Intel Corp,.[1] on Dec. 27, the U.S. District Court for the Western District of Texas found claims of machine-learning patents invalid under Title 35 of the U.S. Code, Section 101, in a motion to dismiss filed under Federal Rule of Civil Procedure 12(b)(6). This decision, on one hand, provides a road map that skilled counsel can follow to draft patents that are more likely to withstand eligibility challenges, but, on the other hand, could make patenting artificial intelligence inventions more nuanced absent due care.

For a worker losing his or her job to automation, knowing that an AI programming job is being created elsewhere is of little solace. "Instead, we believe that--like all previous labor-saving technologies--AI will enable new industries to emerge, creating more new jobs than are lost to the technology," the report's authors, led by Thomas Malone, director of the MIT Center for Collective Intelligence, conclude. "Though these technologies will eliminate some jobs, they will create many others," the report's team of authors, led by BCG's Rainer Strack. "For example, eliminating 10 million jobs and creating 10 million new jobs would appear to have negligible impact. Computers tend to perform well in tasks that humans find difficult or time-consuming to do, "but they tend to work less effectively in tasks that humans find easy to do," the report notes. For a worker losing his or her job to automation, knowing that an AI programming job is being created elsewhere is of little solace. "Instead, we believe that--like all previous labor-saving technologies--AI will enable new industries to emerge, creating more new jobs than are lost to the technology," the report's authors, led by Thomas Malone, director of the MIT Center for Collective Intelligence, conclude. "Though these technologies will eliminate some jobs, they will create many others," the report's team of authors, led by BCG's Rainer Strack. "For example, eliminating 10 million jobs and creating 10 million new jobs would appear to have negligible impact.

Well, is artificial intelligence a job-killer or not? We keep hearing both sides, from projections of doom for many professions that will necessitate things such as universal basic income to help sidelined workers, to projections of countless unfilled jobs needed to build and manage AI-powered enterprises. For a worker losing his or her job to automation, knowing that an AI programming job is being created elsewhere is of little solace. Perhaps the reality will be somewhere in between. An MIT report released at the end of last year states recent fears about AI leading to mass unemployment are unlikely to be realized. "Instead, we believe that--like all previous labor-saving technologies--AI will enable new industries to emerge, creating more new jobs than are lost to the technology," the report's authors, led by Thomas Malone, director of the MIT Center for Collective Intelligence, conclude.

We consider the problem of obtaining coherence in a propositional knowledge base using techniques from Belief Change. Our motivation comes from the field of formal ontologies where coherence is interpreted to mean that a concept name has to be satisfiable. In the propositional case we consider here, this translates to a propositional formula being satisfiable. We define belief change operators in a framework of nonmonotonic preferential reasoning.We show how the introduction of defeasible information using contraction operators can be an effective means for obtaining coherence.