Best Open-Source GPT-3 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
  • BLOOM
  • GLaM by Google
  • Wu Dao 2.0
  • Chinchilla by DeepMind
  • EleutherAI

Introduction to Popular OpenAI Solution – GPT-3

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.

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.

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 open-source alternative

In conclusion, GPT-3 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:

Comparison of Open Source Web Crawlers for Data Mining and Web Scraping: Pros&Cons

Data mining and web scraping are two important tasks for anyone looking to gather data from the internet. There are a number of open source web crawlers available to help with these tasks, but which one is the best?

In this blog post, we compare the pros and cons of the most popular open source web crawlers to help you make the best decision for your needs.

The Best open-source Web Crawling Frameworks in 2022

In my search for a suitable back-end crawler for my startup, I looked at many open source solutions. After some initial research, I narrowed the choice down to the 10 systems that seemed to be the most mature and widely used: 

  • Scrapy (Python), 
  • Heritrix (Java),
  • Apache Nutch (Java),
  • PySpider (Python), 
  • Web-Harvest (Java),
  • MechanicalSoup (Python), 
  • Apify SDK (JavaScript),
  • Jaunt (Java),
  • Node-crawler (JavaScript),
  • StormCrawler (Java).

What is the best open source Web Crawler that is very scalable and fast?

As a starting point I wanted the crawler services of choice to satisfy the properties described in Web crawling and Indexes:

  • Robustness: The Web contains servers that create spider traps, which are generators of web pages that mislead crawlers into getting stuck fetching an infinite number of pages in a particular domain. Crawlers must be designed to be resilient to such traps. Not all such traps are malicious; some are the inadvertent side-effect of faulty website development.
  • Politeness: Web servers have both implicit and explicit policies regulating the rate at which a crawler can visit them. These politeness policies must be respected.
  • Distributed: The crawler should have the ability to execute in a distributed fashion across multiple machines.
  • Scalable: The crawler architecture should permit scaling up the crawl rate by adding extra machines and bandwidth.
  • Performance and efficiency: The crawl system should make efficient use of various system resources including processor, storage and network band-width.
  • Freshness: In many applications, the crawler should operate in continuous mode, obtaining fresh copies of previously fetched pages. A search engine crawler, for instance, can thus ensure that the search engine’s index contains a fairly current representation of each indexed web page. For such continuous crawling, a crawler should be able to crawl a page with a frequency that approximates the rate of change of that page.
  • Quality: Given that a significant fraction of all web pages are of poor utility for serving user query needs, the crawler should be biased towards fetching “useful” pages first.
  • Extensible: Crawlers should be designed to be extensible in many ways — to cope with new data formats, new fetch protocols, and so on. This demands that the crawler architecture be modular.
Table: comparison of TOP 3 open source crawlers in
terms of various parameters (IJSER)

I also had a wish list of additional features that would be nice to have. Instead of just being scalable I wanted to the crawler to be dynamically scalable, so that I could add and remove machines during continuous web crawls. I also wanted to the crawler to be able to export data into a variety of storage backends or data pipelines like Amazon S3, HDFS, or Kafka.

Focused vs. Broad Crawling

Before getting into the meat of the comparison let’s take a step back and look at two different use cases for web crawlers: Focused crawls and broad crawls.

In a focused crawl you are interested in a specific set of pages (usually a specific domain). For example, you may want to crawl all product pages on amazon.com. In a broad crawl the set of pages you are interested in is either very large or unlimited and spread across many domains. That’s usually what search engines are doing. This isn’t a black and white distinction. It’s a continuum. A focused crawl with many domains (or multiple focused crawls performed simultaneously) will essentially approach the properties of a broad crawl.

Now, why is this important? Because focused crawls have a different bottleneck than broad crawls.

When crawling one domain (such as amazon.com) you are essentially limited by your politeness policy. You don’t want to overwhelm the server with thousands of requests per second or you’ll get blocked. Thus, you need to impose an artificial limit of requests per second. This limit is usually based on server response time. Due to this artificial limit, most of the CPU or network resources of your server will be idle. Having a distributed crawler using thousands of machines will not make a focused crawl go any faster than running it on your laptop.

Architecture of a Web crawler.

In the case of broad crawl, the bottleneck is the performance and scalability of the crawler. Because you need to request pages from different domains you can potentially perform millions of requests per second without overwhelming a specific server. You are limited by the number of machines you have, their CPU, network bandwidth, and how well your crawler can make use of these resources.

