Elon Musk's last update on Neuralink -- his company that is working on technology that will connect the human brain directly to a computer -- featured a pig with one of its chips implanted in its brain. Now Neuralink is demonstrating its progress by showing a Macaque with one of the Link chips playing Pong. At first using "Pager" is shown using a joystick, and then eventually, according to the narration, using only its mind via the wireless connection. Monkey plays Pong with his mind https://t.co/35NIFm4C7T Today we are pleased to reveal the Link's capability to enable a macaque monkey, named Pager, to move a cursor on a computer screen with neural activity using a 1,024 electrode fully-implanted neural recording and data transmission device, termed the N1 Link.

The first wireless commands to a computer have been demonstrated in a breakthrough for people with paralysis. The system is able to transmit brain signals at "single-neuron resolution and in full broadband fidelity", say researchers at Brown University in the US. A clinical trial of the BrainGate technology involved a small transmitter that connects to a person's brain motor cortex. Trial participants with paralysis used the system to control a tablet computer, the journal IEEE Transactions on Biomedical Engineering reports. The participants were able to achieve similar typing speeds and point-and-click accuracy as they could with wired systems.

The first wireless brain-computer interface (BCI) system is not only giving people with paralysis the ability to type on computer screens with their minds, but the innovation is also giving them freedom to do so anywhere. Traditional BCIs are tethered to a large transmitter with long cables, but a team from Brown University has cut the cords and replaced them with a small transmitter that sits atop the user's head. The redesigned equipment is just two inches in diameter and connects to an electrode array within the brain's motor cortex by means of the same port used by wired systems. The trials, dubbed BrainGate,' showed two men paralyzed by spinal injuries were able to type and click on a tablet just by thinking of the action, and did so with similar point-and-click accuracy and typing speeds as those with a wired system. A participant in the BrainGate clinical trial uses wireless transmitters that replace the cables normally used to transmit signals from sensors inside the brain.

Prof. Newton Howard, a Brain and Cognitive Scientist, the former Director of the MIT Mind Machine Project at the Massachusetts Institute of Technology and currently a Professor of Computational Neuroscience and Functional Neurosurgery at the University of Oxford, where he directs the Oxford Computational Neuroscience Laboratory participates in Risk Roundup to discuss "Mind Control Technology". Since the beginning of times, we humans have been creating tools to help us interact with the world around us. Now we are moving inwards and developing the tools to help us communicate with the world inside us. While the nature of tools has evolved from physical to digital, and now neural, our brain is effectively becoming the tool for interaction, communication, collaboration, and control. From electrode in many different shapes being implanted in the human brain to transmit and receive signals to non-invasive devices that translate brain waves into commands that control not only computer but also body parts are already becoming a reality.

Through the developmental process, they acquire basic physical skills (such as reaching and grasping), perceptional skills (such as object recognition and phoneme recognition), and social skills (such as linguistic communication and intention estimation) (Taniguchi et al., 2018). This open-ended online learning process involving many types of modalities, tasks, and interactions is often referred to as lifelong learning (Oudeyer et al., 2007; Parisi et al., 2019). The central question in next-generation artificial intelligence (AI) and developmental robotics is how to build an integrative cognitive system that is capable of lifelong learning and humanlike behavior in environments such as homes, offices, and outdoor. In this paper, inspired by the human whole brain architecture (WBA) approach, we introduce the idea of building an integrative cognitive system using a whole brain probabilistic generative model (WB-PGM) (see 2.1). The integrative cognitive system can alternatively be referred to as artificial general intelligence (AGI) (Yamakawa, 2021). Against this backdrop, we explore the process of establishing a cognitive architecture for developmental robots. Cognitive architecture is a hypothesis about the mechanisms of human intelligence underlying our behaviors (Rosenbloom, 2011). The study of cognitive architecture involves developing a presumably standard model of the humanlike mind (Laird et al., 2017).

