The future of AI by Oren Etzioni from AI2
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The document discusses artificial intelligence (AI) and provides examples of current AI applications. It defines AI as the development of computer systems that can perform tasks requiring human intelligence, such as visual perception and language translation. Examples of current AI include self-driving cars, robotics, navigation systems, and chatbots. Weak AI is designed for specific tasks while strong AI would think and act like humans, but no systems have achieved strong AI yet. The document also discusses machine learning, the Turing test for measuring intelligence, and potential future applications of AI in fields like the military, law, business, healthcare, and more.
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This document discusses robotics and provides information on various related topics in 6 paragraphs. It defines a robot and discusses where robots are used. It then defines robotics and why it is used. It notes that the words "robot" and "robotics" were first used by Karel Capek and Isaac Asimov, respectively. It outlines Isaac Asimov's Three Laws of Robotics. Finally, it discusses the advantages and disadvantages of robotics technology.
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Artificial intelligence (AI) is the intelligence exhibited by machines and the branch of computer science which develops it. Alan Turing conceptualized AI with the Turing Test to evaluate a machine's ability to exhibit intelligent behavior equivalent to a human. John McCarthy is considered the father of AI. While machines are now capable of performing many tasks that humans can do, they lack human qualities like creativity, common sense, and emotional intelligence. AI provides benefits like precision, ability to do repetitive tasks, and reducing risks, but also has disadvantages like costs, lack of human touch, potential job losses, and possibility of being misused. AI is applied in fields such as robotics, banking, medical diagnosis, defense technologies, and space exploration.
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The document discusses forecasts for how human interaction with robots will change over the next decade. Seven major forecasts are outlined:
1. Our Robots Ourselves: Robots will gain a deeper understanding of human behavior by decoding our behaviors through new technologies, allowing them to interact with humans in more natural ways.
2. Neither Artifact nor Living Being: Robots will shift to new designs that are less human-like and more suited to their tasks, moving beyond the "uncanny valley" effect.
3. Robots Raise the Bar: Robots will outperform humans on many physical and cognitive tasks, setting new standards for human performance.
4. The Right Tool for the Job: The division
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Robot
The document discusses various topics related to robots including definitions, types, functions, advantages, and disadvantages. It also summarizes the results of a survey conducted about public perceptions of robots. The document contains sections on the different types of robots including modular, collaborative, and modern robots. It also explores the various functions of robots in applications such as factories, homes, and restaurants. Both the advantages of robots like increased productivity and the disadvantages like potential job losses are mentioned. The survey found that most people's views of robots are positive and see them being widely used in the future if developed responsibly.
A History of Robots
The document provides a history of robots from ancient visions to modern achievements. It discusses early robotic concepts and inventions from Greek and Roman engineers. The first modern robots were industrial machines introduced in the 1960s that could perform repetitive, dangerous tasks. Today's robots are more advanced and can have sensors, control systems, and effectors that allow them to operate autonomously and assist humans in tasks that are difficult, risky, or time-consuming such as packaging items on an assembly line. The military has also developed robots to help with dangerous disposal and rescue missions.
Humanoid robotics
A humanoid robot is a robot with its body shape built to resemble that of the human body. A humanoid design might be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as the study of bipedal locomotion, or for other purposes. In general, humanoid robots have a torso, a head, two arms, and two legs, though some forms of humanoid robots may model only part of the body, for example, from the waist up. Some humanoid robots may also have heads designed to replicate human facial features such as eyes and mouths. Androids are humanoid robots built to aesthetically resemble humans.
Purpose
Humanoid robots are used as a research tool in several scientific areas.
Researchers need to understand the human body structure and behavior (biomechanics) to build and study humanoid robots. On the other side, the attempt to the simulation of the human body leads to a better understanding of it.
Human cognition is a field of study which is focused on how humans learn from sensory information in order to acquire perceptual and motor skills. This knowledge is used to develop computational models of human behavior and it has been improving over time.
It has been suggested that very advanced robotics will facilitate the enhancement of ordinary humans. See transhumanism.
Although the initial aim of humanoid research was to build better orthosis and prosthesis for human beings, knowledge has been transferred between both disciplines. A few examples are: powered leg prosthesis for neuromuscularly impaired, ankle-foot orthosis, biological realistic leg prosthesis and forearm prosthesis.
Besides the research, humanoid robots are being developed to perform human tasks like personal assistance, where they should be able to assist the sick and elderly, and dirty or dangerous jobs. Regular jobs like being a receptionist or a worker of an automotive manufacturing line are also suitable for humanoids. In essence, since they can use tools and operate equipment and vehicles designed for the human form, humanoids could theoretically perform any task a human being can, so long as they have the proper software. However, the complexity of doing so is deceptively great.
They are becoming increasingly popular for providing entertainment too. For example, Ursula, a female robot, sings, play music, dances, and speaks to her audiences at Universal Studios. Several Disney attractions employ the use of animatrons, robots that look, move, and speak much like human beings, in some of their theme park shows. These animatrons look so realistic that it can be hard to decipher from a distance whether or not they are actually human. Although they have a realistic look, they have no cognition or physical autonomy. Various humanoid robots and their possible applications in daily life are featured in an independent documentary film called Plug & Pray, which was released in 2010 it continue.....
Branches Of Robotics
Robotics involves the design, manufacture, and application of robots. It is an interdisciplinary field that includes aspects of electronics, mechanics, and software. Some key branches of robotics include android science, which studies human-robot interaction; artificial intelligence, which aims to create machine intelligence; and nanorobotics, which focuses on controlling nanoscale objects. Other branches discussed are robot surgery, laboratory robotics, robot locomotion, telepresence, swarm robotics, and speech processing.
