If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
GitHubbers couldn't get enough of machine learning in 2018. The data doesn't lie: the 2018 State of the Octoverse report showed that ML and data science projects ranked highly on all kinds of categories. From most popular to most contributed, fastest growing and more, machine learning was on your mind and on your forks last year. That said, machine learning and data science are kind of broad topics. What did developers really care about last year?
This tutorial is the second book in the'in a weekend' series – after Classification and Regression in a weekend. The idea of the'in a weekend' series of books is to study one complex section of code in a weekend to master the concept. Cloud computing changes the development paradigm. Specifically, it combines development and deployment (the DevOps approach). In complex environments, the developer has to know more than the coding.
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered by problems such as poor generalization. To address this limitation, in this work, we propose a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs.
Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer canceling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices. Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel – allowing a hotel chain to adjust its marketing strategy accordingly. To investigate how machine learning can aid in this task, the ExtraTreesClassifer, logistic regression, and support vector machine models were employed in Python to determine whether cancellations can be accurately predicted with this model. For this example, both hotels are based in Portugal.
There is no doubt that Hollywood has played a part in convincing society that artificial intelligence is a self-aware danger to humanity – but the underlying truth is much more mundane. In place of'power-hungry robots', AI systems are sophisticated algorithms that make sense of the 21st century's most precious commodity: Data. Drawing from test results and treatment histories, to lifestyle information and symptom diaries, AI algorithms are scanning these bottomless pits of information for clues and patterns that could revolutionise the way diseases are diagnosed, and care is delivered. The team at healthcare marketing agency Perfect Storm, Bristol, talk us through some of the ways in which AI is building the NHS of the future. Clinicians use a plethora of imaging tests, such as X-rays, CT scans and MRIs.
The unprecedented implications of digital health innovations, being co-produced by the mainstreaming and integration of artificial intelligence (AI), the Internet of Things (IoT), and cyber-physical systems (CPS) in healthcare, are examined in a new technology horizon-scanning article. This digital transformation of healthcare is facilitated by the rapid rise in Big Data and real-time Big Data analytics. The detailed findings are published in OMICS: A Journal of Integrative Biology, the peer-reviewed interdisciplinary journal published by Mary Ann Liebert, Inc., publishers. Click here to read the full-text article free on the OMICS: A Journal of Integrative Biology website until July 12, 2019. Vural Özdemir, MD, PhD, DABCP, Editor-in-Chief of OMICS: A Journal of Integrative Biology is the author of the article entitled "The Big Picture on the'AI Turn' for Digital Health: The Internet of Things and Cyber-Physical Systems."
Automated recognition and talent management are on the list of the US Army's AI Task Force, as a result of its year-long collaboration with the Carnegie Mellon University on incorporating Artificial Intelligence (AI) into US Army systems. The task force's access to sensors, different types of electro-mechanical devices, and computing capabilities are enabling them to create AI for other applications as per the plan in the 2018 directive, which states, "The Army is establishing the Army-AI Task Force (A-AI TF) that will narrow an existing AI capability gap by leveraging current technological applications to enhance our warfighters, preserve peace, and, if required, fight to win." Five university staffers at National Robotics Engineering Center, an integral part of CMU's Robotics Institute, have formed an AI Hub to work directly with the Army task force. The task force is starting to fill gaps in its systems with AI. In March, the US Army invested US$72 million in a five-year AI fundamental research effort to research and discover capabilities for augmenting military personnel, optimizing operations, increasing readiness, and reducing casualties. According to the Combat Capabilities Development Command Army Research Laboratory, which is the US Army's corporate laboratory (ARL), in March, CMU will lead a consortium of multiple universities to work in collaboration with the Army lab to accelerate R&D of advanced algorithms, autonomy and AI to enhance national security and defense.
In the heart of Oxford's Ashmolean Museum, surrounded by countless monuments to human achievement, the founders of Mind Foundry regaled the audience with their firsthand account of the inception of the company. In a world that so often fearmongers the advent of democratised artificial intelligence (AI), Professor Stephen Roberts and Professor Michael Osborne pose a more optimistic vision for the future of human and machine, not one of AIs, but rather of augmented human intelligence. Since 2015, the AI for Business report has been a staple of the Raconteur publishing calendar, positioning us to narrate the future of this nascent, but rapidly growing, industry. Over that period, we have watched avidly as AI has transitioned from technology to a category on the Gartner Hype Cycle, spawning more emergent technologies than perhaps any other transitive category since big data. Much to this accord, Mind Foundry has debuted as one of the first-mover commercial machine-learning platform-as-a-service (ML PaaS) or automated machine-learning (AutoML) offerings in the market, presenting a solution which promises to keep humans relevant in the age of machine-learning.