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Image GPT

We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples.

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We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples. By establishing a correlation between sample quality and image classification accuracy, we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting.


Introduction

Unsupervised and self-supervised learning, or learning without human-labeled data, is a longstanding challenge of machine learning. Recently, it has seen incredible success in language, as transformer models like BERT, GPT-2, RoBERTa, T5, and other variants have achieved top performance on a wide array of language tasks. However, the same broad class of models has not been successful in producing strong features for image classification. Our work aims to understand and bridge this gap.

Transformer models like BERT and GPT-2 are domain agnostic, meaning that they can be directly applied to 1-D sequences of any form. When we train GPT-2 on images unrolled into long sequences of pixels, which we call iGPT, we find that the model appears to understand 2-D image characteristics such as object appearance and category. This is evidenced by the diverse range of coherent image samples it generates, even without the guidance of human provided labels. As further proof, features from the model achieve state-of-the-art performance on a number of classification datasets and near state-of-the-art unsupervised accuracy on ImageNet.

Evaluation Dataset Our Result Best non-iGPT Result
Logistic regression on learned features (linear probe) CIFAR-10

96.3

iGPT-L 32×32 w/ 1536 features

95.3

SimCLR w/ 8192 features

CIFAR-100

82.8

iGPT-L 32×32 w/ 1536 features

80.2

SimCLR w/ 8192 features

STL-10

95.5

iGPT-L 32×32 w/ 1536 features

94.2

AMDIM w/ 8192 features

ImageNet

72.0

iGPT-XLa 64×64 w/ 15360 features

76.5

SimCLR w/ 8192 features

Full fine-tune CIFAR-10

99.0

iGPT-L 32×32, trained on ImageNet

GPipe, trained on ImageNet
ImageNet 32×32

66.3

iGPT-L 32×32

70.2

Isometric Nets

To highlight the potential of generative sequence modeling as a general purpose unsupervised learning algorithm, we deliberately use the same transformer architecture as GPT-2 in language. As a consequence, we require significantly more compute in order to produce features competitive with those from top unsupervised convolutional nets. However, our results suggest that when faced with a new domain where the correct model priors are unknown, a large GPT-2 can learn excellent features without the need for domain-specific architectural design choices.

Completions

Model Input

Completions right

Model-generated completions of human-provided half-images. We sample the remaining halves with temperature 1 and without tricks like beam search or nucleus sampling. While we showcase our favorite completions in the first panel, we do not cherry-pick images or completions in all following panels.

Samples

Model-generated image samples. We sample these images with temperature 1 and without tricks like beam search or nucleus sampling. All of our samples are shown, with no cherry-picking. Nearly all generated images contain clearly recognizable objects.


From language GPT to image GPT

In language, unsupervised learning algorithms that rely on word prediction (like GPT-2 and BERT) have been extremely successful, achieving top performance on a wide array of language tasks. One possible reason for this success is that instances of downstream language tasks appear naturally in text: questions are often followed by answers (which could help with question-answering) and passages are often followed by summaries (which could help with summarization). In contrast, sequences of pixels do not clearly contain labels for the images they belong to.

Even without this explicit supervision, there is still a reason why GPT-2 on images might work: a sufficiently large transformer trained on next pixel prediction might eventually learn to generate diverse samples with clearly recognizable objects. Once it learns to do so, an idea known as “Analysis by Synthesis” suggests that the model will also know about object categories. Many early generative models were motivated by this idea, and more recently, BigBiGAN was an example which produced encouraging samples and features. In our work, we first show that better generative models achieve stronger classification performance. Then, through optimizing GPT-2 for generative capabilities, we achieve top-level classification performance in many settings, providing further evidence for analysis by synthesis.

Towards general unsupervised learning

Generative sequence modeling is a universal unsupervised learning algorithm: since all data types can be represented as sequences of bytes, a transformer can be directly applied to any data type without additional engineering. Our work tests the power of this generality by directly applying the architecture used to train GPT-2 on natural language to image generation. We deliberately chose to forgo hand coding any image specific knowledge in the form of convolutions or techniques like relative attention, sparse attention, and 2-D position embeddings.

