Deep Reinforcement Learning (DRL) is one of the most popular and advanced AI algorithms in use today. These systems combine the effectiveness of Machine Learning protocols with the programmability of Deep Neural Networks to produce astonishing results. Today, DRL AI algorithms have helped solve some of science’s oldest mysteries and unlock patterns and solutions that humans easily overlooked.
Recently, a group of researchers introduced a new concept to the market that could help bring DRL algorithms to more businesses, researchers, and even everyday users in the future. The system uses a blockchain market and incentives to streamline adoption. Here’s everything you need to know about how blockchain is set to play an increasingly important role in the DRL community.
What Makes DRL AI Different?
One of the main advantages of DRL AI algorithms is that they can transfer their knowledge from scenarios over to new situations. This capability makes them ideally suited for self-learning in new environments based on model data. DRL AI systems can examine a new situation and create complex answers that can support everything from driving autonomous vehicles to locating cancer cells in your liver or kidney.
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Source – Analyticsvidhya
- DRL AI systems are among the most advanced and have become the go-to option for the interpretation and deciphering of high-dimensional raw input data. This info can come in the form of images, videos, and more. As such, there has been a strong pivot towards utilizing and expanding this tech across industries. Some analysts believe that DRL AI could one-day power general AI systems that can be sent to learn new tasks from people before adopting an autonomous solution.
Another reason why researchers are fond of DRL AI systems is that they offer unmatched scalability. Scalability concerns have limited AI technology for years. DRL systems can scale to meet the demands of the masses. Additionally, they can input data from the scaled-up network to improve performance further.
DRL AI Drawbacks
DRL AI algorithms are among the most difficult to program and deploy. Part of the reason why it is so difficult to create these AI algorithms is the sheer number of variables and data required for them to operate correctly. For one, the system needs to take into account various environmental interactions, parameters, situations, timing, and past programming. All of these processes occur in milliseconds to provide a smooth AI experience for users.
Creating an advanced and responsive DRL algo can be an expensive task requiring expert assistance. These algorithms need to be first created, data imported and tested, and then fine-tuned to improve results. This approach takes time and funding, adding to the overall requirements of the tech.
How can the average Person Access DRL AI tech
These requirements make DRL algorithms one of the most expensive types of AI systems to model. The cost of AI programmers, testing, data gathering, and modeling leave most users far from the starting line in terms of implementing this tech into their models.
How Blockchain Technology Could Help Drive DRL Evolution Moving Forward
There are several ways in which blockchain technology could help to improve access to DRL tech moving forward. History has shown that blockchain protocols can help to drive adoption and expand access to services if used properly. Already, the AI Machine Learning sector has seen numerous AI-powered integrations that have helped drive adoption and innovation.
Machine Learning as a Service (MLaaS) models are in place that enable users to create, trade, shop, and integrate advanced ML algorithms across a secure blockchain ecosystem. In most instances, this approach includes a marketplace that incentivizes users to develop and offer AI models and other services to the community.
Notably, this model has proven to be a great way to open the door for new users and eliminate many of the roadblocks to adoption. As such, it’s no surprise that a similar approach is now on the table for DRL AI systems. The DRL as a Service (DRaaS) framework helps to solve some of the biggest issues with DRL and could help usher in a new age of AI accessibility, innovation, and capabilities.
DRLaaS
The DRLaaS as a framework approach allows companies to utilize DRL services without the need to fully invest in an in-house AI algorithm. This approach can significantly lower costs for businesses and researchers who seek tolerable AI models in their operations. Users can select what features they need and when they need them. This strategy reduces unnecessary costs and ensures that the community offers incentives to developers.
How the DRLaaS Framework Works
The researchers began by selecting a blockchain that could scale, provided high programmability, and had a proven track record of success. After examining multiple options, the team decided it was best to create the DRLaaS framework on the Consortium Blockchain. Notably, Consortium is a privileged permission blockchain that operates like a private network but under the governance of a consortium.
Consortium leverages a unique Proof-of-Authority algorithm that integrates vetted nodes to ensure scalability, increase performance, and provide quality. Notably, Consortium integrates a properties file storage system called the InterPlanetary File System (IPFS). This protocol is highly effective at preventing tampering and enables users to leverage smart contracts to request AI tasks.
Users of the new system can put forth DRL module requests to the community. These requests can be answered by developers who have already created or have the ability to develop the corresponding DRL algorithm. They receive compensation for their efforts in the form of reward tokens. This incentivization system has proven effective at helping to build AI communities and was crucial in driving the ML AI sector forward.
Why the DRLaas Option is a Smart Solution – DRL
One of the main reasons why the DRLaaS option is sure to see more support is due to the fact that it’s so expensive to create DRL algorithms. Hardware costs for these systems can be insanely high. To put programming a DRL AI system into perspective, the researchers cited the DOTA DRL AI algorithm recently put forth by a team of researchers.
