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In addition to laser-assisted bioprinting, other light-based 3D bioprinting techniques include digital light processing (DLP) and two-photon polymerization (TPP)-based 3D bioprinting. DLP uses a digital micro-mirror device to project a patterned mask of ultraviolet (UV)/visible range light onto a polymer solution, which in turn results in photopolymerization of the polymer in contact [56, 57]. DLP can achieve high resolution with rapid printing speed regardless of the layer’s complexity and area. In this method of 3D bioprinting, the dynamics of the polymerization can be regulated by modulating the power of the light source, the printing rate, and the type and concentrations of the photoinitiators used. TPP, on the other hand, utilizes a focused near-infrared femtosecond laser of wavelength 800 nm to induce polymerization of the monomer solution [56]. TPP can provide a very high resolution beyond the light diffraction limit since two-photon absorption only happens in the center region of the laser focal spot where the energy is above the threshold to trigger two-photon absorption [56].

The recent development of the integrated tissue and organ printer (ITOP) by our group allows for bioprinting of human scale tissues of any shape [45]. The ITOP facilitates bioprinting with very high precision; it has a resolution of 50 μm for cells and 2 μm for scaffolding materials. This enables recapitulation of heterocellular tissue biology and allows for fabrication of functional tissues. The ITOP is configured to deliver the bioink within a stronger water-soluble gel, Pluronic F-127, that helps the printed cells to maintain their shape during the printing process. Thereafter, the Pluronic F-127 scaffolding is simply washed away from the bioprinted tissue. To ensure adequate oxygen diffusion into the bioprinted tissue, microchannels are created with the biodegradable polymer, polycaprolactone (PCL). Stable human-scale ear cartilage, bone, and skeletal muscle structures were printed with the ITOP, which when implanted in animal models, matured into functional tissue and developed a network of blood vessels and nerves [45]. In addition to the use of materials such as Pluronic F-127 and PCL for support scaffolds, other strategies for improving structural integrity of the 3D bioprinted constructs include the use of suitable thickening agents such as hydroxyapatite particles, nanocellulose, and Xanthan and gellan gum. Further, the use of hydrogel mixtures instead of a single hydrogel is a helpful strategy. For example, the use of gelatin-methacrylamide (GelMA)/hyaluronic acid (HA) mixture instead of GelMA alone shows enhanced printability since HA improves the viscosity of mixture while crosslinking of GelMA retains post-printing structural integrity [58].

To date, several studies have investigated skin bioprinting as a novel approach to reconstruct functional skin tissue [44, 59,60,61,62,63,64,65,66,67]. Some of the advantages of fabrication of skin constructs using bioprinting compared to other conventional tissue engineering strategies are the automation and standardization for clinical application and precision in deposition of cells. Although conventional tissue engineering strategies (i.e., culturing cells on a scaffold and maturation in a bioreactor) might currently achieve similar results to bioprinting, there are still many aspects that require improvements in the production process of the skin, including the long production times to obtain large surfaces required to cover the entire burn wounds [67]. There are two different approaches to skin bioprinting: in situ bioprinting and in vitro bioprinting. Both these approaches are similar except for the site of printing and tissue maturation. In situ bioprinting involves direct printing of pre-cultured cells onto the site of injury for wound closure allowing for skin maturation at the wound site. The use of in situ bioprinting for burn wound reconstruction provides several advantages, including precise deposition of cells on the wound, elimination of the need for expensive and time-consuming in vitro differentiation, and the need for multiple surgeries [68]. In the case of in vitro bioprinting, printing is done in vitro and the bioprinted skin is allowed to mature in a bioreactor, after which it is transplanted to the wound site. Our group is working on developing approaches for in situ bioprinting [69]. An inkjet-based bioprinting system was developed to print primary human keratinocytes and fibroblasts on dorsal full-thickness (3 cm × 2.5 cm) wounds in athymic nude mice. First, fibroblasts (1.0 × 105 cells/cm2) incorporated into fibrinogen/collagen hydrogels were printed on the wounds, followed by a layer of keratinocytes (1.0 × 107 cells/cm2) above the fibroblast layer [69]. Complete re-epithelialization was achieved in these relatively large wounds after 8 weeks. This bioprinting system involves the use of a novel cartridge-based delivery system for deposition of cells at the site of injury. A laser scanner scans the wound and creates a map of the missing skin, and fibroblasts and keratinocytes are printed directly on to this area. These cells then form the dermis and epidermis, respectively. This was further validated in a pig wound model, wherein larger wounds (10 cm × 10 cm) were treated by printing a layer of fibroblasts followed by keratinocytes (10 million cells each) [69]. Wound healing and complete re-epithelialization were observed by 8 weeks. This pivotal work shows the potential of using in situ bioprinting approaches for wound healing and skin regeneration. Clinical studies are currently in progress with this in situ bioprinting system. In another study, amniotic fluid-derived stem cells (AFSCs) were bioprinted directly onto full-thickness dorsal skin wounds (2 cm × 2 cm) of nu/nu mice using a pressure-driven, computer-controlled bioprinting device [44]. AFSCs and bone marrow-derived mesenchymal stem cells were suspended in fibrin-collagen gel, mixed with thrombin solution (a crosslinking agent), and then printed onto the wound site. Two layers of fibrin-collagen gel and thrombin were printed on the wounds. Bioprinting enabled effective wound closure and re-epithelialization likely through a growth factor-mediated mechanism by the stem cells. These studies indicate the potential of using in situ bioprinting for treatment of large wounds and burns.

