4 ms·

Artificial Intelligence: Foundations of Computational Agents

115 points by rramadass 2y ago

simonw 2y ago
Because I collect definitions of "agent", here's the one this book uses: > An agent is something that acts in an environment; it does something. Agents include worms, dogs, thermostats, airplanes, robots, humans, companies, and countries. https://artint.info/3e/html/ArtInt3e.Ch1.S1.html https://artint.info/3e/html/ArtInt3e.Ch1.S1.html I think of this as the "academic" definition, or sometimes the "thermostat" definition (though maybe I should call it the "worms and dogs" definition). Another common variant of it is from Peter Norvig and Stuart Russell's classic AI text book "Artificial Intelligence: A Modern Approach": http://aima.cs.berkeley.edu/4th-ed/pdfs/newchap02.pdf http://aima.cs.berkeley.edu/4th-ed/pdfs/newchap02.pdf > Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
- fouc 2y ago
  written 2023 , pre-LLM hype
  - avmich 2y ago
    LLM hype was firmly with us with the coming of ChatGPT, November 2022. Records in signing up aren't for nothing.
  - nextos 2y ago
    I don't think so. I own the printed version and it starts discussing GPT in page 6 already.
- andirk 2y ago
  I was literally compiling a list of "agent" synonyms at lunch today. My favorite and most accurate so far is "doer".
- nine_k 2y ago
  So basically an agent is a procedure, by this definition: it takes parameters (environment) and acts upon that by executing side effects. An email filter is an agent. A database trigger is an agent.
  - nivertech 2y ago
    > it takes parameters (environment) I think it's better to imagine agent as something that physically placed inside the Environment, and actually modifying/changing/mutating it in place. > An email filter is an agent. A database trigger is an agent. you're missing the "I" (Intelligence) part - the filtering logic in the email filter, or a business logic in the DB trigger/stored procedure/CGI script/AWS Lambda function/etc. But yes, an agent doesn't have to be Intelligent, it can be a Dumb Agent / NPC / Zero-Intelligence Trader.
    - sureglymop 2y ago
      Can you explain the "intelligence" part? Can't one derive a decision tree of any "intelligent agent" that is in essence no different than a classically programmed algorithm?
      - FollowingTheDao 2y ago
        Just replying here to tell you I replied to a question (cisco) you asked me in case you miss it. Thanks!
      - nivertech 2y ago
        Yes, for Computational Agents you will either code "Agent Intelligence"/"Agent Cognition" algorithmically, or using AI/ML/LLM (either by pre-training, or using continous re-training for Adaptive Agents). Useful abstaractions: - FSM/State Machines - Behavior Trees - Behavior Action Trees - Workflow Orchestration - Dataflow (mostly for pipelines transforming LLM Prompt into LLM Reponse) Another option is to outsource it to a Human, like it was in the ALICE program[1], e.g. Human-in-the-Loop, Participatory Simulation, RLHF, Whole-brain computer simulation, like in The Age of Em[2] (SciFi). See: https://news.ycombinator.com/item?id=43409240 https://news.ycombinator.com/item?id=43409240 --- 1. https://www.media.mit.edu/projects/participatory-simulations/overview/ https://www.media.mit.edu/projects/participatory-simulations... https://ccl.northwestern.edu/papers/partsims/cscl/ https://ccl.northwestern.edu/papers/partsims/cscl/ 2. https://ageofem.com/ https://ageofem.com/
- nivertech 2y ago
  It always amazed me that different branches of CS, AI/ML and Complex Systems/Complexity Sciences have different views on agents. Objects in OOP - something which can have properties/attributes, and methods (verbs/commands). Usually modeled after a real-life/domain enitites. Aggregates in Domain-Driven Design (DDD) - transactional clusters of objects modeling a collections of entities in the domain. Actors in Actor Model / Active Objects - a something we can sen messages to, and receive messages from, and which may have some business logic. Agent-Based Modeling and Simulations (ABM) defines agents as a proxy for a decision maker. Digital Twins - a more realistic proxy/replica for a real life person, object, or process. Multi-Agent Systems (MAS) in how to use asgents to solve or optimize a real problem in production. RL/MARL (Muti-Agent Reinforcement Learning) on how to train an ML algorithm without supervision (i.e. a labeled dataset), by placing agents in an environment capable to automatically provide rewards/punishment feedback. LLM Agents - dynamically generated intelligent business process workflows (including Robotic Process Automation - RPA aka Tool Use/Function Call).
  - nivertech 2y ago
    Also: Smart Contracts - agents with an attached cryptocurrency wallet/account/address, capable to receive and make payments autonomously.
