Blockchain + AI: Decentralized Machine Learning Platforms Changing the Game

Image: Cottonbro studio / Pexels

The convergence of blockchain technology and artificial intelligence, as demonstrated by the meteoric rise of meme tokens like Pepe Coin, is ushering in a new era of decentralized computing that promises to democratize access to advanced machine learning capabilities. This revolutionary combination is not just changing how AI models are trained and deployed – it's fundamentally transforming the economic landscape of technological innovation.

As we examine this technological synergy more closely, it becomes clear how decentralized systems are reshaping the traditional power structures in AI development.

The Foundation of Decentralized AI

Tech giants with vast computing resources and proprietary datasets have long dominated traditional AI development. Companies like Google, Amazon, and Microsoft have maintained a virtual monopoly on advanced AI capabilities, creating a significant barrier to entry for smaller players and independent researchers. However, the introduction of blockchain technology and cryptocurrency incentives is rapidly changing this paradigm.

Decentralized machine learning platforms leverage blockchain's distributed nature to create vast networks of computing power. These networks function like a global supercomputer, where participants can contribute their unused computing resources in exchange for cryptocurrency tokens. This model not only makes AI development more accessible but also more efficient and cost-effective.

The Economic Incentive Model

The brilliance of combining cryptocurrency with decentralized AI lies in its economic incentive structure. Participants in these networks are rewarded with native tokens for contributing resources, whether that's computing power, data, or AI models. This creates a positive feedback loop where:

1. Contributors are incentivized to provide more resources to the network
2. Developers gain access to affordable computing power and datasets
3. Users benefit from increasingly sophisticated AI services
4. The overall ecosystem grows in value as more participants join

Network participants can earn tokens by providing various resources:

• Computing power for training AI models
• Storage space for distributed datasets
• High-quality data for training purposes
• Validated AI models ready for deployment
• Verification services for ensuring data quality

Technical Infrastructure and Implementation

The technical architecture of these platforms typically consists of several key components. Smart contracts manage the distribution of computational tasks and token rewards, ensuring transparent and automatic execution of agreements between parties. Distributed storage solutions like IPFS (InterPlanetary File System) handle the massive datasets required for AI training, while blockchain networks maintain an immutable record of transactions and model provenance.

Federated learning techniques are often employed to train AI models across distributed networks without centralizing sensitive data. This approach allows multiple parties to contribute to model development while maintaining data privacy and reducing bandwidth requirements.

Challenges and Future Developments

Despite the promising potential, decentralized AI platforms face several challenges that need to be addressed for widespread adoption:

• Security concerns remain paramount, as distributed networks must protect against malicious actors while maintaining performance. Platform developers are implementing sophisticated verification mechanisms and reputation systems to ensure network integrity.
• Scalability presents another significant challenge, as blockchain networks must handle the massive computational requirements of AI training while maintaining reasonable transaction speeds and costs. Layer-2 solutions and improved consensus mechanisms are being developed to address these limitations.
• The regulatory landscape around both cryptocurrency and AI remains uncertain in many jurisdictions, potentially affecting platform development and adoption. Industry leaders are actively engaging with regulators to establish clear frameworks that protect users while fostering innovation.

Impact on the AI Industry

The rise of decentralized machine learning platforms is democratizing access to AI technology in unprecedented ways. Small businesses and independent researchers can now access computing resources and datasets that were previously available only to large corporations. This democratization is leading to the following:

• Increased diversity in AI development and applications
• More rapid innovation through collaborative development
• Lower barriers to entry for AI startups
• Greater competition in the AI services market
• Improved transparency in AI model development

Looking Ahead

The future of decentralized AI platforms appears bright, with several emerging trends likely to shape the industry:

• Edge computing integration will enable more efficient processing of AI tasks by leveraging distributed computing resources closer to data sources. This will reduce latency and improve real-time applications.
• Cross-chain interoperability will allow AI resources to be shared across different blockchain networks, creating a more connected and efficient ecosystem. This will enable greater flexibility in resource allocation and token utilization.

