How about "Introduction to AI: Understanding the Basics"? It's a simple yet relevant topic that can cover fundamental concepts of artificial intelligence in a concise manner.
The document discusses different definitions of artificial intelligence, including studying how to make computers solve problems requiring knowledge and intelligence, creating machines that perform intelligent functions, and studying mental faculties through computational models. It also examines what intelligence is, different approaches to AI such as symbol systems and reasoning, and challenges like the Turing Test which aim to determine if a machine can exhibit intelligent behavior. Key debates in AI are discussed around what constitutes intelligence and how to define and measure intelligent behavior in machines.
The document introduces an artificial intelligence course, discussing why AI is studied, potential benefits, and definitions of AI. It explores different approaches to AI like systems that act intelligently by passing the Turing test or thinking rationally. The document also provides a brief history of AI, discussing pioneers in the field and important questions and challenges in developing intelligent systems.
The document introduces an artificial intelligence course, discussing why AI is studied, potential benefits, and definitions of AI. It explores different approaches to AI like systems that act intelligently by passing the Turing test or thinking rationally. The document also provides a brief history of AI, discussing pioneers in the field and important questions and challenges in developing intelligent systems.
This document provides an introduction to an artificial intelligence course. It discusses why AI is an important field of study and provides definitions of AI from several experts. It also explores different approaches to AI like acting humanly by passing the Turing test, thinking humanly by understanding brain function, thinking rationally through logic, and acting rationally to achieve goals. The document examines key issues and questions in AI and outlines important foundations and history. It analyzes components of AI systems and properties of different environments agents can operate in.
The document discusses artificial intelligence and provides definitions of AI from various sources. It examines different approaches to AI such as systems that act humanly by passing the Turing test, think humanly by modeling the brain, think rationally by using logic, and act rationally by achieving goals. The document also discusses the history and components of AI systems, including agents, environments, and the PEAS framework for describing tasks.
This document provides an overview of an introductory lecture on artificial intelligence and expert systems. It discusses the Turing Test, definitions of artificial intelligence, a brief history of AI including important figures and milestones, and examples of what current AI systems can and cannot do.
Most of the examples listed can currently be done to some degree by AI/robotic systems, though often with limitations compared to human capabilities. Here are a few highlights of what has and hasn't been fully achieved:
- Decent table tennis play has been achieved through computer vision, motion planning, and robotics, though not at a professional human level across all situations.
- Autonomous driving has progressed significantly in structured environments like highways, but unconstrained mountain roads with tight curves present greater challenges due to limitations in perception, prediction, and control for high-speed maneuvering.
- Driving autonomously through dense urban environments like city centers is extremely difficult given the complex interactions between many road users and need to understand and
The document discusses different definitions of artificial intelligence, including studying how to make computers solve problems requiring knowledge and intelligence, creating machines that perform intelligent functions, and studying mental faculties through computational models. It also examines what intelligence is, different approaches to AI such as symbol systems and reasoning, and challenges like the Turing Test which aim to determine if a machine can exhibit intelligent behavior. Key debates in AI are discussed around what constitutes intelligence and how to define and measure intelligent behavior in machines.
The document introduces an artificial intelligence course, discussing why AI is studied, potential benefits, and definitions of AI. It explores different approaches to AI like systems that act intelligently by passing the Turing test or thinking rationally. The document also provides a brief history of AI, discussing pioneers in the field and important questions and challenges in developing intelligent systems.
The document introduces an artificial intelligence course, discussing why AI is studied, potential benefits, and definitions of AI. It explores different approaches to AI like systems that act intelligently by passing the Turing test or thinking rationally. The document also provides a brief history of AI, discussing pioneers in the field and important questions and challenges in developing intelligent systems.
This document provides an introduction to an artificial intelligence course. It discusses why AI is an important field of study and provides definitions of AI from several experts. It also explores different approaches to AI like acting humanly by passing the Turing test, thinking humanly by understanding brain function, thinking rationally through logic, and acting rationally to achieve goals. The document examines key issues and questions in AI and outlines important foundations and history. It analyzes components of AI systems and properties of different environments agents can operate in.
The document discusses artificial intelligence and provides definitions of AI from various sources. It examines different approaches to AI such as systems that act humanly by passing the Turing test, think humanly by modeling the brain, think rationally by using logic, and act rationally by achieving goals. The document also discusses the history and components of AI systems, including agents, environments, and the PEAS framework for describing tasks.