  - bsenftner 2y ago
    How's about: LLM Agent: any packaging of the use of AI such that the details of using said AI are packaged, hidden, and the user of this LLM Agent does not need to concern themselves with AI at all, only the intelligence services provided by what is now a simulated personality the user can willfully self deceive they are working with a human.
    - nivertech 2y ago
      aka "delegation", "ousourcing", "serverless", and "not my busienss, do it ASAP, I don't care how" ;)
      - bsenftner 2y ago
- dr_dshiv 2y ago
  How are agents different from daemons? And are both essentially cybernetic feedback loops?
  - jsemrau 2y ago
    (1) Daemons run usually in the background while agents are main loop. (2) Daemons are predefined and can't adapt to changes easily -- this is a function of narrow AI vs general AI (not to be confused with AGI) (3) Daemons have few interactions with the environment while for the agent the environment (tools, sensors, plans, memory, and context) is everything. These are just a few from the top of my head.
- jsemrau 2y ago
  Add this please ->https://jdsemrau.substack.com/about https://jdsemrau.substack.com/about
  - potatoman22 2y ago
    To save a click: "We define cognitive autonomous agents as an entity designed to perform tasks autonomously by combining four key components: goals, reasoning/planning, memory, and tools."
    - jsemrau 2y ago
      Thank you!
- UltraSane 2y ago
  agent has almost as many definitions as set.
  - aithrowawaycomm 2y ago
    I would say the situation for "agent" is about 10,000 times worse than it is for "set," since all the definitions for set are essentially different ways to make Frege / Cantor more rigorous. The underlying scientific concept hasn't actually changed that much: "belonging to a set and a few certain operations are primitives, join these primitives with formal logic." This is the idea behind a famous intro book, Paul Halmos's Naive Set Theory. Even a set theory with Russell's paradox is scientifically defensible, it just needs refinement. In contrast, "agent" discusses a huge range of scientific concepts, and I have yet to see a single definition of agent that holds up to scientific scrutiny. This book has managed to define "agent" in a way that is entirely equivalent to "physical object" - putting worms and thermostats in the same category broadens the category to utter uselessness. By this definition, Jupiter is an agent. The only utility of definitions like this is for dishonest people to cheat at arguments: claiming simple tools are agents, then arguing they are like dogs and humans, which are also agents. It's a total waste of time, coming from AI's shameless disrespect for scientific standards.
    - fc417fc802 2y ago
      > By this definition, Jupiter is an agent. Is it? What task was it designed to carry out autonomously? Where are the inputs and the logic hiding? > for dishonest people to cheat at arguments Only if the goal is to deceive. If the communication is well intentioned then there is nothing inherently wrong with it despite it not being to your liking.
      - aithrowawaycomm 2y ago
        The problem is they also put "worms" in the same category, and they aren't designed by humans to do anything! Why is it that the natural laws of a worm responding to Earth's environment are distinct from the natural laws of Jupiter responding to the solar system's environment? I suppose because of complexity. But then why is a thermostat different from Jupiter despite being considerably simpler? I suppose because it was designed by humans and can be controlled. But then what about the worm, which is just as natural as Jupiter? "Thermostat" is especially problematic because a cheap thermostat is very simple to describe completely as a thermo-electric balance equation: it is certainly simpler to describe than an irregular ball rolling down an irregular hill. Yet apparently the thermostat is an agent and the ball is not. The definition is just incoherent! "Sometimes an agent is deterministic and in this case the term only includes manmade tools, other times an agent is an apparently nondeterministic automaton and in this case we can include natural life." It only allows "agent" to be labelled ad hoc, and in particular blurs the distinction between "nondeterministic tool" and "lifeform" in ways that are scientifically unjustifiable. The only people this pointless word game benefits are liars like Sam Altman and Mustafa Suleyman; if people are well-intentioned then these definitions bring nothing but confusion.
- sc077y 2y ago
  The problem I see with this definition is that we have things called RAG agents which don't technically act in any environment except for provide information.
  - 1dom 2y ago