In June 2022, Amazon re: MARS, the company’s in-person event that explores advancements and practical applications within machine learning, automation, robotics, and space (MARS), took place in Las Vegas. The event brought together thought leaders and technical experts building the future of artificial intelligence and machine learning, and included keynote talks, innovation spotlights, and a series of breakout-session talks.

Now, in our re: MARS revisited series, Amazon Science is taking a look back at some of the keynotes, and breakout session talks from the conference. We’ve asked presenters three questions about their talks, and provide the full video of their presentation.

On June 24, Alexa AI-Natural Understanding employees Craig Saunders, director of machine learning, and Devesh Pandey, principal product manager, presented their talk, “Human-like reasoning for an AI”. Their presentation focused on how Amazon is developing human-like reasoning for Alexa, including how Alexa can automatically recover from errors such as recognizing “turn on lights” in a noisy environment (instead of “turn off lights”) when the lights are already on.

GPT Chat is a large language model trained by OpenAI, its function is to assist users in generating human-like text based on the input provided to it. It can assist with a wide range of tasks, such as answering questions, providing explanations, and generating original text. It’s designed to generate natural-sounding text, and it’s constantly learning and improving. It’s able to process and generate text at scale, making it a powerful tool for natural language processing and generation. It’s ultimate goal is to make it easier for people to interact with computers and access information using natural language.

Give it a try: https://openai.com/blog/chatgpt/

I had GPT Chat rewrite an article… More.


We’ve trained a model called which interacts in a conversational way. The dialogue format makes it possible for to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.

We are excited to introduce to get users’ feedback and learn about its strengths and weaknesses. During the research preview, usage of is free. Try it now at chat.openai.com.

Try

After six decades we have finally reached controlled fusion “ignition.” Here is how it works and what it means (and doesn’t mean):

At the Lawrence Livermore National Lab (LLNL) the National Ignition Facility (NIF) starts with the Injection Laser System (ILS), a ytterbium-doped optical fiber laser (Master Oscillator) that produces a single very lower power, 1,053 nanometer (Infrared Light) beam. This single beam is split into 48 Pre-Amplifiers Modules (PAMs) that create four beams each (192 total). Each PAM conducts a two-stage amplification process via xenon flash lamps.


Self-coding and self-updating AI algorithms appear to be on the horizon. There are talks about Pitchfork AI, a top-secret Google Labs project that can independently code, refactor, and use both its own and other people’s code.

This type of AI has actually been discussed for a long time, and DeepMind mentioned it at the beginning of the year along with the AlphaCode AI, which, according to them, “code programs in competitive level” as a middle developer. However, since February, there hasn’t been any more interesting news.

Human preferences on any topic have become diverse. Coming up with a statement that the majority of the population agrees with seems to be a challenge. Researchers at DeepMind, an AI company, accepted this challenge, trained a large language model, and fine-tuned it. They have to assume that human preferences are static and homogeneous to build the model.

The model generates statements to maximize approval among a group of people with diverse preferences. The research team fine-tuned the 70 billion parameter model, which was provided by thousand moral and political questions, and human written responses were provided for those questions. Then a reward model was trained in order to give weight to different opinions. Their best model was able to achieve more than a 65 percent preference rate.

The model was very sensitive when they tested it by just feeding part of the responses of the group of people then, the rest of the people’s opinion, which was not included, had a significant variance. Thus, the individual contribution of each consensus is equally important. There are many complicated NLP tasks like reading comprehension, fluent language generation, etc., which helped form the foundations for this LLM.