We constructed a hippocampal formation (HPF)-inspired probabilistic generative model (HPF-PGM) using the structure-constrained interface decomposition method. By modeling brain regions with PGMs, this model is positioned as a module that can be integrated as a whole-brain PGM. We discuss the relationship between simultaneous localization and mapping (SLAM) in robotics and the findings of HPF in neuroscience. Furthermore, we survey the modeling for HPF and various computational models, including brain-inspired SLAM, spatial concept formation, and deep generative models. The HPF-PGM is a computational model that is highly consistent with the anatomical structure and functions of the HPF, in contrast to typical conventional SLAM models. By referencing the brain, we suggest the importance of the integration of egocentric/allocentric information from the entorhinal cortex to the hippocampus and the use of discrete-event queues.

Over the course of the next decade humans will integrate more with technology to'upgrade' our lives including brain chips and exoskeletons, a new report claims. Produced by dentsu, a global advertising and digital agency, the report looks at ways the world could change over the next 10 years and the impact on global brands. 'As brands assess the impact of a seismic year and look to chart a new path to recovery, these trends provide them with a roadmap for the next decade,' the firm wrote in the executive summary to the report. One key area of change will be the continued rise of the'synthetic society' as people increasingly incorporate the latest technology into their lives. The study suggests people could even use brain chips to aid memory and exoskeletons to make us faster and stronger. Dentsu predict there will be a number of'key events' over the next decade including the FIFA eWorld Cup becoming the most watched sporting event in the world Over the next decade as automation takes away jobs and technology becomes a larger part of our lives, we will see a'human dividend' appear. Study authors claim this will come in the form of a premium on human skills robots can't do or that can't easily be automated.

Standard few-shot experiments involve learning to efficiently match previously unseen samples by class. We claim that few-shot learning should be long term, assimilating knowledge for the future, without forgetting previous concepts. In the mammalian brain, the hippocampus is understood to play a significant role in this process, by learning rapidly and consolidating knowledge to the neocortex over a short term period. In this research we tested whether an artificial hippocampal algorithm, AHA, could be used with a conventional ML model analogous to the neocortex, to achieve one-shot learning both short and long term. The results demonstrated that with the addition of AHA, the system could learn in one-shot and consolidate the knowledge for the long term without catastrophic forgetting. This study is one of the first examples of using a CLS model of hippocampus to consolidate memories, and it constitutes a step toward few-shot continual learning.

