Real-world production ML systems consist of two main components: data and code. Data is clearly the leader, and rapidly taking center stage. Data defines the quality of almost any ML-based product, more so than code or any other aspect. In Feature Store as a Foundation for Machine Learning, we have discussed how feature stores are an integral part of the machine learning workflow. They improve the ROI of data engineering, reduce cost per model, and accelerate model-to-market by simplifying feature definition and extraction.

We all can relate to thinking about whether route A will take less time than route B, if the average return on investment X is more than investment Y, and if movie ABC is better than movie XYZ. In all these cases, we are testing some hypotheses we have in our minds. Setting up hypotheses, proving/disproving them using data, and helping businesses make decisions is like bread and butter for Data Scientists. Data Scientists often rely on probabilities to understand the likelihood of observing data by chance and use that to make conclusions around a hypothesis. Hence, there are always scenarios of making errors while making conclusions around our assumed hypothesis. The below post is written to provide an intuitive yet detailed explanation of Type-I and Type-II errors that happen during statistical hypothesis testing.

In statistics, hypothesis testing is a form of inference using data to draw certain conclusions about the population. First, we make an assumption about the population which is known as the Null Hypothesis. It is denoted by H₀. Then we define the Alternate Hypothesis which is the opposite of what is stated in the Null Hypothesis, denoted by Hₐ. After defining both the Null Hypothesis and Alternate Hypothesis we perform what is known as a hypothesis test to either accept or reject the Null Hypothesis.

Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization.10 To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books, or other products) to customers. While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations. These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underlie how customers react to visual information displays and make decisions. As another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the Earth's surface is changing over time.50 These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of a6utomatically detected and annotated objects and information. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels. The breadth and complexity of these and many other data science scenarios means the modern data scientist requires broad knowledge and experience across a multitude of topics. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise. Considering this bottleneck, it is not surprising there is increasing interest in automating parts, if not all, of the data science process.

It may sound obvious, perhaps even clichéd, but this mantra is something that must be remembered in ongoing political negotiations over Horizon Europe, which could see Switzerland and the UK excluded from EU research projects. We need more, not fewer, researchers collaborating to solve today's and tomorrow's challenges. By closely working with Swiss and British researchers, who have long played key roles, Horizon Europe projects will benefit – as they have in the past. This is the motivation behind ETH Zurich, which collaborates with IBM Research on nanotechnology, leading the Stick to Science campaign. This calls on all three parties – Switzerland, the UK and the EU – to try and solve the current stalemate and put Swiss and British association agreements in place.

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While engineering, finance, and commerce have profited immensely from novel algorithms, they are not the only ones. Large-scale computation has been an integral part of the toolkit in the physical sciences for many decades - and some of the recent advances in AI have started to change how scientific discoveries are made. There has been a lot of excitement about prominent achievements in the physical sciences, like using machine learning to render an image of a black hole or the contribution of AlphaFold towards protein folding. This article will cover some of the more prominent usages of AI in chemistry, the parent discipline of the aforementioned protein folding problem. One of the chief goals of chemistry is to understand matter, its properties, and the transformations it can undergo.

Hypothesis testing for small-sample scenarios is a practically important problem. In this paper, we investigate the robust hypothesis testing problem in a data-driven manner, where we seek the worst-case detector over distributional uncertainty sets centered around the empirical distribution from samples using Sinkhorn distance. Compared with the Wasserstein robust test, the corresponding least favorable distributions are supported beyond the training samples, which provides a more flexible detector. Various numerical experiments are conducted on both synthetic and real datasets to validate the competitive performances of our proposed method. As a fundamental problem in statistics, hypothesis testing plays a key role in general scientific discovery areas such as anomaly detection and model criticism. The goal of hypothesis testing is to determine which one among given hypotheses is true within a certain error probability level.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

The development in the number and scale of universities throughout the world, as well as the expansion of their research endeavors as a method of enhancing their reputations and attracting both students and sponsors, is driving demand in this lucrative academic publishing sector. Because publishing metrics have become the key indicator of academic achievement and the primary motivator for career development, they have become the primary gauge of academic performance and the primary incentive for career progress. The concept "publish or perish" has become norm many fields. As a result, the rate of scientific publishing has increased exponentially in recent decades, with output rates approaching 2.5 million per year by 2017. The proliferation of so-called "predatory" journals, which provide speedy publishing without peer review or considerable editorial control, is another result of this increase in demand for publication channels.To counter the current science climate, Open Science has emerged.