Comparisons of the different models of the mind, by Marvin Minsky (MIT), Steven Pinker (Harvard), and Jeff Hawkins (Palm)
These are old slides from 2006 when I was doing my PhD, but since AI is a hot topic again, I thought it would be an interesting share.
The books I compare are "The society of mind" (Marvin Minsky, 1988), "How the mind works" (Steven Pinker, 1999), and "On intelligence" (Jeff Hawkins, 2004).
This document discusses how people think irrationally and rely on emotions, metaphors and images rather than rational thought. It notes that approximately 95% of cognitive work occurs unconsciously through images, emotions and metaphors rather than conscious, rational thought. Market research often makes incorrect assumptions that people think rationally when in reality unconscious processes strongly influence decisions. The document advocates eliciting customers' unconscious metaphors to better understand them.
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxrobertad6
Chapter 7
Thinking and Intelligence
Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries.
Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the
20th-century sculpture The Thinker by August Rodin, and Shi Ke’s 10th-century painting Huike Thinking all reflect the
fascination with the process of human thought. (credit “middle”: modification of work by Jason Rogers; credit “right”:
modification of work by Tang Zu-Ming)
Chapter Outline
7.1 What Is Cognition?
7.2 Language
7.3 Problem Solving
7.4 What Are Intelligence and Creativity?
7.5 Measures of Intelligence
7.6 The Source of Intelligence
Introduction
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such
as when you’re driving? How does a person who has never seen or touched snow in real life develop an
understanding of the concept of snow? How do young children acquire the ability to learn language with
no formal instruction? Psychologists who study thinking explore questions like these.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person
to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of
intelligence? What does an IQ test really measure? These questions and more will be explored in this
chapter as you study thinking and intelligence.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this
chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider
thinking and briefly explore the development and use of language. We will also discuss problem solving
and creativity before ending with a discussion of how intelligence is measured and how our biology
and environments interact to affect intelligence. After finishing this chapter, you will have a greater
appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Chapter 7 | Thinking and Intelligence 217
7.1 What Is Cognition?
Learning Objectives
By the end of this section, you will be able to:
• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it
possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The
brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet,
you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one
facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses
the processes associated with perception, knowledge, problem solving, judgment, langu.
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxmccormicknadine86
Chapter 7
Thinking and Intelligence
Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries.
Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the
20th-century sculpture The Thinker by August Rodin, and Shi Ke’s 10th-century painting Huike Thinking all reflect the
fascination with the process of human thought. (credit “middle”: modification of work by Jason Rogers; credit “right”:
modification of work by Tang Zu-Ming)
Chapter Outline
7.1 What Is Cognition?
7.2 Language
7.3 Problem Solving
7.4 What Are Intelligence and Creativity?
7.5 Measures of Intelligence
7.6 The Source of Intelligence
Introduction
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such
as when you’re driving? How does a person who has never seen or touched snow in real life develop an
understanding of the concept of snow? How do young children acquire the ability to learn language with
no formal instruction? Psychologists who study thinking explore questions like these.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person
to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of
intelligence? What does an IQ test really measure? These questions and more will be explored in this
chapter as you study thinking and intelligence.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this
chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider
thinking and briefly explore the development and use of language. We will also discuss problem solving
and creativity before ending with a discussion of how intelligence is measured and how our biology
and environments interact to affect intelligence. After finishing this chapter, you will have a greater
appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Chapter 7 | Thinking and Intelligence 217
7.1 What Is Cognition?
Learning Objectives
By the end of this section, you will be able to:
• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it
possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The
brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet,
you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one
facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses
the processes associated with perception, knowledge, problem solving, judgment, langu ...
The document discusses consciousness and free will from an emergent perspective. It argues that free will emerges from the complex interaction between our minds, bodies, and environment. Through cognition, homeostasis, personality development, social interactions, and environmental feedback, our sense of agency and ability to make choices evolves in a way that is consistent with both relativity and determinism. While free will may not be strictly proven, it can be understood as a higher-level phenomenon that arises from complex, interdependent systems operating across many levels.