    Is that a problem with the definition of agents, or a problem with sticking the word "agent" on something that doesn't meet the definition of an agent?
  - deleted 2y ago
    [deleted]
  - bsenftner 2y ago
    Is that not a summary and translation agent? For some reason only the user knows, they do not want to or cannot read the entire RAG source, so they use their summary and translation agent to give them summaries and to translate the technical jargon they do not understand. That Agent becomes a teacher of the RAG source. I see no problem with the Agent definition when given this perspective.
- mportela 2y ago
  FWIW I had a professor that defined "robotics" in the same way (we even had a quite philosophical debate on whether automatic doors are robots). I ended up liking Norvig and Russell's definition better by appending the word "autonomously". Another interesting word that is quite out fashion nowadays is "cybernetics": "Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent action." [1] Having studied Control Engineering in college, to me, all these things are feedback control loops. [2] [1] https://en.wikipedia.org/wiki/Cybernetics https://en.wikipedia.org/wiki/Cybernetics [2] https://en.m.wikipedia.org/wiki/Feedback https://en.m.wikipedia.org/wiki/Feedback
- antonkar 2y ago
  Agent - time-like, energy-like (you need a GPU to compute it). An agent changes the shape of the environment it operates in, including its own shape. You can count agents, their volume of operations, their speed of changing shapes (volumetric speed), acceleration… The Big Bang had zero intelligence (with maximal potential intelligence) but was and still is maximally agentic. Same way we have many definitions of life (virus is alive or not? It’s very agentic, COVID killed millions but the shape, intelligence is small. Same with computer viruses), we have many definitions of agency, better to use the broadest and most physical one. Intelligence - space-like, matter-like (LLM is a bunch of vectors, a static geometric shape, you just need memory to store it). It’s a static geometric shape. It can have analogs of volume, mass and density. The static 4D spacetime of the universe or multiverse is maximally intelligent but non-agentic.
deleted 2y ago
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throwaway81523 2y ago
Book is from 2023, link should be edited for that.
bbor 2y ago
A) Looks really good, will be checking it out in depth as I get time! Thanks for sharing. B) The endorsements are interesting before you even get to the book; I know all textbooks are marketed, but this seems like quite the concerted effort. For example, take Judea Pearl's quote (an under-appreciated giant): This revised and extended edition of Artificial Intelligence: Foundations of Computational Agents should become the standard text of AI education. Talk about throwing down the gauntlet - especially since Russell looks up to him as a personal inspiration! (Quick context for those rusty on academic AI: Russell & Norvig's 1995 (4th ed in 2020) AI: A Modern Approach ("AIAMA") is the de facto book for AI survey courses, supposedly used in 1500 universities via 9 languages as of 2023.[1]) I might be reading drama into the situation that isn't necessary, but it sure looks like they're trying to establish a connectionist/"scruffy", ML-based, Python-first replacement for AIAMA's symbolic/"neat", logic-based, Lisp-first approach. The 1st ed hit desks in 2010, and the endorsements are overwhelmingly from scruffy scientists & engineers. Obviously, this mirrors the industry's overall trend[2]... at this point, most laypeople think AI is ML. Nice to see a more nuanced--yet still scruffy-forward--approach gaining momentum; even Gary Marcus is on board, a noted Neat! C) ...Ok, after writing an already-long comment (sorry) I did a quantitative comparison of the two books, which I figured y'all might find interesting! I'll link a screenshot[3] and the Google Sheet itself[4] below, but here's some highlights b/w "AMA" (the reigning champion) and "FCA" (the scrappy challenger): 1. My thesis was definitely correct; by my subjective estimation, AMA is ~6:3 neat:scruffy (57%:32%), vs. a ~3:5 ratio for FCA (34%:50%). 2. My second thesis is also seemingly correct: FCA dedicates the last few pages of every section to "Social Impact", aka ethics. Both books discuss the topic in more depth in the conclusion, representing ~4% of each. 3. FCA seems to have some significant pedagogical advantages, namely length (797 pages vs. AMA's 1023 pages) and the inclusion of in-text exercises at the end of every section. 4. Both publish source code in multiple languages, but AMA had to be ported to Python from Lisp, whereas FCA is natively in Python (which, obviously, dominates AI atm). The FCA authors actually wrote their own "psuedo-code" Python library, which is both concerning and potentially helpful. 