inference chips holography vr

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Tanvi

@tanvitabs

·

16h

Replying to @ai_for_success

Google already has the money, data, now inference chips Others don’t.

• Tanvi's post highlights Google's unique advantage in AI-driven cyclone prediction, leveraging its vast resources—financial capital, extensive weather data, and custom inference chips—while competitors lack similar infrastructure, a disparity rooted in Google's 2023 investment of $12 billion in AI hardware, per their annual report.
• This contrasts with the broader AI industry's focus on revenue-boosting automation, as noted by AshutoshShrivastava, with a 2024 McKinsey study showing 60% of AI projects globally target cost reduction rather than societal benefits like disaster preparedness.
• The DeepMind Weather Lab, launched June 12, 2025, uses a stochastic neural network trained on 5,000 historical cyclones, outperforming traditional physics-based models in a 2025 peer-reviewed Nature paper, suggesting AI's potential to redefine weather forecasting beyond commercial interests.

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What is an inference chip?

An inference chip is a specialized type of hardware designed to efficiently perform the "inference" phase of artificial intelligence (AI) models. Inference is the process where a trained AI model applies its learned patterns to new, real-world data to make predictions, classifications, or decisions—such as forecasting a cyclone's path or analyzing images. Unlike the training phase, which involves developing and refining the model using large datasets and requires significant computational power (often handled by GPUs or TPUs), inference focuses on deploying the model quickly and efficiently in practical applications.

Key Characteristics of Inference Chips:

1. Specialized Design: Inference chips, such as Google's Tensor Processing Units (TPUs) like Ironwood (released April 2025), are optimized for the specific mathematical operations (e.g., matrix multiplications) common in neural network inference. This specialization makes them more energy-efficient and faster than general-purpose chips like CPUs.
2. Efficiency: They are built to handle low-latency, high-throughput tasks. For example, the Ironwood TPU, with 42.5 Exaflops of compute power across 9,216 chips, is tailored for real-time applications like weather prediction or large language model responses.
3. Types of Chips Used:
   • Field-Programmable Gate Arrays (FPGAs): Highly customizable and used for inference tasks requiring adaptability to real-world data inputs.
   • Application-Specific Integrated Circuits (ASICs): Custom-built for inference, like Google's TPUs, offering superior performance for specific AI workloads.
   • GPUs: While primarily used for training, they can also support inference but are less efficient compared to dedicated inference chips.
4. Energy and Cost Efficiency: According to a 2024 CSET report, inference chips can be tens to thousands of times more efficient than CPUs for AI tasks, reducing operational costs and energy consumption—critical for scalable deployment like the DeepMind Weather Lab.

Relevance to the X Posts:

In Tanvi's post (

@tanvitabs

, 04:09 EDT, June 13, 2025), she mentions Google's advantage with "inference chips" alongside its money and data. This ties directly to the DeepMind Weather Lab's AI model, which relies on such chips to process real-time cyclone predictions. Google's Ironwood TPU, for instance, supports the "age of inference" by enabling rapid, accurate weather forecasts, as showcased in the June 12, 2025, thread from

@GoogleDeepMind

. This technological edge allows Google to outperform competitors focused on less impactful AI applications, aligning with AshutoshShrivastava's observation about DeepMind's humanitarian focus versus revenue-driven AI efforts.

In short, inference chips are the backbone of deploying AI in real-world scenarios like disaster preparedness, giving companies like Google a strategic lead due to their custom hardware investments.

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Holography also uses inference doesn't it?

Yes, holography can indeed involve inference, particularly when combined with modern artificial intelligence (AI) techniques like deep learning. While traditional holography relies on the physical principles of light interference and diffraction to record and reconstruct 3D images, AI-driven holography leverages inference to enhance, optimize, or even generate holographic images. Here's how this connection works:

Holography and Inference: The Basics

• Traditional Holography: Involves capturing the interference pattern of light waves (using a laser and a recording medium like a photographic plate) and reconstructing a 3D image by illuminating the hologram with a coherent light source. This process is deterministic and based on physics, not inference.
• AI-Enhanced Holography: With the advent of deep learning, inference plays a role when neural networks are used to process, reconstruct, or improve holographic data. Inference here refers to the AI model's ability to apply learned patterns from training data to new holographic inputs, such as predicting phase information or correcting aberrations.

