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AI Res Art Res

The Era of AI-Generated Art and Authorship Rights

art and AI
Generated by Dezgo

Written by Isabel Cheng on Digilah (Student Tech Research)

My Bio:

Hello, I am Isabel. I am a rising junior in high school at Stamford American International School (SAIS). As AI-generated art rapidly gains popularity and advances, questions such as its ownership, copyright, and how it applies to existing intellectual property frameworks arise. Hence, I want to explore the different perspectives toward AI-generated art and how that should be applied to the law with my research article below.

Section 1: Understanding AI-Generated Art

If you are familiar with the concept of Artificial Intelligence (AI), you know that they are algorithm models that programmers train on extensive databases. They analyze vast amounts of data fed to them and learn patterns so they know how to respond to act. However, the technology itself cannot think on its own or create original ideas.

Likewise, AI-generated artworks are created through machine learning models. The databases of images and artworks they are fed with are often without the permission of their owners. With that said, how is AI-generated art affecting the artistic landscape, and where does this technology stand in terms of authorship and the legal status of AI as a creator?

Section 2: Popular Generative AI Websites

Let’s talk about some of the most popular AI generative websites. First off, is Midjourney. Launched in early 2022, it is one of the most recognized AI image/art generators. Though it has a $10-20 USD monthly subscription fee, it is known for giving the best quality pictures fast, which has attracted a substantial user base.

Midjourney Showcase Page (Source: Midjourney Feed)

Leonardo AI, also launched in 2022, has a relatively beginner-friendly interface along with refined tools for paying professionals too. Its free version operates on a token system that resets on a daily basis, allowing users to generate images based on prompts. It produces images relatively well based on the prompts, and compared to Midjourney, it is more customizable because it has a larger variety of style options.

Leonardo AI Home Page (Source: Leonardo AI)

Ideogram, another AI generator that is gaining traction, also caters to a wide audience. It has a range of pricing plans catering to users of different needs. It has a free version where you are allowed up to 10 prompts a day. Its consistent performance in text-to-image generation makes it a reliable choice for many users.

Ideogram Explore Page (Source: Ideogram AI)

Dalle-E 3 is another popular image-generative AI in the market, particularly in AI artworks like drawings and paintings. Though it is still not fully developed, you can access it via ChatGPT Plus.

Notably, Dall-E 3 is one of the few AI image generators that states explicitly on its website that it is designed to refuse requests for images mimicking living artists’ styles. They also allow creators to opt their art out of future training of their image generation models.

However, challenges remain in ensuring artists receive recognition. Is a fake death certificate enough to make Dall-E 3 accept requests for mimicking a living artist’s style? What about the artists who are unaware that their work is being used to train AI models?

With that said, let us dive into the growing concerns about the unauthorized usage of artists’ art styles to train image-generating models.

Dall-E 3 Index Page (Source: OpenAI)

Section 3: Artist Recognition and Copyright Challenges in the Age of AI

Since 2023, the advent of AI generation tools has prompted many to be curious and experiment with them. This includes artists, and an increasing number of them are utilizing these tools to enhance their own work, with some feeding their own artworks into AI systems, so that after inputting a prompt, they are able to produce a new piece of art in their own unique style, all in a matter of minutes.

One wouldn’t exist without the other, and this raises questions about the relationship between traditional and AI-generated art.

In Singapore, the Copyright Act protects original creative work. The owner who made an original art piece is usually automatically given a copyright, and they are granted the right to reproduce, distribute, and display the work.

 However, when it comes to work generated by AI, a critical question arises: Does the creator of the AI, the user of the AI, the work of artists who trained the AI, or the AI itself hold the copyright? The existing framework primarily addresses human authorship, leaving a grey area for AI-generated work.

As of now, the Singapore law does not explicitly recognize AI as the creator, which complicates ownership and copyright claims to their generated artwork.  