If all you want it scrapes data from a couple of domains then looking for a web-scale crawler may be overkill. In this case take a look at services like import.io (from $299 monthly), which is great at scraping specific data items from web pages.

Scrapy (described below) is also an excellent choice for focused crawls.

Meet with Scrapy, Heritrix and Apache Nutch

Scrapy

Website: http://scrapy.org

Language: Python

Scrapy is a Python framework for web scraping. It does not have built-in functionality for running in a distributed environment so that it’s primary use case are focused crawls. That is not to say that Scrapy cannot be used for broad crawling, but other tools may be better suited for this purpose, particularly at a very large scale. According to the documentation the best practice to distribute crawls is to manually partition the URLs based on domain.

What stands out about Scrapy is its ease of use and excellent documentation. If you are familiar with Python you’ll be up and running in just a couple of minutes.

Scrapy has a couple of handy built-in export formats such as JSON, JSON lines, XML and CSV. Scrapy was built for extracting specific information from websites, not necessarily getting for a full dump of the HTML and indexing it. The latter requires some manual work to avoid writing the full HTML content of all pages to one gigantic output file. You would have to chunk the files manually.

Without the ability to run in a distributed environment, scale dynamically, or running continuos crawls, Scrapy is missing some of the key features I was looking for. However, if you need easy to use tool for extracting specific information from a couple of domains then Scrapy is nearly perfect. I’ve successfully used in in several projects and have been very happy with it.

Pros:

  • Easy to setup and use if you know Python
  • Excellent developer documentation
  • Built-in JSON, JSON lines, XML and CSV export formats

Cons:

  • No support for running in a distributed environment
  • No support for continuous crawls
  • Exporting large amounts of data is difficult

How to develop custom web crawler on Python for OLX? You can do it by guide from Adnan.


Heritrix

Website: webarchive.jira.com

Language: Java

Heritrix is developed, maintained, and used by The Internet Archive. Its architecture is described in this paper and largely based on that of the Mercator research project. Heritrix has been well-maintained ever since its release in 2004 and is being used in production by various other sites.

Heritrix runs in a distributed environment by hashing the URL hosts to appropriate machines. As such it is scalable, but not dynamically scalable. This means you must decide on the number of machines before you start crawling. If one of the machines goes down during your crawl you are out of luck.

The output format of Heritrix are WARC files that are written to the local file system. WARC is an efficient format for writing multiple resources (such as HTML) and their metadata into one archive file. Writing data to other data stores (or formats) is currently not supported, and it seems like doing so would require quite a few changes to the source code.

Continuous crawling is not supported, but apparently it is being worked on. However, as with many open source projects the turnaround time for new features can be quite long and I would not expect support for continuous crawls to be available anytime soon.

Heritrix is probably the most mature out of the source projects I looked it. I have found it easier to setup, configure and use than Nutch. At the same time it is more scalable and faster than scrapy. It ships together with a web frontend that can be used for monitoring and configuring crawls.

Pros:

  • Mature and stable platform. It has been in production use at archive.org for over a decade
  • Good performance and decent support for distributed crawls

Cons:

  • Does not support continuous crawling
  • Not dynamically scalable. This means, you must decide on the number of servers and partitioning scheme upfront
  • Exports ARC/WARC files. Adding support for custom backends would require changing the source

Further Reading:


Apache Nutch

Website: nutch.apache.org

Language: Java

Instead of building its own distributed system Nutch makes use of the Hadoop ecosystem and uses MapReduce for its processing ( details ). If you already have an existing Hadoop cluster you can simply point Nutch at it. If you don’t have an existing Hadoop cluster you will need to setup and configure one. Nutch inherits the advantages (such as fault-tolerance and scalability), but also the drawbacks (slow disk access between jobs due to the batch nature) of the Hadoop MapReduce architecture.

It is interesting to note that Nutch did not start out as a pure web crawler. It started as an open-source search engine that handles both crawling and indexing of web content. Even though Nutch has since become more of a web crawler, it still comes bundled with deep integration for indexing systems such as Solr (default) and ElasticSearch(via plugins). The newer 2.x branch of Nutch tries to separate the storage backend from the crawling component using Apache Gora, but is still in a rather early stage. In my own experiments I have found it to be rather immature and buggy. This means that if you are considering using Nutch you will probably be limited to combining it with Solr and ElasticSearch Web Crawler, or write your own plugin to support a different backend or export format.