This document provides an overview of an introductory lecture on artificial intelligence and expert systems. It discusses the Turing Test, definitions of artificial intelligence, a brief history of AI including important figures and milestones, and examples of what current AI systems can and cannot do.
Most of the examples listed can currently be done to some degree by AI/robotic systems, though often with limitations compared to human capabilities. Here are a few highlights of what has and hasn't been fully achieved:
- Decent table tennis play has been achieved through computer vision, motion planning, and robotics, though not at a professional human level across all situations.
- Autonomous driving has progressed significantly in structured environments like highways, but unconstrained mountain roads with tight curves present greater challenges due to limitations in perception, prediction, and control for high-speed maneuvering.
- Driving autonomously through dense urban environments like city centers is extremely difficult given the complex interactions between many road users and need to understand and
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
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Introduction to AI and explain the magic of the magic of Nosql and explain in the following terms of Nosql and disadvantage
1. AI Definitions
• The study of how to make programs/computers do
things that people do better
• The study of how to make computers solve problems
which require knowledge and intelligence
• The exciting new effort to make computers think …
machines with minds
• The automation of activities that we associate with
human thinking (e.g., decision-making, learning…)
• The art of creating machines that perform functions
that require intelligence when performed by people
• The study of mental faculties through the use of
computational models
• A field of study that seeks to explain and emulate
intelligent behavior in terms of computational
processes
• The branch of computer science that is concerned
with the automation of intelligent behavior
Thinking
machines or
machine
intelligence
Studying
cognitive
faculties
Problem
Solving and
CS
2. So What Is AI?
• AI as a field of study
– Computer Science
– Cognitive Science
– Psychology
– Philosophy
– Linguistics
– Neuroscience
• AI is part science, part engineering
• AI often must study other domains in order to implement systems
– e.g., medicine and medical practices for a medical diagnostic system,
engineering and chemistry to monitor a chemical processing plant
• AI is a belief that the brain is a form of biological computer and
that the mind is computational
• AI has had a concrete impact on society but unlike other areas of
CS, the impact is often
– felt only tangentially (that is, people are not aware that system X has AI)
– felt years after the initial investment in the technology
3. What is Intelligence?
• Is there a “holistic” definition for intelligence?
• Here are some definitions:
– the ability to comprehend; to understand and profit from experience
– a general mental capability that involves the ability to reason, plan, solve
problems, think abstractly, comprehend ideas and language, and learn
– is effectively perceiving, interpreting and responding to the environment
• None of these tells us what intelligence is, so instead, maybe we
can enumerate a list of elements that an intelligence must be able
to perform:
– perceive, reason and infer, solve problems, learn and adapt, apply
common sense, apply analogy, recall, apply intuition, reach emotional
states, achieve self-awareness
• Which of these are necessary for intelligence? Which are
sufficient?
• Artificial Intelligence – should we define this in terms of human
intelligence?
– does AI have to really be intelligent?
– what is the difference between being intelligent and demonstrating
intelligent behavior?
4. Physical Symbol System Hypothesis
• A physical symbol system (PSS)
– consists of symbols (patterns) which are combinable into
expressions
– there are processes which operate on these symbols to create
new symbols and expressions
• consider for instance English as a physical symbol system
• symbols are the alphabet
• expressions are words and sentences
• the processes are the English grammar and parsers and dictionaries
• The PSS Hypothesis states that a PSS has the necessary
and sufficient means for intelligent action
– a computer is a PSS
• if the PSS Hypothesis is true, then it should be possible to program a
computer to produce intelligent actions
• this is the (or a) goal of AI
• if the PSS Hypothesis is not true, what are the consequences for AI?
5. The Turing Test
• 1950 – Alan Turing
devised a test for
intelligence called the
Imitation Game
– Ask questions of two
entities, receive answers
from both
– If you can’t tell which of the
entities is human and which
is a computer program, then
you are fooled and we
should therefore consider the
computer to be intelligent
Questions
Answers Answers
Which is the person?
Which is the computer?
