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) …
A human-sized jellyfish lurking off the coast of the English Channel stunned divers who said, 'it's an experience we'll never forget.' Scientists have discovered a new blob-like species of ctenophore, or comb jelly, off Puerto Rico. The creature, named Duobrachium sparksae, was first spotted during a 2015 dive led by the NOAA Office of Ocean Exploration and Research. An undersea drone captured high-definition video of the comb jelly during the dive. "NOAA Fisheries scientists Mike Ford and Allen Collins, working shoreside, spotted it and recognized it as novel," said NOAA, in a statement.
This article is a response to an article arguing that an AI Winter maybe inevitable. However, I believe that there are fundamental differences between what happened in the 1970s (the fist AI winter) and late 1980s (the second AI winter with the fall of Expert Systems) with the arrival and growth of the internet, smart mobiles and social media resulting in the volume and velocity of data being generated constantly increasing and requiring Machine Learning and Deep Learning to make sense of the Big Data that we generate. For those wishing to see a details about what AI is then I suggest reading an Intro to AI, and for the purposes of this article I will assume Machine Learning and Deep Learning to be a subset of Artificial Intelligence (AI). AI deals with the area of developing computing systems that are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. The rapid growth in Big Data has driven much of the growth in AI alongside reduced cost of data storage (Cloud Servers) and Graphical Processing Units (GPUs) making Deep Learning more scalable.
This tutorial covers the entire ML process, from data ingestion, pre-processing, model training, hyper-parameter fitting, predicting and storing the model for later use. Let's see the whole picture Recreating the entire experiment without PyCaret requires more than 100 lines of code in most libraries. The library also allows you to do more advanced things, such as advanced pre-processing, ensembling, generalized stacking, and other techniques that allow you to fully customize the ML pipeline and are a must for any data scientist. PyCaret is an open source, low-level library for ML with Python that allows you to go from preparing your data to deploying your model in minutes. Allows scientists and data analysts to perform iterative data science experiments from start to finish efficiently and allows them to reach conclusions faster because much less time is spent on programming.
Multi-agent reinforcement learning (MARL) has shown recent success in increasingly complex fixed-team zero-sum environments. However, the real world is not zero-sum nor does it have fixed teams; humans face numerous social dilemmas and must learn when to cooperate and when to compete. To successfully deploy agents into the human world, it may be important that they be able to understand and help in our conflicts. Unfortunately, selfish MARL agents typically fail when faced with social dilemmas. In this work, we show evidence of emergent direct reciprocity, indirect reciprocity and reputation, and team formation when training agents with randomized uncertain social preferences (RUSP), a novel environment augmentation that expands the distribution of environments agents play in.
The World Intellectual Property Organization's (WIPO) first report of a series called WIPO Technology Trends, an extensive study of patent applications and other scientific documents, offers clues to the next big thing in AI. Rather than treating'AI' as a single homogeneous discipline (see our guide to AI terminology), the WIPO report divides it into AI techniques, AI functional applications and AI application fields, offering a finer-grained analysis. AI techniques are advanced forms of statistical and mathematical models used in AI, including machine learning, logic programming, ontology engineering, probabilistic reasoning and fuzzy logic. Machine learning is included in more than one third of all identified inventions and represents 89 per cent of AI filings, the report finds. Between 2013 and 2016, filings related to deep learning rocketed by about 175 per cent.
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.