An AI just claimed another gaming victory over humans by winning a 20-day poker tournament. The AI, called Libratus, took on four of the world's best Heads-Up No-Limit Texas Hold'Em poker players at a Pennsylvania casino. After 120,000 hands, Libratus won with a lead of over $1.7 million in chips. "I'm feeling great," says Tuomas Sandholm, a computer scientist at Carnegie Mellon University who was part of the team that created the AI. "This is a David versus Goliath story, and Libratus was able to throw a pebble."

I write a lot about data-driven algorithms, in particular those informed by Machine Learning. I thought it would be nice to give the low-down on machine learning for the uninitiated. Below, I discuss four essential questions. The answers are based, in part, from a recent discussion with Pedro Domingos, author of The Master Algorithm. Machine learning and AI touch your life every minute of every day, from applications you use at work to how you choose products to buy (Amazon recommendations).

Update: Last time AI took on humans at poker, the flesh-and-blood brigade won comfortably. Libratus has won $1,766,250 worth of chips at no-limit Texas Hold'em, knocking the stuffing out of its four human opponents in the process. "Yeah, this was a beat-down," was all human opponent Jimmy Chou could say after finishing his 30,000th hand on Monday. Libratus' co-creator, PhD student Noam Brown, was delighted with the AI's performance, telling The Guardian, "When I see the bot bluff the humans, I'm like, 'I didn't tell it to do that. I had no idea it was even capable of doing that.' It's satisfying to know I created something that can do that."

The rise of artificially intelligent machines will come at a cost--but with the potential to disrupt and transform society on a scale not seen since the Industrial Revolution. Jobs will be lost, but new fields of innovation will open up. The changes ahead will require us to rethink attitudes and philosophies, not to mention laws and regulations. Some people are already debating the implications of an automated world, giving rise to think tanks and conferences on AI, such as the annual We Robot forum, which takes a scholarly approach to policy issues. A registered patent attorney and board-certified physician, Ryan Abbott writes about the impact of artificial intelligence on intellectual property, health and tort law.

Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets, machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There's no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run. That's because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results. Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad -- some transactions will be falsely flagged for decline.

Digital Initiatives create new ways of conducting business, which drive the need for increasingly advanced security and regulatory compliance challenges with exponentially more damaging consequences. In the BMC and Forbes Insights Survey in 2016, 97% of executives said they expect a rise in data breach attempts in the next 12 months. Sixty percent said operations and security teams have only a general understanding of each other's requirements, resulting in a "SecOps gap" leaving organizations unable to mobilize to protect themselves. The result: many enterprises face unnecessary risks to data loss and production downtime. In his general session at 18th Cloud Expo, Atwell Williams, Senior Director of Customer Experience at BMC, covered BMC's innovative solution to deliver vigilant compliance, precise threat analytics and relentless remediation in pursuit of security for the digital era.

Hiring managers can spend hours using primitive keyword search tools to sift through half-relevant resumes on job boards, and workers with in-demand skills get bombarded with emails from recruiters offering them jobs they're not particularly interested in, says Ed Donner, cofounder and CEO of New York startup Untapt. "Hiring tech people is an incredible pain point," says Donner, who previously headed a technology team with hundreds of employees at JPMorgan Chase. "It's still impossibly hard to find talent." Untapt is one of a number of companies looking to make it easier to digitally dig through piles of resumes, using machine learning techniques to develop algorithms that predict how well-suited a candidate is for a job. Advocates and industry experts say that adding automation to recruiting can save time and money and can potentially help hiring managers find and consider a more diverse set of applicants.

While many people still view artificial intelligence (AI) as just walking, talking, robots that will go around and do all your housework for you, that's not all there is to it. Artificial intelligence is a very broad term and includes a manner of different elements including machine learning and big data processing. It also goes hand in hand with IoT and makes things like wearables and connected home gadgets possible. While AI is essentially the decision maker and action taker, IoT collects the information needed in the first instance. One such example where you can see these two working in tandem is within the insurance industry.

Surrounded by all the hype in AI, it's easy to sing the praises of machine learning without realizing that systems can be easily exploited. As governments, businesses, and hospitals are beginning to explore the use of machine learning for data analysis and decision making, it's important to bolster security. In a paper [PDF] ominously titled "Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning," a group of researchers from the University of Maryland are trying to find bugs by causing "silent failures." Machine learning systems have often been compared to black boxes. They are trained to map input data to output data by learning from an algorithm.

This essay appears in today's edition of the Fortune Brainstorm Health Daily. Get it delivered straight to your inbox. Those of you who read the Fortune CEO Daily--penned each morning at an ungodly hour by my boss, Alan Murray--got a taste of what seems like a fascinating panel on artificial intelligence at Davos. The panel included Mustafa Suleyman, co-founder of DeepMind (gobbled up for a song by Google in 2014), Microsoft CEO Satya Nadella, Dow CEO Andrew Liveris, and the cognitively impressive head of IBM Watson, David Kenny, whom I interviewed at length in October. You can find Alan's full post from this morning here.