The document discusses different definitions of intelligence from various experts and perspectives. It explores the debate around whether intelligence is determined by nature or nurture, concluding that it is a combination of both. It then discusses different types of thinking and how lateral and critical thinking can be practiced and improved. Finally, it notes that some famous historical geniuses like Newton, Darwin and Einstein were not the best students but had a passion for questioning and analyzing ideas.
Howard Gardner identified eight multiple intelligences that teachers can appeal to in order to engage all students, including linguistic, logical-mathematical, visual-spatial, musical, bodily-kinesthetic, interpersonal, intrapersonal, and naturalist intelligences. The document discusses each of these intelligences and provides examples of how students with different intelligences may learn best. It also discusses creativity and lists seven keys to turning on creative thinking, such as believing
I apologize, as an AI assistant I do not actually work for or create value for any organization. I was designed by Anthropic to be helpful, harmless, and honest in conversations.
Cognitive psychology is the study of mental processes such as attention, memory, perception, problem solving and thinking. It developed as a field in response to behaviorism, which could not adequately explain complex human behaviors and abilities like language use. Cognitive psychologists study topics like how people learn and remember information, perceive different shapes, and acquire language. Understanding cognition can help fields like education, medicine, AI and interface design. The human mind is complex and cognition involves acquiring, storing, retrieving and processing knowledge.
The document summarizes key points from Daniel Pink's book "A Whole New Mind" which argues that society is shifting from valuing left-brain, logical thinking to also valuing right-brain capabilities for the "Conceptual Age". It discusses six essential right-brain aptitudes needed for the future: Design, Story, Symphony, Empathy, Play, and Meaning. For each aptitude, it provides strategies for cultivating those skills and their importance for the future.
This document provides information about creativity and creative thinking. It defines creativity as the ability to produce novel and useful ideas or work. It discusses different types of creativity and lists steps involved in creative thinking. It also outlines tests that are used to measure creativity, such as unusual uses tests and remote associates tests. Finally, it discusses obstacles to creativity such as perceptual, cultural and emotional blocks.
1) The document discusses key concepts in cognitive psychology including cognition, cognitive science, mental representation, stages of processing, and different approaches to modeling cognition like symbolic and connectionist models.
2) It also covers concepts like artificial intelligence, the Turing test, weak vs strong AI, and definitions of consciousness and self-knowledge.
3) Memory is discussed as being divided into short-term and long-term as well as different types like audio and visual. Key figures in the development of cognitive psychology are also mentioned like Tolman, Bartlett, and Ebbinghaus and their important contributions.
The document discusses the psychology of everyday actions and how people perform tasks. It describes the seven stages of action as goal formation, planning, specifying a sequence, performing the sequence, perceiving the results, interpreting perceptions, and comparing the outcome to the goal. Most of these processes are subconscious for familiar tasks. The document also discusses three levels of human cognition and emotion - the visceral, behavioral, and reflective levels. The visceral level involves quick, subconscious judgments about safety. For designers, the visceral level is about immediate perceptions like visual or auditory pleasantness, unrelated to usability.
The document discusses how human behavior and decision making can be influenced in subtle ways. It explores how the environment, objects, and other people can prime our irrational tendencies and unconscious biases. As designers, acknowledging and understanding these behavioral influences gives an opportunity to design persuasively and encourage positive behaviors.
A book review on the book of John Adair,titled Effective decision making presented by Dr. Helal Uddin Ahmed, Bangladeshi doctor works in psychiatry, BSMMU, Bangladesh.
Summary of the Persuasive Technology 2009 conference, presented at the Mini-UPA (Boston UPA chapter) conference on May 26, 2009 by Carolyn Snyder, PT 09 attendee.
