text:
- Nick Bostrom
- Oxfrord Future of Humanity Institute
- Faculty of Philosophy & Oxford Martin School
- University of Oxford
- https://nickbostrom.com
- [Originally published in Int. Jour. of Future Studies, 1998, vol. 2]
- [Reprinted in Linguistic and Philosophical Investigations, 2006, Vol. 5, No. 1, pp. 11-30.]
This paper outlines the case for believing that we will have superhuman artificial intelligence within the first third of the next century. It looks at different estimates of the processing power of the human brain; how long it will take until computer hardware achieve a similar performance; ways of creating the software through bottom-up approaches like the one used by biological brains; how difficult it will be for neuroscience figure out enough about how brains work to make this approach work; and how fast we can expect superintelligence to be developed once there is human-level artificial intelligence.
Definition of "superintelligence"
By a "superintelligence" we mean an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills. This definition leaves open how the superintelligence is implemented: it could be a digital computer, an ensemble of networked computers, cultured cortical tissue or what have you. It also leaves open whether the superintelligence is conscious and has subjective experiences.
Entities such as companies or the scientific community are not superintelligences according to this definition. Although they can perform a number of tasks of which no individual human is capable, they are not intellects and there are many fields in which they perform much worse than a human brain - for example, you can't have real-time conversation with "the scientific community".
Moore's law and present supercomputers
Moore's law <A> states that processor speed doubles every eighteen months. The doubling time used to be two years, but that changed about fifteen years ago. The most recent data points indicate a doubling time as short as twelve months. This would mean that there will be a thousand-fold increase in computational power in ten years. Moore's law is what chip manufacturers rely on when they decide what sort of chip to develop in order to remain competitive.
If we estimate the computational capacity of the human brain, and allow ourselves to extrapolate available processor speed according to Moore's law (whether doing so is permissible will be discussed shortly), we can calculate how long it will take before computers have sufficient raw power to match a human intellect.
The fastest supercomputer today (December 1997) is 1.5 teraops, 1.5*10^12 ops. There is a project that aims to extract 10 teraops from the Internet by having a hundred thousand volunteers install a screen saver on their computers that would allow a central computer to delegate some computational tasks to them. This (so-called metacomputing) approach works best for tasks that are very easy to parallelize, such as doing an exhaustive journey though search space in attempting to break a code. With better bandwidth connections in the future (e.g. optical fibers), large-scale metacomputing will work even better than today. Brain simulations should by their nature be relatively easy to parallelize, so maybe huge brain simulations distributed over the Internet could be a feasible alternative in the future. We shall however disregard this possibility for present purposes and regard the 1.5 Tops machine as the best we can do today. The potential of metacomputing can be factored into our prognosis by viewing it as an additional reason to believe that available computing power will continue to grow as Moore's law predicts.
Even without any technology improvement we can do somewhat better than, for example by doubling the number of chips that we put in the box. A 3 Tops computer has been ordered by the US government to be used in testing and developing the nation's stock pile of nuclear weapons. However, considering that the cost of this machine is $94,000,000, it is clear that even massive extra funding would only yield a very modest increase in computing power in the short term.
How good grounds are there to believe that Moore's law will continue to hold in the future? It is clear that sooner or later it must fail. There are physical limitations on the density with which matter can store and process information. The Bekenstein bound gives an upper limit on the amount of information that can be contained within any given volume using a given amount of energy. Since space colonization would allow at most a polynomial (~t^3) expansion rate (assuming expansion rate is bounded by the speed of light), the exponential increase of available computational power cannot be continued indefinitely, unless new physics is forthcoming.
In my opinion, Moore's law loses its credibility long before we reach absolute physical limits. It probably hasn't got much predictive power beyond, say, the next fifteen years. That is not to say that processor speed will not continue to double every twelve or eighteen months after 2012; only that we cannot use Moore's law to argue that it will. Instead, if we want to make predictions beyond that date, we will have to look directly at what is physically feasible. That will presumably also mean that we have to contend ourselves with a greater uncertainty interval along the time axis. Physical feasibility studies tell us, at best, what will happen given that people want it to happen; but even if we assume that the demand is there, it will still not tell us when it will happen.
In about the year 2007 we will have reached the physical limit of present silicon technology. Moore's law, however, has survived several technological phase transitions before, from relays to vacuum tubes to transistors to integrated circuits to Very Large Scale Integrated circuits (VLSI). There is no reason why present VLSI designs on two-dimensional silicon wafers should be the last word in chip technology. Several ways to overcome the limits of the present technology have been proposed and are being developed.
In the near future, it might for example be possible to use phase shift masks to push the minimum circuit-line width on a microchip down to as little as 0.13 micrometer, even while remaining in the optical range with the lithographic irradiation. Leaving the optical range, we could use x-rays or at least extreme ultraviolet ("EUV", also called "soft x-rays") to attain still finer precision. Failing this, it should be feasible to use electron beam writing, although this production method would be slow and hence expensive. A compromise would be to write some of the gates with an electron beam, especially at bottlenecks where speed is absolutely crucial, and use optical or EUV to write the other elements of the chip.
We can also increase the power of a chip by using more layers, a technique that has only recently been mastered, and by making bigger wafers (up to 300 mm should not be a problem). Drastically bigger chips could be manufactured if there were some error tolerance. Tolerance to error could be obtained by using evolvable hardware (de Garis 1997).
It is also possible to push the physical limits on how small the transistors can be made by switching to new materials, such as gallium arsenide. Quantum transistors are presently being developed, promising a major step forward for circuitry where high switching speed or low energy consumption is essential.
Because of the highly parallel nature of brain-like computations, it should also be possible to use a highly parallel architecture, in which case it will suffice to produce a great number of moderately fast processors, and have then have them connected. You could either put them in the same box which would give you a bus-based multiprocessor (which are quite popular today) or you could link them up to a high-bandwidth local-area network (an option that will be increasingly attractive as the performance of standard networking technology improves). <B>
These are all things that are being developed today. Massive funding is pumped into these technologies <C>. Although the difficulties can appear staggering to a person working in the field, who is constantly focused on the immediate problems, it is fair to say that there is widespread optimism among the experts that the prospects are good that computers will continue to grow more powerful for the foreseeable future.
<A> It is not clear what, exactly, Moore's law says. The law derives its name from Gordon Moore, co-founder of Intel Corp., who back in 1965 noted that microchips were doubling in circuit density every year. In 1975 he made the prediction that from then on, the doubling time would be two years. The actual doubling time has fluctuated a bit, starting at one year, going up to two years, and is now back to approximately one year again. So one ambiguity in citing Moore's law is that it is unclear whether the time constant is supposed to be one year, two years, or whether it is supposed to be whatever the most recent data points indicate. A second ambiguity resides in the fact that the initial statement was phrased in terms of the number on transistors that could be fitted into an area unit, rather than in terms of the speed of the resulting chip. Until now, this distinction hasn't mattered much, because circuitry density and speed have been highly correlated. When we look to the future, however, it is possible that we will achieve increased computing power by other means than by making transistors smaller. It therefore makes sense to reformulate Moore's law into a statement asserting an exponential growth in computing power (per inflation-adjusted dollar) rather than chip density. It is better to apply the label "Moore's law" to this slightly modified hypothesis than to invent a new term for what is basically the same idea.
<B> In the longer term, we also have to consider molecular nanotechnology and maybe quantum computing.
<C> It nowadays takes about 400 engineers to produce a new chip. A modern chip factory may cost over $2 billion. About $20 to $30 billion is spent on microchip R&D every years. These figures have grown over the years, so it should be pointed out that one factor that could slow the pace of development would be if funding begins to level out, as it sooner or later will.
Hardware requirements
The human brain contains about 10^11 neurons. Each neuron has about 5*10^3 synapses, and signals are transmitted along these synapses at an average frequency of about 10^2 Hz. Each signal contains, say, 5 bits. This equals 10^17 ops.<A>
The true value cannot be much higher than this, but it might be much lower. There seems to be great redundancy in the brain; synchronous firing of large pools of neurons is often required if the signal is not to drown in the general noise. An alternative way of calculating the total capacity is to consider some part of the cortex that performs a function that we know how to replicate on digital computers. We calculate the average computer-equivalent processing capacity of a single neuron in that cortical area, and multiply this value with the number of neurons in the brain. Hans Moravec has done this calculation using data about the human retina (Moravec 1997) and compared it with known computational demands of edge extraction in robot vision. He got the value 10^14 ops for the human brain as a whole. That is three orders of magnitude less than the upper bound calculated by assuming that there is no redundancy.
It is hard to see any reason to suppose that the redundancy in the retina should be greater than in the cortex. If anything, one would rather expect it to be the other way around, since edge extraction is more low-level task than higher cognitive processes and therefore presumably more optimized (by evolution and individual learning).
