Tags / 64 quotes from 9 sources
#artificial-intelligence
Beyond Zero and One (Andrew Smart)
The singularity as existential risk
The last few years have produced an increasing amount of popular discourse about the singularity, which describes events in technological development that would lead to human-level machine intelligence, and would lead to computers achieving superintelligence. Following this…
An explosion of books about AI
I am beginning to suspect that the singularity refers simply to an explosion of books about artificial intelligence.
DeepMind and the Gödel Machine
many of the writers cited above would point to dozens of cool AI projects, such as Jürgen Schmidhuber’s Gödel Machine, which can self-improve by writing its own code after it proves the new code is more optimal. It is a stunning model, but has yet to be really programmed…
Reinforcement learning inspired by the brain
DeepMind’s algorithm is directly inspired by theories about how the human brain stores, retrieves, and learns information—called reinforcement learning. The founder of the company, Demis Hassabis, is himself a neuroscientist who began his career as a game developer. Using…
Intelligence derives from being conscious
Within contemporary cognitive science there is a blurry and awkward line between intelligence and consciousness. However, it seems clear to me that much of our intelligence derives from the fact that we are conscious. This is despite the fact that it is well known that most of…
Thinking about thinking, human or machine
What I hope is novel is the synthesis of these areas in a particular way that might generate some further thinking about thinking9—whether that thinking is done by humans, machines, or some kind of freakish hybrid.
The computational stance
create artificial minds. However, despite assuming that the brain is a computational system—what philosopher Riccardo Manzotti calls “the computational stance”—neuroscience is still discovering that…
Random association as AI's missing ingredient
What is the connection of LSD with machine consciousness? The kind of seemingly random association coming from humans on acid may be one of the key missing ingredients that AI has yet to master. Our brains make weird associations constantly. Some people, like the late great…
Watson versus Robin Williams
The massive amount of cultural or semantic information stored in a human brain is readily and appropriately accessible in the right contexts. What are these contexts, and how does the brain identify them? The key to this mechanism is likely the vague and fuzzy logic our brains…
Strong AI versus weak AI
Building an intelligent machine that can match the cognitive capabilities of humans has been the dream of artificial intelligence researchers since the middle of the last century, when Claude Shannon, Alan Turing, and John von Neumann built the theoretical foundation upon which…
Strong AI would have to be conscious
Rather than just a model or smart system, a strong AI system would be an entirely autonomous computer system in no way controlled or influenced by human operators. It could successfully adapt to its environment or even be part of its environment, making intelligent decisions…
Grabbing a coffee cup is harder than chess
Yale University AI researcher and philosopher Drew McDermott points out that current AI systems tend to work in narrow domains and outperform humans only in areas such as chess and numerical computation, where there are clear ground rules. He says, “A robot that can walk into a…
AI returns to the question of consciousness
Initial excitement about creating machine consciousness in the field’s early days faded when it was realized that creating an artificial mind would be a far greater challenge than was originally believed. AI research has since focused on the development of specialized…
Explaining consciousness away
This indifference to consciousness in practical AI research among those who are actually writing code and coming up with algorithms (and for whom, to be absolutely clear, I have a great deal of respect) is inherited from a long tradition in cognitive science and the philosophy…
Data gets the joke seven years later
To take an illustrative fictional example, in an episode of Star Trek: The Next Generation, Data finally gets a bad joke a full seven years after hearing it: Data: Ha, ha, ha! I get it. Ha, ha, ha! I get it. Geordi La Forge: You get what? Data: When you said to Commander Riker…
Machines remember jokes but don't get them
This science fiction example captures one of the salient differences between human and machine intelligence. Humans get jokes within milliseconds but might not remember them for seven years. Data remembers the joke for seven years, but it also takes him that long to get it…
Make AI's hidden assumptions explicit
To reiterate, there are a few fundamental and implicit assumptions in current AI research that have to be made explicit. This is the only way that these assumptions can be acknowledged and improved upon before we can hope to create the world that Kurzweil and others imagine. I…
Chess programs teach nothing about human chess
From a practical scientific perspective, the main benefit of the quest for artificial intelligence is what it will invariably teach us about the brain. But a note of caution is warranted here. As renowned cognitive scientist Douglas Hofstadter points out, the supercomputer…
AI is still building wing-flapping machines
However, when studying human thought and artificial intelligence we do not have something like aerodynamics. We do not know the right physical principles and models that allow humans to think or be conscious. Right now we are convinced it is through computations, but I believe…
AI as building robots with economic theory
Whether or not the field of artificial intelligence deals with this foundational issue could be decisive for its project of building superintelligent machines. It turns on whether computations are ontologically objective versus epistemically objective. If computations are only…
What if the brain doesn't represent the world?
