MIT researchers have demonstrated diminutive drones that can zip around with bug-like agility and resilience, which could eventually perform these tasks. The soft actuators that propel these microrobots are very durable, but they require much higher voltages than similarly-sized rigid actuators. The featherweight robots can't carry the necessary power electronics that would allow them fly on their own. Now, these researchers have pioneered a fabrication technique that enables them to build soft actuators that operate with 75 percent lower voltage than current versions while carrying 80 percent more payload. These soft actuators are like artificial muscles that rapidly flap the robot's wings.

MIT researchers have pioneered a new fabrication technique that enables them to produce low-voltage, power-dense, high endurance soft actuators for an aerial microrobot. When it comes to robots, bigger isn't always better. Someday, a swarm of insect-sized robots might pollinate a field of crops or search for survivors amid the rubble of a collapsed building. MIT researchers have demonstrated diminutive drones that can zip around with bug-like agility and resilience, which could eventually perform these tasks. The soft actuators that propel these microrobots are very durable, but they require much higher voltages than similarly-sized rigid actuators.

Pattern discovery in multidimensional data sets has been a subject of research since decades. There exists a wide spectrum of clustering algorithms that can be used for that purpose. However, their practical applications share in common the post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be a bottleneck of the process, especially in the cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters, but also a conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KnAC), which main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution does not depend on any ready clustering algorithm, nor introduce one. Instead KnAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and model-agnostic. We demonstrate the feasibility of our method on artificially, reproducible examples and on a real life use case scenario.

The classic win-win has a key flaw in that it cannot offer the parties the right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win-win situation in this paper. The proposed method employs Fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiations scenarios such as the Iranian uranium enrichment negotiations, the Iraqi-Jordanian oil deal, and the iron ore negotiation (2005-2009). The presented model has shown to be a useful tool in practice and can be easily generalized to be utilized in other domains as well.

Porosity has been identified as the key indicator of the durability properties of concrete exposed to aggressive environments. This paper applies ensemble learning to predict porosity of high-performance concrete containing supplementary cementitious materials. The concrete samples utilized in this study are characterized by eight composition features including w/b ratio, binder content, fly ash, GGBS, superplasticizer, coarse/fine aggregate ratio, curing condition and curing days. The assembled database consists of 240 data records, featuring 74 unique concrete mixture designs. The proposed machine learning algorithms are trained on 180 observations (75%) chosen randomly from the data set and then tested on the remaining 60 observations (25%). The numerical experiments suggest that the regression tree ensembles can accurately predict the porosity of concrete from its mixture compositions. Gradient boosting trees generally outperforms random forests in terms of prediction accuracy. For random forests, the out-of-bag error based hyperparameter tuning strategy is found to be much more efficient than k-Fold Cross-Validation.

AI/ML models are often exported. For example, large tech companies tend to congregate in particular parts of the world and sell software-as-a-service, platform-as-a-service, even surveillance-as-a-service in neatly-bound packages on a subscription basis to individuals, companies, and authorities around the world [1]. As a service/product/labour, AI/ML systems are also frequently exceptionalized where sleek models are intentionally portrayed to magically appear from thin air, skipping the commodity chain altogether. Software and virtual products are often decoupled from their material entanglements -- divorced from the vast lithium farms of the Atacama Desert, the cold data centers underneath the Alps, the data annotation centers scattered across the world, and the digital graveyards in the Korle Lagoon [2], [3]. As a feature of our capitalist society, all global supply chains have hidden components -- whether it is the obfuscation of sweatshops that operate on child labor or the efforts made towards washing the blood off of the diamond industry.

Its use dates back to early civilizations, and today it is the most popular composite choice in the world. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too. Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti's research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation.

We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.

Brainnwave, founded in 2014, provides an augmented business intelligence platform that leverages advanced machine learning and statistical models to transform data. Now, the Edinburgh-based company has teamed up with a company nearly 60 years its senior: Hatch, which delivers engineering, construction, and consulting solutions for industries spanning mining, metallurgy, energy, and infrastructure. The new co-venture will combine Brainnwave's AI-powered analytics with Hatch's sector knowledge to deliver improved solutions for those industries. At the launch of the co-venture, Hatch announced a Series A investment in Brainnwave, not disclosing the amount of the investment. "This partnership made sense because both organizations are like-minded in their entrepreneurial approach, willingness to do things differently and challenge the status quo, and propensity to develop game-changing solutions," said Steve Coates, CEO and co-founder of Brainnwave.

The fashion technology outfit co-founded by José Neves plans to use AI to improve made-to-order fashion supply chain. PlatformE, a fashion technology group co-founded by Farfetch's José Neves, is buying start-up Cambridge-based Catalyst AI for an undisclosed sum. According to the announcement, the deal will bring Catalyst AI's machine learning tools for optimizing fashion supply chains to PlatformE's services, which focus on on-demand and made-to-order fashion. "We're delighted to strengthen our capabilities with Catalyst AI's innovative intellectual property, and will benefit immensely from the team's expertise and their network of talent in one of the world's leading data science ecosystems," PlatformE co-founder and chief executive officer Gonçalo Cruz said in a statement. For Catalyst AI co-founder and CEO Raymond Siems, "Gonçalo has an unwavering vision for the future of fashion."