If we need 100 Tops to simulate the human brain then the required computational power will be reached sometime between 2004 and 2008, depending on whether we assume a doubling time of 12 or 18 months. This would be the best experimental supercomputers in the world, not necessarily the computers available to AI researchers. Depending on how much funding is forthcoming, it might take up to an additional decade before researchers experimenting with general artificial intelligence have access to machines with this capacity.
This is if we take the retina simulation as a model. As the present, however, not enough is known about the neocortex to allow us to simulate it in such an optimized way. But the knowledge might be available by 2004 to 2008 (as we shall see in the next section). What is required, if we are to get human-level AI with hardware power at this lower bound, is the ability to simulate 1000-neuron aggregates in a highly efficient way.
The extreme alternative, which is what we assumed in the derivation of the upper bound, is to simulate each neuron individually. The number of clock cycles that neuroscientists can expend simulating the processes of a single neuron knows of no limits, but that is because their aim is to model the detailed chemical and electrodynamic processes in the nerve cell rather than to just do the minimal amount of computation necessary to replicate those features of its response function which are relevant for the total performance of the neural net. It is not known how much of the detail that is contingent and inessential and how much needs to be preserved in order for the simulation to replicate the performance of the whole. It seems like a good bet though, at least to the author, that the nodes could be strongly simplified and replaced with simple standardized elements. It appears perfectly feasible to have an intelligent neural network with any of a large variety of neuronal output functions and time delays.
It does look plausible, however, that by the time when we know how to simulate an idealized neuron and know enough about the brain's synaptic structure that we can put the artificial neurons together in a way that functionally mirrors how it is done in the brain, then we will also be able to replace whole 1000-neuron modules with something that requires less computational power to simulate than it does to simulate all the neurons in the module individually. We might well get all the way down to a mere 1000 instructions per neuron and second, as is implied by Moravec's estimate (10^14 ops / 10^11 neurons = 1000 operations per second and neuron). But unless we can build these modules without first building a whole brain then this optimization will only be possible after we have already developed human-equivalent artificial intelligence.
If we assume the upper bound on the computational power needed to simulate the human brain, i.e. if we assume enough power to simulate each neuron individually (10^17 ops), then Moore's law says that we will have to wait until about 2015 or 2024 (for doubling times of 12 and 18 months, respectively) before supercomputers with the requisite performance are at hand. But if by then we know how to do the simulation on the level of individual neurons, we will presumably also have figured out how to make at least some optimizations, so we could probably adjust these upper bounds a bit downwards.
So far I have been talking only of processor speed, but computers need a great deal of memory too if they are to replicate the brain's performance. Throughout the history of computers, the ratio between memory and speed has remained more or less constant at about 1 byte/ops. Since a signal is transmitted along a synapse, on average, with a frequency of about 100 Hz and since its memory capacity is probably less than 100 bytes (1 byte looks like a more reasonable estimate), it seems that speed rather than memory would be the bottleneck in brain simulations on the neuronal level. (If we instead assume that we can achieve a thousand-fold leverage in our simulation speed as assumed in Moravec's estimate, then that would bring the requirement of speed down, perhaps, one order of magnitude below the memory requirement. But if we can optimize away three orders of magnitude on speed by simulating 1000-neuron aggregates, we will probably be able to cut away at least one order of magnitude of the memory requirement. Thus the difficulty of building enough memory may be significantly smaller, and is almost certainly not significantly greater, than the difficulty of building a processor that is fast enough. We can therefore focus on speed as the critical parameter on the hardware front.)
This paper does not discuss the possibility that quantum phenomena are irreducibly involved in human cognition. Hameroff and Penrose and others have suggested that coherent quantum states may exist in the microtubules, and that the brain utilizes these phenomena to perform high-level cognitive feats. The author's opinion is that this is implausible. The controversy surrounding this issue won't be entered into here; it will simply be assumed, throughout this paper, that quantum phenomena are not functionally relevant to high-level brain modelling.
In conclusion we can say that the hardware capacity for human-equivalent artificial intelligence will likely exist before the end of the first quarter of the next century, and may be reached as early as 2004. A corresponding capacity should be available to leading AI labs within ten years thereafter (or sooner if the potential of human-level AI and superintelligence is by then better appreciated by funding agencies).
<A> It is possible to nit-pick on this estimate. For example, there is some evidence that some limited amount of communication between nerve cells is possible without synaptic transmission. And we have the regulatory mechanisms consisting of neurotransmitters and their sources, receptors and re-uptake channels. While neurotransmitter balances are crucially important for the proper functioning of the human brain, they have an insignificant information content compared to the synaptic structure. Perhaps a more serious point is that that neurons often have rather complex time-integration properties (Koch 1997). Whether a specific set of synaptic inputs result in the firing of a neuron depends on their exact timing. The author's opinion is that except possibly for a small number of special applications such as auditory stereo perception, the temporal properties of the neurons can easily be accommodated with a time resolution of the simulation on the order of 1 ms. In an unoptimized simulation this would add an order of magnitude to the estimate given above, where we assumed a temporal resolution of 10 ms, corresponding to an average firing rate of 100 Hz. However, the other values on which the estimate was based appear to be too high rather than too low , so we should not change the estimate much to allow for possible fine-grained time-integration effects in a neuron's dendritic tree. (Note that even if we were to adjust our estimate upward by an order of magnitude, this would merely add three to five years to the predicted upper bound on when human-equivalent hardware arrives. The lower bound, which is based on Moravec's estimate, would remain unchanged.)
Software via the bottom-up approach
Superintelligence requires software as well as hardware. There are several approaches to the software problem, varying in the amount of top-down direction they require. At the one extreme we have systems like CYC which is a very large encyclopedia-like knowledge-base and inference-engine. It has been spoon-fed facts, rules of thumb and heuristics for over a decade by a team of human knowledge enterers. While systems like CYC might be good for certain practical tasks, this hardly seems like an approach that will convince AI-skeptics that superintelligence might well happen in the foreseeable future. We have to look at paradigms that require less human input, ones that make more use of bottom-up methods.
Given sufficient hardware and the right sort of programming, we could make the machines learn in the same way a child does, i.e. by interacting with human adults and other objects in the environment. The learning mechanisms used by the brain are currently not completely understood. Artificial neural networks in real-world applications today are usually trained through some variant of the Backpropagation algorithm (which is known to be biologically unrealistic). The Backpropagation algorithm works fine for smallish networks (of up to a few thousand neurons) but it doesn't scale well. The time it takes to train a network tends to increase dramatically with the number of neurons it contains. Another limitation of backpropagation is that it is a form of supervised learning, requiring that signed error terms for each output neuron are specified during learning. It's not clear how such detailed performance feedback on the level of individual neurons could be provided in real-world situations except for certain well-defined specialized tasks.
A biologically more realistic learning mode is the Hebbian algorithm. Hebbian learning is unsupervised and it might also have better scaling properties than Backpropagation. However, it has yet to be explained how Hebbian learning by itself could produce all the forms of learning and adaptation of which the human brain is capable (such the storage of structured representation in long-term memory - Bostrom 1996). Presumably, Hebb's rule would at least need to be supplemented with reward-induced learning (Morillo 1992) and maybe with other learning modes that are yet to be discovered. It does seems plausible, though, to assume that only a very limited set of different learning rules (maybe as few as two or three) are operating in the human brain. And we are not very far from knowing what these rules are.
Creating superintelligence through imitating the functioning of the human brain requires two more things in addition to appropriate learning rules (and sufficiently powerful hardware): it requires having an adequate initial architecture and providing a rich flux of sensory input.
The latter prerequisite is easily provided even with present technology. Using video cameras, microphones and tactile sensors, it is possible to ensure a steady flow of real-world information to the artificial neural network. An interactive element could be arranged by connecting the system to robot limbs and a speaker.
Developing an adequate initial network structure is a more serious problem. It might turn out to be necessary to do a considerable amount of hand-coding in order to get the cortical architecture right. In biological organisms, the brain does not start out at birth as a homogenous tabula rasa; it has an initial structure that is coded genetically. Neuroscience cannot, at its present stage, say exactly what this structure is or how much of it needs to be preserved in a simulation that is eventually to match the cognitive competencies of a human adult. One way for it to be unexpectedly difficult to achieve human-level AI through the neural network approach would be if it turned out that the human brain relies on a colossal amount of genetic hardwiring, so that each cognitive function depends on a unique and hopelessly complicated inborn architecture, acquired over aeons in the evolutionary learning process of our species.
Is this the case? A number of considerations suggest otherwise. We have to contend ourselves with a very brief review here. For a more comprehensive discussion, the reader may consult Phillips & Singer (1997).
Quartz & Sejnowski (1997) argue from recent neurobiological data that the developing human cortex is largely free of domain-specific structures. The representational properties of the specialized circuits that we find in the mature cortex are not generally genetically prespecified. Rather, they are developed through interaction with the problem domains on which the circuits operate. There are genetically coded tendencies for certain brain areas to specialize on certain tasks (for example primary visual processing is usually performed in the primary visual cortex) but this does not mean that other cortical areas couldn't have learnt to perform the same function. In fact, the human neocortex seems to start out as a fairly flexible and general-purpose mechanism; specific modules arise later through self-organizing and through interacting with the environment.