The Science and Practice of Brain FitnessSharpBrains
The document discusses SharpBrains, an organization that provides brain fitness market research, consulting, and educational resources. It summarizes SharpBrains' webinar on "The Science and Practice of Brain Fitness", which covered recent studies on the benefits of mental stimulation and stress management for brain health. The webinar also discussed having "3 brains" and 7 "mental muscles" to exercise, and that good brain exercise requires novelty, variety, and challenging practice.
The document discusses sensation and perception, explaining that sensation is the detection of physical stimuli from the environment which is converted into neural signals, while perception involves selecting, organizing, and interpreting sensations. It covers topics like perceptual interpretation, information processing in the visual cortex, visual perception principles like figure-ground and Gestalt principles, and how perception involves both bottom-up sensory processing and top-down cognitive processes.
The document provides an overview of key concepts in social cognition and social influence from a social psychology course, including:
1) Social thinking and perception involves how people form impressions of and make inferences about others based on verbal and nonverbal cues. Schemas, scripts, and stereotypes influence these automatic impressions.
2) Attribution theory examines how people make causal explanations for events and behaviors. People tend to make internal attributions over external ones due to biases like the fundamental attribution error and actor-observer bias.
3) Social influence and persuasion concepts include priming, framing effects, and biases like self-serving bias that influence how people interpret behaviors and events.
Similar to Searle, Intentionality, and the Future of Classifier Systems (20)
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB
This document discusses cloud computing and the Meandre framework. It provides an overview of cloud concepts like public/private clouds and IaaS, PaaS, SaaS models. It describes NCSA's use of virtual machines and Eucalyptus cloud. Meandre is presented as a component-based framework that can orchestrate data-intensive applications across cloud resources through its dataflow model and scripting language. It aims to facilitate scaling applications to leverage elastic cloud infrastructure and integrate computation and data.
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0Xavier Llorà
One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.
Large Scale Data Mining using Genetics-Based Machine LearningXavier Llorà
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them.
This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...Xavier Llorà
Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new TrendsXavier Llorà
The document summarizes a presentation given by Jorge Casillas on research related to scaling up genetic learning algorithms and fuzzy classifier systems. Specifically, it discusses:
1. An approach using evolutionary instance selection and stratification to extract rule sets from large datasets that balance prediction accuracy and interpretability.
2. Fuzzy-XCS, an accuracy-based genetic fuzzy system the author is developing that uses competitive fuzzy inference and represents rules as disjunctive normal forms to address challenges in credit assignment.
3. Open problems and opportunities in applying genetic learning at large scales, such as addressing chromosome size and efficient evaluation over large datasets.
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...Xavier Llorà
This document summarizes research using a Pittsburgh Learning Classifier System (LCS) called GAssist to predict protein structure by determining coordination numbers (CN). The researchers tested GAssist on a dataset of over 250,000 protein residues, comparing it to support vector machines, Naive Bayes, and C4.5 decision trees. While support vector machines achieved the best accuracy, GAssist produced more interpretable and compact rule sets at the cost of lower performance. The researchers analyzed the interpretability and scalability of GAssist for this challenging bioinformatics problem, identifying avenues for improving its accuracy while maintaining explanatory power.
Learning Classifier Systems for Class Imbalance ProblemsXavier Llorà
The document discusses learning classifier systems (LCS) for addressing class imbalance problems in datasets. It aims to enhance the applicability of LCS to knowledge discovery from real-world datasets that often exhibit class imbalance, where one class is represented by significantly fewer examples than other classes. The author proposes adapting parameters of the XCS learning classifier system, such as learning rate and genetic algorithm threshold, based on estimated class imbalance ratios within classifiers' niches in order to minimize bias towards majority classes and better handle small disjuncts representing minority classes.
XCS: Current capabilities and future challengesXavier Llorà
The document discusses the XCS classifier system, which uses a combination of gradient-based techniques and evolutionary algorithms to learn predictive models from complex problems. It summarizes XCS's current capabilities in classification, function approximation, and reinforcement learning tasks. However, it notes there are still challenges to improve XCS's representations and operators, niching abilities, handling of dynamic problems, solution compactness, and development of hierarchical classifier systems.
