- How Did Humans Become So Creative? A Computational Approach
- The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities, 2019
- On the Measure of Intelligence - (2019) Franc ̧ois Chollet | Paper Explained Part1 | Part2 | Part3 | Part4
- When Will AI Exceed Human Performance? Evidence from AI Experts
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- Predictions of human-level AI timelines
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- AI Podcast Series - Lex Fridman | Apple Podcast
- The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
- Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD
- The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents - (2012) Nick Bostrom
- There is no Artificial General Intelligence
- Artificial General Intelligence — A gentle introduction, Pei Wang
- What Do We Know about AI Timelines?
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- Toward a Unified Artificial Intelligence
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- IEEE Special Report: The Singularity
- Beyond MAD?: the race for artificial general intelligence
- We’re entering the AI twilight zone between narrow and general AI
- The 21st Century Singularity and Global Futures - A Big History Perspective
- The 21st Century Singularity and its Big History Implications: A re-analysis
- Superintelligence — Semi-hard Takeoff Scenarios
- The timescale of artificial intelligence: Reflections on social effects
- Nick Bostrom's | Simulation and Superintelligence
- Future Progress in Artificial Intelligence: A Survey of Expert Opinion, Müller, Vincent C. and Bostrom, Nick (2016)
- Reframing Superintelligence Comprehensive AI Servicesas General Intelligence
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- The Hanson-Yudkowsky : AI-Foom Debate
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- 995 experts opinion: AGI / singularity by 2060
- Max Plank Institute: Superintelligence Cannot be Contained: Lessons from Computability Theory
- Turing Test and the Practice of Law: The Role of Autonomous Levels of AI Legal Reasoning
- The Why, What and How of Artificial General Intelligence Chip Development
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- Updates and Lessons from AI Forecasting
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- A Survey of Embodied AI: From Simulators to Research Tasks
- A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
- Towards A Measure Of General Machine Intelligence
- The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence
- The AGI hype train is running out of steam
- Real Superintelligence (RSI): Disrupting ANI, AGI, ASI as parts of human-threating and fake AI
- Why AI is Harder Than We Think
- Prospective Learning: Back to the Future
- Existence and perception as the basis of AGI (Artificial General Intelligence)
- Analysis of Neural Fragility: Bounding the Norm of a Rank-One Perturbation Matrix
- The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence
- Existence and perception as the basis of AGI (Artificial General Intelligence)
- 2022 NLU: Year in Review
- A First-Principles Theory of Neural Network Generalization
- Making RL Tractable by Learning More Informative Reward Functions: Example-Based Control, Meta-Learning, and Normalized Maximum Likelihood
- Which Mutual Information Representation Learning Objectives are Sufficient for Control?
- PICO: Pragmatic Compression for Human-in-the-Loop Decision-Making
- Distilling neural networks into wavelet models using interpretations
- What Can I Do Here? Learning New Skills by Imagining Visual Affordances
- Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
- The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
- google-research/ibc - Official implementation of Implicit Behavioral Cloning, as described in our CoRL 2021 paper, see more at https://implicitbc.github.io/
- Unsupervised Learning of Compositional Energy Concepts NeurIPS 2021
- Building Next Generation Web-based ML Applications Using TensorFlow.js With Google
- To create AGI, we need a new theory of intelligence
- Peter Thiel: Artificial General Intelligence Isn't Happening
- Who Will Create the First Artificial General Intelligence? Tesla?