As these platforms mature, we can expect to see increasingly sophisticated applications in fields such as healthcare, finance, and scientific research. The combination of blockchain's security and transparency with AI's analytical capabilities creates possibilities for solving complex problems in ways previously not possible.

The convergence of blockchain and AI through decentralized machine learning platforms represents a significant shift in how we develop and deploy artificial intelligence. By democratizing access to AI resources and creating economic incentives for participation, these platforms are fostering a new era of collaborative innovation that promises to accelerate technological progress while making it more accessible to all.

by Web Desk via Digital Information World

Who’s Funding Open Source? The $1.7B Question Finally Answered

We can say undoubtedly that open source is an engine powered by humans. Past research shared more than one means for measuring value, investments, costs, and the aggregate for open source.

However, how exactly open source gets funded or where the investment comes from remains an uncharted subject, leaving plenty of queries in people’s minds. It’s a matter of limited visibility and comprehension.

Thanks to insights from GitHub and Linux Foundation who collaborated with researchers from LISH to get more insights on this aspect today. The study’s main goal had to do with measuring organization-driven investments with great interest and how companies invest in open-source software.

Such insights are used to put forward recommendations for better monitoring and investments and to design a more sustainable and very impactful open-source industry. Now the audience entails those from OSPOs, leads in the engineering sector, and C-Level executives.

All the emails for responses were sent to mailing lists at GitHub and Linux Foundation. Other partner foundations such as TODO Group were a part of this and replies from nearly 501 companies arose around the globe.

After diving in, we saw many companies’ funding behaviors and possible misalignments. This includes changes for improvements. In that report, we saw the following findings:

Many firms have different categories for open source. Close to 44% have either an OSPO while 24% consume with 21% making contributions. 18% release projects and 16% influence them through leadership positions or roles for maintenance.

Most organizations don’t know how to make a contribution or where to make it. They lack clarity in terms of contributions. Meanwhile, the median responding group invests close to $520K of the yearly value to OSS.

Responding firms invest close to $1.7B in open source each year and that can go up to $7.7B throughout the whole open source sector each year. Interestingly, 86% of all the investments come from contribution labor through employees and contractors. They’re working to fund the firms while the other 14% direct the financial transactions.

Respondents invest nearly $162M in contractors which make up 57% of the community while 37% goes to foundations and 4% is directed to maintainers. The rest of the 1% heads on over to bounties. Security efforts are more focused on matters like bugs and maintenance while just 6% feel extensive security audits are necessary.

Image: DIW-Aigen

Read next: Blogs Dominate LLM Referrals, E-Commerce Struggles to Gain Traction
by Dr. Hura Anwar via Digital Information World

Blogs Dominate LLM Referrals, E-Commerce Struggles to Gain Traction

Since the release of AI tools like ChatGPT, there has been a noticeable shift in search behavior, with many users opting for AI platforms over traditional search engines. A study by Previsible, which analyzed over 30 websites, revealed that tools like Perplexity and ChatGPT are becoming significant alternatives to Google. This trend indicates that Google’s search dominance has plateaued, with users increasingly favoring AI-driven solutions for resolving their queries.

The study found that ChatGPT and Perplexity account for approximately 37% of referral traffic from AI language models, while CoPilot and Gemini contribute 12–14% each. The finance sector leads in LLM-driven traffic, capturing 84% of referrals, largely due to integrations offered by models like Perplexity that enable seamless access to financial data. Blog content dominates LLM referral traffic, receiving 77.35% of visits, followed by homepage traffic at 9.04%, news content at 8.23%, and guides at 2.35%. E-commerce, however, faces challenges in capturing LLM traffic, with product pages accounting for less than 0.5% of referrals.

