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Expert Systems

Heyday lands $6M to build a knowledge base from the services you already use – TechCrunch


Ever spend much too long trying -- and failing -- to rediscover articles you've partially read? This reporter's been there, and it seems I'm not the only one. According to 2021 Carnegie Mellon study on browser tab usage, many participants admitted to feeling overwhelmed by the amount of tabs they kept open but were compelled not to close them out of fear of missing out on valuable information. Samiur Rahman is familiar with the feeling -- so much so that he co-created a product, Heyday, to alleviate it. Launched in 2021, Heyday is designed to automatically save web pages and pull in content from cloud apps, resurfacing the content alongside search engine results and curating it into a knowledge base. Investors include Spark Capital, which led a $6.5 million seed round in the company that closed today.

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.

Supreme Court shoots down NY rule that set high bar for concealed handgun licenses

FOX News

Fox News Flash top headlines are here. Check out what's clicking on The Supreme Court Thursday ruled 6-3 that New York's regulations that made it difficult to obtain a license to carry a concealed handgun were unconstitutionally restrictive, and that it should be easier to obtain such a license. The existing standard required an applicant to show "proper cause" for seeking a license, and allowed New York officials to exercise discretion in determining whether a person has shown a good enough reason for needing to carry a firearm. Stating that one wished to protect themselves or their property was not enough.

Why Symbolic AI Is Extremely Critical for Business Operations?


Even as many businesses experiment with AI using rudimentary machine learning (ML) and deep learning (DL) models, a new sort of AI called symbolic AI is emerging from the lab, with the potential to transform both AI's function and its relationship with its human overseers. There are two groups in AI history: symbolic AI and non-symbolic AI, each of which takes a distinct approach to build an intelligent system. The symbolic method tried to create an intelligent system with explainable actions based on rules and knowledge, whereas the non-symbolic method aimed to create a computational system modeled after the human brain. The ultimate objective of computer science is to create an AI system capable of thinking, logic, and learning. Most AI systems today, on the other hand, only have one of the two abilities: learning or reasoning.

AI in Healthcare Industry


Artificial Intelligence is proving its prominence in every industry out there and the healthcare industry is no different. From patient care to Administrative processes AI has huge potential in the healthcare industry. There are many research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks. We have seen robots performing surgeries or assisting doctors with more precision and flexibility. Algorithms are outperforming radiologists in detecting dangerous tumors and advising researchers on how to build cohorts for expensive clinical trials.

Net-zero rules set to send cost of new homes and extensions soaring

The Guardian > Energy

New building regulations aimed at improving energy efficiency are set to increase the price of new homes, as well as those of extensions and loft conversions on existing ones. The rules, which came into effect on Wednesday in England, are part of government plans to reduce the UK's carbon emissions to net zero by 2050. They set new standards for ventilation, energy efficiency and heating, and state that new residential buildings must have charging points for electric vehicles. The moves are the most significant change to building regulations in years, and industry experts say they will inevitably lead to higher prices at a time when a shortage of materials and high labour costs is already driving up bills. Brian Berry, chief executive of the Federation of Master Builders, a trade group for small and medium-sized builders, says the measures will require new materials, testing methods, products and systems to be installed.

Pinaki Laskar on LinkedIn: #ai #machinelearning #programming


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 What's the difference between a knowledge based system and an expert system? KBS/ES Knowledge Bases Automated reasoning engines (Inference engines, theorem provers, classifiers), - "expert system" refers to the type of task the system is trying to assist with – to replace or aid a human expert in a complex task requiring expert knowledge; - "knowledge-based system" refers to the architecture of the system – that it represents knowledge explicitly, rather than as procedural code; While the earliest knowledge-based systems were almost all expert systems, the same tools and architectures can and have since been used for a whole host of other types of systems. Virtually all expert systems are knowledge-based systems, but many knowledge-based systems are not expert systems. Expert systems is going as a computer system emulating the decision-making ability of a human expert, solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. The knowledge base represents facts and rules about the world, via KRR formalisms, as ontologies, frames, conceptual graphs or logical assertions.

Kavanaugh threat: WaPo column urges readers not to assign blame because both sides have 'deranged individuals'

FOX News

Fox News correspondent David Spunt has the latest on Congress' response to the failed assassination attempt of Justice Brett Kavanaugh on'Special Report.' Washington Post deputy editorial editor Ruth Marcus wants to make sure people are aware "deranged individuals do deranged things" on "both ends of the political spectrum" before assigning blame for the man who was arrested near the Maryland home of Supreme Court Justice Brett Kavanaugh. On Wednesday, an armed California man identified as Nicholas John Roske was carrying a gun, knife and pepper spray when arrested outside Kavanaugh's home. He told officers that he wanted "to give his life purpose" and purchased the gun and other items for the purpose of breaking into Kavanaugh's home and killing the justice and then himself. A piece published Thursday night by Marcus headlined, "The Kavanaugh threat exposed weaknesses in judicial security -- and our discourse," admitted the incident "could have ended in unfathomable tragedy" but urged readers not to assign blame or dismiss people who created the environment that "fueled" the assassination attempt.

Graph Neural Networks Combined with Semantic Reasoning Deliver 'Total AI' -


The ability for machines to reason not just identify patterns in massive data amounts, but make rule or logic based inferences on domain specific knowledge is foundational to Artificial Intelligence. The growing momentum around Neuro-Symbolic AI and the increasing reliance on Graph Analytics demonstrate how important these developments are for the enterprise. Combining AI s symbolic knowledge processing with its statistical branch (typified by machine learning) produces the best prescriptive outcomes, delivers total AI, and is swiftly becoming necessary to tackle enterprise scale applications of mission-critical processes like foretelling equipment failure, optimizing healthcare treatment, and maximizing customer relationships. Their underlying graph capabilities are ideal for applying machine learning s advanced pattern recognition to high-dimensional, non-Euclidian datasets that are too complex for other machine learning types. Organizations get two forms of reasoning in one framework by fusing GNN reasoning capabilities around relationship predictions, entity classifications, and graph clustering, with classic semantic inferencing available in Knowledge Graphs.