For centuries, eternal life has been a common quest for women and men around the world. Even today, tech tycoons like Jeff Bezos and Sam Altman are investing in startups that aim to reverse ageing by making tweaks to animal cells. While this sounds like a wonderful new era of carefree living, one expert has cast doubt over whether it would even be a good idea. Dr Stephen Cave, a philosopher at the University of Cambridge, said an extended human lifespan would be'absolutely catastrophic'. Attempts to radically lengthen lifespans will put too much pressure on Earth's resources, he claims – and could mean humanity will go extinct sooner.

Here are the results of the KDnuggets Poll inspired by this blog: Relax! As advances in AI continue to progress in leaps and bounds, accessibility to data science at a base level has become increasingly democratized. Traditional entry barriers to the field such as a lack of data and computing power have been swept aside with a continuous supply of new data startups popping up(some offering access for as little as a cup of coffee a day) and all powerful cloud computing removing the need for expensive onsite hardware. Rounding out the trinity of prerequisites, is the skill and know-how to implement, which has arguably become the most ubiquitous aspect of data science. One does not need to look far to find online tutorials touting taglines like "implement X model in seconds", "apply Z method to your data in just a few lines of code". In a digital world, instant gratification has become the name of the game.

Here are the most popular June 2021 stories on KDnuggets. As you see, a lot of attention was on several blogs on the topic of demand for Data Scientists in 10 years, which was also a topic for KDnuggets poll in June: Relax! Data Scientists will not go extinct in 10 years. Stories in green font are also the winners of KDnuggets Top Blog Rewards for June. Most Viewed - Platinum Badge ( 32,000 UPV) 5 Tasks To Automate With Python, by Dylan Roy (*) Data Scientists Will be Extinct in 10 Years, by Mikhail Mew (*) How to Generate Automated PDF Documents with Python, by Mohammad Khorasani (*) Most Viewed - Gold Badge ( 16,000 UPV) How I Doubled My Income with Data Science and Machine Learning, by Terence Shin Top 10 Data Science Projects for Beginners, by Natassha Selvaraj (*) Pandas vs SQL: When Data Scientists Should Use Each Tool, by Matthew Przybyla (*) Five types of thinking for a high performing data scientist, by Anand Rao Will There Be a Shortage of Data Science Jobs in the Next 5 Years?, by Pranjal Saxena (*) What will the demand for Data Scientists be in 10 years?

As someone working in data science for over a decade, it is frustrating to see people prophesying on how the field will get extinct in 10 years. The typical reason given is how emerging AutoML tools will eliminate the need for practitioners to develop their own algorithms. I find such opinions especially frustrating because it dissuades a beginner from taking data science seriously enough to excel in it. Frankly, it is a disservice to the data science community to see such prophecies about a field where the demand is only going to increase even further! Why would any sane person invest their finite time and energy in learning something that will become extinct soon?

Reinforcement learning is a type of dynamic programming that focuses on results operating a process of compensation and retribution. It understands by combining with its ecosystem. They find compensation for going on properly and discipline for operating incorrectly. The operator determines without interference from a mortal by expanding its profit and decreasing its discipline. A neural network is a type of machine learning which creates itself after the human brain, setting up an artificial neural network that via a method provides the computer to understand by integrating different circumstances.

Back in 2010, a team of self-described "data nerds" founded Sumo Logic. The vision was to build a platform to deliver–as a cloud native service–machine data analytics for anybody. It definitely turned out to be the right focus. Consider that today Sumo Logic analyzes 100 petabytes of data on a daily basis (to put this into perspective, a petabyte consists of 1,000 terabytes or about five years of the Earth Observing System). The platform also performs 30 million searches and handles a whopping 500 trillion record queries every day. Because of this, Sumo Logic has a unique view of some of the most important trends in technology.

Back in 2010, a team of self-described "data nerds" founded Sumo Logic. The vision was to build a platform to deliver–as a cloud native service–machine data analytics for anybody. It definitely turned out to be the right focus. Consider that today Sumo Logic analyzes 100 petabytes of data on a daily basis (to put this into perspective, a petabyte consists of 1,000 terabytes or about five years of the Earth Observing System). The platform also performs 30 million searches and handles a whopping 500 trillion record queries every day.

Without a doubt, artificial intelligence (AI) is one of the most important new technologies in the world, with the power to create entirely new industries and professions. Stanford professor Andrew Ng, in fact, has called AI "the new electricity" for its potential world-changing possibilities. So it's perhaps only natural to ask: What impact will AI have on libraries and the world of literature? Perhaps most obviously, AI machines will soon have the power to take over the role of librarians. They will have the power to become not just the stewards of the world's information and literature, but also trusted mentors, research advisors, and content matter experts.

The world is changing, and it's all about your voice. In my house, there are voicebots everywhere. In the kitchen, I use the Google Home speaker to ask about recipes and the weather. In my office, there's an Amazon Echo connected to a high-end stereo, so I can control my music collection by voice. You'd think that would be enough, except that I have another Echo Dot in my family room, and another Echo in an upstairs room.

Cash is getting less attention and people are looking forward to paying companies with mobile apps and other technologies instead, according to a new survey by online invoicing and payment firm Viewpost, which surveyed 1,000 U.S consumers for its analysis. More than half of respondents say payments between companies and customers will soon be done through mobile apps. Only 11 percent of Americans think companies will bill them by paper in the future, and 54 percent believe companies will bill customers through automatic payments from their bank account or credit card. "People are willing to embrace a more convenient, frictionless payments future," Viewpost CEO Max Eliscu said in a released statement. Electronic payments are already popular today, with services like PayPal, Apple Pay, Android Pay and apps like Venmo.