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) …
DeepMind has been trying to bridge the gap between AI and biology for quite some time now. All their endeavours revolve around solving the problem of intelligence in machines. The straightforward trivial tasks for humans can be very, very sophisticated and almost for devices. While human brains are hardcoded with millions of years of learning, the machines have many limitations when it comes to data. They can be fed with data that has been documented or prepared by humans, the magnitude of which is historically insignificant when compared to humans.
Over the past few months, I've been working on a fascinating project with one of the world's largest pharmaceutical companies to apply SAS Viya computer vision to help identify potential quality issues on the production line as part of the validated inspection process. As I know the application of these types of AI and ML techniques are of real interest to many high-tech manufacturing organisations as part of their Manufacturing 4.0 initiatives, I thought I'd take the to opportunity to share my experiences with a wide audience, so I hope you enjoy this blog post. For obvious reasons, I can't share specifics of the organisation or product, so please don't ask me to. But I hope you find this article interesting and informative, and if you would like to know more about the techniques then please feel free to contact me. Quality inspections are a key part of the manufacturing process, and while many of these inspections can be automated using a range of techniques, tests and measurements, some issues are still best identified by the human eye.
A Brain-Computer Interface (BCI) is a system that extracts and translates the brain activity patterns of a subject (humans or animals) into messages or commands for an interactive application. The brain activity patterns are signals obtained with Electroencephalography (EEG). The concept of controlling devices solely with our minds is nothing new. Science fiction and Hollywood movies have been known to depict this. Several studies and experiments have been conducted, such as monkeys controlling robotic arms to feed itself, controlling a wheelchair and controlling cursors to type about eight words per minute.
Cover song identification is an important problem in the field of Music Information Retrieval. Most existing methods rely on hand-crafted features and sequence alignment methods, and further breakthrough is hard to achieve. In this paper, Convolutional Neural Networks (CNNs) are used for representation learning toward this task. We show that they could be naturally adapted to deal with key transposition in cover songs. Additionally, Temporal Pyramid Pooling is utilized to extract information on different scales and transform songs with different lengths into fixed-dimensional representations.
The advent of artificial intelligence (AI) has brought a host of technologies that make everyday tasks easier. The most widely used examples include providing more relevant internet searches, predicting the next word in a text message, identifying the face in a photo on social media, and routing commuters around trouble spots in traffic. The AI discipline of deep learning also has the potential to revolutionize healthcare in ways that researchers are only beginning to explore. A recent announcement by Alphabet's (NASDAQ:GOOGL)(NASDAQ:GOOG) Google highlights one example of how research is taking leaps ahead as physicians begin using AI as a tool. A study published in the scientific journal Nature showed that Google's AI system could detect breast cancer in mammograms more accurately than human radiologists.
Lately, I've been working on a couple of scenarios that have reminded me of the importance of feature extraction in deep learning models. As a result, I would like to summarize some ideas I've outlined before about some of the principles of knowledge quality in deep learning and model and the applicability of representation learning to those scenarios. Understanding the characteristics of input datasets is an essential capability of machine learning algorithms. Given a specific input, machine learning models need to infer specific features about the data in order to perform some target actions. Representation learning or feature learning is the subdiscipline of the machine learning space that deals with extracting features or understanding the representation of a dataset.
The current landscape of machine learning research suggests that modern methods based on deep learning are at odds with good old-fashioned AI methods. Deep learning has proven to be a very powerful tool for feature extraction in various domains, such as computer vision, reinforcement learning, optimal control, natural language processing and so forth. Unfortunately, deep learning has an Achilles heel, the fact that it cannot deal with problems that require combinatorial generalization. An example is learning to predict quickest routes in Google Maps based on map input as an image, an instance of the Shortest Path Problem. A plethora of such problems exists like (Min,Max)-Cut, Min-Cost Perfect Matching, Travelling Salesman, Graph Matching and more.
The recipes for those proteins--called genes--are encoded in our DNA. An error in the genetic recipe may result in a malformed protein, which could result in disease or death for an organism. Many diseases, therefore, are fundamentally linked to proteins. But just because you know the genetic recipe for a protein doesn't mean you automatically know its shape. Proteins are comprised of chains of amino acids (also referred to as amino acid residues).
To bridge the gap between the data we're collecting and the way organizations interface with it, we need to address some uncomfortable realities. As we step into the next decade, there's a growing sense – almost an inevitable momentum – that we're headed towards a golden age of AI. Over the past year, we've witnessed incredible advances in applying artificial intelligence techniques to image recognition, language processing, planning, and information retrieval. There are more amusing applications, too, including one team teaching AI how to craft puns. See also: Will the Consumerization of AI Set Unrealistic Expectations?
An agreed upon definition of machine learning is, a computer program is said to have learned when it's performance measure P at task T improves with experience E. Under the definition of Supervised Learning, we get this diagram. Here the experience would be the training data required to improve the algorithm. In practice we put this data into the Design Matrix. Design Matrix [dəˈzīn ˈmātriks]: term -- if a single input can be represented as a vector, putting all of the training examples, i.e the vectors, into 1 matrix makes the entire input aspects of the training data. This is not all of the experience.