Most state-of-the-art machine learning techniques revolve around the optimisation of loss functions. Defining appropriate loss functions is therefore critical to successfully solving problems in this field. We present a survey of the most commonly used loss functions for a wide range of different applications, divided into classification, regression, ranking, sample generation and energy based modelling. Overall, we introduce 33 different loss functions and we organise them into an intuitive taxonomy. Each loss function is given a theoretical backing and we describe where it is best used. This survey aims to provide a reference of the most essential loss functions for both beginner and advanced machine learning practitioners.

In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only based on AI confidence indicating AI's correctness likelihood (CL) but ignored humans' CL, hindering optimal team decision-making. To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level. We first modeled humans' CL by approximating their decision-making models and computing their potential performance in similar instances. We demonstrated the feasibility and effectiveness of our model via two preliminary studies. Then, we proposed three CL exploitation strategies to calibrate users' trust explicitly/implicitly in the AI-assisted decision-making process. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence. We further provided practical implications for more human-compatible AI-assisted decision-making.

Jazmia Henry July 18, 2022 Summary Discussions about reducing the bias present in algorithms have been on the rise since the mid 2010s. AI ethicists, DEI practitioners, Sociologists, Data Scientists and Social Justice Advocates have decried the lack of understanding of the harms that algorithms pose to people who belong to historically marginalized groups. These cries have become increasingly accepted in industry since 2020, but little is understood of how algorithm and Machine Learning (ML) model builders should go about mitigating bias in models that are intended for deployment. This chapter is written with the Data Scientist or MLOps professional in mind but can be used as a resource for policy makers, reformists, AI Ethicists, sociologists, and others interested in finding methods that help reduce bias in algorithms. I will take a deployment centered approach with the assumption that the professionals reading this work have already read the amazing work on the implications of algorithms on historically marginalized groups by Gebru, Buolamwini, Benjamin and Shane to name a few. If you have not read those works, I refer you to the "Important Reading for Ethical Model Building " list at the end of this paper as it will help give you a framework on how to think about Machine Learning models more holistically taking into account their effect on marginalized people. In the Introduction to this chapter, I root the significance of their work in real world examples of what happens when models are deployed without transparent data collected for the training process and are deployed without the practitioners paying special attention to what happens to models that adapt to exploit gaps between their training environment and the real world. The rest of this chapter builds on the work of the aforementioned researchers and discusses the reality of models performing post production and details ways ML practitioners can identify bias using tools during the MLOps lifecycle to mitigate bias that may be introduced to models in the real world. Introduction "Whether AI will help us reach our aspirations or reinforce the unjust inequalities is ultimately up to us." - Joy Buolowini, 'Facing the Coded Gaze' AI: More than Human Whether you're driving your car using a GPS system, call on Alexa or Siri to turn on your favorite tune, go on social media to perform a well-earned scroll down memory lane, or go to Google search to find a gift to buy for a friend, you have encountered a Machine Learning model.

Schools and educational institutions in the US and elsewhere are announcing bans on the recently released AI-powered chatbot ChatGPT out of fear that students could use the technology to complete their assignments. However, bans may be practically impossible given how difficult it is to detect when text is composed by ChatGPT. Is it instead time to rethink how students are taught and evaluated? "Educators are starting to question what it means to… assess student learning if an AI can write an essay …

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML. You do not need any prior machine learning knowledge to take this course.

Ask the new artificial intelligence tool ChatGPT to write an essay about the cause of the American Civil War and you can watch it churn out a persuasive term paper in a matter of seconds. That's one reason why New York City school officials this week started blocking the impressive but controversial writing tool that can generate paragraphs of plausible text. The decision by the largest US school district to restrict the ChatGPT website on school devices and networks could have ripple effects to other schools. Teachers are scrambling to figure out how to prevent cheating using the tool. The creators of ChatGPT say they are also looking for ways to detect misuse. The free tool has been around for just five weeks but is already raising tough questions about the future of AI in education, the tech industry and a host of professions.

Educators across the U.S. are sounding the alarm over ChatGPT, an upstart artificial intelligence that can write term papers for students based on keywords without clear signs of plagiarism. "I have a lot of experience of students cheating, and I have to say ChatGPT allows for an unprecedented level of dishonesty," said Joy Kutaka-Kennedy, a member of the American Educational Research Association and education professor at National University. "Do we really want professionals serving us who cheated their way into their credentials?" Trey Vasquez, a special education professor at the University of Central Florida, recently tested the next-generation "chatbot" with a group of other professors and students. They asked it to summarize an academic article, create a computer program, and write two 400-word essays on the use and limits of AI in education.

If you haven't had your head under a rock over the holiday season, you will know that an artificial intelligence (AI) software application called ChatGPT came tearing out of the gates of a company called OpenAI in December, a little over a month ago. There was almost no pre-launch advertising, but it took just one week to attract one million users, faster than any software launch in history, even those with $100-million advertising campaigns. Within its short life, ChatGPT has generated a library of heated controversy, a cesspool of Twitter insults and counter-insults, dire predictions about the end of humanity as we know it and breathless prose about a brave new world. What in the world is this thing, and what does it portend? The field of AI has burned quietly for decades, all the way back to the 1950s.

Recently, I gave a talk to a group of K-12 teachers and public school administrators in New York. The topic was artificial intelligence, and how schools would need to adapt to prepare students for a future filled with all kinds of capable A.I. tools. But it turned out that my audience cared about only one A.I. tool: ChatGPT, the buzzy chat bot developed by OpenAI that is capable of writing cogent essays, solving science and math problems and producing working computer code. ChatGPT is new -- it was released in late November -- but it has already sent many educators into a panic. Students are using it to write their assignments, passing off A.I.-generated essays and problem sets as their own.

This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.