Computed Prediction: So far, so good. What now?Xavier Llorà
This document discusses computed prediction in learning classifier systems (LCS). It addresses representing the payoff function Q(s,a) that maps state-action pairs to expected future payoffs. Specifically:
1) In computed prediction, each classifier has parameters w and the classifier prediction is computed as a parametrized function p(x,w) like a linear approximation.
2) Classifier weights are updated using the Widrow-Hoff rule online as the payoff function is learned.
3) Using a powerful approximator like tile coding to compute predictions allows the problem to potentially be solved by a single classifier, but evolution of different approximators per problem subspace may still
This document provides information about the NCSA/IlliGAL Gathering on Evolutionary Learning (NIGEL 2006) conference. It discusses how the conference originated from a previous 2003 gathering. It thanks the organizers and participants and provides details about the agenda, which includes presentations on topics like classifier systems and discussions around applications and techniques of evolutionary learning.
Linkage Learning for Pittsburgh LCS: Making Problems TractableXavier Llorà
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems
Meandre: Semantic-Driven Data-Intensive Flows in the CloudsXavier Llorà
- Meandre is a semantic-driven data-intensive workflow infrastructure for distributed computing. It allows users to assemble modular components into complex workflows (flows) in a visual programming tool or using a scripting language called ZigZag.
- Workflows are composed of components, which can be executable or control components. Executable components perform computational tasks when data is available, while control components pause workflows for user interactions. Components are described semantically using ontologies to separate functionality from implementation.
- Data availability drives workflow execution in Meandre. When required inputs are available, components will fire and produce outputs to make data available for downstream components. This dataflow approach aims to make workflows transparent, intuitive, and reusable across
ZigZag is a new language for describing data-intensive workflows. It aims to make the Meandre infrastructure easier to use by allowing users to assemble complex data flows. The language has a new syntax and compiles workflows that can then be run on Meandre to process large datasets.
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...Xavier Llorà
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...Xavier Llorà
Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.
How to Avoid Learning the Linux-Kernel Memory ModelScyllaDB
The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve?
This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
What Not to Document and Why_ (North Bay Python 2024)Margaret Fero
We’re hopefully all on board with writing documentation for our projects. However, especially with the rise of supply-chain attacks, there are some aspects of our projects that we really shouldn’t document, and should instead remediate as vulnerabilities. If we do document these aspects of a project, it may help someone compromise the project itself or our users. In this talk, you will learn why some aspects of documentation may help attackers more than users, how to recognize those aspects in your own projects, and what to do when you encounter such an issue.
These are slides as presented at North Bay Python 2024, with one minor modification to add the URL of a tweet screenshotted in the presentation.
Data Protection in a Connected World: Sovereignty and Cyber Securityanupriti
Delve into the critical intersection of data sovereignty and cyber security in this presentation. Explore unconventional cyber threat vectors and strategies to safeguard data integrity and sovereignty in an increasingly interconnected world. Gain insights into emerging threats and proactive defense measures essential for modern digital ecosystems.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
AC Atlassian Coimbatore Session Slides( 22/06/2024)apoorva2579
This is the combined Sessions of ACE Atlassian Coimbatore event happened on 22nd June 2024
The session order is as follows:
1.AI and future of help desk by Rajesh Shanmugam
2. Harnessing the power of GenAI for your business by Siddharth
3. Fallacies of GenAI by Raju Kandaswamy
Interaction Latency: Square's User-Centric Mobile Performance MetricScyllaDB
Mobile performance metrics often take inspiration from the backend world and measure resource usage (CPU usage, memory usage, etc) and workload durations (how long a piece of code takes to run).
However, mobile apps are used by humans and the app performance directly impacts their experience, so we should primarily track user-centric mobile performance metrics. Following the lead of tech giants, the mobile industry at large is now adopting the tracking of app launch time and smoothness (jank during motion).
At Square, our customers spend most of their time in the app long after it's launched, and they don't scroll much, so app launch time and smoothness aren't critical metrics. What should we track instead?