Furthermore, Singapore’s legal system emphasizes that copyright protection is only given if the work is original. Since AI-generated art is often trained on databases with existing art, it raises questions about whether the art it generates meets the originality requirement. This could limit the ability of artists to claim copyright over AI-generated work that they incorporate their style or elements into.

As Singapore and countries around the world navigate these complexities, there is a growing consensus that intellectual property laws need to be updated to better fit the world today. Policymakers and legal experts should work together with artists and AI experts to shape a new legal landscape that protects creativity and authorship rights in this new age of AI.

Section 4: Protecting Artists – Tools and Views

Artists are increasingly turning to AI-generative-art poisoning software such as Nightshade, Glaze, Have I Been Trained, and more to protect their work from being used in unauthorized AI training.

These tools allow creators to alter the pixels of their artworks that are imperceptibly different from human eyes but will get picked up by machine learning algorithms and act as a poisoned data sample, resulting in deformed outputs from AI models.

Glaze More Info Page (Source: The University of Chicago)

However, the development of all these poisoning tools is not perfect, and most end up compromising the quality of the artwork. Additionally, some artists view the anti-poisoning software as another unnecessary step, given that another training model could come out to invalidate this approach.

Furthermore, another perspective on AI-generated art is that there is no difference between AI learning and an artist. Most artists take inspiration from other artists anyway and make their own art as a derivative of that.

AI needs artists, but if you look at human history, modern art is built upon thousands and thousands of years of art from different cultures across the globe. Artists need artists too, suggesting that AI could be seen as an extension of this creative process.

Contrary to that though, another valid argument is the idea that AI art is theft? It’s a common consensus that it takes at least 10,000 hours to learn a new skill. Most artists invest years practicing and honing their craft, while all companies do is download datasets of millions of artworks and use them to train their algorithm models.

With that, the models can then make combinations from what they “learned” and instantly generate pieces, often profiting from it through consumers without having done anything artistic themselves.

This situation is unfair to artists, as no human can feasibly digest billions of images and create amalgamations of all of them in seconds. This could lead to a culture of complacency, ultimately stifling creativity and innovation. It is concerning to consider that all “new” and “original” art could end in the next few years if lawmakers do not adequately protect artists’ rights.

However, a lot of this issue involves how AI interacts with the current intellectual copyright laws, further driving the need for policymakers to amend them.

As previously noted, law and legal experts should work together with artists and AI experts to consider adding something like an AI Commission system or requiring AI developers to get permission/credit from artists whose art is used in training datasets.

Conclusion

In conclusion, the rise of AI-generated art is both an opportunity and a challenge for artists and the legal system. Especially with the evolution of AI technology, education, and awareness are crucial for both artists and lawmakers to navigate this new landscape.

Legal frameworks need to reform and adapt so that artists, AI, AI creators, and users get fair recognition for their contributions. As we move forward, it becomes clearer that the ambiguity surrounding artistic authorship around the world has to be changed, fostering an environment where creativity can thrive alongside technological advancement.

References

https://docs.midjourney.com/docs/plans https://leonardo.ai/

https://ideogram.ai/t/explore

https://www.midjourney.com/showcase

https://openai.com/about

https://openai.com/index/dall-e-3

https://glaze.cs.uchicago.edu

https://dezgo.com/text2image/sdxl

https://lawgazette.com.sg/feature/generative-ai

https://www.channelnewsasia.com/singapore/ai-art-copyright-law-artificial-intelligence-authorship-originality-3339396#:~:text=In%20Singapore%2C%20the%20law%20holds,concerns%20around%20protecting%20their%20work.

Most asked questions

Which are the commonly used AI-generative-art poisoning softwares?

Artists are increasingly turning to AI-generative-art poisoning software such as Nightshade, Glaze, Have I Been Trained, and more to protect their work from being used in unauthorized AI training.

How can I access Dalle-E 3?

Dalle-E 3 is a popular image-generative AI in the market. Though it is still not fully developed, you can access it via ChatGPT Plus.

