But new UC Berkeley research suggests that Siri and other digital helpers could someday learn the algorithms that humans have used for centuries to create and understand metaphorical language. Mapping 1,100 years of metaphoric English language, researchers at UC Berkeley and Lehigh University in Pennsylvania have detected patterns in how English speakers have added figurative word meanings to their vocabulary. The results, published in the journal Cognitive Psychology, demonstrate how throughout history humans have used language that originally described palpable experiences such as "grasping an object" to describe more intangible concepts such as "grasping an idea." "The use of concrete language to talk about abstract ideas may unlock mysteries about how we are able to communicate and conceptualize things we can never see or touch," said study senior author Mahesh Srinivasan, an assistant professor of psychology at UC Berkeley. "Our results may also pave the way for future advances in artificial intelligence."

Which machine learning algorithm should I use?

Scientists have developed a new'mind reading' artificial intelligence system that can decode complex human thoughts just by measuring brain activity. The AI system indicates that the mind's building blocks for constructing complex thoughts are formed by the brain's various sub-systems and are not word-based. "We have finally developed a way to see thoughts of that complexity in the fMRI signal. The discovery of this correspondence between thoughts and brain activation patterns tells us what the thoughts are built of," said Marcel Just from Carnegie Mellon University (CMU) in the US. Researchers demonstrated that the brain's coding of 240 complex events, sentences like the shouting during a trial scenario uses an alphabet of 42 meaning components, or neurally plausible semantic features.

For years, the X Prize Foundation has funded competitions that ask participants to make sci-fi a reality: a device to extract water from thin air, like Star Trek's replicator; a tool to instantly diagnose disease, like the Star Trek tricorder; a crime alert network, inspired by Minority Report. But for its latest competition, Seat14C, the organization is putting the fiction first--by asking writers to envision what humanity will need in the future. Starting today, 22 new science fiction stories go live on the Seat14C website, courtesy of genre luminaries like Margaret Atwood and Charlie Jane Anders. Each story details the future from the perspective of a different passengers on a plane that traveled through a wormhole 20 years into the future. Other writers will then compete to tell the story of the passenger in seat 14C.

Marketers are investing heavily in content marketing, and many are looking to emerging technologies like artificial intelligence (AI) to help shape their strategies, new research suggests. Data from BrightEdge, an enterprise search engine optimization (SEO) and content performance marketing company, and SurveyMonkey looked at the probability that US market leaders will use AI or deep learning to develop their 2017 content marketing efforts. While a large share of respondents (57.1%) said they're unlikely to use AI or deep learning in their content marketing, a significant number felt differently. For example, nearly a third (31.4%) of respondents said they were somewhat likely to use AI to help flesh out their content marketing strategy. And an additional 8.7% said they were very likely to do so.

The widespread implementation of electronic health records (EHRs) has proved to be a bumpy ride for many. But the sheer amount of data available in digital form carries with it plenty of potential. Recent work by scientists from IBM and Sutter Health developed artificial intelligence that can uncover pre-diagnostic heart failure through EHRs. A study, published in Circulation: Cardiovascular Quality and Outcomes, included a model that used 1,684 heart failure cases along with 13,525 sex, age-category and clinic matched controls for modeling purposes. "Model performance was most strongly influenced by the diversity of data, basic feature construction and the length of the observation window," wrote Kenny Ng, research staff member in the Center for Computational Health and first author of the study. "In raw form, EHR data are highly diverse, represented by thousands of variants for disease coding, medication orders, laboratory measures, and other data types.

Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.

I finally beat the S&P 500 by 10%. This might not sound like much but when we're dealing with large amounts of capital and with good liquidity, the profits are pretty sweet for a hedge fund. More aggressive approaches have resulted in much higher returns. It all started after I read a paper by Gur Huberman titled "Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar," (with Tomer Regev, Journal of Finance, February 2001, Vol. "A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed's stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday. It closed above 30 in the three following weeks. The enthusiasm spilled over to other biotechnology stocks. The potential breakthrough in cancer research already had been reported, however, in the journal Nature, and in various popular newspapers including the Times! Thus, enthusiastic public attention induced a permanent rise in share prices, even though no genuinely new information had been presented."

Some foundations of statistical science have been questioned recently, especially the use and abuse of p-values. See also this article published in FiveThirtyEight.com. Statistical tests of hypotheses rely on p-values and other mysterious parameters and concepts that only the initiated can understand: power, type I error, type II error, or UMP tests, just to name a few. Pretty much all of us have had to learn this old stuff (pre-dating the existence of computers) in some college classes. Sometimes results from a statistical test will be published in a mainstream journal - for instance about whether or not global warming is accelerating - using the same jargon that few understand, and accompanied by misinterpretations and flaws in the use of the test itself.

I have a background in Computer Science and worked with nearly every programming language on the planet. I graduated with highest distinction during my masters program. I've worked on projects ranging from Robotics, Web Apps, Mobile Apps to Embedded Systems. These courses will help you achieve your goals.