AI R&D: Learning, Adapting and Growing

Anytime Artificial Intelligence (AI) is brought up in conversation, the mind can jump anywhere from Hal in 2001: A Space Odyssey to Jarvis in Iron Man. While it may seem outlandish to believe that advancement in AI is close to the movies, scientists and tech pioneers have created systems that are making that reality. Today, there are platforms that can perform facial, object and speech recognition using AI that learns, adapts and grows as datasets become larger. Star Shot Research is investing resources to contribute the AI development community.

Experimental AI for Developing Robust Systems

Star Shot Research is pushing the boundaries of existing AI development to further advance how systems can learn, adapt and grow on their own for the benefit of humanity. Our pursuits fall into three main categories: Embedded Systems, Cognitive Science and Assistive Technologies.

Embedded Systems

Star Shot Research is seeking to understand and develop computationally intelligent embedded systems. These systems can work together or independently with increased perception, adaptability, reconfigurability, resiliency, self-optimization, and autonomy for the purposes of sharing information across non-conventional network infrastructure.

This solution space involves embedded on-board processing, non-conventional neuromorphic systems and applications, tools to increase the productivity of developing applications, methods and architectures.

Star Shot Research has identified benefits of investing in this area of study to be a reduction in stakeholder decision latencies/response time, decrease system costs and system-of-system development times.

Cognitive Science

Star Shot Research is exploring how to evolve learning theories of mind-based complex representations and computational procedures. Designing the integration and interaction of humanistic sensors in a neural network requires a method for how to evaluate and understand a multi-modal world. With this method, Star Shot Research can identify and define the information paths for problem solving and decision making. For each path identified, Star Shot Research uses the below Learning Theory Questions to shape AI cognitive algorithms:

  1. What is the appropriate loss function (the error between the output and given target value)?
  2. What are the admissible set of functions?
  3. What is the appropriate constructed structure of an admissible set?
  4. What are the minimal number of functions that can be used on a constructed structure?

It is important to note that these principles are wholly dependent on using a set of functions that are finite. Star Shot Research’s vision is to identify a variety of methods that can account for infinite sets of functions.

Assistive Technologies

Star Shot Research has been investigating new methods and techniques of applying AI systems to improve the assistive technologies for special needs and disabled individuals. Our Analysis of Alternatives have included technologies such as robotic exoskeletons, “smart” prosthetic limbs, and brain-machine interfaces. While this AI vertical is relatively new to Star Shot Research, there has been promising results so far in this research.

Applied AI for Operational Advancement

Star Shot Research is invested in the AI domain to push the operational advancement of our customers. Now with the availability of existing AI frameworks, AI Application Programming Interfaces (APIs), and AI platform services, the applications are endless for any organization. We aim to help bridge the gap in understanding how to leverage these existing technologies for your organization’s goals. Our team is experienced in defining the operational problem(s), developing algorithms that shape the solution space and designing the interface to available Commercial-Off-the-Shelf (COTS) AI.

Star Shot Research AI R&D

Since 1956 scientists have been discussing how language simulation, neural nets and complexity theory can be utilized for the development of AI. Star Shot Research is proud to be continuing this area of study. We are committed to understanding the theory of learning and other features of intelligence so that it can be precisely described for a machine to be able to use as a basis of self-reliance. With this understanding, Star Shot Research’s objective is to develop AI capabilities to enhance our world.