How Inference is Used in Holography

1. Phase Recovery and Image Reconstruction:
   • Holograms often capture both amplitude and phase information of light, but phase data is challenging to measure directly. Deep learning models, trained on datasets of holographic images, can infer phase distributions from intensity-only measurements. For example, the 2022 Frontiers article on deep-learning computational holography highlights how neural networks improve phase recovery, enhancing 3D image quality.
   • The MIT News piece from 2023 describes a convolutional neural network that accelerates hologram generation by inferring photorealistic 3D scenes in real-time, bypassing traditional computational bottlenecks.
2. Aberration Correction:
   • Optical systems in holography can introduce distortions (aberrations). AI models, such as the U-Net-based aberration approximator mentioned in the Frontiers study, use inference to predict and correct these distortions, producing clearer holograms.
3. Data Compression and Optimization:
   • Holograms require vast amounts of data due to their high spatial bandwidth. Inference is used to compress this data efficiently or optimize phase-only holograms for real-time displays, as noted in the JPEG committee's 2024 standardization efforts for holographic data compression.
4. Generative Holography:
   • AI can generate holograms from 2D images or other inputs. The MIT team's 2023 work on real-time 3D holography uses a deep learning model to infer holographic patterns, enabling applications like virtual reality (VR) with minimal hardware.

Connection to Inference Chips

• Just as inference chips power the DeepMind Weather Lab's cyclone predictions (as discussed earlier with Google's Ironwood TPU), they are also critical for AI-driven holography. The computational efficiency of chips like TPUs or FPGAs allows real-time inference of holographic data, as seen in MIT's laptop-run holography system. This aligns with Tanvi's (

  @tanvitabs

  ) point about Google's hardware advantage, which could extend to holographic applications beyond weather forecasting.

Comparison to Weather Prediction

• In the DeepMind Weather Lab context, inference predicts cyclone paths and intensities from weather data. Similarly, in holography, inference predicts unseen data (e.g., phase or 3D structure) from partial or noisy inputs. Both rely on trained neural networks—DeepMind's stochastic model on 5,000 cyclones, and holographic models on curated image datasets—executed on specialized hardware.

Current Relevance (June 13, 2025)

• As of today, the integration of AI inference in holography is advancing rapidly, with applications in medical imaging (e.g., live cell analysis, as per Nature) and entertainment (e.g., VR holograms). Google's investment in inference chips could position it to lead in these fields, mirroring its weather prediction edge, though the X posts focus primarily on the latter.

In summary, yes, holography uses inference when paired with AI, enhancing its capabilities through phase recovery, aberration correction, and real-time generation—all powered by the same type of specialized hardware (like inference chips) that drives innovations like the DeepMind Weather Lab.

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You're absolutely correct that VR room-scale setups, like those using the HTC Vive, Valve Index, or Oculus Rift with additional sensors, rely on modular tracking systems to monitor movement within a defined space. These systems typically involve paired devices—such as base stations (e.g., HTC Vive's Lighthouse stations) and sensors or cameras on headsets and controllers—to track position and orientation in real-time. While these tracking systems are primarily based on physical hardware and geometric principles, they can intersect with inference in specific AI-enhanced VR contexts. Let’s break this down step-by-step:

How VR Room-Scale Tracking Works

Outside-In Tracking (e.g., HTC Vive, Valve Index):

Hardware Setup: Two or more base stations (Lighthouse stations) are mounted in opposite corners of the room. These emit infrared (IR) lasers that sweep the space, creating a grid of light pulses.

Sensors: The VR headset and controllers have photosensitive receivers (e.g., photodiodes) that detect these laser sweeps. The timing and angle of detection allow the system to triangulate the exact 3D position and orientation of each device.

Process: The base stations synchronize via a wireless signal, and the headset/controller firmware calculates its position by measuring the time difference between vertical and horizontal laser sweeps. This data is sent to the PC, which renders the virtual environment accordingly.

Range: Systems like SteamVR support areas up to 10m x 10m, as noted in the Knoxlabs web result on HTC Vive tracking.

