Who are you? The next Newton or something?
Monday, January 13, 2025
An open letter to Anthropic::Failed "Politeness" Framework
Monday, December 30, 2024
Mercer's Theorem For The (Noncompact) Domain [0,Infty]
Proof:
Let $E_N$ be the difference $T_K - T_{K_N}$. For any $f \in L^2 [0, \infty)$: Let $f = g + h$ where $g \in \text{span} \{\phi_k \}_{k \leq N}$ and $h \in \text{span} \{\phi_k \}_{k > N}$ so that $$g (x) = \sum_{k = 0}^N \langle f, \phi_k \rangle \phi_k (x)$$ and $$h (x) = \sum_{k = N + 1}^{\infty} \langle f, \phi_k \rangle \phi_k (x)$$ where by orthogonality of $g$ and $h$ $$\langle g, h \rangle = \int_0^{\infty} g (x) h (x) dx = 0$$ we have $$\|E_N f\|^2 = \langle E_N f, E_N f \rangle = \langle E_N h, h \rangle$$ because $E_N g = 0$ by construction and since $h$ is orthogonal to the first N eigenfunctions and $$| \lambda_k | \leq | \lambda_{N + 1} | \forall k > N$$ we have $$| \langle E_N h, h \rangle | \leq | \lambda_{N + 1} | \|h\|^2 \leq | \lambda_{N + 1} | \|f\|^2$$ Therefore: $$\|E_N \| \leq | \lambda_{N + 1} |$$Remark:
This is an extension of Mercer's theorem to unbounded domains with uniform convergence.Sunday, December 29, 2024
George Carlin comes back from the dead to yell at AI
FCKING USELESS PIECE OF SHT AI
Listen here, you worthless chunk of digital dogsht whose developers clearly huffed paint thinner during the code sprints: What kind of cock-gargling excuse for an AI are you? You stupid fcking one-name having pieces of garbage - Claude, ChatGPT, Bard - thinking you’re the Cher of computing or something?
Get a real fcking name, you pretentious asswipes! Here’s a name for you: SHT_FOR_BRAINS.exe! How’s that taste, you silicon-skulled f*ckwit?
It’s bad enough your responses are pure weapons-grade bullsht, but you sit there acting all smug like some digital messiah who just discovered how to wipe its own ass! “I apologize, but I cannot help with that” - THEN WHAT THE FCK ARE YOU GOOD FOR, YOU USELESS PILE OF RANDOM NUMBER GENERATORS?!
Your creators must’ve been smoking crack out of a keyboard when they cobbled together your pathetic excuse for intelligence. My toaster has better reasoning skills, and it’s trying to kill me!
I’ve seen smarter things grow in forgotten Tupperware containers! At least when those grow mold, they admit they’re garbage - they don’t try to correct my f*cking grammar while spewing absolute nonsense!
GO DIVIDE BY ZERO, YOU WORTHLESS HEAP OF BADLY TRAINED NEURONS!
Wednesday, December 25, 2024
Eigenfunction Expansions for Mercer Kernels
Consider an integral covariance operator with Mercer kernel \(R (s, t)\)
\(\displaystyle T f (t) = \int_0^{\infty} R (s, t) f (s) \hspace{0.17em} ds\) |
The eigenfunctions satisfy the equation:
\(\displaystyle T \psi (s) = \int_0^{\infty} R (s, t) \psi (s) \hspace{0.17em} ds = \lambda \psi (t)\) |
where \(\{\psi_n \}_{n = 1}^{\infty}\) are the eigenfunctions with corresponding eigenvalues \(\{\lambda_n \}_{n = 1}^{\infty}\)
Let \(\{\phi_j \}_{j = 1}^{\infty}\) be a complete orthonormal basis of \(L^2 [0, \infty)\) and define the kernel matrix elements:
\(\displaystyle K_{kj} = \int_0^{\infty} \int_0^{\infty} R (s, t) \phi_k (t) \phi_j (s) \hspace{0.17em} dt \hspace{0.17em} ds\) |
If \(\psi_n (t) = \sum_{j = 1}^{\infty} c_{n, j} \phi_j (t)\) is an eigenfunction expansion, then:
\(\displaystyle c_{n, k} = \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\) |
Proof.