- Paradigms of Artificial General Intelligence and Their Associated Risks
- The General Theory of General Intelligence: A Pragmatic Patternist Perspective
- A Metamodel and Framework for Artificial General Intelligence From Theory to Practice
- Patterns of Cognition: Cognitive Algorithms as Galois Connections Fulfilled by Chronomorphisms On Probabilistically Typed Metagraphs
- Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
- Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
- A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis
- Transformers in Vision: A Survey
- Reinforcement Learning for Ridesharing: An Extended Survey
- A Literature Survey of Recent Advances in Chatbots
- Video Transformers: A Survey
- Survey and Perspective on Social Emotions in Robotics
- Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents
- LaMDA: Language Models for Dialog Applications
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- Stitch it in Time: GAN-Based Facial Editing of Real Videos
- Transferability in Deep Learning: A Survey
- DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
- Physical Activity Recognition by Utilising Smartphone Sensor Signals
- Designer Modeling through Design Style Clustering
- Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
- PyTorch Tabular: A Framework for Deep Learning with Tabular Data
- alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction
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- Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
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- imodels: leveraging the unreasonable effectiveness of rules
- Competitive programming with AlphaCode
- Why It’s So Hard to Regulate Algorithms
- Now Physical Jobs Are Going Remote Too
- Artificial intelligence system rapidly predicts how two proteins will attach
- The Ecosystem Path to General AI
- Compression, The Fermi Paradox and Artificial Super-Intelligence
- On the link between conscious function and general intelligence in humans and machines
- Towards A Measure Of General Machine Intelligence
- Memorization and Generalization in Neural Code Intelligence Models
- General Intelligence Requires Rethinking Exploration
- Bridging the Gap between Artificial Intelligence and Artificial General Intelligence: A Ten Commandment Framework for Human-Like Intelligence
- Computable Artificial General Intelligence
- The Embeddings World and Artificial General Intelligence
- Towards artificial general intelligence via a multimodal foundation model
- Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence
- [Deep Learning and Artificial General Intelligence: Still a Long Way to Go](https://arxiv.org/pdf/2203.14963.pdf
- Navigating Conceptual Space; A new take on Artificial General Intelligence
- Existence and perception as the basis of AGI (Artificial General Intelligence)
- Approaches to Artificial General Intelligence: An Analysis
- The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence
- Auxiliary Learning as a step towards Artificial General Intelligence
- ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence
- Graph Coloring with Physics-Inspired Graph Neural Networks
- AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
- FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK
- Explainable AI through the Learning of Arguments
- Sim2Real Object-Centric Keypoint Detection and Description
- Causal Explanations and XAI
- Computational Complexity of Segmentation
- Computational Metacognition
- Augmented Business Process Management Systems: A Research Manifesto
- A BRIEF OVERVIEW OF PHYSICS-INSPIRED METAHEURISTIC OPTIMIZATION TECHNIQUES
- Don’t Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
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- Bioinspired Cortex-based Fast Codebook Generatio
- How to build a cognitive map: insights from models of the hippocampal formation
- Hybrid Quantum Algorithms for Quantum Monte Carlo
- Multimodal Bottleneck Transformer (MBT): A New Model for Modality Fusion
- Robust Graph Neural Networks
- 12 Graphs That Explain the State of AI in 2022 The 2022 AI Index talks jobs, investments, ethics, and more
- Andrew Ng predicts the next 10 years in AI
- Google uses deep learning to design faster, smaller AI chips
- Nvidia reveals H100 GPU for AI and teases ‘world’s fastest AI supercomputer’
- top-artificial-intelligence-writers-you-should-know-about-in-2022
- Ethics and AI: 3 Conversations Companies Need to Have
- The Movement to Decolonize AI: Centering Dignity Over Dependency
- a-report-from-the-universities-of-oxford-and-bologna-will-help-protect-society-from-unethical-ai-with-a-world-first-approach-to-support-organisations-to-meet-future-eu-regulations
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- A Decade of Open Robotics
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- MIT research suggests AI can learn to identify images using synthetic data
- DeepMind’s new AI gives historians a powerful new tool to interpret the past
- The 2022 AI Index: Industrialization of AI and Mounting Ethical Concerns
- Impossibility of Collective Intelligence
- Approaches to Artificial General Intelligence: An Analysis
- Computable Artificial General Intelligence
- Artificial Open World for Evaluating AGI: a Conceptual Design
- A WORLD-SELF MODEL TOWARDS UNDERSTANDING INTELLIGENCE
- A General Framework for the Representation of Function and Affordance: A Cognitive, Causal, and Grounded Approach, and a Step Toward AGI
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- Needs-aware Artificial Intelligence: AI that ‘serves [human] needs’
- LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015
- AGI via Combining Logic with Deep Learning
- Robustness to fundamental uncertainty in AGI alignment
- Gato, the latest from Deepmind. Towards true AI?