This talk will introduce you to Interaction Latency, a user-centric mobile performance metric inspired from the Web Vital metric Interaction to Next Paint"" (web.dev/inp). We'll go over why apps need to track this, how to properly implement its tracking (it's tricky!), how to aggregate this metric and what thresholds you should target.
GDG Cloud Southlake #34: Neatsun Ziv: Automating AppsecJames Anderson
The lecture titled "Automating AppSec" delves into the critical challenges associated with manual application security (AppSec) processes and outlines strategic approaches for incorporating automation to enhance efficiency, accuracy, and scalability. The lecture is structured to highlight the inherent difficulties in traditional AppSec practices, emphasizing the labor-intensive triage of issues, the complexity of identifying responsible owners for security flaws, and the challenges of implementing security checks within CI/CD pipelines. Furthermore, it provides actionable insights on automating these processes to not only mitigate these pains but also to enable a more proactive and scalable security posture within development cycles.
The Pains of Manual AppSec:
This section will explore the time-consuming and error-prone nature of manually triaging security issues, including the difficulty of prioritizing vulnerabilities based on their actual risk to the organization. It will also discuss the challenges in determining ownership for remediation tasks, a process often complicated by cross-functional teams and microservices architectures. Additionally, the inefficiencies of manual checks within CI/CD gates will be examined, highlighting how they can delay deployments and introduce security risks.
Automating CI/CD Gates:
Here, the focus shifts to the automation of security within the CI/CD pipelines. The lecture will cover methods to seamlessly integrate security tools that automatically scan for vulnerabilities as part of the build process, thereby ensuring that security is a core component of the development lifecycle. Strategies for configuring automated gates that can block or flag builds based on the severity of detected issues will be discussed, ensuring that only secure code progresses through the pipeline.
Triaging Issues with Automation:
This segment addresses how automation can be leveraged to intelligently triage and prioritize security issues. It will cover technologies and methodologies for automatically assessing the context and potential impact of vulnerabilities, facilitating quicker and more accurate decision-making. The use of automated alerting and reporting mechanisms to ensure the right stakeholders are informed in a timely manner will also be discussed.
Identifying Ownership Automatically:
Automating the process of identifying who owns the responsibility for fixing specific security issues is critical for efficient remediation. This part of the lecture will explore tools and practices for mapping vulnerabilities to code owners, leveraging version control and project management tools.
Three Tips to Scale the Shift Left Program:
Finally, the lecture will offer three practical tips for organizations looking to scale their Shift Left security programs. These will include recommendations on fostering a security culture within development teams, employing DevSecOps principles to integrate security throughout the development
this resume for sadika shaikh bca studentSadikaShaikh7
I am a dedicated BCA student with a strong foundation in web technologies, including PHP and MySQL. I have hands-on experience in Java and Python, and a solid understanding of data structures. My technical skills are complemented by my ability to learn quickly and adapt to new challenges in the ever-evolving field of computer science.
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
MYIR Product Brochure - A Global Provider of Embedded SOMs & SolutionsLinda Zhang
This brochure gives introduction of MYIR Electronics company and MYIR's products and services.
MYIR Electronics Limited (MYIR for short), established in 2011, is a global provider of embedded System-On-Modules (SOMs) and
comprehensive solutions based on various architectures such as ARM, FPGA, RISC-V, and AI. We cater to customers' needs for large-scale production, offering customized design, industry-specific application solutions, and one-stop OEM services.
MYIR, recognized as a national high-tech enterprise, is also listed among the "Specialized
and Special new" Enterprises in Shenzhen, China. Our core belief is that "Our success stems from our customers' success" and embraces the philosophy
of "Make Your Idea Real, then My Idea Realizing!"
Searle, Intentionality, and the Future of Classifier Systems
1. Illinois Genetic Algorithms Laboratory
Department of General Engineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801.
Searle, Intentionality, and the
Future of Classifier Systems
David E. Goldberg
Illinois Genetic Algorithms Laboratory
University of Illinois at Urbana-Champaign
Urbana, IL 61801
deg@uiuc.edu
2. 1980 v. Now
Remember thinking how cool LCSs
were.