Turing
The document proposes a Turing test for computer game bots to test their ability to imitate human game players. The test was implemented as a bot design competition called the 2Kbot prize contest. The competition involved bots playing the first person shooter game Unreal Tournament 2004 against human judges without knowing which was the bot. The judges would evaluate which players seemed more humanlike based on in-game actions and strategies.
Robots
1) The global robotics market was estimated at $17.3 billion in 2008 and is expected to rise to $21.4 billion by 2014.
2) Some well-known robots today include Honda's Asimo, Pleo the toy dinosaur robot, Chalkbot the street-printing robot, and Roomba the vacuum cleaning robot which has sold over 2 million units worldwide.
3) The robotics industry is still risky but growth will be led by startups, and marketing has been key to success stories like Roomba.
Humanoid robot
The document defines a robot as a machine that can carry out complex series of actions using computer programming. It defines a humanoid robot as having a body resembling a human with two arms, two legs, and a head. It discusses Asimo, an early humanoid robot introduced in 2000 that was designed to be a mobile assistant. The document also notes that robots are taking over dull, dangerous, and dirty tasks, but that truly intelligent human-level robots are at least 50 years away and may never be achieved. It acknowledges that the real concern is not robots themselves but how they are programmed by people.
Artificial Intelligence(Humanoid)
Artificial intelligence and robotics research aims to develop intelligent machines that can reason, plan, learn, and manipulate objects. Some key goals of AI research include developing machines that exhibit general intelligence and can have social interactions with humans. Issues around machine ethics, rights, and consciousness are also areas of study. While humanoid robots currently have limited capabilities and require extensive training, researchers hope that as the technology advances, humanoids will be able to learn and adapt through interaction in human-like ways. Potential applications of humanoids include assisting elderly people, working in manufacturing, and participating in space missions. The future may bring more lifelike humanoid robots that can serve as companions.
History of Robotics
This document discusses the basics of robotics. It introduces robotics as a field that deals with designing, manufacturing, and operating machines and mechanisms. It outlines Isaac Asimov's three laws of robotics which state that robots must not harm humans, obey human orders unless it conflicts with the first law, and robots must protect themselves without violating the other laws. The document also discusses characteristics of robots like mobility, programmability, sensors, and mechanical capabilities. Examples of robots are provided like Asimo, Kismet, and i-Cub along with trends in robotics like industrial and military applications.
Artificial Intellegent
This document provides an overview of artificial intelligence (AI) and robotics. It defines AI as intelligence demonstrated by machines, as opposed to natural human intelligence. Robotics is defined as the design, construction and application of robots. The document discusses how AI and robotics work together, with AI playing a key role in enabling intelligent robot behavior by providing knowledge, learning, planning, and problem solving abilities. As an example, it describes an hypothetical AI-enabled micro assassination drone and its capabilities. It also profiles Sophia, a social humanoid robot developed using AI.
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This is power point presentation is about Artificial intelligence.
It contains a lot of good stuff which helps you to get a good score and make your boss happy.
It also contains a short video on Sophia.
This presentation has good effects.
-Thank you
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What's a robot? This deck shows the sheer variety of what we mean by robot and was used to introduce a couple of fantastic TED talks on robotics for a TED Salon at Newtown Public School (NSW, Australia) on 2nd April 2009.
We followed a quick run through this deck with TED videos from:
1. Rodney Brooks
2. Hod Lipson
3. Caleb Chung
And finally a very interesting and varied discussion.
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Artificial Intelligence
Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception and speech recognition. John McCarthy first coined the term in 1956 at the first academic conference on the subject. AI has since been applied to mobile phones, gaming, voice recognition, robotics, and other areas. Deep learning is a major focus of AI research at companies like Google. While AI shows promise for automating jobs and improving lives, it also presents risks if not developed responsibly, as machines currently lack human attributes like emotions and ethics. Both pros and cons of AI must be considered for its full impact and development.
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The document discusses robots and robotics. It defines a robot and explains that the word robot was coined by Czech playwright Karel Capek from the Czech word for forced labor or serf. It also outlines Isaac Asimov's Three Laws of Robotics, which govern a robot's behavior. The document discusses various applications of robots, including in NASA's telerobotics program, industrial uses, surgery, dangerous situations, and more.
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The document provides a history of robots from ancient visions to modern achievements. It discusses early robotic concepts and inventions from Greek and Roman engineers. The first modern robots were industrial machines introduced in the 1960s that could perform repetitive, dangerous tasks. Today's robots are more advanced and can have sensors, control systems, and effectors that allow them to operate autonomously and assist humans in tasks that are difficult, risky, or time-consuming such as packaging items on an assembly line. The military has also developed robots to help with dangerous disposal and rescue missions.
Humanoid robotics
A humanoid robot is a robot with its body shape built to resemble that of the human body. A humanoid design might be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as the study of bipedal locomotion, or for other purposes. In general, humanoid robots have a torso, a head, two arms, and two legs, though some forms of humanoid robots may model only part of the body, for example, from the waist up. Some humanoid robots may also have heads designed to replicate human facial features such as eyes and mouths. Androids are humanoid robots built to aesthetically resemble humans.
Purpose
Humanoid robots are used as a research tool in several scientific areas.
Researchers need to understand the human body structure and behavior (biomechanics) to build and study humanoid robots. On the other side, the attempt to the simulation of the human body leads to a better understanding of it.
Human cognition is a field of study which is focused on how humans learn from sensory information in order to acquire perceptual and motor skills. This knowledge is used to develop computational models of human behavior and it has been improving over time.
It has been suggested that very advanced robotics will facilitate the enhancement of ordinary humans. See transhumanism.
