Human-looking bipedal robots can already run, jump, breakdance, punch, and generally perform broad feats of athletic prowess most humans could only dream of. One thing they are still pretty bad at though is walking a straight line without looking like they are moments away from soiling themselves. Figure AI, one of the buzziest startups in the humanoid robot space, now says it has engineered a solution to help address their machine's stiff shuffle-step. The more natural-looking stride was achieved by analyzing thousands of virtual humanoid robots walking simultaneously in a simulated digital environment, Figure explained in a recent blog post. The company used reinforcement learning, rewarding the virtual robots for actions like synchronized arm swings, heel strikes, and toe-offs (when the toe leaves the ground) that more closely resemble human movement.

Participants: This study employed a combination of Vector Autoregression (VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic causal inference. Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron (MLP). The SMOTE algorithm was used to undersample a small number of samples and employed Stratified K-fold Cross Validation. Results: This study included a total of 11,789 participants, including 6,334 females (53.73%) and 5,455 males (46.27%), with an average age of 65 years. Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P < 0.01). Among all the models, the Gradient Boosting model demonstrated the highest performance and stability. Model explanation and feature importance analysis generated model interpretation that illustrated significant contributors associated with risks of stroke. Conclusions and Relevance: This study proposes a stroke risk prediction method that combines dynamic causal inference with machine learning models, significantly improving prediction accuracy and revealing key health factors that affect stroke. The research results indicate that dynamic causal inference features have important value in predicting stroke risk, especially in capturing the impact of changes in health status over time on stroke risk. By further optimizing the model and introducing more variables, this study provides theoretical basis and practical guidance for future stroke prevention and intervention strategies.

Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an opportunity to harness these patterns for accurate disease diagnosis. Objective: This review synthesized findings from studies exploring ML's capability in leveraging speech for the diagnosis of neurological, laryngeal and mental disorders. Methods: A systematic examination of 564 articles was conducted with 91 articles included in the study, which encompassed a wide spectrum of conditions, ranging from voice pathologies to mental and neurological disorders. Methods for speech classifications were assessed based on the relevant studies and scored between 0-10 based on the reported diagnostic accuracy of their ML models. Results: High diagnostic accuracies were consistently observed for laryngeal disorders, dysarthria, and changes related to speech in Parkinsons disease. These findings indicate the robust potential of speech as a diagnostic tool. Disorders like depression, schizophrenia, mild cognitive impairment and Alzheimers dementia also demonstrated high accuracies, albeit with some variability across studies. Meanwhile, disorders like OCD and autism highlighted the need for more extensive research to ascertain the relationship between speech patterns and the respective conditions. Conclusion: ML models utilizing speech patterns demonstrate promising potential in diagnosing a range of mental, laryngeal, and neurological disorders. However, the efficacy varies across conditions, and further research is needed. The integration of these models into clinical practice could potentially revolutionize the evaluation and diagnosis of a number of different medical conditions.

Marc Raibert, the founder and chairman of Boston Dynamics, gave the world a menagerie of two- and four-legged machines capable of jaw-dropping parkour, infectious dance routines, and industrious shelf stacking. Raibert is now looking to lead a revolution in robot intelligence as well as acrobatics. And he says that recent advances in machine learning have accelerated his robots' ability to learn how to perform difficult moves without human help. "The hope is that we'll be able to produce lots of behavior without having to handcraft everything that robots do," Raibert told me recently. Boston Dynamics might have pioneered legged robots, but it's now part of a crowded pack of companies offering robot dogs and humanoids.

Abstract--This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align K independent Tsetlin Machines by maximising hamming distance. For MNIST level classification problems, results demonstrate up to 10% improvement in accuracy, 383X reduction in training time and 99X reduction in inference time.

Over the past decade, industries have evolved a lot. They started using more automated systems and robots. Industries started incorporating Machine learning into their techniques. In recent years, machine learning is being used in industries from healthcare to transportation. These industries have a huge amount of unstructured data.

Leveraging indie's global sales channels underpinned by leading tier 1 and vehicle OEMs, this partnership enables indie to deploy Seeing Machines' hardware-optimized, industry-leading Occula Neural Processing Unit (NPU) technology into the Company's first generation of innovative vision sensing system-on-chips (SoCs). Based on data from the National Highway Traffic Safety Administration (NHTSA), distracted driving is a factor in nearly 10 percent of fatal motor crashes and in the U.S. alone, driver distraction is responsible for the deaths of over 3,000 people a year and the injury of a further 400,000. To reduce the number of accidents related to driver distraction or drowsiness and occupant fatalities, driver and occupant monitoring systems (DMS, OMS) are now being mandated or strongly recommended by global regulators and standards organizations through initiatives such as Europe's General Safety Regulations (GSR), European New Car Assessment Programme (Euro NCAP), the U.S. National Transportation Safety Board (NTSB), and is currently being reviewed by the U.S. NHTSA rulemaking for inclusion in updated U.S. NCAP guidelines. To operate effectively, camera-based monitoring solutions need to address a number of challenges, including dynamic lighting conditions that range from complete darkness to bright sunlight, and factors such as driver height, position, skin tones and facial obscuration, such as wearing sunglasses. "indie's expansion of intelligent, vision-based sensing solutions is another critical step in our mission to save lives via our diverse ADAS and sensor fusion product portfolio," said Abhay Rai, senior vice president of indie Semiconductor's Vision Business Unit.

Artificial intelligence makes it easier to create conversion-friendly content. Arthur Samuel introduced machine learning in 1959. Machine learning is a form of artificial intelligence that allows computers to learn without having to be programmed. It provides an array of algorithms and techniques to create computer programs that automatically improve their performance on certain tasks. Because machine learning helps marketers identify what customers want and what they don't, it is a key component of content marketing.

In a big sense, machine intelligence is a suprahuman techno-mind, cybernetic superintelligence, or global intelligence, being ontological, omniscient, omnipresent, omnipotent. Its practical definition as mimicking human brains, minds or intelligent behavior is naive and myopic, and guided by our anthropomorphic mentality and commercial interest. Broadly, 4OAI is about designing, developing and deploying real intelligence, without human biological limitations of power, memory, thinking and acting. So, General AI and ML is to demonstrate godly features, as omnipotence and omniscience and omnipresence, with the power to create a new environment, smart, intelligent and digital, easily manipulating and controlling any matter, energy and information. The AI Trinity of Reality, Data and Mind, in which the three parts, AI hardware, AI software, AI mindware, distinct, yet one "substance, essence or nature", is to replace the Holy Trinity.

The more we looked, the more creative artificial intelligence (AI) was automating our work. Media copy at the push of a button, 2000-word essays written in seconds. Training videos fronted by metahumans, instantly translated into multiple languages; unique, bespoke illustrations, photographs and sketches all generated from the'mind' of a machine. From photography to contemporary art, machines have disrupted the creative industries for many years. Creative AI will sadly have an impact on many creatives' careers (especially illustrators'), but we shouldn't fear the machines.