Strongly supporting this view is the fact that cortical lesions, even sizeable ones, can often be compensated for if they occur at an early age. Other cortical areas take over the functions that would normally have been developed in the destroyed region. In one study, sensitivity to visual features was developed in the auditory cortex of neonatal ferrets, after that region's normal auditory input channel had been replaced by visual projections (Sur et al. 1988). Similarly, it has been shown that the visual cortex can take over functions normally performed by the somatosensory cortex (Schlaggar & O'Leary 1991). A recent experiment (Cohen et al. 1997) showed that people who have been blind from an early age can use their visual cortex to process tactile stimulation when reading Braille.
There are some more primitive regions of the brain whose functions cannot be taken over by any other area. For example, people who have their hippocampus removed, lose their ability to learn new episodic or semantic facts. But the neocortex tends to be highly plastic and that is where most of the high-level processing is executed that makes us intellectually superior to other animals. (It would be interesting to examine in more detail to what extent this holds true for all of neocortex. Are there small neocortical regions such that, if excised at birth, the subject will never obtain certain high-level competencies, not even to a limited degree?)
Another consideration that seems to indicate that innate architectural differentiation plays a relatively small part in accounting for the performance of the mature brain is the that neocortical architecture, especially in infants, is remarkably homogeneous over different cortical regions and even over different species:
Laminations and vertical connections between lamina are hallmarks of all cortical systems, the morphological and physiological characteristics of cortical neurons are equivalent in different species, as are the kinds of synaptic interactions involving cortical neurons. This similarity in the organization of the cerebral cortex extends even to the specific details of cortical circuitry. (White 1989, p. 179).
One might object that at this point that cetaceans have much bigger cortices than humans and yet they don't have human-level abstract understanding and language <A>. A large cortex, apparently, is not sufficient for human intelligence. However, one can easily imagine that some very simple difference between human and cetacean brains can account for why we have abstract language and understanding that they lack. It could be something as trivial as that our cortex is provided with a low-level "drive" to learn about abstract relationships whereas dolphins and whales are programmed not to care about or pay much attention to such things (which might be totally irrelevant to them in their natural environment). More likely, there are some structural developments in the human cortex that other animals lack and that are necessary for advanced abstract thinking. But these uniquely human developments may well be the result of relatively simple changes in just a few basic parameters. They do not require a large amount of genetic hardwiring. Indeed, given that brain evolution that allowed Homo sapiens to intellectually outclass other animals took place over a relatively brief period of time, evolution cannot have embedded very much content-specific information in these additional cortical structures that give us our intellectual edge over our humanoid or ape-like ancestors.
These considerations (especially the one of cortical plasticity) suggest that the amount of neuroscientific information needed for the bottom-up approach to succeed may be very limited. (Notice that they do not argue against the modularization of adult human brains. They only indicate that the greatest part of the information that goes into the modularization results from self-organization and perceptual input rather than from an immensely complicated genetic look-up table.)
Further advances in neuroscience are probably needed before we can construct a human-level (or even higher animal-level) artificial intelligence by means of this radically bottom-up approach. While it is true that neuroscience has advanced very rapidly in recent years, it is difficult to estimate how long it will take before enough is known about the brain's neuronal architecture and its learning algorithms to make it possible to replicate these in a computer of sufficient computational power. A wild guess: something like fifteen years. This is not a prediction about how far we are from a complete understanding of all important phenomena in the brain. The estimate refers to the time when we might be expected to know enough about the basic principles of how the brain works to be able to implement these computational paradigms on a computer, without necessarily modelling the brain in any biologically realistic way.
The estimate might seem to some to underestimate the difficulties, and perhaps it does. But consider how much has happened in the past fifteen years. The discipline of computational neuroscience did hardly even exist back in 1982. And future progress will occur not only because research with today's instrumentation will continue to produce illuminating findings, but also because new experimental tools and techniques become available. Large-scale multi-electrode recordings should be feasible within the near future. Neuro/chip interfaces are in development. More powerful hardware is being made available to neuroscientists to do computation-intensive simulations. Neuropharmacologists design drugs with higher specificity, allowing researches to selectively target given receptor subtypes. Present scanning techniques are improved and new ones are under development. The list could be continued. All these innovations will give neuroscientists very powerful new tools that will facilitate their research.
This section has discussed the software problem. It was argued that it can be solved through a bottom-up approach by using present equipment to supply the input and output channels, and by continuing to study the human brain in order to find out about what learning algorithm it uses and about the initial neuronal structure in new-born infants. Considering how large strides computational neuroscience has taken in the last decade, and the new experimental instrumentation that is under development, it seems reasonable to suppose that the required neuroscientific knowledge might be obtained in perhaps fifteen years from now, i.e. by year 2012.
<A> That dolphins don't have abstract language was recently established in a very elegant experiment. A pool is divided into two halves by a net. Dolphin A is released into one end of the pool where there is a mechanism. After a while, the dolphin figures out how to operate the mechanism which causes dead fish to be released into both ends of the pool. Then A is transferred to the other end of the pool and a dolphin B is released into the end of the pool that has the mechanism. The idea is that if the dolphins had a language, then A would tell B to operate the mechanism. However, it was found that the average time for B to operate the mechanism was the same as for A.
Why the past failure of AI is no argument against its future success
In the seventies and eighties the AI field suffered some stagnation as the exaggerated expectations from the early heydays failed to materialize and progress nearly ground to a halt. The lesson to draw from this episode is not that strong AI is dead and that superintelligent machines will never be built. It shows that AI is more difficult than some of the early pioneers might have thought, but it goes no way towards showing that AI will forever remain unfeasible.
In retrospect we know that the AI project couldn't possibly have succeeded at that stage. The hardware was simply not powerful enough. It seems that at least about 100 Tops is required for human-like performance, and possibly as much as 10^17 ops is needed. The computers in the seventies had a computing power comparable to that of insects. They also achieved approximately insect-level intelligence. Now, on the other hand, we can foresee the arrival of human-equivalent hardware, so the cause of AI's past failure will then no longer be present.
There is also an explanation for the relative absence even of noticeable progress during this period. As Hans Moravec points out:
[F]or several decades the computing power found in advanced Artificial Intelligence and Robotics systems has been stuck at insect brain power of 1 MIPS. While computer power per dollar fell [should be: rose] rapidly during this period, the money available fell just as fast. The earliest days of AI, in the mid 1960s, were fuelled by lavish post-Sputnik defence funding, which gave access to $10,000,000 supercomputers of the time. In the post Vietnam war days of the 1970s, funding declined and only $1,000,000 machines were available. By the early 1980s, AI research had to settle for $100,000 minicomputers. In the late 1980s, the available machines were $10,000 workstations. By the 1990s, much work was done on personal computers costing only a few thousand dollars. Since then AI and robot brain power has risen with improvements in computer efficiency. By 1993 personal computers provided 10 MIPS, by 1995 it was 30 MIPS, and in 1997 it is over 100 MIPS. Suddenly machines are reading text, recognizing speech, and robots are driving themselves cross country. ( Moravec 1997 )
In general, there seems to be a new-found sense of optimism and excitement among people working in AI, especially among those taking a bottom-up approach, such as researchers in genetic algorithms, neuromorphic engineering and in hardware implementations of neural networks. Many experts who have been around, though, are wary not again to underestimate the difficulties ahead.
Once there is human-level AI there will soon be superintelligence
Once artificial intelligence reaches human level, there will be a positive feedback loop that will give the development a further boost. AIs would help constructing better AIs, which in turn would help building better AIs, and so forth.
Even if no further software development took place and the AIs did not accumulate new skills through self-learning, the AIs would still get smarter if processor speed continued to increase. If after 18 months the hardware were upgraded to double the speed, we would have an AI that could think twice as fast as its original implementation. After a few more doublings this would directly lead to what has been called "weak superintelligence", i.e. an intellect that has about the same abilities as a human brain but is much faster.
Also, the marginal utility of improvements in AI when AI reaches human-level would also seem to skyrocket, causing funding to increase. We can therefore make the prediction that once there is human-level artificial intelligence then it will not be long before superintelligence is technologically feasible.
A further point can be made in support of this prediction. In contrast to what's possible for biological intellects, it might be possible to copy skills or cognitive modules from one artificial intellect to another. If one AI has achieved eminence in some field, then subsequent AIs can upload the pioneer's program or synaptic weight-matrix and immediately achieve the same level of performance. It would not be necessary to again go through the training process. Whether it will also be possible to copy the best parts of several AIs and combine them into one will depend on details of implementation and the degree to which the AIs are modularized in a standardized fashion. But as a general rule, the intellectual achievements of artificial intellects are additive in a way that human achievements are not, or only to a much lesser degree.