Despite a lot of prior experience with Hadoop and Hadoop-based projects I have found Nutch quite difficult to setup and configure, mostly due to a lack of good documentation or real-world examples.

Nutch (1.x) seems to be a stable platform that is used in production by various organization, CommonCrawl among them. It has a flexible plugin system, allowing you to extend it with custom functionality. Indeed, this seems to be necessary for most use cases. When using Nutch you can expect spending quite a bit of time writing your own plugins or browsing through source code to make it fit your use case. If you have the time and expertise to do so then Nutch seems like a great platform to built upon.

Nutch does not currently support continuous crawls, but you could write a couple of scripts to emulate such functionality.

Pros:

  • Dynamically scalable (and fault-tolerant) through Hadoop
  • Flexible plugin system
  • Stable 1.x branch

Cons:

  • Bad documentation and confusing versioning. No examples.
  • Inherits disadvantages of Hadoop (disk reads, difficult setup)
  • No built-in support for continuous crawls
  • Export limited to Solr/ElasticSearch (on 1.x branch)

Further reading:


PYSpider Web Crawler

PYSpider has been regarded as a robust web crawler open source developed in Python. It has a distributed architecture with modules like fetcher, scheduler, and processor. 

Some of its basic features include: 

  •  It has a powerful web-based User Interface with which scripts can be edited and an inbuilt dashboard that provides functionalities like task monitoring, project management, and results viewing and conveys which part is going wrong. 
  •  Compatible with JavaScript and heavy AJAX websites. 
  •  It can store data with supported databases like MYSQL, MongoDB, Redis, SQLite, and ElasticSearch.
  •  With PYSpider, RabbitMQ, Beanstalk, and Redis can be used as message queues. 

Advantages: 

  •  It provides robust scheduling control. 
  •  It supports JavaScript-based website crawling. 
  •  It provides many features, such as an understandable web interface to use and a different backend database. 
  •  It provides support to databases like SQLite, MongoDB, and MYSQL. o It facilitates faster and easier to handle scraping. 

Disadvantages: 

  •  Its deployment and setup are a bit difficult and time taking. 

Documentation: http://docs.pyspider.org/ 

Conclusion

Open-source web scrapers are quite powerful and extensible but are limited to developers. 

Not surprisingly, there isn’t a “perfect” web crawler out there. It’s all about picking the right one for your use case. Do you want to do a focused or a broad crawl? Do you have an existing Hadoop cluster (and knowledge in your team)? Where do you want your data to end up in?

Out of all the crawlers I have looked at Heritrix is probably my favorite, but it’s far from perfect. That’s probably why some people and organizations have opted to build their own crawler instead. That may be a viable alternative if none of the above fits your exact use case. If you’re thinking about building your own, I would highly recommend reading through the academic literature on web crawling, starting with these:

What are your experiences with web crawlers? What are you using and are you happy with it? Did I forget any? I only had limited time to evaluate each of the above crawlers, so it is very possible that I have overlooked some important features. If so, please let me know in the comments.

Do you Know What Google use Java?


Need Top 50 open source web crawlers List for data mining?

Complete Guide to DALL-E

https://i.redd.it/fux6ua81r0591.png

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

https://www.reddit.com/r/weirddalle/ topic:

References

  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”. 
  21. https://www.businessinsider.com/dall-e-mini

Authors

Free Social Network Analysis Software – Who is Watching You?

You spend a lot of time on social media, don’t you? Don’t worry, most of us do. And if you are like us, you have probably been at least a little bit curious about who is actually looking at your profile the most. Maybe a former friend or someone you like is secretly lurking your profile? Well now there is a mobile app that can show you your top profile views.

Check out the mobile app Social Network Analyzer.

SNA screenshot

Social Network Analyzer was created by Bilal Raad. This innovative app retrieves a full list of your social network profiles – like Facebook, Twitter, and Instagram. Then it goes on to work analyzing your top interactions like comments, likes, and chats – it even goes as far as showing you your profile views. In order to find out who views your profile you have to interpret the list a little bit. But don’t worry, it is easy. All you have to do is exclude the people that you normally interact with, the rest will be your viewers. There is a free version and a paid version. Check out the free version first and try it out, if you like what you see you will have the option to switch to the paid version. The free version shows you a part of the list but for a complete list you will have to purchase the full version. If you do not want to pay, there are still bonuses that allow you to unlock the top spots. All you have to do is share Social Network Analyzer on your Twitter or Facebook profile.