- On the Controllability of Artificial Intelligence: An Analysis of Limitations
- Google Deepmind Intros Generalist AI Which May Lead to AGI
- GPT-3 prompts: Technical progress or just AI alchemy?
- Trends in AI — June 2022
- Towards artificial general intelligence via a multimodal foundation model
- The Mathematics of Artificial Intelligence
- Meta’s Yann LeCun strives for human-level AI
- Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- When Brain-inspired AI Meets AGI
- Purposeful and Operation-based Cognitive System for AGI
- Safety without alignment
- a survey on GPT-3
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace
- A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
- A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
- A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
- BloombergGPT: A Large Language Model for Finance
- Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
- Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure
- Advances in apparent conceptual physics reasoning in ChatGPT-4
- Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams
- GPT-4 Technical Report
- GPTEval: NLG Evaluation using GPT-4 with Better Human Alignment
- Mind meets machine: Unravelling GPT-4’s cognitive psychology
- A Survey of Large Language Models
- ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
- GPT is becoming a Turing machine: Here are some ways to program it
- Natural Selection Favors AIs over Humans
- Machine Theory of Mind
- Fractal AI: A fragile theory of intelligence
- Theory of Cognitive Relativity: A Promising Paradigm for True AI
- Theory of Minds: Understanding Behavior in Groups Through Inverse Planning
- Towards a framework for the evolution of artificial general intelligence
- The Hanabi Challenge: A New Frontier for AI Research
- A Conceptual Bio-Inspired Framework for the Evolution of Artificial General Intelligence
- Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs
- AI-GAs: AI-generating algorithms, an alternateparadigm for producing general artificial intelligence
- The Technology of Mind and a New Social Contract
- The General Theory of General Intelligence: A Pragmatic Patternist Perspective
- A Metamodel and Framework for Artificial General Intelligence From Theory to Practice
- Counterfactual Planning in AGI Systems
- The Principles of Deep Learning Theory
- A Theory of Consciousness from a Theoretical Computer Science Perspective 2: Insights from the Conscious Turing Machine
- A Theoretical Computer Science Perspective on Consciousness
- What Is Consciousness? Artificial Intelligence, Real Intelligence, Quantum Mind, And Qualia
- Consciousness and Automated Reasoning
- Representation Internal-Manipulation (RIM): A Neuro-Inspired Computational Theory of Consciousness
- The Consciousness Prior
- Information Flow Theory (IFT) of Biologic and Machine Consciousness: Implications for Artificial General Intelligence and the Technological Singularity
- A theory of consciousness: computation, algorithm, and neurobiological realization
- Neural Consciousness Flow
- Can Computers overcome Humans? Consciousness interaction and its implications
- Consciousness is Pattern Recognition
- An Artificial Consciousness Model and its relations with Philosophy of Mind
- Is Intelligence Artificial?