Modern neuroscience, psychology, and artificial intelligence often implicitly assume that our senses determine the contents of our conscious experience. The assumption is that the brain is primarily reflexive and simply exists to respond to the environment. The task of AI is to…
Eyes only count photons
all the eye is doing is catching and counting photons. The millions of photoreceptors in our eyes only react to the number of photons they catch. Our brains then construct the colors, shapes, objects, depths, motion, or textures we see based partly on how many photons the…
The self-model model of consciousness
The current thinking in AI is that consciousness is a self-model, which is a device (or mental model) that a machine or human uses to answer questions about how it interacts with the world. Dennett, for example, argues that the evolution of the self-model was driven by the…
Must machines acquire subjectivity?
believe that the objective mind-independent world does exist. First-person conscious experience is intrinsically subjective, relative and uncertain. How can the field of artificial intelligence use the objective, third-person, logical methods of computer science to create a…
Intelligence as an invention of each brain
What does the notion of consciousness-as-useful-hallucination imply for artificial intelligence? It requires a paradigm shift within artificial intelligence, from seeing intelligence and consciousness as being about perceiving objective truth with a “view from nowhere” in…
What if there is no such thing as computation?
This metaphysical assumption—that human thought is based on objective computational rules—determines much of the methodology used in research on human psychology and artificial thought. But what if this assumption is wrong? What if there is no such thing as a computation?
The physicalist starting point
Again, Searle lays out in very clear terms the basic issue about foundational ontology as it relates to consciousness: We know independently that the world consists entirely of physical particles in fields of force (or whatever the ultimately correct physics tell us are the…
Gödel and the limits of AI
Roger Penrose argues that because humans can grasp Gödel’s theory, the mind is not a formal logical system. He argues that the source of our intuitions and consciousness must come from a new kind of quantum physics. Leonid Perlovsky says that within AI the consequences of…
Epigrams On Programming
Artificial intelligence
A year spent in artificial intelligence is enough to make one believe in God.
From Encyclopedia To Ontology: Toward Dynamic Representation Of The Discipline Of Philosophy
Problem: “philosophy of x” vs. “x”
In numerous places in the expert-supplied taxonomies, we also noticed that cat- egory names sometimes included a “philosophy of” prefix and sometimes did not; examples include “artificial intelligence” vs. “philosophy of artificial intelligence,” “connectionism” vs. “philosophy…
IFI Insights: Inventing the Metaverse
The rise of the metaverse
Nobody can say what the prospects of the metaverse will hold or how far out in the future it will fully materialize. Still, companies see the opportunity and are placing wagers now. Investment bank Citi recently predicted a metaverse market at between $8 trillion and $13…
Introducing Artificial Intelligence (Henry Brighton and Howard Selina)
Searle does not deny strong AI
It is important to note that Searle is not denying the possibility of Strong Al. Indeed, Searle believes that we are nothing more than complex machines, and therefore we can build machines that think and understand. Searle’s objection is with the notion that machine…
Strong AI as a closet dualism
For Searle, to argue anything else means you must believe in a form of dualism, the position that the mental realm has no causal connection with the physical realm. According to Searle, this is precisely the position many AI researchers take. They believe that their models have…
Cognition as the manipulation of symbols
The classical approach to AI encompasses a set of principles and practices used to explore the validity of cognitivism, and specifically, to investigate the hypothesis proposed by Newell and Simon. Cognition is best understood as the formal manipulation of symbolic structures.
Classical AI's engineering projects
The classical approach to AI has resulted in engineering projects such as the following, which we shall examine in more detail later. •Chess-playing computers capable of beating the best human players. •Attempts to equip computers with common-sense knowledge.
The early failure of machine translation
In 1963, after spending $20 million on research into machine translation, the US funding agency concluded: “There is no immediate or predictable prospect of useful machine translation” – National Academy of Sciences National Research Council, 1963. Faced with a hard problem, AI…
Samuel's checkers program learns to win
As a result of learning from its experience, the program carried on improving quickly and soon beat a checkers champion. The champion remarked after defeat in 1965 …
A program can outplay its designer
This victory of machine over man is widely cited, and for good reason. It demonstrates an important lesson: the capabilities of an artificial agent are not necessarily restricted by the capabilities of the designer. Samuel’s program plays better checkers than he does.
How chess computers look ahead
In chess, winning board positions are situated deep in the game tree. Chess-playing computers cannot reach these positions by search. It would take too long. Instead, they look ahead only a certain distance. Using a measure which reflects how advantageous a given board position…
Heuristics: good enough approximations
These tactical rules of thumb are called heuristics and crop up in AI systems everywhere. Heuristics don’t guarantee success or correctness, but offer a good approximation. Heuristics are used when more exhaustive and precise methods are intractable.
AI builds on logic as thought
Logical reasoning requires the manipulation of sentences described in a logical language. These sentences can be interpreted as representing concepts such as states of affairs in the world – or knowledge. Using computers to automate this process, Al has taken the “logic as…
Can common sense be formalized?
The CYC project is entering its final phase, with Lenat predicting a 50% chance of success. Apart from the practical benefits of a successful CYC project, the theoretical objective is to test Newell and Simon’s hypothesis. Is commonsense something that we can formalize and…
Shakey and the sense-model-plan-act cycle
Shakey’s design mirrors the traditional view that an agent should be decomposed into four functional components. This model revolves around the sense-model-plan-act cycle. First, the agent senses the world. Then a model of the world is constructed on the basis of the sensory…
Shakey ignored the real world
The planning design was monolithic. After the plan was set into action, Shakey largely ignored feedback from the real world. For example, if someone secretly removed the block Shakey was interested in, he would become very confused.