Although LLM-driven traffic currently represents only 0.25% of total traffic across the analyzed sectors, its growth is significant. ChatGPT referrals have surged by 900% in the events industry and by over 400% in the finance and e-commerce sectors during the 90-day study period. Growth has been consistent across most models, with the exception of CoPilot. If these trends continue, LLM-driven traffic could grow by approximately 200% every 90 days, potentially representing up to 20% of total website traffic within a year.

Previsible has introduced a free Looker Studio dashboard to help businesses monitor and analyze LLM-driven traffic. This tool integrates with Google Analytics 4 (GA4) to provide insights into traffic trends, popular landing pages, and content performance, enabling businesses to optimize their strategies effectively.

While AI tools are emerging as valuable sources of website traffic, businesses must ensure that pursuing AI-driven traffic does not negatively impact their sales. Although LLM-driven traffic remains a small fraction of overall activity, its rapid growth highlights substantial opportunities for businesses to adapt to evolving user behaviors and maximize the potential of AI-generated traffic.

Read next:

• Google Maps Are Nearly Impossible To Use In The West Bank, Shocking New Investigation Reveals

• Browser Extension Honey is Scamming Influencers Out of Their Money and They Are Not Even Aware of It

• Hyper Connectivity in Workplaces Leaves Employees Overwhelmed and Anxious
by Arooj Ahmed via Digital Information World

Hyper Connectivity in Workplaces Leaves Employees Overwhelmed and Anxious

According to a new study by University of Nottingham, it was found that digital work is increasing the anxiety and productivity in employees. The study talks about how digital workplaces are leading to psychological and physical problems in the employees because they need to be constantly online and in touch with technology. Where digital workplaces have provided endless benefits, they are also causing some issues and organizations need to address them if they need to keep their employees’ well being in check. The researchers named this new feeling as “Digital Workplace Technology Intensity (DWTI)” which talks about the emotional and mental efforts employees need to put in order to stay connected always and check their notifications for any work. They also have to deal with information overload as well as technical problems that can arise during work.

Digital workplaces allow flexible and collaborative work that employees can perform anywhere, but employees also feel overburdened with the constant workload which makes them feel more fatigued and put a strain on their mental health. There is a sense of pressure on employees who are working digitally because they need to always be updated, active and keep up with messages related to work. Even when they are on a vacation or enjoying leisure time, there is always a pressure on them to check their work emails in case they miss something.

The study did some in-depth interviews with 14 employees aged 27-60 from different industries who work in a digital workplace. The results of the interview showed five key characteristics that employees had to face. First one was hyper connectivity, which blurs the lines between their professional and personal lives as the employees feel that they have to be connected with their work all the time. Another thing was productivity anxiety as employees say that they fear being called unproductive when they are working remotely. There was also FOMO (fear of missing out) in professional settings as employees feel that they may miss important updates or messages if they are not connected all the time. There’s also “techno overwhelm” with many digital tools for communication and work, which can lead to technical difficulties anytime too.

Employees say that it is very difficult to leave work at work because there are a lot of tech tools and online connectivity options that you can work anytime and anywhere. The researchers also mentioned some suggestions for the employers like developing stronger workplace skills in employees, addressing issues related to tech platforms that overwhelm the employees, ensuring that employees are establishing boundaries between the personal and professional life and understanding their needs and preferences while they are digitally working.

Image: DIW-Aigen

Read next: Google Maps Are Nearly Impossible To Use In The West Bank, Shocking New Investigation Reveals
by Arooj Ahmed via Digital Information World

US Secretary of Commerce Says America Should Focus on Investments, Not Banning China’s Chipmaking Potential

American Secretary of Commerce has plenty to say about the country’s decision to ban and sanction China for its chip manufacturing progress. Gina Raimondo explained in her recent interview that the decision was unwise and could limit the nation from progressing.

Referring to the act as a fool’s errand, she further explained how the Biden Administration's behavior on this was startling. Biden’s CHIPS and Science Act promotes bans against a host of Chinese firms. If that was not enough, he urged war on the nation’s semiconductor industry by encouraging allies like Japan and the Netherlands to avoid buying advanced tech from that country.

