In the problem of active sequential hypotheses testing (ASHT), a learner seeks to identify the true hypothesis $h^*$ from among a set of hypotheses $H$. The learner is given a set of actions and knows the outcome distribution of any action under any true hypothesis. While repeatedly playing the entire set of actions suffices to identify $h^*$, a cost is incurred with each action. Thus, given a target error $\delta>0$, the goal is to find the minimal cost policy for sequentially selecting actions that identify $h^*$ with probability at least $1 - \delta$. This paper provides the first approximation algorithms for ASHT, under two types of adaptivity. First, a policy is partially adaptive if it fixes a sequence of actions in advance and adaptively decides when to terminate and what hypothesis to return. Under partial adaptivity, we provide an $O\big(s^{-1}(1+\log_{1/\delta}|H|)\log (s^{-1}|H| \log |H|)\big)$-approximation algorithm, where $s$ is a natural separation parameter between the hypotheses. Second, a policy is fully adaptive if action selection is allowed to depend on previous outcomes. Under full adaptivity, we provide an $O(s^{-1}\log (|H|/\delta)\log |H|)$-approximation algorithm. We numerically investigate the performance of our algorithms using both synthetic and real-world data, showing that our algorithms outperform a previously proposed heuristic policy.

In the past year or two, many companies have shared their data discovery platforms (the latest being Facebook's Nemo). Based on this list, we now know of more than 10 implementations. I haven't been paying much attention to these developments in data discovery and wanted to catch up. By the end of this, we'll learn about the key features that solve 80% of data discoverability problems. We'll also see how the platforms compare on these features, and take a closer look at open source solutions available.

If you are interested in sponsoring our event series, please contact Dr Samantha Kanza. Arctoris is an Oxford-based research company that is transforming drug discovery for biotech and AI-driven drug discovery companies, pharmaceutical corporations and academia. Arctoris developed and operates Ulysses, the world's first fully automated drug discovery platform. Accessible remotely, the platform enables researchers worldwide to perform their research rapidly, with more accuracy, transparency, and full reproducibility. Arctoris accelerates drug discovery programmes from idea to clinical testing, combining human ingenuity with the power of robotics.

We are living through an AI renaissance thought wholly unimaginable just a few decades ago -- automobiles are becoming increasingly autonomous, machine learning systems can craft prose nearly as well as human poets, and almost every smartphone on the market now comes equipped with an AI assistant. Oxford professor Michael Woolridge has spent the past quarter decade studying technology. In his new book, A Brief History of Artificial Intelligence, Woolridge leads readers on an exciting tour of the history of AI, its present capabilities, and where the field is heading into the future. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher. In his 1962 book, The Structure of Scientific Revolutions, the philosopher Thomas Kuhn argued that, as scientific understanding advances, there will be times when established scientific orthodoxy can no longer hold up under the strain of manifest failures.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. ROME – Pope Francis urged governments on Monday to use the coronavirus crisis as a revolutionary opportunity to create a world that is more economically and environmentally just -- and where basic health care is guaranteed for all. Francis made the appeal in his annual foreign policy address to ambassadors accredited to the Holy See, an appointment that was postponed for two weeks after he suffered a bout of sciatica nerve pain that made standing and walking difficult. Francis urged the governments represented in the Apostolic Palace to contribute to global initiatives to provide vaccines to the poor and to use the pandemic to reset what he said was a sick economic model that exploits the poor and the Earth. Pope Francis delivers his blessing from his studio window overlooking St. Peter's Square, at the Vatican, Sunday, Feb. 7, 2021.

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"