If the idea of a chatbot is intimidating, remember, we're already inviting sophisticated chatbots with artificial intelligence capabilities into our homes …

But given that artificial intelligence is one of Google's primary areas of focus, expect Google to showcase lots of new, eye-popping demos that show …

The global "Artificial Intelligence Market" market gives a huge platform for different firms, manufacturers, and organizations, such as, H2O, Braina, …

The latest invocation of these nuggets of wisdom comes in an article about low-level artificial intelligence.

Robots and artificial intelligence can help us build the infrastructure we need, here and around the world.

Google recently announced they've started using a "neural matching" algorithm to better understand overall concepts and search queries. According to a Tweet by Google's Danny Sullivan, this neural matching algorithm is affecting 30% of searches. Sullivan explains that this AI algorithm is used to better connect words to concepts. Which basically means Google's gotten a lot better at understanding synonyms (or as Sullivan calls them, "super synonyms"). For example, ever heard of something called the "soap opera effect?"

The idea of Kickstarter first formed in the mind of Perry Chen in 2001. A native New Yorker, Chen was 25, living in New Orleans and working as a musician. He wanted to bring a pair of DJs he loved down to perform during Jazz Fest. He sorted out a venue, organised things with their management, but in the end the event didn't happen – Chen didn't have the funds to pay for the show if not enough people turned up. In his frustration, a thought occurred to him: "What if people could go to a website and pledge to buy tickets for a show? And if enough money was pledged, they would be charged and the show would happen. Over the years that followed, Chen held on to that simple idea. He moved back to New York in 2005, still more intent on making music than starting an internet company – he had no background in technology – but the thought wouldn't go away. He became friends with a music journalist, Yancey Strickler, who got sold on the idea, too. They talked about it with, Charles Adler, a designer and DJ, and the three of them formulated ideas and spoke to mates of mates who knew code or to people who might help fund such a thing. Eventually, in April 2009, eight years after the idea had first come to Chen, the three of them launched their website and waited at their laptops to see if other people thought it was a good idea too. In the first few days, a few emails trickled in, from people pitching ideas, wondering how the thing might work. And then, after a couple of weeks, a young singer-songwriter from Athens, Georgia, launched a project to fund her album, Allison Weiss Was Right All Along. "My name is Allison Weiss and I'm recording a new EP this summer.

Veg out this weekend and still get all the best deals online on robot vacuums, sheets, and more. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Finally it's your time to sleep in, relax a bit, and just generally do the things that you enjoy most, for no other reason besides the fact that you finally can. It's also the perfect time to indulge in a little retail therapy, but if you're not too keen on dealing with big crowds in stores, don't worry.

If there was something that always frustrated me was not fully understanding Bayesian inference. Sometime last year, I came across an article about a TensorFlow-supported R package for Bayesian analysis, called greta. Back then, I searched for greta tutorials and stumbled on this blog post that praised a textbook called Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath. I had found a solution to my lingering frustration so I bought a copy straight away. I spent the last few months reading it cover to cover and solving the proposed exercises, which are heavily based on the rethinking package. I cannot recommend it highly enough to whoever seeks a solid grip on Bayesian statistics, both in theory and application. This post ought to be my most gratifying blogging experience so far, in that I am essentially reporting my own recent learning. I am convinced this will make the storytelling all the more effective. As a demonstration, the female cuckoo reproductive output data recently analysed by Riehl et al., 2019 [1] will be modelled using In the process, we will conduct the MCMC sampling, visualise posterior distributions, generate predictions and ultimately assess the influence of social parasitism in female reproductive output. You should have some familiarity with standard statistical models. If you need to refresh some basics of probabilities using R have a look into my first post. I hope you enjoy as much as I did!