Advancements in the power of machine learning have brought with them major data privacy concerns. This is especially true when it comes to training machine learning models with data obtained from the interaction of users with devices such as smartphones. So the big question is, how do we train and improve these on-device machine learning models without sharing personally-identifiable data? That is the question that we'll seek to answer in this look at a technique known as federated learning. The traditional process for training a machine learning model involves uploading data to a server and using that to train models.

Controversial facial recognition firm Clearview AI has been found to have extensive ties to far-right individuals and movements. Clearview AI has come under scrutiny for scraping billions of photos from across the internet and storing them in a database for powerful facial recognition services. Privacy activists criticise the practice as the people in those images never gave their consent. "Common law has never recognised a right to privacy for your face," Clearview AI lawyer Tor Ekeland said recently. "It's kind of a bizarre argument to make because [your face is the] most public thing out there."

More and more industrial tasks are being performed by robots, but human operators are still needed for the more complex manipulation actions, such as handling and processing food products. "If our aim is to automate some or all these tasks in the food industry, or in other areas, we have to equip the robots with new knowledge via learning. They have to learn the so-called soft skills first so that they will be able to execute operations at the same level as humans in the future," explained Ekrem Misimi, who is a SINTEF researcher developing robot learning technology as part of the iProcess project. In order to teach the robots these complex manipulation skills, a combination of visual and tactile learning is required. In other words, they must learn to see and feel simultaneously.

My last post, AI and COVID-19: Securing the Intensified Reliance on AI Prime Operational Qualities discussed the "safe" and "efficient" operational features as the "prime operational" aspects desirable in an environment where there is an intensified reliance on AI. Even before all of this, but definitely in the'day-after' COVID-19, we can expect AI (in varying flavors) to be integrated into countless applications where its capabilities serve to enhance their function. April 16, 2020: AI can help make sense of the massive amount of COVID-19 related data. Monitoring physical movement can provide certain insight, but will that really be useful in this fight? Let's pretend, for the moment, that end user privacy is in fact effectively protected (Google says its Community Mobility Reports data, for example, is aggregated and anonymized).

Everguard.ai, a developer of AI-based worker safety solutions, announced the commercial release of Sentri360 – an end-to-end safety solution that alerts management and workers in real-time to potential safety hazards. Worker safety is a critical concern for employers and underwriters representing major morale, productivity, and economic impacts. Annually in the US, there are more than 100,000 injuries, 5,000-plus fatalities, which results in more than $60 billion in direct costs and $30 billion-plus in worker compensation claims. Sentri360, developed for the steel industry, will improve worker safety and in addition improve operations and margins as well. Sentri360 solution is tackling the problem of workplace safety by continuously monitoring worker behavior while generating a more complete situational understanding of the worker's environment.

In this paper, we formulate the adaptive learning problem---the problem of how to find an individualized learning plan (called policy) that chooses the most appropriate learning materials based on learner's latent traits---faced in adaptive learning systems as a Markov decision process (MDP). We assume latent traits to be continuous with an unknown transition model. We apply a model-free deep reinforcement learning algorithm---the deep Q-learning algorithm---that can effectively find the optimal learning policy from data on learners' learning process without knowing the actual transition model of the learners' continuous latent traits. To efficiently utilize available data, we also develop a transition model estimator that emulates the learner's learning process using neural networks. The transition model estimator can be used in the deep Q-learning algorithm so that it can more efficiently discover the optimal learning policy for a learner. Numerical simulation studies verify that the proposed algorithm is very efficient in finding a good learning policy, especially with the aid of a transition model estimator, it can find the optimal learning policy after training using a small number of learners.

Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. Design of such predictive models requires choice of appropriate variables, right transformation methods of the variables, and tuning of the parameters of the models. In this work, we present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. The granular data is aggregated into three slots in a day, and the aggregated data is used for building and training the forecasting models. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data. We build eight classification and eight regression models based on statistical and machine learning approaches. In addition to these models, a deep learning regression model using a long-and-short-term memory (LSTM) network is also built. Extensive results have been presented on the performance of these models, and the results are critically analyzed.

Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the traditional CNNs. However, CapsNets require extremely intense computations and are difficult to be deployed in their original form at the resource-constrained edge devices. This paper makes the first attempt to quantize CapsNet models, to enable their efficient edge implementations, by developing a specialized quantization framework for CapsNets. We evaluate our framework for several benchmarks. On a deep CapsNet model for the CIFAR10 dataset, the framework reduces the memory footprint by 6.2x, with only 0.15% accuracy loss. We will open-source our framework at https://git.io/JvDIF in August 2020.

The power grid frequency is the central observable in power system control, as it measures the balance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions and may thus greatly improve power system stability. Here, we develop a weighted-nearest-neighbor (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hour are more precise than averaged daily profiles and could increase the efficiency of frequency control actions. Furthermore, we gain an increased understanding of the specific properties of different synchronous areas by interpreting the optimal prediction parameters (number of nearest neighbors, the prediction horizon, etc.) in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional external perturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequency time series, allowing better understanding of the underlying dynamics.

Accurate segmentation of kidneys and kidney tumors is an essential step for radiomic analysis as well as developing advanced surgical planning techniques. In clinical analysis, the segmentation is currently performed by clinicians from the visual inspection images gathered through a computed tomography (CT) scan. This process is laborious and its success significantly depends on previous experience. Moreover, the uncertainty in the tumor location and heterogeneity of scans across patients increases the error rate. To tackle this issue, computer-aided segmentation based on deep learning techniques have become increasingly popular. We present a multi-scale supervised 3D U-Net, MSS U-Net, to automatically segment kidneys and kidney tumors from CT images. Our architecture combines deep supervision with exponential logarithmic loss to increase the 3D U-Net training efficiency. Furthermore, we introduce a connected-component based post processing method to enhance the performance of the overall process. This architecture shows superior performance compared to state-of-the-art works using data from KiTS19 public dataset, with the Dice coefficient of kidney and tumor up to 0.969 and 0.805 respectively. The segmentation techniques introduced in this paper have been tested in the KiTS19 challenge with its corresponding dataset.