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) …
The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer -- think of Mary Shelley's Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed time and again to make machines act and think like the human brain. But this time could be different because of a major breakthrough -- deep learning, where data structures are set up like the brain's neural network to let computers learn on their own. Together with advances in computing power and scale, AI is making big strides today like never before. Get the entire 10-part series on Timeless Reading in PDF.
Deep learning, a field in machine learning relying on learning data representations as opposed to task-specific algorithms, is experiencing a great and fairly sudden storm of enthusiasm and excitement after over twenty years of relative quietness. Delivering big technology breakthroughs recently, it has been regarded as a major driver towards artificial intelligence by many observers. The AI industry has spurred countless investments and acquisitions over the past years. In 2017, AI startups in the UK raised £488m, according to Pitchbook, more than twice as much as in the previous year. Furthermore, the country hosted four of the biggest acquisitions of AI startups over the past five years, including Google/DeepMind, Apple/VocalIQ, Microsoft/SwiftKey and Twitter/Magic Pony.
If you go to college and take a course "Machine learning 101", this might be the first example of machine learning your teacher will show you: Imagine you work for a real estate agency, and you want to predict, for how much a house will sell. You have some historical data -- you know that house A has been sold for $500 000, house B for $600 000, and house C for $550 000. You also know something about properties of the houses -- you know the size of the house in square meters, number of rooms in the house, and the year the house was build. The goal of the real estate agency is to predict, for how much a new house D will sell, given its known properties (size, age and number of rooms of the house). In ML terminology, the known properties of the house are called "features" or "indicators" (we use the term "indicators" in Signals, because this term has been historically used in trading).
This studentship is funded by the EPSRC DTP covering fees and stipend (RCUK rate) Start date September 2018 for 3.5 years. Applicants must be from the UK/EU and have obtained (or be about to obtain) a minimum 2:1 Bachelors degree in a relevant subject area. Applications should be submitted online, select PhD Health Sciences on the application form. For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website). Interviews will be held in Manchester in May 2018.
"AI," "big data," and "machine learning" are all trending buzzwords, and you might be curious about how they apply to your domain. You might even have startups beating down your door, pitching you their new "AI-powered" product. So how can you know which problems in your business are amenable to machine learning? To decide, you need to think about the problem to be solved and the available data, and ask questions about feasibility, intuition, and expectations. Machine learning can help automate your processes, but not all automation problems require learning.
March Madness--the NCAA college basketball championship playoffs--is among the most popular sporting events in the US, thanks in part to the wide-ranging contest that has evolved around predicting which teams will progress through the tournament. This year, almost $10.4 million is on the line in office pools or more organized competitions, and more than 40 million Americans will fill out their own versions of the playoff brackets to take part, according to the American Gaming Association. The chances of predicting a perfect bracket, which no one has ever done, are at least 1 in 128 billion and could be as remote as 1 in 9.2 quintillion. Now machine learning is taking a shot. Kaggle, the online platform for predictive modeling and analytics competitions that was acquired by Google parent company Alphabet last year, is hosting a competition for both the NCAA men's and women's tournaments.
NARRATOR: The future unfolds before our eyes, but is it always beyond our grasp? What was once the province of the gods has now come more clearly into view, through mathematics and data. Out of some early observations about gambling, arose tools that guide our scientific understanding of the world and more, through the power of prediction. BOATSWAIN'S MATE 1 LUKE SCHAFFER (United States Coast Guard): Keep a good look out. NARRATOR: …every day mathematics and data combine to help us envision what might be. LIBERTY VITTERT (University of Glasgow): It's the best crystal ball that humankind can have. NARRATOR: Take a trip on the wings of probability, into the future. MONA CHALABI (The Guardian, United States Edition): We are thinking about luck or misfortune, but they just, basically, are a question of math, right? The Orange County Fair, held in Southern California: in theory, these crowds hold a predictive power that can have startling accuracy, but it doesn't belong to any individual, only the group. And even then, it has to be viewed through the lens of mathematics. The theory is known as the "wisdom of crowds," a phenomenon first documented about a hundred years ago. Statistician Talithia Williams is here to see if the theory checks out and to spend some time with the Fair's most beloved animal, Patches, a 14-year-old ox. TALITHIA WILLIAMS (Harvey Mudd College): It was a fair, kind of like this one, where, in 1906, Sir Francis Galton came across a contest where you had to guess the weight of an ox, like Patches, you see here behind me. NARRATOR: After the ox weight-guessing contest was over, Galton took all the entries home and analyzed them statistically. To his surprise, while none of the individual guesses were correct, the average of all the guesses was off by less than one percent. But is it still true? TALITHIA WILLIAMS: So, here's how I think we can test that today. What if we ask a random sample of people, here at the fair, if they can guess how many jellybeans they think are in the jar, and then we take those numbers and average them and see if that's actually close to the true number of jellybeans?
As the amount of data that needs to be processed continues to increase, more and more IT teams are turning to cloud computing to help manage their large workloads. Workload Automation plays a vital role in managing virtual and cloud resources and can mean the difference between successful, cost-efficient cloud computing, and hidden-cost ridden operations. A Workload Automation solution that offers automated provisioning and deprovisioning of virtual and cloud-based resources, based on both historical and predictive analytics, can introduce a form of machine learning into your cloud environment and help you optimize your resource usage. The EMA Radar Report commends ActiveBatch Workload Automation on its standout cloud features, such as Smart Queue and Managed Queue, and its prebuilt integrations with VMware, Amazon EC2, Microsoft Azure, and System Center Virtual Machine Manger. The report states that these features and capabilities "make ActiveBatch a strong choice for anyone relying on hybrid or multi-cloud to optimize resource usage."
In this blog, I will show you how to implement a trading strategy using the regime predictions made in the previous blog. Do read it, there is a special discount for you at the end of this. There is one thing that you should keep in mind before you read this blog though: The algorithm is just for demonstration and should not be used for real trading without proper optimization. First, I imported the necessary libraries. If you do not have this package, I suggest you install it first or change your data source to google.
Artificial intelligence is a fascinating but not particularly accessible technology. Project DataREACH, currently in trial mode in Cameroon, wants to change that by giving doctors in all corners of the globe access to advanced AI to help diagnose illnesses and spot troubling health trends. "Our goal is to bridge the gap between the medical data now being gathered on the ground in developing nations, and the cutting-edge [AI] research and application...from the West," Project DataREACH founder and CEO Vikash Singh told PCMag. Singh's app allows clinicians to collect patient data like height, weight, blood pressure, cholesterol, family history, and location. That data is then analyzed via machine learning to assist physicians in evaluating the risk of noncommunicable diseases, including diabetes and cardiovascular issues.