Most searched queries

Midjourney

Ideogram

Leonardo AI

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Categories
Ad Res AI Res

AI transforms advertising industry Dynamics

Generative AI

Written by Diem-Quynh Do on Digilah (Tech Thought Leadership).

My Bio:

Hi, I am Quynh. I am currently a 3rd year Communication Studies major at Nanyang Technological University. Back in my first year of university, generative AI was largely an unknown topic. Fast forward to the present, it is the central point of every discussion about the use of technology in communication and media. Witnessing this swift change is the motivation for me to reflect on my experience with these tools and ponder the question: Is generative AI the future of my industry?

Read my research article below:

Generative AI (Gen AI) took center stage as the prominent buzzword of 2023. This technology revolutionized every single industry for its capacity to quickly generate various types of content including text, images, audio, and more. Its extraordinary capacities led to significant workforce changes and stood behind some of the major layoffs in the past year. That said, how is Gen AI transforming the advertising landscape, and where does it stand in an industry where it does what it does best – generating text and images?

The buzz around Generative AI

Gen AI first existed in the ’60s century in the form of chatbots. However, it was not until recent years that they became widely accessible and adopted by internet users worldwide. While Gen AI can assist all sectors in their day-to-day tasks, the implication of the current technology, which is to generate various types of media, remains the most relevant in the advertising industry.

In their original forms, text generators stand as a revolutionary tool for communication specialists and advertisers with their ability to churn out ad copy and refine written pieces in no time. Image generators are also widely utilized and proven to be a cost-effective and time-efficient alternative.

But the potential of Gen AI does not stop there. Big players in the tech industries are also bringing generative AI into their products. Adobe Photoshop, the household name for all professional creatives, introduced their AI-powered Generative Fill in early 2023. This feature was widely celebrated, as it helps to significantly reduce the time spent on creating and editing media assets.

 Adobe Photoshop Generative Fill (Source: Adobe Support)

Canva, catering to non-professionals, also incorporated AI to swiftly assist users in discovering templates along with the AI image generator and other capabilities.

Canva’s Magic Studio, powered by AI (Source: Canva)

Google also introduced a new Gen AI tool for their Google Performance Max partners. This innovative tool will aid advertisers in asset creation, ad copy generation, and more.

Google’s AI-powered ads tool (Source: Google)

These tech giants saw a trend in advertisers’ habits: a desire for tools that facilitate swift content creation, allowing more time for other meaningful tasks. It is also a strategic move to cut the budget on outsourcing designers and writers and produce content in-house with the help of AI.

The confined box of AI tools

While Gen AI exhibits extraordinary capabilities, it fundamentally remains a product crafted by humans and acquires knowledge through the datasets fed into the system. Therefore, its generating capacity is limited by what is available in the training dataset.

A prominent example is a trend that blew up on TikTok. Taking advantage of the new image-generative feature of ChatGPT, users create prompts that ask AI to generate different sets of images. All went well until a user took a step further and asked ChatGPT to generate an image of a hamburger without cheese. To our surprise, AI failed at this simple task despite the creator’s effort to rephrase and work around the instruction.

The user is asking AI to create a hamburger without cheese (Video: https://www.tiktok.com/@teddywang86/video/7315228986072681733)

The internet community went wild, with many joking about how “they said AI will take over the world”. But what is left after a good laugh is the acknowledgment that AI’s capacity is limited. As most hamburgers in the training dataset have cheese, AI faces difficulty creating one without it.

Generally, Gen AI tools’ capability is limited by specific tasks assigned to them and the quality of the data used for their training. They can only perform certain tasks or only have the knowledge up until when it was trained. Those who use Gen AI in their work must have a good understanding of the limits of this technology, use it to assist only in relevant tasks, and double-check the quality of the work.

How far is AI from us?

Just as advertising professionals must undergo training and years of experience to excel at their job, Gen AI also requires training to adapt to the specific tasks that it is assigned.  It is undoubted that with enough time and investment, Gen AI will become even better at producing results and catering to specific industries, including advertising.

