Brains@Bay Meetup: The Increasing Role of Sensorimotor Experience in Artifici...Numenta
We receive information about the world through our sensors and influence the world through our effectors. Such low-level data has gradually come to play a greater role in AI during its 70-year history. I see this as occurring in four steps, two of which are mostly past and two of which are in progress or yet to come. The first step was to view AI as the design of agents which interact with the world and thereby have sensorimotor experience; this viewpoint became prominent in the 1980s and 1990s. The second step was to view the goal of intelligence in terms of experience, as in the reward signal of optimal control and reinforcement learning. The reward formulation of goals is now widely used but rarely loved. Many would prefer to express goals in non-experiential terms, such as reaching a destination or benefiting humanity, but settle for reward because, as an experiential signal, reward is directly available to the agent without human assistance or interpretation. This is the pattern that we see in all four steps. Initially a non-experiential approach seems more intuitive, is preferred and tried, but ultimately proves a limitation on scaling; the experiential approach is more suited to learning and scaling with computational resources. The third step in the increasing role of experience in AI concerns the agent’s representation of the world’s state. Classically, the state of the world is represented in objective terms external to the agent, such as “the grass is wet” and “the car is ten meters in front of me”, or with probability distributions over world states such as in POMDPs and other Bayesian approaches. Alternatively, the state of the world can be represented experientially in terms of summaries of past experience (e.g., the last four Atari video frames input to DQN) or predictions of future experience (e.g., successor representations). The fourth step is potentially the biggest: world knowledge. Classically, world knowledge has always been expressed in terms far from experience, and this has limited its ability to be learned and maintained. Today we are seeing more calls for knowledge to be predictive and grounded in experience. After reviewing the history and prospects of the four steps, I propose a minimal architecture for an intelligent agent that is entirely grounded in experience.
Brains@Bay Meetup: The Increasing Role of Sensorimotor Experience in Artifici...Numenta
We receive information about the world through our sensors and influence the world through our effectors. Such low-level data has gradually come to play a greater role in AI during its 70-year history. I see this as occurring in four steps, two of which are mostly past and two of which are in progress or yet to come. The first step was to view AI as the design of agents which interact with the world and thereby have sensorimotor experience; this viewpoint became prominent in the 1980s and 1990s. The second step was to view the goal of intelligence in terms of experience, as in the reward signal of optimal control and reinforcement learning. The reward formulation of goals is now widely used but rarely loved. Many would prefer to express goals in non-experiential terms, such as reaching a destination or benefiting humanity, but settle for reward because, as an experiential signal, reward is directly available to the agent without human assistance or interpretation. This is the pattern that we see in all four steps. Initially a non-experiential approach seems more intuitive, is preferred and tried, but ultimately proves a limitation on scaling; the experiential approach is more suited to learning and scaling with computational resources. The third step in the increasing role of experience in AI concerns the agent’s representation of the world’s state. Classically, the state of the world is represented in objective terms external to the agent, such as “the grass is wet” and “the car is ten meters in front of me”, or with probability distributions over world states such as in POMDPs and other Bayesian approaches. Alternatively, the state of the world can be represented experientially in terms of summaries of past experience (e.g., the last four Atari video frames input to DQN) or predictions of future experience (e.g., successor representations). The fourth step is potentially the biggest: world knowledge. Classically, world knowledge has always been expressed in terms far from experience, and this has limited its ability to be learned and maintained. Today we are seeing more calls for knowledge to be predictive and grounded in experience. After reviewing the history and prospects of the four steps, I propose a minimal architecture for an intelligent agent that is entirely grounded in experience.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
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GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
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UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
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Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
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:
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
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The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
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Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
1. How should we represent visual scenes?
Common-Sense Core,
Probabilistic Programs
Josh Tenenbaum
MIT Brain and Cognitive Sciences
CSAIL
Joint work with Noah Goodman, Chris Baker, Rebecca Saxe,
Tomer Ullman, Peter Battaglia, Jess Hamrick and others.
2. Core of common-sense reasoning
Human thought is structured around a basic
understanding of physical objects, intentional
agents, and their relations.
“Core knowledge” (Spelke, Carey, Leslie, Baillargeon, Gergely…)
Intuitive theories (Carey, Gopnik, Wellman, Gelman, Gentner, Forbus, McCloskey…)
Primitives of lexical semantics (Pinker, Jackendoff, Talmy, Pustejovsky)
Visual scene understanding (Everyone here…)
From scenes to stories…
The key questions:
(1) What is the form and content of human common-sense
theories of the physical world, intentional agents, and their
interaction?
(2) How are these theories used to parse visual experience
into representations that support reasoning, planning,
communication?
3. A developmental perspective
A 3 year old and her dad:
Dad: “What's this a picture of?”
Sarah: “A bear hugging a panda bear.”
...
Dad: “What is the second panda bear
doing?”
Sarah: “It's trying to hug the bear.”
Dad: “What about the third bear?”
Sarah: “It’s walking away.”