As a consequence of its generality, our method requires significantly more compute to achieve competitive performance in the unsupervised setting. Indeed, contrastive methods are still the most computationally efficient methods for producing high quality features from images. However, in showing that an unsupervised transformer model is competitive with the best unsupervised convolutional nets, we provide evidence that it is possible to trade off hand coded domain knowledge for compute. In new domains, where there isn’t much knowledge to hand code, scaling compute seems an appropriate technique to test.

Approach

We train iGPT-S, iGPT-M, and iGPT-L, transformers containing 76M, 455M, and 1.4B parameters respectively, on ImageNet. We also train iGPT-XL, a 6.8 billion parameter transformer, on a mix of ImageNet and images from the web. Due to the large computational cost of modeling long sequences with dense attention, we train at the low resolutions of 32×32, 48×48, and 64×64.

While it is tempting to work at even lower resolutions to further reduce compute cost, prior work has demonstrated that human performance on image classification begins to drop rapidly below these sizes. Instead, motivated by early color display palettes, we create our own 9-bit color palette to represent pixels. Using this palette yields an input sequence length 3 times shorter than the standard (R, G, B) palette, while still encoding color faithfully.

Experimental results

There are two methods we use to assess model performance, both of which involve a downstream classification task. The first, which we refer to as a linear probe, uses the trained model to extract features from the images in the downstream dataset, and then fits a logistic regression to the labels. The second method fine-tunes the entire model on the downstream dataset.

Since next pixel prediction is not obviously relevant to image classification, features from the final layer may not be the most predictive of the object category. Our first result shows that feature quality is a sharply increasing, then mildly decreasing function of depth. This behavior suggests that a transformer generative model operates in two phases: in the first phase, each position gathers information from its surrounding context in order to build a contextualized image feature. In the second phase, this contextualized feature is used to solve the conditional next pixel prediction task. The observed two stage performance of our linear probes is reminiscent of another unsupervised neural net, the bottleneck autoencoder, which is manually designed so that features in the middle are used.

Feature quality depends heavily on the layer we choose to evaluate. In contrast with supervised models, the best features for these generative models lie in the middle of the network.

Our next result establishes the link between generative performance and feature quality. We find that both increasing the scale of our models and training for more iterations result in better generative performance, which directly translates into better feature quality.

Hover to see sample images up

Each line tracks a model throughout generative pre-training: the dotted markers denote checkpoints at steps 131K, 262K, 524K, and 1000K. The positive slopes suggest a link between improved generative performance and improved feature quality. Larger models also produce better features than smaller models. iGPT-XL is not included because it was trained on a different dataset.

When we evaluate our features using linear probes on CIFAR-10, CIFAR-100, and STL-10, we outperform features from all supervised and unsupervised transfer algorithms. Our results are also compelling in the full fine-tuning setting.

Pre-trained on ImageNet
Evaluation Model Accuracy w/o labels w/ labels
CIFAR-10
Linear Probe
ResNet-152 94.0 check
SimCLR 95.3 check
iGPT-L 32×32 96.3 check
CIFAR-100
Linear Probe
ResNet-152 78.0 check
SimCLR 80.2 check
iGPT-L 32×32 82.8 check
STL-10
Linear Probe
AMDIM-L 94.2 check
iGPT-L 32×32 95.5 check
CIFAR-10
Fine-tune
AutoAugment 98.5
SimCLR 98.6 check
GPipe 99.0 check
iGPT-L 99.0 check
CIFAR-100
Fine-tune
iGPT-L 88.5 check
SimCLR 89.0 check
AutoAugment 89.3
EfficientNet 91.7 check

A comparison of linear probe and fine-tune accuracies between our models and top performing models which utilize either unsupervised or supervised ImageNet transfer. We also include AutoAugment, the best performing model trained end-to-end on CIFAR.

