Worrying about Artificial Intelligence (AI) and Internet of Things (IOT) destroying human opportunity of wealth creation today is a lot like worrying about overpopulation that could occur in Iceland, once overpopulated countries start facing civil wars, apartheid and people have no other options left but to migrate, although the feasibility of both the events happening in the coming decades is relatively high, if a strong Governance Framework is not implemented globally. At the moment nine countries pose a serious threat to technological progress and human welfare in the coming 50 years 2018–2068, these nine countries are expected to account for half of the world's projected population increase: Bangladesh, Brazil, China, India, Indonesia, Nigeria, Pakistan, Uganda and the United States of America. Out of these nine, China, Brazil and the United States have a firm resolve to slow down the growth rate by adopting the best possible methods to do it. Others will have no other option but to plan a stark reduction in human population in the coming decades. The exponential population growth and seemingly drastic reduction of Earths finite resources along with the threat of war, might restrain AI from reaching a conclusion that AI cannot keep up its pace with growing human numbers, which are detrimental to AI and human existence itself. This population growth is not a direct product of increasing population but also directly proportional to improved survival rate, lower child mortality rate and rapid improvements in the field of medicine.

Latent Semantic Analysis (LSA) and Word2vec are some of the most widely used word embeddings. Despite the popularity of these techniques, the precise mechanisms by which they acquire new semantic relations between words remain unclear. In the present article we investigate whether LSA and Word2vec capacity to identify relevant semantic dimensions increases with size of corpus. One intuitive hypothesis is that the capacity to identify relevant dimensions should increase as the amount of data increases. However, if corpus size grow in topics which are not specific to the domain of interest, signal to noise ratio may weaken. Here we set to examine and distinguish these alternative hypothesis. To investigate the effect of corpus specificity and size in word-embeddings we study two ways for progressive elimination of documents: the elimination of random documents vs. the elimination of documents unrelated to a specific task. We show that Word2vec can take advantage of all the documents, obtaining its best performance when it is trained with the whole corpus. On the contrary, the specialization (removal of out-of-domain documents) of the training corpus, accompanied by a decrease of dimensionality, can increase LSA word-representation quality while speeding up the processing time. Furthermore, we show that the specialization without the decrease in LSA dimensionality can produce a strong performance reduction in specific tasks. From a cognitive-modeling point of view, we point out that LSA's word-knowledge acquisitions may not be efficiently exploiting higher-order co-occurrences and global relations, whereas Word2vec does.

It's hard to believe how fast 2017 has already gone by here at BigML. It has been a banner year with many firsts thanks to the Machine Learning freight train running on all cylinders across the global economy. Gone are the days, when we often found ourselves describing what Machine Learning is and why it matters for businesses. Instead, here we are in the closing days of 2017 exchanging ideas on new use cases Machine Learning can be applied towards with business leaders. When things happen so fast, one can sometimes find it a challenge to stop and reflect on milestones and achievements.

In the next stage of artificial intelligence adoption, banks will use AI to help understand the intentions and emotions of customers and enable better interactions, according to a new report from Accenture (NYSE: ACN). A new report from Accenture has revealed that banks are set to adopt artificial intelligence (AI) to better understand customers' intentions and emotions, with the aim of enabling better interactions. The report, Accenture Banking Technology Vision 2017, draws on the analysis of an advisory board of more than two dozen individuals, interviews with technology luminaries and industry experts, and results of a survey of 579 bank executives in 31 countries across North America, Europe, Asia Pacific, Africa and South America. The goal of the survey was to identify the key issues and priorities for technology adoption and investment. According to the report, more than three-quarters (78%) of bankers believe that AI will enable simpler user interfaces that will help banks create a more human-like customer experience.

A recent piece from Angelica Mari of ZDNet shows that the number of Brazilian banks that see artificial intelligence (AI) as a strategic priority is higher than many mature markets. About 30 percent of local institutions in the country are seeing AI playing an important role in their innovation plans, according to GFT Technologies' Digital Banking Expert Survey. By comparison, 23 percent of sector firms in the UK and Mexico see AI as crucial in their strategy, while only 17 percent of US banks perceive the technology as an important aspect of their overall plans, the study from the financial services vendor says. Banking giant Bradesco is the highest profile supporter of AI technology in Brazil and has been piloting IBM's Watson for over a year. Earlier this year, the bank has announced its plans to make its Watson-based artificial intelligence system available to end consumers.

Remember the last burger you really enjoyed – try to summon up its rich, juicy taste in your mind and its chewy, firm-yet-soft-yet-crunchy texture. Try to recall how the taste filled your mouth with flavour as you bit into it. Remember how satisfying it was. Now think about how it might have tasted without any meat in it. Farming the meat for beef burgers takes a hefty toll on the environment around the world.

These and many other insights are from the Salesforce Fourth Annual State of Marketing - Marketing Embraces the AI Revolution published last week. The report is available for download here (50 pp., PDF, no opt-in). The survey is based on interviews with 3,500 marketers worldwide conducted by Salesforce Research through a third-party survey firm in April 2017. The 3,500 respondents are full-time marketing leaders in Australia, New Zealand, Brazil, Canada, France, Germany, Japan, Netherlands, U.K., Ireland and U.S. Respondents were segmented into high-performing, moderate-performing or under-performing groups. High-performing organizations are defined as those who are "extremely satisfied" with the current outcomes realized as a direct result of their company's marketing investment.

It took Amazon a while to get its streaming music strategy truly off the ground -- its Music Unlimited service, with competes with Spotify, Apple Music and the like, only launched last fall. But today, both Music Unlimited and the Echo smart speaker lineup are expanding in a big way: Amazon has announced that both are available in 28 new countries, most of which are found across Europe and South America. Pricing for Music Unlimited will vary by area, but Amazon says it'll offer the same three plans it currently does -- including an Echo-only plan, the standard individual plan for smartphones, computers and other devices and a family plan for multiple users. Amazon's also not discussing pricing for Echo hardware, as that also will vary from country to country. But launching both the hardware and service at the same time is a smart move, as the company says its music service is designed with voice control in mind. Of course, Spotify and the like work just as well, but having both the Echo and Music Unlimited available at the same time will ensure new customers can use their new speakers to their fullest extent.

Rafael Yuste thinks neuroscientists have been looking at the brain too close. "It's just like a TV screen--if you're watching a movie and could only look at an individual pixel, you would never understand what's going on," he says. "What neuroscientists have been doing since [the father of neuroscience, Santiago Ramon y] Cajal, is looking at the single pixels of the brain--one neuron at a time. So that's why we need these methods to see the whole screen, to see what's playing in our brains." The methods in question were on display in a recent study he and his graduate student, Christopher Dupre, conducted, recording the activity of all neurons in the Hydra vulgaris, a centimeter-long hydroid, while the animal swam between two pieces of glass.

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multicollinearity in urban indicators, it is common to find controversial conclusions about the influence of some urban indicators on crime. Machine learning ensemble-based algorithms can handle well such problems. Here, we use a random forest regressor to predict crime and quantify the influence of urban indicators on homicides. Our approach can have up to $97\%$ of accuracy on crime prediction and the importance of urban indicators is ranked and clustered in groups of equal influence, which are robust under slightly changes in the data sample analyzed. Our results determine the rank of importance of urban indicators to predict crime, unveiling that unemployment and illiteracy are the most important variables for describing homicides in Brazilian cities. We further believe that our approach helps in producing more robust conclusions regarding the effects of urban indicators on crime, having potential applications for guiding public policies for crime control.