But China did not sit back in silence. It chose to tell the world that it would come out stronger than before and if that meant spending more funds to strengthen its chip-making industry, then so be it.

This is why Sec Raimondo says the act was foolish as China is winning the tech race and is now even more powerful without US support. She feels this is what a true winner does. Despite the long list of export controls in America, most companies can still procure banned chips via the black market.

Innovation from China is not coming to a slow. So many firms and organizations are left with no choice but to pursue a long list of goals despite massive roadblocks coming through via American sanctions.

These were the statements made on the occasion of Trump's returning back into the White House for a second time next year. Some states certainly have the majority of Republican strongholds and they keep on benefitting.

Now, Trump feels the Chip Deal wasn’t too bad. Instead of providing direct funding, the new administration would prefer reducing taxes, enabling tariffs, and reducing regulations while unleashing American energy.

Due to this major uncertainty related to the CHIPS Act, so many subsidy applicants continue to rush and get the right funding in place. Trump always makes plans to strengthen permits for any firm that hopes to invest a billion dollars in America. This would come at the cost of getting some reviews and regulations waived.

It’s also the major reason why SoftBank wants to make $100B investments in the world of AI and other tech. Now even if the Secretary does agree about some rules holding the country back, Raimondo does admit that some firms cannot work with impunity.

What do you think about the Secretary’s comments on America adding sanctions on China and its semiconductor technology for chips?

Image: DIW-Aigen

Read next:

• How Can AI Help You Create Stunning Digital Designs?

• From AI Surges to Data Breaches: 2024 Internet Activity Reaches New Heights

• BMJ Study Finds Cognitive Weaknesses in AI Models, Challenging Human Replacement Claims
by Dr. Hura Anwar via Digital Information World

How Can AI Help You Create Stunning Digital Designs?

Artificial intelligence (AI) is revolutionizing the way we create digital designs, providing tools and capabilities that enhance creativity, streamline workflows, and open up new possibilities for design innovation. From automating repetitive tasks to generating unique visual elements, AI empowers designers to focus on what truly matters: crafting stunning, impactful designs. Here’s a closer look at how AI is transforming the digital design landscape.

Automating Routine Tasks

One of AI’s most valuable contributions to digital design is its ability to handle repetitive and time-consuming tasks. Tools like Adobe Photoshop’s Content-Aware Fill, Canva, and Figma’s auto-layout feature allow designers to automate processes such as resizing images, aligning elements, and generating layout variations.

For instance, AI algorithms can analyze an existing design and suggest optimal arrangements for components, such as text and imagery, across different devices and screen sizes. This automation not only saves time but also ensures consistency, enabling designers to maintain focus on more creative aspects of their work.

Offering Design Suggestions and Inspiration

AI tools are excellent collaborators during the ideation phase of design projects. Applications like Runway ML, Adobe Sensei, and even ChatGPT can suggest design elements, color palettes, and typography combinations based on project goals or industry trends. By leveraging these insights, designers can jumpstart their creative process and develop innovative concepts faster.

Additionally, AI can analyze vast amounts of data to predict design trends and user preferences. This ensures that your designs remain contemporary and relevant, increasing their impact in competitive markets.

The Role of AI-Driven Art Generators

AI-driven art generators, such as DALL·E, MidJourney, and Stable Diffusion, have taken the design world by storm, enabling the creation of unique and stunning visuals with minimal effort. These tools generate artwork based on text prompts, producing results that range from photorealistic to highly abstract. Platforms like cgdream.ai go beyond simple generation, offering features like consistent character rendering, style transfer, and 3D-to-image integration, making it a powerful tool for designers looking to innovate and save time.

For example, a designer working on a sci-fi-themed app might use an AI generator to create a surreal space landscape as part of the interface or promotional material. Similarly, a marketing team could use AI tools to create striking social media visuals tailored to specific campaigns.