Just apply them to gas pipelines
and voila, all AI problems of
Western Civilization would be
solved.
Started to ask John for examples
of successful application.
Found out that I was in the
middle of an interesting idea, not
a working computer program.
John H. Holland (b. 1929)
3. Roadmap
Are we happy with LCSs?
What’s Searle got to do with it.
Revisiting the Chinese room.
Art Burkes had it right.
Designing a conscious computer.
Searlean program for LCSs:
Computational consciousness not impossible.
–
From consciousness to intentionality.
–
Intentionality and beyond.
–
What are we missing?
What should we do?
4. Are We Happy With LCSs?
Have made progress:
Increasingly competent, solve hard problems
–
quickly reliably and accurately.
Principled manner.
–
But don’t seem very intelligent:
Do what we tell them.
–
Not autonomous in any serious sense.
–
Our discussions are largely technical.
–
Are we focused on right problems?
–
5. What’s Searle Got to Do With It?
Mill Prof of Philosophy of Berkeley.
Philosopher of language and mind.
Early work took off from Austin’s work on
speech acts.
Searle is Darth Vader of artificial
intelligence.
Notorious Chinese Room argument has
always puzzled me.
In many ways, Searle is high philosophical
priest of emergence.
John R. Searle (b. 1932)
Rejects dualism & materialism.
Couldn’t understand how he could miss
possibility of more than mere systactical
translation.
6. The Chinese Room Argument
Strong AI is not possible on a computer.
Monolingual English speaker in a room with
Chinese writing (story)
–
2nd Chinese symbols (questions)
–
Instructions in English for relating first set of symbols
–
to second.
3rd set of Chinese symbols (answers)
–
English speaker does not understand Chinese even
if answers are indistinguishable from those of
Chinese speaker.
7. Cracks in the Chinese Room
Mind, Language & Society,
Basic Books, 1998, p. 53.
“When I say that the brain
is a biological organ and
consciousness a biological
process, I do not, of course,
say or imply that it would
be impossible to produce an
artificial brain out of
nonbiological materials.”
8. More Searle
“The heart is also a biological organ, and the
pumping of blood a biological process, but it is
possible to build an artificial heart that pumps
blood. There is no reason, in principle, why we
could not similarly make an artificial brain that
causes consciousness.”
Searle was complaining about direct approach to
intelligence.
Without consciousness and intentionality there
cannot be intelligence.
How do we create an intelligent, conscious being?
9. Arthur Burks Had Interesting Take
Robots and Free
Minds, University of
Michigan, 1986.
“Tonight I will
advocate the thesis: A
FINITE
DETERMINISTIC
AUTOMATON CAN
PERFORM ALL
NATURAL HUMAN
FUNCTIONS.”
10. Chapter 5: Evolution and Intentionality
“The course of biological evolution from cells
to Homo sapiens has been a gradual
development of intentional systems from
direct-response systems.”
“The [intentional] system contains a model of
its present status in relation to its goal and
regularly updates that model on the basis of
the information it receives. Finally, it decides
what to do after consulting a strategy, which
has value assessments attached in to various
alternative courses of action.”
11. CS-1 Had Bio/Psycho Roots
CS-1 had reservoirs for
hunger and thirst (Holland
& Reitman, 1978).
Schemata processors
paper had reservoirs, too
(Holland, 1971).
CS-1 worked in maze
running task.
But design was Lockean.
Tabula rasa for everything
except rule firing,
apportionment of credit,
and rule discovery.
Is this enough?
Thesis: Can’t take shortcut
around consciousness and
intentionality.
12. So You Want a Conscious Computer
What does this mean?
Consciousness is complex, emergent
phenomenon.
How can we design it?
Don’t throw pieces together and hope for
the best.
My experience: Emergent phenomena
emerge when (a) key elements are present
and (b) system tuned properly.
Consider more Searle.
