From Turing to Humanoid Robots: A fascinating view of the AI journey

Ramon Lopez de MantarasArtificial Intelligence Research Institute (IIIA)

CSIChttp://www.iiia.csic.es/~mantaras

Outline- Turing on AI From Turing to Dartmouth

- Two views on AI: Weak AI vs. Strong AI

-The road traveledAchievements of (Weak) AI

-The (long) road aheadFrom Integrated Systems to Strong AI

-Conclusions

Turing on AIIn 1948 Turing predicted that by the end of the 20th century there would be intelligent computers capable of performing logical deductions, acquire new knowledge inductively, by experience and by evolution and capable of communicating by means of humanized interfaces. He also speculated about a connection between randomness and creative intelligence by suggesting to add radium in to the ACE in the hope that the random decay of radiation would give its inputs the desired unpredictability.In his famous 1950 paper he also speculated about the emulation of the mind of a child and giving it an appropriate education to obtain an adult mind (mental development)

From Turing to Dartmouth1948 Hixon Symposium on Cerebral Mechanisms in Behavior in Caltech (McCulloch on NNs, von Neumann, Lashley on limitations of behaviourism)

Session on Learning Machines at the 1955 Western Computer Conference in L.A. (Clark & Farley on Hebbian learning in NNs; Selfridge on image classification; Newell on chess; Pitts on NNs)

1956 Summer Research Project on Artificial Intelligence in Dartmouth College (McCarthy, Minsky, Newell, Simon, Shaw, Selfridge, Solomonoff, Rochester, Shannon, Samuel, Bernstein)

Two views on AI

-The view of the founding fathers:The science and engineering of replicating, even surpassing (singularity?), human-level intelligence in machines (“strong AI”)

-The view in the early 80’s (after the “AI winter”):The science and engineeringof designing machines with the capability to perform tasks that, when done by humans, we agree that they require intelligence (“weak AI”)

Strong versus Weak AIThe Strong AI case

Strong AI refers to AI that matches (or even exceeds) general human-level intelligence (intelligent machines will have mental states, consciousness, etc.)

Example: The robots from the movies (HAL 9000, Matrix, Terminator, I Robot, etc.)

The goal of human-level intelligence remains elusive but has inspired and still inspires our work on AI even though most efforts are on building weak AI (or “idiots savants”)

Strong versus Weak AIThe Weak AI case (or the “idiots savants”)

Machines already exhibit specialized intelligences without worrying about having mental states, consciousness, etc.

All current forms of AI are “weak AI”

We have achieved impressive results along the traveled “weak AI” road

The road traveled

The road traveledAI is everywhere (though most of the time is not visible!):-Fuel injection systems in our cars designed using AI algorithms.-Jet turbines are designed using genetic algorithms.-10.000 engineers carry out 2.600 maintenance works nightly on Hong Kong’s subway, scheduled by an AI system-There are a millions of AI-powered specialized robots in people’s homes and robots running on the surface of Mars.-Computer games (NPCs) use many AI techniques (including ML)-Web search engines use AI techniques-Automatic detection of credit card fraudulent transactions use ML algorithms-Routing of cell phone calls is based on AI-Detection of consumer habits is based on AI (ML)-Complex mathematical theorems have been proven by automatic theorem provers (i.e. Robbins conjecture)-There are robots that play soccer-An ML system revals passing patterns in soccer teams-There are AI systems composing beautiful music and systems performing music expressively (among other artistic applications)

The road traveledWe have achieved many of the things that the field’s founders used as motivators, but not always in the way the “founding fathers” imagined:

-there is an impressive variety of application achievements. Most of them based on the availability of very large sets of data processed by very high performance computers, and not on emulating human’s mental processes:

-the world’s best chess players are computers-self-driving cars have successfully run milions of miles (gathers 1 Gb/sec of data to make predictions about its surroundings)-there are high-performance speech recognition systems.-Watson outperformed the best “Jeopardy” players (and now… turns medic)-an ML system, trained on data from 133.000 patients, can predict heart attacks 4 hours before they happen

The road traveledIn spite of all these great successes along specialized lines in each of the areas of AI, we do not seem to be getting any closer to “general AI” because:

1-We have given up the explainability of the AI systems (as well as the cognitive plausability of AI models)

the “reasoning” made by today’s massive data-driven AI is a massively complex statistical

analysis of an immense number of datapoints. We have traded the “why” for simply the “what”2-We have focused on the isolated components of AI but not on the whole AI itself

We have wonderful bricks but, to build the house, we need an architecture and the cement to tie the bricks together (sensing, knowledge acquisition & representation, reasoning, communication, action, planning, etc)

The long road ahead:Future Challenges

The road ahead: Integrated systemsIntelligence seems to emerge from a complex combination of many specialized abilities, such as sensing, reasoning, learning, planning, socializing, and communicating.

But not a mere juxtaposition of these abilities!Rather, there is some set of deep interdependencies that tie these elements together. For example:

-learning must result on knowledge that needs to be represented so that reasoners, planners, etc can use it efficiently.-perception requires reasoning and learning and

viceversa.Most important challenge: We need to think about how all the components of an artificial intelligence should work together and how they need to be connected (the architecture!). We need to focus on comprehensive, totally integrated systems.Integrated systems might be a necessary step towards strong (human-level) AI (assuming this is a realistic goal!).

