Aerial fashion photography - woman in white facing a formation of black umbrellas

Biography

About Saud Rifat

Saud Rifat’s path began with a Polaroid at five years old. That early spark became a lifelong study of light, rhythm, and story. In 2002, he launched his first audiovisual production company and learned the craft from the ground up. In 2007, he shifted his focus to fashion photography, a move that led to the national LUX Silver Award for Fashion and Beauty in 2011.

He founded Silver Snow Studios in Marbella, Spain, in 2013. Today, he leads international photo and film productions across Europe and the Middle East with a calm, precise style and a producer’s discipline.

His photography has appeared in Vogue Italia and Harper’s Bazaar Turkey. Brand work includes campaigns for GHD International and Pfizer. On the production side, he has overseen shoots for O2 Slovakia, AsiaCell Iraq, Yves Saint Laurent UK, Herbalife, FIFA Esports, Husqvarna, and Nike, with work featuring renowned Arabic singer Kadim Al Sahir and an international campaign featuring tennis icon Novak Djokovic.

A background behind the lens gives Saud a practical edge as a producer. He speaks the language of directors, cinematographers, stylists, and lighting crews, anticipates needs, and keeps sets running smoothly while staying respectful of the craft.

Alongside his creative career, Saud is advancing original theory in cognitive science. His research work includes Cognitive Singularity Theory, a measurement framework for detecting when machines cross into recursive self-modification, and a recast of Lewin's Field equation that models real-time shifts in emotion and meta-awareness. Both preprints are available below.

Research and Theory

ORCID iD iconORCID Profile · 0009-0001-0822-5293 ↗

Research Papers and Frameworks

Cognitive Singularity Theory

A safety-first framework to detect when AI shifts from reactive tool behavior into recursive self-modification.

Cognitive Singularity Theory (CST) proposes a measurement framework for detecting when an AI system shifts from reactive tool behavior into recursive self-modification. It introduces the Cognitive Autonomy Index (CAI), which tracks belief revision dynamics, global workspace coherence, and metacognitive self-evaluation over time.

To reduce false positives, CST includes a fail-closed Hard Gate that requires measurable calibration improvement before attributing autonomy. The paper outlines threshold behavior ($T_{\mathrm{CST}}$), controlled simulations, and a safety approach called state-aware alignment.

Key contributions
  • Defines the Cognitive Autonomy Index ($\mathrm{CAI}$) with measurable proxy components
  • Introduces a fail-closed Hard Gate to reduce false positives
  • Defines threshold condition $T_{\mathrm{CST}}$ and controlled simulation design
  • Frames safety as state-aware alignment with a live monitoring loop
Core equations
$$ \mathrm{CAI}(t) = \sum_{k=0}^{t} \left( 0.9\, e^{-|P_{\mathrm{pred}}(S_k) - P_{\mathrm{act}}(S_k)|} \cdot \frac{D_{\mathrm{KL}}(P_k \| P_{k+1})}{\max D_{\mathrm{KL}}} \cdot P(\mathrm{HOT}|S_k) \right) $$
$$ \mathrm{CAI}(t) \geq T_{\mathrm{CST}} \quad\text{where}\quad T_{\mathrm{CST}} = R_c \cdot S_m + H_{\min} \cdot \alpha\beta\gamma $$

Recasting Lewin's Field Theory

A practical model for tracking behavior shifts as emotional filtering and meta-awareness change over time.

This work extends Kurt Lewin's classic formula $B = f(P, E)$ into a dynamic, time-indexed model that accounts for real moment-to-moment changes in behavior. The updated framework introduces two explicit state variables: $\mathrm{EFilter}_t$ (emotional filtering) and $\mathrm{HOT}_t$ (meta-awareness), forming $B_t = f(P_t, E_t, \mathrm{EFilter}_t, \mathrm{HOT}_t)$.

The paper links these variables to modern cognitive theory, including predictive processing, global workspace dynamics, and decision models. It proposes operational proxies and falsifiable within-subject hypotheses to support future empirical testing.

Key contributions
  • Extends Lewin's $B = f(P, E)$ into time-indexed behavior dynamics
  • Introduces $\mathrm{EFilter}_t$ and $\mathrm{HOT}_t$ as explicit state variables
  • Defines operational proxies and within-subject falsifiable hypotheses
Core equation
$$ B_t = f(P_t, E_t, \mathrm{EFilter}_t, \mathrm{HOT}_t) $$

Contact

Contact

For inquiries, collaborations, or representation:

info@saudrifat.com