Closing the gap between neuroscience and machine learning
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I discuss some of the differences between neuroscience and the machine learning communities and how we can get them communicating with each other to bring us better understanding of the brain and artificial intelligence.
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Artificial intelligence (AI) is the simulation of human intelligence by machines, especially computer systems. AI works by ingesting large amounts of labeled training data to analyze patterns and correlations in order to make predictions. The main types of AI are reactive machines (task-specific without memory), limited memory systems (can use past experiences), theory of mind systems (understand human emotions and intentions), and self-aware systems (have consciousness). AI is important because it can provide insights by analyzing large amounts of data faster than humans and in some cases perform tasks better. However, AI also has disadvantages such as being expensive, requiring expertise, and only knowing what it has been exposed to through data.
arti-ficial-inte-lligence
Artificial intelligence and its history, definitions, limitations, and applications are summarized. AI aims to create machine intelligence through computer programs that can perform tasks normally requiring human intelligence. While AI has made progress in applications like games and speech recognition, truly intelligent machines have not been achieved. Limitations remain in developing programs that can match full human intelligence and adapt to new situations. Different programming languages like Lisp, Python, and Prolog are used to develop AI systems.
Artificial intelligence- The science of intelligent programs
Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.
Artificialintelligence
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
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The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
UNIT1-AI final.pptx
The document provides an overview of an artificial intelligence course syllabus and outlines. It discusses key concepts in AI including intelligent agents and environments. The syllabus covers what AI is, its history and current status, the scope of AI applications, intelligent agents and environments, problem formulations, and search techniques. It then outlines the history of AI from its origins in the 1950s and discusses various AI problems and applications including gaming, natural language processing, expert systems, vision systems, speech recognition, handwriting recognition, and intelligent robots.
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Introduction to AI
Introduction to AI for Freshmen class of Industrial Engineer at Rajamangala University of Technology Rattanakosin (Nakhon Pathom Campus)
Artificial_Intelligence_ppt_presentation.pptx
Artificial intelligence (AI) is the science and engineering of creating intelligent machines, especially computer programs. There are several major branches of AI, including perceptive systems that approximate human senses, robotics that perform tasks, expert systems that store knowledge, learning systems that adapt based on feedback, and neural networks that mimic the human brain. AI can be classified based on how the knowledge is designed, such as symbolic AI where knowledge is explicitly programmed or connectionist AI where an artificial neural network is taught. The main components of AI are speech synthesis, speech recognition, and logical reasoning, while common programming languages used are Prolog and Lisp. AI has many applications including robotics, medical diagnosis, video games, expert systems, natural language
Artificial intelligence : what it is
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
Ai introduction
Artificial intelligence (AI) is intelligence exhibited by machines. It is the branch of computer science which deals with creating computers or machines that are as intelligent as humans. The document discusses the history and evolution of AI from its foundations in 1943 to modern applications. It also defines different types of AI such as narrow AI, artificial general intelligence, and artificial super intelligence. Popular AI techniques like machine learning, deep learning, computer vision and natural language processing are also summarized.
Artificial intelligence
Artificial intelligence can be defined as the branch of computer science that is concerned with automation of computer system in an intelligent manner as like as humans.
Artificial Intelligence focuses on developing computer programs to solve complex problems and process.
What humans can do, now can be performed by machines too just, because of artificial intelligence.
AI is used because, it saves a whole lot of time and manpower.
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
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Artificial intelligence- The science of intelligent programs
Artificial intelligence- The science of intelligent programs
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt
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Closing the gap between neuroscience and machine learning
- 1. Closing the gap between neuroscience and machine learning Jason Toy @jtoy
- 2. Neuroscience • Scientific study of the nervous system • Why? How do humans and living beings function and learn. What are we? • Many sub disciplines: cognitive science, computational neuroscience, neurochemistry, neuro anatomy, clinical neuroscience, etc
- 5. Machine learning • study of computer science that often uses statistical methods to give the computers the ability to learn • Silo'd away • Why? Can we make computers intelligent like humans using current computer science paradigms
- 7. Differences between computers and human intelligence • Hardware • Software • Need new algorithms? • New hardware? • What is missing?
- 8. Differences between computers and human intelligence - hardware • Programs in computer vs embodied cognitions • Digital vs analog? Hybrid models? • Parallel computation vs sequential?
- 9. Differences between computers and human intelligence - software • Single objective function vs dynamic goals • text/images vs multimodal sensory • ML models learn strong features vs casual models for explaining and understanding
- 10. Reinforcement learning • Multi arm bandits - slot machines • Marketing and landing page optimization • Robots
- 14. sensorimotor vs RL ?
- 15. Sensenet: My contribution & attempt at closing the gap • Sensenet: An open source touch and sensorimotor simulator for neuroscience and machine learning research • https://github.com/jtoy/sensenet
- 16. Why sensorimotor? • Most ML algorithms are passive and static: CNNs, classification • Single objective function models in classification/RL • vision vs embodied sense • Next gen algorithms in the sensorimotor system - touch / motion / mapping / location / rotations
- 17. Goal of SenseNet Project • Aid in discovery of new AI algorithms through touch/sensorimotor simulations • Get computer science and neuroscience communities interested in touch and sensorimotor research
- 20. SenseNet components • 4 main components: • Physics and Touch Simulator • Simple extendable API for RL, sensorimotor, and touch abstractions • Standardized benchmarks and reference • Dataset of 3D objects dataset for manipulation
- 21. Physics and Touch Simulator • SenseNet provides different physics and touch environments as a reference • Hard and soft body collision detection • Tunable and tweakable physics and touch apparatuses • Current touch sensor acts as edge detector (similar to merkel cells)
- 22. Bullet Physics
- 24. Get to human like sensors
- 25. SenseNet API • Physics in C++, everything else in python • Researchers interact with the API in python • Reinforcement learning API / OpenAI gym comp • Developing API to add touch sensors and skin • Can use standard robotics tools to add agents (URDF/stl)
- 26. Environments / Agents • W Full Hand Index finger only Touch Wand ~7 agent types currently
- 27. SenseNet API & Touch Agents • Current API: reinforcement learning API (gym) • In development: • APIs to enable multiple reward functions. • Skin API, sensorimotor API • Embodied Agents: Hand, single finger, touch wand, disembodied touch agents, custom agents
- 29. SenseNet Dataset
- 30. SenseNet Dataset
- 32. Sensenet experiments • Can we train a simulated hand to touch and classify objects • Can we train a simulated hand to touch and classify textures • Can we train a simulated hand to touch and manipulate objects
- 34. Summary • Computer .......... humans • deep learning is awesome, but ........ • " We urge researchers in neuroscience and AI to find a common language, allowing a free flow of knowledge that will allow continued progress" - Demis Hassabis, Founder of DeepMind
- 35. Jason Toy @jtoy jtoy@jtoy.net Want to contribute? Questions? Feedback http://github.com/jtoy/sensenet