We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence research has made great achievements in solving specific applications, but we're still far from the kind of general-purpose AI systems that scientists have been dreaming of for decades. Among the solutions being explored to overcome the barriers of AI is the idea of neuro-symbolic systems that bring together the best of different branches of computer science. In a talk at the IBM Neuro-Symbolic AI Workshop, Joshua Tenenbaum, professor of computational cognitive science at the Massachusetts Institute of Technology, explained how neuro-symbolic systems can help to address some of the key problems of current AI systems. Among the many gaps in AI, Tenenbaum is focused on one in particular: "How do we go beyond the idea of intelligence as recognizing patterns in data and approximating functions and more toward the idea of all the things the human mind does when you're modeling the world, explaining and understanding the things you're seeing, imagining things that you can't see but could happen, and making them into goals that you can achieve by planning actions and solving problems?"

Artificial General Intelligence (AGI) is an umbrella term for all artificial intelligence endeavours aimed at creating something that performs at or above the human level on most or all cognitive tasks. AI derives its meaning from the acronym Artificial Intelligence, which refers to a machine's ability to exhibit "intelligence." This is not necessarily human-level intelligence; instead, it suggests an agent's capability for learning, planning, and problem-solving. In contrast, Artificial General Intelligence should allow an agent to use these capabilities towards any cognitive task that humans can achieve with ease: e.g., deducing whether or not a novel is a detective story or flying an airplane. Artificial General Intelligence can be considered the ultimate manifestation of Artificial Intelligence because it applies to any task that a human brain can perform.

This article is part of our coverage of the latest in AI research. Artificial intelligence research has made great achievements in solving specific applications, but we're still far from the kind of general-purpose AI systems that scientists have been dreaming of for decades. Among the solutions being explored to overcome the barriers of AI is the idea of neuro-symbolic systems that bring together the best of different branches of computer science. In a talk at the IBM Neuro-Symbolic AI Workshop, Joshua Tenenbaum, professor of computational cognitive science at the Massachusetts Institute of Technology, explained how neuro-symbolic systems can help to address some of the key problems of current AI systems. Among the many gaps in AI, Tenenbaum is focused on one in particular: "How do we go beyond the idea of intelligence as recognizing patterns in data and approximating functions and more toward the idea of all the things the human mind does when you're modeling the world, explaining and understanding the things you're seeing, imagining things that you can't see but could happen, and making them into goals that you can achieve by planning actions and solving problems?"

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Russian model who previously called Vladimir Putin a "psychopath" has been found dead with her body stuffed inside a suitcase, a report says. Gretta Vedler, 23, went missing a year ago after the anti-Putin social media rant, but the two events do not appear to be connected. "Vedler's ex-boyfriend Dmitry Korovin, 23, has now confessed to strangling her to death before driving her 300 miles to the Lipetsk region and abandoning the body in the boot of a car.." the Daily Star reports.

For programmers, algorithms and data structures are their most essential subjects--a programmer's bread and butter if you will. If you want to enter the field of programming and hit the ground running, you'll need to master the most common data structures and boost your resume with in-demand skills. Here, we'll explore the eight most important data structures every programmer should know, including what they do and where to use them. To start, let's gain a fundamental understanding of what a data structure is. Data structures are methods of storing and organizing data in a computer system so that operations can be performed upon them more efficiently.

This article is part of our coverage of the latest in AI research. Artificial intelligence research has made great achievements in solving specific applications, but we're still far from the kind of general-purpose AI systems that scientists have been dreaming of for decades. Among the solutions being explored to overcome the barriers of AI is the idea of neuro-symbolic systems that bring together the best of different branches of computer science. In a talk at the IBM Neuro-Symbolic AI Workshop, Joshua Tenenbaum, professor of computational cognitive science at the Massachusetts Institute of Technology, explained how neuro-symbolic systems can help to address some of the key problems of current AI systems. Among the many gaps in AI, Tenenbaum is focused on one in particular: "How do we go beyond the idea of intelligence as recognizing patterns in data and approximating functions and more toward the idea of all the things the human mind does when you're modeling the world, explaining and understanding the things you're seeing, imagining things that you can't see but could happen, and making them into goals that you can achieve by planning actions and solving problems?"

Concepts are key building blocks of higher level human understanding. Explainable AI (XAI) methods have shown tremendous progress in recent years, however, local attribution methods do not allow to identify coherent model behavior across samples and therefore miss this essential component. In this work, we study concept-based explanations and put forward a new definition of concepts as low-dimensional subspaces of hidden feature layers. We novelly apply sparse subspace clustering to discover these concept subspaces. Moving forward, we derive insights from concept subspaces in terms of localized input (concept) maps, show how to quantify concept relevances and lastly, evaluate similarities and transferability between concepts. We empirically demonstrate the soundness of the proposed Sparse Subspace Clustering for Concept Discovery (SSCCD) method for a variety of different image classification tasks. This approach allows for deeper insights into the actual model behavior that would remain hidden from conventional input-level heatmaps.

MOUNTAIN VIEW, CA, USA, Mar 9, 2022 – Synopsys is pleased to announce the Simpleware Release T-2022.03. The latest release of Simpleware software includes many new features and improvements, including the new shoulder CT tool in the Simpleware AS Ortho module, contour measurements, improved 3D printing capabilities, and aortic valve analysis. Join us on March 30, 2022 to see the new features in action. Register to watch live or to receive the on-demand recording to view at your own convenience. Synopsys' Simpleware software provides an industry-leading, comprehensive 3D image processing platform for handling 3D scan data.

Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a de-facto standard to produce specifications in many application domains (e.g., planning, business process management, run-time monitoring, reactive synthesis). Several studies approached the respective satisfiability problem. In this paper, we investigate the problem of extracting the unsatisfiable core in LTLf specifications. We provide four algorithms for extracting an unsatisfiable core leveraging the adaptation of state-of-the-art approaches to LTLf satisfiability checking. We implement the different approaches within the respective tools and carry out an experimental evaluation on a set of reference benchmarks, restricting to the unsatisfiable ones. The results show the feasibility, effectiveness, and complementarities of the different algorithms and tools.

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 intellect is artificial intelligence today? It is as smart as its dump, dull and deficient (3d). Today's quasi-AI is biased, black box, oblique, weak and narrow. It could blindly and unknowingly perform strictly what it was designed for, videogame/chess/strategic games playing, self-driving, language translation, face recognition, fraud detection, speech communication, product recommendation, pattern matching, generating poetry or music or images or faces or new molecules, etc. It is all relying on statistical relationships in raw input data sets to generate some patterns that humans find useful.