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) …
While technology has evolved, allowing newspapers to serve up digital content personalized to the subscriber and offering customer portals to boost subscriptions, the same can't be said for the print side of newspaper businesses. Organizations are still having to manage and allocate resources toward task-heavy print processes instead of focusing on digitally transforming their business models. And so, as the move to digital accelerates, there are some critical steps that can be taken to help close the print and digital divide. According to a survey conducted by Naviga in 2020, approximately 30 percent of all newsroom resources are dedicated to the print newspaper manufacturing process. That's a third of all resources tethered to delivering a physical paper.
Talking Machines podcasts feature conversations in today's popular areas of machine learning. They appeal to both machine learning professionals and enthusiasts. Talks are usually about NIPS (Neural Computing Systems), and guests are usually top practitioners. Data Skeptic explains certain concepts in data science in short sections. Longer interviews with practitioners and experts on interesting data-related topics are also included.
The drug discovery ecosystem is changing rapidly. The rise of robotics and AI enables the emergence of a new model of data-driven drug discovery. Bringing together recent advances in life sciences automation and machine learning applications for drug discovery, new partnerships evolve that allow for game-changing improvements in the drug discovery process. The webinar will provide an overview on large-scale data and metadata capture enabled by end-to-end automation, going beyond what is currently possible in traditional wet lab operations, and will present case studies showing the impact on biotech and pharma operations, providing actionable insights for biopharma leaders. Disclaimer Regarding Audio/Video Recording: a) By participating in this Webinar, you will be participating in an event where photography, video and audio recording may occur. b) By participating in this webinar, you consent to interview(s), photography, audio recording, video recording and its/their release, publication, exhibition, or reproduction to be used for news, web casts, promotional purposes, telecasts, advertising, inclusion on web sites, or for any other purpose(s) that Invitrocue, its vendors, partners, affiliates and/or representatives deems fit to use. You release Invitrocue, its employees, and each and all persons involved from any liability connected with the taking, recording, digitising, or publication of interviews, photographs, computer images, video and/or or sound recordings.
If you're a buyer of technology stocks and solutions, you are most likely familiar with Gartner, Inc. (NYSE: IT) and the power it holds in the space. The firm, founded in 1979 by Gideon Gartner, now has a market capitalization of $10B and it employs a team of 1,900 analysts who glean insights from more than 380,000 client interactions each year. When it comes to industry research, Gartner is the 800-pound gorilla. And when Gartner publishes market research, people pay attention. Take for example the last pieces the firm released over the last two weeks: Gartner's February 2018 Magic Quadrant for Data Science and Machine-Learning Platforms and the Magic Quadrant for Analytics and Business Intelligence Platforms.
We cannot fathom how deeply Artificial Intelligence (or AI for short) is involved in our day-to-day tasks – from simple chatbots helping us in online shopping to taking the help of Robo-Advisors to make investment-related decisions. Almost every leading company is utilizing AI tech, an industry that is speculated to be worth around $390 billion by 2025. As easy as it sounds, AI is still a dynamic category of technology, and it can be exhausting to keep up with its countless subsets. However, if you are considering investing in AI-driven companies, it is salient to gather vast knowledge about the types of investments that are available and knowing which type of AI is trending the most. Now that more companies are starting to rely successfully on AI with tremendous profits, stocks for AI companies are becoming highly attractive to potential investors.
In a new perspective piece "Transparency and reproducibility in artificial intelligence" published this week in the journal Nature, an international group of scientists including CUNY Graduate School of Public Health and Health Policy (CUNY SPH) Associate Professor Levi Waldron raised concerns about the lack of transparency in publication of artificial intelligence algorithms for health applications. The authors raise concerns about a recent publication in which a group including Google Health reported using artificial intelligence to diagnose breast cancer from mammogram images more accurately than expert human radiologists. The authors contend that restrictive data access procedures, lack of published computer code, and unreported model parameters make it impractically difficult for any other researchers to confirm or extend this work. The piece also highlights tensions over what are appropriate measures to protect patient privacy while allowing the broader research community to contribute methodology and to correct potential errors that could set back progress to the detriment of other patients. "This back-and-forth is one high-profile example of the current state of struggles over who controls data that has played out for decades in the biomedical sciences and other fields," says Professor Waldron.
Focus on research in Artificial Intelligence (AI) is nowadays growing more and more every year, particularly in fields such as Deep Learning, Reinforcement Learning and Natural Language Processing (Figure 1). State of the art research in AI is usually carried out in top universities research groups and research-focused companies such as Deep Mind or Open AI, but what if you would like to give your own contribution in your spare time? In this article, we are going to explore different possible approaches you can take in order to be always up to date with the latest in research and how to provide your own contribution. One of the main problems which have affected the AI research field is the possible inability to efficiently reproduce models and results claimed in some publications (Reproducibility Challenge). In fact, many research articles published every year contains just an explanation of the derided topic and model developed but no source code to reproduce their results.
Moreover, the undersigned agrees to cooperate in any claim or other action seeking to protect or enforce any right the undersigned has granted to AAAI in the article/paper. If any such claim or action fails because of facts that constitute a breach of any of the foregoing warranties, the undersigned agrees to reimburse whomever brings such claim or action for expenses and attorneys' fees incurred therein. The foregoing right shall not permit the posting of the article/paper in electronic or digital form on any computer network, except by the author or the author's employer, and then only on the author's or the employer's own web page or ftp site.
"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models. This book is to accompany the usual free tutorial videos and sample code from youtube.com/sentdex. This topic is one that warrants multiple mediums and sittings. Having something like a hard copy that you can make notes in, or access without your computer/offline is extremely helpful.
On 23 September 2020, the Committee of Ministers approved the progress report of the Ad hoc Committee on Artificial Intelligence (CAHAI), which sets out the work undertaken and progress towards the fulfilment of the committee's mandate since it was established on 11 September 2019. The progress report sets out a clear roadmap for action towards a Council of Europe legal instrument based on human rights, the rule of law and democracy. Its clear relevance has also been confirmed and reinforced by the recent COVID-19 pandemic. The preliminary feasibility study, providing indications on the legal framework on the design, development of artificial intelligence based on Council of Europe's standards is expected to be examined by the CAHAI at its forthcoming third plenary meeting in December 2020.