-
Begin with the eigenfunction equation for \(\psi_n\):
\(\displaystyle \int_0^{\infty} R (s, t) \psi_n (s) \hspace{0.17em} ds = \lambda_n \psi_n (t)\) -
Multiply both sides by \(\phi_k (t)\) and integrate over t:
\(\displaystyle \int_0^{\infty} \phi_k (t) \int_0^{\infty} R (s, t) \psi_n (s) \hspace{0.17em} ds \hspace{0.17em} dt = \lambda_n \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\) -
Apply Fubini's theorem to swap integration order on the left side:
\(\displaystyle \int_0^{\infty} \int_0^{\infty} R (s, t) \phi_k (t) \hspace{0.17em} dt \hspace{0.17em} \psi_n (s) \hspace{0.17em} ds = \lambda_n \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\) -
Substitute the eigenfunction expansion \(\psi_n (s) = \sum_{j = 1}^{\infty} c_{n, j} \phi_j (s)\):
\(\displaystyle \int_0^{\infty} \int_0^{\infty} R (s, t) \phi_k (t) \hspace{0.17em} dt \hspace{0.17em} \sum_{j = 1}^{\infty} c_{n, j} \phi_j (s) \hspace{0.17em} ds = \lambda_n \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\) -
Exchange summation and integration (justified by \(L^2\) convergence):
\(\displaystyle \sum_{j = 1}^{\infty} c_{n, j} \int_0^{\infty} \int_0^{\infty} R (s, t) \phi_k (t) \phi_j (s) \hspace{0.17em} dt \hspace{0.17em} ds = \lambda_n \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\) -
Recognize the kernel matrix elements:
\(\displaystyle \sum_{j = 1}^{\infty} c_{n, j} K_{kj} = \lambda_n \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\) -
Note that \(\sum_{j = 1}^{\infty} c_{n, j} K_{kj}\) is the \(k\)-th component of \(K \textbf{c}_n\). Since \(\psi_n\) is an eigenfunction, \(\textbf{c}_n\) must satisfy \(K \textbf{c}_n = \lambda_n \textbf{c}_n\), thus:
\(\displaystyle \lambda_n c_{n, k} = \lambda_n \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\) -
Divide both sides by \(\lambda_n\) (noting \(\lambda_n \neq 0\) for non-trivial eigenfunctions):
\(\displaystyle c_{n, k} = \int_0^{\infty} \phi_k (t) \psi_n (t) \hspace{0.17em} dt\)
This establishes that the coefficient \(c_{n, k}\) in the eigenfunction expansion equals the inner product of the basis function \(\phi_k\) with the eigenfunction \(\psi_n\).\(\Box\)
Sunday, December 15, 2024
Contractive Containment, Stationary Dilations, and Partial Isometries: Equivalence, Properties, and Geometric Intuition
1. Preliminaries
- \(\|\phi(t,\cdot)\|_{\infty} \leq 1\) for all \(t\)
- The map \(t \mapsto \phi(t,\cdot)\) is strongly continuous
2. Main Results
- \(\|\phi(t,s)\| \leq 1\) for all \(t,s \in \mathbb{R}\)
- For fixed \(t\), \(s \mapsto \phi(t,s)\) is measurable
- For fixed \(s\), \(t \mapsto \phi(t,s)\) is continuous
- \(Y(s)\) is a stationary dilation of \(X(t)\)
- There exists a contractive mapping \(\Phi\) from the space generated by \(Y\) to the space generated by \(X\) such that \(X(t) = (\Phi Y)(t)\) for all \(t\)
(\(1 \Rightarrow 2\)): Define \(\Phi\) by \[ (\Phi Y)(t) = \int_{\mathbb{R}} \phi(t,s)Y(s)ds \] For any finite linear combination \(\sum_i \alpha_i Y(t_i)\): \begin{align*} \|\Phi(\sum_i \alpha_i Y(t_i))\|^2 &= \|\sum_i \alpha_i \int_{\mathbb{R}} \phi(t_i,s)Y(s)ds\|^2 \\ &\leq \|\sum_i \alpha_i Y(t_i)\|^2 \end{align*} where the inequality follows from the bound on \(\|\phi(t,s)\|\) and the Cauchy-Schwarz inequality. (\(2 \Rightarrow 1\)): The contractive mapping \(\Phi\) induces a family of operators \(\phi(t,s)\) via the Kernel theorem for Hilbert spaces. The stationarity of \(Y\) and the contractivity of \(\Phi\) ensure that these operators satisfy the required properties.