Ready to find out who is secretly viewing your social media profiles? Go download Social Network Analyzer. The app is currently available on iOS and Android devices. Head to your app store and search for “Social Network Analyzer” to download it today.

4 Apps Like Social Network Analyzer

  • InstaGhost is a great application which lets everyone to get noticed with the interactive users who have just stopped posting anymore and who is fed up with using Instagram.
  • Crowdfire is an all in one solution for managing your social media concerns under one platform.
  • Wish to get a huge number of Likes on Instagram? Need to be well known like VIPs like Kim Kardashian, Dan Bilzerian, and others? Utilize a final application – Get Likes for Instagram.
  • SocialViewer for Instagram is another intuitive app for Instagram users which enables its users to calculate all their account activity and access data for each user who is interested with your profile freshly.

Meet Free Social Network Analysis Tools

Socilab

Socilab is an online tool that lets you visualize and analyze LinkedIn network using methods derived from social-scientific research. It displays a number of network measures drawn from sociological research on professional networks, and percentile bars comparing your aggregate network measures to past users. Also, there is a messaging feature that allows you to type and send a message to the selected LinkedIn contacts.

JUNG

JUNG stands for Java Universal Network/Graph Framework. This Java application provides an extendible language for the analysis, modeling and visualization of data that could be represented as a graph or network.JUNG supports numerous graph types (including hypergraphs) with any properties.

It enables customizable visualizations, and includes algorithms from graph theory, social network analysis and data mining. However, it is limited by the amount of memory allocated to Java.

Netlytic

Netlytic is a cloud-based text analyzer and social network visualizer that can automatically summarize large dataset of text and visualize social networks from conversations on social media sites like Twitter, YouTube, online forums, and blog comments. The tool is mainly developed for researchers to identify key and influential constituents, and discover how information flow in a network.

NodeXL

NodeXL is an open-source template for Microsoft Excel for network analysis and visualization. It allows you to enter a network edge list in a worksheet, click a button and visualize your graph, all in the familiar environment of the Excel window.

The tool supports extracting email, YouTube, Facebook. Twitter, WWW, and Flickr social network. You can easily manipulate and filter underlying data in spreadsheet format.

Open-source Image Recognition Library

What is the best image recognition app?

  • Google Image Recognition. Google is renowned for creating the best search tools available. …
  • Brandwatch Image Insights. …
  • Amazon Rekognition. …
  • Clarifai. …
  • Google Vision AI. …
  • GumGum. …
  • LogoGrab. …
  • IBM Image Detection.

Is there an app that can find an item from a picture?

Google Goggles: Image-Recognition Mobile App. The Google Goggles app is an image-recognition mobile app that uses visual search technology to identify objects through a mobile device’s camera. Users can take a photo of a physical object, and Google searches and retrieves information about the image.

Virtual Churches: Is VR the Future of Religious Technology?

In the 18th century, the American colonies experienced an enormous swell of religious fervor known as the First Great Awakening.

Leaving the cold, dark churches and their solemnly delivered sermons for exuberant traveling preachers in the town square, the colonials must have felt a profound spiritual transformation.

Since that First Great Awakening, there have been many other breakthroughs in the way people worship and interact spiritually: televangelists, megachurches, and online churches, just to name a few.

They’re all part of the constantly evolving way that we experience religion, and for the church to stay relevant, it will need to continue to embrace innovation by listening for new voices coming from the town square.

VR is one of the most recent examples of technology altering the way we worship, and in this article, we’ll look at how your Virtual Church could become a virtual church.

What is virtual reality?

Virtual reality uses computer-generated 360-degree images to immerse the viewer in a comprehensive, realistic experience.

When I recently came home from a summer in Washington, D.C., I brought my little sister a virtual reality viewer that let her visit the national monuments without ever leaving her room. She loved it, and spent hours that night looking around the Jefferson Memorial, the Washington Monument, the Capitol Building, and (my favorite) the Lincoln Memorial.

Virtual reality offers an escape from ordinary content consumption, but it also offers an exciting educational experience.

Students can work with virtual reality in the classroom to visit historical sites, watch a demonstration, or learn about the stars. Employees can train on the job without entering into dangerous or uncomfortable situations in real life.

Virtual reality can also be a portal to faith and spirituality for those who might not otherwise get the chance.

Virtual reality and the Church

L. Michelle Salvant, founder of Mission:VR, considers virtual reality to be vital for faith formation.

“We have a lot of people talking about faith,” she says. “But I know for a surety that when you can experience someone’s faith and their hope, when you can go inside of their life and feel it, conversion will increase.”