Given the resurgence of interest in unsupervised and self-supervised learning on ImageNet, we also evaluate the performance of our models using linear probes on ImageNet. This is an especially difficult setting, as we do not train at the standard ImageNet input resolution. Nevertheless, a linear probe on the 1536 features from the best layer of iGPT-L trained on 48×48 images yields 65.2% top-1 accuracy, outperforming AlexNet.

Contrastive methods typically report their best results on 8192 features, so we would ideally evaluate iGPT with an embedding dimension of 8192 for comparison. However, training such a model is prohibitively expensive, so we instead concatenate features from multiple layers as an approximation. Unfortunately, our features tend to be correlated across layers, so we need more of them to be competitive. Taking 15360 features from 5 layers in iGPT-XL yields 72.0% top-1 accuracy, outperforming AMDIM, MoCo, and CPC v2, but still underperforming SimCLR by a decent margin.

Method Input Resolution Features Parameters Accuracy
Rotation original 8192 86M 55.4
iGPT-L 32×32 1536 1362M 60.3
BigBiGAN original 16384 86M 61.3
iGPT-L 48×48 1536 1362M 65.2
AMDIM original 8192 626M 68.1
MoCo original 8192 375M 68.6
iGPT-XL 64×64 3072 6801M 68.7
SimCLR original 2048 24M 69.3
CPC v2 original 4096 303M 71.5
iGPT-XL 64×64 3072 x 5 6801M 72.0
SimCLR original 8192 375M 76.5

A comparison of linear probe accuracies between our models and state-of-the-art self-supervised models. We achieve competitive performance while training at much lower input resolutions, though our method requires more parameters and compute.

Because masked language models like BERT have outperformed generative models on most language tasks, we also evaluate the performance of BERT on our image models. Instead of training our model to predict the next pixel given all preceding pixels, we mask out 15% of the pixels and train our model to predict them from the unmasked ones. We find that though linear probe performance on BERT models is significantly worse, they excel during fine-tuning:

CIFAR-10
ImageNet

Comparison of generative pre-training with BERT pre-training using iGPT-L at an input resolution of 322 × 3. Bold colors show the performance boost from ensembling BERT masks. We see that generative models produce much better features than BERT models after pre-training, but BERT models catch up after fine-tuning.

While unsupervised learning promises excellent features without the need for human-labeled data, significant recent progress has been made under the more forgiving framework of semi-supervised learning, which allows for limited amounts of human-labeled data. Successful semi-supervised methods often rely on clever techniques such as consistency regularization, data augmentation, or pseudo-labeling, and purely generative-based approaches have not been competitive for years. We evaluate iGPT-L on a competitive benchmark for this sub-field and find that a simple linear probe on features from non-augmented images outperforms Mean Teacher and MixMatch, though it underperforms FixMatch.

Model 40 labels 250 labels 4000 labels
Improved GAN 81.4 ± 2.3
Mean Teacher 67.7 ± 2.3 90.8 ± 0.2
MixMatch 52.5 ± 11.5 89.0 ± 0.9 93.6 ± 0.1
iGPT-L 73.2 ± 01.5 87.6 ± 0.6 94.3 ± 0.1
UDA 71.0 ± 05.9 91.2 ± 1.1 95.1 ± 0.2
FixMatch RA 86.2 ± 03.4 94.9 ± 0.7 95.7 ± 0.1
FixMatch CTA 88.6 ± 03.4 94.9 ± 0.3 95.7 ± 0.2

A comparison of performance on low-data CIFAR-10. By leveraging many unlabeled ImageNet images, iGPT-L is able to outperform methods such as Mean Teacher and MixMatch but still underperforms the state of the art methods. Our approach to semi-supervised learning is very simple since we only fit a logistic regression classifier on iGPT-L’s features without any data augmentation or fine-tuning—a significant difference from specially designed semi-supervised approaches.

Limitations

While we have shown that iGPT is capable of learning powerful image features, there are still significant limitations to our approach. Because we use the generic sequence transformer used for GPT-2 in language, our method requires large amounts of compute: iGPT-L was trained for roughly 2500 V100-days while a similarly performing MoCo model can be trained in roughly 70 V100-days.