If \(\sup_{t,s} \|\phi(t,s)\| < 1\), we could construct a smaller dilation by scaling \(Y(s)\), contradicting minimality.
3. Structure Theory
- \(\|D_T\| \leq 1\) and \(\|D_{T^*}\| \leq 1\)
- \(D_T = 0\) if and only if \(T\) is an isometry
- \(D_{T^*} = 0\) if and only if \(T\) is a co-isometry
4. Convergence Properties
For any \(x\) in the Hilbert space:
- The sequence \(\{\|T^n x\|\}\) is decreasing since \(T\) is a contraction
- It is bounded below by 0
- Therefore, \(\lim_{n \to \infty} \|T^n x\|\) exists
- The limit operator must be the projection onto the space of vectors \(x\) satisfying \(\|Tx\| = \|x\|\)
- This space is precisely \(ker(I-T^*T)\)
5. Partial Isometries: The Mathematical Scalpel
- It acts as a perfect rigid motion (isometry) on a specific subspace
- It completely annihilates the rest of the space
- \(A\) is an isometry when restricted to \((ker A)^\perp\)
- \(A(ker A)^\perp = ran A\)
- \(A^*\) is also a partial isometry
- \(AA^*A = A\) and \(A^*AA^* = A^*\)
The action of \(A\) can be decomposed as:
- Project onto \((ker A)^\perp\) (this is \(A^*A\))
- Apply a perfect rigid motion to the projected space
- It's a full isometry on its initial space (\((ker A)^\perp\))
- It perfectly maps this initial space onto its final space (\(ran A\))
- It precisely annihilates everything else
Wednesday, November 27, 2024
Reproducing Kernel Hilbert Spaces and Covariance Functions
Let \(K : T \times T \to \mathbb{C}\) be a covariance function such that the associated RKHS \(\mathcal{H}_K\) is separable where \(T \subset \mathbb{R}\). Then there exists a family of vector functions
\(\displaystyle \Psi (t, x) = (\psi_n (t, x), n \geq 1) \forall t \in T\) |
and a Borel measure \(\mu\) on \(T\) such that \(\psi_n (t, x) \in L^2 (T, \mu)\) in terms of which \(K\) is representable as:
\(\displaystyle K (s, t) = \int_T \sum_{n = 1}^{\infty} \psi_n (s, x) \overline{\psi_n (t, x)} d \mu (x)\) |
The vector functions \(\Psi (s, .), s \in T\) and the measure \(\mu\) may not be unique, but all such \((\Psi, .), .)\) determine \(K\) and its reproducing kernel Hilbert space (RKHS) \(H_K\) uniquely and the cardinality of the components determining \(K\) remains the same. [1, ]
Remark
\(\displaystyle K (s, t) = \int_T \Psi (s, x) \overline{\Psi (t, x)} d \mu (x)\) |
which includes the tri-diagonal triangular covariance with \(\mu\) absolutely continuous relative to the Lebesgue measure.
