Multi-Agent Systems with Rollback Mechanisms

Dean Mai
artificial intelligence, software

Enterprise demand for AI today isn’t about slotting in isolated models or adding another conversational interface. It’s about navigating workflows that are inherently messy: supply chains that pivot on volatile data, financial transactions requiring instantaneous validation, or medical claims necessitating compliance with compounding regulations. In these high-stakes, high-complexity domains, agentic and multi-agent systems (MAS) offer a structured approach to these challenges with intelligence that scales beyond individual reasoning. Rather than enforcing top-down logic, MAS behave more like dynamic ecosystems. Agents coordinate, collaborate, sometimes compete, and learn from each other to unlock forms of system behavior that emerge from the bottom up. Autonomy is powerful, but it also creates new unique fragilities concerning system reliability and data consistency, particularly in the face of failures or errors.

Garbage Collection Tuning In Large-Scale Enterprise Applications

Dean Mai
artificial intelligence, software

Garbage collection (GC) is one of those topics that feels like a solved problem until you scale it up to the kind of systems that power banks, e-commerce, logistics firms, and cloud providers. For many enterprise systems, GC is an invisible component: a background process that “just works.” But under high-throughput, latency-sensitive conditions, it surfaces as a first-order performance constraint. The market for enterprise applications is shifting: everyone’s chasing low-latency, high-throughput workloads, and GC is quietly becoming a choke point that separates the winners from the laggards.

Specialization and Modularity in AI Architecture with Multi-Agent Systems

Dean Mai
artificial intelligence, software

The evolution from monolithic large language models (mono-LLMs) to multi-agent systems (MAS) reflects a practical shift in how AI can be structured to address the complexity of real-world tasks. Mono-LLMs, while impressive in their ability to process vast amounts of information, have inherent limitations when applied to dynamic environments like enterprise operations.

Adopting Function-as-a-Service (FaaS) for AI workflows

Dean Mai
artificial intelligence, software

Unstructured data encompasses a wide array of information types that do not conform to predefined data models or organized in traditional relational databases. This includes text documents, emails, social media posts, images, audio files, videos, and sensor data. The inherent lack of structure makes this data difficult to process using conventional methods, yet it often contains valuable insights that can drive innovation, improve decision-making, and enhance customer experiences.

Architectural Paradigms for Scalable Unstructured Data Processing in Enterprise

Dean Mai
artificial intelligence

Unstructured data encompasses a wide array of information types that do not conform to predefined data models or organized in traditional relational databases. This includes text documents, emails, social media posts, images, audio files, videos, and sensor data. The inherent lack of structure makes this data difficult to process using conventional methods, yet it often contains valuable insights that can drive innovation, improve decision-making, and enhance customer experiences.

AI roadmap for enterprise adoption

Dean Mai

While the advent of ChatGPT sparked tremendous excitement for AI’s transformative potential, practical implementation reveals that sophisticated enterprise adoption demands more than just large language models (LLMs). Leading organizations now recognize the importance of model diversity – integrating proprietary, third-party and task-specific models. This evolving multi-model approach creates massive potential for startups to develop foundational tools and drive the advancement of enterprise AI into the next era.

Edge computing and the Internet of Things: Investing in the future of autonomy

Dean Mai
artificial intelligence

One of the most ubiquitous technological advancements making its way into devices we use every single day is autonomy. Autonomous technology via the use of artificial intelligence (AI) and machine learning (ML) algorithms enables core functions without human interference. As the adoption of ML becomes more widespread, more businesses are using ML models to support mission-critical operational processes. This increasing reliance on ML has created a need for real-time capabilities to improve accuracy and reliability, as well as reduce the feedback loop.

The Rise of MSSPs and MDRs

Dean Mai
security

The cybersecurity industry is facing two major challenges: an increase in cybercrime and sophisticated attacks alongside a vast deficiency of cybersecurity practitioners to fill open positions. There are currently more than 4.7 million overall cybersecurity employees, with over 400,000 hired this year alone. Despite this hiring increase, recent data reveals a need for 3.4 million additional cybersecurity workers worldwide in order to effectively secure assets. Cybercrimes rose more than 600% over the last year, causing many organizations to increase their cybersecurity budgets with the goal of hiring even more security experts.

