Benevolent hackers found a smart way around Windows 10's Cortana voice assistant. Microsoft last week issued a patch to Windows 10 machines for a vulnerability that allowed hackers to use Cortana voice commands to sneak past the operating system's lock screen protections. But just what could hackers have done, and what could they still do to unpatched machines? In videos put together for Forbes, Israeli researchers from the Technion Israeli Institute of Technology showed what was possible, whether it was executing a program or viewing a private document (such as a list of passwords) from behind the lockscreen, proving just how problematic Microsoft's Cortana can be. The weakness was found separately by McAfee researchers, and Yuval Ron and Ron Marcovich, software engineering students at the Technion Israeli Institute of Technology, as part of a project overseen by independent security researchers Amichai Shulman and Tal Be'ery.

Technologically, the 20-year jump from 2015 to 2035 will be huge. During that time some elements of our world will change beyond recognition while others will stay reassuringly (or disappointingly) familiar. Consider the 20 years to 2015. Back in 1995 we were in the early days of the internet, we worked in cubicles and our computers were chunky and powered by Windows 95. There were no touch screen phones or flat screen TVs; people laughed at the idea of reading electronic books, and watching a home movie meant loading a clunky cassette into your VCR.

Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.

Whether consumers know it or not, three next-generation technologies are playing a major role in shaping their experience with brands -- and the future of consumer goods marketing: artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). To keep pace and effectively compete in an increasingly connected marketplace, brands are investing in these three technologies to continually fine-tune their customer strategies, using hyper-personalized information across touchpoints. Have you ever wondered how Netflix makes movie and TV show recommendations, how Facebook prompts friends to be tagged in photos, and how Alexa, Siri and Google Now assist in our day-to-day activities? These are real-life examples of machine learning -- a subset of AI. ML uses a customer's historic data and behavioral patterns to create high-quality predictions of their future behavior.

Whether Father's Day, Mother's Day, a birthday or simply "just because," buying gifts can feel like counting grains of sand -- i.e., it's not easy. Meanwhile, there are 250,ooo -- 300,000 e-commerce companies in the U.S. all vying for the attention of shoppers. How are consumers possibly expected to decide where to spend their hard-earned money? And how can retailer s create a more personalized shopping experience, rather than facing the same demise as the 6,700 retail locations that closed their doors in 2017? In short, the retail sector is in a sticky wicket.

Artificial intelligence is no longer a far-off concept; today AI and machine learning part of our everyday lives. For instance, Siri, Alexa and Google Home use neural networks for natural language processing to analyze the human voice and respond accordingly. Nest Learning Thermostat uses AI algorithms to control the climate in your home, while Nest Cam uses AI and facial recognition to reduce false alarms. The security screener at the airport uses machine learning to augment human intelligence. Netflix's online learning serves the perfect movie based on your previous selections.

Matchmaking has been around since the late 1950s. From punch card questionnaires to now finding your significant other through an app, it has evolved with technology. Online dating sites provide singles with options based on shared interests to race preference. It has been said that online dating will be a thing of the past, but companies like Match.com which launched in 1995 and Eharmony.com in 2000 prove that there is longevity in this business.

Voice shopping using smart speakers and smartphone apps is starting to gain traction among consumers, opening up a new "conversational commerce" channel and potentially disrupting the retail sector. Devices such as Amazon's Alexa-powered speakers and Google Home, which use artificial intelligence (AI) to respond to voice commands, are offering new choices to consumers who are looking for more convenient ways to order goods and services. Voice shopping is expected to jump to US$40 billion (S$54 billion) annually in 2022 in the United States, from US$2 billion today, according to a survey this year by OC&C Strategy Consultants. "People are liking the convenience and natural interaction of using voice," said Ms Victoria Petrock of the research firm eMarketer. "Computing in general is moving more toward voice interface because the technology is more affordable, and people are responding well because they don't have to type."

Here are five things in technology that happened this past week and how they affect your business. Facebook has announced the global rollout of a new policy that will now let users file a complaint about businesses they've had a problem with if they bought something after clicking on one of their ads. If enough people complain about a business, it could lead Facebook to ban the company from running ads. The new policy is meant to help Facebook fight back against advertising abuse on its platform and trying to prevent "bad shopping experiences," which can cost customers and make them frustrated with Facebook, too. The good news for your business is that if you're on the up and up, do a good job, and provide good products and service you'll be fine.

Machine learning, data science, and data analytics or scientists are emerging fields growing into various sub-fields, helping companies improve their efficiency and performance at certain stages during the operations and services. Hence, understanding these technologies is very important to realize their right use and benefits into various sectors. So, here we have discussed what data science is and what is machine learning with few sets of examples. Data science is a term used for dealing with big data that includes data collection, cleansing, preparation, and analysis for various purposes. A data scientist collects data from multiple sources and after analysis, applies into predictive analysis or machine learning and sentiment analysis to extract the critical information from the data sets.