The demand for superintelligence
Given that superintelligence will one day be technologically feasible, will people choose to develop it? This question can pretty confidently be answered in the affirmative. Associated with every step along the road to superintelligence are enormous economic payoffs. The computer industry invests huge sums in the next generation of hardware and software, and it will continue doing so as long as there is a competitive pressure and profits to be made. People want better computers and smarter software, and they want the benefits these machines can help produce. Better medical drugs; relief for humans from the need to perform boring or dangerous jobs; entertainment -- there is no end to the list of consumer benefits. There is also a strong military motive to develop artificial intelligence. And nowhere on the path is there any natural stopping point where technofobics could plausibly argue "hither but not further".
It therefore seems that up to human equivalence, the driving forces behind improvements in AI will easily overpower whatever resistance might be present. When the question is about human-level or greater intelligence then it is conceivable that there might be strong political forces opposing further development. Superintelligence might be seen to pose a threat to the supremacy, and even to the survival, of the human species. Whether by suitable programming we can arrange the motivation systems of the superintelligences in such a way as to guarantee perpetual obedience and subservience, or at least non-harmfulness, to humans is a contentious topic. If future policy-makers can be sure that AIs would not endanger human interests then the development of artificial intelligence will continue. If they can't be sure that there would be no danger, then the development might well continue anyway, either because people don't regard the gradual displacement of biological humans with machines as necessarily a bad outcome, or because such strong forces (motivated by short-term profit, curiosity, ideology, or desire for the capabilities that superintelligences might bring to its creators) are active that a collective decision to ban new research in this field cannot be reached and successfully implemented.
Depending on degree of optimization assumed, human-level intelligence probably requires between 10^14 and 10^17 ops. It seems quite possible that very advanced optimization could reduce this figure further, but the entrance level would probably not be less than about 10^14 ops. If Moore's law continues to hold then the lower bound will be reached sometime between 2004 and 2008, and the upper bound between 2015 and 2024. The past success of Moore's law gives some inductive reason to believe that it will hold another ten, fifteen years or so; and this prediction is supported by the fact that there are many promising new technologies currently under development which hold great potential to increase procurable computing power. There is no direct reason to suppose that Moore's law will not hold longer than 15 years. It thus seems likely that the requisite hardware for human-level artificial intelligence will be assembled in the first quarter of the next century, possibly within the first few years.
There are several approaches to developing the software. One is to emulate the basic principles of biological brains. It is not implausible to suppose that these principles will be well enough known within 15 years for this approach to succeed, given adequate hardware.
The stagnation of AI during the seventies and eighties does not have much bearing on the likelihood of AI to succeed in the future since we know that the cause responsible for the stagnation (namely, that the hardware available to AI researchers was stuck at about 10^6 ops) is no longer present.
There will be a strong and increasing pressure to improve AI up to human level. If there is a way of guaranteeing that superior artificial intellects will never harm human beings then such intellects will be created. If there is no way to have such a guarantee then they will probably be created nevertheless.
- Postscript I
- (25 October, 1998)
The U.S. Department of Energy has ordered a new supercomputer from IBM, to be installed in the Lawrence Livermore National Laboratory in the year 2000. It will cost $85 million and will perform 10 Tops. This development is in accordance with Moore's law, or possibly slightly more rapid than an extrapolation would have predicted.
Many steps forward have been taken during the past year. An especially nifty one is the new chip-making techniques being developed at Irvine Sensors Corporation (ISC). They have found a way to stack chips directly on top of each other in a way that will not only save space but, more importantly, allow a larger number of interconnections between neigboring chips. Since the number of interconnections has been a bottleneck in hardware implementations of neural networks, this breakthrough could prove very important. In principle, it should allow you to have an arbitrarily large cube of neural network modules with high local connectivity and moderate non-local connectivity.
Postscript II (28 August, 2000) Is progress still on schedule? - In fact, things seem to be moving somewhat faster than expected, at least on the hardware front. (Software progress is more difficult to quantify.) IBM is currently working on a next-generation supercomputer, Blue Gene, which will perform over 10^15 ops. This computer, which is designed to tackle the protein folding problem, is expected to be ready around 2005. It will achieve its enormous power through massive parallelism rather than through dramatically faster processors. Considering the increasing emphasis on parallel computing, and the steadily increasing Internet bandwidth, it becomes important to interpret Moore's law as a statement about how much computing power can be bought for a given sum of (inflation adjusted) money. This measure has historically been growing at the same pace as processor speed or chip density, but the measures may come apart in the future. It is how much computing power that can be bought for, say, 100 million dollars that is relevant when we are trying to guess when superintelligence will be developed, rather than how fast individual processors are.
Postscript III (30 October, 2005) The fastest supercomputer today is IBM's Blue Gene/L, which has attained 260 Tops (2.6*10^14 ops). The Moravec estimate of the human brain's processing power (10^14 ops) has thus now been exceeded.
The 'Blue Brain' project was launched by the Brain Mind Institute, EPFL, Switzerland and IBM, USA in May, 2005. It aims to build an accurate software replica of the neocortical column within 2-3 years. The column will consist of 10,000 morphologically complex neurons with active ionic channels. The neurons will be interconnected in a 3-dimensional space with 10^7 -10^8 dynamic synapses. This project will thus use a level of simulation that attempts to capture the functionality of individual neurons at a very detailed level. The simulation is intended to run in real time on a computer preforming 22.8*10^12 flops. Simulating the entire brain in real time at this level of detail (which the researchers indicate as a goal for later stages of the project) would correspond to circa 2*10^19 ops, five orders of magnitude above the current supercomputer record. This is two orders of magnitude greater than the estimate of neural-level simulation given in the original paper above, which assumes a cruder level of simulation of neurons. If the 'Blue Brain' project succeeds, it will give us hard evidence of an upper bound on the computing power needed to achieve human intelligence.
Functional replication of the functionality of early auditory processing (which is quite well understood) has yielded an estimate that agrees with Moravec's assessment based on signal processing in the retina (i.e. 10^14 ops for whole-brain equivalent replication).
No dramatic breakthrough in general artificial intelligence seems to have occurred in recent years. Neuroscience and neuromorphic engineering are proceeding at a rapid clip, however. Much of the paper could now be rewritten and updated to take into account information that has become available in the past 8 years.
Molecular nanotechnology, a technology that in its mature form could enable mind uploading (an extreme version of the bottom-up method, in which a detailed 3-dimensional map is constructed of a particular human brain and then emulated in a computer), has begun to pick up steam, receiving increasing funding and attention. An upload running on a fast computer would be weakly superintelligent -- it would initially be functionally identical to the original organic brain, but it could run at a much higher speed. Once such an upload existed, it might be possible to enhance its architecture to create strong superintelligence that was not only faster but functionally superior to human intelligence.
Postscript IV (12 March, 2008)
I should clarify what I meant when in the abstract I said I would "outline the case for believing that we will have superhuman artificial intelligence within the first third of the next [i.e. this] century". I chose the word "case" deliberately: In particular, by outlinining "the case for", I did not mean to deny that one could also outline a case against. In fact, I would all-things-considered assign less than a 50% probability to superintelligence being developed by 2033. I do think there is great uncertainty about whether and when it might happen, and that one should take seriously the possibility that it might happen by then, because of the kinds of consideration outlined in this paper.
There seems to be somewhat more interest now in artificial general intelligence (AGI) research than there was a few years ago. However, it appears that as yet no major breakthrough has occurred.
The author would like to thank all those who have contributed comments on earlier versions of this paper. The very helpful suggestions by Hal Finney, Robin Hanson, Carl Feynman, Anders Sandberg, and Peter McCluskey were especially appreciated.
Bostrom N. 1996. "Cortical Integration: Possible Solutions to the Binding and Linking Problems in Perception, Reasoning and Long Term Memory". Tech. report, available from https://nickbostrom.com/old/cortical.
Cohen L., G. et al. 1997. "Functional relevance of cross-modal plasticity in blind humans". Nature 389: 180-83.
de Garis, H. 1997. Home page. http://www.hip.atr.co.jp/~degaris/
Hameroff & Penrose http://psyche.cs.monash.edu.au/psyche-index-v2.html
Koch, C. 1997. "Computation and the single neuron". Nature 385: 207-10.
Moravec, H. 1998. "When will computer hardware match the human brain?" Journal of Transhumanism, vol. 1. At http://www.transhumanist.com
Moravec, H. 1997. http://www.frc.ri.cmu.edu/~hpm/book97/ch3/retina.comment.html
Morillo, C., R. 1992. "Reward event systems: reconceptualizing the explanatory roles of motivation, desire and pleasure". Phil. Psych. Vol. 5, No. 1, pp. 7-32.
Phillips W. A. & Singer W. 1997. "In Search of Common Foundations for Cortical Computations". Behavioural and Brain Sciences, 20, 657-722.
Quartz S. R. & Sejnowski T. J. 1997. "The neural basis of cognitive development: A constructivist manifesto". Behavioural and Brain Sciences, 20, 537-596.
Schlaggar, B. L. & O'Leary, D. D. M. 1991. "Potential of visual cortex to develop an array of functional units unique to somatosensory cortex". Science 252: 1556-60.
Sur, M. et al. 1988. "Experimentally induced visual projections into auditory thalamus and cortex". Science 242: 1437-41
White, E. L. 1989. Cortical Circuits: Synaptic Organization of the Cerebral Cortex. Structure, Function and Theory.


