Making the Most of AI and GPT-like Models: An examination of the industries most suitable for integration

Paulina Lewandowska

19 Jan 2023
Making the Most of AI and GPT-like Models: An examination of the industries most suitable for integration

Introduction

The way businesses work is being revolutionized by artificial intelligence (AI) and language models like GPT. AI is quickly becoming a crucial tool for businesses wanting to stay competitive in today's fast-paced economy, from automating monotonous processes to offering insightful analysis and predictions. In this article, we'll look at how companies are using GPT-like models and AI to boost productivity, boost efficiency, and boost revenue. We will look at the numerous uses of AI in the corporate world, from customer service to financial analysis. We'll also look at how GPT-like models are specifically employed in content generation and natural language processing to scale up communication and human-computer interaction.

AI implementation by sector

AI and models like GPT can be particularly beneficial in a variety of sectors, including but not limited to:

SectorApplications
Natural Language Processing (NLP)Language Translation, Text Summarization, Question Answering
Content CreationAutomated written content generation (news articles, product descriptions, social media posts)
BusinessAutomating tasks (customer service, sales, marketing), Financial forecasting and analysis
HealthcareMedical Diagnosis, Drug Discovery, Personalized Medicine
EducationPersonalized learning experience, Grading and providing feedback
Transport and logisticsSelf-driving cars, Supply chain management
RoboticsObject recognition, Navigation, Manipulation
GamingRealistic and engaging gameplay, New types of games

GPT-like Models in NLP and Content Creation: Automating Writing and Personalizing Content

GPT-like models have demonstrated substantial skills in the areas of Natural Language Processing (NLP) and content generation. Language translation, text summarization, and question answering are just a few of the natural language processing activities that are catered to by language models like GPT-3.

Automated writing is one of the most well-liked uses of GPT-like models in NLP and content generation. GPT-3 is capable of producing written content such as blog entries, product descriptions, and social media updates automatically. By automating the process of content production, GPT-3 can save enterprises a significant amount of time and resources because it can produce cohesive and grammatically sound text. This makes GPT-3 perfect for jobs like writing reports, email drafts, and chatbot scripts for customer assistance.

GPT-like models have uses in personalized content creation for clients in addition to automated authoring. By evaluating consumer data and creating content that is specific to the user's interests and preferences, GPT-3, for instance, can be used to provide personalized product suggestions or targeted advertising. This might aid companies in enhancing their marketing initiatives and raising client involvement.

Automation: Using AI to Simplify Business Processes 

Automating processes is one of the most obvious ways that organizations are utilizing AI. AI can handle a wide range of monotonous activities, from customer care chatbots to automated financial analysis, freeing up employees to concentrate on more worthwhile work. Simple customer care requests, like responding to frequently asked queries, can be handled by AI-powered chatbots, while more sophisticated systems can even manage complicated problems. Additionally, financial analysis tasks like fraud detection and trend prediction can be automated using machine learning models.

AI-Assist in Healthcare Revolution: Diagnosis and Treatment 

AI is being applied in a variety of ways in the healthcare sector to improve efficiency and precision. AI-powered systems, for instance, can help clinicians diagnose illnesses by reviewing medical images and making recommendations. This raises diagnostic precision while lowering the possibility of human error. Drug development is another area where AI is being used in healthcare. AI is capable of analyzing enormous amounts of data, including genetic data, to find potential novel treatments and medications. AI is also being used to develop individualized treatment regimens for patients, which take into consideration aspects like medical history, genetics, and other personal traits.

Intelligent tutoring and Personalized Learning with AI in Education 

Similar to how it is being used in business, AI is being used in education to help teachers grade assignments and give feedback to students. Based on a student's skills, shortcomings, and preferred learning style, AI is used to generate customised learning plans for them. Additionally, it contributes to the development of intelligent tutoring programs that support teachers by offering tailored feedback and assistance to students both within and outside of the classroom. AI is also being used to automate grading and assessment, which can assist save teachers time and increase the effectiveness of the educational system.

AI-Optimized Logistics and Transportation: Supply Chain Management to Self-Driving Cars 

AI is also being used by the transportation and logistics sector to boost productivity and cut expenses. The development of self-driving automobiles is one example of how AI can be used to enhance road safety and lower the frequency of accidents brought on by human mistake. Another area where AI may be used to optimize is supply chain management. This is done by forecasting demand, analyzing data, and making better decisions. AI can also improve fleet management by tracking the whereabouts and condition of vehicles, anticipating maintenance requirements, and increasing productivity.

Enhancing Capabilities and Real-world Functionality of AI-Powered Robotics 

AI is being applied in the field of robotics to enhance the capabilities and usefulness of robots. Robots are now able to recognize and interact with items in the real world thanks to AI, for instance in the field of object recognition. Another area where AI is applied to help robots autonomously navigate in challenging settings is navigation. AI can also be utilized to enhance the manipulation abilities of robots, allowing them to carry out a larger variety of activities, like grabbing and manipulating real-world objects. Robots are improving their ability to work in real-world settings and do tasks that were previously insurmountable thanks to the incorporation of AI.