Mission:VR partnered with Covalent Reality to create an environment for the virtual reality platform Google Cardboard called BelieveVR.

BelieveVR uses 360-degree cameras to follow the stories of people of faith as they struggle to overcome spiritual challenges. The first story, called “Healed,”follows Florida pastor Nicky E. Collins as she finds spiritual support during her battle with breast cancer. After the premiere of the short film, Salvant said viewers were “uplifted” by the experience, which they called “awesome” and “touching.”

That’s just one example of how virtual reality is affecting the worship experience.

#1 Case: Church Online Platform

Church Online is developing a virtual reality platform to supplement its online presence.

#2 Case: Virtual Reality Church

The Virtual Reality Church uses the software platform AltspaceVR (free for most Android VR devices) to bring their congregants a 360-degree church experience.

What is VR Church?

In recent times the Church has been slow to venture into new technology; with many pastors siting allegiance to the good old pen and paper over an iPad. Indeed technology is often met with mistrust; but this was not always the case for God’s people. In 1454 one of these new technologies in it’s infancy was used to print the Gutenberg bible. At one point the pen and paper was a new technology, and God has embraced that in order to communicate his word. As Christian’s it is our mandate to communicate God’s loving kindness through whatever means we have available to us. Advanced technology and recent breakthroughs such as virtual reality present new media channels through which the Church can communicate the timeless message of Jesus.

VR Church is not so much about being a Church as it is about making use of technology to engage in the mission of the Church. Virtual Reality is an upcoming media channel that can be used like any other to communicate God’s words. Virtual Reality is different from many channels though in that it presents more ways for us to communicate God’s word that other channels: it is actually most similar to theatre in that respect. An important aspect of this channel is that we can communicate God’s word using different learning modalities: kinaesthetic, audio and visual learning styles are all represented and this has been shown to help with memorisation, as well as engaging a wide range of people.

So what specifically can we do with virtual reality that we couldn’t do already? Well, in a quick moment in our busy lives we can put on our VR headset and:

  • Be transported to a new and beautiful place which reflects the words of the Psalms
  • Be completely immersed in scenes from the bible
  • Find a quiet and still place to pray and worship
  • Join in a unique communal prayer room

There are a lot of different possibilities for how we might engage with God in virtual reality, what is left to do now is to get out there and present these ideas so that might be experienced and enjoyed.

#3 Case: SecondLife

SecondLife, one of the earliest virtual reality software systems, offers three different churches in its platform, and you can start exploring for free.

The National Shrine of the Divine Mercy in Second Life

No one expects any of these platforms to replace traditional churches, and even a megachurch would be ill advised to buy hundreds of high-end Oculus Rift headsets for their congregation.

But as VR devices become more and more affordable and ubiquitous (Google Cardboard can turn your smartphone into a VR headset for about $15, for example), virtual reality will become another channel for people to communicate on.

For people who can’t physically come to church, people who want to enhance their experience with new technology, or people who want to come back to the church, virtual reality offers another opportunity to do so.

Using virtual reality in the church brings a holistic spiritual experience to those who can’t or wouldn’t normally go to church. Making faith accessible to more people was one of the major goals Salvant hoped to achieve with her church software. People with disabilities are less likely to attend a service, so virtual reality quite literally opens the door for hundreds of thousands of disabled religious people across the country.

Clearly, VR is making its way into the spiritual community. But what are some ways you can use it in your own church?

1. Live stream in VR

I love watching live streams. They’re like a little window into someone else’s life. My church at home sets up a live stream at every Mass for those who can’t make it. But VR takes it to a whole new level. Your congregants can meet in a common space that requires no travel time, no cleanup, and no reservation.

VahanaVR by Orah

Software such as VahanaVR by Orah, or Facebook Spaces for Oculus Rift provides real-time hangout spaces for any kind of event, meeting, or service. Your congregants don’t necessarily need a headset. All you need to broadcast in 3D is:

  • the software (VahanaVR sells for $2,195; Facebook Spaces is free on Oculus devices)
  • a 360-degree camera (which start at about $100)
  • a connection to a livestreaming platform such as Facebook, YouTube, or Twitter

Anyone who wants to watch can simply click the link, put on a headset or watch on their computer screen, and become immersed in the experience.

2. VR small groups

You can also apply virtual reality to smaller groups of people. Instead of meeting at the church for department decisions, your staff can simply log onto an online VR platform.