Relatedly, we model low resolution inputs using a transformer, while most self-supervised results use convolutional-based encoders which can easily consume inputs at high resolution. A new architecture, such as a domain-agnostic multiscale transformer, might be needed to scale further. Given these limitations, our work primarily serves as a proof-of-concept demonstration of the ability of large transformer-based language models to learn excellent unsupervised representations in novel domains, without the need for hardcoded domain knowledge. However, the significant resource cost to train these models and the greater accuracy of convolutional neural-network based methods precludes these representations from practical real-world applications in the vision domain.

Finally, generative models can exhibit biases that are a consequence of the data they’ve been trained on. Many of these biases are useful, like assuming that a combination of brown and green pixels represents a branch covered in leaves, then using this bias to continue the image. But some of these biases will be harmful, when considered through a lens of fairness and representation. For instance, if the model develops a visual notion of a scientist that skews male, then it might consistently complete images of scientists with male-presenting people, rather than a mix of genders. We expect that developers will need to pay increasing attention to the data that they feed into their systems and to better understand how it relates to biases in trained models.

Conclusion

We have shown that by trading off 2-D knowledge for scale and by choosing predictive features from the middle of the network, a sequence transformer can be competitive with top convolutional nets for unsupervised image classification. Notably, we achieved our results by directly applying the GPT-2 language model to image generation. Our results suggest that due to its simplicity and generality, a sequence transformer given sufficient compute might ultimately be an effective way to learn excellent features in many domains.

If you’re excited to work with us on this area of research, we’re hiring!

Source: https://openai.com/blog/image-gpt/

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Things to Know about Free Form Templates

A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to […]

The post Things to Know about Free Form Templates appeared first on 1redDrop.

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A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to view and also work with the form files. 

It will create a new free form template by copying and storing those files within a folder. A form template (.XSN) file designing or creation of a single file will include various supporting files. Users may fill out the online form by accessing the .XML form file, which is a form template.

Designing Free Form Templates

There are numerous processes that define free form template design, and are as follows:

  • Designing the form’s appearance – the instructional text, labels, and controls
  • Controls will assist with user interaction behavior on the form template. You can design a specific section to appear or disappear when the user chooses a particular option
  • Whether the form template may include some additional views. For a permit application form design, for example, you have to provide different views for each person. One view especially for the electrical contractor, next for the receiving agent, and finally, the investigator. He or she will deny or approve the permit application
  • Next, you need to know how & where to store the form data. Designing free from templates will allow users to submit their data within the database either online or direct access. If not, they can also store the same in any specific shared folder
  • It is essential to design the other elements, colors, and fonts within the form template
  • Users must be able to personalize the form. Allowing users to include various rows within the optional section, repeating section, or a repeating table
  • Users should receive a notification when they forget to input a mandatory field or make mistakes within the form
  • After completing the free form templates design, you can publish the same online using a .XSN file format

Club Signup Form

A simple registration form can help your Club Signup Form creation process go smoother. This signup form could be an ideal solution for a new club membership registration for any organization or club.

Application Form

Application form templates are much easier to use & set-up to streamline your application process. You can customize this online form and utilize the same for numerous applications. Make use of this application form as a job application form, volunteer applications, contest entries, or high school scholarship applications. It is an ideal solution for scholarship programs, nonprofit organizations, business owners, and many such users and use cases.

Scheduling Form

Scheduling form templates are handy and can be used for numerous appointment booking requirements. A scheduling form is also utilized for various appointment scheduling or online reservations and booking purposes. Regardless of your business requirement, it is easy to customize the form template.

Concept Testing Survey

While testing a new design or concept, it is essential to gather the responses quickly. Freeform templates for a concept testing survey make it much easier to gather product feedback and reach the target audience. It is essential to conduct market research while planning to release a new product. A mobile-friendly form will allow you to utilize the survey questions for collecting the product’s consumer input quickly.

