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How Good Are You Really?
by Dr. Arnie Gotfryd

Even when a person's conduct does not seem worthy of favorable judgment, one should endeavor to find redeeming virtue within him. - The Rebbe, Shabbos Behaalos'cha, 5741.

Who doesn't mess up?
Obviously we all do. And when we do, the thought of someone (or Someone) constantly watching and keeping score can be very intimidating. Think about the kid who covers his eyes and says "You can't see me!" Our inner child wants to wish away those critical eyes, too.
But then there are new beginnings. It may not be easy but we can all get there. The process is, basically, deal with it: Reflect, regret, restore, and poof... we are good to go with a nice clean slate. Then we retry, but oops. Error! And the cycle starts again. Refail, reflect. Regret, restore. Renew, retry. Then oops - Error! R-r-r-r-r-r... Error! R-r-r-r-r-r... Error! It can get pretty discouraging but fortunately there is another ingredient - Forgiveness.
True there are watchful eyes, whether they belong to someone we know, Some One we don't, or even ourselves. But where there is forgiveness, there can be a new beginning. The magic of forgiveness lies in positive perception, focusing on the hidden good, but how does that work practically?
Love is blind. If I love myself I will cover my faults. If I love you, I will overlook yours. But what about the Omniscient One? Surely He is not blind. How does infinite awareness square up with forgive and forget? Does He pull out an err-brush to cover up our faults? In a way, yes.
To understand this a little better, let's explore a recent advance in image-enhancement (and after all, whose image does not need enhancing?). It's called Compressed Sampling (CS) and it's an algorithm (mathematical process) that excels at transforming very incomplete and messy data sets into crisp and accurate representations of reality.
CS was first stumbled upon in 2004 by CalTech Professor Emmanuel Candes and in the short time since, over one thousand papers have expanded on it, millions of dollars have poured into developing it, and because of it, Candes himself has won the $500,000 Waterman Prize, the National Science Foundation's highest honor.
The practical implications are immense. For instance, medical imaging can be done with just 10% of the radiation, saving 90% of the time, cost and most importantly health risks. Now, instead of creating huge audio or video files and then compressing and storing those, you can record a very low-definition file, apply the CS algorithm, and voila. Here is a low-resolution photo enhanced by CS.
So what is the trick? How does the algorithm work? The simple answer is... simplicity.
To explain, let's say you have two pictures of a scene - one is sharp with a million pixels and the other is grainy with just 100,000. There are nearly an infinite number of ways to fill in the latter to become a million-pixel image, nearly all of them wrong. The right way to reconstruct the rich realm of reality from a motley muddle of maybes turns out to be the simplest.
The heart of the CS algorithm is what the mathematicians call sparsity. It assumes that the most accurate guesses are also the simplest. First the program inserts the biggest blocks of color that fit between similar pixels. When the program is done with big blocks, it then moves on to fill in with progressively smaller blocks. The final solution, and the one that most accurately mirrors reality every time, is the one with the smallest number of blocks. Billions of alternatives will always be wrong, but the simplest one wins every time.
Now we can return to our original question. How can Justice and Forgiveness coexist? To resolve the paradox maybe we can apply some lessons from the CS algorithm.
Let's picture, for a moment, the One Above gazing down at one of his hapless humans and determines that 90% of his deeds are folly and 10% are purposeful. The overall picture is a mess. It seems that Justice will condemn but Forgiveness will exonerate. How can we get the two working together to yield an accurate yet positive picture?
Let's make one simple assumption - that the bloke means well, that between his good deeds, deep down, the fellow really is good and wants to do good. All that other nonsense he does is just static, error, glitches. They don't form a coherent picture - it's only the good deeds that do. But since he has so few of them, there's just a trace of that.
Like CS, we start with the biggest consistent area that's good, and fill that in with the biggest block of goodness that fits. Once the biggest goodness blocks are mapped, we move on to slightly smaller stretches of goodness and fit goodness blocks in there wherever we can. By the time we work our way down to the splotches, most of the space is already filled with good. A nice picture indeed.
But, you may ask, why doesn't the ugly side sometimes form an even more consistent picture? Again the answer is in the imagery. There are an infinity of angles at which one falls, but only one at which one stands. All the mess-ups are noisy and scattered, but all the goodness is consistent and harmonious.
Things aren't always the way the seem. Our sages teach that no matter how messy things may appear, the true picture of man in the world is a positive one. It may sound simplistic, but then again, sometimes the best solutions are indeed the simplest. Think good and it will be good.
Moshiach NOW!




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