How Collaboration Across Platforms Could Supercharge AI Performance

How Collaboration Across Platforms Could Supercharge AI Performance

Collaboration across platforms enables AI models to access a wider range of data and expertise, leading to faster learning and improved performance through diverse insights and resources. This synergy accelerates AI advancements across industries.

Artificial intelligence (AI) has been one of the central points of discussion across the world over the past year. It seems that news organizations publish more news about billion-dollar deals, multi-million dollar investments, and AI tools emerging in a new industry every day. In fact, 2023 has acted as an accelerator for AI technology, with AI deal size increasing by 48% YTD

Yet, each advancement in AI has been deeply individualistic, with multiple leading tech firms and start-ups focusing on fixing the same problems or developing similar technology. Instead of creating a streamlined system that draws upon all AI resources we have available to us, each company is racing to create the same thing. 

While these siloed forms of artificial intelligence still provide a powerful technological solution, the focus on competition and hidden development is stifling potential progress. When we look at other industries, one of the central methods to streamline progress has been collaboration across individuals, companies, or even whole democracies.

Forging A New Era Of Productivity

Across history, collaboration has been a central force for productivity, enabling people to achieve more compared to individual pursuits. Famous partnerships, like James Watson and Francis Crick finding the structure of the DNA molecule, or Larry Page and Sergey Brin developing the internet, are central developments that could not have been done alone.

Collaboration is humanity’s greatest strength, allowing us to come together, share ideas, and develop toward common goals. In almost every area of life, be it in science, philosophy, art, literature, or any major field, collaboration appeared in connection with major breakthroughs. 

Even in a more microcosmic sense, people rarely work alone in business settings. Alongside having direct reports, people are put into larger teams and departments to surround themselves with other people. By forging these environments where people can actively work together, businesses are able to get more from their employees and strive toward productivity.

During the COVID pandemic, when many people were unable to see their coworkers face-to-face, there was a sudden surge in collaborative technologies. In short, platforms like Zoom allowed people to regain a sense of collaboration that they had lost without in-person contact. By bouncing ideas off one another, combining resources, and putting two or more brains on one task, we can go further than individual pursuits. 

Although human collaboration has brought people together to work on a common good, the production of new technologies does not display the same focus on this core trait. Collaboration has allowed for huge leaps in AI, yet the end products are siloed.

Artificial intelligence networks are extremely powerful, yet currently function in a vacuum.

A Movement Toward Collaborative AI

A 2023 study by PwC revealed that the demand for AI computation is growing faster than the general supply. As new industries discover the utility of AI tools, they seek to incorporate this technology into their work stacks, requiring increasingly specific functions. Unable to keep up with global demand, companies that currently focus on creating and distributing AI tools need to turn to a new solution.

In order to overcome this lack of availability, HyperCycle has proposed a scalable computing solution. By using neural-based architecture, HyperCycle is proposing a component that allows distinct AI networks to communicate with one another. Instead of many separate AI systems working on disparate functions, this would forge an internet of AI networks.

This highly scalable solution comes in the form of decentralized node software, which AI developers can download and embed into their models. Across a blend of AI, virtual machines, and decentralized technology, this provides a simple yet effective solution that facilitates connections between AI networks. 

As we move away from the central idea that AI networks have to work in silos, we will be able to reach a new era of utility. This component provides a similar advancement that was made when the internet became widely available. Instead of individual platforms, we will reach toward a corporate AI network that works on a modular level.

An example of how this could pose as useful comes when focusing on what specific AI companies currently offer. Zoox is a California-based AI mobility solution that uses artificial intelligence to help with pathfinding and safety protocols in self-driving cars. While operational, Zoox could refine its services by integrating with already established AI companies. 

A company like Drata, which uses AI to collect information for compliance and security reasons, could perfectly integrate with Zoox. Instead of spending years in development, linking these AI tools together would provide each with the other’s capabilities, streamlining progress and empowering AI companies to reach the next level of development even faster.

With HyperCycle’s software, these AI networks could communicate, using one another’s resources to access a highly scalable and simple solution. When done at scale, the connection between several AI networks has the potential to scale exponentially, with collaboration forging a pathway toward progress.

Final Thoughts

If collaboration is the leading quality that has allowed us to reach dazzling peaks in technology, art, science, and business, then embedding the ability to collaborate across AI networks will unlock a new era of utility. While AI tools are already extremely impressive and useful, their inability to communicate with other networks severs them short of their full potential.

The architecture that HyperCycle offers will help to scale AI as a computing solution, forging pathways for connection that allow different AI networks to collaborate. By pooling resources together and creating the internet of AI, we will create a global network of highly powerful tools and systems.

Collaboration in AI can further streamline development, increase research potential, and drive utility for all users around the world. 

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