Could a population of Frankenstein's monsters have driven humans to extinction? Nathaniel Dominy, an evolutionary biologist at Dartmouth College, and Justin Yeakel, an ecologist at the University of California, Merced, pose just that ghoulish question in a study published Friday in the journal BioScience ahead of Halloween. And, according to their research, the answer is yes. That scenario may have come to pass in the fictional world of Mary Shelley's novel "Frankenstein" had the monster's creator, Victor Frankenstein, kept a promise to his creation to build him a female companion. But fortunately for humans, say Dr. Dominy and Dr. Yeakel, Dr. Frankenstein went back on his word.

This is the continuation of my mini-series on sentiment analysis of movie reviews, which originally appeared on recurrentnull.wordpress.com. Last time, we had a look at how well classical bag-of-words models worked for classification of the Stanford collection of IMDB reviews. As it turned out, the "winner" was Logistic Regression, using both unigrams and bigrams for classification. The best classification accuracy obtained was .89 So, bag-of-words models may be surprisingly successful, but they are limited in what they can do.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.

Until recently, [Europe's] contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO. London is Europe's startup centre, mixing capital, proximity to markets, and world-class research hubs.

The world famous physicist and cosmologist said that as AI becomes more advanced it could bring dangers such as "powerful autonomous weapons, or new ways for the few to oppress the many". He was speaking at the launch of The Leverhulme Centre for the Future of Intelligence (CFI) in Cambridge, which will explore the implications - good and bad - of the rapid development of AI. It will look into applications ranging from increasingly "smart" smartphones to robot surgeons and Terminator-style military droids. It is not the first time Professor Hawking has warned about the potential dangers, having previously said that AI could end mankind if it is misused. That is why the new centre is "crucial to the future of our civilisation and of our species", he said.

Stock market cheats, A.I. will soon be looking for you. The sheer volume of transactions conducted in financial markets renders market surveillance by regulatory groups difficult, but machine learning and artificial intelligence tools will soon be employed to ferret out cheaters, according to Reuters. The Financial Industry Regulatory Authority (FINRA) is developing A.I. that it will start testing in 2017 along with its existing surveillance and detection mechanisms. NASDAQ and the London Stock Exchange Group intend to start using artificial intelligence to spot trade irregularities and violation patterns this year. Related: Future AI assistants like Siri could be trained by Joey from'Friends' Reuters reports that financial firms already use artificial intelligence for picking stocks and for monitoring their own firms' compliance.

Video games aren't just for fun – they can make a political point, too. Responding to current affairs is a growing trend in gaming, and it is more evident than ever in the run-up to the US presidential election. There are dozens of Donald Trump-themed indie titles on PC game store Steam. And bigger developers are also incorporating political themes in their games. Chris Baker, a former creative director at news and entertainment website Buzzfeed, launched GOP Arcade with a friend this year.

High-speed body scanners powered by advanced artificial intelligence are to be trialled across US airports and subway stations. According to The Guardian, documents filed by Evolv Technology with the US Federal Communications Commission (FCC) show plans to test the new systems at Denver International airport, Los Angeles' Union Station metro and Union Station in Washington DC. Evolv's scanners use millimetre-wave radio frequencies to scan a person's body. This setup is currently used by the full-body scanners found in airports, but whereas those devices slow down the screening process, the new scanners can finish their work in a fraction of a second. This means individuals can simply walk through the gate without having to stop.

The TV show The IT Crowd played off of stereotypes on what it's like to work in IT. Basically, it meant working in the basement, no one except the CEO remembering you exist, and impatiently asking people if they've tried turning the device off and on again. But the Internet of Things is changing all of that, as people in all roles and professions realize how IBM's Watson machine learning platform could impact them and their work. "It's resonating with so many people, not just CTOs," said Harriet Green, VP and GM of Internet of Things, Commerce and Education at IBM. Green was interviewed by Dave Vellante (@dvellante), and John Furrier (@furrier), hosts of theCUBE*, from the SiliconANGLE Media team, during the IBM World of Watson event in Las Vegas, NV. Exciting client use cases abound. From Olli, a 3-D printed driverless vehicle that uses Watson to talk, to a deputy superintendent demonstrating how Watson can be used in classrooms to interact with students.

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before. Deep Learning is becoming the method of choice for its exceptional accuracy and capturing capacity for unstructured data.