But in this specific case, the learning curve might be steeper for AI than it is for humans. The supporting arguments often center around two key aspects: flexibility and originality.

Text AI generators, most prominently ChatGPT, are known for their limited flexibility in writing. While they can be instructed to adopt different styles, some users remain unsatisfied or frustrated as the result does not precisely meet their expectations.

A simple instruction to ‘energize’ a piece of text may return a copy that is oversaturated with emojis and buzzwords. The absence of ‘empathy’ in current AI technology also hinders their ability to produce written pieces that effectively address the intricacies and nuances of communication tasks.

The conversation surrounding originality often involves copyright infringement. This concern originated from the fact that third-party material might have been used in the process without permission. While referencing past artworks is a common practice in the creative industry, the added touch of human creativity somewhat mitigates these concerns.

To conclude, the fact that Gen AI does not seem to have ‘creativity’ on its own will continue to pose concerns about its range of capabilities and the originality of the product created. Yet, Gen AI remains a powerful tool that can elevate human creativity by offering new ideas and bringing in new thought dimensions.

Most asked questions

Do AI tools have a confined set of capabilities?

Does ChatGPT always give satisfactory results?

How will Gen AI help the advertising industry to evolve with time?

Most asked queries

ChatGPT

Canva

Adobe Photoshop

Copyright infringement

Generative AI

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Categories
Med Res

Precision Revolution: CRISPR, AI and the Future of Biotechnology and Pharmaceuticals

drug

Written by : Oswald Yap Tingzhe on Digilah (Student Research)

My Bio

I am a 2nd year student at Nanyang Technological University. The inspiration for this research article is the rapid and advanced development of AI and CRISPR technology in the medical field. AI’s rapid data analysis transforms drug development, while CRISPR precision revolutionizes genetic modification. Together, these two incredible technologies result in groundbreaking developments, shaping the future of healthcare with innovative treatments and personalized medicine.

Read my article below:

Section 1: CRISPR Technology

CRISPR technology is derived from bacterial immune systems to facilitate precise gene modification. It is made up of short repetitive DNA sequences that contain “spacer” sequences, which contain viral genetic information.

By utilizing Cas9 enzyme as molecular scissors, RNA molecules can guide CRISPR to target and edits specific genes precisely. With this capability, scientists can introduce new genes or modify existing sequences accurately.

CRISPR has a profound impact on treating genetic disease, because it can modify faulty genes that are responsible for these hereditary conditions. Therefore, CRISPR can treat diseases that cannot be treated through therapeutic interventions.

This makes CRISPR a revolutionary tool in the pursuit of precise and effective treatments for many genetic diseases. This is due to its precision, versatility, and transformative impact.

A group of scientists from China has demonstrated that CRISPR can eliminate or inactivate carcinogenic viral infection (cancers are caused by gene mutations). They have proven that CRISPR can treat cancer when it is applied to human viruses, such as hepatitis B virus and HPV16.

For example, HPV16 and HPV18 viruses can induce cervical cancers by Papillomavirus E6 and E7 viral proteins. Bacterial CRISPR/Cas RNA guided endonuclease can be reprogrammed to HPV-transformed cells to knockout (delete) E6 and E7 genes.

One of the clinical challenges faced is the off-target effects even though CRISPR can edit and modify gene precisely. Many researchers have reported that the CRISPR-Cas9 technology cause gene modification in other undesired genomic loci. As a result, this will reduce the efficacy of gene modification.

To reduce the off-target effects of CRISPR-Cas9, a scientist from Harvard university has modified the Cas9 protein to enhance the recognition of target DNA. Hence, it can improve the on-target specificity and efficiency of CRISPR-Cas9 technology.

Section 2: AI accelerates drug development

The advanced algorithms of artificial intelligence (AI) can revolutionize the development of drug by analyzing extensive dataset with speed and accuracy. 

