Meta has debuted a new AI capable of besting human opponents in the game of Diplomacy. The game has been seen "as a near-impossible grand challenge" for AI, Meta wrote in a blog post about the AI, called CICERO. Diplomacy is especially difficult, even compared to complex games like chess and go, because it requires a mastery not of hard and fast rules, but of soft skills. Players must know the art of understanding other people's perspectives and needs, wants and wonts; make complex, living plans that can change with human whims; and then persuade other players to work with them and against others. Because it relies on social -- not strategic, logical, or mathematical -- skills, Diplomacy has long been seen as a "near-impossible" challenge for an AI.

This is one of those things you only find out after getting a job as a data scientist. Imagine that someone simply showed up with an app to you and said that if you follow what this app recommends, you will reach your goals in life. Take the job the app ranks highest. Date the person the app shows it's the best match. Just take the medicine the app recommends.

As technology evolves at a rapid rate – especially technology that incorporates artificial intelligence (AI) capabilities – so too does the potential for bias, disconnect, misuse of data, and the automation of impersonal actions or decisions. With the vast amounts of data collected, stored, and exchanged, capitalist societies risk the commoditization of personal data at the expense of the individual, instead of using personal data to foster valuable individual and societal relationships. In business, AI and machine learning are increasingly used as part of smart systems that analyze large amounts of data to identify trends that will benefit the business, like capturing more consumers and increasing profits, as opposed to building long-lasting relationships. AI shouldn't only be focused on the business' bottom line. In fact, a recent AI and empathy survey by our company of 6,000 consumers from North America, the United Kingdom, Australia, Japan, Germany, and France found that 69% of consumers think businesses have a moral obligation to do what's right for the consumer, beyond what is legally required.

If you were about to build a physical, hardware robot, how do you go about it? Probably (if you are not insane), you will start by asking yourself, "What it's for?" What is the purpose of the thing? This functional requirement starts the process of the design and answers most of the upcoming problems that you'll have, whether you're building a hardware robot, a chatbot, a voice assistant or even a full-duplex multimodal videobot. I've had dozens of executives tell me that they want to build a chatbot or to use AI (or VR or blockchain or whatever), but they don't know why.

As we discussed in the previous article, the Top 3 Challenges of Records Management, records management automation is the best way to address these challenges. But what is automation, really? Within these two main categories there are seven types of automation we typically deal with in the records management world. They can use fingerprinting, linguistic analysis, or both as methods of automation. All of them help us to classify content correctly against the file plan, and in some cases, we can build relationships between content for event better classification. This also helps us to enhance search and retrieval of information.

The stereotypical salesperson - smartly dressed with their Rolodex of contacts - is an image of a bygone era. Even interacting via phone and email is starting to feel outdated thanks to advanced communication methods. Video collaboration software like Google Hangouts and Skype reduce the need for physical interactions. Social media and messaging apps also afford an instant line of communication. The ways to interact with each other seem endless, yet somehow it feels as if personal relationships are becoming more distant.

Facebook recently caused a bit of a commotion as rumours circulated that it had to shut down two of its chatbots after they started talking gibberish to each other, as if inventing and learning a new language or a secretive way to communicate. Mark Zuckerberg and Elon Musk have also publicly wrangled over the future of Artificial Intelligence (AI) and machine learning, while the Western tabloid media continues to try to generate mass panic about robots pinching our jobs. Sadly, the "Facebots" in question were in fact using verbal shorthand to better compute tasks. They weren't planning to take over the world (just yet, anyway!) but finding a more efficient route to task completion. Let's have less of the doom-mongering, more about the opportunities that AI, machine learning and automation can offer us in the real future of work.