But this feels too hard to approach now, so what about
looking at younger children (e.g.12 months or younger)?
4. Intuitive physics and psychology
Southgate and Csibra, 2009
(13 month olds)
Heider and Simmel, 1944
7. Probabilistic generative models
• early 1990’s-early 2000’s
– Bayesian networks: model the causal processes that
give rise to observations; perform reasoning, prediction,
planning via probabilistic inference.
– The problem: not sufficiently flexible, expressive.
8. Scene understanding as an
inverse problem
The “inverse Pixar” problem:
World state (t)
graphics
Image (t)
9. Scene understanding as an
inverse problem
The “inverse Pixar” problem:
physics
… World state (t-1) World state (t) World state (t+1) …
graphics
Image (t-1) Image (t) Image (t+1)
10. Probabilistic programs
• Probabilistic models a la Laplace.
– The world is fundamentally deterministic (described by a program),
and perfectly predictable if we could observe all relevant variables.
– Observations are always incomplete or indirect, so we put probability
distributions on what we can’t observe.
• Compare with Bayesian networks.
– Thick nodes. Programs defined over unbounded sets of objects, their
properties, states and relations, rather than traditional finite-
dimensional random variables.
– Thick arrows. Programs capture fine-grained causal processes
unfolding over space and time, not simply directed statistical
dependencies.
– Recursive. Probabilistic programs can be arbitrarily manipulated
inside other programs. (e.g. perceptual inferences about entities that make
perceptual inferences, entities with goals and plans re: other agents’ goals and plans.)
• Compare with grammars or logic programs.
11. Probabilistic programs for “inverse
pixar” scene understanding
• World state: CAD++
• Graphics
– Approximate Rendering
• Simple surface primitives
• Rasterization rather than ray tracing (for each primitive, which
pixels does it affect?)
• Image features rather than pixels
– Probabilities:
• Image noise, image features
• Unseen objects (e.g., due to occlusion)
12. Probabilistic programs for “inverse
pixar” scene understanding
• World state: CAD++
• Graphics
• Physics
– Approximate Newton (physical simulation toolkit, e.g. ODE)
• Collision detection: zone of interaction
• Collision response: transient springs
• Dynamics simulation: only for objects in motion
– Probabilities:
• Latent properties (e.g., mass, friction)
• Latent forces
14. Modeling stability judgments
physics
… World state (t-1) World state (t) World state (t+1) …
graphics
Image (t-1) Image (t) Image (t+1)
15. Modeling stability judgments
physics
… World state (t-1) World state (t) World state (t+1) …
Prob. approx. rendering
Image (t-1) Image (t) Image (t+1)
16. Modeling stability judgments
physics
… World state (t-1) World state (t) World state (t+1) …
Prob. approx. rendering
Image (t-1) Image (t) Image (t+1)
17. Modeling stability judgments
Prob.
approx.
Newton
… World state (t-1) World state (t) World state (t+1) …
Prob. approx. rendering
Image (t-1) Image (t) Image (t+1)
18. Modeling stability judgments
Prob.
approx.
Newton
… World state (t-1) World state (t) World state (t+1) …
Prob. approx. rendering
Image (t-1) Image (t) Image (t+1)
= perceptual uncertainty
19. Modeling stability judgments
(Hamrick,
Battaglia,
Tenenbaum,
Cogsci 2011)
Perception: Approximate posterior with block positions normally distributed
around ground truth, subject to global stability.
Reasoning : Draw multiple samples from perception.
Simulate forward with deterministic approx. Newton (ODE)
Decision: Expectations of various functions evaluated on simulation outputs.
28. The flexibility of common sense
(“infinite use of finite means”, “visual Turing test”)
• Which way will the blocks fall?
• How far will the blocks fall?
• If this tower falls, will it knock that one over?
• If you bump the table, will more red blocks or
yellow blocks fall over?
• If this block had (not) been present, would the
tower (still) have fallen over?
• Which of these blocks is heavier or lighter than
the others?
• …
39. If you bump the table…
(Battaglia, & Tenenbaum, in prep)
Mean human
judgment
Model prediction
(expected proportion of red vs. yellow blocks that fall)
40. Experiment 1: Cause/ Prevention Judgments
(Gerstenberg, Tenenbaum,
Goodman, et al., in prep)
41. Modeling people’s cause/prevention judgments
• Physics Simulation Model
p(B|A) – p(B| not A)
0 if ball misses
p(B|A)
1 if ball goes in
p(B| not A): assume
sparse latent Gaussian
perturbations on B’s
velocity.
51. Conclusions
From scenes to stories… What contents of stories are
routinely accessed through visual scenes? How can we
represent that content for reasoning, communication,
prediction and planning?
Focus on core knowledge present in preverbal infants:
intuitive physics, intuitive psychology.
Representations using probabilistic programs: thick nodes
(e.g. CAD++), thick arrows (physics, graphics, planning),
recursive (inference about inference, goals about goals).
Challenges for future work: (1) Integrating physics and
psychology. (2) Efficient inference. (3) Learning.
