On Tuesday, CERN will launch a new podcast series on artificial intelligence. The series looks forward to the first edition of the Sparks! Serendipity Forum in September, when over 30 leading thinkers will converge on the laboratory for high-level multidisciplinary discussions designed to spark ethical innovation. To whet your appetite for the forum, the podcasts bring a selection of the Sparks delegates together in pairs. Think of these conversations like collisions in the LHC.

Statistics Fundamentals (7/9) Hypothesis Testing Statistical Hypothesis Testing: Theory and Python Welcome to Statistics Fundamentals 7, Hypothesis Testing. This course is for beginners who are interested in statistical analysis. Description Welcome to Statistics Fundamentals 7, Hypothesis Testing. This course is for beginners who are interested in statistical analysis. And anyone who is not a beginner but wants to go over from the basics is also welcome!

Sometimes it seems paradoxical to call the famous bell curve "normal". Among all the assumptions made by traditional statistical theory, the normality assumption is notorious for the frequency it doesn't hold. My aim in this article is to show a way to test hypotheses when the normality assumption of traditional hypothesis tests is violated. In this scenario, we can't rely on theoretical results, so we need to depart from theory's ivory tower and double the bet on our data. To get there, first I briefly review what hypothesis testing is, focusing on an intuitive grasp of the reasoning behind it (no equations allowed!). Then I proceed to a case study motivated by a business problem where the normality assumption doesn't hold. This makes matters concrete and will direct our discussion. After the problem is explained, I will show that bootstrapping is a good way to fill the gaps left by theory without changing anything in the reasoning at the heart of hypothesis testing. In particular, I will show that bootstrapping leads to the right conclusion about the test. I conclude this article with a critical evaluation of bootstrapping and similar methods, pointing out their pros and cons. Many data scientists have trouble understanding hypothesis testing.

I walked into my adviser's office, overflowing with frustration and confusion about the advice I had received at a recent career development workshop. It reiterated what I had heard so many times before: I should follow my dream, and if I didn't yet know what that was, I should live with career uncertainty until I figured it out. But as an international student working in the United States, taking time to explore wasn't an option for me. After listening to me rant, my adviser calmly looked across his desk. He told me that instead of focusing on finding a dream job, I should think about what I am good at and what makes me happy at least 80% of the time. This advice surprised me at first, but it ended up being exactly what I needed to hear. > “I should think about what I am good at and what makes me happy at least 80% of the time.” I had spent the previous 22 years following my childhood dream—becoming a professor of marine biology. However, in grad school I saw how applying for grants is a constant source of worry for many professors. I realized I did not want to be responsible for the salaries of my hypothetical lab members. About 4 years into the program, I decided I did not want to pursue a career in research after all. I began to attend career panels, which all followed a worryingly similar template. I would walk into the room with other excited graduate students and collect my free cookies and coffee, confident that the panelists would have the magical answers I needed. Instead, they would talk—again—about following their dreams. The message: I just needed to find a new dream. It would mean taking time off from work to self-reflect and discover a new path. But I couldn't stay in the country without a visa. For most academic researchers, obtaining a university-sponsored visa is relatively straightforward. But outside of academia, it is infinitely more complex, requiring a company that has a job opening and is willing to foot the bill for a work visa. As well-meaning as the panelists were, they fell silent when I brought up this dilemma. I felt totally lost. Finally, I went to my adviser for help. We hadn't talked much about my career plans over the years, but I felt I needed a new perspective from someone who knew me well. When he offered his advice, I was taken aback at first. What happened to “if you love what you do, you'll never work a day in your life”? My adviser assured me there is seldom such a job. Every job has its ugly bits. But as long as you're happy most of the time, you can struggle through the parts you don't like. He also said it was important to find a job I was good at, especially because my visa applications required me to make the case that I would benefit the country. I was relieved to finally have helpful, practical advice. But I discovered that finding overlap between what I like and what I'm good at was not easy. I love scuba diving, but the physical demands are a challenge for me. I'm good at teaching, as evidenced by my friends nagging me to teach them chemistry and microbiology during my high school and undergraduate years and getting rave reviews from my students when I was a teaching assistant, but I don't like repeating the same content every year. Through my teaching experience, however, I also learned that I love telling stories about science. Maybe science communication would offer the overlap I was looking for. To test the waters, during my “spare time” in grad school I started a blog about the history of scientific discoveries. I found that I loved the freedom to choose what to write about, and I never encountered a challenge I didn't enjoy. As for whether I was any good at it, the signs were promising. My writing got noticed, eventually by people at my institution, and I was given opportunities to write press releases and stories for the university's news bureau. After 3 years of writing, I was offered a position as a science writer. It's nothing like my childhood dream. But I am happy—more than 80% of the time.

