Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery and development. The mapping of non-equilibrium synthesis phase diagrams has recently been accelerated via high throughput experimentation but still limits materials research because the parameter space is too vast to be exhaustively explored. We demonstrate accelerated synthesis and exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis and characterization along with a hierarchy of AI methods that efficiently reveal the structure of processing phase diagrams. SARA designs lateral gradient laser spike annealing (lg-LSA) experiments for parallel materials synthesis and employs optical spectroscopy to rapidly identify phase transitions. Efficient exploration of the multi-dimensional parameter space is achieved with nested active learning (AL) cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments as well as end-to-end uncertainty quantification. With this, and the coordination of AL at multiple scales, SARA embodies AI harnessing of complex scientific tasks. We demonstrate its performance by autonomously mapping synthesis phase boundaries for the Bi$_2$O$_3$ system, leading to orders-of-magnitude acceleration in establishment of a synthesis phase diagram that includes conditions for kinetically stabilizing $\delta$-Bi$_2$O$_3$ at room temperature, a critical development for electrochemical technologies such as solid oxide fuel cells.

In a digital, global world where information is projected to double every 12 hours ([ 1 ][1]), the memorization of facts will become less of a commodity than the ability to think, find patterns, and generate new ideas from old parts ([ 2 ][2], [ 3 ][3]). Thus, a cradle-to-career approach to educating children must be mindful of how children learn to learn, not just what they learn ([ 4 ][4]). Combining insight, scientific acumen, and exquisite narrative, The Intellectual Lives of Children allows readers to peer into the minds of infants, toddlers, and preschoolers as they explore and learn in everyday moments, emphasizing what constitutes real learning. Children are bursting with playful curiosity. By age 3, they ask questions about everything they see—Why does a tree have leaves? Why does the Sun come up each day?—and by age 5, they pose even deeper questions, about God and morals. These questions not only provide fodder for knowledge, they help children discover the causal relationships among things—all with adult mentors by their side. Children also need time to explore. One child might collect dead things like worms and slugs, and another, assorted leaves of different shapes and colors. These collections, Engel argues, become treasured resources for the discovery of patterns, and they invite even more inquisitiveness. Indeed, the adults who guide this exploration by asking questions themselves reinforce curiosity and innovation. Hidden in these playful encounters are rich opportunities for learning. Yet explorations take time—the time to meander and discover, the unscheduled time to be bored. As Engel writes, “when children are allowed to dive into a topic thoroughly, they…connect isolated facts in order to generate new ideas.” They learn grit and they learn to have agency over their own learning. As such, the real mental work for children takes place in plain sight as they play—when a child builds a platform of chairs and pillows to retrieve cookies from an out-of-reach cookie jar and when she uses kitchen utensils to fish for the toy that is lodged under the couch. As adults, we often overlook the fact that learning is happening during periods of unstructured play, or we dismiss these intervals as unproductive. Hurried parents often lack the ability to carve out that time, fearing that their children might be late for their next scheduled activity. “Watch and listen for twenty minutes in almost any school in the United States and it becomes clear that the educational system does not concern itself with children's intellectual lives,” admonishes Engel in the opening pages of the book. Instead, she hopes to reenvision schools as “idea factories” built on inspiring curiosity and problem solving: “Imagine assessing students' progress under some new headings: poses interesting questions, speculates,…articulates important problems and spends time solving them.” In one lovely example, Engel describes a teacher who challenged her students to construct a record-breaking straw chain that would eventually measure 3.8 miles. “Winning the record would be fun, but the enduring benefit would be coming to grips with vast quantities,” explains the teacher, whose goal was to help the children to better understand the sheer depth of the Mariana Trench. The puzzles and problems that captivate children and the ways they set about solving them are reminiscent of how philosophers Karl Popper and Thomas Kuhn conceptualized the thinking of scientists ([ 5 ][5], [ 6 ][6]). Both children and scientists bring the tools in their respective arsenals to bear on things that matter to them. Their learning is not linear and is certainly not funneled through flashcards ([ 7 ][7]). In the past few decades, developmental science has made great strides in understanding the mental richness of infants, toddlers, and preschoolers. Engel's book helps parents and educators see what scientists have learned, offering tips for how to make the learning even more apparent. For example, she encourages parents to see children as active thinkers and suggests that by asking open-ended questions and letting them explore, children will be better prepared to thrive in a complex and ever-changing world. 1. [↵][8]1. S. Sorkin , “Thriving in a world of ‘knowledge half-life’,” Enterprising Insights, 5 April 2019. 2. [↵][9]1. R. M. Golinkoff, 2. K. Hirsh-Pasek , Becoming Brilliant (APA Press, 2016). 3. [↵][10]1. D. H. Pink , A Whole New Mind (Penguin, 2006). 4. [↵][11]1. K. Hirsh-Pasek, 2. H. S. Hadani, 3. E. Blinkoff, 4. R. M. Golinkoff , “A new path to education reform: Playful learning promotes 21st-century skills in schools and beyond,” The Brookings Institution: Big Ideas Policy Report, 28 October 2020. 5. [↵][12]1. K. Popper , The Logic of Scientific Discovery (Hutchinson, 1959). 6. [↵][13]1. T. S. Kuhn , The Structure of Scientific Revolutions (Univ. of Chicago Press, 1962). 7. [↵][14]1. A. Gopnik, 2. A. N. Meltzoff, 3. P. K. Kuhl , The Scientist in the Crib (William Morrow, 1999). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #xref-ref-1-1 "View reference 1 in text" [9]: #xref-ref-2-1 "View reference 2 in text" [10]: #xref-ref-3-1 "View reference 3 in text" [11]: #xref-ref-4-1 "View reference 4 in text" [12]: #xref-ref-5-1 "View reference 5 in text" [13]: #xref-ref-6-1 "View reference 6 in text" [14]: #xref-ref-7-1 "View reference 7 in text"