- How to avoid ethically relevant Machine Consciousness
- Mysteries of Visual Experience
- Model-based actor-critic: GAN (model generator) + DRL (actor-critic) => AGI
- Neuro-Symbolic VQA: A review from the perspective of AGI desiderata
- Simulation Intelligence: Towards a New Generation of Scientific Methods
- A Psychopathological Approach to Safety Engineering in AI and AGI
- Provable limitations of deep learning
- Deep Learning: A Critical Appraisal
- Measure, Manifold, Learning, and Optimization: A Theory Of Neural Networks
- Predicting Research Trends From Arxiv
- Brief Review of Computational Intelligence Algorithms
- We analyzed 16,625 papers to figure out where AI is headed next, 2019
- Troubling Trends in Machine Learning Scholarship
- AI Enabling Technologies: A Survey, MIT 2019
- A Berkeley View of Systems Challenges for AI
- Vincent C. Müller - "Challenges for Artificial Cognitive Systems"
- AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
- Go-Explore: a New Approach for Hard-Exploration Problems
- Neurosymbolic AI: The 3rd Wave
- Scaling down Deep Learning
- Thinking Fast and Slow in AI
- Understanding Attention: In Minds and Machines
- Model-based actor-critic: GAN (model generator) + DRL (actor-critic) => AGI
- The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain
- Model-based actor-critic: GAN (model generator) + DRL (actor-critic) => AGI
- Learning What To Do by Simulating the Past
- AGI logic tutorial - July2021
- AGI via Combining Logic with Deep Learning
- Mind and matter: Modeling the human brain with machine learning
- A Survey of Human-in-the-loop for Machine Learning
- A Survey on Neural Network Interpretability
- Approaches to Artificial General Intelligence: An Analysis
- Progress in artificial intelligence
- AI Impacts Library
- PRIMO.ai - Benchmarks
- Measuring the Progress of AI Research
- paperswithcode.com/trends
- A critical analysis of metrics used for measuring progress in artificial intelligence
- Directory of AI Benchmarks
- MLPerf Training benchmark
- MLPerf Inference Benchmark
- Demystifying the MLPerf Training Benchmark Suite
- The Vision Behind MLPerf: Benchmarking ML Systems, Software Frameworks and Hardware Accelerators
- MLPerf: A Benchmark Suite for Machine Learning - David Patterson (UC Berkeley)
- MLPerf: A Benchmark Suite for Machine Learning - Gu-Yeon Wei (Harvard University)
- DAWNBench: An End-to-End Deep LearningBenchmark and Competition
- AIBench Training: Balanced Industry-Standard AI Training Benchmarking
- ai-benchmark - Smartphone Hardwares & Mobile SoC's
- deep500/deep500 | AI500
- Fathom: Reference Workloads for Modern Deep Learning Methods
- Measuring Inference Performance of Machine-Learning Frameworks on Edge-class Devices with the MLMark™Benchmark
- Ultra-low Power Machine Learning with ULPMark-ML
- The Murky World Of AI Benchmarks
- Benchmarking Contemporary Deep Learning Hardware and Frameworks:A Survey of Qualitative Metrics
- Survey and Benchmarking of Machine Learning Accelerator
- Algorithmic Progress in Six Domains
- Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks
- Consortium of Tech Firms Sets AI Benchmarks
- Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training
- SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
- gluebenchmark
- Nvidia Dominates Latest MLPerf Training Benchmark Results
- ML Perf v0.7 Results Released -- NVIDIA Breaks 16 AI Performance Records
- Open Source Growth Benchmarks and the 20 Fastest-Growing OSS Startups
- Benchmarking Natural Language Understanding Services for building Conversational Agents
- Recent advances in conversational NLP : Towards the standardization of Chatbot building
- Benchmarking TinyML Systems: Challenges and Direction
- MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It
- paperswithcode.com/sota
- stateoftheart.ai
- State of AI Report 2020 | 2019 | 2018
- Deep Learning State of the Art (2020) | MIT Deep Learning Series | 2019
- AI/AGI - state of the art in 2019
- State-of-Art-Reviewing: A Radical Proposal to Improve Scientific Publication
- Overview: State-of-the-Art Machine Learning Algorithms per Discipline & per Task
- ONNX Model Zoo
- A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective
- A Survey of Data Quality Measurement and Monitoring Tools
- datasets for machine-learning research
- arXiv dataset and metadata of 1.7M+ scholarly papers across STEM
- Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods
- The Big Bad NLP Database
- Deep Learning for Vision-based Prediction: A Survey
- Driving Datasets Literature Review
- PMLB v1.0: an open source dataset collection for benchmarking machine learning methods
- Data and its (dis)contents: A survey of dataset development and use in machine learning research
- Taxonomy of Big Data: A Survey
- A Survey of Big Data Machine Learning Applications Optimization in Cloud Data Centers and Networks
- Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities
- Data Science: A Comprehensive Overview
- Machine Learning in Python: Main developments andtechnology trends in data science, machine learning,and artificial intelligence
- How can AI Automate End-to-End Data Science?