These algorithms can identify potential drug candidates more efficiently than traditional methods. Due to its ability to identify intricate pattern and relationships within the extensive and diverse data, it can lead to more informed decision-making.

The complex algorithm in AI can also reduce the time and resources required for early-stage research. Hence, this innovative application of AI marks a paradigm shift, creating the hope of streamlining drug discovery to bring novel and effective treatments to patients more swiftly.

AI has emerged as a possible solution to the problems caused by chemical space of atoms in the pharmaceutical industry. 

The AI algorithms have been increased in computer-aided drug design (CADD) due to the development of technologies and high-performance computer.

The two most common methods of CADD are structure-based drug design and ligand-based drug design. The structure-based drug design analyzes the three-dimensional of proteins, while the ligand-based CADD uses the information of studied active and inactive molecules.

Machine learning computational algorithms, such as support vector machine (SVM), has ensured to improve the activity of bioactive components. 

The combined methods of both deep-learning and machine-learning has increased the ability, strength, and standard of the evolved products.

In the field of orthopedics, the large amount of data with the inclusion of ML has helped orthopedic surgeons in many aspects of the application. 

For example, the advances in this field to assess the impact on the musculoskeletal system of human beings. This is done to provide value-based healthcare and serving the patients in a better manner.

Section 3: Synergy of CRISPR and AI

The integration of CRISPR and AI has led to a new era of unprecedented advancements in the development of drug discovery. 

As a result, many critical challenges can be addressed. There will be more novel solutions and the pace of scientific breakthroughs will be accelerated.

The synergy of CRISPR and AI potential drug targets to be identified rapidly in the process of drug discovery and the assessment of their therapeutic viability. 

It would be impossible for human researchers to decipher the massive genomic information. AI algorithms make it possible due to its ability to analyze vast data sets, identifying patterns and relationships.

Furthermore, AI has an important role in optimizing the process of predicting the outcomes of genetic editing in CRISPR experiments. 

This is because the algorithms can anticipate the effects of specific gene edits after studying the previous CRISPR data. In this way, it can learn from past experiments.

The ability of AI to predict results in experiments not only speeds up the experiment, but also reduce risks to increase accuracy. Therefore, the synergy of CRISPR and AI can revolutionize the landscape of biotechnology.

For example, machine-learning (ML) models are trained using existing datasets and can be used to predict the on/off-target effects of the testing datasets (genomic information). 

The current ML models are based on regression-based methods, classification-based methods, and ensemble-based methods.

The advanced ML models enable deep-learning (DL) methods to be applied in the CRISPR-Cas9 system. The models in CIRSPR-Cas9 system consists of multiple layers of interconnected compute units.

The algorithm takes the encoded gRNA-DNA sequence in length 23 in the matrix as input. The convolution layer applies various filters of different sizes to the input matrix.

The next layer performs batch normalization to the output of convolution layer to boost learning and prevent over-fitting.

The last layer (pooling layer) further filters the normalized data from the previous layer. The output of this layer is then passed through multiple layers of deep learning neural network.

The last layer of this network passes the result to the stop layer that will predict whether the input is off-target or on-target.

Conclusion

In conclusion, the combination of CRISPR and AI has led to a revolutionary era in biotechnology. The coupling of the precision of CRISPR in genetic modification and AI in drug development has resulted in a groundbreaking development in drug discovery.

This showcases the transformative potential of this dynamic collaboration.

References:

https://sci-hub.se/https://doi.org/10.1093/bfgp/elaa001

https://link.springer.com/article/10.1007/s11030-021-10217-3

https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/computer-aided-drug-design

https://link.springer.com/article/10.1186/s12967-022-03765-1#Sec14

https://www.sciencedirect.com/science/article/pii/B9780323911726000200?ref=pdf_download&fr=RR-2&rr=8464441bdb385ffa

Most asked questions

Can AI be more efficient than traditional methods in predicting diseases?

How AI can be used to decipher DNA?

