, locate and fix expensive authorized glitches, collaborate with legal knowledgeable professionals, Describe valid provisions by executing an AI-based scoring platform and many more. It also enables one to assess the contract together with people at an identical marketplace to Be sure yours is more ordinary.

In Smart Decision Making

One of the most important goals of Artificial Intelligence will be to Help in creating smarter business decisions. Salesforce Einstein that’s a comprehensive AI for CRM (Customer Relationship Management), has managed to accomplish so quite effortlessly.

As Albert Einstein said:

“The meaning of genius is taking the complex and making it basic.”

                                                                       Smart Decision Making

Salesforce Einstein is removing the complexity of Artificial Intelligence and allowing organizations to deliver brighter, and more personalized consumer experiences. Propelled by creative Machine Learning, Deep Learning, Natural Language Processing, along with predictive modeling, Einstein is implemented in large scale businesses such as discovering useful insights, forecasting promote behaviour and producing improved and better decisions.

In Complex Problems

During the years, AI has improved in straightforward Machine Learning Algorithms to innovative machine learning concepts like Deep Learning. This progress in AI has helped companies solve sophisticated issues such as fraud detection, medical investigation, climate forecasting and so on.

                                                                       Solve Complex Problems

Let consider the use case of how PayPal uses Artificial Intelligence in fraud detection. By use of deep learning, PayPal is now able to identify possible fraudulent activities very quickly.

PayPal acquired $235 billion in payments from several billion transactions by its more than a hundred and seventy million clients.

Machine learning and deep learning algorithms mine the information out of the customer’s purchasing history as well as reviewing patterns of fraud saved in its own databases also may tell if a specific transaction is fraudulent or maybe not.

In Economy

No matter if AI is known as a hazard into this earth, it’s believed to contribute in excess of $15 trillion into the entire world market from the year 2030.

As per a recent report by PwC, the progressive advances in AI increases the international GDP by up to 14 percent between today and 2030, the equal of an extra $15.7 trillion contribute into the world’s economy.

                                                                                 In Economy

It’s also stated that the most important financial gains in AI Is likely to be in China and North America. These countries will account for almost 70 percent of the global financial impact. The exact same report also demonstrates the greatest impact of Artificial Intelligence will be at the fields of robotics and healthcare.

The report also claims that approximately $6.6 trillion of the anticipated GDP growth can result in productivity benefits, especially within the coming decades. Big contributors to the growth include the automation of routine tasks and the evolution of smart robots and equipment which may perform all human-level activities.

Currently, a lot of the tech giants are already in the process for applying AI like a remedy to laborious tasks. But businesses who are slow to embrace these AI-based alternatives may wind up at a significant competitive disadvantage.

Managing Repetitive Tasks

Performing repetitive tasks can become very monotonous and time-consuming. Using AI for tiresome and regular actions can enable us to focus on the most essential activities inside our to do checklist.

A good example of this kind of AI is the virtual financial helper used by the Bank Of America, known as Erica.

Erica implements AI and ML methods to appeal to the bank’s customer service demands. It does this by simply creating credit report upgrades, easing bill installments along with helping customers with simple transactions process.

                                                                      Managing Repetitive Tasks

Erica’s capabilities have already been enlarged to assist clients make smarter economic choices, by offering them with personalized insights.

At the time of 2019, Erica has surpassed 6 million users and give services Over 35 million customer service requests.


In the research McKinsey found that manufacturers which glow personalization deliver 5 to 8 instances the marketing ROI and raise their own earnings by over 10 percent over businesses which is not  personalized. Personalization may be a very time consuming task, but it might be simplified by way of artificial intelligence. In truth, it has never been better to a target clients using the most suitable item.

A real example of this is the UK based style company ‘Thread’ which works by using AI to provide personalized outfits strategies for every customer.


Most clients could adore a private stylist, particularly just one Which comes free of cost. But staffing ample stylists to get 650,000 clients would be high priced. As an alternative, UK-based style corporation Thread makes use of AI to present personalized outfits strategies for every one of its own customers. Clients take model quizzes to supply data in their private style.

Every week, clients get personalized tips which they could vote down or up. Thread’s utilizes a Machine Learning algorithm named Thimble which works by using consumer information to discover patterns and also understand precisely the kind of the purchaser. Additionally, it proposes clothes predicated around the consumer’s preference.

Global Defense

Even the most advanced robots on earth are being assembled with international defense applications in mind. This isn’t any surprise since any cutting-edge technology first gets executed in military software. Though almost all of those applications do not see the light of day, one case we are aware of is your AnBot.

                                                                             Global Defense

The AI-based robot created by the Chinese is an armed police Robot made from the country’s National Defense University. Capable of reaching maximum speeds of 11 miles per hour, the system is intended to patrol locations and, even in case of danger, can deploy an “electrically charged riot control tool.”

The intelligent machine stands in a height of 1.6m and can spot individuals with criminal records. The AnBot has contributed to improving protection by preserving a track of almost any questionable activity happening around its vicinity.

AI for Lead Generation — Here is another utilization of Artificial Intelligence in the present time that by using AI you can create automatic sales Leads for your Business.

Disaster Management

For almost all people, Accurate weather calling makes holiday planning simpler, however, the tiniest progress in forecasting the current weather impacts the marketplace.