- Human-AI Collaboration in Data Science: Exploring DataScientists’ Perceptions of Automated AI
- Data Science through the looking glass and what we found there
- Software Engineering for Machine Learning:A Case Study
- Hidden Technical Debt in Machine Learning Systems
- Machine Learning Testing:Survey, Landscapes and Horizons
- Ten Research Challenge Areas in Data Science
- Explainable Artificial Intelligence: a Systematic Review
- Architecture for High-Throughput Low-Latency Big Data Pipeline on Cloud
- DevOps for ML and other Half-Truths: Processes and Tools for the ML Life Cycle
- MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale
- Data science on industrial data -- Today's challenges in brown field applications
- Data Science and Digital Systems: The 3Ds of Machine LearningSystems Design
- 2020 Machine Learning Roadmap
- data-engineer-roadmap
- visenger/awesome-mlops
- Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure
- Automated Machine Learning: State-of-The-Art and Open Challenges
- Deep Models for Relational Databases
- Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterprise
- Emerging Architectures for Modern Data Infrastructure
- Challenges in Deploying Machine Learning: a Survey of Case Studies
- Convergence of Edge Computing and DeepLearning: A Comprehensive Survey
- Applications of Deep Neural Networks
- Automating Data Science: Prospects and Challenges
- Ice Core Science Meets Computer Vision: Challenges and Perspectives
- Green AI
- AI at Scale | MSR1 | MSR2
- Intelligence at Scale in Action
- Large-Scale Intelligent Microservices
- A Survey on Large-scale Machine Learning
- Deploying Scientific AI Networks at Petaflop Scale
- Towards Federated Learning at Scale: System Design
- System Design for Large Scale Machine Learning
- Lessons Learned from Building Scalable Machine Learning Pipelines
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
- Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
- The Computational Limits of Deep Learning, MIT
- High Performance I/O For Large Scale Deep Learning
- On Scale-out Deep Learning Training for Cloud
- Deep Learning At Scale and At Ease
- A Survey on Distributed Machine Learning
- Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools
- LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations
- Deep Learning on Mobile Devices – A Review
- Survey of Machine Learning Accelerators
- Exascale Deep Learning for Scientific Inverse Problems
- The Lives and Death of Moore's Law
- Fundamental Limits to Moore's Law
- Limits on Fundamental Limits to Computation
- Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
- Natural Language Generation at Scale: A Case Study for Open Domain Question Answering
- Scaling Scaling Laws with Board Games
- High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models
- From Distributed Machine Learning to Federated Learning: A Survey
- A Comprehensive Survey and Performance Analysis of Activation Functions in Deep Learning
- A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities
- Survey on Large Scale Neural Network Training
- Compute Trends Across Three Eras of Machine Learning
- Computational Lens on Cognition: Study Of Autobiographical Versus Imagined Stories With Large-Scale Language Models
- Adversarial Attacks and Defences: A Survey
- Ten ways to fool the masses with machine learning
- Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
- Challenges of Privacy-Preserving Machine Learning in IoT
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- How to avoid machine learning pitfalls: a guide for academic researchers
- Is History Converging? Again?
