I'll forewarn that I'm no string theorist and Susskind's work is not therefore fully wonted to me (and likely I couldn't understand it if it were) so I do not fully know the context (of the supposed quote that entropy is hidden information).
But what he maybe means by "hidden" information is one or both of two things: the first theoretical, the second practical:
- The Kolmogorov complexity $K(\Omega)$ for a given system $\Omega$ (more precisely: the complexity of the system's unambiguous description) is in general not computable. $K(\Omega)$ is related to the concept of Shannon entropy $S_{Sha}(\Omega)$ (see footnote);
- Both a system's Kolmogorov complexity and Shannon entropy are masked from macroscopic observations by statistical correlations between microscopic components of the systems: thermodynamic systems the measurable entropy $S_{exp}(\Omega)$ (which is usually the Boltzmann) equals the true Shannon entropy $S_{Sha}(\Omega)$ plus any mutual information $M(\Omega)$ (logarithmic measure of statistical correlation) between the system's components: $S_{exp}(\Omega)= S_{Sha}(\Omega) + M(\Omega)$
Hopefully the following explanations will show you why these ideas of "hidden" are in no way related to being "destroyed" or even "unrecoverable".
A system's Kolmogorov complexity is the size (wontedly measured in bits) of the smallest possible description of the system's state. Or, as user @Johannes wonderfully put it: it's the minimum number of yes / no questions one would have to have answered to uniquely specify the system. Even if you can unambiguously and perfectly describe a system's state, there is in general no algorithm to decide whether a more compressed description can be equivalent. See the discussion of the Uncomputability theorem for a Kolmogorov complexity on Wikipedia for example. So in this sense, the true entropy of a thing is hidden from an observer, even though the thing and a perfect description of it are fully observable by them.
So much for the hiddenness of the entropy (quantity of information). But what of the information itself? Uncomputability of Kolmogorov complexity bears on this question too: given that the amount of entropy describing a system state is uncomputable, there is in general no way of telling whether that system's state has been reversibly encoded into the state of an augmented system if our original system merges with other systems: otherwise put in words more applicable to black holes: there is no algorithm that can tell whether our original system's state is encoded in the state of some other system that swallows the first one up.
For a discussion on the second point i.e. how the experimentally measured entropy and the Kolmogorov complexity differ, please see my answer here I also discuss there why information might not be destroyed in certain simple situations, to wit: if the relevant laws of physics are reversible, then
The World has to remember in some way how to get back to any state it has evolved from (the mapping between system states at different times is one-to-one and onto).
This is a more general way of putting the unitary evolution description given in other answers.
Afterword: Charles Bennett in his paper "The thermodynamics of computation-a review" puts forward the intriguing and satisfying theory that the reason that physical chemists can't come up with a failsafe algorithm for calculating entropies of the molecules they deal with is precisely this uncomputability theorem (note that there does not rule out algorithms for certain specific cases, so the theorem can't prove that's why physical chemists can't calculate entropies, but it's highly plausible in the same sense that one could say that one reason why debugging software is a hard problem is Turing's undecidability of the halting problem theorem).
Footnote: Shannon entropy is a concept more readily applicable to systems which are thought of as belonging to a stochastic process when one has a detailed statistical description of the process. In contrast Kolmogorov complexity applies more to "descriptions" and one must define the language of the description to fully define $K(\Omega)$. Exactly how they are related (or even if either is relevant) in questions such as those addressed in the black hole information paradox is a question whose answer probably awaits further work beyond physics community "views" (as put in another answer) about whether or not information outlives the underlying matter and energy thrown into a black hole.
Another footnote (26th July 13): See also the Wikipedia page on the Berry Paradox, and a wonderful talk by Gregory Chaitin called "The Berry Paradox" and given at a Physics - Computer Science Colloquium at the University of New Mexico. The Berry Paradox introduces (albeit incompletely, but in everyday words) the beginnings of the ideas underlying Kolmogorov Complexity and indeed lead Chaitin to his independent discovery of the Kolmogorov Complexity, even though the unformalised Berry Paradox is actually ambigious. The talk also gives some poignant little examples of dealing personally with Kurt Gödel.