Programming the DOTA algorithm took 51 thousand CPUs and 512 GPUs. For the average researcher, user, or business, there is no way for them to access this much computational power affordably. Purchasing this equipment isn’t the only financial roadblock. There is still a need to source data, program the AI systems, and other tasks associated with implementation. In the end, DRLaaS may be the only way for most firms to access these powerful tools securely.
Now, businesses can leverage the top tier of expertise and domain knowledge to create customized AI solutions to meet their needs. The DRLaaS option enables these firms to utilize AI systems when needed, reducing unnecessary exposure and costs significantly.
Researchers
The researchers behind the study include a variety of engineers, analyst developers, and more. Specifically, the study credits authors Ahmed Alagha, Hadi Otrok, Shakti Singh, Rabeb Mizouni, and Jamal Bentaharas as the main authors. Now, the team seeks to expand its operations and implementation to the market, driving more access for the masses.
DRL Technology Applications
There are many DRL AI applications in operation today. these systems play vital roles in autonomous vehicles, navigation, healthcare, gaming, and more. Here are a few prime applications for the DRL system that could help to improve current options significantly.
DRL Healthcare
DRL technology continues to play a vital role in next-generation healthcare practices. These advanced AI systems have proven to be extremely good at determining patterns across massive data models, enabling researchers to see previously unnoticed correlations. Already there are AI systems that can help determine your organ health, scan brain wave activity, and locate damaged cells.
DRL Military
The use of DRL tech in the military is on the rise. This technology has been put to use in future warfare systems including the growing number of drone swarms. Drone swarms are a method of attacking an enemy using massive amounts of autonomous drones. This approach is meant to overwhelm air defenses.
DRL AI systems also play a vital role in accessing targets. Today’s military surveillance systems integrate DRL to keep track of the slightest changes in the surveilled area, revealing hidden weapons and potential targets more effectively. In the future, there will be complete AI kill chains that eliminate the use of human approval when determining a target.
Similar Approaches Taken by Blockchain Projects
Several blockchain projects have used similar crowdsourcing methods to drive innovation in industries. At first, this style of blockchain integration was used for cloud computing services. Then it expanded into data sharing and computational exchanges. Today, you can find crowdsourced marketplaces for advanced computations including AI across the market. Here are a few prime examples.
Golem (GLM)
Golem is an AI-focused decentralized computing marketplace that enables users to secure rewards in the form of tokens for sharing their unused computational power. The protocol enables anyone to gain access to massive computing power without the need to purchase expensive hardware. At the core of Golem’s approach is a Decentralized marketplace. Here, users can secure GLM tokens that provide CPU power to developers, researchers, and users.
Notably, Golem enables users to take large computational tasks and delegate them to smaller solutions that are spread out amongst the community of active users. This strategy lowers costs and improves efficiency and decentralization. As such, Golem is a popular project that was among the first to provide computation as a service using blockchain systems. Today, it is recognized as a pioneering platform that has secured a strong user base.
Render (RENDER)
Render is an industrialized AI-powered blockchain-based system that provides near-limitless computational power to AI developers. The protocol originally focused on using its massive decentralized computational power to help develop and process the massive amount of high-def video and effects needed for today’s gaming, research, and entertainment.
Render empowers the scientific, entertainment, and research communities via its unique and proven approach. The platform utilizes Ethereum for validation while integrating the OctaneRender protocol to improve 3d processing performance. Those providing CPU power to the community earn RENDER tokens based on the amount of computational power they provide.
Publicly Traded Company Developing Innovative AI Solutions
Multiple AI firms are developing AI that will one day interact with your life. In many instances, you might think of massive firms like Microsoft (MSFT -2.32%) when discussing AI development. However, there are several publicly traded AI options worth checking out. Here’s one example of a firm utilizing AI to provide much-needed services to the market.
1. BrainChip (BRCHF)
Brainchip is an innovative software and hardware provider for the AI health industry. The platform introduces a neuromorphic computing model that was designed to represent how neurons work in the brain. This strategy has proven to be energy efficient and provide high performance.
BrainChip is a pioneer in the AI field and is leading the way in the field of AIoT (Artificial Intelligence of Things) networks. This concept combines the low cost and availability of IoT (Internet of Things) smart devices, with the capability of advanced AI algorithms. The results are a highly capable system that can monitor operations, discover problems before they occur, and present viable solutions promptly.
DRL Request Crowdsourcing – The Way to Go
The introduction of a DRLaaS option will help to drive integration across the market. This approach has a proven track record of helping to expand user understanding and access in other AI sectors. As such, it’s perfect to apply to the DRL market. You can expect to see a lot more AI integration as DRL services become available to the masses moving forward.
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