The road aheadExample of Integrated SystemBuilding a multipurpose, social, robot that can accumulate diverse knowledge over long periods of time (never ending learning) and that can use it effectively to decide what to do and how to do it.Requirements-A robot’s knowledge must be grounded in the physical world and capable of learning by interacting with the world (“embodied cognition”)-Because learning is prone to error, and the world is not deterministic, reasoning with such learned knowledge must deal with uncertainty-The representation languages must be expressive enough to represent the complex connections between objects, places, actions, people, time, and causation (understanding these requires “common sense” knowledge).-Also requires natural language understanding (Watson does not understand anything!) and scene understanding (these require “common sense” knowledge).-We should be able to evaluate the progress (beyond Turing test)

Big failures in scene understanding!

A red and white bus in frontof a building

The road aheadThe field is ripe to develop such systems because:-we have a variety of scalable learning methods that are both relational and statistical, for instance SRL.-the development and rapid deployment of ubiquitous sensing and actuator devices makes it possible to create AI systems robustly grounded in direct experience with the world and learn from interacting with the world (i.e. work on Developmental Robotics)-there are a growing number of successful applications of behavior understanding based on computer vision and ML-we have made substantial progress in automatically extracting from the web named-entities and facts relating these entities using “Learning by Reading” (work of T. Mitchell, et al.)-we have made substantial progress in other ML techniques and particularly on learning by experience, by imitation, transfer learning, deep learning, and “never ending learning” (for instance CMU’s NELL ans NEIL systems)-we have made substantial progress in MAS to model social cognition and behavior -we have an ever increasing amount of computational power.

Developmental Robotics: Learning the musical instrument and playing by imitation(in collaboration with Imperial College)

The road ahead: Very ambitious predictions-Robotic scientists that will serve as

companions in discovery by formulating hypothesis and pursuing their confirmation (initial work on the ADAM and EVE systems by R. King et al. "The Automation of Science". Science 324 (5923): 85–89)

-AI will play a central role in solving challenges in energy, the environment, and in healthcare.

-A team of robots will beat the world’s human soccer champion team. (H. Kitano)

-AI and other sciences (biology, material sciences, nanotechnology, economics,…) will come together and will have wide-ranging influences on our ideas about AI and on the machines we will build.

Hydrogen muscle for silent robots

Copper and nickel-based metal hydride powder is compressed into peanut-sized pellets and secured in a vessel. Hydrogen is pumped in to “charge” the pellets with the gas. A heater coil surrounds the vessel. Heat breaks the weak chemical bonds and releases the stored hydrogen.

(Kim & Vanderhoff, Smart Mat. and Struct., 18, 2009 DOI: 10.1088/0964-1726/18/12/125014)

Inflatable rubber tubesurrounded by Kevlarfibre braiding

Artificial cartilage

Chen, Briscoe, Armes, Klein; Lubrication at Physiological Pressures by Polyzwitterionic Brushes, Science 323, 2009

Each moleculargroup attracts 25 water molecules

Performs well in pressures up to 5 megapascals

60 nm backbone

Touch sensitive artificial skin1977 2008

Capacitive copper contacts

A layer of silicone rubber acts as a spacerbetween those contacts and an outer layerof Lycra that carries a metal contact aboveeach copper contact. The whole constitutesa pressure-sensing capacitor that can detecta touch as light as 1 gram.

(Schmitz et al. IEEE Transactions on Robotics, 27(3). 2011)

Carbon, or metal, charged polymer coats thefingers and palm. The transversal electrical resistance varies as a function of the pressure. Detected a touch greater than 20 grams. Applied to tactile object recognition.

(López de Mántaras. PhD Thesis, Univ. Paul Sabatier.1977)

Touch sensitive artificial skin (cont.)

Conclusions-AI is a well stablished research discipline with demonstrated successess and clear trajectories for its immediate future (but no “singularity”: the brain is much too complex!).-AI techniques are everywhere (although often are invisible): AI Algorithms increasingly run our lives: They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs.”-Most exciting opportunities for research lie on the interdisciplinary boundaries of AI with biology, linguistics, economics, material sciences, etc. That will provide insights and technologies towards building large-scale integrated systems.-AI is mature enough to undertake research on integrated systems (perhaps leading towards the goals of “strong AI”) and not only working on massive data-driven AI.-Fragmentation of the field, funding, and inadequate education curricula are also strong limiting factors

BUT……progress will be slow because there is no direct significant funding to pursue the “strong AI” goals of human-level intelligence (although there is, and will be, funding for integrated projets particularly in robotics because it requires significant integration)

because the field is dominated by massive data-driven AI

…and because AI suffers from fragmentation (separate conferences and over-specialized college curricula)

Final thoughtsNo matter how sophisticated will be future Artificial Intelligences they will be necessarily different to human intelligences because:

THE BODY SHAPES THE WAY WE THINK

These artificial intelligences will be alien to human needs and therefore we should put limits on the developments of AI

And…”KEEP CALM AND FORGET ABOUT THE SINGULARITY”