5. Finally, FCA includes sections explicitly focused on data structures, rather than just weaving them into discussions of algorithms & behavioral patterns. I for one think this is a really great idea, and where I see most short-term advances in unified (symbolic + stochastic) AI research coming from! Lots of gold to be mined in 75 years of thought. Apologies, as always, for the long comment -- my only solace is that you can quickly minimize it. I should start a blog where I can muse to my heart's content... TL;DR: This new book is shorter, more ML-centric, and arguably uses more modern pedagogical techniques; in general, it seems to be a slightly more engineer-focused answer to Russell & Norvig's more academic-focused standard text. [1] AIAMA: https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Modern_Approach https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Mod... [2] NGRAM: https://books.google.com/ngrams/graph?content=%28Machine+Learning%2FAI%29%2C%28Expert+Systems%2FAI%29&year_start=1950&year_end=2022&corpus=en&smoothing=3 https://books.google.com/ngrams/graph?content=%28Machine+Lea... [3] Screenshot: https://imgur.com/a/x8QMbno https://imgur.com/a/x8QMbno [4] Google Sheet: https://docs.google.com/spreadsheets/d/1Gw9lxWhhTxjjTstyAKliwdy75VHXLtCDxCPFJhe5pPQ/edit?usp=sharing https://docs.google.com/spreadsheets/d/1Gw9lxWhhTxjjTstyAKli...
light_triad 2y ago
Here's a few more definitions of agents: Agents are a coupling of perception, reasoning, and acting with preferences or goals. They prefer some states of the world to other states, and they act to try to achieve the states they prefer most (this book) AI agents are rational agents. They make rational decisions based on their perceptions and data to produce optimal performance and results. An AI agent senses its environment with physical or software interfaces (AWS) An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools (IBM) Agents are like layers on top of the language models that observe and collect information, provide input to the model and together generate an action plan and communicate that to the user — or even act on their own, if permitted (Microsoft) Assumptions: Focus on rationality vs. goal-seeking vs. autonomy Whether tool use is emphasized Architectural specificity Relationship to users Decision-making framework (optimality vs. preference satisfaction)
- EGreg 2y ago
  I am really not sure where agents would ever be better than workflows. Can you give me some examples? Workflows means some organization signed off on what has to be done. Checklists, best practices, etc. Agents on the other hand have a goal and you have no idea or what they’re going to do to achieve it. I think of an agent’s guardrails as essentially a “blacklist” of actions, while a workflow is a “whitelist”. To me, agents are a gimmick the same way that real-time chat, or video, is a gimmick. It is good for entertainment but actually has negative value for getting actual work done. Think of it this way… just as models have a tradeoff between explore and exploit, the agents can be considered as capable of exploration while the workflows exploit best practices. Over time and many tasks, everything is standardized into best practices, so the agents become worse than completely standardized workflows. They may be useful to tinker at the edges but not to make huge decisions. Like maybe agents can be used to set up some personalized hooks for users at the edges of some complex system. https://medium.com/@falkgottlob/many-ai-agents-are-actually-ai-workflows-or-automations-in-disguise-7e377017df98 https://medium.com/@falkgottlob/many-ai-agents-are-actually-...
  - 2y ago
Peteragain 2y ago
To add to the mix, agents are nominally proactive, rather than a tool wielded by someone. This (again nominally) means having goals, although the goals can often be in the observer's mind rather than the agent itself. Reasoning with goals is trivial for humans but the algorithms get hairy.
deleted 2y ago
[deleted]
antonkar 2y ago
Intelligence - space-like, matter-like (LLM is a bunch of vectors, a static geometric shape, you just need memory to store it). It’s a static geometric shape. It can have analogs of volume, mass and density. The static 4D spacetime of the universe or multiverse is maximally intelligent but non-agentic. Agent - time-like, energy-like (you need a GPU to compute it). An agent changes the shape of the environment it operates in, including its own shape. You can count agents, their volume of operations, their speed of changing shapes (volumetric speed), acceleration… The Big Bang had zero intelligence (with maximal potential intelligence) but was and still is maximally agentic