Federated machine learning as a distributed architecture for real-world implementations

Dean Mai
artificial intelligence

Present performance of machine learning systems—optimization of parameters, weights, biases—at least in part relies on large volumes of training data which, as any other competitive asset, is dispersed, distributed, or maintained by various R&D and business data owners, rather than being stored by a single central entity. Collaboratively training a machine learning (ML) model on such distributed data—federated learning, or FL—can result in a more accurate and robust model than any participant could train in isolation.

Intuitive physics and domain-specific perceptual causality in infants and AI

Dean Mai
cognitive psychology

More recently, cognitive psychology and artificial intelligence (AI) researchers have been motivated by the need to explore the concept of intuitive physics in infants’ object perception skills and understand whether further theoretical and practical applications in the field of artificial intelligence could be developed by linking intuitive physics’ approaches to the research area of AI—by building autonomous systems that learn and think like humans.

Virtual reality as a tool for stress inoculation training in Special Operations Forces

Dean Mai
vr & ar, cognitive psychology

Stress is a common factor in tactical fast-paced scenarios such as in firefighting, law enforcement, and military—especially among Special Operations Forces (SOF) units who are routinely required to operate outside the wire (i.e., in hostile enemy territory) in isolated, confined, and extreme (ICE) environments (albeit seldom such environment is long-duration by choice).

Speculating through design fiction

Dean Mai

Despite the significant technological progress in sustainable transportation, computer vision and urban network infrastructures, many key issues are yet to be answered about how these autonomous vehicles will be integrated on the roadways, how willingly people will adopt them as a part of their daily lives and what new types of human-centered design interaction will emerge as part of the built environment.

Qualia and artificial neuron bridges

Dean Mai
artificial intelligence

Is intelligence a prerequisite for experience, or only for expression of that experience? What if the occurrence of higher-order, self-reflexive states are not necessary and sufficient for consciousness? Although humans tend to believe that we perceive the true reality, the fact is that subjective image generated in our brains are far from being a truthful representation of real world.

Psychometric intelligence, coalition formation and domain-specific adaptation

Dean Mai
neuro

The remarkable intricacy of human general intelligence has so far left psychologists being unable to agree on its common definition. Learning from past experiences and adapting behavior accordingly have been vital for an organism in order to prevent its distinction or endangerment in a dynamic competing environment. The more phenotypically intelligent an organism is the faster it can learn to apply behavioral changes in order to survive and the more prone it is to produce more surviving offspring.

Improvisational intelligence as a domain-specific adaptation

Dean Mai
neuro

The human brain is remarkable in its complexity design. A myriad of constantly evolving, reciprocally sophisticated computational systems, engineered by natural selection to use information to adaptively regulate physiology, behavior and cognition. Our brain defines our humanity. Systematically, through multitudinous generations, both the human brain structure (hardware) and its neural algorithms (software) have been fine-tuned by evolution to enable us adapt better to environment.

Swarm intelligence: from natural to artificial systems

Dean Mai
artificial intelligence

Complexity is natively intervened within data: if an operation is decomposable into rudimentary steps whose number varies depending on data complexity, exploiting a data sequence as a whole (collective effort of colony members in the specific task), rather than a single data input, can conduce to a much faster result. By forming a closed-loop system among large populations of independent agents, the ‘Swarm’, high-level intelligence can emerge that essentially exceeds the capacity of the individual participants. The intelligence of the universe is social.

Legal personhood for artificial intelligences

Dean Mai
software, artificial intelligence, innovation

There are innumerable examples of other ways in which information technology has caused changes in the existing legislative structures. The law is naturally elastic, and can be expanded or amended to adapt to the new circumstances created by technological advancement. The continued development of artificial intelligence, however, may challenge the expansive character of the law because it presents an entirely novel situation.

Load More