Credit Card Order Form

It is not always a complex process to provide an online credit card payment form for the customers. This form template will allow you to access numerous services or products for collecting card payment information. You can utilize this yet-another endless and simple payment form.

Employment Application Form

The employment application form for recruitment will assist the HR team to gather the required information from candidates. During the interview or application process, you can easily remove any expensive follow-ups. Some of the fields are contact information, employment history, useful information, etc. as well as an outline of the job description, consent for background checks, military service record, anticipated start date, any special skills, and many more. It is optional to enable notifications for the form owners to receive an alert or email when a new employment application is submitted.

Source: https://1reddrop.com/2020/10/24/things-to-know-about-free-form-templates/?utm_source=rss&utm_medium=rss&utm_campaign=things-to-know-about-free-form-templates

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Are Chatbots Vulnerable? Best Practices to Ensure Chatbots Security

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Rebecca James
credit IT Security Guru

A simple answer is a Yes! Chatbots are vulnerable. Some specific threats and vulnerabilities risk chatbots security and prove them a wrong choice for usage. With the advancement in technology, hackers can now easily target the hidden infrastructure of a chatbot.

The chatbot’s framework has an opportunity for the attackers ready to inject the malicious codes or commands that might unlock the secured data of the customers and your business. However, the extent of the attack’s complexity and success might depend on the messaging platform’s security.

Are you thinking about how chatbots are being exposed to attacks? Well! Hackers are now highly advanced. They attack the chatbots in two ways, i.e., either by social engineering attack or by technical attacks.

  • An evil bot can impersonate a legal user by using backup data of the possibly targeted victims by social engineering attack. All such data is collected from various sources like the dark web and social media platforms. Sometimes they use both sources to gain access to some other user’s data by a bot providing such services.
  • The second attack is technical. Here also attackers can turn themself into evil bots who exchange messages with the other bots. The purpose is to look for some vulnerabilities in the target’s profile that can be later exploited. It can eventually lead to the compromise of the entire framework that protects the data and can ultimately lead to data theft.

To ensure chatbots security, the bot creators must ensure that all the security processes are in place and are responsible for restoring the architecture. The data flow via the chatbot system should also be encrypted both in transit and rest.

To further aid you in chatbot security, this article discusses five best practices to ensure chatbots security. So, let’s read on.

The following mentioned below are some of the best practices to ensure the security of chatbots.

It’s always feared that data in transit can be spoofed or tampered with the sophistication of cybercriminals’ technology and smartness. It’s essential to implement end-to-end encryption to ensure that your entire conversation remains secured. It means that by encryption, you can prevent any third person other than the sender and the receiver from peeping into your messages.

Encryption importance can’t be neglected in the cyber world, and undoubtedly the chatbot designers are adapting this method to make sure that their chatbot security is right on the point. For more robust encryption, consider using business VPNs that encrypt your internet traffic and messages. With a VPN, you can also prevent the threats and vulnerabilities associated with chatbots.

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3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

Moreover, it’s a crucial feature of other chat services like WhatsApp and other giant tech developers. They are anxious to guarantee security via encryption even when there’s strict surveillance by the government. Such encryption is to fulfill the legal principles of the GDPR that says that companies should adopt measures to encrypt the users’ data.

User identity authentication is a process that verifies if the user is having secure and valid credentials like the username and password. The login credentials are exchanged for having a secure authentication token used during the complete user session. If you haven’t, then you should try out this method for boosting user security.

Authentication timeouts are another way to ensure your chatbots security. This method is more common in banks as the token can be used for the predetermined time.

Moreover, two-factor authentication is yet another method to prove user identity. Users are asked to verify identity either by a text message or email, depending on the way they’ve chosen. It also helps in the authorization process as it permits access to the right person and ensures that information isn’t mishandled or breached.

The self-destructive message features open another way for enhancing chatbot security. This option comes in handy when the user provides their personally identifiable information. Such information can pose a serious threat to user privacy and should be destroyed or deleted within a set period. This method is handier when you’re associated with backing or any other financial chatbots.