Edit 2nd August 2013 Answers to Prathyush's questions:
I could not understand the connection between thermodynamic entropy and kolmogorov complexity, Please can you comment on that. Esp the part "So in this sense, the true entropy of a thing is hidden from an observer, even though the thing and a perfect description of it are fully observable by them. " If you know the exact state of the system, then in physics entropy is zero, whether we can simplify the description does not come into picture
First let's try to deal with
If you know the exact state of the system, then in physics entropy is zero, whether we can simplify the description does not come into picture
Actually, whether or not there is possible simplification is central to the present problem. Suppose our description of our system $\Omega$ is $N_\Omega$ bits long. Moreover, suppose we have worked very hard to get the shortest full description we can, so we hope that $N_\Omega$ is somewhere near the Kolmogorov complexity $K(\Omega) < N_\Omega$. Along comes another "swallower" system $\Sigma$, which we study very carefully until we have what we believe is a full description of $\Sigma$, which is $N_\Sigma$ bits long. Again, we believe that $N_\Sigma$ is near $\Sigma$'s Kolmogorov complexity $K(\Sigma) < N_\Sigma$ The swallower $\Sigma$ absorbs system $\Omega$ - so the two systems merge following some physical process. Now we study our merged system very carefully, and find that somehow we can get a full description whose length $N_{\Omega \cup \Sigma}$ is much shorter than $N_\Omega + N_\Sigma$ bits long. Can we say that the merging process has been irreversible, in the sense that if we ran time backwards, the original, separated $\Omega$ and $\Sigma$ would not re-emerge? The point is we cannot, even if $N_{\Omega \cup \Sigma} \ll N_\Omega + N_\Sigma$. Why? Because we can never be sure that we truly did find the shortest possible descriptions of $\Omega$ and $\Sigma$. There is no way of telling whether $K(\Omega) = N_\Omega, K(\Sigma) = N_\Sigma$.
Ultimately what it being driven at here is the question of whether time evolutions in physics are one-to-one functions, i.e. given an ending state for a system, does this always unambiguously imply a unique beginning state? Our great central problem here is, forgive some floridity of speech, that we do not know how Nature encodes the states of her systems. Figuratively speaking, the coding scheme and codebook are what physicists make their business to work out. Kolmogorov Complexity, or related concepts, are presumed to be relevant here because it is assumed that if one truly knows how Nature works, then one knows what the maximally compressed (in the information theoretic sense) configuration space for a given system is and thus the shortest possible description of a system's state is a number that names which of the points in the configuration space a particular system is at. If the number of possible points in the ending configuration space - the ending Kolmogorov complexity (modulo an additive constant) - is less than the number of possible points in the beginning space, then we can say in general the process destroys information because two or more beginning states map to an ending state. Finding hidden order in seemingly random behaviour is a difficult problem: that fact makes cryptography work. Seemingly random sequences can be generated from exquisitely simple laws: witness Blum Blum Shub or Mersenne Twisters. We might observe seemingly random or otherwise fine structure in something and assume we have to have a hugely complicated theory to describe it, whereas Nature might be using a metaphorical Mersenne twister all along and summing up exquisite structure in a few bits in Her codebook!
Now let's try to deal with:
I could not understand the connection between thermodynamic entropy and kolmogorov complexity, Please can you comment on that.
One interpretation of the thermodynamic entropy is that it is an approximation to the "information content" the system, or the number of bits needed to wholly specify a system given only its macroscopic properties. Actually your comment "I could not understand the connection between thermodynamic entropy and kolmogorov complexity" is a very good answer to this whole question! - we don't in general know the link between the two and that thwarts efforts to know just how much information it really takes to encode a system's state unambiguously.
But the concepts are linked in some cases. The classic example here is the Boltzmann $H$-entropy for a gas made up of statistically independent particles:
$H = -\sum_i p_i \log_2 p_i$
where $p_i$ is the probability that a particle is in state number $i$. The above expression is in bits per particle (here I've just rescaled units so that the Boltzmann constant $k_B = \log_e 2$).
If indeed the particles' occupations of the states are truly random and statistically independent, then it can be shown through the Shannon Noiseless Coding Theorem that the number of bits needed to encode the states of a large number $N$ of them is precisely $H$ bits per particle. This is the minimum number of bits in the sense that if one tries to construct a code that assigns $H-\epsilon$ bits per particle then, as $N\rightarrow\infty$ the probability of coding failure approaches unity, for any $\epsilon > 0$. Conversely, if we are willing to assign $H+\epsilon$, then there always exists a code such that the probability of wholly unambiguous coding approaches unity as $N\rightarrow\infty$ for any $\epsilon > 0$. So, in this special case, the Boltzmann entropy equals the Kolmogorov complexity as $N\rightarrow\infty$: we have to choose $H+\epsilon$ bits per particle, plus a constant overhead to describe how the coding works in the language we are working with. This overhead spread over all the particles approaches nought bits per particle as $N\rightarrow\infty$.