text:
Holy Synod
The Holy Synod is the ecclesiastical governing body for a church. It is presided over by a primate of a specific ecclesiastical area and consists, as members, of all the bishops to whom the primate is accountable. Holy Synod is used as a formal name for the body itself, as distinct from synod, which more often refers to the actual meetings of these bodies.
All autocephalous and some autonomous churches have their own church-wide holy synods, and there may also be local ones (often called eparchial synods), as well, especially if a particular church has territory spread out over a large area. Membership in the holy synod is determined by the traditions and canonical documents of a particular church. In some churches, all bishops who hold the title of metropolitan are considered members (e.g., the Church of Antioch), while in others, all active bishops—whether diocesan or auxiliary—are considered to be members (e.g., the Orthodox Church in America). Local synods typically consist of the primate and all diocesan bishops within the territory.
Holy Synod in this usage is distinct from an Ecumenical Council, which may also informally be called a holy synod and consists of bishops from throughout the Orthodox Church, acting together to deal with Church-wide issues.

subset: commoncrawl


text:
When unexpected behaviour is noticed in a program, GNU/Linux provides a wide
variety of command-line tools for diagnosing problems. The use of gdb
, the
GNU debugger, and related tools like the lesser-known Perl debugger, will be
familiar to those using IDEs to set breakpoints in their code and to examine
program state as it runs. Other tools of interest are available however to
observe in more detail how a program is interacting with a system and using its
Debugging with gdb
You can use gdb
in a very similar fashion to the built-in debuggers in modern
IDEs like Eclipse and Visual Studio. If you are debugging a program that you've
just compiled, it makes sense to compile it with its debugging symbols added
to the binary, which you can do with a gcc
call containing the -g
If you're having problems with some code, it helps to also use -Wall
to show
any errors you may have otherwise missed:
$ gcc -g -Wall example.c -o example
The classic way to use gdb
is as the shell for a running program compiled in
C or C++, to allow you to inspect the program's state as it proceeds towards
its crash.
$ gdb example
Reading symbols from /home/tom/example...done.
At the (gdb)
prompt, you can type run
to start the program, and it may
provide you with more detailed information about the causes of errors such as
segmentation faults, including the source file and line number at which the
problem occurred. If you're able to compile the code with debugging symbols as
above and inspect its running state like this, it makes figuring out the cause
of a particular bug a lot easier.
(gdb) run
Starting program: /home/tom/gdb/example
Program received signal SIGSEGV, Segmentation fault.
0x000000000040072e in main () at example.c:43
43 printf("%d\n", *segfault);
After an error terminates the program within the (gdb)
shell, you can type
to see what the calling function was, which can include the
specific parameters passed that may have something to do with what caused the
(gdb) backtrace
#0 0x000000000040072e in main () at example.c:43
You can set breakpoints for gdb
using the break
to halt the program's run
if it reaches a matching line number or function call:
(gdb) break 42
Breakpoint 1 at 0x400722: file example.c, line 42.
(gdb) break malloc
Breakpoint 1 at 0x4004c0
(gdb) run
Starting program: /home/tom/gdb/example
Breakpoint 1, 0x00007ffff7df2310 in malloc () from /lib64/ld-linux-x86-64.so.2
Thereafter it's helpful to step through successive lines of code using
. You can repeat this, like any gdb
command, by pressing Enter
repeatedly to step through lines one at a time:
(gdb) step
Single stepping until exit from function _start,
which has no line number information.
0x00007ffff7a74db0 in __libc_start_main () from /lib/x86_64-linux-gnu/libc.so.6
You can even attach gdb
to a process that is already running, by finding the
process ID and passing it to gdb
$ pgrep example
$ gdb -p 1524
This can be useful for redirecting streams of output for a task that is taking an unexpectedly long time to run.
Debugging with valgrind
The much newer valgrind can be used as a debugging tool in a similar way. There are many different checks and debugging methods this program can run, but one of the most useful is its Memcheck tool, which can be used to detect common memory errors like buffer overflow:
$ valgrind --leak-check=yes ./example
==29557== Memcheck, a memory error detector
==29557== Copyright (C) 2002-2011, and GNU GPL'd, by Julian Seward et al.
==29557== Using Valgrind-3.7.0 and LibVEX; rerun with -h for copyright info
==29557== Command: ./example
==29557== Invalid read of size 1
==29557== at 0x40072E: main (example.c:43)
==29557== Address 0x0 is not stack'd, malloc'd or (recently) free'd
The gdb
and valgrind
tools can be used together for a very thorough
survey of a program's run. Zed Shaw's Learn C the Hard Way includes a
really good introduction for elementary use of valgrind
with a deliberately
broken program.
Tracing system and library calls with ltrace
The strace
and ltrace
tools are designed to allow watching system calls and
library calls respectively for running programs, and logging them to the screen
or, more usefully, to files.
You can run ltrace
and have it run the program you want to monitor in this
way for you by simply providing it as the sole parameter. It will then give you
a listing of all the system and library calls it makes until it exits.
$ ltrace ./example
__libc_start_main(0x4006ad, 1, 0x7fff9d7e5838, 0x400770, 0x400760
srand(4, 0x7fff9d7e5838, 0x7fff9d7e5848, 0, 0x7ff3aebde320) = 0
malloc(24) = 0x01070010
rand(0, 0x1070020, 0, 0x1070000, 0x7ff3aebdee60) = 0x754e7ddd
malloc(24) = 0x01070030
rand(0x7ff3aebdee60, 24, 0, 0x1070020, 0x7ff3aebdeec8) = 0x11265233
malloc(24) = 0x01070050
rand(0x7ff3aebdee60, 24, 0, 0x1070040, 0x7ff3aebdeec8) = 0x18799942
malloc(24) = 0x01070070
rand(0x7ff3aebdee60, 24, 0, 0x1070060, 0x7ff3aebdeec8) = 0x214a541e
malloc(24) = 0x01070090
rand(0x7ff3aebdee60, 24, 0, 0x1070080, 0x7ff3aebdeec8) = 0x1b6d90f3
malloc(24) = 0x010700b0
rand(0x7ff3aebdee60, 24, 0, 0x10700a0, 0x7ff3aebdeec8) = 0x2e19c419
malloc(24) = 0x010700d0
rand(0x7ff3aebdee60, 24, 0, 0x10700c0, 0x7ff3aebdeec8) = 0x35bc1a99
malloc(24) = 0x010700f0
rand(0x7ff3aebdee60, 24, 0, 0x10700e0, 0x7ff3aebdeec8) = 0x53b8d61b
malloc(24) = 0x01070110
rand(0x7ff3aebdee60, 24, 0, 0x1070100, 0x7ff3aebdeec8) = 0x18e0f924
malloc(24) = 0x01070130
rand(0x7ff3aebdee60, 24, 0, 0x1070120, 0x7ff3aebdeec8) = 0x27a51979
--- SIGSEGV (Segmentation fault) ---
+++ killed by SIGSEGV +++
You can also attach it to a process that's already running:
$ pgrep example
$ ltrace -p 5138
Generally, there's quite a bit more than a couple of screenfuls of text
generated by this, so it's helpful to use the -o
option to specify an output
file to which to log the calls:
$ ltrace -o example.ltrace ./example
You can then view this trace in a text editor like Vim, which includes syntax
highlighting for ltrace
I've found ltrace
very useful for debugging problems where I suspect improper
linking may be at fault, or the absence of some needed resource in a chroot
environment, since among its output it shows you its search for libraries at
dynamic linking time and opening configuration files in /etc
, and the use of
devices like /dev/random
or /dev/zero
Tracking open files with lsof
If you want to view what devices, files, or streams a running process has open,
you can do that with lsof
$ pgrep example
$ lsof -p 5051
For example, the first few lines of the apache2
process running on my home
server are:
# lsof -p 30779
apache2 30779 root cwd DIR 8,1 4096 2 /
apache2 30779 root rtd DIR 8,1 4096 2 /
apache2 30779 root txt REG 8,1 485384 990111 /usr/lib/apache2/mpm-prefork/apache2
apache2 30779 root DEL REG 8,1 1087891 /lib/x86_64-linux-gnu/libgcc_s.so.1
apache2 30779 root mem REG 8,1 35216 1079715 /usr/lib/php5/20090626/pdo_mysql.so
Interestingly, another way to list the open files for a process is to check the
corresponding entry for the process in the dynamic /proc
# ls -l /proc/30779/fd
This can be very useful in confusing situations with file locks, or identifying whether a process is holding open files that it needn't.
Viewing memory allocation with pmap
As a final debugging tip, you can view the memory allocations for a particular
process with pmap
# pmap 30779
30779: /usr/sbin/apache2 -k start
00007fdb3883e000 84K r-x-- /lib/x86_64-linux-gnu/libgcc_s.so.1 (deleted)
00007fdb38853000 2048K ----- /lib/x86_64-linux-gnu/libgcc_s.so.1 (deleted)
00007fdb38a53000 4K rw--- /lib/x86_64-linux-gnu/libgcc_s.so.1 (deleted)
00007fdb38a54000 4K ----- [ anon ]
00007fdb38a55000 8192K rw--- [ anon ]
00007fdb392e5000 28K r-x-- /usr/lib/php5/20090626/pdo_mysql.so
00007fdb392ec000 2048K ----- /usr/lib/php5/20090626/pdo_mysql.so
00007fdb394ec000 4K r---- /usr/lib/php5/20090626/pdo_mysql.so
00007fdb394ed000 4K rw--- /usr/lib/php5/20090626/pdo_mysql.so
total 152520K
This will show you what libraries a running process is using, including those in shared memory. The total given at the bottom is a little misleading as for loaded shared libraries, the running process is not necessarily the only one using the memory; determining "actual" memory usage for a given process is a little more in-depth than it might seem with shared libraries added to the picture.