From Realistic Gameplay to Game Development: AI in the Gaming Industry 

AI is also employed in the video game industry to build new game genres and more realistic and captivating gameplay. By offering more lifelike AI-controlled characters and environments, game AI is one application of AI that aims to improve gameplay realism and engagement.

AI can also be applied to the creation of new game mechanisms and game genres, such as games that adjust to the preferences and skill level of the player. AI can speed up the process of finding and fixing flaws in games, giving gamers a better gaming experience. Game testing can also be improved by AI. We may anticipate even more advancements in the application of AI to gaming as it continues to develop, pushing the limits of what is feasible in the gaming sector.

Conclusion

In conclusion, the incorporation of AI and models resembling the GPT into numerous industries and businesses is proving to be quite advantageous. These technologies are transforming how we conduct business, improve medical diagnosis in healthcare, personalize education, optimize logistics and transportation, and even transform the gaming sector. GPT-like models have enormous potential for content creation and natural language processing. Businesses should be aware of the advantages and potential of these technologies. The integration of AI and GPT-like models in several industries has a promising future.

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Aethir Tokenomics – Case Study

Kajetan Olas

22 Nov 2024
Aethir Tokenomics – Case Study

Authors of the contents are not affiliated to the reviewed project in any way and none of the information presented should be taken as financial advice.

In this article we analyze tokenomics of Aethir - a project providing on-demand cloud compute resources for the AI, Gaming, and virtualized compute sectors.
Aethir aims to aggregate enterprise-grade GPUs from multiple providers into a DePIN (Decentralized Physical Infrastructure Network). Its competitive edge comes from utlizing the GPUs for very specific use-cases, such as low-latency rendering for online games.
Due to decentralized nature of its infrastructure Aethir can meet the demands of online-gaming in any region. This is especially important for some gamer-abundant regions in Asia with underdeveloped cloud infrastructure that causes high latency ("lags").
We will analyze Aethir's tokenomics, give our opinion on what was done well, and provide specific recommendations on how to improve it.

Evaluation Summary

Aethir Tokenomics Structure

The total supply of ATH tokens is capped at 42 billion ATH. This fixed cap provides a predictable supply environment, and the complete emissions schedule is listed here. As of November 2024 there are approximately 5.2 Billion ATH in circulation. In a year from now (November 2025), the circulating supply will almost triple, and will amount to approximately 15 Billion ATH. By November 2028, today's circulating supply will be diluted by around 86%.

From an investor standpoint the rational decision would be to stake their tokens and hope for rewards that will balance the inflation. Currently the estimated APR for 3-year staking is 195% and for 4-year staking APR is 261%. The rewards are paid out weekly. Furthermore, stakers can expect to get additional rewards from partnered AI projects.

Staking Incentives

Rewards are calculated based on the staking duration and staked amount. These factors are equally important and they linearly influence weekly rewards. This means that someone who stakes 100 ATH for 2 weeks will have the same weekly rewards as someone who stakes 200 ATH for 1 week. This mechanism greatly emphasizes long-term holding. That's because holding a token makes sense only if you go for long-term staking. E.g. a whale staking $200k with 1 week lockup. will have the same weekly rewards as person staking $1k with 4 year lockup. Furthermore the ATH staking rewards are fixed and divided among stakers. Therefore Increase of user base is likely to come with decrease in rewards.
We believe the main weak-point of Aethirs staking is the lack of equivalency between rewards paid out to the users and value generated for the protocol as a result of staking.

Token Distribution

The token distribution of $ATH is well designed and comes with long vesting time-frames. 18-month cliff and 36-moths subsequent linear vesting is applied to team's allocation. This is higher than industry standard and is a sign of long-term commitment.

  • Checkers and Compute Providers: 50%
  • Ecosystem: 15%
  • Team: 12.5%
  • Investors: 11.5%
  • Airdrop: 6%
  • Advisors: 5%

Aethir's airdrop is divided into 3 phases to ensure that only loyal users get rewarded. This mechanism is very-well thought and we rate it highly. It fosters high community engagement within the first months of the project and sets the ground for potentially giving more-control to the DAO.

Governance and Community-Led Development

Aethir’s governance model promotes community-led decision-making in a very practical way. Instead of rushing with creation of a DAO for PR and marketing purposes Aethir is trying to make it the right way. They support projects building on their infrastructure and regularly share updates with their community in the most professional manner.

We believe Aethir would benefit from implementing reputation boosted voting. An example of such system is described here. The core assumption is to abandon the simplistic: 1 token = 1 vote and go towards: Votes = tokens * reputation_based_multiplication_factor.

In the attached example, reputation_based_multiplication_factor rises exponentially with the number of standard deviations above norm, with regard to user's rating. For compute compute providers at Aethir, user's rating could be replaced by provider's uptime.

