Communication plays a big part in our everyday lives. We talk to different people in different languages, but what about communicating with technology? Nowadays, everyone has some sort of device, and we often use it to find answers to our questions. Such as asking Siri, "where can I find the nearest sushi place?" we are verbally asking a question/making a statement. But here's the thing, computers don't just speak English; they are written in complex code with totally different syntax than we speak.

Over the last decade, Artificial intelligence (AI) has become embedded in every aspect of our society and lives. From chatbots and virtual assistants like Siri and Alexa to automated industrial machinery and self-driving cars, it's hard to ignore its impact. Today, the technology most commonly used to achieve AI is machine learning – advanced software algorithms designed to carry out one specific task, such as answering questions, translating languages or navigating a journey – and become increasingly good at it as they are exposed to more and more data. Worldwide, spending by governments and business on AI technology will top $500 billion in 2023, according to IDC research. But how will it be used, and what impact will it have? Here, I outline what I believe will be the most important trends around the use of AI in business and society over the next 12 months.

Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolated data silos by cooperatively training models among organizations without breaching privacy and security. However, FL faces heterogeneity from various aspects, including data space, statistical, and system heterogeneity. For example, collaborative organizations without conflict of interest often come from different areas and have heterogeneous data from different feature spaces. Participants may also want to train heterogeneous personalized local models due to non-IID and imbalanced data distribution and various resource-constrained devices. Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL. In this survey, we comprehensively investigate the domain of heterogeneous FL in terms of data space, statistical, system, and model heterogeneity. We first give an overview of FL, including its definition and categorization. Then, We propose a precise taxonomy of heterogeneous FL settings for each type of heterogeneity according to the problem setting and learning objective. We also investigate the transfer learning methodologies to tackle the heterogeneity in FL. We further present the applications of heterogeneous FL. Finally, we highlight the challenges and opportunities and envision promising future research directions toward new framework design and trustworthy approaches.

Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content -- making relevancy a moving target, to which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment. We model users as boundedly-rational decision makers whose causal beliefs are encoded by a causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.

As artificial intelligence continues to develop and become a bigger part of our lives, many people believe AI should have regulations or guidelines in place. The White House agrees with those citizens, releasing a "bill of rights" dedicated solely to artificial intelligence. It would be a massive understatement to say artificial intelligence has affected our lives. Whether that's a positive or negative is entirely up to you. Virtual assistants like Alexa and Siri are integrated into many devices that people interact with everyday.

High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommendation models. Although synchronous AR training is designed to have higher training efficiency, asynchronous PS training would be a better choice for training speed when there are stragglers (slow workers) in the shared cluster, especially under limited computing resources. An ideal way to take full advantage of these two training modes is to switch between them upon the cluster status. However, switching training modes often requires tuning hyper-parameters, which is extremely time- and resource-consuming. We find two obstacles to a tuning-free approach: the different distribution of the gradient values and the stale gradients from the stragglers. This paper proposes Global Batch gradients Aggregation (GBA) over PS, which aggregates and applies gradients with the same global batch size as the synchronous training. A token-control process is implemented to assemble the gradients and decay the gradients with severe staleness. We provide the convergence analysis to reveal that GBA has comparable convergence properties with the synchronous training, and demonstrate the robustness of GBA the recommendation models against the gradient staleness. Experiments on three industrial-scale recommendation tasks show that GBA is an effective tuning-free approach for switching. Compared to the state-of-the-art derived asynchronous training, GBA achieves up to 0.2% improvement on the AUC metric, which is significant for the recommendation models. Meanwhile, under the strained hardware resource, GBA speeds up at least 2.4x compared to synchronous training.

Artificial Intelligence will have a long and lasting impact on our lives. It is already at work in many of the devices we use and it will only become more indispensable as time goes on. And it's not just about machines doing things that we would otherwise do ourselves, like driving cars or organizing our lives. AI will also make all sorts of things cheaper, more convenient, and safer–from medical diagnoses to movie recommendations to home security. In the past few years, artificial intelligence (AI) has made incredible strides. We've seen AI beat humans in complex games like Go and chess, and it is now being used in a variety of applications such as autonomous vehicles, facial recognition, and fraud detection.

Data Science combines statistical and computational skills together with Machine Learning for data-driven problem solving. This rapidly growing area includes large-scale data analysis, DevOps and deep learning, and has applications in many tech and finance related areas, amongst others. Data Science and Machine Learning distinguishes itself from other computer guided decision methods by creating prediction algorithms using data. Well known examples of these applications include spam detectors, movie and music recommendation systems as well as speech recognition tools. Data Science and Machine Learning platforms provide a variety of basic building blocks that aid in creating different types of data science solutions.

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