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Tesla Kills Dojo for AI6! Here’s Why

Questions to inspire discussion.

🚗 Q: How will AI6 be used in Tesla vehicles? A: AI6 will be used for FSD inference, with two chips in every car, enabling advanced autonomous driving capabilities.

🤖 Q: What role will AI6 play in Optimus? A: AI6 will enable on-device learning and reinforced learning in Optimus, enhancing its AI capabilities.

🔋 Q: Will AI6 be used in other Tesla products? A: AI6 will be integrated into every edge device produced by Tesla, including Tesla Semi, Mega Pack, and security cameras.

Technical Specifications.

💻 Q: What is the architecture of AI6? A: AI6 will use a cluster model of individual chips with a software layer on top, similar to Dojo 3 for training.

Continue reading “Tesla Kills Dojo for AI6! Here’s Why” | >

IVAE: an interpretable representation learning framework enhances clustering performance for single-cell data

Variational autoencoders (VAEs) serve as essential components in large generative models for extracting latent representations and have gained widespread application in biological domains. Developing VAEs specifically tailored to the unique characteristics of biological data is crucial for advancing future large-scale biological models.

Through systematic monitoring of VAE training processes across 31 public single-cell datasets spanning oncological and normal conditions, we discovered that reducing the β β value which corresponds to lower disentanglement of VAE significantly improves unsupervised clustering metrics in single-cell data analysis. Based on this finding, we innovatively developed iVAE with an irecon module that, when benchmarked against 8 established dimensionality reduction methods across 5 clustering performance metrics, exhibited superior capabilities in representing single-cell transcriptomic data.

Scientists just proved a fundamental quantum rule for the first time

Scientists have, for the first time, experimentally proven that angular momentum is conserved even when a single photon splits into two, pushing quantum physics to its most fundamental limits. Using ultra-precise equipment, the team captured this elusive process—comparable to finding a needle in a haystack—confirming a cornerstone law of nature at the photon level.

Terra Quantum Brings Quantum Gravity to Quantum Computing: Advance Reduces Errors Without Added Complexity

Terra Quantum has published a groundbreaking advance in quantum error correction that redefines how we scale quantum computing. In the peer-reviewed paper “QMM-Enhanced Error Correction: Demonstrating Reversible Imprinting and Retrieval for Robust Quantum Computation”, Terra Quantum scientists present QMM-Enhanced Error Correction, a hardware-validated, measurement-free method for suppressing quantum errors, based on principles derived initially from quantum gravity.

At the heart of this innovation is the Quantum Memory Matrix (QMM), a cosmology-inspired concept that models space-time as a lattice of finite-dimensional memory cells. Terra Quantum has now translated this deep theoretical idea into a functional quantum circuit. Validated on IBM’s superconducting processors, the QMM layer functions as a lightweight, unitary “booster” that enhances fidelity without mid-circuit measurements or added two-qubit gates, offering a powerful alternative to traditional surface codes.

“We have taken a concept rooted in quantum gravity and made it plug-and-play for today’s quantum processors,” said Florian Neukart, Chief Product Officer at Terra Quantum. “QMM-enhanced error correction works out of the box on existing hardware, requires no architectural changes, and delivers measurable gains. For industries building quantum solutions now, not in 10 years. This is a game-changer.

Terra Quantum has introduced QMM-Enhanced Error Correction, a hardware-validated, measurement-free method that suppresses quantum errors.

Johns Hopkins APL Takes a Quantum Approach to Tracking Online Trends

Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated that a quantum algorithm can be used to speed up an information analysis task that classical computers struggle to perform.

The innovation tackles a key element of information operations: tracking and attributing topics and narratives as they emerge and evolve online, which can help analysts spot indications of potential terrorist acts, for example. This involves using computers to perform what’s known as semantic text similarity analysis, or comparing the similarities within a textual dataset — not just the similarity of the words, but the meaning behind them, which makes it possible to identify related texts even if they don’t share any common keywords.

“The amount of open-source text data online — on social media platforms especially — is growing dramatically, and our ability to analyze all of that data has not kept pace with our ability to collect it,” said Roxy Holden, a mathematician at APL and principal investigator of this effort. “Intelligence analysts have limited resources, so finding better ways to automate this kind of analysis is critical for the military and the intelligence community.”

APL researchers have demonstrated that a quantum algorithm can be used to speed up an information analysis task that classical computers struggle to perform.

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A research team headed by the University of Zurich has developed a powerful new method to precisely edit DNA by combining cutting-edge genetic engineering with artificial intelligence. This technique opens the door to more accurate modeling of human diseases and lays the groundwork for next-generation gene therapies.

Precise and targeted DNA editing by small point mutations as well as the integration of whole genes via CRISPR/Cas technology has great potential for applications in biotechnology and gene therapy. However, it is very important that the so-called gene scissors do not cause any unintended genetic changes, but maintain genomic integrity to avoid unintended side effects. Normally, double-stranded breaks in the DNA molecule are accurately repaired in humans and other organisms. But occasionally, this DNA end joining repair results in genetic errors.

Gene editing with greatly improved precision Now, scientists from the University of Zurich (UZH), Ghent University in Belgium and the ETH Zurich have developed a new method which greatly improves the precision of genome editing. Using artificial intelligence (AI), the tool called Pythia predicts how cells repair their DNA after it is cut by gene editing tools such as CRISPR/Cas9. “Our team developed tiny DNA repair templates, which act like molecular glue and guide the cell to make precise genetic changes,” says lead author Thomas Naert, who pioneered the technology at UZH and is currently a postdoc at Ghent University.

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