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) …
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit some of these methods to improve the conventional algorithm based approach used in astrophysics today to detect exoplanets. We used the popular time-series analysis library'TSFresh' to extract features from lightcurves. For each lightcurve, we extracted 789 features.
IBM has been banging the drum for years about the role artificial intelligence can play to support everything from cancer treatment to retail personalization. More recently, though, IBM has started to prioritize practical advertising applications of its cognitive computing system, Watson. Last month, IBM brought its AI to ad tech through partnerships with Xandr, Magnite, Nielsen, MediaMath, LiveRamp and Beeswax. The move followed a steady stream of Watson Advertising announcements involving AI in advertising. In January, IBM built Advertising Accelerator, a tool that helps predict the best ads to run and tests creative versions in real time during a campaign.
Next week, Adobe is rolling out'visual similarity recommendations' which offer AI-powered product suggestions based on what consumers are considering purchasing. And this on-the-fly use of visual interpretation and recommendation is just the start. Now that more people are shopping online during the pandemic, brands need to facilitate the myriad ways people hunt, browse and discover products. But it's not so easy to do that if a shopper doesn't quite know what she wants until she sees it. Enter AI and visual similarity.
As businesses race towards bringing high-quality products to the markets as quickly and as efficiently as they can, automation sets a strong foundation for streamlining tedious tasks, optimizing workflows, eliminating manual efforts, and minimizing administrative errors. Analysts expect the global process automation market to cross $114 billion by 2025. In the quest to automate, enterprises are testing the limits of solutions. They are exploring different automation avenues and modes. Terms like RPA and BPA are under discussion.
New software checks for potential conflicts of interest by flagging whether the authors of a manuscript, the editors dealing with it, or the peer reviewers refereeing it have been co-authors on papers in the past. Frontiers, a Swiss publisher of open-access journals, has rolled out the Artificial Intelligence Review Assistant (AIRA) to check for potential conflicts of interest. The software flags whether the authors of a manuscript, as well as the editors and peer reviewers handling it, have been co-authors on previous papers. Said Frontiers' Kamila Markram, "AIRA is designed to direct the attention of human experts to potential issues in manuscripts." Other publications are using similar artificial intelligence (AI) tools, but some researchers note that conflicts of interest can be subjective and difficult to unravel.
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Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and nonconvex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures, and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals. In 1884, Edwin Abbott wrote Flatland: A Romance of Many Dimensions (1) (Figure 1). This book was written as a satire on Victorian society, but it has endured because of its exploration of how dimensionality can change our intuitions about space. Flatland was a 2-dimensional (2D) world inhabited by geometrical creatures.
There are many great articles about Artificial Intelligence (AI) and its benefits for business and society. However, many of these articles are too technical for the average reader. I love reading about AI, but I sometimes think to myself, 'Gee, I wish the author had explained this in simple English.' I will try and explain AI and its related technologies in simple terms, using real-life examples, as though I were talking to someone at a party. Your colleagues or your (close) friends may tolerate your endless and complex ramblings, but I guarantee you that people at parties are far less forgiving.
Whatever the ultimate impact may be of a report by UK experts in algorithmic bias, the document already has succeeded where many analyst reports have failed. The new report, on bias in algorithmic decision making, comes from the government-funded Centre for Data Ethics and Innovation. It takes the position that AI decision making must be ethical to be successful, and AI ethics must be viewed as impacting individual people -- because it does. Thinking about AI ethics in terms of industries, regions, demographics and data sets is an easy out. It lets everyone involved spread harms across faceless multitudes.