🤖🦾 3D Printing Semiconductors, Peak Data, and Deep Learning by Physical Dynamics

In partnership with

ATLANT 3D: Shaping the Future of Atomic-Scale Manufacturing for the Semiconductor Industry

Founded in 2018 by Maksym Plakhotnyuk, Ivan Kundrata, and Julien Bachmann, ATLANT 3D was built to pioneer advanced 3D printing technology for atomic-scale manufacturing.

Their solutions support next-generation devices, 2D nanoelectronics, 5G infrastructure, MEMS, sensors, and optical systems. They’ve even developed the first-ever in-space atomic layer printer, enabling on-demand manufacturing aboard the International Space Station.

In September 2022, ATLANT 3D secured a €15M Series A funding round led by West Hill Capital, with participation from existing investors, including Sony Group Corporation.

Future of Computing News

⚛️ Israel’s First Superconducting Quantum Computer Now Operational (Quantum Computing Report)

⚛️ Redesigning the quantum computer: Researchers in the US have come up with a new modular architecture for scaling a superconducting quantum computer (EENews Europe)

🦾 TSMC says first advanced U.S. chip plant ‘dang near back’ on schedule. Here’s an inside look at the Arizona fab (CNBC)

🦾 Chip Cities Rise in Japan’s Fields of Dreams (Bloomberg)

🦾 NVIDIA Unveils Its Most Affordable Generative AI Supercomputer (Nvidia)

🦾 Nvidia Introduces Device Aimed at Small Companies, Hobbyists for AI Use (WSJ)

🦾 Microsoft acquires twice as many Nvidia AI chips as tech rivals (FT)

🦾 Self-Assembly Trick Makes Transistors and Diodes (IEEE Spectrum)

🤖 “We’ve achieved peak data and there’ll be no more,” OpenAI’s former chief scientist told a crowd of AI researchers (The Verge)

🤖 Falcon 3 series sets new benchmarks for open-source LLMs on a single GPU (the Decoder)

🤖 Introducing Phi-4: Microsoft’s Newest Small Language Model Specializing in Complex Reasoning (Microsoft)

Funding News

🤖 AI unicorn Zhipu raises $412 million in new funding round (technode)

🤖 SandboxAQ Announces More Than $300 Million of Funding to Drive Next Era of AI (Quantum Insider)

🦾 AMD backs $333M funding round for cloud infrastructure provider Vultr (Silicon Angle)

🦾 SK Hynix secures $458m in CHIPS Act funding (DCD)

💎 EU invests €81m in Spanish synthetic diamonds factory to boost semiconductor production (Innovation News Network)

⚛️ Indian Quantum Tech Startup Quanfluence Raises $2 Million in Seed Funding (Quantum Computing Report)

⚛️ BlueQubit raises $10M to take Quantum software into real-world applications (TechCrunch)

⚛️ BosonQ Psi Raises $3M in Seed Funding to Advance Quantum-Inspired Simulation Technology (Quantum Insider)

🌐 Databricks Valuation Hits US$62bn After Latest VC Round (Technology Magazine)

Good Reads

🎈 The RAM myth (purplesyringa)

“The RAM myth is a belief that modern computer memory resembles perfect random-access memory. Cache is seen as an optimization for small data: if it fits in L2, it’s going to be processed faster; if it doesn’t, there’s nothing we can do.”

🤖 AI will be dead in five years (Erik Gahner Larsen)

“There is this joke that people do statistics in R, machine learning in Python, and AI in PowerPoint. (I did not say it is a funny joke.)”

🤖 Computing inside an AI (Will Whitney)

“I propose a new mode of interaction, where models play the role of computer (e.g. phone) applications: providing a graphical interface, interpreting user inputs, and updating their state. In this mode, instead of being an “agent” that uses a computer on behalf of the human, AI can provide a richer and more powerful computing environment for us to use.”

🦾 Addressing interconnect challenges for enhanced computing performance (Science)

“The shrinking footprint of integrated circuits is now shifting the limits of performance from the transistors themselves to the interconnections between them.“

🦾 Reservoir direct feedback alignment: deep learning by physical dynamics (Nature Communications Physics)

“Here, we present an alternative training algorithm that combines two emerging concepts: reservoir computing (RC) and biologically inspired training.”

🦾 Kernel approximation using analogue in-memory computing (Nature Machine Intelligence)

“Kernel functions are vital ingredients of several machine learning (ML) algorithms but often incur substantial memory and computational costs. We introduce an approach to kernel approximation in ML algorithms suitable for mixed-signal analogue in-memory computing (AIMC) architectures.”

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