Accurate climate forecasting makes it possible for farmers to earn crucial decisions about planting and harvesting. This makes transport simpler and safer & most of all it’s might be utilized to predict natural disasters which influence the lifestyles of many.

                                                                        Disaster Management

After decades of study, IBM surfaced together with all the weather company and obtained lots of info. The venture gave IBM entry into this weather company’s mathematical versions, which furnished lots of weather conditions data it might propel to IBM’s AI stage Watson to try to boost predictions.

Back in 2016 that the weather company claimed their versions utilized greater than a hundred terabytes of third party data each day.

The item with this biography would be your AI established IBM deep thunder. Even the system supplies highly customized information to business customers using hyper-local predictions — in a 0.2 into 1.2-mile resolution. This info is helpful for transport providers, utility businesses, and also merchants.

Enhances Lifestyle

From the current years, Artificial Intelligence has progressed out of the science fiction movie storyline into a vital portion of our lives. Due to the fact the development of AI from the 1950s, we’ve observed exponential increase in its prospective. We utilize AI established digital assistants like Siri, Cortana, and Alexa to socialize together with all our mobiles as well as other apparatus; It can be utilised to foresee lethal diseases like ALS and leukemia.

                                                                           Enhances Lifestyle

Amazon tracks our surfing habits then serves products up it thinks we want to buy, and also Google determines exactly what contributes give us predicated on our search activity.

Irrespective of being contemplated a danger AI still proceeds to assist people In many manners. Like the way Eliezer Yudkowsky, co-founder and study fellow in Machine Intelligence Research Institute said:

“By a long shot, the most serious peril of Artificial Intelligence is that individuals conclude too soon that they get it.”

For this particular note, I’d love to complete by requesting one personally, just how can you presume Artificial Intelligence may help people create a much better universe?

Therefore for this particular, we arrived at a conclusion with this section Benefits Of Artificial Intelligence. Stay tuned in for a lot more blogs in the many trending technologies.  


Author’s Bio
Name :- Ikhlas Mohd. Saqib

Location:- Jaipur, Rajasthan, India

Designation:- SEO Executive

I am an SEO executive in the Edunbox and there i handle all the SEO related and Content Writing works.

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Is AI the future of Web and App Design?

Are robots taking over the world of web design?

Not too long ago we discussed the advantages of building sites with PHP, a practice that some people assume is akin to using a piece of chalk and a board to write your shopping list (oh how wrong they are). And on a somewhat related note, we’ve seen a glut of social media posts and articles of late claiming that the end is near for website and app developers and designers. The story goes that these prehistoric creatures using that chalk and board code known as PHP are relics of the past and that the future is artificial intelligence.

But is this a real threat? Can Artificial Intelligence (AI) really take over our jobs and leave app and web developers as penniless paupers? Let’s take a look at how plausible this actually is.

Can AI design a website?

The simple answer to that is yes, of course AI can build a website. In fact, Wix ADI has been doing it for quite some time now. However, it all depends on how much functionality, customizability, and uniqueness you want in your website. While robots can and will always build cheap and functional websites, they can’t quite cut it when it comes to creativity.

With sites such as Wix, Bookmark, and Jimdo Dolphin, there tends to be a cookie-cutter feel to the overall design of the site. Yes, they are unique in that the colors and copy are different, but the layout, placement of objects and so forth make them all look like sister sites with different color palettes. With this in mind, there will always be a market for design professionals that can create truly unique websites that allow a business to stand out from the crowd.

What about apps?

Interestingly, the mobile app market is one that is increasingly driven by AI. As the average consumer looks for more shortcuts and life hacks to make their lives more convenient, mobile app developers are turning to AI to help personalize user app experience. In the now flooded market, it’s the app that can offer personal recommendations based on a user’s habits that is the most successful. And while consumers often bemoan the fact that their phone sometimes seems to know exactly what they’re thinking, it’s this AI-powered convenience that they seek out.

The most interesting aspect about app development, however, is that despite the need for AI-driven apps, the actual design and creation of these applications is still the work of the trusted app developer. Yes, we mean the human kind. Although we want AI in our mobile apps, we want and prefer a human to be the one that puts it there.

So where does AI fit in?

Coffee Computer Work Design Notebook Creativity

AI – the assistant that doesn’t need coffee.

With research by Deloitte suggesting that 35% of UK jobs are at high risk of replacement, it’s clear that industry leaders feel that AI has a significant role to play in our future. But clearly that role is not one of singlehanded app or website design. Sure, AI can help churn out simple apps and websites at a phenomenal rate but for the high-end jobs, a human touch is still required and likely always will be. So does it have a place at all? Yes, it does, and that role is as the assistant in chief.

Let’s take the recruitment industry as an example. Companies looking to hire employees and find suitable candidates for a role are turning to AI software and tools to help them in their search. Yet they are not relying on that software to interview their candidates or make a final decision because that would be madness, right? The same can be said for the use of AI in any industry including website and app development.

AI can play Robin to your Batman, Lois Lane to your Superman, or Steve Trevor to your Wonder Woman. In the world of design and development it can be your faithful sidekick that ensures that you leave no stone unturned. So instead of worrying about AI taking your job, embrace the future and make artificial intelligence work for you. AI is the future, but it’s a future on your terms.