"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
NSF grant to UCLA computer science professors Todd Millstein and Guy Van den Broeck will support research to democratize emerging AI-based technology. Two computer scientists at the UCLA Samueli School of Engineering have received a four-year, $947,000 research grant from the National Science Foundation to make machine learning – a branch of artificial intelligence where computer programs learn and improve on their own – more widely available and easier to work with. "Machine learning has been really successful in the past decade, leading to state-of-the-art techniques for language translation, face recognition and other compelling applications, but these advances have mainly come from experts with specialized knowledge at major technology companies and at universities," said Todd Millstein, professor of computer science and the principal investigator on the research. "Our primary goal with this research is to democratize machine learning, so that programs that utilize it can be written by anyone." Machine learning technologies are powered by probability models.
October is historically the most volatile month for stocks, but is this a persistent signal or just noise in the data? Follow me on Twitter (twitter.com/pdquant) Over the past 32 years, October has been the most volatile month on average for the S&P500 and December the least, in this article we will use simulation to assess the statistical significance of this observation and to what extent this observation could occur by chance. We will be using Daily S&P500 data for this analysis, in particular we will use the raw daily closing prices from 1986 to 2018 (which is surprisingly hard to find so I've made it publicly available). The inspiration for this post came from Winton, which we will be reproducing here, albeit with 32 years of data vs their 87 years.
In the middle of the historic city of Bristol in England, about 150 engineers are currently designing the most sophisticated computer AI chip in the world. The "Colossus" has 1216 processors fitted on a chip characterized by the size of a postage stamp. Designed specifically for artificial intelligence (AI) applications, the AI chip draws its name from the computer that was used by cryptographers at Bletchley Park during World War II. "[Colossus] was all top-secret for decades after the war, so the Americans thought they invented everything first. Now it is clear to the world that they didn't," claimed Simon Knowles, the inventor of the novel AI chip.
Increasingly, businesses are using ML to strengthen their competitive advantage and drive innovation. Is your organisation embracing this shift or are you falling behind? If you are on the "bias-for-action" side of the scale and have already started steering your organisation towards digital & ML transformation, are you confident you are doing so in the right way? Over the past decade, data has become increasingly important and has even been described as the "new oil". Organisations with extensive user data can leverage data to increase sales and customer retention.
Two Princeton University computer science professors will lead a new Google AI lab opening in January in the town of Princeton. The lab is expected to expand New Jersey's burgeoning innovation ecosystem by building a collaborative effort to advance research in artificial intelligence. The lab, at 1 Palmer Square, will start with a small number of faculty members, graduate and undergraduate student researchers, recent graduates and software engineers. The lab builds on several years of close collaboration between Google and professors Elad Hazan and Yoram Singer, who will split their time working for Google and Princeton. The work in the lab will focus on a discipline within artificial intelligence known as machine learning, in which computers learn from existing information and develop the ability to draw conclusions and make decisions in new situations that were not in the original data.
Artificial intelligence (AI) has a perception problem, as many people think of the technology primarily as a job killer. However, collaboration between humans and AI opens the opportunity of putting the design and manufacturing of goods of all kinds on a new, better foundation by curating intelligence. That's why we should rethink our expectations for machine intelligence and how it will affect our future. The role of a human as the most intelligent creature on earth may not last much longer. Technologies like artificial intelligence and machine learning are taking on operations that could previously only be conducted with human intelligence – and in some cases they're doing even better than we do.
Business-to-consumer (B2C) businesses have made it a priority to incorporate machine learning into customer-facing functions, integrating it into sales and marketing. For business-to-business (B2B) companies, however, translating data into actionable marketing strategies can be a more difficult proposition. Selling to organizations invariably requires embarking on a much longer and more complex journey, culminating in an order of much higher value than in the consumer realm. With hundreds of thousands, if not millions, of dollars at stake, a misguided marketing investment could lead to financial losses. "The availability of data and the importance of having the focus on the full customer journey is coming a little later to the B2B world," says Laura Beaudin, a partner at Bain & Co. "A lot of expectations in terms of customers manifested themselves in the consumer world before they brought those expectations to their business-purchasing world."
The robots are coming, if not already here: A 2017 study conducted by the McKinsey Global Institute revealed that up to 800 million jobs worldwide are at risk of being replaced by automation by 2030. According to our calculations, you have two options as far as the machine apocalypse goes: One, you could wait anxiously until your job is inevitably stolen by a C-3PO wannabe, or two, you could one-up the robots by training to work in the field of Artificial Intelligence. Let's suppose you go with the second option (probably the wiser choice, all things considered). Now, how do you go about getting said AI training? A good place to start is -- where else -- online.
With more and more artificial intelligence (AI) solutions emerging each year, it's safe to say that this technology is here to stay. Statista predicts that the global AI market will be worth more than $10.5 billion by 2020, and forward-thinking businesses continue to incorporate AI into their everyday operations in the form of automation and customer service chatbots. If your business hasn't yet jumped on the AI bandwagon, you might be wondering what you're missing -- and whether you really need to find out. While it's true that many businesses have found worthwhile use cases for this technology, it may not necessarily be the right time for your company to explore AI. According to Forbes Technology Council members, here are nine questions to determine if artificial intelligence is a smart investment for your business right now.