Graph matching (GM) (also called graph alignment or network reconciliation) refers to a class of computational techniques to identify node correspondences across related networks based on structural information. GM has applications in a variety of domains, including data fusion, privacy, computer vision, and in computational biology. For example, in computational biology, a coarse description of the metabolic machinery of a particular species is via a protein-protein interaction (PPI) network, which essentially captures which protein can react with which other protein in that species. Across species, the PPI networks tend to be strongly correlated, because evolution transfers metabolic processes from species to species. Therefore, by identifying correspondences among proteins in different species (so-called orthologs), one is able to transfer biological knowledge from one species to the other. However, crucially, the actual proteins tend to be chemically different across species, because random mutations alter these proteins over time without affecting their function. It is therefore not possible to find correspondences between proteins in different species simply by examining their amino-acid sequences. GM computes such correspondences by exploiting the correlation across networks in different species. A similar challenge arises in social networks: suppose a set of users have accounts in several social networks.

Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state of the art. We conclude that classic approaches constitute an important and strong baseline.

Singapore's Ngee Ann Polytechnic (NP) and London-based Centre for Finance, Technology and Entrepreneurship (CFTE) will jointly launch an industry-led AI in Finance (AIF) online course on June 24, 2018. Through this course, both NP and CFTE hope to support and to nurture talent in Fintech and to boost Fintech development in their respective regions and around the world. The course is accredited by SkillsFuture Singapore and is in the process of obtaining accreditation with The Institute of Banking and Finance Singapore. It aims to update finance professionals and technologists on the AI revolution and create an online community of learners and experts in AI to connect and network for future collaborations. Over 20 finance and technology thought leaders and insiders will come together to share key fundamentals and real-life case studies on how AI is reshaping the finance industry worldwide.

As the uses of Artificial Intelligence (AI) technology expand into every industry, companies like yours might be looking to hire experienced AI and, more specifically, machine learning (ML) talent from a small pool of available candidates stateside. But how can you find, entice, and recruit ML talent when you're competing with major tech giants like Google, Amazon, and Microsoft? The answer lies in getting creative with your recruitment and hiring strategies. The first thing to know when reevaluating recruitment strategies for high-end machine learning (ML) or other AI roles is that you'll need to adapt strategies based on the experience level you're looking for. What works for a Jr. ML Engineer won't work for recruiting Sr. AI Researcher. To access the talent you're looking to hire, you need to go where they'll be found.

The Statistics and Data Science Center (SDSC) at the Massachusetts Institute of Technology is launching a new online micromasters in statistics and data science. Currently under development by MIT faculty, the program will be available through edX in the fall and will feature a curriculum covering foundational knowledge of data science's methods and tools, in-depth coverage of probability and statistics and opportunities to experiment with data analysis techniques and machine learning algorithms. "The demand for data scientists is growing rapidly," said Krishna Rajagopal, dean for digital learning, in a prepared statement. "This new program increases the supply of professionals who are masters of the data science of today, and who have the foundational understanding needed to keep on top of the data science of tomorrow." MIT's micromasters programs are open to anyone who wants to enroll with no application process.

Singapore, South Korea, and Germany topped a recent survey of how countries across the world are dealing with rapidly advancing artificial intelligence (AI) technology primed to automate millions of jobs in the coming years. Commissioned by ABB, The Economist Intelligence Unit created their Automation Readiness Index using data and interviews with industry stakeholders, economists, government officials, and NGOs. The index ranks countries by how prepared they are for what they call "intelligent automation." There have long been questions about the future of automation and the adoption of AI-backed robotics that can do more than just replicate physical human tasks. Businesses are showing little hesitation in exploring AI, but governments continue to struggle in preparing populations for the coming changes to the workforce.

Enrollment in the society, founded in 1946, is open only to individuals who score in the 98th percentile or higher on a pre-approved intelligence test. To join this clever club, hopefuls must demonstrate excellence in verbal-, spatial-, and mathematical-reasoning skills. These four problems are what today's aspirants might see on a typical IQ assessment.

Sony Corp. and Carnegie Mellon University aim to jointly develop a home-use robot for food preparation that utilizes artificial intelligence technology. Sony and the U.S. school have concluded an agreement to collaborate on AI and robotics research. Their initial research and development efforts will focus on optimizing food preparation, cooking and delivery. They want to release a product within five years. "The technology necessary for a robot to handle the complex and varied task of food preparation and delivery could be applied to a broader set of skills and industries," Sony said in a statement.

There are a lot of neat things going on in deep reinforcement learning. One of the coolest things from last year was OpenAI and DeepMind's work on training an agent using feedback from a human rather than a classical reward signal. There's a great blog post about it at Learning from Human Preferences, and the original paper is at Deep Reinforcement Learning from Human Preferences. I've seen a few recommendations that reproducing papers is a good way of levelling up machine learning skills, and I decided this could be an interesting one to try with. It was indeed a super fun project, and I'm happy to have tackled it - but looking back, I realise it wasn't exactly the experience I thought it would be. If you're thinking about reproducing papers too, here are some notes on what surprised me about working with deep RL.

Existing applications include a huge amount of knowledge that is out of reach for deep neural networks. This paper presents a novel approach for integrating calls to existing applications into deep learning architectures. Using this approach, we estimate each application's functionality with an estimator, which is implemented as a deep neural network (DNN). The estimator is then embedded into a base network that we direct into complying with the application's interface during an end-to-end optimization process. At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application. Using this 'Estimate and Replace' method, we were able to train a DNN end-to-end with less data and outperformed a matching DNN that did not interact with the external application.