There were a few things that sent me into a panic about turning 40, but the biggest -- looming larger than the golden ring of a book deal or a staff job or, like, finally going back to yoga -- was what it meant for me to still be single and actively looking for a partner at that age. Not so much even that I was single, but that I cared and what that implied. It just felt really basic, to be frank. There are plenty of things I simply do not give a single solitary fuck about when it comes to what women my age are supposed to be doing. So why did this one detail bother me?

There is no doubt the search industry has evolved. Just one look at how search engine results pages are currently laid out shows how things have changed. We have come a long way from 10 blue links. But have we gone far enough? At SXSW earlier this month, information access was a hot topic.

If you choose to pay for membership, you'll be able to see your matches' activity report, like in the image below. It shows how likely they are to send the first message, how long it usually takes them to reply and the last time they were active. You'll also see read receipts when the other person has already seen your messages. That way, you won't have to waste time waiting for responses that'll never come. Finally, you'll get 6,000 "Beans" to spend on the service's various features, such as the ability to see you and your matches' mutual friends.

Tinder has repeatedly made top ten lists of the best and most popular dating apps, but is it effective? A recent survey showed that despite its high usership, not even five percent of the young people swiping right are looking for relationships. The majority of people on the app are young: an estimated 80 percent of Tinder users qualify as millennials, according to a survey by LendEDU, a website that provides student loan guidance. But only 4.16 percent of those users are looking for a relationship. Twenty-two percent said they were looking for a hookup, while 44.44 percent said they used it "confidence-boosting procrastination."

The future of machine learning technology in marketing sounds promising. Machines will not only fulfill ordinary, everyday tasks but accomplish things humans cannot even conceive of by deriving predictive models with powerful, automated technology. Customers stand to benefit too, as machine learning will apply advanced algorithms to offer spot-on, personalized recommendations, improving customer experiences and brand interactions. And while it's easy to get caught up in the hype and promise, remember what really matters in growing a business -- the people who buy and use the products. Customer empathy will continue to be a key attribute startups must master in order to compete with, and outperform the giants in their industry.

In his regular courses at Georgia Tech, the computer science professor had at most a few dozen students. But his online class had 400 students -- students based all over the world; students who viewed his class videos at different times; students with questions. Maybe 10,000 questions over the course of a semester, Goel says. It was more than he and his small staff of teaching assistants could handle. "We were going nuts trying to answer all these questions," he says.

These days, job hunting looks a lot like online dating: Seemingly infinite scrolling, a few messages that lead to nowhere, and even fewer human interactions that--more often than not--don't turn out the way you thought they would. Recruiting startup WayUp hopes to convince job searchers that it doesn't have to be such "a miserable process," says co-founder and CEO Liz Wessel. WayUp offers a "smart" platform that uses data to match seekers with potential employers. Wessel says that the site collects about 40 data points per applicant and, armed with that information, is able to suggest "the right jobs to the right people" better than job sites like Indeed and Monster, or professional social network LinkedIn. On Thursday, company announced that it has raised a $18.5 million series B funding round, led by Trinity Ventures, with participation from WayUp's existing investors, including General Catalyst, BoxGroup, Lerer-Hippeau Ventures, Index Ventures, SV Angel, and Female Founders Fund.

SAP Ariba unveiled an AI-powered assistant named Procurement today to speak with buyers and suppliers about orders in a conversational interface. The announcement was made onstage at SAP Ariba Live conference in Las Vegas. "Through its bot, the company plans to enable buyers and suppliers to converse with their SAP Ariba applications much as they would Siri or Alexa," the company said in a statement shared with VentureBeat. "Leveraging machine learning, the bot will be able to train and learn about a user's preferences and a company's policies and procedures and guide actions in line with them to reduce errors and speed processing." The Procurement bot will be part of "a portfolio of bot applications that SAP is developing by the end of the year," SAP said in a statement.

My mother waited two months for her Amazon Echo to arrive. Then, she waited again -- leaving it in the box until I came to help her install it. Any device that requires vocal instructions makes my mother skeptical. She has bad memories of Siri. "She could not understand me," my mom told me.

AI is already influencing all areas of business – but should prove especially useful for the finance industry. This will, however, require a significant change in the way lenders approach their customer base, according to Richard Harris, Head of International Operations at Feedzai, which specializes in machine learning services for businesses, speaking at White Clarke Group's Auto Captives Summit in November. Harris argues that - with computing power now a fraction of what it was a decade ago - every lender can integrate artificial intelligence and machine learning into their business to provide highly personalized services that focus on individuals, not customer groups. He told the Summit: "In the past, what we did was build scorecards or we built rules. We made decisions about people by putting them in a bucket. Such as: where are you from? Where did you go to school? How long have you been in employment? How much is your house worth? All these criteria of stability and probability were used to make decisions about individuals. The time has come to stop putting people in boxes. You've got all this processing power; it's cheap. You've got machine learning algorithms which can look down to a very fine-grained level of detail at hundreds of different factors. You don't need to group people anymore. "Let's treat people as individuals!