Handling missing data in time series classification remains a significant challenge in various domains. Traditional methods often rely on imputation, which may introduce bias or fail to capture the underlying temporal dynamics. In this paper, we propose TANDEM (Temporal Attention-guided Neural Differential Equations for Missingness), an attention-guided neural differential equation framework that effectively classifies time series data with missing values. Our approach integrates raw observation, interpolated control path, and continuous latent dynamics through a novel attention mechanism, allowing the model to focus on the most informative aspects of the data. We evaluate TANDEM on 30 benchmark datasets and a real-world medical dataset, demonstrating its superiority over existing state-of-the-art methods. Our framework not only improves classification accuracy but also provides insights into the handling of missing data, making it a valuable tool in practice.

We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natural system that produce a given set of observed measurements. Recent work has shown impressive results using deep learning, but we note that there is a trade-off between model performance and computational time. For some applications, the computational time at inference for the best performing inverse modeling method may be overly prohibitive to its use. We present a new method that leverages multiple manifolds as a mixture of backward (e.g., inverse) models in a forward-backward model architecture. These multiple backwards models all share a common forward model, and their training is mitigated by generating training examples from the forward model. The proposed method thus has two innovations: 1) the multiple Manifold Mixture Network (MMN) architecture, and 2) the training procedure involving augmenting backward model training data using the forward model. We demonstrate the advantages of our method by comparing to several baselines on four benchmark inverse problems, and we furthermore provide analysis to motivate its design.

With much of the world now focused on the goal of net zero carbon emissions by the year 2050, industry leaders are asking: How can businesses be more sustainable? At the same time, another revolution has sprung: digital transformation. Companies are busy adopting tools and technologies to make processes more efficient and competitive. In the pursuit of global optimization, businesses have a lot to gain from thinking about these two movements in conjunction. Let's say a company with sustainability baked into its business model has decided to invest time and focus on driving more efficient operations and reducing waste.

Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivated to develop new methods for tactile-based object interaction. However, tactile sensing presents the challenge of being an active sensing modality: a touch sensor provides sparse, local data, and must be used in conjunction with effective exploration strategies in order to collect information. In this work, we focus on the process of guiding tactile exploration, and its interplay with task-related decision making. We propose TANDEM (TActile exploration aNd DEcision Making), an architecture to learn efficient exploration strategies in conjunction with decision making. Our approach is based on separate but co-trained modules for exploration and discrimination. We demonstrate this method on a tactile object recognition task, where a robot equipped with a touch sensor must explore and identify an object from a known set based on binary contact signals alone. TANDEM achieves higher accuracy with fewer actions than alternative methods and is also shown to be more robust to sensor noise.

Senseonics has taken their Eversense implantable CGM before an FDA panel, and Bigfoot has announced they have closed their series B $55 million financing round. The FDA has approved Dexcom's G6 system, pushing shares in the company higher in early trading. And while they didn't announce anything recently, our friends at Tandem are feeling the love as shares continue to surge higher in anticipation of their new system, which has low glucose suspend. Simply put, when it comes to diabetes devices, CGM and insulin pumps, the joint is jumping. But before everyone breaks into an unstoppable happy dance, I'd advise they slow their roll.

Digital challenger bank Tandem has turned to Personetics' artificial intelligence (AI) for personalised insights and advice. Powered by Personetics' Cognitive Banking Brain, the bank will integrate the new offerings within its digital banking. Tandem's chief executive, Ricky Knox says: "Together with our use of open banking we can use AI to get to know our customers better, predict their needs, and help them make better decisions about their money with as little effort as possible on their part. We want to do the heavy lifting for them, so they can go about their lives and not worry about day-to-day finances." Examples of personalised insights delivered by the Personetics solution include unusual spending activity; tips on how to avoid fees; and opportunities for savings or investment. Knox adds: "While AI is promising, it was important for us to work with a partner such as Personetics that has proven experience in financial services.

Artificial intelligence (AI) and the Internet of Things (IoT) have got a great deal of press in the last few years, and with good reason -- they'll both transform the way enterprises do business. And now a new Gartner report says that the synergy between them will be so powerful that enterprises should consider rolling them out in tandem. The report, "AI on the Edge: Fusing Artificial Intelligence and IoT Will Catalyze New Digital Value Creation," puts it this way: "Artificial Intelligence and the Internet of Things are symbiotic technologies that will be the foundation of a new platform for digital business value creation. CIOs engaged in IoT initiatives should leverage these capabilities for strategic advantage." The report starts out by noting that using AI and IoT in tandem will become commonplace.

When the time comes to discuss artificial intelligence (AI) integration in a particular business unit, you'd think the chief information officer (CIO) would be the obvious go-to person -- but it's the chief data officer (CDO) who should be on your radar. AI activity often falls under the care of the CDO because it involves using data, technology and analytics to make informed research and product decisions. There has been steady growth in CDO appointments in the past two years, and the role is coming of age in 2017, according to a recent Forrester report. In 2015, the global average of organizations with a CDO was 45 percent, and increased to 47 percent in 2016, the firm reports. Meanwhile, Gartner estimates by 2019, 90 percent of large organizations will have a CDO.

Both Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that evoke futuristic, sci-fi, and generally far-out imagery in us. But the truth is that AI and IoT are already our mundane reality and will continue to become prominent aspects of our lives in the near and far future. Chaney Ojinnaka, the founder, and CEO of VendorMach says in his recently published article that AI has been romanticized into this abstract concept conjuring images of robots doing our housework for us. But what do these terms really denote and what is their relation? Well, both concepts are strongly interrelated, and one can't really exist without the other.