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The Role of Symbolic AI and Machine Learning in Robotics


Robotics is a multi-disciplinary field in computer science dedicated to the design and manufacture of robots, with applications in industries such as manufacturing, space exploration and defence. While the field has existed for over 50 years, recent advances such as the Spot and Atlas robots from Boston Dynamics are truly capturing the public's imagination as science fiction becomes reality. Traditionally, robotics has relied on machine learning/deep learning techniques such as object recognition. While this has led to huge advancements, the next frontier in robotics is to enable robots to operate in the real world autonomously, with as little human interaction as possible. Such autonomous robots differ to non-autonomous ones as they operate in an open world, with undefined rules, uncertain real-world observations, and an environment -- the real world -- which is constantly changing.

Does cognitive computing offer the next wave of analytics beyond data science?


"AI" may be a hot buzzword – and a global market expected to grow to nearly $310 billion by 2026 – but what exactly does artificial intelligence mean? The definition of AI can be a willowy one to pin down because its application is so broad and ranging in degree of complexity, scope, algorithmic underpinnings and methodologies used. For these reasons, there is an increased call for a more advanced, specific definition of AI beyond "the simulation of human intelligence processes by machines." Some consider the AI next step – and ultimate evolution – to be cognitive computing. "It's such a vast amorphic term that AI has come to mean right now," said Stephen DeAngelis, founder and CEO of Enterra Solutions.

2022 Trends in Intelligent Bots: Knowledge Worker Empowerment - insideBIGDATA


Whether in the form of Robotic Process Automation, chatbots, or some other type of digital assistants, the presence of intelligent bots is substantially increasing across the data ecosystem … in more ways than one. The diversification of the number of tasks these bots can perform is multiplying, as is the intrinsic complexity of those jobs, which unambiguously benefits knowledge workers worldwide. Whether dynamically engaging in natural language interactions with contact center agents, for example, or issuing and answering queries from a certified knowledge base, intelligent bots are integral for not only automating these data exchanges, but also implementing the ensuing action required to complete workflows. "Over the next one to two years we'll see tens of thousands more knowledge workers deploy digital assistants to reduce complexity, achieve error-free work, help their customers by drastically reducing their'on-hold' times and, most importantly, eliminate the frustration that arises from performing repetitive, manual tasks," presaged Automation Anywhere CTO Prince Kohli. These capabilities, of course, are naturally augmented by coupling intelligent bots with the sundry of Artificial Intelligence manifestations that are more pervasive today than they ever were before.

A Logic-Based Explanation Generation Framework for Classical and Hybrid Planning Problems

Journal of Artificial Intelligence Research

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.

3 Ways to Solve Your AI FOMO Before it Hurts Your Business


The pandemic has accelerated the onset of new technologies across industries. With artificial intelligence (AI) expected to grow by 21%, reaching $62 billion in 2022, it's no wonder nearly half of CIOs said they've either already deployed AI or plan to add it to their tech stacks in the next year. And with record-high numbers of Americans leaving their jobs, AI has moved from a "nice-to-have" technology to an essential way to optimize your teams' work and keep everyone feeling productive. The question for most businesses is no longer whether to adopt AI -- it's how to best integrate it into processes that scale with the company. Even though companies have spent the last two years implementing new technologies, often under tight deadlines and without a clear plan of what will come next, the process can still feel intimidating.

Pinaki Laskar on LinkedIn: #machinelearning #artificialintelligence #algorithms


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How much might cost Real AI Model? Encyclopedic Intelligent Systems has developed the first real model of the Real/Causal AI, including the following elements of its Universal Intelligent Platform, I-World: Machine World Model; Master Algorithm, Causal.World; World Data Framework, World.Data; Global Knowledge Base, World.Net; Domain Knowledge Base, Domain.Net; The Development has reached a stage of a proof of principle, concept and mechanism in which the best AI technology stack of hardware, software and dataware is constructed and tested to explore and demonstrate the feasibility of the Real/Causal AI Model. It creates a world-data mapping of all possible entities, their relationships and behaviors, binding causes and effects. To build truly AI machines of infinitely powerful digital intelligence, we need to encode, program or teach them what the world is with all its complex cause-effect relationships. What makes machine intelligence and learning a true and real AI is the powerful underlying causal master algorithms used to reveal the causal patterns in the world's data universe.

Two minutes NLP -- Quick Intro to Knowledge Base Question Answering


Knowledge base question answering (KBQA) aims to answer a natural language question over a knowledge base (KB) as its knowledge source. A knowledge base (KB) is a structured database that contains a collection of facts in the form subject, relation, object, where each fact can have properties attached called qualifiers. For example, the sentence "Barack Obama got married to Michelle Obama on 3 October 1992 at Trinity United Church" can be represented by the tuple Barack Obama, Spouse, Michelle Obama, with the qualifiers start time 3 October 1992 and place of marriage Trinity United Church . Popular knowledge bases are DBpedia and WikiData. Early works on KBQA focused on simple question answering, where there's only a single fact involved.

AI takes aim at employee turnover


Employees are quitting jobs at record rates and companies are having a hard time luring them back. Exacerbating the problem is the fact that employees are now frequently working from home, making it harder for managers to identify employees who are unhappy. Plus, getting new hires up to speed is more challenging when they can't attend in-person training sessions or shadow experienced employees. To solve all these issues, companies are increasingly turning to artificial intelligence. But there's a limit to how much AI can do.

How Support Automation Enhances Clinical Trial Management


The life of a clinical study relies on data from documentation, meetings, emails and calls; all of which can be overwhelming for patients, clinical trial teams and associates. Although mundane, documenting, executing and collecting data is crucial to move a trial from phase to phase. Clinical trial teams face a multitude of competing priorities, from evaluating hundreds of potential patients to maintaining compliance and recording patient progress. No aspect or step can be neglected for a trial to succeed, especially regarding patient recruitment and retention. To help streamline these facets, AI-powered support automation platforms provide clinical trial management teams an interactive and informative interface with integrated cloud storage, intelligent document processing and a centralized knowledge base.