More than 320 million years ago, a reptile-like amniote ancestor abandoned aquatic habitats and fully adapted to life on land. This transition was arguably the most impactful event in vertebrate history and catalyzed evolutionary innovations. Soon after, sauropsids (birds and reptiles) diverged from the ancestors of mammals. Racing to survive in their new environments, birds and mammals evolved cognitive abilities unmatched by other vertebrates, such as vocal learning in songbirds and spoken language in humans. The evolution of the brain areas supporting these behaviors has been a dilemma for neuroscientists. On page 695 of this issue, Colquitt et al. ([ 1 ][1]) disentangle molecular similarities and differences between the song nuclei of birds and the cerebral cortex of mammals and propose that these brain areas have distinct evolutionary origins. Vocal learning circuits are part of the pallium, the roof of the most anterior vesicle of the developing neural tube. In mammals, pallial neurons are generally arranged into layers; the neocortex, in the dorsal part of the pallium, has six layers. In birds, there is no layering: The pallium is a collection of nuclei, including a set of large nuclei protruding from the ventrolateral wall of the pallium, called the dorsal ventricular ridge (DVR). Reptiles, such as turtles and lizards, also have a DVR. In the brains of other vertebrates, the pallium is simpler and there is nothing clearly comparable to the neocortex or the DVR. Thus, when the ancestors of mammals and sauropsids parted ways, they each evolved their own brain specializations—the neocortex and DVR—from expansions of the dorsal and the ventral pallium, respectively. ![Figure][2] Vocal processing circuits in birds and mammals In birds, neural circuits in the dorsal ventricular ridge (DVR, simplified schematic) resemble circuits in the mammalian neocortex. However, songbird DVR circuits are genetically related to the mammalian ventral pallium and not to the neocortex (dorsal pallium), as indicated by transcription factor expression. Moreover, in songbirds, these circuits include γ-aminobutyric acid (GABA)–ergic interneurons from the lateral ganglionic eminence (LGE), which, in mammals, are restricted to parts of the ventral pallium. RA, robust nucleus of the arcopallium GRAPHIC: KELLIE HOLOSKI/ SCIENCE Despite their anatomical differences, the bird DVR and the mammalian neocortex harbor neural circuits with similar organization and function. The DVR and neocortex both have specialized visual, auditory, and somatosensory areas and are involved in high cognitive functions, such as vocal learning, planning, and abstraction. Notably, processing of sensory inputs in the avian DVR follows some of the same characteristic rules that define the “canonical” cortical microcircuit in mammals ([ 2 ][3]). These circuits have three major components: input neurons that receive sensory information relayed by the thalamus, intratelencephalic neurons that process this information locally, and output neurons that project to motor control centers ([ 3 ][4], [ 4 ][5]) (see the figure). Given these functional similarities, several neuroscientists refer to the DVR as the “bird's cortex,” even though its neurons are not arranged in layers. Yet, functional similarities are not sufficient to answer a key question: Do DVR and neocortical circuits trace back to the same neurons in the ancestor of birds, reptiles, and mammals? The equivalent circuit hypothesis claims so and proposes that DVR nuclei are homologous to layers of the mammalian neocortex ([ 3 ][4], [ 4 ][5]). But this hypothesis is at odds with the observation that the DVR develops from the ventral pallium and the neocortex develops from the dorsal pallium. These are two distinct parts of the pallium that exist in all vertebrates ([ 5 ][6]). To understand the evolutionary relationships of DVR and neocortical neuron types, Colquitt et al. collected single-cell transcriptomics data from two DVR nuclei that form part of the song circuits—HVC (proper name) and RA (robust nucleus of the arcopallium)—in zebra finches and Bengalese finches. Comparison with transcriptomes from the pallia of the red-eared slider turtle ([ 6 ][7]) and mouse ([ 7 ][8]) revealed that DVR nuclei and neocortical layers express similar genes related to neuronal function, as expected from the equivalent circuit hypothesis. However, the expression of these genes is regulated by different sets of transcription factors. DVR neurons express transcription factors enriched in the mouse ventral pallium but not in the neocortex [as also reported in reptiles ([ 6 ][7])]. Transcription factors specify and maintain cellular identities, and cells that express the same transcription factors have the same core genetic identity and are typically considered homologous ([ 4 ][5], [ 8 ][9]). For this reason, the results from Colquitt et al. strengthen the idea that the DVR and neocortex trace back to separate regions of the ancestral pallium ([ 5 ][6], [ 6 ][7]). This suggests that complex behaviors, such as vocal learning, require neuronal operations that can be implemented only if neurons are wired up in a certain way, explaining why similar circuits evolved independently in birds and mammals. In an unexpected twist, Colquitt et al. discover another key difference between the DVR and neocortex. By examining γ-aminobutyric acid (GABA)–ergic interneurons (inhibitory neurons that project locally), they find that the entire songbird pallium is populated by a type of interneuron that is largely absent in the neocortex. These interneurons, born in the lateral ganglionic eminence (LGE), migrate to ventral pallium areas in mammals, such as the amygdala and olfactory bulb. This implies that the cortex-like microcircuits described in the avian DVR may engage an entire class of interneurons that has no counterpart in the mammalian neocortex. The study by Colquitt et al. is a teaser for what is to come. Single-cell genomics, one of the most powerful tools to understand neuronal diversity, is revolutionizing the study of brain evolution. But in single-cell comparative studies, the power and accuracy of bioinformatic analyses depend on the breadth and depth of the data. Colquitt et al. sampled a small portion of the DVR in two closely related species of finches. On the mammalian side, ventral pallium single-cell data are scarce. More data—ideally whole-pallium cell-type transcriptomes from multiple species, including amphibians and fish—are needed to clarify in detail the relationships of ventral pallium cell types in vertebrates. An evolutionary view of the DVR that acknowledges its ventral pallium nature forces reexamination of the functional analogies with the neocortex. The independent expansions of the DVR and neocortex endowed birds and mammals with the capacity to process and integrate sensory inputs in complex ways, but perhaps for different uses. Unlike the neocortex in mammals, the DVR in birds is poorly connected with entorhinal-hippocampal circuits; instead, one of its main projection targets is the hypothalamus, resembling parts of the mammalian amygdala in the ventral pallium ([ 9 ][10]). Did early mammals benefit from an enhanced representation of space to tackle complex navigation tasks? Did the bird brain specialize in computing stimulus valence? A closer look at the differences between the DVR and neocortex is needed to understand the cognitive specializations of birds and mammals. 1. [↵][11]1. B. M. Colquitt et al ., Science 371, eabd9704 (2021). [OpenUrl][12][Abstract/FREE Full Text][13] 2. [↵][14]1. A. Calabrese, 2. S. M. N. Woolley , Proc. Natl. Acad. Sci. U.S.A. 112, 3517 (2015). [OpenUrl][15][Abstract/FREE Full Text][16] 3. [↵][17]1. H. J. Karten , Curr. Biol. 23, R12 (2013). [OpenUrl][18][CrossRef][19][PubMed][20] 4. [↵][21]1. S. D. Briscoe, 2. C. W. Ragsdale , Science 362, 190 (2018). [OpenUrl][22][Abstract/FREE Full Text][23] 5. [↵][24]1. J. H. Kaas 1. L. Puelles et al ., in Evolution of Nervous Systems, J. H. Kaas, Ed. (Elsevier, ed. 2, 2017), pp. 519–555. 6. [↵][25]1. M. A. Tosches et al ., Science 360, 881 (2018). [OpenUrl][26][Abstract/FREE Full Text][27] 7. [↵][28]1. A. Zeisel et al ., Cell 174, 999 (2018). [OpenUrl][29][CrossRef][30][PubMed][31] 8. [↵][32]1. D. Arendt et al ., Nat. Rev. Genet. 17, 744 (2016). [OpenUrl][33][CrossRef][34][PubMed][35] 9. [↵][36]1. L. L. Bruce, 2. T. J. Neary , Brain Behav. Evol. 46, 224 (1995). [OpenUrl][37][CrossRef][38][PubMed][39][Web of Science][40] [1]: #ref-1 [2]: pending:yes [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: #ref-6 [8]: #ref-7 [9]: #ref-8 [10]: #ref-9 [11]: #xref-ref-1-1 "View reference 1 in text" [12]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DColquitt%26rft.auinit1%253DB.%2BM.