Most searched queries

Machine learning

Genome

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Categories
Digi Res

IoT and Smart Devices: The Hidden Dangers

IoT

Written by Ekta Yadav on Digilah (Student Tech Research).

My Bio:

I am Ekta Yadav studying at the University of Mumbai, currently pursuing my final year of BTech. I closely relate to this article on IoT and the dangers of smart devices as these technologies have increasingly integrated into my daily life. 

While the convenience they offer is undeniable, the potential security vulnerabilities and data privacy concerns have always been on my mind. This article reinforces my commitment to staying informed and taking the necessary steps to safeguard my data and privacy in this interconnected world.

My research article:

The rapid proliferation of IoT, or the Internet of Things and smart devices has revolutionized our daily lives, introducing a new level of convenience and connectivity. 

However, it is essential to recognize the hidden dangers that come with these advancements.

From security vulnerabilities to potential privacy breaches, the impact of IoT can be far-reaching. In this article, we will explore the key risks associated with IoT and provide effective prevention measures to safeguard ourselves and our data.

One of the primary concerns surrounding IoT is the security vulnerabilities that exist within the devices and networks. IoT devices often lack robust security measures, making them attractive targets for cybercriminals. 

Weak passwords, outdated firmware, and insecure communication protocols can lead to unauthorized access, data breaches, and even device manipulation.

To mitigate security risks, it is crucial for manufacturers to prioritize security by implementing encryption, authentication mechanisms, and regular firmware updates.

Users should also play their part by practicing sound security hygiene, such as using unique and strong passwords, enabling two-factor authentication, and keeping their devices up to date with the latest security patches.

The proliferation of IoT devices has led to an unprecedented amount of data being generated and collected. This raises significant concerns about data privacy and the potential misuse or unauthorized access to personal information. 

IoT devices can gather sensitive data about individuals, including their location, behaviours, and preferences. Manufacturers and service providers must adopt transparent privacy policies and provide clear consent mechanisms to ensure users have control over their data.

Educating users about the risks and best practices for IoT security is crucial. Users should be aware of common threats like phishing attacks and understand the importance of updating their devices with the latest security patches. 

By raising awareness and promoting best practices, we empower users to protect themselves and their devices from potential cyber threats.

The lack of standardized protocols and interoperability among IoT devices poses challenges. Industry-wide collaboration is necessary to establish standardized protocols, promote interoperability, and enhance security across devices. 

Governments and organizations should encourage collaboration to drive the adoption of secure and interoperable standards, fostering seamless integration and improved security.

Governments can play a pivotal role in addressing IoT risks by implementing robust regulations. These regulations can set minimum security standards for IoT devices, ensuring manufacturers adhere to secure coding practices and prioritize user safety. 

Regular audits, certifications, and stringent enforcement can hold manufacturers accountable and promote a safer IoT ecosystem.

The potential consequences of IoT vulnerabilities can be profound.

 

 A compromised IoT device can not only lead to the loss of personal data but also be harnessed as part of a botnet for large-scale cyberattacks. 

Furthermore, privacy breaches can have long-lasting effects on individuals, eroding trust in technology. Recognizing these impacts reinforces the importance of preventive measures.

To address the challenges posed by IoT security, regulatory frameworks are necessary. Governments and regulatory bodies play a crucial role in establishing minimum security standards for IoT devices and holding manufacturers accountable for their products’ security. 

Regulations can ensure that devices undergo rigorous testing and meet specific security requirements before they are introduced into the market. 

Regular audits, certifications, and compliance checks can help identify vulnerabilities and enforce security practices.

Additionally, regulations can outline data protection and privacy requirements, ensuring that user data is handled responsibly and transparently.

As the IoT landscape continues to expand, it is essential to address the hidden dangers associated with these devices.

By implementing effective prevention measures, such as prioritizing security, safeguarding data privacy, promoting cybersecurity education, establishing interoperability standards, and implementing robust regulations, we can mitigate the risks and unlock the full potential of IoT. 

