The dream of building a machine that can think like a human stretches back to the origins of electronic computers. But ever since research into artificial intelligence (AI) began in earnest after World War II, the field has gone through a series of boom and bust cycles called "AI summers" and "AI winters." Each cycle begins with optimistic claims that a fully, generally intelligent machine is just a decade or so away. Funding pours in and progress seems swift. Over the last ten years, we've clearly been in an AI summer as vast improvements in computing power and new techniques like deep learning have led to remarkable advances.

Can those memories serve to enhance retention of learnings and help-built connections or patterns we could not do in a conscious state? What is also intriguing is the work being done in projects like "Deep Dream" which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like hallucinogenic appearance in the deliberately over-processed images. We may indeed be in the cusp of the "next big thing". But this is not new, AI pretty much has been at the forefront of all major conversations pertaining to technology over the past few years. From self-driving cars to intuitive virtual assistants, AI has been turning every science-fiction trope into reality.

Wall Street is big business, and it is about to become even bigger with the rise of big data. It is every investor's dream to have prior knowledge of the direction of the market before it happens, which is why financial investment firms are driven to mine for data rather than for gold in the information economy. Traditionally, investors have based their decisions on fundamentals, intuition, and analysis drawn from traditional data sources, such as quarterly earnings reports, financial statement filings to the U.S. Securities and Exchange Commission (SEC), historical market data, institutional research reports and sometimes the so-called "expert networks." The new data-driven paradigm, fueled by new alternative data sources, high performance computing and predictive analytics, offers a more robust framework to generate data-driven investment theses. Data – from satellite images of areas of interest, automated drones, people-counting sensors, container ships' positions, credit card transactional data, jobs and layoffs reports, cell phones, social media, news articles, tweets, online search queries – is now the most valuable commodity for Wall Street.

Researchers fed the robot nearly 5,000 complete songs -- from Beethoven to the Beatles to Lady Gaga to Miles Davis -- and more than 2 million motifs, riffs and licks of music. Aside from giving the machine a seed, or the first four measures to use as a starting point, no humans are involved in either the composition or the performance of the music. The first two compositions are roughly 30 seconds in length. Ph.D. student Mason Bretan is the man behind the machine. He's worked with Shimon for seven years, enabling it to "listen" to music played by humans and improvise over pre-composed chord progressions.

Shimon, a four-armed, marimba playing robot, is writing and playing its own music using deep learning. This is the first of its two songs. A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. Researchers fed the robot nearly 5,000 complete songs -- from Beethoven to the Beatles to Lady Gaga to Miles Davis -- and more than 2 million motifs, riffs and licks of music.