Although the initial aim of humanoid research was to build better orthosis and prosthesis for human beings, knowledge has been transferred between both disciplines. A few examples are: powered leg prosthesis for neuromuscularly impaired, ankle-foot orthosis, biological realistic leg prosthesis and forearm prosthesis.
Besides the research, humanoid robots are being developed to perform human tasks like personal assistance, where they should be able to assist the sick and elderly, and dirty or dangerous jobs. Regular jobs like being a receptionist or a worker of an automotive manufacturing line are also suitable for humanoids. In essence, since they can use tools and operate equipment and vehicles designed for the human form, humanoids could theoretically perform any task a human being can, so long as they have the proper software. However, the complexity of doing so is deceptively great.
They are becoming increasingly popular for providing entertainment too. For example, Ursula, a female robot, sings, play music, dances, and speaks to her audiences at Universal Studios. Several Disney attractions employ the use of animatrons, robots that look, move, and speak much like human beings, in some of their theme park shows. These animatrons look so realistic that it can be hard to decipher from a distance whether or not they are actually human. Although they have a realistic look, they have no cognition or physical autonomy. Various humanoid robots and their possible applications in daily life are featured in an independent documentary film called Plug & Pray, which was released in 2010 it continue.....
Branches Of Robotics
Robotics involves the design, manufacture, and application of robots. It is an interdisciplinary field that includes aspects of electronics, mechanics, and software. Some key branches of robotics include android science, which studies human-robot interaction; artificial intelligence, which aims to create machine intelligence; and nanorobotics, which focuses on controlling nanoscale objects. Other branches discussed are robot surgery, laboratory robotics, robot locomotion, telepresence, swarm robotics, and speech processing.
Turing
The document proposes a Turing test for computer game bots to test their ability to imitate human game players. The test was implemented as a bot design competition called the 2Kbot prize contest. The competition involved bots playing the first person shooter game Unreal Tournament 2004 against human judges without knowing which was the bot. The judges would evaluate which players seemed more humanlike based on in-game actions and strategies.
Robots
1) The global robotics market was estimated at $17.3 billion in 2008 and is expected to rise to $21.4 billion by 2014.
2) Some well-known robots today include Honda's Asimo, Pleo the toy dinosaur robot, Chalkbot the street-printing robot, and Roomba the vacuum cleaning robot which has sold over 2 million units worldwide.
3) The robotics industry is still risky but growth will be led by startups, and marketing has been key to success stories like Roomba.
Humanoid robot
The document defines a robot as a machine that can carry out complex series of actions using computer programming. It defines a humanoid robot as having a body resembling a human with two arms, two legs, and a head. It discusses Asimo, an early humanoid robot introduced in 2000 that was designed to be a mobile assistant. The document also notes that robots are taking over dull, dangerous, and dirty tasks, but that truly intelligent human-level robots are at least 50 years away and may never be achieved. It acknowledges that the real concern is not robots themselves but how they are programmed by people.
Artificial Intelligence(Humanoid)
Artificial intelligence and robotics research aims to develop intelligent machines that can reason, plan, learn, and manipulate objects. Some key goals of AI research include developing machines that exhibit general intelligence and can have social interactions with humans. Issues around machine ethics, rights, and consciousness are also areas of study. While humanoid robots currently have limited capabilities and require extensive training, researchers hope that as the technology advances, humanoids will be able to learn and adapt through interaction in human-like ways. Potential applications of humanoids include assisting elderly people, working in manufacturing, and participating in space missions. The future may bring more lifelike humanoid robots that can serve as companions.
History of Robotics
This document discusses the basics of robotics. It introduces robotics as a field that deals with designing, manufacturing, and operating machines and mechanisms. It outlines Isaac Asimov's three laws of robotics which state that robots must not harm humans, obey human orders unless it conflicts with the first law, and robots must protect themselves without violating the other laws. The document also discusses characteristics of robots like mobility, programmability, sensors, and mechanical capabilities. Examples of robots are provided like Asimo, Kismet, and i-Cub along with trends in robotics like industrial and military applications.
Artificial Intellegent
This document provides an overview of artificial intelligence (AI) and robotics. It defines AI as intelligence demonstrated by machines, as opposed to natural human intelligence. Robotics is defined as the design, construction and application of robots. The document discusses how AI and robotics work together, with AI playing a key role in enabling intelligent robot behavior by providing knowledge, learning, planning, and problem solving abilities. As an example, it describes an hypothetical AI-enabled micro assassination drone and its capabilities. It also profiles Sophia, a social humanoid robot developed using AI.
Artificial intelligence
This is power point presentation is about Artificial intelligence.
It contains a lot of good stuff which helps you to get a good score and make your boss happy.
It also contains a short video on Sophia.
This presentation has good effects.
-Thank you
Artificial Intelligence: Should You Be Worried?
An introduction to the what artificial intelligence is and a cultural history of the fear of creation of intelligence. A realistic assessment is made of the so-called singularity and what we really should be worried about: artificial "semi-intelligence." Talk given to the Northern Monmouth County Branch of the AAUW.
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Similar to The future of AI by Oren Etzioni from AI2
Between Hermeneutics and Deceit: Keeping Natural Language Generation in Line
Presented with Dr Albert Meroño-Peñuela at the Digital Humanities Congress (10 September 2022), organised by the University of Sheffield's Digital Humanities Institute. Argues for explicit acknowledgement of hype surrounding AI-driven natural language generation (NLG) systems, using prompt engineering to dispel understandings of language usage as line of thought. Note that the formatting of the slides has been muddled by SlideShare - please download the slides if you wish to see the intended formatting.