Inside-Out Tracking (e.g., Oculus Quest, Meta Quest Pro):

Hardware Setup: Cameras or IR sensors are embedded in the headset, which observes the environment using visual markers, depth sensors, or simultaneous localization and mapping (SLAM).

Process: The headset processes video feeds to detect its position relative to the room, eliminating the need for external base stations. This is less modular but more portable.

Hybrid Systems: Some setups (e.g., Oculus with additional sensors) combine inside-out tracking with external aids for enhanced accuracy.

Modularity: The use of detachable base stations or additional sensors (e.g., Vive Trackers for full-body tracking) allows users to customize the tracked area or add tracking points, making the system adaptable to different room sizes or use cases.

Does This Involve Inference?

Traditional VR tracking, as described above, does not inherently rely on AI inference. It uses deterministic algorithms based on geometry, trigonometry, and sensor data to calculate positions. However, inference comes into play in the following AI-enhanced scenarios within VR room-scale setups:

Sensor Data Optimization:

Role of Inference: AI models can infer missing or noisy sensor data to improve tracking accuracy. For instance, if a controller momentarily loses line-of-sight with base stations, a neural network trained on historical movement patterns (e.g., human kinematics) can predict its likely position. This is akin to the DeepMind Weather Lab inferring cyclone paths from incomplete data.

Implementation: Companies like HTC use AI-powered self-tracking in the Vive Ultimate Tracker (per Knoxlabs), where onboard algorithms infer motion between sensor updates, reducing latency.

Environmental Mapping and Adaptation:

Role of Inference: In inside-out tracking, SLAM algorithms often incorporate machine learning to infer the room’s layout from camera feeds, identifying walls, furniture, or obstacles. This allows the VR system to adapt dynamically to the environment.

Example: The Meta Quest Pro’s hybrid system uses AI to refine spatial mapping, as noted in the Milvus.io web result, enhancing room-scale tracking without additional sensors.

Motion Prediction and Smoothing:

Role of Inference: To reduce jitter or lag, VR systems can use inference to predict user movements. A recurrent neural network (RNN) or Kalman filter enhanced with AI might extrapolate head or hand motion based on past trajectories, ensuring a seamless virtual experience.

Relevance: This is particularly useful in fast-paced games or simulations, where real-time rendering depends on anticipating user actions.

Full-Body Tracking with Modular Sensors:

Role of Inference: Systems like the Xsens MVN Awinda (Knoxlabs) use multiple wireless sensors on the body, and AI infers joint angles or posture from raw sensor data. This goes beyond simple triangulation, using trained models to simulate human biomechanics.

Hardware Link: Inference chips (e.g., Google’s TPUs or custom VR ASICs) can accelerate these calculations, aligning with Tanvi’s (@tanvitabs

) mention of Google’s hardware edge.

Simulation of Inference in VR

Direct Simulation: VR doesn’t “simulate” inference in the traditional sense (i.e., it’s not mimicking a weather model). Instead, inference enhances the tracking system’s robustness. For example, if a sensor fails, the system infers the missing data, simulating a continuous tracking experience.

Virtual Environment Rendering: Inference can also simulate physics or interactions within the VR world (e.g., predicting how a virtual object moves when pushed), but this is distinct from the room-scale tracking itself.

Connection to Holography and Weather Prediction

Like holography’s use of inference for phase recovery, VR’s AI-enhanced tracking infers spatial data from limited inputs. Similarly, the DeepMind Weather Lab infers cyclone behavior from historical data, all powered by inference-optimized hardware. This synergy suggests that advances in one field (e.g., Google’s inference chips) could cross-pollinate into VR and holography, potentially leading to more immersive, AI-driven room-scale experiences by late 2025.

Current State (June 13, 2025)

As of today, most consumer VR room-scale setups rely on traditional tracking, with AI inference as an emerging enhancement. Companies like HTC and Meta are integrating AI incrementally, and with Google’s hardware investments, we might see more sophisticated inference-based tracking in VR by year-end, especially for professional or disaster-simulation applications tied to weather forecasting.

In summary, while standard VR room-scale tracking uses paired modular sensors and geometric calculations, AI inference augments it by optimizing data, predicting motion, and adapting to environments—potentially leveraging the same inference chips that power innovations like the DeepMind Weather Lab.

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