- New Millennium AI and the Convergence of History, 2006 | Update of 2012
- Secular Cycles and Millennial Trends
- Self-Organization and Artificial Life: A Review
- A computational perspective of the role of Thalamus in cognition
- Lenia - Biology of Artificial Life
- A Roadmap for Reverse-Architecting the Brain’s Neocortex
- Whole Brain Emulation - A Roadmap
- The Explanation of Human Intelligence
- Accelerating Change
- The Law of Accelerating Returns by Ray Kurzweil
- The Beginning and the End: The Meaning of Life in a Cosmological Perspective
- The Pace and Proliferation of Biological Technologies
- The Universeof Minds
- Deep learning for molecular generation and optimization - a review of the state of the art
- Human-AI Symbiosis: A Survey of Current Approaches
- A Browsable Petascale Reconstruction of the Human Cortex
- Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence
- Accelerating science with human versus alien artificial intelligences
- Analogies between Biology and Deep Learning https://t.co/AvbYmP1azq A rough short note exploring unusual analogies between biology and deep learning research.
- A Survey of Neural Trojan Attacks and Defenses in Deep Learning
- artificial-life
- Brain’s Magical Future
- BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
- Neurosciences and 6G: Lessons from and Needs of Communicative Brains
- The Roadmap to 6G – AI Empowered Wireless Networks
- The Transhumanist FAQ
- Nanotechnology:The Future is Coming Sooner Than You Think
- BANG Theory : A mix of Bits, Atoms, Neurons and Genes (B.A.N.G.)
- What is Transhumanism?
- The History of ‘Transhumanism’
- Transhumanism: 2000 Years in the Making
- A history of transhumanist thought, NickBostrom
- Immortality 2.0: A Silicon Valley Insider Looks at California’s Transhumanist Movement
- Posthuman Rights:Dimensions of Transhuman Worlds
- Quantum Consciousness, 1994
- On quantum theories of the mind, 1997 - Lawrence Berkeley National Laboratory
- The Hard Problem: A Quantum Approach.
- Quantum effects in the brain: A review
- Quantum Approaches to Consciousness
- The Road to Quantum Artificial Intelligence
- The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
- Implications of Quantum Computing for Artificial Intelligence alignment research
- Computer-inspired Quantum Experiments
- gopala-kr/a-week-in-wild-ai/ai-for-science
- A Survey of Deep Learning for Scientific Discovery
- Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects
- Robust Artificial Intelligence and Robust Human Organizations
- Value Bias and Lost Utility in Multi-Dimensional Gaps
- Global AI Survey: AI proves its worth, but few scale impact | mckinsey
- PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution
- MIT Human-Centered Artificial Intelligence (AI) 2019
- Benchmarking National AI Strategies
- Global Talent in the Age of Artificial Intelligence, INSEAD 2020
- Artificial Intelligence Index Report Stanford, 2019
- Government AI Readiness Index 2020
- Artificial Intelligence, Education, and Entrepreneurship∗
- The Global AI Talent Tracker
- Who’s Ahead in AI Research in 2020? Insights from the International Conference on Machine Learning (ICML 2020)
- Mapped: The State of Facial Recognition Around the World
- AI Strategies & Public Sector Components
- Tackling Climate Change with Machine Learning
- Artificial General Intelligence: Coordination & Great Powers
- The President in Conversation With MIT’s Joi Ito and WIRED’s Scott Dadich
- Intelligence Explosion Microeconomics
- Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo
- The Global Artificial Intelligence Landscape
- Learning from All Vehicles
- AGI Society | AGI Society
- AGI Conferences
- Future of Life Institute
- SingularityNET
- ai-principles
- Cambridge Centre for the Study of Existential Risk
- Oxford Future of Humanity Institute
- Machine Intelligence Research Institute
- AGI Safety Researchers
- AGI Safety Literature Review
- The Threats of Artificial Intelligence Scale (TAI)
- A Survey on Computational Propaganda Detection
- Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
- A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
- A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy
- A Survey on Bias and Fairness in Machine Learning
- The State of AI Ethics Report (June 2020)
- AI Ethics Guidelines Global Inventory
- Potential Risks from Advanced Artificial Intelligence
- Responses to catastrophic AGI risk: a survey
- CAPTCHA:Using Hard AI Problems For Security
- The implausibility of intelligence explosion, François Chollet
- Intelligence Explosion FAQ
- “Singularity” is improperly used in AI
- Why an Intelligence Explosion is Probable
- The Coming Technological Singularity: How to Survive in the Post-Human Era
- Global Solutions vs. Local Solutions for the AI Safety Problem
- Nick Bostrom : Existential Risks - Analyzing Human Extinction Scenarios and Related Hazards
- Singularity Comments
- An overview of models of technological singularity
- The Singularity: A Philosophical Analysis
- Intelligence Explosion:Evidence and Import
- The Human Future: Upgrade or Replacement?