6. Solving the Turing Test: Eliza
• Created by Joseph Weizenbaum
• This system would act like a Rogerian psychoanalyst by
holding a dialog with a person
– the dialog would be consist of the doctor (Eliza) asking
questions, the human responding, and the doctor using the
response to ask another question
• Weizenbaum wrote the program so that it would
generate an English response/question based on a group
of patterns
– If the user sentence matched a pattern, this pattern would be
used to generate the next sentence/question
– A more recent, and much superior version is called A.L.I.C.E,
see http://alicebot.blogspot.com/
• click on “chat with A.L.I.C.E.”
7. Example
Eliza
Dialog
– H: Men are all alike
– E: In what way?
– H: They’re always bugging us about something or
another.
– E: Can you be more specific?
– H: Well, my boyfriend made me come here.
– E: Your boyfriend made you come here?
– H: He says I’m depressed much of the time.
– E: I am sorry to hear you are depressed.
– H: It’s true. I am unhappy.
– E: Do you think coming here will help you not to be
unhappy?
– H: I need some help, that much seems certain.
– E: What would it mean to you if you got some help?
– H: Perhaps I could learn to get along with my mother.
– E: Tell me more about your family
8. Eliza
Rules
(?* ?x) hello (?* ?y)
(How do you do. Please state your problem.)
(?* ?x) I want (?* ?y)
(What would it mean if you got ?y)
(Why do you want ?y)
(Suppose you got ?y soon)
(?* ?x) if (?* ?y)
(Do you really think its likely that ?y)
(Do you wish that ?y)
(What do you think about ?y) (Really-- if ?y)
(?* ?x) no (?* ?y) (Why not?)
(You are being a bit negative)
(Are you saying "NO" just to be negative?)
(?* ?x) I was (?* ?y) (Were you really?)
(Perhaps I already knew you were ?y)
(Why do you tell me you were ?y now?)
(?* ?x) I feel (?* ?y) (Do you often feel ?y ?)
(?* ?x) I felt (?* ?y)
(What other feelings do you have?)
Eliza also has a
rule to swap
‘I’ and ‘my’
to ‘you’ and ‘your’
9. Eliza Pattern Syntax
pat var match any one expression to a variable
constant or to a constant (see below)
segment-pat match against a sequence
single-pat match against one expression
(pat . pat) match the first and the rest of a list
single-pat
(?is var predicate) test predicate on one expression
(?or pat1 pat2 …) match on any of the patterns
(?and pat1 pat2 …) match on every of the expressions
(?not pat) match if expression does not match
segment-pat
((?* var) …) match on zero or more expressions
((?+ var) …) match on one or more expressions
((?? var) …) match zero or one expression
((?if expr) …) test if expression is true
var ?chars variables of the form ?name
constant atom symbols, numbers, chars
10. ALICE Demo
Jerry: my name is jerry.
ALICE: Hey jerry.
Jerry: do you know Turing?
ALICE: A brilliant and tragic figure in the history of computer science.
Jerry: who else do you know?
ALICE: Hugh Loebner, Robby Garner, and Steven Spielberg
Jerry: which film do you like?
ALICE: My favorite movie is Teknolust. Have you seen it?
Jerry: no.
ALICE: I would recommend you check it out.
Jerry: what is it about?
ALICE: Science fiction.
Jerry: do you remember my name?
ALICE: Your name is jerry, seeker.
Notice unlike Eliza, ALICE has a memory, can handle references
(have you seen it? no. I would recommend it.)
11. ALICE vs. Eliza
ALICE talking to ELIZA
ELIZA: I am ELIZA.
ALICE: I am very pleased to meet you ELIZA.
ELIZA: How can I help you?
ALICE: Just keep talking to me as long as you can.
ELIZA: Do you wish to as long as me?
ALICE: OK, I will as long as you.
ELIZA: Do you wish to will as long as me?
ALICE: OK, I will will as long as you.
ELIZA: Do you wish to will will as long as me?
ALICE: OK, I will will will as long as you.
ELIZA: Do you wish to will will will as long as me?
ALICE: OK, I will will will will as long as you.
ELIZA: Do you wish to will will will will as long as me?
ALICE: OK, I will will will will will as long as you.
ELIZA: Do you wish to will will will will will as long as me?
…
Eliza gets stuck on the phrase “I will” and then ALICE gets stuck
on the same phrase
12. How Useful is the Turing Test?
• With Eliza or Alice like rules, we can eventually solve the
Turing Test – it just takes writing enough rules
• Does the system understand what it is responding to?