text:
Asian-Pacific American Heritage Month
May is the month to celebrate the heritage of Asians and Pacific Islanders in the United States. The cultures, history, religion, architecture, and art of the continent of Asia are well represented in the Artstor Digital Library, and you can find a full guide in our Artstor Is… Asian Studies post; resources for Asian-Pacific content are also plentiful, but scattered throughout many collections and require a little more diligence.
A quick way to find content in the Digital Library from a specific country is by going to the Browse area in the lower left corner of the search page and clicking Geography. Considering that Asia-Pacific encompasses the Pacific islands of Melanesia (Fiji, New Caledonia, New Guinea, the Solomon Islands, and Vanuatu), Micronesia (Guam, Kiribati, Marianas, Marshall Islands, the Federated States of Micronesia, Nauru, Palau, and Wake Island), and Polynesia (American Samoa and Samoa, Cook Islands, Easter Island, French Polynesia, Hawaiian Islands, Midway Island, New Zealand, Rotumas, Tonga, and Tuvalu), this might be a little time consuming, so here are some hints:
The main repositories of Asian-Pacific images in Artstor include the Metropolitan Museum of Art, which features art and artifacts from many of the regions listed above, the Peabody Museum of Natural History (Yale University), which has archaeological and ethnographic objects, the Peabody Museum of Archaeology and Ethnology (Harvard University), which has anthropological objects, and Magnum Photos, which includes contemporary photographs of New Guinea by Burt Glinn and Philip Jones Griffiths, of the Marshall Islands by Chris Steele-Perkins, Samoa by Alex Webb, the Cook Islands by Trent Parke, and Easter Island by Thomas Hoepker.
Also of note is Cook's Voyages to the South Seas (Natural History Museum, London), which includes 1,600 images of botanical and zoological illustrations associated with Captain James Cook's expeditions to the South Pacific in the 18th century, and Thomas K. Seligman: Photographs of Liberia, New Guinea, Melanesia and the Tuareg people which, as its title states, includes field photography of New Guinea and Melanesia. Also fruitful, The Native American Art and Culture (National Anthropological Archives, Smithsonian Institution) includes a dozen fascinating photographs of Fiji in 1900 by Charles Haskins Townsend, and the Fowler Museum, the Dallas Museum of Art Collection, the Saint Louis Art Museum, and the Smith College Museum of Artall include art and artifacts from different cultures in Asia Pacific.
And don't miss Re-historicizing Contemporary Pacific Island Art by Marion Cadora, a graduate student at the University of Hawaii at Manoa.
Enjoy the celebrations and don't forget to visit the Library of Congress' official Asian-Pacific American Heritage Month site!


text:
Not really sure where to bring this up but I was watching the pending-edit queue and noticed a user by an eponymous handle retrospectively tagging some seemingly contrived questions with the following:
ithit-webdav-server ithit-ajax-file-browser - some example posts: 1, 2, 3, 4, 5
... notably all questions by the same user or users with low rep - which might go some way to explaining the unusual up-votes given the poor quality of the original posts. "IT Hit" is also a commercial entity.
Other than flagging these questions for being off-topic / not following the guidelines - what's the procedure here? Should I be untagging these posts? Would it generally be acceptable if they were of higher quality?


text:
Welcome to the home of the Portuguese Radio Network — serving 400,000 Portuguese and those of Portuguese descent in northern and central California. KLBS 1330 AM serves the agricultural San Joaquin Valley and beyond, from Stockton in the north to Bakersfield in the south. Broadcasting from Los Baños, KLBS teams up with its sister station, KSQQ 96.1 FM, to serve the largest Portuguese community in the United States.
Portuguese is the third most spoken western language, behind English and Castilian Spanish |
The Portuguese Radio Network draws upon more than thirty years of experience serving its listening audience, dating back to to former KRVE 95.3 in 1974. KLBS was acquired in 1982 and KSQQ came online in 1990. KSQQ broadcasts from the Capital of Silicon Valley, San José, where Portuguese immigrants and their descendants dominate the construction trades; whereas in the San Joaquin Valley, the Portuguese control as much as 60% of the largest dairy industry in the world.
Portuguese is the third most-spoken western language, after English and Castilian Spanish, and fifth overall in the world today. California has prospered from Portuguese immigration since the mid-1800's. Portuguese-speaking immigrants came from Portugal, the Azores, Madeira, Macau, Brazil, and a dozen other countries and island groups where Portuguese is the primary language spoken.
Realize the value of broadcasting your messages to this economic powerhouse — the Portuguese community of California. Join us today!


text:
You are Here: Zante Hotels Zakynthos
Enjoy your nightlife in Zakynthos during your stay. Explore bars and clubs located in the most famous Zante resorts like Laganas, Argassi, Tsilivi and Kalamaki. Watch videos and read the latest tweets about nightlife in Zakynthos Zante island.
Zakynthos (Zante) island has a great number of beaches. They are, either organized like the famous Laganas Beach or Tsilivi Beach or Kalamaki Beach and Vassilikos Beach or secluded paradises like the famous Shipwreck (Navagio) Beach or Gerakas Beach or Xigia Beach. You may find all of them in Zante Island, including many private beaches and beaches of high ecological importance. Find out more:


text:
Any plan can sound good on paper, but what about when life happens — sometimes unexpectedly?
There's no age too young to start learning about responsible financial management.
Crunch the numbers for your home and personal financing, investing and retirement.
— No matter what stage of life you're in, your credit union can make the ride a little smoother. —
There's a first time for everything, but that doesn't mean you have to go unprepared. Here are a few firsts Securityplus can help you with.
So you're ready to make one of the biggest commitments of your life? We're experts at financing the big day expenses — and beyond.
No matter how much you plan, things can still go wrong. During times of unexpected hardship, your credit union is still willing to help.
Whether a new baby or a new pet, a growing family means growing expenses. We can help you prepare for those changes in your budget.
Whether you're looking to vacation or you're relocating, Securityplus wants to make sure finances don't stand in your way.
You've spent most of your life preparing to retire. We can help ensure that you are financially equipped to do so comfortably.


text:
The ID for this specific file is: 27063 >> Download
The ID can be used for searching/referencing this particular file directly. ID does not otherwise have academic significance and can only be found above.
If searching for a particular course it is better to use department and course number. Department code and course number are combined and shown under "Course" below.


text:
Please enter keywords for any or all of the following fields to search the Membership Directory. You must fill in at least one field.
For searches based on the area (city, county, or state) serviced by the ALTA member company or branch visit HomeClosing101.org.
You can also look up a state land title association, and search our ALTA Marketplace to find member companies to meet your business needs.
Please Note: The information in this membership directory is provided for the purpose of connecting title professionals, attorneys, real estate agents, bankers, mortgage brokers (and others in the land evidencing and settlement services industries) and consumers with ALTA® members for legitimate business purposes. ALTA® does not condone or approve of companies or individuals extracting information from this membership directory to build their own marketing database or to conduct wholesale (spam) marketing activities. Please be aware that any company or individual that violates this policy is subject to the Telephone Consumer Protection Act [TCPA] [47 U.S.Code Section 227] which prohibits using fax machines, computers or other devices to send unsolicited advertisements to a fax machine and which provides what can amount to substantial penalties for its violation.


text:
Sophocles, Antigone
n his long life, Sophocles (born ca. 496 B.C., died after 413) wrote more than one hundred plays. Of these, seven complete tragedies remain, among them the famed Oedipus Rex and Oedipus at Colonus. In Antigone, he reveals the fate that befalls the children of Oedipus. With its passionate speeches and sensitive probing of moral and philosophical issues, this powerful drama enthralled its first Athenian audiences and won great honors for Sophocles.
The setting of the play is Thebes. Polynices, son of Oedipus, has led a rebellious army against his brother, Eteocles, ruler of Thebes. Both have died in single combat. When Creon, their uncle, assumes rule, he commands that the body of the rebel Polynices be left unburied and unmourned, and warns that anyone who tampers with his decree will be put to death.
Antigone, sister of Polynices, defies Creon's order and buries her brother, claiming that she honors first the laws of the gods. Enraged, Creon condemns her to be sealed in a cave and left to die. How the gods take their revenge on Creon provides the gripping denouement to this compelling tragedy, which remains today one of the most frequently performed of classical Greek dramas.


text:
Must-Have New Arrivals
Fuel Your Fandom with New Styles
Her Universe is a groundbreaking fangirl fashion company and lifestyle brand founded by actress and entrepreneur Ashley Eckstein, whose mission is to create stylish, fashion-forward merchandise for female sci-fi fans. Hoping to change the perception that science fiction and fantasy is just for boys, Her Universe is the place for fangirls to step into the spotlight and be heard, recognized and rewarded. Her Universe has joined forces with some of the biggest names in the sci-fi/fantasy world to create fangirl apparel and accessories for such well-known names as Star Wars, Doctor Who, Star Trek and Marvel. Today, Her Universe apparel and accessories – including tees, cardigans, jackets, dresses, skirts, jewelry and more – are available retailers nationwide, as well as online at heruniverse.com. Shop now.
*For a limited time only. Restrictions apply. See offer details.


text:
Journal of Gender, Society, Policy & Law I (Jr. Staff) (LAW-800F-002)
NOTE: Effective Fall 2020, all journals have new course numbers and sections. The three-digit number for the journal will remain the same, but the fall courses have the letter "F" to designate the term. The section number will now correspond to the number of credits earned in the semester, e.g., section 001 = 1 credit, section 002 = 2 credits, etc. Spring course numbers will be renumbered similarly, with the letter "S" in the course number to denote spring courses. If you have already added a previous semester's journal number to your plan for fall 2020, you will have to remove it and add the correct course and section. Make sure you are looking for course numbers with the letter "F for fall.
N/A. Please contact the instructor for further information.
Textbooks and Other Materials
The textbook information on this page was provided by the instructor. Students should use this information when considering purchases from the AU Campus Store or other vendors. Students may check to determine if books are currently available for purchase online.
First Class Readings
Not available at this time.