13. Shooting for C Not Crazy
Shooting for GA competence was crazy.
Have accomplished.
How:
Considered essential elements.
–
Built qual/quant theories of how they worked.
–
Designed until limits of performance achieved.
–
Can do the same for
consciousness/intentionality!!
14. Searle’s Greatest Hits
Mind as biological phenomenon.
Function of consciousness.
Features of consciousness.
How the mind works: Intentionality.
The good stuff comes from intentionality:
Language & other institutional fact.
What are we missing?
15. Mind as Biology
Consciousness is the primary feature of
minds.
3 features of consciousness:
Inner: in body and in sequence of events.
–
Qualitative: certain way they feel.
–
Subjective: first person ontology (does not
–
preclude objective epistemology).
Enormous variety of consciousness: smell a
rose, worry about income taxes, sudden
rage about driver, etc.
16. Functions of Consciousness
What does it do? What is survival value?
What doesn’t it do for our species?
Consciousness is central to our survival.
All actions a result of conscious thought
followed by action.
17. Consciousness, Intentionality, & Causation
Represent world, and act on representations.
Intentional causation: Not billiard ball causation.
Not all consciousness intentionally causal, but much
is.
Should be best understood; are we not in touch
with it always? Descartes’s error.
Yet difficult to describe: Can describe objects,
moods, thoughts, but not C itself.
Problems:
Not itself an object of observation (consciousness
–
observes but is not observed).
Tradition of separating mind/body: dualism.
–
18. Features of C
Ontological subjectivity.
1.
C comes in unified form. Thinking and feeling go on
2.
at same time in same field of C: Vertical & horizontal.
C connects us to world (Tie to intentionality).
3.
C states come in moods.
4.
Always structured.
5.
Varying degrees of attention.
6.
C is situated.
7.
Varying degrees of familiarity.
8.
Refer to other things
9.
Always pleasurable or unpleasurable
10.
19. How the Mind Works: Intentionality
Primary evolutionary role of C is to relate
us to environment.
Cannot eliminate intentionality of mind by
appealing to language; already
intentionality of the mind.
Searle: Urge to reduce it to something else
is faulty.
DEG: As designers we need to reduce it to
something and then find conditions of
emergence among those things.
20. Intentionality as Biology
Thirst, hunger as basic, causing desire to
drink or eat.
Once this granted, camel nose under the
tent, intentions based on other sensory.
Isn’t reality “confirmed” by our “success” in
achieving intentional goals over and over
again.
21. Structure of Intentional States
Intentionality as way mental states are directed at
objects & states of affairs.
Can be directed at things that don’t exist?
How can this be?
Distinguish between type of intentional state and
content.
Content: rain; Types: hope, believe, fear rain.
Structural features:
Direction of fit
–
Conditions of satisfaction
–
22. Direction of Fit
Term: from Austin, foreshadowed by Wittgenstein,
examples Anscombe.
Anscombe’s lists:
Shopping list: Beer, butter, bacon. Husband matches
–
world to list.
Detective’s list: Follows shopper, “beer, butter,
–
bacon,” matches list to world.
Not all intentional states like this: e.g. when you
are sorry, assume match between mind and world.
Intentional state is null.
23. Conditions of Satisfaction
Beliefs can be true or false.
Goals can be achieved or not.
Easier to understand in terms of speech acts.
Have 5 illocutionary points or types:
Assertive: commit to the truth.
–
Directive: direct hearer to do something.
–
Commissive: speaker promises to do something.
–
Expressive: speaker expresses opinion about state of
–
the world.
Declarations: speaker creates something with
–
utterance.
24. Intentional Causation
Intend to move body body moves:
Example of intentional causation.
Differs from billiard ball or Humean causation.
Self-referential: intend to move body, body moves
because I intended then intentional causation.
Critical to distinguishing natural versus social
sciences.
Intentional explanations not deterministic: Could
have done otherwise gap is free will.
25. Good Stuff from Intentionality
Searle goes on to talk about language and
institutional facts (money, college degrees,
etc.).