– No, neither Eliza nor Alice understand the text, its just that Alice
has better, more in depth and wider ranging rules
• However, we could build a representation that
models some real-world domain and knowledge
base
– The system can fill in information from the conversation
• this is sort of like a database, or an object with data attributes to be filled in
• we can use a variety of AI representations like scripts, frames, semantic
networks
– Questions can be responded to by looking up the stored data
– In this way, the system is responding, not based merely on “canned”
knowledge, but on knowledge that it has “learned”
• Does this imply that the system knows what it is discussing?
– What does it mean to know something?
13. Table-Lookup vs. Reasoning
• Consider two approaches to programming a Tic-Tac-Toe player
– Solution 1: a pre-enumerated list of best moves given the board
configuration
– Solution 2: rules (or a heuristic function) that evaluate a board
configuration, and using these to select the next best move
• Solution 1 is similar to how Eliza works
– This is not practical for most types of problems
– Consider solving the game of chess in this way, or trying to come up with
all of the responses that a Turing Test program might face
• Solution 2 will reason out the best move
– Given the board configuration, it will analyze each available move and
determine which is the best
– Such a player might even be able to “explain” why it chose the move it did
• We can (potentially) build a program that can pass the Turing
Test using table-lookup even though it would be a large
undertaking
• Could we build a program that can pass the Turing Test using
reasoning?
– Even if we can, does this necessarily mean that the system is intelligent?
14. Slot Filling
• Roger Schank created the
Script representation
– the script describes typical
sequences of actions and
actors for some real-world
situation
– a story (newspaper article) is
parsed and slots are filled in
– SAM (Script Applier
Mechanism) uses the filled in
script to answer questions
• The Script provides the
knowledge needed to
respond like a human and
thus solve the Turing Test
Schank’s Restaurant script
15. The Chinese Room Problem
• From John Searle, Philosopher, in an attempt to
demonstrate that computers cannot be intelligent
– The room consists of you, a book, a storage area (optional),
and a mechanism for moving information to and from the
room to the outside
• a Chinese speaking individual provides a question for you in writing
• you are able to find a matching set of symbols in the book (and
storage) and write a response, also in Chinese
Question (Chinese)
Book of Chinese Symbols
Answer
(Chinese)
Storage You
16. Chinese Room:
An Analogy for a Computer
User Input I/O pathway (bus) Output
Memory Program/Data
(Script) CPU (SAM)
Note: Searle’s original Chinese Room actually was based on a
Script that was implemented in Chinese, our version is just a
variation on the same theme
17. Searle’s Question
• You were able to solve the problem of communicating with the
person/user and thus you/the room passes the Turing Test
• But did you understand the Chinese messages being
communicated?
– since you do not speak Chinese, you did not understand the symbols in the
question, the answer, or the storage
– can we say that you actually used any intelligence?
• By analogy, since you did not understand the symbols that you
interacted with, neither does the computer understand the
symbols that it interacts with (input, output, program code, data)
• Searle concludes that the computer is not intelligent, it has no
“semantics,” but instead is merely a symbol manipulating device
– the computer operates solely on syntax, not semantics
• He defines to categories of AI:
– strong AI – the pursuit of machine intelligence
– weak AI – the pursuit of machines solving problems in an intelligent way
18. But Computers Solve Problems
• We can clearly see that computers solve problems in a
seemingly intelligent way
– Where is the intelligence coming from?
• There are numerous responses to Searle’s argument
– The System’s Response:
• the hardware by itself is not intelligent, but a combination of the
hardware, software and storage is intelligent
• in a similar vein, we might say that a human brain that has had no
opportunity to learn anything cannot be intelligent, it is just the
hardware
– The Robot Response:
• a computer is void of senses and therefore symbols are meaningless to
it, but a robot with sensors can tie its symbols to its senses and thus
understand symbols
– The Brain Simulator Response:
• if we program a computer to mimic the brain (e.g., with a neural
network) then the computer will have the same ability to understand as
a human brain
19. Brain vs. Computer
• In AI, we compare the brain (or the mind) and the
computer
– Our hope: the brain is a form of computer
– Our goal: we can create computer intelligence through
programming just as people become intelligent by learning
But we see that the computer
is not like the brain
The computer performs tasks
without understanding what
its doing
Does the brain understand
what its doing when it solves
problems?
