What if the singularity heralds not AI dominance, but a profound unity consciousness, connecting human and machine minds into a symbiotic, transcendent intelligence?
Scientists used machine learning to discover what they say could be a new way to speed up the process of breaking down plastic significantly, Vice reports.
As detailed in a new paper published in the journal Nature, a research team from the University of Texas at Austin modified an enzyme to break down the individual components of polyethylene terephthalate (PET), a commonly used plastic that makes up a staggering 12 percent of global waste.
Impressively, the modified enzyme also reduced the amount of time it takes for the plastic to degrade from months to a just single week.
Battery technologies that can reliably operate at very low temperatures could be highly valuable for a wide range of applications. These batteries could, for instance, power devices, vehicles, and robotic systems in outer space, deep under the sea, and in other extreme environments.
AMD on Monday announced its new artificial intelligence chips for everything from cutting-edge data centers to advanced laptops, ramping up its challenge to the runaway market leader Nvidia.
Demand has exploded in the past two years for the specialized processors that help develop, train and run AI applications such as ChatGPT.
AMD has emerged as one of Nvidia’s most serious contenders and CEO Lisa Su said the firm’s next-generation processors will rival the top offerings from competitors.
AMD is releasing a chip later this year with 288 GB of cache on it! My high-end PC only has 256 GB of memory in total with only 0.0188 GB of cache on my main chip.
NVIDIA today announced the general availability of NVIDIA ACE generative AI microservices to accelerate the next wave of digital humans, as well as new generative AI breakthroughs coming soon to the platform.
Nvidia Corp. has launched Rubin, its next-generation artificial intelligence (AI) chip platform, which will be available in 2026, according to CEO Jensen Huang. The announcement was made during a presentation at National Taiwan University in Taipei as part of the Computex trade fair.
Rubin platform to include advanced CPUs, GPUs, and networking chips.
The Rubin chip family will include new GPUs, CPUs, and networking processors. The new CPU, called Versa, will be geared to improve AI capabilities. The GPUs, which are critical for powering AI applications, will use next-generation high-bandwidth memory from industry giants including SK Hynix, Micron, and Samsung. Despite the enthusiasm surrounding the introduction, Huang revealed only limited information regarding the Rubin platform’s specific features and capabilities.
Learn the basics of Convolutional Neural Networks (CNNs) in this beginner-friendly guide. Discover how they work, their applications in image recognition, and how they’re changing the world of AI.
Conductive aerogels have gained significant research interests due to their ultralight characteristics, adjustable mechanical properties, and outstanding electrical performance1,2,3,4,5,6. These attributes make them desirable for a range of applications, spanning from pressure sensors7,8,9,10 to electromagnetic interference shielding11,12,13, thermal insulation14,15,16, and wearable heaters17,18,19. Conventional methods for the fabrication of conductive aerogels involve the preparation of aqueous mixtures of various building blocks, followed by a freeze-drying process20,21,22,23. Key building blocks include conductive nanomaterials like carbon nanotubes, graphene, Ti3C2Tx MXene nanosheets24,25,26,27,28,29,30, functional fillers like cellulose nanofibers (CNFs), silk nanofibrils, and chitosan29,31,32,33,34, polymeric binders like gelatin25,26, and crosslinking agents that include glutaraldehyde (GA) and metal ions30,35,36,37. By adjusting the proportions of these building blocks, one can fine-tune the end properties of the conductive aerogels, such as electrical conductivities and compression resilience38,39,40,41. However, the correlations between compositions, structures, and properties within conductive aerogels are complex and remain largely unexplored42,43,44,45,46,47. Therefore, to produce a conductive aerogel with user-designated mechanical and electrical properties, labor-intensive and iterative optimization experiments are often required to identify the optimal set of fabrication parameters. Creating a predictive model that can automatically recommend the ideal parameter set for a conductive aerogel with programmable properties would greatly expedite the development process48.
Machine learning (ML) is a subset of artificial intelligence (AI) that builds models for predictions or recommendations49,50,51. AI/ML methodologies serve as an effective toolbox to unravel intricate correlations within the parameter space with multiple degrees of freedom (DOFs)50,52,53. The AI/ML adoption in materials science research has surged, particularly in the fields with available simulation programs and high-throughput analytical tools that generate vast amounts of data in shared and open databases54, including gene editing55,56, battery electrolyte optimization57,58, and catalyst discovery59,60. However, building a prediction model for conductive aerogels encounters significant challenges, primarily due to the lack of high-quality data points. One major root cause is the lack of standardized fabrication protocols for conductive aerogels, and different research laboratories adopt various building blocks35,40,46. Additionally, recent studies on conductive aerogels focus on optimizing a single property, such as electrical conductivity or compressive strength, and the complex correlations between these attributes are often neglected to understand37,42,61,62,63,64. Moreover, as the fabrication of conductive aerogels is labor-intensive and time-consuming, the acquisition rate of training data points is highly limited, posing difficulties in constructing an accurate prediction model capable of predicting multiple characteristics.
Herein, we developed an integrated platform that combines the capabilities of collaborative robots with AI/ML predictions to accelerate the design of conductive aerogels with programmable mechanical and electrical properties (see Supplementary Fig. 1 for the robot–human teaming workflow). Based on specific property requirements, the robots/ML-integrated platform was able to automatically suggest a tailored parameter set for the fabrication of conductive aerogels, without the need for conducting iterative optimization experiments. To produce various conductive aerogels, four building blocks were selected, including MXene nanosheets, CNFs, gelatin, and GA crosslinker (see Supplementary Note 1 and Supplementary Fig. 2 for the selection rationale and model expansion strategy). Initially, an automated pipetting robot (i.e., OT-2 robot) was operated to prepare 264 mixtures with varying MXene/CNF/gelatin ratios and mixture loadings (i.e.
