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In 2023, the market for autonomous tractors is expected to be worth US$1.5 billion. The total market value is predicted to increase at a phenomenal CAGR (Compound Annual Growth Rate) of 24% from 2023 to 2033, reaching US$ 13 billion.

A team of researchers with the New York State University (NYU) has done the seemingly impossible: they've successfully designed a semiconductor chip with no hardware definition language. Using only plain English - and the definitions and examples within it that can define and describe a semiconductor processor - the team showcased what human ingenuity, curiosity, and baseline knowledge can do when aided by the AI prowess of ChatGPT.

While surprising, it goes further: the chip wasn't only designed. It was manufactured; it was benchmarked, and it worked. The two hardware engineers' usage of plain English showcases just how valuable and powerful ChatGPT can be (as if we still had doubts, following the number of awe-inspiring things it's done already).

Journal Link

Chip-Chat: Challenges and Opportunities in Conversational Hardware Design

Natural DNA is often double-stranded: one strand to encode the genes and one backup strand, intertwined in a double helix. The double helix is stabilized by Watson-Crick interactions, which allow the two strands to recognize and pair with one another. Yet there exists another, lesser-known class of interactions between DNA. These so-called normal or reverse Hoogsteen interactions allow a third strand to join in, forming a beautiful triple helix.

In a recent paper, published in Advanced Materials, researchers from the Gothelf lab debut a general method to organize double-stranded DNA, based on Hoogsteen interactions. The study unambiguously demonstrates that triplex-forming strands are capable of sharply bending or “folding” double-stranded DNA to create compacted structures. The appearance of these structures range from hollow two-dimensional shapes to dense 3D constructs and everything in-between, including a structure resembling a potted flower. Gothelf and co-workers have named their method triplex origami.

Journal Article:

Folding Double-Stranded DNA into Designed Shapes with Triplex-Forming Oligonucleotides

Abstract

Graphene has recently gained significant interest owing to its advantageous physicochemical and biological properties. However, its preparation strategies, main properties, chemical derivatives, and advanced applications in the multidimensional fields of lubrication, electricity, and tissue engineering are rarely reported. Hence, this review presents comprehensive discussions on current states of graphene as effective reinforcements to apply into these fields. First, graphene preparation methods are analyzed, and its main properties and chemical derivatives are discussed. Then, the friction-reduction and antiwear mechanisms of graphene are summarized. Next, the advanced applications of graphene in electricity and tissue engineering are described. Finally, the review is concluded by presenting outlooks on key challenges and future opportunities for extending preparation methods and multidimensional applications of the graphene-based materials.

Abstract

Advances in nanoscience have enabled the synthesis of nanomaterials, such as graphene, from low-value or waste materials through flash Joule heating. Though this capability is promising, the complex and entangled variables that govern nanocrystal formation in the Joule heating process remain poorly understood. In this work, machine learning (ML) models are constructed to explore the factors that drive the transformation of amorphous carbon into graphene nanocrystals during flash Joule heating. An XGBoost regression model of crystallinity achieves an r2 score of 0.8051 ± 0.054. Feature importance assays and decision trees extracted from these models reveal key considerations in the selection of starting materials and the role of stochastic current fluctuations in flash Joule heating synthesis. Furthermore, partial dependence analyses demonstrate the importance of charge and current density as predictors of crystallinity, implying a progression from reaction-limited to diffusion-limited kinetics as flash Joule heating parameters change. Finally, a practical application of the ML models is shown by using Bayesian meta-learning algorithms to automatically improve bulk crystallinity over many Joule heating reactions. These results illustrate the power of ML as a tool to analyze complex nanomanufacturing processes and enable the synthesis of 2D crystals with desirable properties by flash Joule heating.

Abstract

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize.

You can take a look at the technology demoed in this video at Kahnmigo!