Storage and retrieval of data in a computer memory plays a major role in system performance. Traditionally, computer memory organization is static - i.e., they do not change based on the application-specific characteristics in memory access behaviour during system operation. Specifically, the association of a data block with a search pattern (or cues) as well as the granularity of a stored data do not evolve. Such a static nature of computer memory, we observe, not only limits the amount of data we can store in a given physical storage, but it also misses the opportunity for dramatic performance improvement in various applications. On the contrary, human memory is characterized by seemingly infinite plasticity in storing and retrieving data - as well as dynamically creating/updating the associations between data and corresponding cues. In this paper, we introduce Neural Storage (NS), a brain-inspired learning memory paradigm that organizes the memory as a flexible neural memory network. In NS, the network structure, strength of associations, and granularity of the data adjust continuously during system operation, providing unprecedented plasticity and performance benefits. We present the associated storage/retrieval/retention algorithms in NS, which integrate a formalized learning process. Using a full-blown operational model, we demonstrate that NS achieves an order of magnitude improvement in memory access performance for two representative applications when compared to traditional content-based memory.

In exploratory data analysis of known classes of high dimensional data, a central question is how distinct are the classes? The Direction Projection Permutation (DiProPerm) hypothesis test provides an answer to this that is directly connected to a visual analysis of the data. In this paper, we propose an improved DiProPerm test that solves 3 major challenges of the original version. First, we implement only balanced permutations to increase the test power for data with strong signals. Second, our mathematical analysis leads to an adjustment to correct the null behavior of both balanced and the conventional all permutations. Third, new confidence intervals (reflecting permutation variation) for test significance are also proposed for comparison of results across different contexts. This improvement of DiProPerm inference is illustrated in the context of comparing cancer types in examples from The Cancer Genome Atlas.

