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) …
That's why companies like Google are turning to computational photography: using algorithms and machine learning to improve your snaps. The researchers used machine learning to create their software, training neural networks on a dataset of 5,000 images created by Adobe and MIT. Each image in this collection has been retouched by five different photographers, and Google and MIT's algorithms used this data to learn what sort of improvements to make to different photos. Of course, it's worth pointing out that smartphones and cameras already process imaging data in real time, but these new techniques are more subtle and reactive, responding to the needs of individual images, rather than applying general rules.
Decision trees are becoming increasingly popular and can serve as a strong learning algorithm for any data scientist to have in their repertoire, especially when coupled with techniques like random forests, boosting, and bagging. Support vector machines, also known as SVM, are a well-known supervised classification algorithm that create a dividing line between the your differing categories of data. K-Means is a popular unsupervised learning classification algorithm typically used to address the clustering problem. The algorithm begins with randomly selected points and then optimizes the clusters using a distance formula to find the best grouping of data points.
The news web site BuzzFeed did just that, reporting this week that it employed a machine-learning algorithm to first recognize known spy planes, and then combine that model with a large set of flight-tracking data from a commercial web site. The AI project mapped thousands of surveillance flights operated by federal agencies over a four-month period, including a military contractor tracking terrorists in Africa that is also flying surveillance aircraft over U.S. cities, BuzzFeed reported. The ground radars sweep up a flight data transmitted by aircraft transponders, including unique identifiers for each plane. "Given that spy planes tend to fly in tight circles, it put most weight on the planes' turning rates," the web site reported.
This explains why corporations like Google, Facebook, Baidu, and other search engines and social networks with millions of users and almost limitless data have a distinct advantage when it comes to machine learning. Because a lot of the tools being used to develop AI are currently opensource, the possibility of both large and small businesses taking advantage of them to solve very specific problems by optimizing very specific tasks with AI has become a possibility. It also stands to reason that for any positive advantage AI might give honest government administrators, businesses, or individuals, an equally negative advantage could be lent to dishonest organizations or individuals. In terms of an independent, sentient AI system more intelligent than humans, Elon Musk has compared it to knowing that an advanced race of aliens was coming to Earth in the next 10-20 years.
So, my colleague and I were discussing the topic and after a while she said she doesn't understand machine learning & Artificial Intelligence fully. In 1959, Arthur Samuel sparked the discussion on Machine Learning (ML), stating that ML gives, "computers the ability to learn without being explicitly programmed." This is where another algorithmic concept called Artificial Neural Networks and Deep Learning came into being. To know more about Machine Learning and Artificial Intelligence in depth, listen to ResellerClub's Tech Talks revolving on this topic that was aired on 9th August, 2017.
Simply put, machine learning refers to teaching computers how to analyse data for solving particular tasks through algorithms. The system learns how to put data into different groups based on a reference data set. This is directly associated with the kinds of decisions we make every day, whether it's grouping similar products (kitchen goods against beauty products, for instance), or choosing good films to watch based on previous experiences. Having access to a tool that actively uses this data for practical problem solving, such as artificial intelligence, means everyone should and can explore and exploit this.
The rise of advanced data analytics and cognitive technologies has led to an explosion in the use of algorithms across a range of purposes, industries, and business functions. Decisions that have a profound impact on individuals are being influenced by these algorithms--including what information individuals are exposed to, what jobs they're offered, whether their loan applications are approved, what medical treatment their doctors recommend, and even their treatment in the judicial system. What's more, dramatically increasing complexity is fundamentally turning algorithms into inscrutable black boxes of decision making. But these black boxes are vulnerable to risks, such as accidental or intentional biases, errors, and frauds--raising the question of how to "trust" algorithmic systems.
Indian-origin researchers have developed a new system that uses Artificial Intelligence algorithms and a smartphone app to instantly distinguish between genuine and fake versions of the same product. The Artificial Intelligence algorithms then analyse the images to determine authenticity and provide results in real-time. "The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products – corresponding to the same larger product line–exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions," Subramanian explained. The Entrupy method, by contrast, provides a non-intrusive solution to easily distinguish authentic versions of the product produced by the original manufacturer and fake versions of the product produced by counterfeiters.
Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Collaborative information filtering applications such as movie recommender systems co-cluster the accumulated movie rating provided by viewers and the movies they have watched. Using this information, the viewer is recommended other movies by classifying the rating he/she provided to a viewer ratings-movies watched cluster. An entry Cij of the matrix signifies the relation between the data type represented by row i and column j. Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix.
This explains why corporations like Google, Facebook, Baidu, and other search engines and social networks with millions of users and almost limitless data have a distinct advantage when it comes to machine learning. Because a lot of the tools being used to develop AI are currently opensource, the possibility of both large and small businesses taking advantage of them to solve very specific problems by optimizing very specific tasks with AI has become a possibility. Keeping AI and Minds Open is Our Best Defense Making sure that no single corporation or government acquires such an advantage, negating the temptation to exploit a monopoly of technology as has been done countless other times throughout human history, requires the playing field to remain as even as possible. The Elephant in the Room: The Rise of Sentient AI In terms of an independent, sentient AI system more intelligent than humans, Elon Musk has compared it to knowing that an advanced race of aliens was coming to Earth in the next 10-20 years.