115 points by rramadass 2y ago

Because I collect definitions of "agent", here's the one this book uses: > An agent is something that acts in an environment; it does something. Agents include worms, dogs, thermostats, airplanes, robots, humans, companies, and countries. https://artint.info/3e/html/ArtInt3e.Ch1.S1.html https://artint.info/3e/html/ArtInt3e.Ch1.S1.html I think of this as the "academic" definition, or sometimes the "thermostat" definition (though maybe I should call it the "worms and dogs" definition). Another common variant of it is from Peter Norvig and Stuart Russell's classic AI text book "Artificial Intelligence: A Modern Approach": http://aima.cs.berkeley.edu/4th-ed/pdfs/newchap02.pdf http://aima.cs.berkeley.edu/4th-ed/pdfs/newchap02.pdf > Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators

fouc 2y ago
written 2023 , pre-LLM hype
- avmich 2y ago
  LLM hype was firmly with us with the coming of ChatGPT, November 2022. Records in signing up aren't for nothing.
- nextos 2y ago
  I don't think so. I own the printed version and it starts discussing GPT in page 6 already.
andirk 2y ago
I was literally compiling a list of "agent" synonyms at lunch today. My favorite and most accurate so far is "doer".
nine_k 2y ago
So basically an agent is a procedure, by this definition: it takes parameters (environment) and acts upon that by executing side effects. An email filter is an agent. A database trigger is an agent.
- nivertech 2y ago
  > it takes parameters (environment) I think it's better to imagine agent as something that physically placed inside the Environment, and actually modifying/changing/mutating it in place. > An email filter is an agent. A database trigger is an agent. you're missing the "I" (Intelligence) part - the filtering logic in the email filter, or a business logic in the DB trigger/stored procedure/CGI script/AWS Lambda function/etc. But yes, an agent doesn't have to be Intelligent, it can be a Dumb Agent / NPC / Zero-Intelligence Trader.
  - sureglymop 2y ago
    Can you explain the "intelligence" part? Can't one derive a decision tree of any "intelligent agent" that is in essence no different than a classically programmed algorithm?
    - FollowingTheDao 2y ago
      Just replying here to tell you I replied to a question (cisco) you asked me in case you miss it. Thanks!
    - nivertech 2y ago
      Yes, for Computational Agents you will either code "Agent Intelligence"/"Agent Cognition" algorithmically, or using AI/ML/LLM (either by pre-training, or using continous re-training for Adaptive Agents). Useful abstaractions: - FSM/State Machines - Behavior Trees - Behavior Action Trees - Workflow Orchestration - Dataflow (mostly for pipelines transforming LLM Prompt into LLM Reponse) Another option is to outsource it to a Human, like it was in the ALICE program[1], e.g. Human-in-the-Loop, Participatory Simulation, RLHF, Whole-brain computer simulation, like in The Age of Em[2] (SciFi). See: https://news.ycombinator.com/item?id=43409240 https://news.ycombinator.com/item?id=43409240 --- 1. https://www.media.mit.edu/projects/participatory-simulations/overview/ https://www.media.mit.edu/projects/participatory-simulations... https://ccl.northwestern.edu/papers/partsims/cscl/ https://ccl.northwestern.edu/papers/partsims/cscl/ 2. https://ageofem.com/ https://ageofem.com/
nivertech 2y ago
It always amazed me that different branches of CS, AI/ML and Complex Systems/Complexity Sciences have different views on agents. Objects in OOP - something which can have properties/attributes, and methods (verbs/commands). Usually modeled after a real-life/domain enitites. Aggregates in Domain-Driven Design (DDD) - transactional clusters of objects modeling a collections of entities in the domain. Actors in Actor Model / Active Objects - a something we can sen messages to, and receive messages from, and which may have some business logic. Agent-Based Modeling and Simulations (ABM) defines agents as a proxy for a decision maker. Digital Twins - a more realistic proxy/replica for a real life person, object, or process. Multi-Agent Systems (MAS) in how to use asgents to solve or optimize a real problem in production. RL/MARL (Muti-Agent Reinforcement Learning) on how to train an ML algorithm without supervision (i.e. a labeled dataset), by placing agents in an environment capable to automatically provide rewards/punishment feedback. LLM Agents - dynamically generated intelligent business process workflows (including Robotic Process Automation - RPA aka Tool Use/Function Call).
- nivertech 2y ago
  Also: Smart Contracts - agents with an attached cryptocurrency wallet/account/address, capable to receive and make payments autonomously.
- bsenftner 2y ago
  How's about: LLM Agent: any packaging of the use of AI such that the details of using said AI are packaged, hidden, and the user of this LLM Agent does not need to concern themselves with AI at all, only the intelligence services provided by what is now a simulated personality the user can willfully self deceive they are working with a human.
  - nivertech 2y ago
    aka "delegation", "ousourcing", "serverless", and "not my busienss, do it ASAP, I don't care how" ;)
    - bsenftner 2y ago
dr_dshiv 2y ago
How are agents different from daemons? And are both essentially cybernetic feedback loops?
- jsemrau 2y ago
  (1) Daemons run usually in the background while agents are main loop. (2) Daemons are predefined and can't adapt to changes easily -- this is a function of narrow AI vs general AI (not to be confused with AGI) (3) Daemons have few interactions with the environment while for the agent the environment (tools, sensors, plans, memory, and context) is everything. These are just a few from the top of my head.
jsemrau 2y ago
Add this please ->https://jdsemrau.substack.com/about https://jdsemrau.substack.com/about