Abstract:

CMOS technology and its continuous scaling have made electronics and computers accessible and affordable for almost everyone on the globe; in addition, they have enabled the solutions of a wide range of societal problems and applications. Today, however, both the technology and the computer architectures are facing severe challenges/walls making them incapable of providing the demanded computing power with tight constraints. This motivates the need for the exploration of novel architectures based on new device technologies; not only to sustain the financial benefit of technology scaling, but also to develop solutions for extremely demanding emerging applications. This paper presents two computation-in-memory based accelerators making use of emerging memristive devices; they are Memristive Vector Processor and RRAM Automata Processor. The preliminary results of these two accelerators show significant improvement in terms of latency, energy and area as compared to today's architectures and design.

In this paper authors from UCSB and Microsoft Research propose the LONGMEM framework, which enables language models to cache long-form prior context or knowledge into the non-differentiable memory bank and take advantage of them via a decoupled memory module to address the memory staleness problem. They create a revolutionary residual side network (SideNet) to achieve decoupled memory. A frozen backbone LLM is used to extract the paired attention keys and values from the previous context into the memory bank. The resulting attention query of the current input is utilized in the SideNet’s memory-augmented layer to access cached (keys and values) for earlier contexts. The associated memory augmentations are then fused into learning hidden states via a joint attention process.

Paper:

Augmenting Language Models with Long-Term Memory

Progress in drug testing and regenerative medicine could greatly benefit from laboratory-engineered human tissues built of a variety of cell types with precise 3D architecture. But production of greater than millimeter sized human tissues has been limited by a lack of methods for building tissues with embedded life-sustaining vascular networks.

Context is an important part of understanding the meaning of natural language, but most neuroimaging studies of meaning use isolated words and isolated sentences with little context. In this study, we examined whether the results of neuroimaging language studies that use out-of-context stimuli generalize to natural language. We find that increasing context improves the quality of neuroimaging data and changes where and how semantic information is represented in the brain. These results suggest that findings from studies using out-of-context stimuli may not generalize to natural language used in daily life

There is a major health crisis in terms of the shortage of organs. Since 2013, the total number of patients requiring a transplant has doubled while the number of available donor organs has remained relatively the same. According to the Health Resources & Services Administration, every day 17 people die waiting for an organ transplant in the US. This issue is now a public health crisis. Fortunately, due to the advancement of technology, three-dimensional (3D)-printed organs have become a reality.

In 2014, a California-based company called Organovo was the first to successfully engineer commercially available 3D-bioprinted human livers and kidneys. 3D printing in healthcare is used to create living human cells or tissues for regenerative medicine and tissue engineering purposes. The process of 3D printing typically begins with obtaining a sample of a patient’s own cells to grow and expand outside the body in a sterile incubator or bioreactor. These cells are then fed with nutrients called ‘media’ and mixed with a gel that acts as a glue. This mixture is then loaded into a printing chamber to build tissues by building the material up layer by layer.

Currently, the biggest challenge is to get the organs to function as they should. Despite the tremendous amount of progress being made in this field, Dr Anthony Atala and his colleagues at the Wake Forest Institute for Regenerative Medicine are conservative with their estimate about the number of years remaining before fully functioning 3D-printed organs can be implanted into humans.

In spite of the unknown timeline of when bioprinting organs can become an available option to patients, researchers are optimistic about the affordability of it for patients and their caregivers. The cost associated with organ failure is very high: just to keep a patient on dialysis is estimated to cost around C$350,000 ($270,000) in Canada, according to Ferguson and colleagues. According to research published by the American Society of Nephrology, in 2020 the average cost of a kidney transplant was $442,500, while 3D printers retail for upwards of $100,000, depending on their complexity. Adding costs of surgery and maintaining the 3D-printed organs could still be cheaper than a kidney transplant, according to Jennifer Lewis, a professor at Harvard University’s Wyss Institute for Biologically Inspired Engineering.

This is an exciting field that is still being developed and its speculated affordability is a good sign for patients and their caregivers.