Despite a relatively widespread understanding that AI is an inevitable force for winning market share and serving customers, by name estimates some 80-90% of AI projects fail. When AI projects lead to successful deployments and strategic or measurable ROI, it isn't because the right vendor was chosen, or the right algorithm was used. It's because someone lead the charge and not only chose the right project – but won the trust and air cover needed to see it through to deployment. At Emerj, we refer to an "AI catalyst" as: Someone who leads their company to AI adoption and ROI. But what does it mean to "lead" in this context?

Believe it or not, fighter jets, flying cars, natural gas pipelines and plastic bottles may be more alike than you think. They might one day be made with TuFF -- a high-performance short-fiber composite material invented at the University of Delaware that is superstrong, ultra-lightweight and virtually indestructible. It might even be the Superman of materials. Developed by researchers at UD's Center for Composite Materials as part of a Defense Advanced Projects Agency (DARPA) Defense Sciences Office program, TuFF (Tailored Universal Feedstock for Forming) has properties equal to the very best composites used in space and aerospace applications today. And, according to CCM Director Jack Gillespie, the uses for TuFF are starting to take off -- literally.

GM has released more details about its next-generation Ultium batteries, including plans for lithium-metal (Li-metal) technology to boost performance and energy density. The automaker announced that it has signed an agreement to work with SolidEnergy Systems (SES), an MIT spinoff developing prototype Li-metal batteries with nearly double the capacity of current lithium-ion cells. As a reminder, Li-metal batteries replace carbon anodes with lithium metal, allowing for lighter and more powerful cells. The challenge with the technology is increased resistance and "dendrite" filaments that tend to form on the anodes, making batteries short-circuit and heat up. Previous lithium-metal batteries would only work when heated up to 175 degrees F, but SolidEnergy developed an electrolyte coating for lithium metal foil that works at room temperature.

The biggest energy transition in history is well and truly underway, and nowhere is the shift more readily apparent than in the transport industry. Wall Street is almost unanimous that electric vehicles are the future of the industry, with EV sales already outpacing ICE sales in markets such as Norway. That kind of exponential growth can only mean one thing: Explosive demand for the metals that go into those batteries. Demand for battery metals is projected to soar as the transport industry continues to electrify at a record pace. In fact, there's a real danger that current mining technologies might struggle to keep up with the demand for battery metals in the near future. Thankfully, Artificial intelligence (AI) can not only be deployed to help improve the way these crucial elements are mined but can replace them altogether.

Elisabeth Skoda speaks to three industry leaders to find out more about how robotics and automation help tackle today's challenges. In the autumn of 2020, Japanese engineers presented a 18 metre-tall, 25-tonne heavy humanoid robot that could walk, wave its hand and even take the knee. This giant Gundam robot was inspired by a 1970s anime series that subsequently evolved into a multi-billion-dollar franchise. The technical challenges that had to be overcome to achieve this kind of movement were immense, given the weight and size of the robot. On a smaller scale, robots have been a key part of the packaging industry for decades, and while advances maybe have not been as spectacular as Gundam, they are just as impressive.

Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling robotic platforms and intelligent augmentation platforms such as augmented reality devices to perceive and understand the physical world. In this paper, we observe that there are three "drivers" that impose requirements on spatially intelligent agents and thus VPR systems: 1) the particular agent including its sensors and computational resources, 2) the operating environment of this agent, and 3) the specific task that the artificial agent carries out. In this paper, we characterize and survey key works in the VPR area considering those drivers, including their place representation and place matching choices. We also provide a new definition of VPR based on the visual overlap -- akin to spatial view cells in the brain -- that enables us to find similarities and differences to other research areas in the robotics and computer vision fields. We identify numerous open challenges and suggest areas that require more in-depth attention in future works.

Model predictive control is an advanced control approach for multivariable systems with constraints, which is reliant on an accurate dynamic model. Most real dynamic models are however affected by uncertainties, which can lead to closed-loop performance deterioration and constraint violations. In this paper we introduce a new algorithm to explicitly consider time-invariant stochastic uncertainties in optimal control problems. The difficulty of propagating stochastic variables through nonlinear functions is dealt with by combining Gaussian processes with polynomial chaos expansions. The main novelty in this paper is to use this combination in an efficient fashion to obtain mean and variance estimates of nonlinear transformations. Using this algorithm, it is shown how to formulate both chance-constraints and a probabilistic objective for the optimal control problem. On a batch reactor case study we firstly verify the ability of the new approach to accurately approximate the probability distributions required. Secondly, a tractable stochastic nonlinear model predictive control approach is formulated with an economic objective to demonstrate the closed-loop performance of the method via Monte Carlo simulations.

Bayesian optimization has emerged as a powerful strategy to accelerate scientific discovery by means of autonomous experimentation. However, expensive measurements are required to accurately estimate materials properties, and can quickly become a hindrance to exhaustive materials discovery campaigns. Here, we introduce Gemini: a data-driven model capable of using inexpensive measurements as proxies for expensive measurements by correcting systematic biases between property evaluation methods. We recommend using Gemini for regression tasks with sparse data and in an autonomous workflow setting where its predictions of expensive to evaluate objectives can be used to construct a more informative acquisition function, thus reducing the number of expensive evaluations an optimizer needs to achieve desired target values. In a regression setting, we showcase the ability of our method to make accurate predictions of DFT calculated bandgaps of hybrid organic-inorganic perovskite materials. We further demonstrate the benefits that Gemini provides to autonomous workflows by augmenting the Bayesian optimizer Phoenics to yeild a scalable optimization framework leveraging multiple sources of measurement. Finally, we simulate an autonomous materials discovery platform for optimizing the activity of electrocatalysts for the oxygen evolution reaction. Realizing autonomous workflows with Gemini, we show that the number of measurements of a composition space comprising expensive and rare metals needed to achieve a target overpotential is significantly reduced when measurements from a proxy composition system with less expensive metals are available.

The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large high-quality visio-linguistic datasets for learning complementary information (across image and text modalities). In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset (https://github.com/google-research-datasets/wit) to better facilitate multimodal, multilingual learning. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval. WIT has four main and unique advantages. First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing). Second, WIT is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 12K examples) and provides cross-lingual texts for many images. Third, WIT represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, WIT provides a very challenging real-world test set, as we empirically illustrate using an image-text retrieval task as an example.

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically-optimized, functionally graded structures, particularly when those structures are represented as discrete density maps. One na\"ive approach to creating variable-density cellular structures is simply replacing the discrete density map with an unselective type of unit cells having corresponding densities. However, doing so breaks the desired mechanical behavior, as equivalent density alone does not guarantee equivalent mechanical properties. Another approach uses homogenization methods to estimate each pre-defined unit cell's effective properties and remaps the unit cells following a scaling law. However, a scaling law merely mitigates the problem by performing an indirect and inaccurate mapping from the material property space to single-type unit cells. In contrast, we propose a deep generative model that resolves this problem by automatically learning an accurate mapping and generating diverse cellular unit cells conditioned on desired properties (i.e., Young's modulus and Poisson's ratio). We demonstrate our method via the use of implicit function-based unit cells and conditional generative adversarial networks. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (relative error <5%), 2) create functionally graded cellular structures with high-quality interface connectivity (98.7% average overlap area at interfaces), and 3) improve the structural performance over the conventional topology-optimized variable-density structure (84.4% reduction in concentrated stress and extra 7% reduction in displacement).