- potatoman22 2y ago
  To save a click: "We define cognitive autonomous agents as an entity designed to perform tasks autonomously by combining four key components: goals, reasoning/planning, memory, and tools."
  - jsemrau 2y ago
    Thank you!
UltraSane 2y ago
agent has almost as many definitions as set.
- aithrowawaycomm 2y ago
  I would say the situation for "agent" is about 10,000 times worse than it is for "set," since all the definitions for set are essentially different ways to make Frege / Cantor more rigorous. The underlying scientific concept hasn't actually changed that much: "belonging to a set and a few certain operations are primitives, join these primitives with formal logic." This is the idea behind a famous intro book, Paul Halmos's Naive Set Theory. Even a set theory with Russell's paradox is scientifically defensible, it just needs refinement. In contrast, "agent" discusses a huge range of scientific concepts, and I have yet to see a single definition of agent that holds up to scientific scrutiny. This book has managed to define "agent" in a way that is entirely equivalent to "physical object" - putting worms and thermostats in the same category broadens the category to utter uselessness. By this definition, Jupiter is an agent. The only utility of definitions like this is for dishonest people to cheat at arguments: claiming simple tools are agents, then arguing they are like dogs and humans, which are also agents. It's a total waste of time, coming from AI's shameless disrespect for scientific standards.
  - fc417fc802 2y ago
    > By this definition, Jupiter is an agent. Is it? What task was it designed to carry out autonomously? Where are the inputs and the logic hiding? > for dishonest people to cheat at arguments Only if the goal is to deceive. If the communication is well intentioned then there is nothing inherently wrong with it despite it not being to your liking.
    - aithrowawaycomm 2y ago
      The problem is they also put "worms" in the same category, and they aren't designed by humans to do anything! Why is it that the natural laws of a worm responding to Earth's environment are distinct from the natural laws of Jupiter responding to the solar system's environment? I suppose because of complexity. But then why is a thermostat different from Jupiter despite being considerably simpler? I suppose because it was designed by humans and can be controlled. But then what about the worm, which is just as natural as Jupiter? "Thermostat" is especially problematic because a cheap thermostat is very simple to describe completely as a thermo-electric balance equation: it is certainly simpler to describe than an irregular ball rolling down an irregular hill. Yet apparently the thermostat is an agent and the ball is not. The definition is just incoherent! "Sometimes an agent is deterministic and in this case the term only includes manmade tools, other times an agent is an apparently nondeterministic automaton and in this case we can include natural life." It only allows "agent" to be labelled ad hoc, and in particular blurs the distinction between "nondeterministic tool" and "lifeform" in ways that are scientifically unjustifiable. The only people this pointless word game benefits are liars like Sam Altman and Mustafa Suleyman; if people are well-intentioned then these definitions bring nothing but confusion.
sc077y 2y ago
The problem I see with this definition is that we have things called RAG agents which don't technically act in any environment except for provide information.
- 1dom 2y ago
  Is that a problem with the definition of agents, or a problem with sticking the word "agent" on something that doesn't meet the definition of an agent?
- deleted 2y ago
  [deleted]
- bsenftner 2y ago
  Is that not a summary and translation agent? For some reason only the user knows, they do not want to or cannot read the entire RAG source, so they use their summary and translation agent to give them summaries and to translate the technical jargon they do not understand. That Agent becomes a teacher of the RAG source. I see no problem with the Agent definition when given this perspective.
mportela 2y ago
FWIW I had a professor that defined "robotics" in the same way (we even had a quite philosophical debate on whether automatic doors are robots). I ended up liking Norvig and Russell's definition better by appending the word "autonomously". Another interesting word that is quite out fashion nowadays is "cybernetics": "Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent action." [1] Having studied Control Engineering in college, to me, all these things are feedback control loops. [2] [1] https://en.wikipedia.org/wiki/Cybernetics https://en.wikipedia.org/wiki/Cybernetics [2] https://en.m.wikipedia.org/wiki/Feedback https://en.m.wikipedia.org/wiki/Feedback
antonkar 2y ago
Agent - time-like, energy-like (you need a GPU to compute it). An agent changes the shape of the environment it operates in, including its own shape. You can count agents, their volume of operations, their speed of changing shapes (volumetric speed), acceleration… The Big Bang had zero intelligence (with maximal potential intelligence) but was and still is maximally agentic. Same way we have many definitions of life (virus is alive or not? It’s very agentic, COVID killed millions but the shape, intelligence is small. Same with computer viruses), we have many definitions of agency, better to use the broadest and most physical one. Intelligence - space-like, matter-like (LLM is a bunch of vectors, a static geometric shape, you just need memory to store it). It’s a static geometric shape. It can have analogs of volume, mass and density. The static 4D spacetime of the universe or multiverse is maximally intelligent but non-agentic.

