John Conway’s Game of Life, a famous cellular automaton, has been found to have periodic patterns of every possible length.
One of the fundamental and timeless questions of life concerns the mechanics of its inception. Take human development, for example: how do individual cells come together to form complex structures like skin, muscles, bones, or even a brain, a finger, or a spine?
Although the answers to such questions remain unknown, one line of scientific inquiry lies in understanding gastrulation — the stage at which embryo cells develop from a single layer to a multidimensional structure with a main body axis. In humans, gastrulation happens around 14 days after conception.
It’s not possible to study human embryos at this stage, so researchers at the University of California San Diego, the University of Dundee (UK), and Harvard University were able to study gastrulation in chick embryos, which have many similarities to human embryos at this stage.
When Isaac Newton inscribed onto parchment his now-famed laws of motion in 1,687, he could have only hoped we’d be discussing them three centuries later.
Writing in Latin, Newton outlined three universal principles describing how the motion of objects is governed in our Universe, which have been translated, transcribed, discussed and debated at length.
But according to a philosopher of language and mathematics, we might have been interpreting Newton’s precise wording of his first law of motion slightly wrong all along.
Its performance matches the smartest high school mathematicians and is much stronger than the previous state-of-the-art system.
Google DeepMind has created an AI system that can solve complex geometry problems.
(Bloomberg) — Google DeepMind, Alphabet Inc.’s research division, said it has taken a “crucial step” towards making artificial intelligence as capable as humans. It involves solving high-school math problems. Most Read from BloombergWall Street Dials Back Fed Wagers After Solid Data: Markets WrapMusk Pressures Tesla’s Board for Another Massive Stock AwardChina’s Economic Growth Disappoints, Fueling Stimulus CallsChina Population Extends Record Drop on Covid Deaths, Low BirthsApple to Allow Outsi.
Consciousness is one of the most mysterious and fascinating aspects of human existence. It is also one of the most challenging to study scientifically, as it involves subjective experiences that are not directly observable or measurable. David Chalmers, a professor of philosophy and neural science at NYU mentions in his book The Conscious Mind.
“It may be the largest outstanding obstacle in our quest for a scientific understanding of the universe.”
The real questions are: how can we approach the problem of consciousness from a rigorous and objective perspective? Is there a way to quantify and model the phenomena of awareness, feelings, thoughts, and selfhood? There is no definitive answer to this question, but some researchers have attempted to use mathematical tools and methods to study these phenomena. Self-awareness, for instance, is the ability to perceive and understand the things that make you who you are as an individual, such as your personality, actions, values, beliefs, and even thoughts. Some studies have used the mirror test to assess the development of self-awareness in infants and animals.
This is an AI called a Neural Network. But all of the transistors and electronics are replaced with DNA, the molecule of life… all in one test tube.
Papers used for this video.
DNA Neural Networks: https://www.nature.com/articles/s4225…
Computation Via DNA: https://www.nature.com/articles/s4159…
DNA logic circuits: https://www.nature.com/articles/s4146…
Matrices Using DNA: https://onlinelibrary.wiley.com/doi/1…
Continue reading “The Math Behind Building An AI Using DNA #SoME3” »
On top of this, the use of quantification has significantly increased over the last decades with the inflation of metrics, indicators, and scores to rank and benchmark options (Muller, 2018). The case of energy policy making in the European Union is again an effective example. The European Union’s recent energy strategy has been underpinned by the Clean Energy for all Europeans packages, which are in turn supported by a number of individual directives, each one characterized by a series of quantitative goals (European Commission, 2023). The quantification of the impact (impact assessment) is customarily required to successfully promote new political measures (European Commission, 2015a) and is in turn based on quantification, often from mathematical models (Saltelli et al., 2023). The emphasis on producing exact figures to assess the contribution of a new technology, political or economic measure has put many models and their users into contexts of decision-making that at times extends beyond their original intent (Saltelli, Bammer et al., 2020). At the same time, the efforts to retrospectively assess the performance of energy models have been extremely limited, one example being the Energy Modeling Forum in the United States (Huntington et al., 1982). In spite of this, retrospective assessments can be very helpful in understanding the sources of mismatch between a forecast and the actual figures reported a posteriori (Koomey et al., 2003). For example, long-range forecast models are typically based on the assumption of gradual structural changes, which are at stake with the disruptive events and discontinuities occurring in the real world (Craig et al., 2002). This dimension is especially important in terms of the nature and pace of technology change (Bistline et al., 2023 ; Weyant & Olavson, 1999). A further critical element in this approach is the cognitive bias in scenario analysis that naturally leads to overconfidence in the option being explored and results in an underestimate of the ranges of possible outcomes (Morgan & Keith, 2008).
Additionally, in their quest for capturing the features of the energy systems represented, models have increased their complicatedness and/or complexity. In this context, the need to appraise model uncertainty has become of paramount importance, especially considering the uncertainty due to propagation errors caused by model complexification (Puy et al., 2022). In ecology, this is known as the O’Neil conjecture, which posits a principle of decreasing returns for model complexity when uncertainties come to dominate the output (O’Neill, 1989 ; Turner & Gardner, 2015). Capturing and apportioning uncertainty is crucial for a healthy interaction at the science–policy interface, including energy policy making, because it promotes better informed decision-making. Yet Yue et al. (2018) found that only about 5% of the studies covering energy system optimization models have included some form of assessment of stochastic uncertainty, which is the part of uncertainty that can be fully quantified (Walker et al., 2003). When it comes to adequately apportioning this uncertainty onto the input parameters and hypotheses through sensitivity analysis, the situation is even more critical: Only very few papers in the energy field have made the use of state-of-the-art approaches (Lo Piano & Benini, 2022 ; Saltelli et al., 2019). Further to that, the epistemic part of uncertainty, the one that arises due to imperfect knowledge and problem framing, has been largely ignored in the energy modeling literature (Pye et al., 2018). For instance, important sources of uncertainties associated with regulatory lag and public acceptance have typically been overlooked. 1
In this contribution, we discuss three approaches to deal with the challenges of non-neutrality and uncertainty in models: The numerical unit spread assessment pedigree (NUSAP) method, diagnostic diagrams, and sensitivity auditing (SAUD). These challenges are especially critical when only one (set of) model(s) has been selected to contribute to decision-making. One practical case is used to showcase in retrospective the relevance of the issue and the associated problems: the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s.
Members’ SeminarTopic: Towards a mathematical model of the brainSpeaker: Lai-Sang YoungAffiliation: New York University; Distinguished Visiting Professor, Sc…
When learning, patients with schizophrenia or depression have difficulty making optimal use of information that is new to them. In the learning process, both groups of patients give greater weight to less important information and, as a result, make less than ideal decisions.
This was the finding of a several-months-long study conducted by a team led by neuroscientist Professor Dr. med. Markus Ullsperger from the Institute of Psychology at Otto von Guericke University Magdeburg in collaboration with colleagues from the University Clinic for Psychiatry & Psychotherapy and the German Center for Mental Health.
By using electroencephalography (EEG) and complex mathematical computer modeling, the team of researchers discovered that learning deficits in depressive and schizophrenic patients are caused by diminished/reduced flexibility in the use of new information.
