In this paper, we consider robotic tasks which require a desirable outcome to be achieved in the physical world that the robot is embedded in and interacting with. Accomplishing this objective requires designing a filter that maintains a useful representation of the physical world and a policy over the filter states. A filter is seen as the robot's perspective of the physical world based on limited sensing, memory, and computation and it is represented as a transition system over a space of information states. To this end, the interactions result from the coupling of an internal and an external system, a filter, and the physical world, respectively, through a sensor mapping and an information-feedback policy. Within this setup, we look for sufficient structures, that is, sufficient internal systems and sensors, for accomplishing a given task. We establish necessary and sufficient conditions for these structures to satisfy for information-feedback policies that can be defined over the states of an internal system to exist. We also show that under mild assumptions, minimal internal systems that can represent a particular plan/policy described over the action-observation histories exist and are unique. Finally, the results are applied to determine sufficient structures for distance-optimal navigation in a polygonal environment.

When designing a robot's internal system, one often makes assumptions about the structure of the intended environment of the robot. One may even assign meaning to various internal components of the robot in terms of expected environmental correlates. In this paper we want to make the distinction between robot's internal and external worlds clear-cut. Can the robot learn about its environment, relying only on internally available information, including the sensor data? Are there mathematical conditions on the internal robot system which can be internally verified and make the robot's internal system mirror the structure of the environment? We prove that sufficiency is such a mathematical principle, and mathematically describe the emergence of the robot's internal structure isomorphic or bisimulation equivalent to that of the environment. A connection to the free-energy principle is established, when sufficiency is interpreted as a limit case of surprise minimization. As such, we show that surprise minimization leads to having an internal model isomorphic to the environment. This also parallels the Good Regulator Principle which states that controlling a system sufficiently well means having a model of it. Unlike the mentioned theories, ours is discrete, and non-probabilistic.

This paper addresses the lower limits of encoding and processing the information acquired through interactions between an internal system (robot algorithms or software) and an external system (robot body and its environment) in terms of action and observation histories. Both are modeled as transition systems. We want to know the weakest internal system that is sufficient for achieving passive (filtering) and active (planning) tasks. We introduce the notion of an information transition system for the internal system which is a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. An information transition system is viewed as a filter and a policy or plan is viewed as a function that labels the states of this information transition system. Regardless of whether internal systems are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. We establish, in a general setting, that minimal information transition systems exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for modeling a system given input-output relations.

In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. Toward the quest to find the best policies, we establish in a general setting that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for feasible policies.

Building a data processing pipeline is one of the most common problem statements, for which you would have written small scripts or built a full-fledged scalable system based on the amount, and frequency of data. In this article, we will talk about the idea of event-driven scalability, the backbone that will be cost-optimized, and requires a minimum amount of development and operations. Why build an event-driven and scalable data processing pipeline? While working with startups or building a team project or a personal project, that requires a pipeline for data processing, there is always a constraint of cost. We will use a simple example for building a metadata extraction system for e-commerce products.

Why has there been such a sudden explosion of Machine Learning and Artificial Intelligence in security? The truth is that these technologies have been underpinning many security tools for years. Frankly, both tools are necessary precisely because there has been such a rapid increase in the number and complexity of attacks. These attacks carry a high cost for business. Recent studies predict that global annual cybercrime costs will grow from $3 trillion in 2015 to $6 trillion annually by 2021.

Today's cutting-edge research on cloud solutions for manufacturers highlights how artificial intelligence and machine learning have the potential to prevent downtime, improve safety, and reduce material waste. That's exciting for industry leaders, who are always looking for ways to refine these core efforts. At AWS's re:Invent conference in November, AWS CEO Andy Jassy dedicated a considerable portion of his keynote to machine learning, which hasn't happened before. His message was clear: businesses need to start developing an understanding of machine learning now, because this technology will be critical in the future. Even though machine learning is on the horizon, current ML and AI applications aren't the simplest or even the best-suited solution to the problems manufacturers face today.

Today's cutting-edge research on cloud solutions for manufacturers highlights how artificial intelligence and machine learning have the potential to prevent downtime, improve safety, and reduce material waste. That's exciting for industry leaders, who are always looking for ways to refine these core efforts. At AWS's re:Invent conference in November, AWS CEO Andy Jassy dedicated a considerable portion of his keynote to machine learning, which hasn't happened before. His message was clear: businesses need to start developing an understanding of machine learning now, because this technology will be critical in the future. Even though machine learning is on the horizon, current ML and AI applications aren't the simplest or even the best-suited solution to the problems manufacturers face today.

A strong AI model can automate daily tasks to free up resources, thus increasing the level of innovation and productivity. Currently, the emerging technology is used mostly by large enterprises through machine learning and predictive analytics. More companies are incorporating AI into their products to deliver a cutting-edge experience for their consumers. According to Salesforce, by 2020, 57% of business buyers will depend on companies to know what they need before they ask.This will require solid prediction capabilities. Soon AI will be the key to keeping customers.

Everything built by humans is meant to work within a proper environment. Herbert A. Simon, in his book The Sciences of the Artificial, defines things that are created or manipulated by humans as artificial systems. These systems are composed of an inner environment and they interact with the outer environment -- that is, the external world -- through an interface. Examples of artificial systems may include cars, a cotton farm, or a computer program. These systems, even biological ones, exist only to serve humans a predefined purpose.