deleted 2y ago

[deleted]

throwaway81523 2y ago

Book is from 2023, link should be edited for that.

bbor 2y ago

A) Looks really good, will be checking it out in depth as I get time! Thanks for sharing. B) The endorsements are interesting before you even get to the book; I know all textbooks are marketed, but this seems like quite the concerted effort. For example, take Judea Pearl's quote (an under-appreciated giant): This revised and extended edition of Artificial Intelligence: Foundations of Computational Agents should become the standard text of AI education. Talk about throwing down the gauntlet - especially since Russell looks up to him as a personal inspiration! (Quick context for those rusty on academic AI: Russell & Norvig's 1995 (4th ed in 2020) AI: A Modern Approach ("AIAMA") is the de facto book for AI survey courses, supposedly used in 1500 universities via 9 languages as of 2023.[1]) I might be reading drama into the situation that isn't necessary, but it sure looks like they're trying to establish a connectionist/"scruffy", ML-based, Python-first replacement for AIAMA's symbolic/"neat", logic-based, Lisp-first approach. The 1st ed hit desks in 2010, and the endorsements are overwhelmingly from scruffy scientists & engineers. Obviously, this mirrors the industry's overall trend[2]... at this point, most laypeople think AI is ML. Nice to see a more nuanced--yet still scruffy-forward--approach gaining momentum; even Gary Marcus is on board, a noted Neat! C) ...Ok, after writing an already-long comment (sorry) I did a quantitative comparison of the two books, which I figured y'all might find interesting! I'll link a screenshot[3] and the Google Sheet itself[4] below, but here's some highlights b/w "AMA" (the reigning champion) and "FCA" (the scrappy challenger): 1. My thesis was definitely correct; by my subjective estimation, AMA is ~6:3 neat:scruffy (57%:32%), vs. a ~3:5 ratio for FCA (34%:50%). 2. My second thesis is also seemingly correct: FCA dedicates the last few pages of every section to "Social Impact", aka ethics. Both books discuss the topic in more depth in the conclusion, representing ~4% of each. 3. FCA seems to have some significant pedagogical advantages, namely length (797 pages vs. AMA's 1023 pages) and the inclusion of in-text exercises at the end of every section. 4. Both publish source code in multiple languages, but AMA had to be ported to Python from Lisp, whereas FCA is natively in Python (which, obviously, dominates AI atm). The FCA authors actually wrote their own "psuedo-code" Python library, which is both concerning and potentially helpful. 5. Finally, FCA includes sections explicitly focused on data structures, rather than just weaving them into discussions of algorithms & behavioral patterns. I for one think this is a really great idea, and where I see most short-term advances in unified (symbolic + stochastic) AI research coming from! Lots of gold to be mined in 75 years of thought. Apologies, as always, for the long comment -- my only solace is that you can quickly minimize it. I should start a blog where I can muse to my heart's content... TL;DR: This new book is shorter, more ML-centric, and arguably uses more modern pedagogical techniques; in general, it seems to be a slightly more engineer-focused answer to Russell & Norvig's more academic-focused standard text. [1] AIAMA: https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Modern_Approach https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Mod... [2] NGRAM: https://books.google.com/ngrams/graph?content=%28Machine+Learning%2FAI%29%2C%28Expert+Systems%2FAI%29&year_start=1950&year_end=2022&corpus=en&smoothing=3 https://books.google.com/ngrams/graph?content=%28Machine+Lea... [3] Screenshot: https://imgur.com/a/x8QMbno https://imgur.com/a/x8QMbno [4] Google Sheet: https://docs.google.com/spreadsheets/d/1Gw9lxWhhTxjjTstyAKliwdy75VHXLtCDxCPFJhe5pPQ/edit?usp=sharing https://docs.google.com/spreadsheets/d/1Gw9lxWhhTxjjTstyAKli...

