Facebook chairman and CEO Mark Zuckerberg just achieved what most of us have only seen in the movie "Iron Man" and never dreamed would be possible in 2016: He has an artificial intelligence (AI) system running his home, which incidentally, just like you, hates Nickelback. Inspired by Stark's home in "Iron Man," Zuckerberg developed the project and named it "Jarvis," just like in the movie. And oh yeah, it's voiced by Morgan Freeman. So Zuck may have just one-upped "Iron Man" for real. It may be a little less high-tech than the one in the movie, but we're still impressed.

Machine learning algorithms are enabling organizations to supercharge workflow processes across their enterprises. They center around technology that has the ability to learn without being explicitly programmed: machines that can study their mistakes and reprogram themselves to improve their performance over time. Lots of big names are investing R&D dollars into machine learning. Here are some ways it can help improve business operations. A workflow process is the backbone of just about every common business activity, whether it centers around finance, inventory or another back-office task.

Retailers and technology firms are experimenting with chatbots, powered by a combination of machine learning, natural language processing, and live operators, to provide customer service, sales support, and other commerce-related functions. Chris Messina of Uber recently coined the term "conversational commerce" to describe this movement, which he defines as: The net result is that you and I will be talking to brands and companies over Facebook Messenger, WhatsApp, Telegram, Slack, and elsewhere before year's end, and will find it normal. While messaging and voice interfaces are central components, they fit into a larger picture of increasing infusion of technology into our daily lives, which in turn is unlocking new potential for brand-to-consumer interaction. The fact is, technology overall is becoming more deeply woven into our lives, and the entire ecosystem is enjoying tighter cohesion through the increasing availability and sophistication of APIs. Smart companies are finding new and innovative touch points with consumers that are contextual, relevant, highly personal, and, yes, conversational.

Ben Dickson is a software engineer and the founder of TechTalks. As artificial intelligence and machine learning mature and manifest their potential to take on complicated tasks, we've become somewhat expectant that robots can succeed where humans have failed -- namely, in putting aside personal biases when making decisions. But as recent cases have shown, like all disruptive technologies, machine learning introduces its own set of unexpected challenges and sometimes yields results that are wrong, unsavory, offensive and not aligned with the moral and ethical standards of human society. While some of these stories might sound amusing, they do lead us to ponder the implications of a future where robots and artificial intelligence take on more critical responsibilities and will have to be held responsible for the possibly wrong decisions they make. At its core, machine learning uses algorithms to parse data, extract patterns, learn and make predictions and decisions based on the gleaned insights.

There was the voice recognition software that struggled to understand women, the crime prediction algorithm that targeted black neighbourhoods and the online ad platform which was more likely to show men highly paid executive jobs. Concerns have been growing about AI's so-called "white guy problem" and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making. Mortiz Hardt, a senior research scientist at Google and a co-author of the paper, said: "Decisions based on machine learning can be both incredibly useful and have a profound impact on our lives ... Despite the need, a vetted methodology in machine learning for preventing this kind of discrimination based on sensitive attributes has been lacking." A beauty contest was judged by AI and the robots didn't like dark skin The paper was one of several on detecting discrimination by algorithms to be presented at the Neural Information Processing Systems (NIPS) conference in Barcelona this month, indicating a growing recognition of the problem. Nathan Srebro, a computer scientist at the Toyota Technological Institute at Chicago and co-author, said: "We are trying to enforce that you will not have inappropriate bias in the statistical prediction."

How much progress has Facebook co-founder and CEO Mark Zuckerberg made in fulfilling his New Year's resolution for 2016? Zuckerberg said in a Jan. 3 Facebook post that his personal challenge for 2016 was to use artificial intelligence to create a personal assistant, which he described as his own version of Jarvis from Iron Man. On Monday, he offered a detailed update on Jarvis, and highlights follow. So far this year, I've built a simple AI that I can talk to on my phone and computer; that can control my home, including lights, temperature, appliances, music and security; that learns my tastes and patterns; that can learn new words and concepts; and that can even entertain Max. It uses several artificial intelligence techniques, including natural language processing, speech recognition, face recognition and reinforcement learning, written in Python, PHP and Objective C. In this note, I'll explain what I built and what I learned along the way. Before I could build any AI, I first needed to write code to connect these systems, which all speak different languages and protocols.

Presbyterian reverend Thomas Bayes had no reason to suspect he'd make any lasting contribution to humankind. Born in England at the beginning of the 18th century, Bayes was a quiet and questioning man. He published only two works in his lifetime. In 1731, he wrote a defense of God's--and the British monarchy's--"divine benevolence," and in 1736, an anonymous defense of the logic of Isaac Newton's calculus. Yet an argument he wrote before his death in 1761 would shape the course of history.

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data.

Over 3/4 of Marketing Leaders believe the future belongs to Artificial Intelligence. In October 2015, in my post entitled "The Future Of Sales Is AI: Are Your Sales Teams Prepared?" I shared a bold prediction from LeadGenius cofounder Anand Kulkarni: "In just 10 years most salespeoples' jobs will be replaced by artificial intelligence." As I wrote then and as I believe today, sales is not going away. Salespeople are not going away.

At some point in the course of pregnancy, a woman is likely to suspect that the baby she is incubating has somehow hijacked her brain. New research suggests that, in some sense, she's right, and that pregnancy itself is altering her brain like no other experience she's had since adolescence. The places where a pregnant woman's brain shrinks are very specific, the research says. The structural renovations wrought by pregnancy appear to overlap almost perfectly with the brain regions that play a key role in how we understand and interpret the actions, intentions and feelings of others. And the brain of a first-time mother stays changed -- for at least two years after she has given birth, according to the new research, published Monday in the journal Nature Neuroscience. Pregnant women did not lose intellectual ground, the researchers found: as a group, their working memory and memory for words was no better or worse than before pregnancy.