%26rft.volume%253D371%26rft.issue%253D6530%26rft.spage%253Deabd9704%26rft.epage%253Deabd9704%26rft.atitle%253DCellular%2Btranscriptomics%2Breveals%2Bevolutionary%2Bidentities%2Bof%2Bsongbird%2Bvocal%2Bcircuits%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abd9704%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [13]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjE3OiIzNzEvNjUzMC9lYWJkOTcwNCI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3MS82NTMwLzY3Ni5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [14]: #xref-ref-2-1 "View reference 2 in text" [15]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.1408545112%26rft_id%253Dinfo%253Apmid%252F25691736%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [16]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMToiMTEyLzExLzM1MTciO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUzMC82NzYuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [17]: #xref-ref-3-1 "View reference 3 in text" [18]: {openurl}?query=rft.jtitle%253DCurr.%2BBiol.%26rft.volume%253D23%26rft.spage%253DR12%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cub.2012.11.013%26rft_id%253Dinfo%253Apmid%252F23305661%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: /lookup/external-ref?access_num=10.1016/j.cub.2012.11.013&link_type=DOI [20]: /lookup/external-ref?access_num=23305661&link_type=MED&atom=%2Fsci%2F371%2F6530%2F676.atom [21]: #xref-ref-4-1 "View reference 4 in text" [22]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DBriscoe%26rft.auinit1%253DS.%2BD.%26rft.volume%253D362%26rft.issue%253D6411%26rft.spage%253D190%26rft.epage%253D193%26rft.atitle%253DHomology%252C%2Bneocortex%252C%2Band%2Bthe%2Bevolution%2Bof%2Bdevelopmental%2Bmechanisms%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aau3711%26rft_id%253Dinfo%253Apmid%252F30309947%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNjIvNjQxMS8xOTAiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUzMC82NzYuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [24]: #xref-ref-5-1 "View reference 5 in text" [25]: #xref-ref-6-1 "View reference 6 in text" [26]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aar4237%26rft_id%253Dinfo%253Apmid%252F29724907%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [27]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNjAvNjM5MS84ODEiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUzMC82NzYuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [28]: #xref-ref-7-1 "View reference 7 in text" [29]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D174%26rft.spage%253D999%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cell.2018.06.021%26rft_id%253Dinfo%253Apmid%252F30096314%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: /lookup/external-ref?access_num=10.1016/j.cell.2018.06.021&link_type=DOI [31]: /lookup/external-ref?access_num=30096314&link_type=MED&atom=%2Fsci%2F371%2F6530%2F676.atom [32]: #xref-ref-8-1 "View reference 8 in text" [33]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BGenet.%26rft.volume%253D17%26rft.spage%253D744%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrg.2016.127%26rft_id%253Dinfo%253Apmid%252F27818507%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [34]: /lookup/external-ref?access_num=10.1038/nrg.2016.127&link_type=DOI [35]: /lookup/external-ref?access_num=27818507&link_type=MED&atom=%2Fsci%2F371%2F6530%2F676.atom [36]: #xref-ref-9-1 "View reference 9 in text" [37]: {openurl}?query=rft.jtitle%253DBrain%2BBehavior%2Band%2BEvolution%26rft.stitle%253DBrain%2BBehavior%2Band%2BEvolution%26rft.aulast%253DBruce%26rft.auinit1%253DL.%2BL.%26rft.volume%253D46%26rft.issue%253D4-5%26rft.spage%253D224%26rft.epage%253D234%26rft.atitle%253DThe%2Blimbic%2Bsystem%2Bof%2Btetrapods%253A%2Ba%2Bcomparative%2Banalysis%2Bof%2Bcortical%2Band%2Bamygdalar%2Bpopulations.%26rft_id%253Dinfo%253Adoi%252F10.1159%252F000113276%26rft_id%253Dinfo%253Apmid%252F8564465%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: /lookup/external-ref?access_num=10.1159/000113276&link_type=DOI [39]: /lookup/external-ref?access_num=8564465&link_type=MED&atom=%2Fsci%2F371%2F6530%2F676.atom [40]: /lookup/external-ref?access_num=A1995TG69500005&link_type=ISI

Welcome to Thanks, I Love It, our series highlighting something onscreen we're obsessed with this week. There's a lot to enjoy in every episode of Netflix's Blown Away. The competition show for glassblowers has quirky competitors, fun guest judges, oodles of glasswork sex puns (drink every time someone says "glory hole"), and of course, the visual thrill of seeing simple glass turned into stunning works of art. Blown Away's editing relies on slow-motion shots of artists stretching ropes of shiny, glowing glass between their tools and close ups of hands manipulating technicolor goo into fantastic shapes. That focus on the material captures the danger of working with a substance heated to 2000 degrees Fahrenheit; it also makes the glass look absolutely delicious.