light_triad 2y ago

Here's a few more definitions of agents: Agents are a coupling of perception, reasoning, and acting with preferences or goals. They prefer some states of the world to other states, and they act to try to achieve the states they prefer most (this book) AI agents are rational agents. They make rational decisions based on their perceptions and data to produce optimal performance and results. An AI agent senses its environment with physical or software interfaces (AWS) An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools (IBM) Agents are like layers on top of the language models that observe and collect information, provide input to the model and together generate an action plan and communicate that to the user — or even act on their own, if permitted (Microsoft) Assumptions: Focus on rationality vs. goal-seeking vs. autonomy Whether tool use is emphasized Architectural specificity Relationship to users Decision-making framework (optimality vs. preference satisfaction)

EGreg 2y ago
I am really not sure where agents would ever be better than workflows. Can you give me some examples? Workflows means some organization signed off on what has to be done. Checklists, best practices, etc. Agents on the other hand have a goal and you have no idea or what they’re going to do to achieve it. I think of an agent’s guardrails as essentially a “blacklist” of actions, while a workflow is a “whitelist”. To me, agents are a gimmick the same way that real-time chat, or video, is a gimmick. It is good for entertainment but actually has negative value for getting actual work done. Think of it this way… just as models have a tradeoff between explore and exploit, the agents can be considered as capable of exploration while the workflows exploit best practices. Over time and many tasks, everything is standardized into best practices, so the agents become worse than completely standardized workflows. They may be useful to tinker at the edges but not to make huge decisions. Like maybe agents can be used to set up some personalized hooks for users at the edges of some complex system. https://medium.com/@falkgottlob/many-ai-agents-are-actually-ai-workflows-or-automations-in-disguise-7e377017df98 https://medium.com/@falkgottlob/many-ai-agents-are-actually-...
- 2y ago

Peteragain 2y ago

To add to the mix, agents are nominally proactive, rather than a tool wielded by someone. This (again nominally) means having goals, although the goals can often be in the observer's mind rather than the agent itself. Reasoning with goals is trivial for humans but the algorithms get hairy.

deleted 2y ago

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antonkar 2y ago

Intelligence - space-like, matter-like (LLM is a bunch of vectors, a static geometric shape, you just need memory to store it). It’s a static geometric shape. It can have analogs of volume, mass and density. The static 4D spacetime of the universe or multiverse is maximally intelligent but non-agentic. Agent - time-like, energy-like (you need a GPU to compute it). An agent changes the shape of the environment it operates in, including its own shape. You can count agents, their volume of operations, their speed of changing shapes (volumetric speed), acceleration… The Big Bang had zero intelligence (with maximal potential intelligence) but was and still is maximally agentic