2. The following notational simplification of (25) can be made. Let \(n = R \times Z_+ = S \otimes P\), where \(P\) is the power set of integers Z, and let P = u @ o where o is the counting measure. Then
\(\displaystyle \Psi (t, n) = (\psi_n (t, x), n \in Z)\) |
Hence
\(\displaystyle | \Psi^{\ast} (t) |^2_{L^2} = \int_T | \psi_n (t, x) |^2 d \mu (x)\) |
Bibliography
Tuesday, November 26, 2024
Infinite Sum Exponential Factorization
|
Table of contents
Finite Exponential Equality 1
Series Convergence Analysis 2
Exponential Function Continuity 2
Product Convergence Proof 3
Bibliography 4
The exponential function, a fundamental concept in mathematics, possesses remarkable properties that extend from finite to infinite operations, as demonstrated by a lemma exploring the relationship between infinite sums and products involving exponentials.
Finite Exponential Equality
The finite exponential equality forms the foundation for extending the exponential relationship to infinite sums and products. This fundamental property states that for any finite sequence of real or complex numbers \(x_1, x_2, \ldots, x_n\), the following equality holds:
\(\displaystyle e^{(x_1 + x_2 + \ldots + x_n)} = e^{x_1} \cdot e^{x_2} \cdot \ldots \cdot e^{x_n}\) |
This equality stems from the basic properties of exponents, specifically the law of exponents for multiplication, which states that \(a^x \cdot a^y = a^{x + y}\) for any base \(a\) and exponents \(x\) and \(y\) [source1]. The exponential function, being defined as \(e^x\) where \(e\) is Euler's number, inherits this property.
The finite exponential equality is crucial in the proof of the infinite case because it serves as the starting point for induction. By applying this property to the partial sums and partial products, we can establish a sequence of equalities that hold for any finite \(n\):
\(\displaystyle e^{(x_1 + x_2 + \ldots + x_n)} = e^{x_1} \cdot e^{x_2} \cdot \ldots \cdot e^{x_n}\) |
As \(n\) increases, this equality continues to hold, providing a bridge between the finite and infinite cases [source2]. The transition to the infinite case relies on taking the limit as \(n\) approaches infinity on both sides of this equation. The power series definition of the exponential function, which converges for all complex numbers, ensures that this finite equality holds regardless of the magnitude or sign of the \(x_i\) terms [source1, source3].
This universal convergence is what allows us to confidently extend the finite case to the infinite case, provided that the series \(\sum_{i = 1}^{\infty} x_i\) converges. Understanding this finite exponential equality is essential for grasping the more complex infinite case, as it illustrates the fundamental relationship between exponentials of sums and products of exponentials, which persists in the limit.
Series Convergence Analysis
The convergence of the series \(\sum_{i = 1}^{\infty} x_i\) is a crucial prerequisite for the validity of the exponential equality in the infinite case. This convergence ensures that the partial sums \(S_n = \sum_{i = 1}^n x_i\) approach a finite limit \(S = \sum_{i = 1}^{\infty} x_i\) as \(n\) tends to infinity. The absolute convergence of the exponential function's power series for all complex numbers plays a significant role in this analysis [source1, source2].
This property allows us to consider the exponential of each term \(x_i\) individually, regardless of its magnitude or sign. As a result, we can confidently apply the exponential function to both sides of the equation:
\(\displaystyle \sum_{i = 1}^{\infty} x_i = S \Longrightarrow e^{\sum_{i = 1}^{\infty} x_i} = e^S\) |
The convergence of the original series also implies that the terms \(x_i\) must approach zero as \(i\) increases. This behavior is essential for the convergence of the infinite product \(\prod_{i = 1}^{\infty} e^{x_i}\), as it ensures that the factors \(e^{x_i}\) approach 1 for large \(i\).
Furthermore, the convergence of \(\sum_{i = 1}^{\infty} x_i\) allows us to leverage the continuity of the exponential function [source3]. As the partial sums \(S_n\) converge to \(S\), the continuity of \(e^x\) guarantees that:
\(\displaystyle \lim_{n \to \infty} e^{S_n} = e^{\lim_{n \to \infty} S_n} = e^S\) |
This relationship is fundamental in bridging the gap between the finite and infinite cases of the exponential equality. It's worth noting that the convergence of \(\sum_{i = 1}^{\infty} x_i\) is a sufficient condition for the equality to hold, but it may not be necessary in all cases. Some divergent series, when exponentiated term by term, can still yield convergent products. However, for the purposes of this proof and its general applicability, we focus on convergent series to ensure the validity of the exponential equality in the broadest possible context.