Going from left to right, we consider increasingly complex functions. These neighborhoods, in other words, need to become more and more disjoint as the function becomes more complex. Indeed, we quantify "disjointedness" of the neighborhoods via a term denoted by and relate it to the complexity of the function class, and subsequently, its generalization properties. There has been a growing interest in interpretable machine learning (IML), towards helping users better understand how their ML models behave. IML has become a particularly relevant concern especially as practitioners aim to apply ML in important domains such as healthcare [Caruana et al., '15], financial services [Chen et al., '18], and scientific discovery [Karpatne et al., '17]. While much of the work in IML has been qualitative and empirical, in our recent ICLR21 paper, we study how concepts in interpretability can be formally related to learning theory.

In this paper, we propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier. Previous works tend to apply a threshold either on the classification scores or the loss function to reject the inputs that violate the assumption. However, these methods cannot achieve the low False Positive Ratio (FPR) required in safety applications. The proposed method is a rejection option based on hypothesis testing with probabilistic networks. With probabilistic networks, it is possible to estimate the distribution of outcomes instead of a single output. By utilizing Z-test over the mean and standard deviation for each class, the proposed method can estimate the statistical significance of the network certainty and reject uncertain outputs. The proposed method was experimented on with different configurations of the COCO and CIFAR datasets. The performance of the proposed method is compared with the Softmax Response, which is a known top-performing method. It is shown that the proposed method can achieve a broader range of operation and cover a lower FPR than the alternative.

As someone who has spent 13 years in the weeds of data, I witnessed the rise of the "data-driven" trend first hand. Before starting and selling my first data startup, I spent time as a statistical analyst building sales forecasting models in R, a software engineer creating data transformation jobs, and a product manager running A/B tests and analyzing user behaviors. What all these roles had in common was that they gave me an understanding that the context of data -- what it represents, how it was generated, when it was updated last, and the ways it could be joined with other datasets -- is essential to maximizing the data's potential and driving successful outcomes. However, accessing and understanding the context of data is quite difficult. This is because the context of data is often tribal knowledge, meaning it lives only in the brains of the engineers or analysts who have worked with it recently.

Humans, on the other hand, need none of this. On the basis of very limited or incomplete data, we nonetheless come to the right conclusion about many things (yes, we are fallible, but the miracle is that we are right so often). Noam Chomsky's entire claim to fame in linguistics really amounts to exploring this underdetermination problem, which he referred to as "the poverty of the stimulus." Humans pick up language despite very varied experiences with other human language speakers. Babies born in abusive and sensory deprived environments pick up language.

Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.

In this article we introduce the supervised machine learning tool called Feyn. The simulation engine that powers this tool is called the QLattice. The QLattice is a supervised machine learning tool inspired by Richard Feynman's path integral formulation, that explores many potential models that solves a given problem. It formulates these models as graphs that can be interpreted as mathematical equations, allowing the user to completely decide on the trade-off between interpretability, complexity and model performance. We touch briefly upon the inner workings of the QLattice, and show how to apply the python package, Feyn, to scientific problems. We show how it differs from traditional machine learning approaches, what it has in common with them, as well as some of its commonalities with symbolic regression. We describe the benefits of this approach as opposed to black box models. To illustrate this, we go through an investigative workflow using a basic data set and show how the QLattice can help you reason about the relationships between your features and do data discovery.