By using secure protocols, you can also ensure chatbots security. Every security system, by default, has the HTTPS protocol installed in it. If you aren’t an IT specialist, you can also identify it when you view the search bar’s URL. As long as your data is being transferred via HTTPS protocol and encrypted connections, TLS and SSL, your data is secured from vulnerabilities and different types of cyber-attacks.

Thus, make sure to use secure protocols for enhanced security. Remember that when Chatbots are new, the coding and system used to protect it is the same as the existing HIMs. They interconnect with their security systems and have more than one encryption layer to protect their users’ security.

Do you know what the most significant security vulnerability that’s challenging to combat is? Wondering? Well! It’s none other than human error. User behavior must be resolved using commercial applications because they might continue to believe that the systems are flawed.

No doubt that an unprecedented number of users label the significance of digital security, but still, humans are the most vulnerable in the system. Chatbot security continues to be a real big problem until the problem of user errors comes to an end. And this needs education on various forms of digital technology, including chatbots.

Here the customers aren’t the ones who are to be blamed. Like customers, employees can make a mistake, and they do make most of the time. To prevent this, the chatbot developers should form a defined strategy, including the IT experts, and train them on the system’s safe use. Doing so enhances the team’s skillset and allows them to engage with the chatbot system confidently.

However, clients can’t be educated like the employees. But at least you can provide them a detailed road map of securely interacting with the system. It might involve other professionals who can successfully engage customers and educate them on the right way to interact with the chatbots.

Several emerging technologies are keen to play a vital role in protecting the chatbots against threats and vulnerabilities in the upcoming time, among all the most potent method behavior analytics and Artificial Intelligence developments.

  • User Behavioral Analytics: It’s a process that uses applications to study the patterns of user behavior. It enables them to implement complex algorithms and statistical analysis to detect any abnormal behavior that possibly represents a security threat. Analytical tools are quite common and powerful; thus, this methodology can become a fundamental component of the chatbot system.
  • Developments in AI: Artificial technology is a two-end sword that offers benefits and threats simultaneously. But, as AI is predicted to fulfill its potential, it will provide an extra security level to the systems. It is mainly because of its ability to wipe a large amount of data for abnormalities that recognizes security breaches and threats.

The Bottom Line

Security concerns have always been there with new technologies and bring new threats and vulnerabilities with them. Although chatbots are an emerging technology, the security practices that stand behind them are present for a long time and are effective. Chatbots are the innovative development of the current era, and emerging technologies like AI will transform the way businesses might interact with the customers and ensure their security.

Source: https://chatbotslife.com/are-chatbots-vulnerable-best-practices-to-ensure-chatbots-security-d301b9f6ce17?source=rss—-a49517e4c30b—4

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Best Technology Stacks For Mobile App Development

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What’s the Best Tech Stack for Mobile App Development? Read To Know

Which is the Best Tech Stack for Mobile Application Development? Kotlin, React Native, Ionic, Xamarin, Objective-C, Swift, JAVA… Which One?

Image Source: Google

Technology Stack for smartphones is like what blood is for the human body. Without a technology stack, it is hard even to imagine smartphones. Having a smartphone in uncountable hands is rising exponentially. For tech pundits, this is one unmissable aspect of our digital experience wherein tech stack is as critical as ROI.

The riveting experience for a successful mobile app predominantly depends on technology stacks.

The unbiased selection of mobile apps development language facilitates developers to build smooth, functional, efficient apps. They help businesses tone down the costs, focus on revenue-generation opportunities. Most importantly, it provides customers with jaw-dropping amazement, giving a reason to have it installed on the indispensable gadget in present times.

In today’s time, when there are over 5 million apps globally, and by all conscience, these are whopping no.s and going to push the smartphone industry further. But now you could see mobile app development every ‘nook and corner.’ But the fact is not who provides what but understanding the behavioural pattern of users.

So the pertinent question is, which is the ideal tech stack to use for mobile app development?

