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Scientific Discovery


Hypothesis Testing- Test of Mean, Variance, Proportion

#artificialintelligence

Hypothesis testing is used to determine whether the assumption about the value of the population parameter should be rejected or not. There are different types of hypothesis testing and different approaches to perform hypothesis testing. Let's learn about this in detail in this article. The null hypothesis is always formulated in such a way that the assumption is true. If we fail to reject the null hypothesis means no follow-up action is required.


A New Paradigm of Threats in Robotics Behaviors

arXiv.org Artificial Intelligence

Robots applications in our daily life increase at an unprecedented pace. As robots will soon operate "out in the wild", we must identify the safety and security vulnerabilities they will face. Robotics researchers and manufacturers focus their attention on new, cheaper, and more reliable applications. Still, they often disregard the operability in adversarial environments where a trusted or untrusted user can jeopardize or even alter the robot's task. In this paper, we identify a new paradigm of security threats in the next generation of robots. These threats fall beyond the known hardware or network-based ones, and we must find new solutions to address them. These new threats include malicious use of the robot's privileged access, tampering with the robot sensors system, and tricking the robot's deliberation into harmful behaviors. We provide a taxonomy of attacks that exploit these vulnerabilities with realistic examples, and we outline effective countermeasures to prevent better, detect, and mitigate them.


Serendipity Brands is launching a 'Friends' ice cream pint, plus 3 movie-themed lines

FOX News

Learn how to whip up one of Serendipity 3's iconic desserts. Serendipity's new ice cream flavor will be there for you. At least it will be if you're a "Friends" fan who can't resist the brand's newest pint, which takes inspiration from the hit '90s TV show. Serendipity Brands announced it created a Central Perk Coffee Almond Fudge ice cream in partnership with Warner Bros. Consumer Products, according to a press release issued Tuesday. The Central Perk Coffee Almond Fudge comes in a purple carton and features the show's iconic logo and orange couch.


Approximation Algorithms for Active Sequential Hypothesis Testing

arXiv.org Machine Learning

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.


Data Discovery Platforms and Their Open Source Solutions

#artificialintelligence

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.


AI 4 Proteins 2021 Sponsors : AI 4 Scientific Discovery

#artificialintelligence

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.


Hitting the Books: The Brooksian revolution that led to rational robots

Engadget

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.


Pope seeks 'Copernican revolution' for post-COVID economy

FOX News

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.


Play breeds better thinkers

Science

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"


Neural Storage: A New Paradigm of Elastic Memory

arXiv.org Artificial Intelligence

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.