Connectionism models the mind on the brain
Connectionism gained popularity in the 1980s and is often depicted as a radical departure from the classical, symbolic approach to AI. Rather than viewing the processes of the mind as a computer program, connectionism draws a parallel between the processes of the mind and the…
Machine learning spans symbols and networks
Machine Learning is a branch of AI that spans both the classical symbolic approach and connectionism. Here, models of learning capture the ability of an agent to improve itself in light of information in the environment. Often, the ability of connectionist systems to learn is…
Evolution has already solved intelligence
In one sense, the problem of creating intelligent agents has already been solved. Over the course of the earth’s 4.5 billion years of history, evolution has solved the problem over and over again. Mammals arrived 370 million years ago. Our last common ancestor with the apes…
Brooks: get the basics right first
The MIT roboticist Rodney Brooks takes the evolutionary basics as evidence that “hard” tasks like reasoning, planning and language might turn out to be easier to understand once the basics are in place.
Mechanical creatures before mechanical humans
Can our knowledge of evolution inform AI? Brooks believes that it can, and argues that we should first aim to build basic mechanical creatures before we try to build mechanical humans.
The philosophical roots of AI
Many of the concepts central to Al have their roots in the work of philosophers such as Descartes, Hobbes, Leibniz, as we have seen, and the Tractatus Logico-Philosophicus of Ludwig Wittgenstein (1889–1951):
Dreyfus against disembodied intelligence
This argument formed the backbone of one of the foremost critiques of AI. The philosopher Hubert Dreyfus, in the 1970s, declared that AI was misguided in its assumption that disembodied intelligence was possible. On the perceived failure of classical AI, Dreyfus noted …
From critique to engineering principles
The arguments from evolution, biology and philosophy stand in opposition to much of conventional AI research. But to put these arguments into practice, they need to be translated into engineering principles. Three principles characterize the new approach to AI.
Embodiment as the first principle
The First Principle of Embodiment
Bottom-up versus top-down AI design
Given the goal of building an intelligent agent, the methodology frequently adopted by Al is to build from the top downwards. That is, higher-level functions such as knowledge and reasoning are targeted first, with lower-level Junctions swept under the carpet until later. New…
Evolving meaning and communication bottom-up
Although the principles of new AI translate most directly into the field of robotics, they are by no means limited to issues in robotics. A closer treatment of the interaction between agents and their environments can be applied to every branch of AI. Luc Steels, director of…
AI and the Lamarckian evolution of our species
By engineering our own offspring, many have proposed that Artificial Intelligence can lead to the Lamarckian evolution of our species. In contrast to Darwin’s theory of evolution by natural selection, Lamarck proposed that evolution allows characteristics acquired over our…
Mind: A Brief Introduction (John R. Searle)
Mind is to brain as program is to hardware
The idea was based on a convergence of work in philosophy, cognitive psychology, linguistics, computer science, and artificial intelligence. It seemed that we knew the answer to the question that faced us: the way the system works is that the brain is a digital computer and…
The Turing test as a kind of behaviorism
We need a test that will tell us when a machine is genuinely behaving intelligently, and when it is not. This test was also invented by Alan Turing, and is called the Turing test. There are different versions of it, but the basic idea is this: we can side-step all the great…
The research program of cognitive science
And we now have a research project; indeed, it is the research project of cognitive science. We try to discover the programs that are implemented in the brain by designing programs for our commercial machines that will pass the Turing test, and then we ask the psychologists to…
The Chinese room
An argument explicitly directed against Strong AI was put forth by the present author.5 The strategy of the argument is to appeal to one’s first person experiences in testing any theory of the mind. If Strong AI were true, then anybody should be able to acquire any cognitive…
Computers manipulate symbols without meaning
as far as human cognition is concerned, such achievements are simply irrelevant. And they are irrelevant for a deep reason: the computer operates by manipulating symbols. Its processes are defined purely syntactically, whereas the human mind has more than just uninterpreted…
Shivon Zilis - Machine Intelligence
Machine intelligence
I mean “machine intelligence” as a unifying term for what others call machine learning and artificial intelligence. (Some others have used the term before, without quite describing it or understanding how laden this field has been with debates over descriptions.) I would have…
Thesauri Of Historical Periods – A Proposal For Standardization.
Temporal reasoning that ignores space
On one side, computer science concentrates on temporal reasoning in controlled industrial environments, typically using discrete events and time intervals, neglecting spatiotemporal dependencies (e.g. Theodoulidis and Loucopoulos 1991, Ligozat and Vila 1998, Cowley and…
Two Decades Of The Language-action Perspective
Winograd and Flores's critique of AI
In 1986, Terry Winograd and Fernando Flores published Understanding Computers and Cognition—A New Foundation for Design [3]. They offered a sharp critique of the field of artificial intelligence, which was completing 30 years of pursuit of its Holy Grail of expert systems…