In native mobile app development, all toolkits, mobile apps development language, and the SDK are supported and provided by operating system vendors. Native app development thus allows developers to build apps compatible with specific OS environments; it is suitable for device-specific hardware and software. Hence it renders optimized performance using the latest technology. However, since Android & iOS imparts — — a unique platform for development, businesses have to develop multiple mobile apps for each platform.

1. Waz

2. Pokemon Go

3. Lyft

1.Java: The popularity of JAVA still makes it one of the official programming languages for android app development until the introduction of Kotlin. Java itself is at the core of the Android OS. Many of us even see the logo of Java when the device reboots. However, contradictions with Oracle (which owns the license to Java) made Google shift to open-source Java SDK for versions starting from Android 7.0 Nougat

2.Kotlin: According to Google I/O conference in 2019- Kotlin is the officially supported language for Android app development. It is entirely based on Java but has a few additions which make it simpler and easier to work.

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It’s my gut feeling like other developers to say that Kotlin is simply better. It has a leaner, more straightforward and concise code than open-cell Java, and several other advantages about handling null-pointer exceptions and more productive coding.

HERE’S A Programming Illustration Defining the CONCISENESS OF KOTLIN CODE

public class Address {

private String street;

private int streetNumber;

private String postCode;

private String city;

private Country country;

public Address(String street, int streetNumber, String postCode, String city, Country country) {

this.street = street;

this.streetNumber = streetNumber;

this.postCode = postCode;

this.city = city;

this.country = country;

}

@Override

public boolean equals(Object o) {

if (this == o) return true;

if (o == null || getClass() != o.getClass()) return false;

Address address = (Address) o;

if (streetNumber != address.streetNumber) return false;

if (!street.equals(address.street)) return false;

if (!postCode.equals(address.postCode)) return false;

if (!city.equals(address.city)) return false;

return country == address.country;

}

@Override

public int hashCode() {

int result = street.hashCode();

result = 31 * result + streetNumber;

result = 31 * result + postCode.hashCode();

result = 31 * result + city.hashCode();

result = 31 * result + (country != null ? country.hashCode() : 0);

return result;

}

@Override

public String toString() {

return “Address{“ +

“street=’” + street + ‘\’’ +

“, streetNumber=” + streetNumber +

“, postCode=’” + postCode + ‘\’’ +

“, city=’” + city + ‘\’’ +

“, country=” + country +

‘}’;

}

public String getStreet() {

return street;

}

public void setStreet(String street) {

this.street = street;

}

public int getStreetNumber() {

return streetNumber;

}

public void setStreetNumber(int streetNumber) {

this.streetNumber = streetNumber;

}

public String getPostCode() {

return postCode;

}

public void setPostCode(String postCode) {

this.postCode = postCode;

}

public String getCity() {

return city;

}

public void setCity(String city) {

this.city = city;

}

public Country getCountry() {

return country;

}

public void setCountry(Country country) {

this.country = country;

}

}

class Address(street:String, streetNumber:Int, postCode:String, city:String, country:Country) {

var street: String

var streetNumber:Int = 0

var postCode:String

var city: String

var country:Country

init{

this.street = street

this.streetNumber = streetNumber

this.postCode = postCode

this.city = city

this.country = country

}

public override fun equals(o:Any):Boolean {

if (this === o) return true

if (o == null || javaClass != o.javaClass) return false

Val address = o as Address

if (streetNumber != address.streetNumber) return false

if (street != address.street) return false

if (postCode != address.postCode) return false

if (city != address.city) return false

return country === address.country

}

public override fun hashCode():Int {

val result = street.hashCode()

result = 31 * result + streetNumber

result = 31 * result + postCode.hashCode()

result = 31 * result + city.hashCode()

result = 31 * result + (if (country != null) country.hashCode() else 0)

return result

}

public override fun toString():String {

return (“Address{“ +

“street=’” + street + ‘\’’.toString() +

“, streetNumber=” + streetNumber +

“, postCode=’” + postCode + ‘\’’.toString() +

“, city=’” + city + ‘\’’.toString() +

“, country=” + country +

‘}’.toString())

}

}

I’d say KOTLIN IS THE BEST FIND FOR ANDROID APP DEVELOPMENT.Google has dug deeper with some plans ahead since announcing it as an official language. Moreover, it signals Google’s first steps in moving away from the Java ecosystem, which is imminent, considering its recent adventures with Flutter and the upcoming Fuchsia OS.