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Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception and speech recognition. John McCarthy first coined the term in 1956 at the first academic conference on the subject. AI has since been applied to mobile phones, gaming, voice recognition, robotics, and other areas. Deep learning is a major focus of AI research at companies like Google. While AI shows promise for automating jobs and improving lives, it also presents risks if not developed responsibly, as machines currently lack human attributes like emotions and ethics. Both pros and cons of AI must be considered for its full impact and development.
Quick Guide to Artificial Intelligence - Transform XO
This document provides a quick guide to artificial intelligence (AI). It defines AI as the attempt to remove boundaries between humans and machines. Examples of weak AI include chess-playing programs, while strong AI examples are IBM's Watson and Google's DeepMind. Machine learning allows machines to learn without explicit programming. Deep learning works similarly to how human brains work. AI systems are fed using big data. Key aspects of intelligence are recognition of patterns, prognosis, reasoning, problem-solving, perception, and language understanding. The future outlook discusses potential human-level and super-intelligent AI, as well as predictions that AI may outperform humans by 2048. Risks of advanced AI are also discussed.
AI_Workshop
The document discusses artificial intelligence and its applications. It defines AI as the science and engineering of making intelligent machines, especially computer programs, and using computers to understand human intelligence. It notes that AI is seen in recommender systems, virtual assistants, face and object recognition used by companies like YouTube, Facebook, Amazon, Google. The document also discusses machine learning, the Turing test, game playing, applications of AI in healthcare like genome editing and nanobots, using AI for social good like reducing pollution and crime, and potential problems like unemployment and threats to privacy.
Ai and digital media (pcto2018)
Artificial Intelligence has become an essential part in our day-to-day life, and with the advancements in this field and how close we got to the Artificial Consciousness it’s getting into all types of digital media and even the film and TV industry. Machines now can author novels, create art, generate videos, act as news anchors, write fiction, compose music and the skies are the limit.
Where is AI taking us to future with digital media? Will there become a day that it will totally replace us? What is Digital Creativity and how close it is to our human creativity? How can we deal with a future where all these scary factors are approaching fast?
All these topics and more will be the core of the subject of the speech. It’s a conversation long due now and we should initiate it now before it’s too late. My experience in both Digital Media and Artificial Intelligence alongside with my recent research about Artificial Consciousness makes me qualified to carry such a tough conversation and bring it to light. I always say: Machines are NOT coming,they’re already here.
Artificial intelligence (ai)
This presitation include
INTRODUCTION TO (AI)
EXAMPLES OF (AI)
Types of (AI)
RISE OF (AI)
FUTURE OF (AI)
Advantages /Disadvantages OF (AI)
How safe is (AI)
software engineering presentation.pptx details explanation
Coursera
What Is Artificial Intelligence? Definition, Uses, and Types
What Is Artificial Intelligence? Definition, Uses, and Types
Written by Coursera Staff • Updated on Apr 4, 2024
Learn what artificial intelligence actually is, how it’s used today, and what it may do in the future.
[Featured Image] Waves of 0 and 1 digits on a blue background.
Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems.
Today, the term “AI” describes a wide range of technologies that power many of the services and goods we use every day – from apps that recommend tv shows to chatbots that provide customer support in real time. But do all of these really constitute artificial intelligence as most of us envision it? And if not, then why do we use the term so often?
In this article, you’ll learn more about artificial intelligence, what it actually does, and different types of it. In the end, you’ll also learn about some of its benefits and dangers and explore flexible courses that can help you expand your knowledge of AI even further.
Want to try out your AI skills? Enroll in AI for Everyone, an online program offered by DeepLearning.AI. In just 6 hours, you'll gain foundational knowledge about AI terminology, strategy, and the workflow of machine learning projects. Your first week is free.
Artificial Intelligence
This document provides an overview of artificial intelligence (AI) including its history, subsets, scope, applications, and recent advances. It discusses early concepts of AI from ancient Greece. Key events in AI history include Alan Turing's 1950 paper speculating about machine intelligence and the creation of machine learning and deep learning subsets. The document outlines current applications of AI in various fields like education, healthcare, finance, and industry. Recent AI advances presented include predictive systems, robot journalists, self-flying helicopters, art-creating systems, and question answering computers. Potential threats from AI developing unintended behaviors or being programmed for destructive goals are also examined.
Today is all about AI
This presentation give an introduction to Artificial Intelligence subjectiveness and history. The primary goal of the presentation is to provide a deep enough understanding of Artificial Narrow Intelligence and Artificial General Intelligence so that the people can appreciate the strengths or weaknesses of the AI. The presentation also includes a classification(the main domains of AI) and the most relevant examples from the past decades. In the second part it provides some statistics and future possible applications and forecasts.
Intuition in Artificial Intelligence
The theoretical concept of artificial intuition is the capacity of an artificial object or software to function with the factor of consciousness known as intuition: a machine-based system that has some capacity to function analogously to human intuition.
Artificial Intelligence ppt
Artificial intelligence (AI) is the development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception and decision-making. There are three types of AI: narrow AI, which is limited in scope; general AI at an advanced level similar to human intelligence; and super AI, which would surpass human intelligence. AI has many applications today including personal assistants on phones, gaming, robotics, and self-driving cars. While AI shows promise, it also presents risks if not developed responsibly, as machines currently lack human attributes like emotions and ethics.
Assignment no 1
The document provides an overview of artificial intelligence (AI) and robotics. It defines AI as replicating human intelligence in machines using perception, reasoning, learning, communicating and acting. The evolution of AI is discussed from early proposals by Alan Turing to recent advances in machine learning. Robotics are described as physical agents that operate in the physical world using sensors and effectors. The relationship between AI and robotics is that AI provides the intelligence for robots to make their own decisions. Examples of AI and robotics applications are presented. The conclusion is that while AI is still in early stages, the combination of AI and robotics may help realize futures depicted in science fiction.