A demo in Hyperfair

  • Hyperfair, which is usually used for businesses, could be used to meet in a virtual space. Pricing for Hyperfair is not available online.
  • Mozilla’s MozVR is a free, open-source virtual reality framework that allows you to create your own online VR experience and share it with your coworkers or friends (assuming you have advanced programming ability, of course).
  • vTime is a VR social network that allows people to meet in virtual space, and is free on Windows Mixed Reality devices.

Meeting in virtual space provides a chance to meet new people without the pressure of meeting in person. It gives your congregants a chance to talk to like-minded people about their faith, without having to leave home. For people who are new to the church, a virtual reality group just for them can give them a chance to get to know new people before they arrive.

3. VR group retreats

Retreats are expensive. There’s no way around it. From finding a place to accommodate everyone in your group, to catering food for each night, to planning the content and activities, retreats are labor intensive and costly.

What if you could join them from anywhere in the world using your computer?

Meeting in a virtual space (using the same devices and apps outlined in the sections above) can reduce the cost of reserving a building or campground, as well as travel to that location.

Guest speakers can give talks without ever having to leave their homes. People with disabilities can participate without having to worry about whether the space will accommodate their needs.

And the possibilities are endless. In the near future, you might be able to virtually travel all over the world on your retreat without having to pay for tickets or hotels. Because the virtual environment is computer generated, the options are only limited to what the designers and programmers can dream up. Just be prepared for a little push back if you try to convince your congregation that a virtual Hawaiian beachfront is as good as the real thing. We’re not quitethere yet.

How will you include virtual reality in your church?

Media professionals say virtual reality is the future. Classrooms, real estate, and construction are just three industries where virtual reality is already becoming a part of everyday life.

Places of faith and worship are not far behind. Will you incorporate VR in your church experience in the future? How will VR help your church? Tell us what you think in the comments!

10+2 Superior Automated Testing Tools for Mobile Apps

A collection of the best Mobile test automation tools than you can use to test your Mobile Apps.

Testing Tools for Native or cross-platform applications

1. Appium

-free

An open-source mobile test automation tool to test Android and iOS applications. It supports C#, Java, Ruby, and many other programming languages that belong to WebDriver library.

2. Selendroid

-free

Being one of the leading test automation software, Selendroid tests the UI of Androids based hybrid and native applications and mobile web.

3. MonkeyTalk by Oracle

-close

MonkeyTalk automates the functional testing of Android and iOS apps.

4. Bitbar Testing

-from $ 99 / month

It is one of the best platforms to test your iOS and Android devices that are having different screen resolutions, OS versions, and HW platforms.

5. Calabash

-free

Calabash works efficiently with .NET, Ruby, Flex, Java and other programming languages.

6. SeeTest

-have trial

SeeTest Automation is a cross-platform solution. It allows to run the same scripts on different devices.

Android Automation Testing Tools:

1. Robotium

-free
-android

Again an open-source tool to test Android applications of all versions and sub-versions.

2. monkeyrunner

-free
-android

MonkeyRunner is specifically designed for the testing of devices and applications at the framework/functional level.

3. UI Automator

-free
-android

In order to test the user interface of an app, UI Automator creates functional Android UI test cases. It has been recently expanded by Google.

Automation iOS Testing Tools:

1. Frank

-free
-iOS

Frank allows to test only iOS applications and software. The open-source framework combines JSON and Cucumber.

2. KIF for iOS

-free, open-source

KIF stands for Keep It Functional. It is an open source framework developed for iOS mobile app UI testing. It utilizes the Accessibility APIs built into iOS in order to simulate real user interactions. Tests are written in Objective-C, which is already familiar to iOS developers, but not test teams. Apple’s switch to Swift makes its use of Objective-C a disadvantage going forward.

3. iOS Driver for iOS

-free

iOS Driver utilizes Selenium and the WebDriver API for testing iOS mobile apps. Its default is to run on emulators, where execution is faster and scalable. The current version works with devices, but actually executes more slowly in that case. No app source code requires modification and no additional apps are loaded on the device under test. iOS Driver is designed to run as a Selenium grid node, which improves test speed as it enables parallel GUI testing.


At the moment, there appear to be many test framework solutions looking for problems, but that is to be expected as mobile app development and testing tools continue to be developed at a rapid pace. Every framework has its pros and cons, each of which should be weighted relative to the needs of the testing organization and the software products being delivered.


Inspiration