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) …
To Olley, machine learning fills a gap in technology that has existed for a long time: solving complex problems with pattern recognition. "With the majority of Elsevier's revenue coming from technology-based products and services, we started using machine learning in our commercial products, but it's equally applicable to internal IT platforms," Olley says. As part of the executive teams within RBI and Elsevier, Dan continues to drive organic online product growth across the portfolio. Prior to RELX Group, Dan held technology and product management leadership roles with GM Financial, Wunderman Cato Johnson, and IBM, as well as a number of software organizations in the United Kingdom and other international locales.
Current take: Statistics, not machine learning, is the real deal, but unfortunately suffers from bad marketing. On the other hand, to the extent that bad marketing includes misguided undergraduate curriculums, there's plenty of room to improve for everyone. I had two thoughts reading this. Machine learners invent annoying new terms, sound cooler, and have all the fun. They have way less funding and influence than it seems they might deserve.
Get the O'Reilly AI Newsletter and receive weekly AI news and insights from industry insiders. The following piece was first published in the AI newsletter. A recent Forrester survey of business and technology professionals found that 58% of them are researching AI, but only 12% are using AI systems. This is partially because applied AI applications are only now starting to be realized, but it's also because right now AI is hard. It requires very specialized skills and a develop-it-yourself attitude.
Get started with deep learning and neural networks with "Fundamentals of Deep Learning," by Nikhil Buduma. Security data science is booming--reports indicate that the security analytics market is set to reach $8 billion dollars by 2023, with a growth rate of 26%, thanks to relentless cyber attacks. If you want to stay ahead of emerging security threats in 2017, it is important to invest in the right areas. In March 2016, I wrote a piece on the 4 trends to be aware of for 2016; for my 2017 trends post, Cody Rioux from Netflix joins me, bringing his platform perspective. Our goal is to help you formulate a plan for every quarter of 2017 (i.e., 4 trends for 4 quarters).
According to the IBM Institute for Business Value the market will see a rapid adoption of initial cognitive systems. The most likely candidates have moved beyond descriptive and diagnostic, predictive and routine industry-specific capabilities. In fact, the widespread adoption of cognitive systems and artificial intelligence (AI) across various industries is expected to drive worldwide revenues from nearly US$8.0 billion in 2016 to more than US$47 billion in 2020. The analyst firm IDC predicts that the banking, retail, healthcare and discrete manufacturing industries will generate more than 50% of all worldwide cognitive/ AI revenues in 2016. Banking and retail will each deliver nearly US$1.5 billion, while healthcare and discrete manufacturing will deliver the greatest revenue growth over the 2016-2020 forecast period, with CAGRs of 69.3% and 61.4%, respectively.
According to the IBM Institute for Business Value the market will see a rapid adoption of initial cognitive systems. The most likely candidates have moved beyond descriptive and diagnostic, predictive and routine industry-specific capabilities. Seventy percent of survey respondents are currently using advanced programmatic analytics in three or more departments. In fact, the widespread adoption of cognitive systems and artificial intelligence (AI) across various industries is expected to drive worldwide revenues from nearly US$8.0 billion in 2016 to more than US$47 billion in 2020. The analyst firm IDC predictsthat the banking, retail, healthcare and discrete manufacturing industries will generate more than 50% of all worldwide cognitive/ AI revenues in 2016. Banking and retail will each deliver nearly US$1.5 billion, while healthcare and discrete manufacturing will deliver the greatest revenue growth over the 2016-2020 forecast period, with CAGRs of 69.3% and 61.4%, respectively.
Editor's note: The following is an interview with Columbia University Professor Andrew Gelman conducted by Marketing scientist Kevin Gray, in which Gelman spells out the ABCs of Bayesian statistics. Andrew Gelman: Bayesian statistics uses the mathematical rules of probability to combines data with "prior information" to give inferences which (if the model being used is correct) are more precise than would be obtained by either source of information alone. Classical statistical methods avoid prior distributions. In classical statistics, you might include in your model a predictor (for example), or you might exclude it, or you might pool it as part of some larger set of predictors in order to get a more stable estimate. These are pretty much your only choices.
This week we are celebrating Computer Science Education Week around the globe. In this "age of acceleration," in which advances in technology and the globalization of business are transforming entire industries and society itself, it's more critical than ever for everyone to be digitally literate, especially our kids. This is particularly true for women and girls who, while representing roughly 50 percent of the world's population, account for less than 20 percent of computer science graduates in 34 OECD countries, according to this report. This has far-reaching societal and economic consequences. By 2020, the U.S. Bureau of Labor Statistics predicts that there will be 1.4 million computing jobs but just 400,000 computer science students with the skills to apply for those jobs.
Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and clinical information related to the diagnosis, treatment, and history of cancer incidence in the United States. The surveillance effort, coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention, enables researchers and clinicians to monitor cancer cases at the national, state, and local levels. Much of this data is drawn from electronic, text-based clinical reports that must be manually curated--a time-intensive process--before it can be used in research. For example, cancer pathology reports, text documents that describe cancerous tissue in detail, must be individually read and annotated by experts before becoming part of a cancer registry.
Useful quantum computers are closer to becoming a reality as some of the world's biggest corporations try to bring the technology from the lab into the practical world. A quantum computer utilizes subatomic particles called qubits to speed up the solving of complex computations. Near-term expectations for quantum computers range from solving optimization problems to quantum-encrypted communications, and more. With the help of CB Insights' investment, acquisition, and partnership data, we identified 18 corporate groups involved in the development of commercialized quantum computing hardware and software. They are a diverse group of players, ranging from tech industry behemoths to defense contractors to national telecommunications companies.