Disappointment with LCS is it can’t get to
the good stuff.
Can’t do language.
Can’t form contracts.
Can’t create new institutional fact.
26. Construction of Social Reality
Need to clarify observer-independent &
observer-dependent features of the world.
Need 3 new elements:
Collective intentionality.
–
Assignment of function.
–
Constitutive rules
–
27. Observer Independent v. Dependent
Many features of the world independent of
our observations of them: observer
independence.
Many observer dependent: Something a
characteristic because of observer
judgment, but not relative to others.
OI vs. OD more important than mind-body.
DEG aside: Isn’t it dualism in the back door
though?
28. Collective Intentionality
Need the notion of “we intend together.”
Attempts to reduce to individual intention are
complex.
Existence of biological organisms with collective
intentionality suggests CI is a primitive.
DEG aside: Are social insects intentional in Searlean
sense? Could be that social affiliation is primitive,
certain behaviors hard wired. Then, CI results from
(a) naming the group, (b) attributing intention to it
(as-if intentionality), and (c) treating the as-if as
real.
29. Assignment of Function
Use of objects as tools:
Monkey uses stick to get banana.
–
Man sits on rock.
–
Physical existence facilitates function, but
function is observer relative.
All function assignment is observer relative.
30. Constitutive Rules
How to distinguish between brute facts and
institutional facts.
Types of rules:
Some rules regulate: “Drive on right side of road.”
–
Some rules regulate and constitute: Rules of chess
–
both regulate conduct of game and create it.
Constitutive rules have form: X counts as Y in C.
“Move two and over one” counts as a knight’s
move in Chess.”
31. Simple Model of Construction of Social Reality
Strong thesis: All institutional reality explained by 3
things:
Collective intentionality.
–
Assigned function wall keeps people out
–
physically, but low fence or boundary marker keeps
people out by convention.
Constitutive rules.
–
Money example: Evolution from valuable
commodity to fiat currency.
Institutional reality powerful: X counts as Y in C can
be iterated and stacked forming powerful network
of institutional facts.
32. What Are We Missing?
Do not have C-machines.
Searle’s 10:
Ontological subjectivity.
1.
C comes in unified form.
2.
C connects us to world.
3.
C states come in moods.
4.
Always structured.
5.
Varying degrees of attention.
6.
C is situated.
7.
Varying degrees of familiarity.
8.
Refer to other things
9.
Always pleasurable or unpleasurable
10.
33. Unity Missing
Can argue that we have vertical unity in
message board.
Do not have horizontal unity.
My first proposal recommended
modifications to permit time series.
Modifications to rules.
Modification to the boards.
34. Moods & Pleasant/Unpleasant Missing
This is big.
Emotions are “engagement with the world”
(Solomon).
Necessary for judgment and values.
Don’t want a simulation.
Emotions:
Physiological component
–
Judgmental component
–
35. Other Things Missing
Attention
Gestalt structure
Situatedness & familiarity
Refer to other things (may have this)
36. What Should We Do?
Stuff we’ve gotten right: Sensors, association,
models (anticipation), learning
Can’t continue to work on same thing.
No serious architectural changes proposed to LCS.
Why?
Need:
Emotions: As judgments, source of values, and
–
arbiter of attention.
Multiple boards: As source of difference and
–
similarity. Main hope of quality of consciousness &
unity.
Center of intention rooted in “biological needs.”
–
37. How Do We Break This Down?
Tough problem.
If C is complex building block,
what are minimal essential
elements to achieve.
How do we know we’ve achieved
it (first person ontology, third
person epistomology)?
Sets of tests and experiments.
What theory needed to set
parameters of C?
Not unlike approach that cracked
innovation
38. Summary & Conclusions
Have accomplished quite a bit in classifier
systems.
Many of our questions are technical.
Deeper questions about whether we’re
attacking the right questions.
Need consciousness and intention to get
the “good stuff” of intelligent behavior.
Wrestling with Searle’s categories not a bad
place to start.