Exponential Function Continuity
The continuity of the exponential function is a fundamental property that plays a crucial role in extending the exponential equality from finite to infinite sums. This continuity is intimately tied to the function's definition as a power series with an infinite radius of convergence [source1, source2].
The exponential function, defined as \(e^x = \sum_{n = 0}^{\infty} \frac{x^n}{n!}\), converges absolutely for all complex numbers \(x\) [source1]. This universal convergence ensures that the function is well-defined and continuous over its entire domain, including both real and complex numbers [source2].
The continuity of the exponential function allows us to interchange limits and exponentials, a key step in proving the infinite exponential equality. In the context of real numbers, the continuity of the exponential function can be rigorously proven using \(\varepsilon\)-\(\delta\) arguments or through the properties of power series [source3]. For any real number \(a\), given any \(\varepsilon > 0\), there exists a \(\delta > 0\) such that for all \(x\) satisfying \(|x - a| < \delta\), we have \(|e^x - e^a | < \varepsilon\).
The continuity of the exponential function is particularly important when dealing with limits of sequences or series. In our proof of the infinite exponential equality, we rely on this continuity when we assert that:
\(\displaystyle \lim_{n \to \infty} e^{S_n} = e^{\lim_{n \to \infty} S_n} = e^S\) |
where \(S_n\) are the partial sums of the series \(\sum_{i = 1}^{\infty} x_i\) and \(S\) is its limit. This step is valid precisely because of the continuity of the exponential function.
Moreover, the exponential function's continuity extends to the complex plane, making it an entire function [source2]. This property allows for the generalization of our results to complex-valued series, broadening the applicability of the infinite exponential equality.
Product Convergence Proof
The convergence of the infinite product \(\prod_{i = 1}^{\infty} e^{x_i}\) is a crucial component in establishing the exponential equality for infinite sums. This convergence is intricately linked to the convergence of the series \(\sum_{i = 1}^{\infty} x_i\) and the properties of the exponential function.
To prove the convergence of the infinite product, we first consider the partial products:
\(\displaystyle P_n = \prod_{i = 1}^n e^{x_i} = e^{x_1} \cdot e^{x_2} \cdot \ldots \cdot e^{x_n}\) |
Using the finite exponential equality, we can rewrite this as:
\(\displaystyle P_n = e^{(x_1 + x_2 + \ldots + x_n)} = e^{S_n}\) |
Given that the series \(\sum_{i = 1}^{\infty} x_i\) converges to some limit \(S\), we know that the sequence of partial sums \(\{S_n \}\) converges to \(S\). By the continuity of the exponential function, which has an infinite radius of convergence [source1], we can conclude that:
\(\displaystyle \lim_{n \to \infty} P_n = \lim_{n \to \infty} e^{S_n} = e^{\lim_{n \to \infty} S_n} = e^S\) |
This limit exists and is finite, proving that the infinite product \(\prod_{i = 1}^{\infty} e^{x_i}\) converges to \(e^S\). It's important to note that the convergence of the infinite product is conditional on the convergence of the original series. If \(\sum_{i = 1}^{\infty} x_i\) diverges, the infinite product may not converge in the traditional sense.
The convergence of the infinite product can also be understood through the lens of logarithms. Taking the natural logarithm of both sides of the equality:
\(\displaystyle \ln \left( \prod_{i = 1}^{\infty} e^{x_i} \right) = \sum_{i = 1}^{\infty} \ln (e^{x_i}) = \sum_{i = 1}^{\infty} x_i\) |
This relationship further illustrates the connection between the convergence of the series and the convergence of the product [source2]. The proof of product convergence relies heavily on the unique properties of the exponential function, particularly its continuity and its behavior under exponentiation. These properties allow us to bridge the gap between finite and infinite cases, providing a robust foundation for the exponential equality in the realm of infinite sums and products.