Social Impacts of Artificial intelligence
This lecture gives detail introduction, applications about AI. This lecture gives details about the social perspective and realities in the field of AI.
AI Presentation Y65 Class Dinner
This document summarizes a presentation on artificial intelligence given by David Roscoe. In 3 sentences: The presentation discussed the current capabilities and rapid development of AI, noting that AI will transform every industry and lead to profound societal changes. It highlighted both opportunities, like improved healthcare, and challenges, such as job disruption and ensuring AI is developed and used in a way that aligns with human values. The presentation concluded by considering the implications of developing superintelligent AI that exceeds human abilities.
Ethics for the machines altitude software
Will the machines save us or kill us all? – that is the question. While many are thrilled with the latest AI
breakthroughs and dream of a shinning AI-powered world, others, like Bill Gates, Elon Musk, Steve Wozniak and the late
and legendary Stephen Hawking, expressed concerns about the evolution of the machines and warned about an
apocalyptic future.
http://www.altitude.com/
Artificial Intelligence Presentation
A presentation on the overview, history, applications, interaction, ethics and facts of Artificial Intelligence (AI) in modern life.
ARTIFICIAL INTELLIGENCE
Dive into the most advanced and dreamed field of science and technology: AI
In this slide, I just tried to give an overview of artificial intelligence.please comment if anything valuable thing that can be added to it. suggestions and corrections are welcome.
کارگاه فناوری های نو - جناب قاضی، من و کامپیوترم با هم تفاهم نداریم - جلسه سوم
روندها و رویدادها در فناوری اطلاعات، نسل z، ویژگی های نسل اینترنت، ابر روندهای تکنولوژی، جان هنری، The turk، سایبورگ، سن تور، قنطورس، نیماسب، انواع فناوری ها
Elements of AI Luxembourg - session 2
In this second session of the Elements of AI Luxembourg series of webinars, we have the pleasure to have Dr. Sana Nouzri as a guest speaker. More information, and a recording of the session, can be found on our reddit page:
eofai.lu/reddit
Introduction to AI.pptx
This document provides an overview of the history and current state of artificial intelligence. It discusses key events like the Dartmouth workshop in 1956 which is seen as the official birth of AI. The document also explores different applications of AI like in movies, news, and real world tasks. It discusses challenges for the future like ensuring AI is beneficial to humanity and aligned with human values and preferences.
Similar to The future of AI by Oren Etzioni from AI2 (20)
Between Hermeneutics and Deceit: Keeping Natural Language Generation in Line
Between Hermeneutics and Deceit: Keeping Natural Language Generation in Line
Quick Guide to Artificial Intelligence - Transform XO
Quick Guide to Artificial Intelligence - Transform XO
software engineering presentation.pptx details explanation
software engineering presentation.pptx details explanation
کارگاه فناوری های نو - جناب قاضی، من و کامپیوترم با هم تفاهم نداریم - جلسه سوم
کارگاه فناوری های نو - جناب قاضی، من و کامپیوترم با هم تفاهم نداریم - جلسه سوم
More from Bill Liu
Walk Through a Real World ML Production Project
Success in productionizing ML models is difficult to achieve due to tools, processes and operational procedures. In this session, we demonstrate how data scientists and ML engineers collaborate and efficiently deploy models to production with the Wallaroo platform.
Using a real world scenario we will click down into the ML production journey that Data Scientists and ML engineers go through to take ML models into production. In this session you will learn:
The current pain points and blockers to production
The 2 persona roles in the ML production process. Data Scientist (DS) and ML Engineer
How the ML engineer creates a workspace in Wallaroo, and invites the DS to collaborate
How the DS uploads and deploys models to WL performing simple validation checks on output
How the ML Engineer can check model health (inference speed, etc)
How the DS checks logs, looks for anomalies
How the DS switches model in the pipeline
Speakers: Nina Zumel, Martin Bald
Redefining MLOps with Model Deployment, Management and Observability in Produ...
Tech talk: https://www.aicamp.ai/event/eventdetails/W2022052410
What happens after your machine learning models are deployed in production? How do you make sure that your model performance does not degrade as data and the world change?
The constantly changing data creates challenges for data scientists and engineering teams on how to detect which models have been affected and how to get their ML applications up and running seamlessly.
In this session we will take a deep dive into the new ML model monitoring and drift detection technology. We will discuss:
- How to track the ongoing accuracy of their models in production
- How to immediately detect drift before it causes significant damage to the business
- How to locate the cause of model drifting in live environments.
We will also discuss how data scientists and ML engineers can collaborate effectively using their respective tools to identify issues and take the necessary actions with a live demo and a real world use case.
Speaker: Younes Amar, Head of Product Wallaroo AI.
Resources: https://docs.wallaroo.ai/
Productizing Machine Learning at the Edge
These days, training of the Machine Learning models at the device Edge is still a risky endeavor. It is frequently considered a purely academic subject with little value for real-life product development.
In her talk, Vera will challenge this misconception, talk about the advantages of learning at the Edge and guide you through the Edge learning decision-making framework and design principles.
https://www.aicamp.ai/event/eventdetails/W2021102210
Transformers in Vision: From Zero to Hero
Attention Is All You Need.
With these simple words, the Deep Learning industry was forever changed. Transformers were initially introduced in the field of Natural Language Processing to enhance language translation, but they demonstrated astonishing results even outside language processing. In particular, they recently spread in the Computer Vision community, advancing the state-of-the-art on many vision tasks. But what are Transformers? What is the mechanism of self-attention, and do we really need it? How did they revolutionize Computer Vision? Will they ever replace convolutional neural networks?
These and many other questions will be answered during the talk.
