Autonomous agents are a huge trend in consumer, business, industry, and other domains. They're popping up in everything from physical devices -- such as Internet of things (IoT) endpoints and mobile handsets -- to cloud services such as virtual personal assistants and smart advisers. Autonomous IoT devices will allow us to multitask like never before. As we incorporate more of them into our lives, we can offload much of the drudgery we once needed to handle manually. We will let self-driving cars manage our commute, offload the more strenuous yardwork to our robotic household assistants, and depend on personal drones to keep an eye on the neighborhood.
Morever, these algorithms are robust, so don't require problem-specific hand-tuning. One powerful example is sampling from an arbitrary probability distribution, which we need to do often (and efficiently!) when doing inference. The brute force approach, rejection sampling, is problematic because acceptance rates are low: as only a tiny fraction of attempts generate successful samples, the algorithms are slow and inefficient. See this post by Jeremey Kun for further details. Until recently, the main alternative to this naive approach was Markov Chain Monte Carlo sampling (of which Metropolis Hastings and Gibbs sampling are well-known examples). If you used Bayesian inference in the 90s or early 2000s, you may remember BUGS (and WinBUGS) or JAGS, which used these methods. These remain popular teaching tools (see e.g.
With time, smartphones and the technology used in them are constantly evolving. Most features that we often use on our phones right now such as playing augmented reality games or streaming live videos were difficult or nearly impossible a few years back. With the advancement in technology came the smartphones and the sliding keyboard phones or flip phones are a thing of the past right now. The smartphones that exist now feature fingerprint authentication, powerful processors, sharp cameras, and bright displays making many things possible. Still, the technology is subject to constant advancement and we can see many more improvements coming to our phones in future.
Google wants to bring smarts to cool gadgets and devices made using Raspberry Pi 3 or Intel's Edison. The company is chasing makers with open-source tools needed to add artificial intelligence to consumer, industrial, and retail devices made using board computers. The plan may include machine-learning tools, which are central to AI. AI helps Apple's Siri, Amazon's Alexa, and Microsoft's Cortana answer questions, and also helps self-driving cars cruise the streets safely. "We don't have any specifics to announce right now, but we're excited to keep sharing open-source machine learning tools with the community--stay tuned for more this year," a Google spokesman said in an email. Earlier this week, Google published a market research survey in an effort to get a better grip on the maker community and its priorities.
The bot revolution is happening fast for Facebook. After launching third-party bots in April offering everything from forecasts to your boarding pass, the social network says there are now more than 11,000 bots active on Facebook. To celebrate, Facebook is adding a bunch of new features that could show up on your favorite bots soon--if developers enable them, that is. One big problem with bots is they feel a little antiquated. Many of them will only respond to certain commands, which can make the whole experience kind of tedious.