Most of the results above were actually first published in TU Munich's FKI Tech Report series, for which I drew many illustrations by hand, some of them shown in the present page (Sec. The FKI series now plays an important role in the history of Artificial Intelligence, as it introduced several important concepts: Unsupervised Pre-Training for Very Deep Learning (FKI-148-91 [UN0], Sec. In particular, the report FKI-126-90 [AC90] introduced a whole bunch of concepts that are now widely used: Planning with Recurrent World Models (Sec. Later remarkable FKI Tech Reports from the 1990s describe ways of greatly compressing NNs [KO0] [FM] to improve their generalisation capability.
Argo.ai has closed a $2.6 billion investment from Volkswagen to strengthen the self-driving startup's presence across Europe. Commentary: Please join our sister sites in fundraising to help address racism. Pittsburgh-based Argo.ai said in a blog post on Tuesday that the funding, initially invested in July 2019, will be used to bolster its position in Europe with the addition of VW Group's Munich-based Autonomous Intelligent Driving (AID) team. AID is working on the development of intelligent self-driving car technology for use in urban areas and potential applications such as robotic taxis and autonomous shuttles. Now due to be rebranded as Argo Munich, the team's base will also become Argo.ai's
This month, the cover of New Scientist ran the headline, "Is the Universe Conscious?" Mathematician and physicist Johannes Kleiner, at the Munich Center for Mathematical Philosophy in Germany, told author Michael Brooks that a mathematically precise definition of consciousness could mean that the cosmos is suffused with subjective experience. "This could be the beginning of a scientific revolution," Kleiner said, referring to research he and others have been conducting. Kleiner and his colleagues are focused on the Integrated Information Theory of consciousness, one of the more prominent theories of consciousness today. As Kleiner notes, IIT (as the theory is known) is thoroughly panpsychist because all integrated information has at least one bit of consciousness.
Assembling an accurate image of what the inside of an organ looks like is not an easy task. In order to figure out what the inside of a liver or eyeball or brain looks like, said organs often need to be sliced into tiny slivers, which are then individually studied with a microscope. But now, another method of studying the insides of organs--which utilizes AI and a process called "tissue clearing"--is allowing researchers to study these biological structures in a way that's not only far less work-intensive, but also more conducive to understanding how they actually work. Futurism reported on the new process, which has been dubbed "Small-micelle-mediated Human orgAN Efficient clearing and Labeling" or SHANEL. The process is being developed by researchers in Germany, from Helmholtz Zentrum München (a research center), the LMU University Hospital Munich, and the Technical University of Munich.
BERLIN, Nov 21 (Reuters) - German data mining software firm Celonis said on Thursday that it had raised $290 mln in a Series C funding round, putting a $2.5 billion valuation on the company that has been compared with enterprise application giant SAP . The funding round was led by Arena Holdings and investors included Ryan Smith, the founder of customer experience specialist Qualtrics that was bought by SAP for $8 billion a year ago. Celonis, based in Munich and New York, runs a cloud-based service that uses artificial intelligence to mine data and optimise business processes, serving customers including Siemens, 3M, Airbus and Vodafone. "We are in a market that shows enormous momentum," co-CEO and co-founder Bastian Nominacher told Reuters, adding that Celonis would invest the funds raised in its global sales and customer service and in enhancing its cloud platform. The funding round brings total investments into Celonis to $370 million.
MUNICH – We are entering a transformational period in medical science, as traditional research techniques combine with massive computing power and a wealth of new data. Just recently, Google announced that it has developed an artificial intelligence (AI) system capable of outperforming human radiologists in detecting breast cancer. And that is merely the latest example of how machine learning and big data are leading to new medical diagnostics, treatments, and discoveries. To realize AI's enormous potential, however, we must develop a pragmatic and globally agreed approach to governing the collection and use of "real-world data." Like climate change, the COVID-19 pandemic is a perfect example of why we need multilateralism in a globalized world.
MUNICH ― U.S. Defense Secretary Mark Esper on Saturday called out China as America's main adversary and warned allies that letting the Chinese firm Huawei build its next-generation, or 5G, network risks their security cooperation and information sharing arrangements with the U.S. "Reliance on Chinese 5G vendors, for example, could render our partners' critical systems vulnerable to disruption, manipulation and espionage," Esper said in a speech at the high-level Munich Security Conference. "It could also jeopardize our communication and intelligence sharing capabilities, and by extension, our alliances." Adopting Huawei's equipment on allies' 5G networks, Esper said, "could inject serious risk into our defense cooperation." It was a tough statement partially at odds with other U.S. officials, including Secretary of State Mike Pompeo, who offered assurances last week that U.S.-U.K. intelligence sharing remained strong despite Britain's decision to include Huawei in some parts of its nascent 5G network. A day earlier, the White House's point person for international telecommunications policy, Robert Blair, told reporters: "There will be no erosion in our overall intelligence sharing."
Over the last few decades, machine learning has revolutionized many sectors of society, with machines learning to drive cars, identify tumors and play chess--often surpassing their human counterparts. Now, a team of scientists based at the Okinawa Institute of Science and Technology Graduate University (OIST), the University of Munich and the CNRS at the University of Bordeaux have shown that machines can also beat theoretical physicists at their own game, solving complex problems just as accurately as scientists, but considerably faster. In the study, recently published in Physical Review B, a machine learned to identify unusual magnetic phases in a model of pyrochlore--a naturally-occurring mineral with a tetrahedral lattice structure. Remarkably, when using the machine, solving the problem took only a few weeks, whereas previously the OIST scientists needed six years. "This feels like a really significant step," said Professor Nic Shannon, who leads the Theory of Quantum Matter (TQM) Unit at OIST. "Computers are now able to carry out science in a very meaningful way and tackle problems that have long frustrated scientists."
Traditionally, the Friedrich-Alexander University of Erlangen-Nürnberg (FAU) is an exemplary location for Artificial Intelligence (AI), pattern recognition and machine learning. Already in 1975, Prof. Heinrich Niemann established the first chair dealing with AI. His work on pattern recognition yielded fundamental research in the areas of computer vision, speech comprehension and medical imaging. After FAU has raised numerous additional research topics around AI, the Machine Learning and Data Analytics lab was just founded in 2017, supported by a Heisenberg Professorship by the DFG. Further, John McCarthy, the inventor of the AI, holds an honorary doctorate from the FAU.
Birds do not collide when they fly in flocks. We may wonder how they do not and how they flock in a self-organized and well-orchestrated movement. It is a collective intelligence that is encapsulated within the interactions between the birds and the environment. The cohesive self-organized movement of a biological swarm such as flocking birds is commonly studied. Such phenomena have had successful applications in robotics and autonomous vehicles, and it has attracted a renewed interest from the Artificial Intelligence and the Predictive Analytics communities.