What is the scientific method, and what makes it the most efficient approach for generating insight? In The Knowledge Machine , Michael Strevens argues that to answer this question, we must acknowledge the role played by the undisciplined and emotional nature of the humans who carry it out. The book takes readers on a whirlwind tour through the history of science, rendering Arthur Eddington, Louis Pasteur, G. G. Simpson, Lord Kelvin, and many others as “warm-blooded organisms, whose enthusiasms, hopes, and fears mold their thinking far below the threshold of awareness.” When asked what science is and how it functions, researchers offer a range of conflicting responses, notes Strevens. “Some scientists say that the essence of science is controlled or repeatable experiment, forgetting that experiments are of relatively little importance in cosmology or evolutionary biology. Some say advanced mathematical techniques are crucial, forgetting that the discoverers of genetics, for example, had no use for sophisticated math.” Strevens argues that an objective scientific method cannot exist, as all predictions from hypotheses rely on auxiliary assumptions such as the functioning of instruments, whose reliability must be evaluated subjectively. He proposes that the distinguishing feature of science is a procedural agreement, which he refers to as the “iron rule of explanation.” This rule holds that differences in scientific opinion must be settled by empirical testing alone. Thus, a scientist cannot argue for one hypothesis over another because it is more beautiful or more appealing philosophically or because it is better aligned with “God's plan.” The iron rule applies only to official communications. Outside of such venues, scientists may think and believe as they wish. That only data are capable of formally supporting a hypothesis may seem obvious, yet Strevens suggests that such an approach is inherently illogical. Imagine, for example, suggesting to Aristotle that he should restrict himself to data when arguing in favor of a particular theory. He would have pitied your ignorance. What better support for a theory could there be than an elegant chain of philosophical arguments? Strevens argues that modern science owes its success to the relinquishing of deep philosophical understanding in favor of the shallow power to predict empirical observations. As Isaac Newton—whom Strevens sees as the first truly modern scientist—wrote: “I have not as yet been able to deduce from phenomena the reason for these properties of gravity, and I do not feign hypotheses…It is enough that gravity really exists and acts according to the laws that we have set forth” ([ 1 ][1]). Strevens proposes that scientists reason differently in public discourse and private venues. By drawing a clear distinction between formal scientific arguments and informal, behind-the-scenes scientific work, he provides a coherent framework for the divergent ideas of earlier philosophers of science: Karl Popper's ideas on the falsification of hypotheses ([ 2 ][2]) form the basis of formal scientific discourse; Paul Feyerabend's observations highlight the subjectivity of daily work, including the evaluation of assumptions ([ 3 ][3]); and the apparent security of a scientific paradigm guided by the iron rule compels scientists to perform elaborate experiments, thus generating data of otherwise unimaginable quantity and detail—a phenomenon described by Thomas Kuhn ([ 4 ][4]). Strevens frames the toiling life of data generation as the cost scientists pay to gain access to the sacred halls of scientific excellence. What he overlooks is the supreme “pleasure of finding things out” ([ 5 ][5]). In his autobiography, French biologist François Jacob proposed the notion of “night science,” in which scientists generate new ideas and hypotheses in often unstructured thought processes ([ 6 ][6]). This approach, he argued, complements “day science,” wherein new ideas are tested empirically and reported formally. Thinkers such as Aristotle perceived day and night science as intertwined in a single process. Newton and his contemporaries founded modern science by separating them into distinct undertakings. While Strevens's iron rule may indeed be the foundation of modern science's success, the methods scientists use to come up with new ideas remain elusive. 1. [↵][7]1. I. Newton , The Mathematical Principles of Natural Philosophy (Benjamin Motte, 1687). 2. [↵][8]1. K. Popper , The Logic of Scientific Discovery (Hutchinson, 1959). 3. [↵][9]1. P. Feyerabend , Against Method (Verso Books, 1975). 4. [↵][10]1. T. Kuhn , The Structure of Scientific Revolutions (Univ. of Chicago Press, 1962). 5. [↵][11]1. R. Feynman , The Pleasure of Finding Things Out (Perseus Books, 1999). 6. [↵][12]1. F. Jacob , The Statue Within (Cold Spring Harbor Laboratory Press, 1995). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #xref-ref-1-1 "View reference 1 in text" [8]: #xref-ref-2-1 "View reference 2 in text" [9]: #xref-ref-3-1 "View reference 3 in text" [10]: #xref-ref-4-1 "View reference 4 in text" [11]: #xref-ref-5-1 "View reference 5 in text" [12]: #xref-ref-6-1 "View reference 6 in text"

The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. Intuitively, the better the quality of the event data, the better the quality of the model that is discovered. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We will also use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

With a lot of hype going on with the data science field, most of us jump directly into machine learning models and algorithms to make business decisions. All the online courses available fail to teach the very basics of decision making. Hypotheses testing is one of the basic building blocks of decision making and oldest. The earliest use of hypotheses testing was in the 1700s by John Arbuthnot to test whether male and female births are equally likely to occur. In this article, we will be discussing everything about hypotheses testing at the beginner level along with python code making use of the SciPy package.

There has been an upsurge of interest in applying machine-learning (ML) techniques to chemistry, and a number of these applications have achieved impressive predictive accuracies; however, they have done so without providing any insight into what has been learnt from the training data. The interpretation of ML systems (i.e., a statement of what an ML system has learnt from data) is still in its infancy, but interpretation can lead to scientific discovery, and examples of this are given in the areas of drug discovery and quantum chemistry. It is proposed that a research programme be designed that systematically compares the various model-agnostic and model-specific approaches to interpretable ML within a range of chemical scenarios.

Industry partners are often rivals, but not in the current coronavirus vaccine endeavour. Every member of the Consortium is united by a common goal: to accelerate our search for a new treatment or vaccine against COVID-19. The benefits of collaboration are greater speed and accuracy; a freer exchange of ideas and data; and full access to cutting-edge technology. In sum, it supercharges innovation and hopefully means the pandemic will be halted faster than otherwise.

Research across global academic and commercial labs to create a more efficient drug discovery process won recognition today with a special Gordon Bell Prize for work fighting COVID-19. A team of 27 researchers led by Rommie Amaro at the University of California at San Diego (UCSD) combined high performance computing (HPC) and AI to provide the clearest view to date of the coronavirus, winning the award. Their work began in late March when Amaro lit up Twitter with a picture of part of a simulated SARS-CoV-2 virus that looked like an upside-down Christmas tree. Seeing it, one remote researcher noticed how a protein seemed to reach like a crooked finger from behind a protective shield to touch a healthy human cell. "I said, 'holy crap, that's crazy'… only through sharing a simulation like this with the community could you see for the first time how the virus can only strike when it's in an open position," said Amaro, who leads a team of biochemists and computer experts at UCSD.