Objective C is the same for iOS what Java is for Android. Objective-C, a superset of the C programming language( with objective -oriented capabilities and dynamic run time) initially used to build the core of iOS operating system across the Apple devices. However, Apple soon started using swift, which diminishes the importance of Objective -C in comparison to previous compilations.

Apple introduced Swift as an alternative to Objective-C in late 2015, and it has since been continued to be the primary language for iOS app development.Swift is more functional than Objective-C, less prone to errors, dynamic libraries help reduce the size and app without ever compromising performance.

Now, you would remember the comparison we’ve done with Java and kotlin. In iOS, objective-C is much older than swift with much more complicated syntax. Giving cringeworthy feel to beginners to get started with Objective-C.

Image Source: Google

THIS IS WHAT YOU DO WHEN INITIALIZING AN ARRAY IN OBJECTIVE-C:

NSMutableArray * array =[[NSMutableArray alloc] init];

NOW LOOK AT HOW THE SAME THING IS DONE IN SWIFT:

var array =[Int]()

SWIFT IS MUCH MORE ` WHAT WE’VE COVERED HERE.

In cross-platform app development, developers build a single mobile app that can be used on multiple OS platforms. It is made possible by creating an app with a shared common codebase, adapted to various platforms.

Image Source: Google

Popular Cross-platform apps:

  1. Instagram
  2. Skype
  3. LinkedIN

React Native is a mobile app development framework based on JavaScript. It is used and supported by one of the biggest social media platforms- Facebook. In cross-platform apps built using React Native, the application logic is coded in JavaScript, whereas its UI is entirely native. This blog about building a React Native app is worth reading if you want to know why its stakes are higher.

Xamarin is a Microsoft-supported cross-platform mobile app development tool that uses the C# programming language. Using Xamarin, developers can build mobile apps for multiple platforms, sharing over 90% of the same code.

TypeScript is a superset of JavaScript, and is a statically-typed programming language supported by Microsoft. TypeScript can be used along with the React Native framework to make full use of its error detection features when writing code for react components.

In Hybrid mobile app development, developers build web apps using HTML, CSS & JavaScript and then wrap the code in a native shell. It allows the app to be deployed as a regular app, with functionality at a level between a fully native app and a website rendered(web browser).

Image Source: Google
  1. Untappd
  2. Amazon App Store
  3. Evernote

Apache Cordova is an open-source hybrid mobile app development framework that uses JavaScript for logic operations and while HTML5 & CSS3 for rendering. PhoneGap is a commercialized, free, and open-source distribution of Apache Cordova owned by Adobe. The PhoneGap platform was developed to deliver non-proprietary, free, and open-source app development solutions powered by the web.

Ionic is a hybrid app development framework based on AngularJS. Similar to other hybrid platforms, it uses HTML, CSS & JavaScript to build mobile apps. Ionic is primarily focused on the front-end UI experience and integrates well with frameworks such as Angular, Vue, and ReactJS.

To summarize, there are 3 types of mobile apps- Native mobile apps, Cross-platform mobile apps, and Hybrid mobile apps; each offers unique technologies, frameworks, and tools of their own. I have enlisted here the best mobile app technology stacks you could use for mobile app development.

The technologies, tools, and frameworks mentioned here are used in some of the most successful apps. With support from an expert, a well-established mobile app development company, that may give much-needed impetus in the dynamic mobile app development world.

Source: https://chatbotslife.com/best-technology-stacks-for-mobile-app-development-6fed70b62778?source=rss—-a49517e4c30b—4

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