- The Singularity Is Further Than It Appears
- Artificial Intelligence as a Positive andNegative Factor in Global Risk
- BILL JOY - Why the Future Doesn't Need Us
- Ethical behavior in humans and machines - Evaluating trainingd at a quality for beneficial machine learning
- Ethical Artificial Intelligence
- Chappie and the Future of Moral Machines
- Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-based Approaches to Principles for AI
- A Case for Machine Ethics in Modeling Human-Level Intelligent Agents
- sam-altmans-manifest-destiny
- Artificial Intelligence May Doom The Human Race Within A Century, Oxford Professor Says
- Creating Friendly AI 1.0:The Analysis and Design ofBenevolent Goal Architectures
- The Problem with ‘Friendly’ Artificial Intelligence
- The Persistent Peril of the Artificial Slave
- Rise of Concerns about AI: Reflections and Directions
- The Precipice: Existential Risk and the Future of Humanity | Toby Ord | EA Global: London 2019
- Superintelligence Skepticism as a Political Tool
- Countering Superintelligence Misinformation
- The Wrong Cognitive Measuring Stick
- AI Alignment Podcast: AI Alignment through Debate with Geoffrey Irving
- Google developing kill switch for AI
- ‘Press the big red button’: Computer experts want kill switch to stop robots from going rogue
- Specifying AI safety problems in simple environments
- Human compatible : artificial intelligence and the problem of control
- Avoiding Unintended AI Behaviors
- AI safety via debate
- Thinking inside the box: using and controlling an Oracle AI
- Preparing for our posthuman future of artificial intelligence
- Teaching AI, Ethics, Law and Policy
- Linking Artificial Intelligence Principles
- Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society
- On the Connections between Counterfactual Explanations and Adversarial Examples
- Know Your Model (KYM): Increasing Trust in AI and Machine Learning
- The Future Of Work & The Work Of The Future
- Study: The Future of VR, AR and Self-Driving Cars
- Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research
- Unsolved ML Safety Problems
- Poisoning Attacks and Defenses on Artificial Intelligence: A Survey
- Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons
- My AGI safety research—2022 review, ’23 plans
- A Week in Wild AI - gopala-kr/a-week-in-wild-ai | daviddao/awful-ai
- AI Academic Survey- gopala-kr/summary
- AI and Hardware Survey - gopala-kr/DL-on-Silicon
- Computer Vision Tasks and CNN Surveys - gopala-kr/ConvNets OR CV Tasks
- NLP and RNN Surveys - gopala-kr/language-models
- Generative Models - gopala-kr/generative-models
- Auto Encoders - gopala-kr/autoencoders
- Reinforcement Learning Survey - gopala-kr/DRL-Agents
- MetaLearning Research gopala-kr/meta-learning
- State of the Art Models - gopala-kr/SoTA
- AI Applications - gopala-kr/ai-applications
- AI Learning Roadmap - gopala-kr/ai-learning-roadmap
- AI/ML Handson-Notebooks - gopala-kr/notebooks
- Kaggle Solutions - gopala-kr/kaggle-solutions
- Quantum Dots - gopala-kr/Quantum-Dots
- Trending Repositories - gopala-kr/trending-repos
- Transfer Learning Survey - ci-ai/transfer-learning