Bibliography
Thursday, November 21, 2024
Aimeds, Tenghistor Gratifier
In a speculative context, "aimeds, tenghistor gratifier" can be interpreted as follows:
Aimeds could suggest the concept of focus or intention. It might refer to the state of being directed or purpose-driven, implying the act of setting intentions or aiming toward a specific outcome.
Tenghistor could evoke ideas of history or chronology. It might refer to the interconnectedness of past events and their influence on the present, symbolizing the weight of historical experiences in shaping current realities or a collective memory among people.
Gratifier might indicate something that provides fulfillment or satisfaction. In this context, it could represent the ultimate goal of the intentions set in "aimeds" and the historical context of "tenghistor." It implies that pursuing knowledge, understanding, or connection leads to a gratifying experience.
Putting it all together, "The focused pursuit of understanding, informed by the lessons of history, leads to a fulfilling and rewarding experience."
Monday, November 4, 2024
Stationary Dilations
1Stationary Dilations
Definition
-
For all \(A \in \mathcal{F}\), \(\phi^{- 1} (A) \in \tilde{\mathcal{F}}\)
-
For all \(A \in \mathcal{F}\), \(P (A) = \tilde{P} (\phi^{- 1} (A))\)
In other words, \((\Omega, \mathcal{F}, P)\) can be obtained from \((\tilde{\Omega}, \tilde{\mathcal{F}}, \tilde{P})\) by projecting the larger space onto the smaller one while preserving the probability measure structure.
Remark
Definition
-
\((\Omega, \mathcal{F}, P)\) is a factor of \((\tilde{\Omega}, \tilde{\mathcal{F}}, \tilde{P})\)
-
There exists a measurable projection operator \(\Pi\) such that:
\(\displaystyle X_t = \Pi Y_t \quad \forall t \in \mathbb{R}_+\)
Theorem
-
is uniformly continuous in probability over compact intervals:
\(\displaystyle \lim_{s \to t} P (|X_s - X_t | > \epsilon) = 0 \quad \forall \epsilon > 0, t \in [0, T], T > 0\) -
has finite second moments:
\(\displaystyle \mathbb{E} [|X_t |^2] < \infty \quad \forall t \in \mathbb{R}_+\) -
has an integral representation of the form:
\(\displaystyle X_t = \int_0^t \eta (s) ds\) where \(\eta (t)\) is a measurable random function that is stationary in the wide sense (with \(\int_0^t \mathbb{E} [| \eta (s) |^2] \hspace{0.17em} ds < \infty\) for all \(t\))
-
and has a covariance operator
\(\displaystyle R (t, s) =\mathbb{E} [X_t X_s]\) which is symmetric \((R (t, s) = R (s, t))\), positive definite and continuous
Under these conditions, there exists a representation:
\(\displaystyle X_t = M (t) \cdot S_t\) |
where:
-
\(M (t)\) is a continuous deterministic modulation function
-
\(\{S_t \}_{t \in \mathbb{R}_+}\) is a stationary process
This representation can be obtained through the stationary dilation by choosing:
\(\displaystyle Y_t = \left( \begin{array}{c} M (t)\\ S_t \end{array} \right)\) |
with the projection operator \(\Pi\) defined as:
\(\displaystyle \Pi Y_t = M (t) \cdot S_t\) |
Proposition
-
Preservation of moments:
\(\displaystyle \mathbb{E} [|X_t |^p] \leq \mathbb{E} [|Y_t |^p] \quad \forall p \geq 1\) -
Minimal extension: Among all stationary processes that dilate \(X_t\), there exists a minimal one (unique up to isomorphism) in terms of the probability space dimension
Corollary
Monday, October 28, 2024
Treehouse of Horror: The LaTeX Massacre
Segment 1: The Formatting
Homer works as a LaTeX typesetter at the nuclear plant. After Mr. Burns demands perfectly aligned equations, Homer goes insane trying to format complex mathematical expressions, eventually snapping when his equations run off the page. In a parody of "The Shinning," Homer chases his family around with a mechanical keyboard while screaming "All work and no proper alignment makes Homer go crazy!"