Let us embrace the benefits of this technology while remaining vigilant and proactive in protecting ourselves and our digital lives.

Most asked questions

What is Internet of Things(IoT)?

What is a phishing attack?

Most searched queries

Internet of Things

Cybersecurity

Cyber attacks

Categories
Mar Tech Web 3.0 Tech

EMB Gaming

Written by : Saurabh Bhatia on Digilah (Tech Thought Leadership)

Gaming is booming and is expected to keep growing as per World Economic Forum.

Exploding Gaming Industry in India

The gaming industry in India is experiencing explosive growth, expanding at an annual rate of approximately 38%. India is second only to China in terms of growth, with the industry projected to grow four-fold by 2027 and create at least 100,000 jobs each year. While the global gaming revenue is expected to expand at a CAGR of 9.64%, India is on track to grow at an impressive CAGR of 16.22%. The transformation from a simple pastime to a thriving business is nothing short of remarkable.

Challenges and Opportunities

Despite the rapid and lucrative growth of the gaming industry, the constant technological advancements make staying relevant a considerable challenge. Out of the 1000+ game development studios and agencies, only a few are keeping pace with the industry’s evolution.

Expand My Business: A Pioneer in Indian Gaming

Gurgaon-based Series-A funded tech service start-up, Expand My Business (EMB), is one such trailblazer in the Indian gaming industry. Our Game Development vertical is making an impact worldwide. EMB specializes in providing consultation and delivering cutting-edge tech and digital services across domains, positioning itself as the most proficient solution provider.

In just a year, EMB’s gaming division has become a significant contributor internally and has positively impacted its clients’ success. A clear distinction between iGaming and Core Game Development & Gamification has enabled them to penetrate gaming, e-sports, casual and hyper-casual gaming, and AR-VR-based games. The company has delivered more than 30+ game solutions across domains and tech stacks in FY 22-23, aiming to increase project volume tenfold in the current FY as they continue adding new gaming micro-level services to their arsenal.

EMB’s USP

Diversified Expertise and Innovation

EMB, the technical facilitator behind India’s digital gaming solutions, has strengthened its expertise in fantasy and digital card & board games. Making headlines for service expansion in niche domains like blockchain-based gaming, cloud-based gaming, immersive gaming, NFT-enabled gaming, and immersive gaming experiences.

Comprehensive Game Development Services

Not only is Expand My Business a one-stop solution for game development, but it is also an ideal destination for game design and strategic partnerships. The company fosters growth by cultivating high-revenue games and excels in:

Research

·       Guiding game development through studies and information gathering

Design

·       Storyboarding & Storytelling

·       Game Art & Animation

·       Concept Art

·       Casual Art

·       3D Assets

·       2D-3D Animation

·       UX/UI

·       Motion Design

·       Game VFX Services

Audio Production

·       Music & Audio Production

·       Sound Effects

·       Scores

·       Audio Assets

Deployment

·       Game Testing & QA

·       TRC Game Certification

·       Game Porting Services

·       Full Post Production Services

Marketing Services

·       Social Media

·       Digital Advertising

·       Influencer Marketing

·       PR

·       Content Marketing

Full-Game Development

·       Complete the game development process, start to finish

·       A final roadmap cycle

Resource Augmentation

·       Hiring additional resources as needed (artists, developers, designers, testers)

·       3D/2D Artists and Designers

·       Game Developers

·       Game Testers, QA

Legal Services

·       Intellectual property protection

·       Licensing and distribution agreements

·       Compliance with local and international regulations

·       Privacy policies

·       Contract drafting and negotiation

·       Dispute resolution

Future vision

To be a significant contributor to India’s GDP which is fueled by the gaming industry’s extraordinary growth and the comprehensive array of services offered and create a global gaming footprint.

As the world marvels at the gaming industry’s unparalleled growth, choosing the right tech and consulting partner is crucial.

Most asked questions

Which is the trendiest online game?

What is world economic forum?

Most searched queries

Augmentation

GDP

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