Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature - the well-known "catastrophic forgetting" issue. In particular, when a model consecutively learns from different visual domains, it tends to forget the past ones in favor of the most recent. In this context, we show that one way to learn models that are inherently more robust against forgetting is domain randomization - for vision tasks, randomizing the current domain's distribution with heavy image manipulations. Building on this result, we devise a meta-learning strategy where a regularizer explicitly penalizes any loss associated with transferring the model from the current domain to different "auxiliary" meta-domains, while also easing adaptation to them. Such meta-domains, are also generated through randomized image manipulations. We empirically demonstrate in a variety of experiments - spanning from classification to semantic segmentation - that our approach results in models that are less prone to catastrophic forgetting when transferred to new domains.

Machine learning (ML) algorithms may be susceptible to being gamed by individuals with knowledge of the algorithm (a.k.a. Goodhart's law). Such concerns have motivated a surge of recent work on strategic classification where each data point is a self-interested agent and may strategically manipulate his features to induce a more desirable classification outcome for himself. Previous works assume agents have homogeneous preferences and all equally prefer the positive label. This paper generalizes strategic classification to settings where different data points may have different preferences over the classification outcomes. Besides a richer model, this generalization allows us to include evasion attacks in adversarial ML also as a special case of our model where positive [resp. negative] data points prefer the negative [resp. positive] label, and thus for the first time allows strategic and adversarial learning to be studied under the same framework. We introduce the strategic VC-dimension (SVC), which captures the PAC-learnability of a hypothesis class in our general strategic setup. SVC generalizes the notion of adversarial VC-dimension (AVC) introduced recently by Cullina et al. arXiv:1806.01471. We then instantiate our framework for arguably the most basic hypothesis class, i.e., linear classifiers. We fully characterize the statistical learnability of linear classifiers by pinning down its SVC and the computational tractability by pinning down the complexity of the empirical risk minimization problem. Our bound of SVC for linear classifiers also strictly generalizes the AVC bound for linear classifiers in arXiv:1806.01471. Finally, we briefly study the power of randomization in our strategic classification setup. We show that randomization may strictly increase the accuracy in general, but will not help in the special case of adversarial classification under evasion attacks.

The course typically attracts students majoring in the study of religion, who are learning the language to further their research into Hinduism, Buddhism and Sikhism. Reading through her class list, however, Mills found that of six of the 40 enrolled students were actually computer science majors. "I'm always excited when there are students from an unexpected place," she says. The lingual connection between Sanskrit and computer science, it turns out, has been the subject of interest for quite some time. The first well-known publication that examined the relationship was in 1985, when NASA scientist Rick Briggs published a research paper in which he argued that the 3,500-year-old language was the best candidate for programming artificial intelligence technology – namely because of its adherence to rigid grammatical rules.

Advanced Neural Networks in R - A Practical Approach Boost your data science skills - learn to build and train complex neural network using the R program. Neural networks are powerful predictive tools that can be used for almost any machine learning problem with very good results. If you want to break into deep learning and artificial intelligence, learning neural networks is the first crucial step. This course contains four comprehensive sections. Learn to use multilayer perceptrons to make predictions for both categorical and continuous variables.

Artificial Intelligence (AI) is evolving at an exponential rate. Today, it has expanded beyond tech and geographical constraints and is slowly bringing massive changes worldwide. In recent times, AI influencers are driving conversations about AI news and trends across social media and beyond while also offering advice to numerous enterprises. Plus, they also help us keep updated with the recent innovations and information about AI. Analytics Insight brings 10 LinkedIn influencers who share the latest trends in the AI domain through insightful articles on their LinkedIn blogs.

Together, these five new machine learning services help industrial and manufacturing customers embed intelligence in their production processes in order to improve operational efficiency, quality control, security, and workplace safety. The services combine sophisticated machine learning, sensor analysis, and computer vision capabilities to address common technical challenges faced by industrial customers, and represent the most comprehensive suite of cloud-to-edge industrial machine learning services available. This is why more than a hundred thousand customers are using AWS for machine learning, and why customers of all sizes and across all industries are using AWS services to make machine learning core to their business strategy. Companies are increasingly looking to add machine learning capabilities to industrial environments, such as manufacturing facilities, fulfillment centers, and food processing plants. For these customers, data has become the connective tissue that holds their complex industrial systems together.

Welcome back to Mixtape, the TechCrunch podcast that examines diversity, inclusion and the human labor that drives tech. This week, Megan moderated a panel at Sight Tech Global, a conference dedicated to fostering discussion among technology pioneers on how advances in AI and related technologies will alter the landscape of assistive technology. The panel featured three heavy hitters in the accessibility space: Haben Girma (pictured above), the first deafblind person to graduate from Harvard Law School and who is a human rights lawyer advancing disability justice; Lainey Feingold, a disability rights lawyer who was on the team that negotiated the first web accessibility agreement in the U.S. in 2000; and George Kerscher, the chief innovations officer for the DAISY Consortium. Among the topics they discussed were communicating via Zoom and other video platforms in the days of COVID, how tech companies have adhered to the Americans with Disabilities Act, and the need for a culture shift if we're going to realize any significant change. "It's all about a culture change to really make sure technology is accessible for everyone," Feingold told Megan. "And you can't get a culture change, I don't believe, by hammering people.

While many companies are hiring data scientists and other types of analytical and artificial intelligence talent, there is little consensus within and across companies about the qualifications for such roles. The term data scientist might mean a job with a heavy emphasis on statistics, open-source coding, or working with executives to solve business problems with data and analysis. The idea of data scientist "unicorns" who possess all these skills at high levels was never very realistic. As the job has grown more popular and sought-after, an increasing number of professionals have begun to use it to describe their role. Colleges and universities have responded to the demand as well by offering hundreds of new programs on data science and analytics.

The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL based on planning in an abstract space of higher-order skills. We learn the skills in an unsupervised manner using intrinsic rewards and plan over the learned skills using a learned dynamics model. Moreover, our framework permits skill discovery even from offline data, thereby reducing the need for excessive real-world interactions. We demonstrate empirically that LiSP successfully enables long-horizon planning and learns agents that can avoid catastrophic failures even in challenging non-stationary and non-episodic environments derived from gridworld and MuJoCo benchmarks.

The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilities. Such a requirement to transit from application-specific to general intelligence AI chip must consider several factors. This paper provides an overview of this cross-disciplinary field of study, elaborating on the generalisation of intelligence as understood in building artificial general intelligence (AGI) systems. This work presents a listing of emerging AI chip technologies, classification of edge AI implementations, and the funnel design flow for AGI chip development. Finally, the design consideration required for building an AGI chip is listed along with the methods for testing and validating it.