The new year is in full swing, and so was Microsoft this week, with transformative work in artificial intelligence, retail, music, virtual reality and social good. Microsoft advanced its artificial intelligence efforts with plans to acquire Montreal-based Maluuba, one of the world's most impressive, deep-learning research labs for natural language understanding. Microsoft announced it will double the size of the lab in the next two years and give $6 million toward AI research to two Montreal universities. The announcements continue the ambitions of Microsoft to democratize artificial intelligence (AI) and leverage its potential to transform industries and humanity. If you didn't trek out to Joshua Tree, California, for the three-day concert event last year that launched Childish Gambino's latest album, "Awaken My Love," you're in luck: A new virtual reality experience can take you there.

Chatbots are a dime a dozen these days and every bot claims to have AI. Everyone seems to have their own definition of AI. What does AI really mean in the context of chatbots and is that the same as machine learning? Traditionally in computer science literature, what we call chatbots today are referred to as "chatterbots". These chatterbots were one of the first problems tackled under the gambit of AI and popularised because of the Turing test.

A charitable foundation backed by Mark Zuckerberg and his wife said Monday it has bought a Canadian artificial intelligence startup as part of a mission to eradicate disease. The Chan Zuckerberg Initiative did not disclose financial terms of the deal to acquire Toronto-based Meta, which uses AI to quickly read and comprehend scientific papers and then provide insights to researchers. Meta capabilities will be unified in a tool made available for free to scientists. Meta artificial intelligence can analyze insights across millions of papers, finding connections and patterns at scales and speeds impossible for humans to match unassisted. In the field of biomedicine alone, thousands of research papers are published daily.

Entire medical labs built on a computer chip, humans with superhero vision and smart sensors that can detect pollution at the speed of light are just a few predictions IBM has made about life in 2022. The New York firm has released its '5 in 5 list', revealing new innovations that could change the way we work live and interact with each other over the next five years. Humans should expect to see advancements in artificial intelligence, smart sensors, telescopes, sensors and medical devices. IBM predicts that technology that will'listen' to speech patterns and then analyze them with a text analysis algorithm that can identify any issues. Technology will analyze patterns in speech and text that could hold clues to mental illness.

Andrea: First and foremost, this is not an overnight deal rather it is a journey. Your road map and strategies must align with the objectives and use cases we identify as benefiting from these technologies and know that they will evolve. Benefiting from cognitive technologies require that you measure outcomes and continue to tune and train your systems and users. In fact, ensure that you plan to govern the tuning mechanisms and training sets closely whether human built or system generated. The competitive advantages that businesses stand to gain are undeniable and as these technologies continue to evolve they will take on a life of their own.

As an IBM futurist and influencer, I was honored to be part of the World of Watson event (#ibmwow) two months ago in Vegas. Since then, one presentation has been rambling around my soul and my brain. Shannon Vallor, is Chair of the Department of Philosophy at Santa Clara University, and author of Technology and the Virtues. She gave a brilliant talk on AI, Ethics and the Future of Human Flourishing. She pinpointed why AI raises ethical questions we must answer, and conversations we must have.

The report is lengthy but interesting reading. According to Deloitte's Technology, Media and Telecommunications predictions, more than 300m smartphones – or more than one-fifth of phones sold globally in 2017 – will come with machine learning capabilities. But in addition to smartphones, other mobile devices will be able to perform machine learning tasks even without connectivity. Machine learning capabilities are forecast to arrive in tens of millions of drones, tablets, cars, VR or AR devices, medical tools, internet of things (IoT) devices and unforeseen new technologies. As well as the evolution of machine learning, Deloitte predicts that by the end of 2018, spending on IT as a Service (ITaaS) for data centres, software and services will reach in excess of €520bn worldwide, up from €344bn in 2016.

AI developers are making amazing advances. Witness the excitement around AI's progress in search, cancer diagnosis, genomic medicine, autonomous vehicles, Go, smart homes, machine translation, and even lip reading. Progress in such complex problems raises hopes for the development of general-purpose AI that can be deployed in a wide range of intelligent, open-ended interactions with people like computer interface, customer service, planning and advice. It is easy to imagine an enhanced Apple Siri, Amazon Alexa or IBM Watson that engages in conversations with people to answer questions, fulfill commands and even anticipate needs. In fact, unless you watch marketing videos with a very critical eye (like the latest one for Alexa shown below), you might even believe that AI has already reached this point.

How Does Machine Learning Work? One of the hottest trends in the technology sector right now is called machine learning.

Back in October 2015, Google announced that a new system, named RankBrain, will become an integral part of their search engine. The story was first presented in a Bloomberg article. RankBrain is one of the newest additions to Google's algorithm (but not an algorithmic update). It represents a machine learning system that should help Google understand search queries better. So far, Google has provided us with very limited information regarding RankBrain. Due to this fact, we have been left with a lot of room for speculation. Here are some facts and presumptions when it comes to this revolutionary system. This is a machine learning system that should provide users more relevant results to their queries. RankBrain is an automated system that is able to learn by itself without any human interference. Furthermore, a great thing about it is that it can learn from its mistakes and continuously improve its results. Have in mind that this wasn't Google's first project involving machine learning. Google news is based on the same concept.