Perspectives for the future

While it's important to analyze aspects such as supply-side tokenomics, or governance, we must keep in mind that 95% of project's success depends on demand-side. In this regard the outlook for Aethir may be very bright. The project declares $36M annual reccuring revenue. Revenue like this is very rare in the web3 space. Many projects are not able to generate any revenue after succesfull ICO event, due to lack fo product-market-fit.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

Quadratic Voting in Web3

Kajetan Olas

04 Dec 2024
Quadratic Voting in Web3

Decentralized systems are reshaping how we interact, conduct transactions, and govern online communities. As Web3 continues to advance, the necessity for effective and fair voting mechanisms becomes apparent. Traditional voting systems, such as the one-token-one-vote model, often fall short in capturing the intensity of individual preferences, which can result in centralization. Quadratic Voting (QV) addresses this challenge by enabling individuals to express not only their choices but also the strength of their preferences.

In QV, voters are allocated a budget of credits that they can spend to cast votes on various issues. The cost of casting multiple votes on a single issue increases quadratically, meaning that each additional vote costs more than the last. This system allows for a more precise expression of preferences, as individuals can invest more heavily in issues they care deeply about while conserving credits on matters of lesser importance.

Understanding Quadratic Voting

Quadratic Voting (QV) is a voting system designed to capture not only the choices of individuals but also the strength of their preferences. In most DAO voting mechanisms, each person typically has one vote per token, which limits the ability to express how strongly they feel about a particular matter. Furthermore, QV limits the power of whales and founding team who typically have large token allocations. These problems are adressed by making the cost of each additional vote increase quadratically.

In QV, each voter is given a budget of credits or tokens that they can spend to cast votes on various issues. The key principle is that the cost to cast n votes on a single issue is proportional to the square of n. This quadratic cost function ensures that while voters can express stronger preferences, doing so requires a disproportionately higher expenditure of their voting credits. This mechanism discourages voters from concentrating all their influence on a single issue unless they feel very strongly about it. In the context of DAOs, it means that large holders will have a hard-time pushing through with a proposal if they'll try to do it on their own.

Practical Example

Consider a voter who has been allocated 25 voting credits to spend on several proposals. The voter has varying degrees of interest in three proposals: Proposal A, Proposal B, and Proposal C.

  • Proposal A: High interest.
  • Proposal B: Moderate interest.
  • Proposal C: Low interest.

The voter might allocate their credits as follows:

Proposal A:

  • Votes cast: 3
  • Cost: 9 delegated tokens

Proposal B:

  • Votes cast: 2
  • Cost: 4 delegated tokens

Proposal C:

  • Votes cast: 1
  • Cost: 1 delegated token

Total delegated tokens: 14
Remaining tokens: 11

With the remaining tokens, the voter can choose to allocate additional votes to the proposals based on their preferences or save for future proposals. If they feel particularly strong about Proposal A, they might decide to cast one more vote:

Additional vote on Proposal A:

  • New total votes: 4
  • New cost: 16 delegated tokens
  • Additional cost: 16−9 = 7 delegated tokens

Updated total delegated tokens: 14+7 = 21

Updated remaining tokens: 25−21 = 425 - 21 = 4

This additional vote on Proposal A costs 7 credits, significantly more than the previous vote, illustrating how the quadratic cost discourages excessive influence on a single issue without strong conviction.

Benefits of Implementing Quadratic Voting

Key Characteristics of the Quadratic Cost Function

  • Marginal Cost Increases Linearly: The marginal cost of each additional vote increases linearly. The cost difference between casting n and n−1 votes is 2n−1.
  • Total Cost Increases Quadratically: The total cost to cast multiple votes rises steeply, discouraging voters from concentrating too many votes on a single issue without significant reason.
  • Promotes Egalitarian Voting: Small voters are encouraged to participate, because relatively they have a much higher impact.

Advantages Over Traditional Voting Systems

Quadratic Voting offers several benefits compared to traditional one-person-one-vote systems:

  • Captures Preference Intensity: By allowing voters to express how strongly they feel about an issue, QV leads to outcomes that better reflect the collective welfare.
  • Reduces Majority Domination: The quadratic cost makes it costly for majority groups to overpower minority interests on every issue.
  • Encourages Honest Voting: Voters are incentivized to allocate votes in proportion to their true preferences, reducing manipulation.

By understanding the foundation of Quadratic Voting, stakeholders in Web3 communities can appreciate how this system supports more representative governance.

Conclusion

Quadratic voting is a novel voting system that may be used within DAOs to foster decentralization. The key idea is to make the cost of voting on a certain issue increase quadratically. The leading player that makes use of this mechanism is Optimism. If you're pondering about the design of your DAO, we highly recommend taking a look at their research on quadratic funding.

If you're looking to create a robust governance model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure that your DAO will stand out as a beacon of innovation and resilience in the long term.