20. Symbol Grounding
• One problem with the computer is that it works strictly
syntactically
– Op code: 10011101 translates into a set of microcode
instructions such as: move IR16..31 to MAR, signal memory
read, move MBR to AC
– There is no understanding
• x = y + z; is meaningless to the computer
– the computer doesn’t understand addition, it just knows that a certain op
code means to move values to the adder and move the result elsewhere
• do you know what addition means?
– if so, how do you proscribe meaning to +
– how is this symbol grounded in your brain?
– can computers similarly achieve this?
– Recall Schank’s Restaurant script
• does the computer know what the symbols “waiter” or “PTRANS”
represent? or does it merely have code that tells the computer what to
do when it comes across certain words in the story, or how to respond
to a given question?
21. Two AI Assumptions
• We can understand and model cognition without
understanding the underlying mechanism
– That is, it is the model of cognition that is important not the
physical mechanism that implements it
– If this is true, then we should be able to create cognition (mind)
out of a computer or a brain or even other entities that can
compute such as a mechanical device
• This is the assumption made by symbolic AI researchers
• Cognition will emerge from the proper mechanism
– That is, the right device, fed with the right inputs, can learn and
perform the problem solving that we, as observers, call
intelligence
– Cognition will arise as the result (or side effect) of the hardware
• This is the assumption made by connectionist AI researchers
• Notice that while the two assumptions differ, neither is
necessarily mutually exclusive and both support the idea
that cognition is computational
22. Problems with Symbolic AI Approaches
• Scalability
– It can take dozens or more man-years to create a useful
systems
– It is often the case that systems perform well up to a certain
threshold of knowledge (approx. 10,000 rules), after which
performance (accuracy and efficiency) degrade
• Brittleness
– Most symbolic AI systems are programmed to solve a specific
problem, move away from that domain area and the system’s
accuracy drops rapidly rather than achieving a graceful
degradation
• this is often attributed to lack of common sense, but in truth, it is a lack
of any knowledge outside of the domain area
– No or little capacity to learn, so performance (accuracy) is
static
• Lack of real-time performance
23. Problems with Connectionist AI Approaches
• No “memory” or sense of temporality
– The first problem can be solved to some extent
– The second problem arises because of a fixed sized input but
leads to poor performance in areas like speech recognition
• Learning is problematic
– Learning times can greatly vary
– Overtraining leads to a system that only performs well on the
training set and undertraining leads to a system that has not
generalized
• No explicit knowledge-base
– So there is no way to tell what a system truly knows or how it
knows something
• No capacity to explain its output
– Explanation is often useful in an AI system so that the user can
trust the system’s answer
24. So What Does AI Do?
• Most AI research has fallen into one of two categories
– Select a specific problem to solve
• study the problem (perhaps how humans solve it)
• come up with the proper representation for any knowledge needed to
solve the problem
• acquire and codify that knowledge
• build a problem solving system
– Select a category of problem or cognitive activity (e.g.,
learning, natural language understanding)
• theorize a way to solve the given problem
• build systems based on the model behind your theory as experiments
• modify as needed
• Both approaches require
– one or more representational forms for the knowledge
– some way to select proper knowledge, that is, search
25. What is Search?
• We define the state of the problem being solved as the
values of the active variables
– this will include any partial solutions, previous conclusions, user
answers to questions, etc
• while humans are often
able to make intuitive
leaps, or recall solutions
with little thought, the
computer must search
through various
combinations to find a
solution
• To the right is a search
space for a tic-tac-toe
game
26. Search Spaces and Types of Search
• The search space consists of all possible states of the
problem as it is being solved
– A search space is often viewed as a tree and can very well
consist of an exponential number of nodes making the search
process intractable
– Search spaces might be pre-enumerated or generated during
the search process
– Some search algorithms may search the entire space until a
solution is found, others will only search parts of the space,
possibly selecting where to search through a heuristic
• Search spaces include
– Game trees like the tic-tac-toe game
– Decision trees (see next slides)
– Combinations of rules to select in a production system
– Networks of various forms (see next slides)
– Other types of spaces
27.
28.
