diff --git a/README.md b/README.md index 601d4465..a3430146 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ Lectures for INFO8006 - Introduction to Artificial Intelligence, ULiège, Fall 2 - [Lecture 7: Reasoning over time](https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture7.md), [PDF](https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec7.pdf) - [Lecture 8: Learning](https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture8.md), [PDF](https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec8.pdf) - [Lecture 9: Communication](https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture9.md), [PDF](https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec9.pdf) -- Lecture 10: Artificial General Intelligence and beyond +- [Lecture 10: Artificial General Intelligence](https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture10.md), [PDF](https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec10.pdf) ## Exercise sessions diff --git a/figures/lec10/envs.png b/figures/lec10/envs.png new file mode 100644 index 00000000..f70c97b9 Binary files /dev/null and b/figures/lec10/envs.png differ diff --git a/figures/lec10/gameover.png b/figures/lec10/gameover.png new file mode 100644 index 00000000..1d70c0e8 Binary files /dev/null and b/figures/lec10/gameover.png differ diff --git a/figures/lec10/tree.gif b/figures/lec10/tree.gif new file mode 100644 index 00000000..6dd5eee4 Binary files /dev/null and b/figures/lec10/tree.gif differ diff --git a/lecture10.md b/lecture10.md index 71b066f9..4af0ea7b 100644 --- a/lecture10.md +++ b/lecture10.md @@ -15,6 +15,8 @@ http://machineintelligence.org/universal-ai.pdf .center.width-100[![](figures/lec10/ai-in-news.png)] +.footnote[Credits: [Andrej Karpathy, Where will AGI come from?](https://docs.google.com/presentation/d/119VW6ueBGLQXsw-jGMboGP2-WuOnyMAOYLgd44SL6xM)] + --- # ... to popular media @@ -101,11 +103,11 @@ class: smaller - **Artificial general intelligence** (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. - No clear definition, but there is an agreement that AGI is required to do the following: - - *reason*, use strategy, solve puzzle and make judgments under uncertainty; - - *represent knowledge*, including commonsense knowledge; - - *plan*; - - *learn*; - - *communicate* in natural language; + - *reason*, use strategy, solve puzzle and make judgments under uncertainty, + - *represent knowledge*, including commonsense knowledge, + - *plan*, + - *learn*, + - *communicate* in natural language, - integrate all these skills towards *common goals*. --- @@ -189,7 +191,7 @@ $$\bar{\Upsilon} = \max\_\pi \Upsilon(\pi) = \Upsilon(\pi^{AIXI})$$ --- -# Optimal action +# Optimal actions .center.width-100[![](figures/lec10/optimal-action.png)] @@ -203,7 +205,7 @@ rigorously shown by [Hut05] to be optimal in many different senses of the word. --- -# Intractability +# Incomputability .center.width-100[![](figures/lec10/aixi-action.png)] @@ -230,8 +232,9 @@ class: middle, center - A hypothesis for AGI is **whole brain simulation**. - A low-level brain model is built by scanning and mapping a biological brain in detail and copying its state into a computer system. - - The simulation is *so faithful* that it will behave in essentially the same way as the original. -- Ongoing initiatives: BRAIN, Blue Brain Project, Human Brain Project, NeuraLink, etc. + - The simulation is *so faithful* that it would behave in the same way as the original. + - Therefore, the computer-run model would be as intelligent. +- Initiatives: Blue Brain Project, Human Brain Project, NeuraLink, etc. --- @@ -262,9 +265,79 @@ class: middle, center --- -??? +# How did intelligence arise in Nature? + +.center.width-100[![](figures/lec10/tree.gif)] + +--- + +# Artificial life + +- **Artificial life** is the study of systems related to natural life, its processes and its evolution, through the use of *simulations* with computer models, robotics or biochemistry. +- One of its goals is to *synthesize* life in order to understand its origins, development and organization. +- There are three main kinds of artificial life, named after their approaches: + - Software approaches (soft) + - Hardware approaches (hard) + - Biochemistry approaches (wet) +- Artificial life is related to AI since synthesizing complex life forms would, **hypothetically**, induce intelligence. +- The field of AI has traditionally used a top down approach. Artificial life generally works from the bottom up. + +--- + +# Evolution for AGI + +- Evolution may **hypothetically** be interpreted as an (unknown) algorithm. +- This algorithm gave rise to AGI. + - e.g., it induced humans. +- Can we **simulate** the *evolutionary process* to reproduce life and intelligence? +- Note that we can work at a high level of abstraction. + - We don't have to simulate physics or chemistry to simulate evolution. + - We can also bootstrap the system with agents that are better than random. + +--- + +# Evolutionary algorithms + +- Start with a *random population* of **creatures**. +- Each creature is *tested for their ability* to perform a given task. + - e.g., swim in a simulated environment. + - e.g., stay alive as long as possible (without starving or being killed). +- The **most successful survive**. +- Their virtual genes containing coded instructions for their growth are copied, combined and mutated to *make offspring* for a new population. +- The new creatures are tested again, some of which may be improvements on their parents. +- As this cycle of variation and selection continues, creatures with more and more successful behaviors may **emerge**. + +--- + +class: center, middle + +.center[ + +] + +--- + +class: center, middle + +.center[ + +] + +--- + +# Environments for AGI? + +- For the emergence of intelligent creatures, we presumably need environments that **incentivize** the emergence of a *cognitive toolkit*. + - attention, memory, knowledge representation, reasoning, emotions, forward simulation, skill acquisition, ... + +.center.width-60[![](figures/lec10/envs.png)] + +- **Multi-agent** environments are certainly better because of: + - *Variety*: the environment is parameterized by its agent population. The optimal strategy must be derived dynamically. + - *Natural curriculum*: the difficulty of the environment is determined by the skill of the other agents. + +.footnote[Credits: [Andrej Karpathy, Where will AGI come from?](https://docs.google.com/presentation/d/119VW6ueBGLQXsw-jGMboGP2-WuOnyMAOYLgd44SL6xM)] -https://en.wikipedia.org/wiki/Artificial_life --- @@ -284,4 +357,45 @@ Don't fear intelligent machines, work with them. Garry Kasparov --- +# Summary + +- Lecture 1: Foundations +- Lecture 2: Solving problems by searching +- Lecture 3: Adversarial search +- Lecture 4: Constraint satisfaction problems +- Lecture 5: Representing uncertain knowledge +- Lecture 6: Inference in Bayesian networks +- Lecture 7: Reasoning over time +- Lecture 8: Learning +- Lecture 9: Communication +- Lecture 10: Artificial General Intelligence + +--- + # Going further + +- ELEN0062: Introduction to Machine Learning +- INFO8004: Advanced Machine Learning +- INFOXXXX: Deep Learning (Spring 2019) +- INFO8003: Optimal decision making for complex problems +- INFO0948: Introduction to Intelligent robotics +- INFO0049: Knowledge representation +- ELEN0016: Computer vision + +--- + +class: center, middle + +.center.width-70[![](figures/lec10/gameover.png)] + +Thanks for following Introduction to AI! + +--- + +# Readings + +- Bostrom, Nick. Superintelligence. Dunod, 2017. +- Legg, Shane, and Marcus Hutter. "Universal intelligence: A definition of machine intelligence." Minds and Machines 17.4 (2007): 391-444. +- Hutter, Marcus. "One decade of universal artificial intelligence." Theoretical foundations of artificial general intelligence (2012): 67-88. +- Sims, Karl. "Evolving 3D morphology and behavior by competition." Artificial life 1.4 (1994): 353-372. +- Kasparov, Garry. Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, 2017.