These tools democratize access to high-quality visuals, making it easier for non-designers to create professional-grade content. However, they also serve as valuable aids for experienced designers, offering inspiration and saving time during the conceptualization and production stages.

Enhancing Personalization

AI excels at personalization, allowing designers to craft tailored experiences for diverse audiences. By analyzing user data, AI tools can suggest design elements that resonate with specific demographics. For example, AI-driven platforms like Adobe XD can generate interface designs optimized for different user profiles, ensuring an engaging and relevant experience.

Personalization is particularly valuable in e-commerce and marketing. AI can help designers create dynamic content that adjusts based on user behavior, such as personalized product recommendations or targeted promotional visuals.

Improving Collaboration

AI tools are also fostering better collaboration among design teams. Platforms like Figma and Sketch integrate AI features that streamline version control, suggest improvements, and ensure design consistency across projects. These capabilities are especially useful for remote teams, as they allow for real-time collaboration and seamless communication.

By automating project management tasks, such as tracking changes and managing assets, AI enables teams to focus on delivering high-quality designs without getting bogged down by administrative details.

Overcoming Creative Blocks

Creative blocks are a common challenge for designers, and AI can be a helpful ally in overcoming them. Tools like AI art generators, mood board creators, and even text-based assistants can spark ideas and provide fresh perspectives. By experimenting with AI-generated suggestions, designers can break out of creative ruts and explore new directions.

For example, an AI tool might suggest alternative color schemes or design layouts, prompting the designer to think outside the box and refine their vision. Tools like an AI skybox generator can also inspire immersive background designs, offering innovative solutions for creating dynamic environments in games or virtual spaces.

Image: DIW-Aigen
by Asim BN via Digital Information World

BMJ Study Finds Cognitive Weaknesses in AI Models, Challenging Human Replacement Claims

A new study published in The BMJ finds that AI chatbots are showing signs of cognitive impairment just like humans and this pattern is mostly seen in older models. The study is a great challenge to different studies and researches saying that AI is going to replace humans in medicine and teaching because now AI is showing signs of dementia and other cognitive problems like the ones seen in older humans. There are many studies that state that artificial intelligence will be able to do accurate medical diagnosis soon but this study says that it doesn't seem possible now that AI is showing cognitive decline.

Many AI models and LLMs like Google Gemini 1.0 and 1.5, OpenAI's ChatGPT-4 and 4o and Anthropic’s Claude 3.5 were assessed for the studies so the researchers could know which ones are showing cognitive decline. It was found that these AI models, especially the older ones, showed signs of cognitive impairment and performed the worst on tests which were done on them. Researchers used Montreal Cognitive Assessment (MoCA) on the models which are used to test early signs of dementia in older people. The maximum score of the test is 30 and includes questions related to language, attention, executive functions, memory and visuospatial skills, and a score above 26 is considered normal.

The LLMs were tested and were asked questions according to the test Gemini 1.0 scored the lowest with 16 out of 30. The highest score was achieved by GPT-4o (26 out of 30), followed by Claude and GPT-4 (25 out of 30). A practicing neurologist did all the tests and evaluated the results. The test showed that all AI models did the worst in visuospatial skills and executive tasks as well as a clock drawing test. Gemini models also didn't do well in delayed recall tasks where a sequence of five-word sentences is memorized and then recalled.

Most of the AI models which were assessed did well in language, naming, abstraction and attention. The researchers say that results of this test shows that AI models cannot perform perfectly in a clinical setting because they are showing some signs of weaknesses. So, this means that AI models aren't going to replace humans anytime soon because they are experiencing cognitive impairment and as long as this issue isn't solved, humans are going to take the lead. Researchers also suggested treating AI models with cognitive impairment the same way we treat human patients with similar issues.

Read next: Survey Predicts Global Warming, Economic Struggles, and AI Job Shifts in 2025

by Arooj Ahmed via Digital Information World