AI will change the legal profession, just not how you are expecting. Fernando Garcia is looking forward to the day when he can get his hands on Beagle, an automated contract analysis system powered by artificial intelligence that reads contracts in seconds, highlights key information visually with easy-to-read graphs and charts and gets "smarter" with each reviewed contract. Also on his bucket list is an offering by yet another Canadian legal tech startup, Blue J Legal, that also uses AI to scan legal documents, case files and decisions to predict how courts will rule in tax decisions. At a time when the majority of in-house counsel are under intense pressure to shave costs and run a lean team, such powerful tools are a godsend. "There's always that pressure to do more with less, so when a tool comes along that can provide more efficiency, more risk mitigation and can let you do your job better and focus on providing value added, it is a strategic advantage," notes Garcia, general counsel, government affairs and corporate secretary with Nissan Canada Inc. "It's going to fundamentally change our job." This fundamental change has been a long time coming. Nearly two decades ago, the former justice of the High Court of Australia, Michael Kirby, remarked with uncanny prescience in a speech before the Bombay High Court in Mumbai that "it would be a bold observer" who would deny the possibility of artificial intelligence to "enhance" lawyering and judicial-making.

We built the internet, we got online porn. We invented haptic engines that can automatically convey a remote sense of touch, we got teledildonics. Technological advances and the shifting of sexual boundaries are intertwined, and this is the world Bryony Cole tackles on her podcast, Future of Sex. After a career in technology, including a stint at Microsoft, she was fascinated by the way digital platforms were entering the bedroom. "I made it my mission to uncover what the hell was going to happen to our intimate lives," she laughed.

Multilabel classification is a classification problem where multiple target labels can be assigned to each observation instead of only one, like in multiclass classification. It can be regarded as a special case of multivariate classification or multi-target prediction problems, for which the scale of each response variable can be of any kind, for example nominal, ordinal or interval. Originally, multilabel classification was used for text classification (McCallum, 1999; Schapire and Singer, 2000) and is now used in several applications in different research fields. For example, in image classification, a photo can belong to the classes mountain and sunset simultaneously. Zhang and Zhou (2008) and others (Boutell et al., 2004) used multilabel algorithms to classify scenes on images of natural environments.

Things change pretty much on a daily basis in the world of SEO. Since the announcement of Google's AI machine learning algorithm – RankBrain – in 2015, one of the most discussed topics in SEO galleries is: With Google admitting RankBrain being one of the top three ranking factors, these discussions have become even more worthwhile. In past 3-4 months, we also saw a spike in the number of SERPed members asking the same question. And, multiple posts claiming 2017 as the year of AI and Voice Search, we think it is the right time to dive deeper to understand more about it. To get more clarity on this topic, we decided to go straight to the big guns and find out what they think about it. The responses from each expert are compiled below. Fasten your seat belts and get ready for an awesome ride. Albert Mora is the CEO and co-founder of Seolution, an SEO agency for Shopify e-commerce sites. He has been doing SEO from 1997 and has around 20 years of experience. Follow Albert on Twitter here. Since the beginning of the Internet, artificial intelligence has played a relevant role in the operation of search engines. Logically, the algorithms have been evolving, but the fundamental underlying principle remains the same: search engines want to deliver quality search results to the users. For this reason, if you want a long term sustainable SEO results, you must think about the users first, not about the search engines. Alex has more than 15 years of experience in Digital Marketing, and he is working online since 2002.

Have you ever dreamt of gazing into the eyes of chatbot while explaining your frustration with a service? If so, that would be oddly specific, but you'd also be in luck, thanks to an ambitious New Zealand startup. The company, which has the eerie name of Soul Machines, has created a virtual chatbot that can not only portray human emotion, but also read human facial expressions. The aim is to take chatbot service to the next level by humanizing the interaction between man and machine, basically by making them more like us. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017!

Bennett Schwartz is one of the nation's leading memory experts, and when I visited him in his office at Florida International University, he was standing at his desk. A soft sunlight crowded the room. Large windows framed the palm tree-lined quad outside. Dressed in a short-sleeved shirt and slacks, Schwartz appeared to be quietly talking to himself, with hushed, mumbled words, and for a long moment, it seemed as if he was some sort of monk, living in another, more esoteric world. Schwartz turned around, putting the book away with an easy gesture. It turned out that when I walked into the room, Schwartz was honing his Scrabble skills. He had a Scrabble tournament the next day, and he was practicing words from a book devoted to the game.

The artificial intelligence (AI) industry is undergoing unprecedented growth. AI development is finding its way into all areas of business and, increasingly, into creative endeavors. Despite the obvious difficulties, Canva, The Grid, and Autodesk have all tried to market AI design algorithms, with varying degrees of failure. Adobe is understood to be working on AI enhancements to its Creative Cloud product range. Eventually, someone will arrive at a mathematical formula that approximates the creative process.

The landscape of data is ever-changing, meaning analysts need to evolve both their thinking and data collection methods to stay ahead of the curve. In many cases, data that might have been considered unique, uncommon or unattainably expensive just a few years ago is now widely used and often very affordable. It is the analysts who take advantage of these untapped data sources, while they remain untapped, who can reap the rewards by gaining a competitive advantage before the rest of their industry or peers catch on. This type of data is often referred to as alternative data, and with the ever-increasing levels of data available in the modern world comes the opportunity to gain unique insights, competitive industry advantage, and boosted profits. It is perhaps no surprise then to hear that the scramble to get hold of such data has been dubbed the new gold rush.

It covers self-study tutorials and end-to-end projects on topics like: Loading data, visualization, modeling, algorithm tuning, and much more...

How to Cut Cake Fairly.