text:
Winter Sleigh Ride by Rodel Gonzalez
Mickey and Minnie enjoying a winter day on their sleigh.
Artwork inspired by Walt Disney Studios' 1928/1942 most famous animated classic film characters of all time – Mickey and Minnie Mouse.
Disney Limited Edition Collection
- Giclee on Canvas
- Edition Size: 195
- Image Size: 32" x 16"
- Hand-numbered and signed by artist - Rodel Gonzalez
- Studio Embellished, which adds hand-painted accents to the canvas
- Certificate of Authenticity.
Option: Rolled, Stretched, or *Gallery Wrapped
*Best Seller: Gallery Wrap is ready to hang. The back of the artwork is affixed with a wire for easy wall hanging. (wall hook not included)
There's a reason Disney's movies resonate with so many—the stories are rich with character, inspiration and so much more. The talented artists behind our Disney Art Collection have recreated those ever-lasting stories in each painting for us to enjoy and treasure for years to come.
- Premium Canvas
- Gicleé canvas print using archival inks and materials
- Long-life pigment inks that outperform conventional press inks.
- Made in the USA
Free Shipping
Free standard shipping with a purchase of $149 or more on all orders in the United States (excluding PO Boxes, Alaska and Hawaii).
All art is carefully handled, inspected and hand-package. Unframed canvases and lithographs will ship in a reinforced tube. Unframed cels will be packaged and shipped in a flat box.
International Shipping
We ship to Australia, Canada, China, Germany, Hawaii, Japan, and the UK. Please contact us if you live in another country not listed.
You may be required to pay duties and taxes to claim your package, depending on your local customs office.
All items shipped are fully insured.
14-day returns. Return policy exclusions apply. Learn more.
Damaged Items:
We inspect each artwork before it is shipped. If, for any reason, your artwork shows up in other-than-perfect condition, contact us within 48 hours of receipt, and we will repair or replace framing and artwork at no additional charge.
Genuine & Authentic
All artwork is officially licensed and shipped with a Certificate of Authenticity.


text:
The safe, secure and completely free way to transfer large files around the Web.
It is a classic problem: you need to send files to someone, or they need to send them to you, and there's no way except email. But they are too large or your administrator won't let you transfer the files by email at all. ZendTo to the rescue!
ZendTo is a completely free web-based system, which you can run on your own server with complete safety and security. It runs from any Linux / Unix server or virtualisation system, there is no size limit and it will send files 50% faster than by email.
Technical support is provided by email or an active mailing list. All completely free.


text:
Demonstrators protesting the shooting death of an unarmed black man said to be "mentally perturbed" gathered in El Cajun for a second night of mostly peaceful protests, chanting slogans and holding signs.
Several hundred people took to the streets of the San Diego suburb, marching from the shooting scene to City Hall and back, shouting Alfred Okwera Olango's name, taunting police and periodically blocking traffic.
The 28-year-old refugee from the Uganda was fatally shot by police after allegedly taking a "shooting stance" while holding an object during a confrontation with officers in shopping center's parking lot, El Cajon police said. Police later confirmed Olango was holding a vape smoking device.
The crowd staged a boisterous but peaceful rally near the site of Olango's death at the Broadway Plaza Shopping Center on Broadway, chanting "No justice, no peace," and holding "Black Lives Matter" signs.
A tense situation unfolded earlier Wednesday evening after some sort of scuffle prompted the crowd to scatter, and forced police to call in back up.
Officers in riot gear could be seen forming lines and blocking off streets, but mostly kept their distance. Demonstrators milled about the streets, but the mood grew more relaxed and the crowds gradually diminished.
Protests against the police shooting began earlier in the day, when crowds gathered and walked in between cars traveling in the opposite direction along Broadway.
Some protesters cursed as they marched while others screamed, "No violence! No violence!"
A dozen San Diego County Sheriff's deputies stood in a line across Broadway between Ballantyne Street and Mollison Avenue, in an apparent attempt to block protesters from getting on to State Route 67.
One woman, who was protesting with her son, told NBC 7 that she worries about him because he's black and she's white.
"It's just to bring attention to what's going on," Sara Bennett said. "I worry about him every day because of what's going on and how is it, they see me as a white woman and him as a black man. Its not fair, we need to be together."
Her son, Josiah, said he was out in the streets because he was tired of his mom worrying about him whenever he left the house.
"I shouldn't have to worry if I'm white, brown, black, purple, green," he said. "Doesn't matter. I want to feel safe and hang out and do what I want without police looking at me because I'm a certain color."
Protesters also held a candlelight memorial for Olango at the shooting scene on Broadway. They said his name multiple times and held a moment of silence before heading to the street for a sit-in.
Parkway Plaza in El Cajon, on the 400 block of Fletcher Parkway off Interstate 8, temporarily closed their doors Wednesday because of the protesters.
"By request of local authorities and out of an abundance of caution, Parkway Plaza will be temporarily closed due to demonstrations in the area. We will let you know when we are scheduled to reopen," the Plaza said in a statement on their Facebook page.
El Cajon Police said the mall chose to close on its own.
On Tuesday, officers with the El Cajon Police Department (ECPD) shot and killed Alfred Olango, a Ugandan refugee, during an encounter in the parking lot of a shopping center in the 800 block of Broadway. Police said Olango was reported to be "acting erratically" and did not follow orders to remove his hands from the pockets of his pants.
Police said at one point he pulled an object from his pants and pointed it at officers in a "shooting stance." Authorities later revealed the object was a vaping device.
The killing of Olango sparked outrage in the community amid racial tensions nationwide stemming from deadly police shootings of unarmed black men.
An NBC 7 news crew captured one confrontation between a protester and a sheriff's deputy.
"Pigs! Be scared! [You're all] in riot gear because you're all f---ing cowards!" the protester said.
Suddenly a woman nearby chimed in, directing her comment to the deputy, "No, you guys aren't. You guys are doing your jobs."
"You may not be part of it, but your buddies are," another man shouted at law enforcement officers. "It's time to expose everybody."
A man wearing a shirt with the words #BlackLives tried to walk across the line formed by law enforcement but was quickly stopped by a deputy.
The man shouted that he was trying to get to his son on the other side of the line.
"Stop! Go back!" the deputy told the man.
"Are you kidding me?" the man asked the deputy. "You wonder why we gotta problem with you all? Are you serious?"
"Is not following police orders a justification for killing? This is Murder," said Christopher Rice-Wilson, the Associate Director at Alliance San Diego who was one of the protesters.
Olango emigrated to the U.S. in 1991 when he was 12 years old.


text:
This Privacy Policy describes the way in which, Morandin Estates Pty Ltd (The Wine Crate) will handle personal information that you provide to us through this website or through any other form of communication with us, our agents and contractors.
Morandin Estates Wines is bound by the National Privacy Principles (NPPs) comprised in Schedule 3 to the Privacy Act 1988 (Cth). Our Privacy Policy may change from time to time and any variation will be reflected in this Policy.
Before using our website or providing any personal information to us, please ensure you have read this entire Privacy Policy and checked it for any changes.
This Privacy Policy applies to any personal information of yours which we collect or that is otherwise provided to us. Personal information is information or an opinion, whether true or not and whether recorded in a material form or not, about you where your identity is apparent, or can reasonably be ascertained, from the information or opinion.
The type of personal information we collect may include (but is not limited to):
your name;
date of birth – please note it is against Liquor Laws to sell or supply alcohol to, or obtain alcohol on behalf of, a person under the age of 18 years. In order to process any orders placed through our website, we may ask you to provide additional details or require you to verify your age;
telephone number;
email address;
information about your wine preferences.
Additionally, as you use this website, certain information is also collected through various technologies, such as cookies, internet tags or web beacons and navigational data collection. This information is collected anonymously and is used to provide improved services to website users, customise the website based on your preferences, compile and analyse statistics and trends and otherwise administer and improve the website for your use.
We collect your personal information in order to provide the goods and services that you have requested from us to you. For example, when using this website, we collect your personal information in order to process and administer your order and related purposes.
If you do not provide some or all of the personal information we request, we may not be able provide you with our goods and services.
We may also use your personal information to send you marketing materials from time to time about our other products and services (including by email, direct mail, telemarketing, SMS and MMS).
In order to do these things, we may disclose your personal information to third parties, including:
our related companies; or
outsourced service providers, such as data processors, warehouse providers, mailing houses, and delivery service providers.
By providing your personal information to us, you consent to us using and disclosing your personal information in order to send you marketing materials about our other products and services. However, you may opt-out of receiving such marketing materials any time by emailing us at email@example.com
Personal information that you provide with respect to your wine orders is processed by our service provider in United States of America. By making an order, you consent to us transferring your personal information to our service provider in United States of America for that purpose.
Personal information that you provide to us with respect to your membership and wine orders may be processed through service providers based outside Australia. By becoming a member or making an order, you consent to us transferring your personal information to our service providers worldwide for that purpose, using reasonable security measures.
We will generally provide you with access to any of your personal information we hold (subject to the exceptions that apply under the NPPs).
Before we provide you with access to your personal information we will require some proof of identity. For most requests, your information will be provided free of charge.
If you would like:
to request access to your personal information;
to make a complaint about the way we have handled your personal information;
further information about the way that we manage personal information that we hold; or
would like to update (for example, if you have changed your address)
Please contact the Office on +61 412 447 466 or email email@example.com


text:
Over at PrawfsBlawg, John Pfaff (Fordham) provides a cautionary reminder that most empiricists cannot hear often enough. Re-defining key variables can influence results and re-defined variables are frequently difficult to detect, particularly in large, complex, longitudinal datasets. That is, too often, secondary analyses are undertaken without necessary due-diligence involving the underlying data. Among Pfaff's take-aways:
"If nothing else, this is a strong warning against casually running empirical models, a growing problem in legal scholarship. Legal academics shouldn't just get their IT departments to install Stata on their computers, download some data, and then start running some regressions. It can take years to fully understand what a dataset looks like, what it is really measuring, its strengths and weaknesses. People who just run some quick regressions and then send them off to a law review are likely moving knowledge backwards, not forwards, since the risk of bad results is too great."


