Machine learning (ML) and deep learning models are extensively used for parameter optimization and regression problems. However, not all inverse problems in ML are ``identifiable,'' indicating that model parameters may not be uniquely determined from the available data and the data model's input-output relationship. In this study, we investigate the notion of model parameter identifiability through a case study focused on parameter estimation from motion sensor data. Utilizing a bipedal-spring mass human walk dynamics model, we generate synthetic data representing diverse gait patterns and conditions. Employing a deep neural network, we attempt to estimate subject-wise parameters, including mass, stiffness, and equilibrium leg length. The results show that while certain parameters can be identified from the observation data, others remain unidentifiable, highlighting that unidentifiability is an intrinsic limitation of the experimental setup, necessitating a change in data collection and experimental scenarios. Beyond this specific case study, the concept of identifiability has broader implications in ML and deep learning. Addressing unidentifiability requires proven identifiable models (with theoretical support), multimodal data fusion techniques, and advancements in model-based machine learning. Understanding and resolving unidentifiability challenges will lead to more reliable and accurate applications across diverse domains, transcending mere model convergence and enhancing the reliability of machine learning models.

The demand for a huge amount of data for machine learning (ML) applications is currently a bottleneck in an empirically dominated field. We propose a method to combine prior knowledge with data-driven methods to significantly reduce their data dependency. In this study, component-based machine learning (CBML) as the knowledge-encoded data-driven method is examined in the context of energy-efficient building engineering. It encodes the abstraction of building structural knowledge as semantic information in the model organization. We design a case experiment to understand the efficacy of knowledge-encoded ML in sparse data input (1% - 0.0125% sampling rate). The result reveals its three advanced features compared with pure ML methods: 1. Significant improvement in the robustness of ML to extremely small-size and inconsistent datasets; 2. Efficient data utilization from different entities' record collections; 3. Characteristics of accepting incomplete data with high interpretability and reduced training time. All these features provide a promising path to alleviating the deployment bottleneck of data-intensive methods and contribute to efficient real-world data usage. Moreover, four necessary prerequisites are summarized in this study that ensures the target scenario benefits by combining prior knowledge and ML generalization.

The Weekly Wrapup is an analysis of the week's insurance tech news from the editors of Digital Insurance. The staff of Digital Insurance has just returned from our third Dig In: The Digital Future of Insurance conference, held over three days in Austin, Texas. The biggest Dig In so far included nearly 1,300 attendees and 200 speakers. Key themes discussed over the course of the conference included the impact of drones on claims, how new risks drive transformation strategies and what the next age of insurance digitalization will look like. On the technology side, artificial intelligence was a particularly hot topic.

It is common to subsample Markov chain output to reduce the storage burden. Geyer (1992) shows that discarding $k-1$ out of every $k$ observations will not improve statistical efficiency, as quantified through variance in a given computational budget. That observation is often taken to mean that thinning MCMC output cannot improve statistical efficiency. Here we suppose that it costs one unit of time to advance a Markov chain and then $\theta>0$ units of time to compute a sampled quantity of interest. For a thinned process, that cost $\theta$ is incurred less often, so it can be advanced through more stages. Here we provide examples to show that thinning will improve statistical efficiency if $\theta$ is large and the sample autocorrelations decay slowly enough. If the lag $\ell\ge1$ autocorrelations of a scalar measurement satisfy $\rho_\ell\ge\rho_{\ell+1}\ge0$, then there is always a $\theta<\infty$ at which thinning becomes more efficient for averages of that scalar. Many sample autocorrelation functions resemble first order AR(1) processes with $\rho_\ell =\rho^{|\ell|}$ for some $-1<\rho<1$. For an AR(1) process it is possible to compute the most efficient subsampling frequency $k$. The optimal $k$ grows rapidly as $\rho$ increases towards $1$. The resulting efficiency gain depends primarily on $\theta$, not $\rho$. Taking $k=1$ (no thinning) is optimal when $\rho\le0$. For $\rho>0$ it is optimal if and only if $\theta \le (1-\rho)^2/(2\rho)$. This efficiency gain never exceeds $1+\theta$. This paper also gives efficiency bounds for autocorrelations bounded between those of two AR(1) processes.

Artificial intelligence is set to take a key role in the design and engineering of new cars, dreaming up lighter, stronger and more complex structures than humans can envision. Just as computing power exceeds the mathematical capability of the human mind, smart software capable of innovation and problem solving is set to push product development into new territory. Hack Rod, a team of designers, engineers, geeks, Hollywood insiders and stunt drivers is working on a way to harness the power of artificial intelligence in tandem with powerful design software produced by Autodesk. Experimenting with connectivity surrounding the emerging "internet of things", the Hack Rod crew built a basic sports car, fitted it with dozens of race car-like sensors, and set about testing, racing and crashing the vehicle. They then fed millions of data points into a computer powered by NVidia processors capable of machine learning, and asked Autodesk's "Dreamcatcher" software to take that information and use it to design a better car.

Artificial intelligence is set to take a key role in the design and engineering of new cars, dreaming up lighter, stronger and more complex structures than humans can envision. Just as computing power exceeds the mathematical capability of the human mind, smart software capable of innovation and problem solving is set to push product development into new territory. Hack Rod, a team of designers, engineers, geeks, Hollywood insiders and stunt drivers is working on a way to harness the power of artificial intelligence in tandem with powerful design software produced by Autodesk. Experimenting with connectivity surrounding the emerging "internet of things", the Hack Rod crew built a basic sports car, fitted it with dozens of race car-like sensors, and set about testing, racing and crashing the vehicle. They then fed millions of data points into a computer powered by NVidia processors capable of machine learning, and asked Autodesk's "Dreamcatcher" software to take that information and use it to design a better car.