This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. Job-seeking firm FirstJob debuted a recruitment chatbot called Mya on Monday, according to Venture Beat. The chatbot -- which the firm says will automate roughly 75% of the recruitment process -- uses a combination of artificial intelligence and natural language processing (NLP) to vet job applicants based on factors like qualifications and extra curricular activity, and to answer applicant questions regarding company culture, policies, and benefits. This will streamline the overall recruitment process, freeing up time for human agents to close deals and finish contracts.

If you can't be bothered chatting with Facebook's news and weather bots, maybe a pizza bot can change your mind. Pizza Hut is launching a Facebook and a Twitter chatbot this fall that can take your order and show you current deals. The Facebook bot even comes with something extra: it can connect your FB with your Pizza Hut account, so it can list your past orders. You know, in case somebody asks you to prove that you've never had Hawaiian before. But what if you eat healthy and don't like pizza?

The entire AI system learns your environment and optimizes performance in real-time. The Expert System makes decisions regarding which parameters to tune based on patterns fed by the learning module. The Expert System also creates Client Behavior Profiles and uses fingerprinting to tailor Device-to-Access Point interactions.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.

In his iconic Star Wars series, George Lucas envisioned a world in a galaxy far, far away, where, among other things, doctors were droids and bots. In this world, a droid surgeon fitted Luke Skywalker with a bionic hand after a fight with Darth Vader, a bot midwife oversaw the delivery of Princess Leia and droids treated Luke Skywalker for hypothermia after his rescue from the icy planet of Hoth. Time and time again, robots, rather than humans, provided healthcare. Lucas viewed medical care as algorithmic, and therefore well within the capacity of intelligent machines. Does the world of healthcare in the Star Wars films -- where bots are the new docs -- mirror our own not-so-distant future of medicine?

In a recent issue of Nature Scientific Reports, a multidisciplinary team of UCLA researchers combined a new form of microscopy called photonic time-stretch imaging with deep learning. With this powerful new technique, they were able to capture 36 million video frames per second. Not content with simply developing a new technique, the team then utilized the augmented microscope to determine which strains of algae provide the most lipids for subsequent refining into biofuels. They also were able to detect cancer cells more accurately and in less time than current techniques. Yes, one methodology that tackled both biofuels and cancer.

The General Data Protection Regulation (GDPR), approved at the end of April and scheduled to enter into force in 2018, does not only deal with EU citizens' privacy. That means that all EU citizens will be able to ask to know the rationale governing companies' algorithmic decisions. As an Oxford paper explains, this is theoretically a good thing: exposing the workings and criteria behind algorithmic behaviors can help to ensure that such criteria are fair and not, for instance, informed by discriminatory ideas. However, a little bit of hand-wringing on the techie's part is understandable: explaining why an AI algorithm acts the way it does is not only an industrial secret, it is also very hard, given the intrinsic complexity of machine-learning technology.

The General Data Protection Regulation (GDPR), approved at the end of April and scheduled to enter into force in 2018, does not only deal with EU citizens' privacy. One of the measures included in the new bill aims to restrict so-called "automated individual decision-making." Such restrictions can be applied to algorithms using machine-learning tools that are becoming more and more widespread among the world's tech giants. The regulation will legally prevent these programs to take decisions that "significantly affect" EU nationals: for instance, these algorithms will not be used to evaluate somebody's "performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements." As AI and machine-learning are going through a worldwide boom, GDPR's wet blanket rule is unlikely to go down well in the tech circles. But what could be even more worrisome for AI fans is another part of the bill: a measure dubbed "right to explanation."

AlphaGo's uncanny success at the game of Go was taken by many as a death knell for the dominance of the human intellect, but Google researcher David Silver doesn't see it that way. Instead, he sees a world of potential benefits. As one of the lead architects behind Google DeepMind's AlphaGo system, which defeated South Korean Go champion Lee Se-dol 4 games to 1 in March, Silver believes the technology's next role should be to help advance human health. "We'd like to use these technologies to have a positive impact in the real world," he told an audience of A.I. researchers Tuesday at the International Joint Conference on Artificial Intelligence in New York. With more possible board combinations than there are atoms in the universe, Go has long been considered the ultimate challenge for A.I. researchers.

This is a group for anyone interested in Deep Learning technology, employment, or business opportunities. All skills, levels of experience, and professional backgrounds are welcome. I started this group to help people network with one another and move their careers forward. Looking forward to exploring what this technology can do with everybody.