Jeff Hawkins has a principle that intuitively makes a lot of sense, yet is something that Deep Learning research has not emphasized enough. This is the notion of embodied learning. That is, biological systems learn from interacting with the environment. Hawkins is of the opinion that the brain learns by interacting with its environment. The classic Deep Learning training procedure is one of the crudest teaching methods that one can possibly imagine.
Many tasks in which humans excel are extremely difficult for robots and computers to perform. Especially challenging are decision-making tasks that are non-deterministic and, to use human terms, are based on experience and intuition rather than on predetermined algorithmic response. A good example of a task that is difficult to formalize and encode using procedural programming is image recognition and classification. For instance, teaching a computer to recognize that the animal in a picture is a cat is difficult to accomplish using traditional programming. Artificial intelligence (AI) and, in particular, machine learning technologies, which date back to the 1950s, use a different approach.
Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled.
Project-based learning opportunities come in all forms at MIT, as Melanie Chen discovered during her internship at Lincoln Laboratory this year. A computer science major, she served as a teaching assistant, curriculum developer, and mentor to high school students participating in Cog*Works, part of the Beaver Works Summer Institute. Now, finishing up her fall sophomore semester, Chen is finding plenty of opportunities to apply the lessons she has learned from her hands-on experience teaching others. "One of the greatest skills I've learned is effective communication," she notes. "Whether it's students, peers, colleagues, or mentors, I've learned what it takes to be able to create a trusting relationship, so that we can effectively work on a project of this scale together."
It has almost become regular news for this to occur, and these rapid pendulum swings are looking more and more normal for gold's trading pattern. Of course, as consistently reported, this trading pattern has been heavily dependent on geopolitical tensions. So much so, in fact, that analysts are calling this type of uncertainty the new normal. A recent report by Citi analysts stated, "Event-driven bids for gold seem to be occurring more frequently and may be the new normal […] In short, even as the rates and forex channel dominate the outlook for gold pricing, the yellow metal is increasingly being used by investors as a policy and tail risk hedge". This closely ties in to what was discussed in previous articles: gold has become something of a paper trading commodity, such that it it heavily reactive to events and bases itself in very reactive movement.
Machine Learning, thinking systems, expert systems, knowledge engineering, decision systems, neural networks - all synonymous loosely woven words in the evolving fabric of Artificial Intelligence. Of these Machine Learning (ML) and Artificial Intelligence (AI) are often debated and used interchangeably. In very abstract terms, ML is a structured approach for deriving meaningful predictions/insights from both structured and unstructured data. ML methods employ complex algorithms that enable analytics based on data, history and patterns. The field of data science continues to scale new heights enabled by the exponential growth in computing power over the last decade.
New KDnuggets Poll is asking: Which Data Science / Machine Learning methods and tools you used in the past 12 months for work or a real-world project? Please vote below and we will summarize the results and examine the trends in early December. Poll Which Data Science / Machine Learning methods and tools you used in the past 12 months for a real-world application? Kaggle survey asked: What data science methods are used at work? and the top answers were Gradient Boosted Machines
The research was led by Marco Zorzi at the University of Padova and funded with a starting grant from the European Research Centre (ERC). The project – GENMOD – demonstrated that it is possible to build an artificial neural network that observes the world and generates its own internal representation based on sensory data. For example, the network was able by itself to develop approximate number sense, the ability to determine basic numerical qualities, such as greater or lesser, without actually understanding the numbers themselves, just like human babies and some animals. "We have shown that generative learning in a probabilistic framework can be a crucial step forward for developing more plausible neural network models of human cognition," Zorzi says. Tests on visual numerosity show the network's capabilities, and offer insight into how the ability to judge the amount of objects in a set emerges in humans and animals without any pre-existing knowledge of numbers or arithmetic.
"By identifying patterns in molecular behavior, the learning algorithm or'machine' we created builds a knowledge base about atomic interactions within a molecule and then draws on that information to predict new phenomena," explains New York University's Mark Tuckerman, a professor of chemistry and mathematics and one of the paper's primary authors. The research team created a machine that can learn complex interatomic interactions, which are normally prescribed by complex quantum mechanical calculations, without having to perform such intricate calculations. To weigh the viability of the tool, they examined how the machine predicted the chemical behavior and then compared their prediction with our current chemical understanding of the molecule. The results revealed how much the machine could learn from the limited training data it had been given.