text:
Other Community Events Planned To Raise Awareness About Hearing:
World Gallery Fine Art Presents: The Perfect Wave-- Art auction and raffle to benefit the Surfrider Foundation's Save Trestles. September 5, 2008 6-11pm. 1980 Placentia Ave. Costa Mesa, CA. 92627
Paddle for Clean Water: Both the Washington DC and San Diego Chapters are using their Paddle events to raise awareness about the toll road hearing. click here to see the happenings in San Diego, and click here for DC Paddle.
Community Organizing Meeting: Join Surfrider and Sierra Club a few days before the hearing to learn more about meeting procedures and the best way to have a successful hearing. Time and Location TBD.
Jack Doing his Own Community Awareness


text:
Scoped type variables
Scoped Type Variables are an extension to Haskell's type system that allow free type variables to be re-used in the scope of a function.
The GHC Users Guide has a Scoped Type Variables section.
As an example, consider the following functions:
{-# LANGUAGE ScopedTypeVariables #-}
mkpair1 :: forall a b. a -> b -> (a,b)
mkpair1 aa bb = (ida aa, bb)
ida :: a -> a -- This refers to a in the function's type signature
ida = id
mkpair2 :: forall a b. a -> b -> (a,b)
mkpair2 aa bb = (ida aa, bb)
ida :: b -> b -- Illegal, because refers to b in type signature
ida = id
mkpair3 :: a -> b -> (a,b)
mkpair3 aa bb = (ida aa, bb)
ida :: b -> b -- Legal, because b is now a free variable
ida = id
Scoped type variables make it possible to specify the particular type of a function in situations where it is not otherwise possible, which can in turn help avoid problems with the Monomorphism restriction.
This feature should be better documented in the Wiki, but this is a start.
Avoiding Scoped Type Variables
Although Scoped Type Variables are often a simple solution, they are not available in all compilers. Often there is a solution that is Haskell 98. First, there is
asTypeOf :: a -> a -> a
asTypeOf a b = a
It is used like x `asTypeOf` y
and has the same value like x
but type inference asserts that x
and y
have the same type.
Sometimes it helps to divide a big function into smaller ones and give each of the small functions a signature. This also helps reading the program.
If this does not help, too, then use a helper function. E.g. if you want to determine the size of an object a pointer points to, then you might define a function like
sizeOfPtr :: Ptr a -> Int
sizeOfPtr = sizeOf . (undefined :: Ptr a -> a)
sizeOfPtr :: Ptr a -> a -> Int
sizeOfPtr _ a = sizeOf a
sizeOf :: Ptr a -> Int
sizeOf ptr = sizeOfPtr ptr undefined
See also
- Haskell-Cafe on sizeOf on a type
- Haskell-Cafe on What is a rigid type variable?
- Haskell-Cafe on asserting the type of a binding in a "do" expression


text:
Caring for groups of livestock and groups of young children share many similarities when it comes to disease prevention and control. I am reminded of this a week after Hand, Foot, and Mouth Disease (HFMD) came home with our daughter for the third time since August. Since they often inhabit the same spaces, they eat, drink, and play together. Both young children and livestock taste surfaces while exploring their environments. It's wonderful for developing social skills and also wonderful for spreading pathogens. Neither toddlers or livestock can effectively wash their bodies after every encounter with an infected individual or contaminated surface. As caregivers, we have to do our best to prevent disease from entering the system, because once it is there, control becomes increasingly challenging. Some illnesses can be treated effectively with antibiotics, but the more we use antibiotics, the greater resistance is built within the bacterial population. Not to mention, that viruses (like HFMD) cannot be treated with antibiotics. Given all this, the best way to fight illness is through prevention.
Beginning on January 1, 2017 Veterinary Feed Directives (VFDs) will be required for use of any fed antibiotics for livestock that are also medically important for humans. A VFD is similar to a prescription, but does not need to be filled by a pharmacist, only approved by your veterinarian. Feed stores can continue to sell feeds and minerals containing antibiotics, but the seller must have a current VFD to buy them. Antibiotic feeds have been used for years as ways to prevent and treat bacterial illnesses in livestock and this has helped improve herd health. In conjunction, antibiotic feeds have been used unethically by some parties to promote weight gain or to compensate for sub-par management practices. Research has shown and concluded that overuse of antibiotics increases resistance to their effectiveness in the long run. Therefore, it is important for human and animal health to only use antibiotics when disease is a present threat (not just suspected) and in an ethical manner.
There are many ways to stop disease before it starts and they have been identified for livestock producers in quality assurance (QA) guidelines. To quote the Good Production Practices (GPP) factsheet, "It is every animal owner's responsibility to assure that proper management and welfare are at the core of animal care." There are ten core GPPs:
- Use an appropriate veterinarian/client/patient relationship as the basis for medication decision-making.
- Establish and implement an efficient and effective health management plan.
- Use antibiotics responsibly.
- Properly store and administer animal health products.
- Follow proper feed processing protocols.
- Establish effective animal identification, medication records and withdrawal times.
- Practice good environmental stewardship.
- Maintain proper workplace safety.
- Provide proper animal care.
- Utilize tools for continuous improvement.
These are the core guidelines for herd health. Inevitably, disease will still get through our barriers on occasion. When it does consult your veterinarian about how to treat the herd, whether it be with medication, isolation of infected animals, or improved practices. The best things you can do in preparation for VFD implementation in 2017 is to establish and maintain a relationship with your veterinarian and follow QA guidelines.


text:
How I Live Now ROSO 1
Omschrijving: ""Every war has turning points and every person too.""
Fifteen-year-old Daisy is sent from Manhattan to England to visit her aunt and cousins she's never met: three boys near her age, and their little sister. Her aunt goes away on business soon after Daisy arrives. The next day bombs go off as London is attacked and occupied by an unnamed enemy.
As power fails, and systems fail, the farm becomes more isolated. Despite the war, it's a kind of Eden, with no adults in charge and no rules, a place where Daisy's uncanny bond with her cousins grows into something rare and extraordinary. But the war is everywhere, and Daisy and her cousins must lead each other into a world that is unknown in the scariest, most elemental way.
A riveting and astonishing story.
vanaf ca. 14 jaar


text:
Garth Knox, viola & fiddle
with Agnès Vesterman, cello & Sylvain Lemêtre, percussion
ECM Records CD 2157
Dance music in multiple forms, from the saltarello, a Venetian dance dating back to the Fourteenth century, to Breton and Celtic folk music, as well as transcriptions of medieval era compositions, Renaissance era consort music, and contemporary fare, are featured on Saltarello, violist Garth Knox's latest ECM CD. Among the early music slections, Particularly impressive is a Vivaldi concerto, performed in a duo arrangement for viola d'amore and cello. Its interpreters, Knox and Agnès Vesterman, take this continuo less opportunity to accentuate a supple contrapuntal interplay between soloist and bass line. Equally lovely is a piece that combines music by Hildegard and Machaut in a kind of medieval style mash-up. Also stirring is this duo's version of John Dowland's most famous piece, Lachrimae, perhaps known best in its incarnation as the song "Flow My Tears."
Knox, who is a past member of both Ensemble Intercontemporain and the Arditti String Quartet, also performs the disc's newer material with consummate musicality: he also has the bedeviling habit of making virtuosic writing sound far too easy to play (his poor violist colleagues!). Knox's own composition, "Fuga Libre," combines jazz rhythms and neo-baroque counterpoint with ever more complicated harmonic tension points and several instances in which Knox demonstrates various extended playing techniques. Meanwhile, Kaaija Saariaho's Vent Nocturne, an eerily evocative and tremendously challenging piece for viola and electronics, is given a haunting, sonically sumptuous rendering.
Tomorrow night, Knox celebrates the release of the CD at LPR (details below). Early music, new pieces by and for Knox, and lovely comestibles on menu and on tap? Sounds like my evening's planned!
Event Details
Tuesday May 22nd – Doors open at 6:30, show starts at 7:30
158 Bleecker Street, NYC| 212.505.FISH