ONE Saturday night last year, 11 people went looking for love. Like countless speed daters before them, they met in a room draped with curtains, the lights on low. In one hand they held traditional glasses of bubbly, but in the other were sheets of paper they had filled with their personal data. This twist on speed-dating was part of an experiment run by a team at Newcastle University in the UK. They wanted to know what would happen in a world where instead of vetting potential dates by their artfully posed selfies or carefully crafted dating-site profiles, we looked at data gathered by their computers and phones.

An AI created by scientists at the Massachusetts Institute of Technology (MIT) uses machine learning to detect suspicious activity - getting it right 85% of the time. The system uses an algorithm called "AI2", that detects anomalies, in conjunction with a human expert, because AI2 on its own can lead to false positives, according to MIT News. "The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions," said one of the researchers behind the project, Kalyan Veeramachaneni. "That human-machine interaction creates a beautiful, cascading effect." The merging of artificial intelligence and what researchers call "analyst intuition" has allowed for this new system to be successful in its early development, Veeramachaneni and fellow scientist Ignacio Arnaldo said.

MIT scientists have built a hybrid human/artificial intelligence (AI) machine that they claim can learn how to detect 85% of cyber attacks – that's roughly three times better than previous benchmarks – while reducing false positive rates by a factor of 5. Nitesh Chawla, professor of computer science at Notre Dame University, said in a statement from MIT that the machine "has the potential to become a line of defense against attacks such as fraud, service abuse and account takeover." Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx demonstrated the platform, called AI2, in a paper titled "AI2: Training a big data machine to defend". As the researchers describe the current state of the art, today's security systems are typically driven by either humans – so-called "analyst-driven solutions" – or by machine. The problem with security systems based on fixed rules is that they miss attacks that don't match those rules. Machine-learning approaches, as the name suggests, rely on an adaptive process that can trigger annoying numbers of false positives.

Business intelligence has gone from static reports that tell you what happened, to interactive dashboards where you can drill into information to try and understand why it happened. New big data sources, including Internet of Things (IoT) devices, are pushing businesses from those reactive analytics – whether you look back once a month to spot trends or once a day to check for problems – to proactive analytics that give you alerts and real-time dashboards. That makes better use of operational data, which is more useful while it's still current, before conditions change. "There's a demand for real-time dashboards," says Herain Oberoi from Microsoft's Cortana Analytics team. "A lot of businesses want to get the pulse of their business. But dashboards show things that have already happened."

Chatbots, also called Conversational Agents or Dialog Systems, are a hot topic. Microsoft is making big bets on chatbots, and so are companies like Facebook (M), Apple (Siri), Google, WeChat, and Slack. There is a new wave of startups trying to change how consumers interact with services by building consumer apps like Operator or x.ai, bot platforms like Chatfuel, and bot libraries like Howdy's Botkit. Microsoft recently released their own bot developer framework. Many companies are hoping to develop bots to have natural conversations indistinguishable from human ones, and many are claiming to be using NLP and Deep Learning techniques to make this possible.

Artificial Intelligence has made huge advances in recent years in many areas, including language processing, vision, and machine learning; we are also seeing the emergence of platforms that integrate different kinds of AI, such as IBM Watson (Arria is a Watson ecosystem partner). Within medicine, I see a lot of excitement about using many aspects of AI; of course Natural Language Generation (NLG), but also using predictive analytics to anticipate potential problems, using machine learning to build diagnostic algorithms, using natural language processing to identify relevant research findings, using computer vision to analyze scans, and using robotics to assist surgeons and other clinicians. Natural Language Generation (NLG) software systems generate narratives that summarize, explain, and communicate complex data sets to people. The huge amount of data available in the modern world can overwhelm people; NLG humanizes the flood of data so that it helps rather than overwhelms people. NLG systems use data analysis and artificial intelligence techniques to analyze complex data sets, and computational linguistic techniques to communicate the results of the analysis in a high-quality narrative text.

However, the project to merge social science and artificial intelligence ran into more than a few bumps in the road. In a 1990 book, sociologist Harry Collins suggests why. Collins argued that every community "knows" certain tacit things that are difficult if not impossible to fully represent computationally. Or, in other words, "computers can act intelligently to the degree that humans act mechanically." Lots of human activities (like voting, greeting, praying, shopping, or writing a love letter) are "polymorphic"--socially shaped based on an understanding of how society expects the action to be performed.

This year marks the 60th anniversary of the first Dartmouth Conference, the research project that is credited with introducing the term'Artificial Intelligence' (AI) to the world. Clearly, AI isn't a new concept, and Facebook's announcement that it will enable businesses to deliver automated customer support, online shopping guidance, content and interactive experiences to its users through chatbots is the latest iteration of the technology. As with most news from Facebook, the announcement has caused quite a stir - only few days later people were already hailing Bots as'The New Apps'! It's worth taking moment to look behind all the hype around chatbots and understand the technology that sits at their core. Although they are still in their development phase, chatbots are becoming more readily accepted by the general public, who probably don't even really think of them as a form of AI.

The new microscope features something called "photonic time stretch". Essentially, the microscope takes pictures of blood cells with the help of flashing lasers. Alongside optics that boost clarity within images, the new microscope can track information not possible in the past. Then, the AI uses deep learning to distinguish the difference between healthy and cancer riddled white blood cells.

Tech companies are betting big on conversational bots as the next big thing. But you might want to be nice to those bots. CBC Radio technology columnist Dan Misener says the way we treat them reveals a lot about who we are as humans. Because the chatbots are coming, and we're just starting to see the first wave. Facebook recently unveiled its plans for chatbots that live inside its Messenger app.