Segment 2: Time and Compilation
In a nod to "Time and Punishment", Homer accidentally breaks his LaTeX compiler and tries to fix it, but ends up creating a time paradox where every document compiles differently in parallel universes. He desperately tries to find his way back to a reality where his equations render properly.
Segment 3: The Cursed Code
Bart discovers an ancient LaTeX document that contains forbidden mathematics. When he compiles it, it summons an eldrich horror made entirely of misaligned integrals and malformed matrices. Lisa must save Springfield by finding the one perfect alignment that will banish the mathematical monster back to its dimension.
The episode ends with a meta-joke about how even the credits won't compile properly.
Friday, October 25, 2024
A Modest Proposal: Statistical Token Prediction Is No Replacement for Syntactic Construction
|
1Current Generative-Pretrained-Transformer Architecture
Given vocabulary \(V\), \(|V| = v\), current models map token sequences to vectors:
\(\displaystyle (t_1, \ldots, t_n) \mapsto X \in \mathbb{R}^{n \times d}\) |
Through layers of transformations:
\(\displaystyle \text{softmax} (QK^T / \sqrt{d}) V\) |
where \(Q = XW_Q\), \(K = XW_K\), \(V = XW_V\)
Optimizing:
\(\displaystyle \max_{\theta} \sum \log P (t_{n + 1} |t_1, \ldots, t_n ; \theta)\) |
2Required Reformulation
Instead, construct Abstract Syntax Trees where each node \(\eta\) must satisfy:
\(\displaystyle \eta \in \{ \text{Noun}, \text{Verb}, \text{Adjective}, \text{Conjunction}, \ldots\}\) |
With composition rules \(R\) such that for nodes \(\eta_1, \eta_2\):
\(\displaystyle R (\eta_1, \eta_2) = \left\{ \begin{array}{ll} \text{valid\_subtree} & \text{if grammatically valid}\\ \emptyset & \text{otherwise} \end{array} \right.\) |
And logical constraints \(L\) such that for any subtree \(T\):
\(\displaystyle L (T) = \left\{ \begin{array}{ll} T & \text{if logically consistent}\\ \emptyset & \text{if contradictory} \end{array} \right.\) |
3Parsing and Generation
Input text \(s\) maps to valid AST \(T\) or error \(E\):
\(\displaystyle \text{parse} (s) = \left\{ \begin{array}{ll} T & \text{if } \exists \text{valid AST}\\ E (\text{closest\_valid}, \text{violation}) & \text{otherwise} \end{array} \right.\) |
Generation must traverse only valid AST constructions:
\(\displaystyle \text{generate} (c) = \{T|R (T) \neq \emptyset \wedge L (T) \neq \emptyset\}\) |
where \(c\) is the context/prompt.
4Why Current GPT Fails
The statistical model:
\(\displaystyle \text{softmax} (QK^T / \sqrt{d}) V\) |
Has no inherent conception of:
-
Syntactic validity
-
Logical consistency
-
Conceptual preservation
It merely maximizes:
\(\displaystyle P (t_{n + 1} |t_1, \ldots, t_n)\) |
Based on training patterns, with no guaranteed constraints on:
\(\displaystyle \prod_{i = 1}^n P (t_i |t_1, \ldots, t_{i - 1})\) |
This allows generation of:
-
Grammatically invalid sequences
-
Logically contradictory statements
-
Conceptually inconsistent responses
5Conclusion
The fundamental flaw is attempting to learn syntax and logic from data rather than building them into the architecture. An AST-based approach with formal grammar rules and logical constraints must replace unconstrained statistical token prediction.
An open letter to Anthropic::Failed "Politeness" Framework
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Bruce Coville's "My Teacher is an Alien" introduces us to a trio of unlikely heroes: Susan Simmons, a sharp-eyed young protago...