Conferences




  • Association for Computational Linguistics (ACL)
  • European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
  • International Conference on Machine Learning (ICML)
  • International Conference on Learning Representations (ICLR)
  • International Conference on Intelligent Robots and Systems (IROS)
  • Conference on Knowledge Discovery and Data Mining (KDD)
  • Conference on Neural Information Processing Systems (NeurIPS)

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Theory

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A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error. For the best performance in the context
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Hardware

Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.
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Ethics

Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. The evolvement of AI systems raises a lot questions in the realm of ethics and morality. AI can be well equipped in making decisions in certain fields such technical and scientific which rely heavily on data and historical information. These decisions rely on objectivity and logical reasoning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases. Other forms of ethical challenges, n
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