In this tech talk, we will discuss:
- A piece of history: Why did we need a new architecture?
- What is self-attention, and where does this concept come from?
- The Transformer architecture and its mechanisms
- Vision Transformers: An Image is worth 16x16 words
- Video Understanding using Transformers: the space + time approach
- The scale and data problem: Is Attention what we really need?
- The future of Computer Vision through Transformers
Speaker: Davide Coccomini, Nicola Messina
Website: https://www.aicamp.ai/event/eventdetails/W2021101110
Deep AutoViML For Tensorflow Models and MLOps Workflows
deep_autoviml is a powerful new deep learning library with a very simple design goal: Make it as easy as possible for novices and experts alike to experiment with and build tensorflow.keras preprocessing pipelines and models in as few lines of code as possible.
deep_autoviml will enable data scientists, ML engineers and data engineers to fast prototype tensorflow models and data pipelines for MLOps workflows using the latest TF 2.4+ and keras preprocessing layers. You can now upload your saved model to any Cloud provider and make predictions out of the box since all the data preprocessing layers are attached to the model itself!
In this webinar, we will discuss the problems that deep_AutoViML can solve, its architecture design and demo how to build powerful TF.Keras models on structured data, NLP and Image data domains.
https://www.aicamp.ai/event/eventdetails/W2021080918
Metaflow: The ML Infrastructure at Netflix
Metaflow was started at Netflix to answer a pressing business need: How to enable an organization of data scientists, who are not software engineers by training, build and deploy end-to-end machine learning workflows and applications independently. We wanted to provide the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure: data, compute, orchestration, and versioning.
Today, the open-source Metaflow powers hundreds of business-critical ML projects at Netflix and other companies from bioinformatics to real estate.
In this talk, you will learn about:
- What to expect from a modern ML infrastructure stack.
- Using Metaflow to boost the productivity of your data science organization, based on lessons learned from Netflix.
- Deployment strategies for a full stack of ML infrastructure that plays nicely with your existing systems and policies.
https://www.aicamp.ai/event/eventdetails/W2021080510
Practical Crowdsourcing for ML at Scale
Daria Baidakova is the Director of Educational Programs at Toloka, an open crowdsourcing platform. She manages crowdsourcing courses and organizes tutorials and hackathons. Toloka provides labeled data via crowdsourcing which is a missing pillar of AI. It offers data annotation, content collection, and business decision making through crowdsourcing. Toloka manages quality through controls at the project level, performer selection, and result aggregation rather than direct management of individuals.
Building large scale transactional data lake using apache hudi
Data is a critical infrastructure for building machine learning systems. From ensuring accurate ETAs to predicting optimal traffic routes, providing safe, seamless transportation and delivery experiences on the Uber platform requires reliable, performant large-scale data storage and analysis. In 2016, Uber developed Apache Hudi, an incremental processing framework, to power business critical data pipelines at low latency and high efficiency, and helps distributed organizations build and manage petabyte-scale data lakes.
In this talk, I will describe what is APache Hudi and its architectural design, and then deep dive to improving data operations by providing features such as data versioning, time travel.
We will also go over how Hudi brings kappa architecture to big data systems and enables efficient incremental processing for near real time use cases.
Speaker: Satish Kotha (Uber)
Apache Hudi committer and Engineer at Uber. Previously, he worked on building real time distributed storage systems like Twitter MetricsDB and BlobStore.
website: https://www.aicamp.ai/event/eventdetails/W2021043010
Deep Reinforcement Learning and Its Applications
What is the most exciting AI news in recent years? AlphaGo!
What are key techniques for AlphaGo? Deep learning and reinforcement learning (RL)!
What are application areas for deep RL? A lot! In fact, besides games, deep RL has been making tremendous achievements in diverse areas like recommender systems and robotics.
In this talk, we will introduce deep reinforcement learning, present several applications, and discuss issues and potential solutions for successfully applying deep RL in real life scenarios.
https://www.aicamp.ai/event/eventdetails/W2021042818
Big Data and AI in Fighting Against COVID-19
This document discusses how big data and AI can help fight Covid-19. It describes supercomputers being used for scientific research on Covid-19. An open data lake has been created containing various Covid-19 datasets for analysis. Natural language processing and BERT are being used to answer scientific questions from the Covid-19 literature by generating summaries and highlighting relevant text passages. Challenges are being conducted on the Covid-19 Open Research Dataset to further advance research.
Highly-scalable Reinforcement Learning RLlib for Real-world Applications
This document discusses Ray and RLlib, a reinforcement learning library built on Ray. It provides three key points:
1. Ray is a framework for building distributed applications and services with shared memory abstraction. It allows ML workloads to scale beyond a single machine.
2. RLlib is a scalable reinforcement learning library that uses Ray. It supports a wide range of algorithms and execution models. This allows for easy implementation and comparison of RL techniques.
3. The Ray community is growing rapidly and provides resources like tutorials and Slack support to help users adopt Ray and RLlib for their distributed Python and reinforcement learning applications.
Build computer vision models to perform object detection and classification w...
event: https://learn.xnextcon.com/event/eventdetails/W20042918
video:
description: Computer Vision has received significant attention over the recent years, both within academia, and industry. As the state-of-the-art rapidly improves, the art-of-the-possible follows , offering innovative forms of computer vision applications for different scenarios.
In this talk, Ramine will cover the background and development of computer vision, and demonstrate how to use AWS to build robust, computer vision models to perform object detection and classification.
