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 new app can help give you the perfect Instagram shot by removing bystanders and tourists from photos in busy locations. Named Bye Bye Camera, the developer describes it as an art project and an app'for the post-human era'. Artist Damjanski created the software with Do Something Good, an'incubation collective' where coders and artists pool their resources to create projects. The app uses the same AI tools found in facial recognition software to identify people using an object detection algorithm called YOLO (You Only Look Once). It then uses a separate tool to fill in the space left behind with what Adobe has dubbed'context-aware fill'.
The CEO of Instagram has defended the company's decision not to take down a deepfaked video of Mark Zuckerberg two weeks after the doctored video was reported. Adam Mosseri told CBS' Gayle King - in his first US television interview since taking over the platform last year - that the company hasn't yet formulated an official policy on AI-altered video called'deepfakes', and until then taking action would be'inappropriate.' Mosseri said, 'I don't feel good about it,' but said there is no rush to remove the video, in part because'the damage is done.' Mosseri's comments about deepfakes come as a response to King's questioning about a faked video of Facebook CEO Mark Zuckerberg taken from an actual interview with CBSN in 2017. The doctored video features a fairly convincing Zuckerberg next to a superimposed CBSN logo talking about how Facebook wields power over its users.
TEHRAN - Iran said Tuesday it will further free itself from the 2015 nuclear deal in defiance of new American sanctions as U.S. President Donald Trump warned the Islamic republic of "overwhelming" retaliation for any attacks. Tensions between Iran and the U.S. have spiraled since last year when Trump withdrew the United States from the deal under which Tehran was to curb its nuclear program in exchange for relief from economic sanctions. The two arch-rivals have been locked in an escalating war of words since Iran shot down a U.S. surveillance drone in what it said was its own airspace, a claim the US vehemently denies. On Monday, Washington stepped up pressure by blacklisting Iran's supreme leader Ayatollah Ali Khamenei and top military chiefs, saying it would also sanction Foreign Minister Mohammad Javad Zarif later in the week. Tehran was defiant on Tuesday, saying the new US sanctions against Iran showed Washington was "lying" about an offer of talks.
Members of the public are getting the chance to take a free ride in a self-driving car in Detroit as part of a nonprofit coalition's effort to clear up misunderstanding and confusion about the technology (April 5) AP, AP Fully autonomous vehicles that can drive themselves in nearly any situation aren't roaming the streets just yet, but almost every major technology company and automaker is working on getting to that point as quickly as possible. These companies are making huge strides in creating a world where we can hop into a car, tell it where to take us, and safely arrive – with no human input needed. If you need some convincing about how these vehicles will transform our world, consider these four hard-to-believe facts. Research from IHS Markit shows that in nearly two decades, more than 30 million self-driving vehicles will be sold each year. That means that 26% of new cars will have autonomous mobility by that year.
Machine Learning has seen a tremendous rise in the last decade, and one of its sub-fields which has contributed largely to its growth is Deep Learning. The large volumes of data and the huge computation power that modern system possess has given Data Scientist, Machine Learning Engineers, and others to achieve ground-breaking results in the Deep Learning and continue to bring in new developments in this field. In this blog post, we would cover the deep learning data sets that you could work with as a Data Scientist but before that, we would provide an intuition about the concept of Deep Learning. A sub-field of Machine Learning, the working structure of Deep Learning is similar to our brain known as the Artificial Neural Networks. It is similar to our nervous system where each neuron connected to each other.
Already heard talking about Machine Learning in conferences, meetups or in my first article and want to learn more? You're in the right place, the second step for you will be to discover the different kind of machine learning. I'll introduce them to you through many practical examples. ML comes in two flavors: supervised and unsupervised learning. The one you'll choose only depends on your purpose.
Plant-derived secondary metabolites play a vital role in the food, pharmaceutical, agrochemical and cosmetic industry. Metabolite concentrations are measured after extraction, biochemistry and analyses, requiring time, access to expensive equipment, reagents and specialized skills. Additionally, metabolite concentration often varies widely among plants, even within a small area. A quick method to estimate the metabolite concentration class (high or low) will significantly help in selecting trees yielding high metabolites for the metabolite production process. Here, we demonstrate a deep learning approach to estimate the concentration class of an intracellular metabolite, azadirachtin, using models built with images of leaves and fruits collected from randomly selected Azadirachta indica (neem) trees in an area spanning 500,000 sqkms and their corresponding biochemically measured metabolite concentrations.
This article is my entry for CodeProject's AI competition "Image Classification Challenge"[ ]. My goal was to teach a neural network to play a game of tic tac toe, starting from only knowing the rules. Tic tac toe is a solved game. A perfect strategy[ ] exists so a neural network is a bit overkill and will not perform as well as existing programs and humans can. Described from a high level: when the AI needs to make a move, it iterates over all possible moves, generates the board after making a given move, and uses the neural network to see how good the position is after performing that move.
Does this star have a planet? A new algorithm could help astronomers predict, on the basis of a star's chemical fingerprint, whether that star will host a giant gaseous exoplanet. "It's like Netflix," Natalie Hinkel, a planetary astrophysicist at the Southwest Research Institute in San Antonio, Texas, told Eos. Netflix "sees that you like goofy comedy, science fiction, and kung fu movies--a variety of different patterns" to predict whether you'll like a new movie. Likewise, her team's machine learning algorithm "will learn which elements are influential in deciding whether or not a star has a planet."
Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. I would say that it is "identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data" in the context of "mapping data from one'raw' form into another..." all the way up to "training a statistical model" which I like to think of data preparation as encompassing, or "everything from data sourcing right up to, but not including, model building."