When a thermodynamic system is at "equilibrium" and the particle state occupations statistically independent, we can plug the Boltzmann probability distribution
$p_i = \mathcal{Z}^{-1} e^{-\beta E_i}$
into the $H$ and show that it gives the same as the Clausius entropy $S_{exp}$ derived from experimental macrostates.
If there is correlation between particle occupations, similar comments in principle apply to the Gibbs's Entropy, if the joint state probability distributions are known for all the particles. However, the joint probability distributions are in general impossible to find, at least from macroscopic measurements. See the paper Gibbs vs Boltzmann Entropy by E. T. Jaynes, as well as many other works by him on this subject). Moreover, user Nathaniel of Physics Stack Exchange has an excellent PhD thesis as well as several papers which may be of interest. The difficulty of measuring the Gibbs' Entropy is yet another difficulty with this whole problem. I also gave another answer summarizing this problem.
A final way to link KC to other concepts of entropy: you can, if you like, use the notion of KC to define what we mean by "random" and "statistically independent". Motivated by the Shannon Noiseless Coding theorem, we can even use it to define probabilities. A sequence of variables is random if there is no model (no description) that can be used to describe their values other than to name their values. The degree of "randomness" in a random variable can be thought of like this: you can find a model that describes the sequence of variables somewhat - but it is only approximate. A shorter description of a random sequence is to define a model and its boundary conditions, then to code that model and conditions as well as the discrepancies between the observed variables and the model. If the model is better than guessing, this will be a pithier description than simply naming the values in full. Variables are "statistically independent" if there is no description, even in principle, that can model how the value of some variables affects the others and thus the pithiest description of the sequence is to name all the separate variables in full. This is what correlation functions between rvs do, for example: the knowledge of value of X can be used to reduce the variance of a second correlated variable Y through a linear model involving the correlation co-efficient (I mean, reduce the variance in the conditional probability distribution). Finally, we can turn the Shannon Noiseless Coding Theorem on its head and use it to define probabilities through the KC: the probability that discrete rv $X$ equals $x$ is $p$ if the following holds. Take a sequence of rvs and for each one record the sequence of truth values $X=x$" or $X\neq x$ and "find the pithiest possible description" (we shall need an "oracle" because of the uncomputability of KK) of this truth value sequence and its length in bits and bits per sequence member. The probability "p" is then the number such that $-p\log_2 p - (1-p)\log_2 (1-p)$ equals this bits per sequence member, as the sequence length $\rightarrow\infty$ (taking the limit both improves the statistical estimates and spreads the fixed length overhead in describing the coding scheme over many sequence members, so that this overhead does not contribute to the bits per sequence member). This approach gets around some of the philosophical minefield that arises in even defining randomness and probability - see the Stanford Dictionary of Philosophy entry "Chance Versus Randomness for some flavor of this.
Lastly:
If you know the exact state of the system, then in physics entropy is zero
Here our problems are the subtle distinctions (1) between an instance of an ensemble of systems, all assumed to be members of the same random process or "population" and the ensemble itself, (2) Information and Thermodynamic entropies and (3) unconditional and conditional information theoretic entropies.
When I said that "the true entropy of a thing is hidden from an observer, even though the thing and a perfect description of it are fully observable by them" well of course the information theoretic Shannon entropy, conditioned on the observer's full knowledge of the system is nought. By contrast, the thermodynamic entropy will be the same as it is for everybody. By yet another contrast, the information theoretic entropy for another observer who does not have full knowledge is nonzero. What I was driving at in this instance is the Kolmogorov Complexity, or the number of yes/no questions needed to specify a system from the same underlying statistical population, because this quantity, if it can be calculated before and after a physical process, is what one can use to tell whether the process has been reversible (in the sense of being a one-to-one function of system configuration).
I hope that these reflexions help you Prathyush on your quest to understand the indestructability, or otherwise, of information in physics.