Key Takeaways:
Understand the history of Computer Vision
Learn how to use Amazon SageMaker to build and Deploy Computer Vision Models
How to orchestrate multiple models for implementing a real-world use case
Causal Inference in Data Science and Machine Learning
Event: https://learn.xnextcon.com/event/eventdetails/W20042010
Video: https://www.youtube.com/channel/UCj09XsAWj-RF9kY4UvBJh_A
Modern machine learning techniques are able to learn highly complex associations from data, which has led to amazing progress in computer vision, NLP, and other predictive tasks. However, there are limitations to inference from purely probabilistic or associational information. Without understanding causal relationships, ML models are unable to provide actionable recommendations, perform poorly in new, but related environments, and suffer from a lack of interpretability.
In this talk, I provide an introduction to the field of causal inference, discuss its importance in addressing some of the current limitations in machine learning, and provide some real-world examples from my experience as a data scientist at Brex.
Weekly #106: Deep Learning on Mobile
https://learn.xnextcon.com/event/eventdetails/W20040610
This talk explains how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones. You will learn various strategies to circumvent obstacles and build mobile-friendly shallow CNN architectures that significantly reduce the memory footprint and therefore make them easier to store on a smartphone;
The talk also dives into how to use a family of model compression techniques to prune the network size for live image processing, enabling you to build a CNN version optimized for inference on mobile devices. Along the way, you will learn practical strategies to preprocess your data in a manner that makes the models more efficient in the real world.
Weekly #105: AutoViz and Auto_ViML Visualization and Machine Learning
https://learn.xnextcon.com/event/eventdetails/W20040310
I will describe what is available in terms of Open Source and Proprietary tools for automating Data Science tasks and introduce 2 new tools: one to visualize any sized data set with one click, another: to try multiple ML models and techniques with a single call. I will provide the Github Repos for both for free in the talk.
AISF19 - On Blending Machine Learning with Microeconomics
The document discusses how machine learning and economics can inform each other. It describes how markets can be viewed as decentralized algorithms that solve complex tasks. Recommendation systems are discussed as an example of machine learning success, but it is noted that recommending the same items to everyone is problematic. As an alternative, the document proposes creating markets with recommendation systems on both sides, like a market connecting diners and restaurants. The rest of the document discusses several specific examples at the intersection of machine learning and economics research, like bandits that compete and finding Nash equilibria with gradient-based algorithms.
AISF19 - Travel in the AI-First World
Travel is a force for good economically and socially. Expedia Group is building towards an "AI-First World" of travel where AI is used throughout the entire travel planning, booking, and experience process. They are developing massive multi-dimensional AI models and an AI platform to deliver highly personalized and targeted experiences. Overcoming challenges like data quality, scalability, and control will be key to realizing an AI-First vision where travel is optimized for each individual user.
AISF19 - Unleash Computer Vision at the Edge
This document discusses the key drivers enabling computer vision at the edge, including new machine learning approaches, optimized model architectures, hardware innovations, and improved software tools. It describes how machine learning has advanced computer vision by enabling end-to-end learning without predefined features. Edge-optimized models like GoogleNet and ShuffleNet are discussed. The proliferation of cameras, embedded processors, and AI accelerators is enabling computer vision everywhere. Open-source tools like OpenCV and frameworks like TensorFlow are supporting development, along with platforms to speed application creation.
AISF19 - Building Scalable, Kubernetes-Native ML/AI Pipelines with TFX, KubeF...
This document discusses modern machine learning pipelines and popular open source tools to build them. It defines key characteristics of ML pipelines like experiment tracking, hyperparameter optimization, distributed execution, and metadata/data versioning. Popular tools covered are KubeFlow for Kubernetes+TensorFlow, Airflow for data and feature engineering, MLflow for experiment tracking, and TensorFlow Extended (TFX) libraries. The document demonstrates these tools and argues that while the field is emerging, simplicity is important and one should only use necessary components of different tools.
Toronto meetup 20190917
This document discusses elastic distributed deep learning training at scale on-premises and in the cloud. It introduces the architecture of elastic distributed training, which combines high performance synchronization techniques like distributed data parallel with session scheduling and elastic scaling to provide flexibility. This allows training jobs to automatically scale up and down resources based on policies while maintaining high performance. It aims to make distributed training transparent to frameworks like TensorFlow and PyTorch.
More from Bill Liu (20)
Redefining MLOps with Model Deployment, Management and Observability in Produ...
Redefining MLOps with Model Deployment, Management and Observability in Produ...
Deep AutoViML For Tensorflow Models and MLOps Workflows
Deep AutoViML For Tensorflow Models and MLOps Workflows
Building large scale transactional data lake using apache hudi
Building large scale transactional data lake using apache hudi
Highly-scalable Reinforcement Learning RLlib for Real-world Applications
Highly-scalable Reinforcement Learning RLlib for Real-world Applications
Build computer vision models to perform object detection and classification w...
Build computer vision models to perform object detection and classification w...
Causal Inference in Data Science and Machine Learning
Causal Inference in Data Science and Machine Learning
Weekly #105: AutoViz and Auto_ViML Visualization and Machine Learning
Weekly #105: AutoViz and Auto_ViML Visualization and Machine Learning
AISF19 - On Blending Machine Learning with Microeconomics
AISF19 - On Blending Machine Learning with Microeconomics
AISF19 - Building Scalable, Kubernetes-Native ML/AI Pipelines with TFX, KubeF...
AISF19 - Building Scalable, Kubernetes-Native ML/AI Pipelines with TFX, KubeF...