29. Search Algorithms and Representations
• Breadth-first
• Depth-first
• Best-first (Heuristic Search)
• A*
• Hill Climbing
• Limiting the number of
Plies
• Minimax
• Alpha-Beta Pruning
• Adding Constraints
• Genetic Algorithms
• Forward vs Backward
Chaining
• We will study various forms of
representation and uncertainty
handling in the next class
period
• Knowledge needs to be
represented
– Production systems of some form
are very common
• If-then rules
• Predicate calculus rules
• Operators
– Other general forms include
semantic networks, frames,
scripts
– Knowledge groups
– Models, cases
– Agents
– Ontologies
30. A Brief History of AI: 1950s
• Computers were thought of as an electronic brains
• Term “Artificial Intelligence” coined by John McCarthy
– John McCarthy also created Lisp in the late 1950s
• Alan Turing defines intelligence as passing the Imitation
Game (Turing Test)
• AI research largely revolves around toy domains
– Computers of the era didn’t have enough power or memory to
solve useful problems
– Problems being researched include
• games (e.g., checkers)
• primitive machine translation
• blocks world (planning and natural language understanding within the
toy domain)
• early neural networks researched: the perceptron
• automated theorem proving and mathematics problem solving
31. The 1960s
• AI attempts to move beyond toy domains
• Syntactic knowledge alone does not work, domain
knowledge required
– Early machine translation could translate English to Russian
(“the spirit is willing but the flesh is weak” becomes “the
vodka is good but the meat is spoiled”)
• Earliest expert system created: Dendral
• Perceptron research comes to a grinding halt when it is
proved that a perceptron cannot learn the XOR operator
• US sponsored research into AI targets specific areas –
not including machine translation
• Weizenbaum creates Eliza to demonstrate the futility of
AI
32. 1970s
• AI researchers address real-world problems and solutions through
expert (knowledge-based) systems
– Medical diagnosis
– Speech recognition
– Planning
– Design
• Uncertainty handling implemented
– Fuzzy logic
– Certainty factors
– Bayesian probabilities
• AI begins to get noticed due to these successes
– AI research increased
– AI labs sprouting up everywhere
– AI shells (tools) created
– AI machines available for Lisp programming
• Criticism: AI systems are too brittle, AI systems take too much
time and effort to create, AI systems do not learn
33. 1980s: AI Winter
• Funding dries up leading to the AI Winter
– Too many expectations were not met
– Expert systems took too long to develop, too much money to
invest, the results did not pay off
• Neural Networks to the rescue!
– Expert systems took programming, and took dozens of man-
years of efforts to develop, but if we could get the computer
to learn how to solve the problem…
– Multi-layered back-propagation networks got around the
problems of perceptrons
– Neural network research heavily funded because it promised
to solve the problems that symbolic AI could not
• By 1990, funding for neural network research was
slowly disappearing as well
– Neural networks had their own problems and largely could
not solve a majority of the AI problems being investigated
– Panic! How can AI continue without funding?
34. 1990s: ALife
• The dumbest smart thing you can do is staying alive
– We start over – lets not create intelligence, lets just create
“life” and slowly build towards intelligence
• Alife is the lower bound of AI
– Alife includes
• evolutionary learning techniques (genetic algorithms)
• artificial neural networks for additional forms of learning
• perception and motor control
• adaptive systems
• modeling the environment
• Let’s disguise AI as something new, maybe we’ll get
some funding that way!
– Problems: genetic algorithms are useful in solving some
optimization problems and some search-based problems, but
not very useful for expert problems
– perceptual problems are among the most difficult being
solved, very slow progress
35. Today: The New (Old) AI
• Look around, who is doing AI research?
• By their own admission, AI researchers are not doing “AI”, they
are doing
– Intelligent agents, multi-agent systems/collaboration
– Ontologies
– Machine learning and data mining
– Adaptive and perceptual systems
– Robotics, path planning
– Search engines, filtering, recommendation systems
• Areas of current research interest:
– NLU/Information Retrieval, Speech Recognition
– Planning/Design, Diagnosis/Interpretation
– Sensor Interpretation, Perception, Visual Understanding
– Robotics
• Approaches
– Knowledge-based
– Ontologies
– Probabilistic (HMM, Bayesian Nets)
– Neural Networks, Fuzzy Logic, Genetic Algorithms