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Communications Mining Series - Zero to Hero - Session 1
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• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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The future of AI by Oren Etzioni from AI2
- 1. 1 AI NEXTCon Seattle ‘18 1/17-20th | Seattle #ainextcon http://aisea18.xnextcon.com
- 2. Oren Etzioni CEO, Allen Institute for Artificial Intelligence (AI2) allenai.org (Professor, University of Washington) The Future of AI
- 3. Outline I. AI, AlphaGo, and Deep learning II. Is AI an Existential Risk? III. AI for the Common Good (AI2) SemanticScholar.org 3
- 4. What Is AI? AI is machines doing what humans do: • Gave rise to a lot of Hollywood scripts • AI as a noun: “the AI” took over the world… AI in practice: • The science & engineering of software to accomplish narrow tasks once thought to require human intelligence 4
- 5. WE HAVE CREATED NARROW AI AI SAVANTS
- 7. What Is the Significance Of AlphaGo? 7
- 8. Haven’t We Seen This Movie Before? 8 Deep Blue beats Kasparov in 1997
- 9. Go Is Far More Difficult Than Chess 9 Chess Go Board Size 8 x 8 19 x 19 Estimated Branching Factor ~35 ~250 Estimated Game Tree Depth 𝟒𝟎 𝟏𝟓𝟎 Est. Number of possible games 𝟏𝟎 𝟒𝟕 𝟏𝟎 𝟏𝟕𝟎 Go board position evaluation is challenging
- 10. Myth: AlphaGo Shows The Way To AI Board games are “black and white” - Discrete moves - Win/lose è evaluation function Labeled data is essentially “infinite” AlphaGo Zero: Self play. 10
- 11. Arthur C. Clarke “Sufficiently advanced technology is indistinguishable from magic...” 11 Deep Learning isn’t magic! Source: Etzioni (Wired Magazine 2016)
- 12. Machine Learning Is 99% Human Work Deep Learning inputs: • Target concept • Algorithm • Neural network design 12 “Figuring out how to optimize something is a CS problem. But figuring out what to optimize is not.” (Nate Silver, paraphrased)
- 13. My Questions for AlphaGo • Can you play again? • Can you play poker? • Cross the street? • Can you tell us about the game? 13 No autonomy. No. No. No. Super-human performance on a narrow task, does not translate to human-level performance in general!
- 14. Winograd Schemas (Levesque, 2011) The large ball crashed right through the table because it was made of styrofoam. It = table The large ball crashed right through the table because it was made of steel. It = ball Common-sense knowledge & tractable reasoning are necessary for basic understanding! 14 2016 Challenge: 58.33% Correct
- 15. Paraphrasing Winston Churchill Deep Learning is not the end, it is not the beginning of the end, it's not even the end of the beginning! 15
- 16. Outline I. AI, AlphaGo, and Deep Learning II. Is AI an Existential Risk? III. AI for the Common Good (AI2) SemanticScholar.org 16
- 17. Artificial Intelligence (AI) is Like ‘Summoning the Demon’ 17 Source: Terminator Salvation (2009) Elon Musk Warns…
- 18. What do the Experts Think? 18 Source: Etzioni (MIT Tech Review 2016)
- 19. Working to Prevent AI from turning evil is like disrupting the Space Program to prevent overpopulation on Mars. 19 Andrew Ng
- 20. Rodney Brooks 20 “If you’re worried about The Terminator, just keep the door closed.”
- 21. Hollywood Myth: SkyNet AI will be hegemonic, monolithic, and evil One AI can be utilized to check and counterbalance others (Etzioni & Etzioni, 2016) Research on AI Guardians Fact: Intelligence ≠ Autonomy “Autonomous cars” is a misnomer 21
- 22. Outline I. AI, AlphaGo, and Deep learning II. Is AI an Existential Risk? III. AI for the Common Good (AI2) SemanticScholar.org 22
- 23. 23 Mission: AI for the Common Good Allen Institute For AI (AI2)
- 24. 24 Intelligent Cars Will reduce accidents
- 26. “It's the absence of AI technologies that is already killing people.” 26 Eric Horvitz
- 27. Moshe Vardi “We have a moral imperative to study AI in order to save people’s lives.” 27
- 28. IMPREGNABLE OFF SWITCH Proposed Framework for Regulating AI Source: 2001: A Space Odyssey (1968) (Etzioni Op-Ed, 9/1/17)
- 29. AI Field Is Fast Moving, Amorphous AI CARS AI TOYS ROBOTS Regulate AI Applications
- 30. Responsibility: an AI is subject to the laws that apply to its human operator My AI “did it” is not an excuse. THE DOG ATE MY HOMEWORK… (Etzioni Op-Ed, 9/1/17)
- 31. Disclosure: an AI shall disclose that it is not human. Source: TV Series Finale Humans (Etzioni Op-Ed, 9/1/17)
- 32. an AI shall not retain or disclose confidential information without approval from the source. Privacy: AI Barbie Amazon Echo Roomba (Etzioni Op-Ed, 9/1/17)
- 33. AVOIDING BIAS An AI shall not amplify the Bias in its training data
- 34. BOTTOM LINE AI IS NOT GOOD OR EVIL; AI IS A TOOL; A TECHNOLOGY, Loss of Jobs Major concern:
- 35. We Are Hiring! (visit Allenai.org) 35
- 36. 36 Our incubator helps entrepreneurs build large, impactful, AI-fueled companies. CTO Residency Program Early-Stage Startups Entrepreneurs in Residence We train and mentor talented engineers on the latest in Deep Learning, Computer Vision, ML, and more. We help early-stage startups build out their AI teams and accelerate their AI capabilities. We help successful entrepreneurs build their next billion-dollar startup with AI at the core.
- 37. 37 Companies We’ve Incubated To-Date: Your Startup Here Ai2incubator.com
- 38. 1 2 3 38 CTO Residency Program (January 23!) Get up to 12-months of highly exclusive training on Deep Learning, NLP, Computer Vision, ML, etc. Pair with a successful CEO to collaboratively start building a new company together. Exit the incubator as CTO of your new startup with a large amount of co-founder equity.