Everyone knows the frustration of talking to banking customer service on the phone. You call in with a straightforward question and get the dreaded interactive voice response (IVR) recording. Before you know it, you're listening to a robotic voice ask how it can direct your call and having to give short, over-enunciated responses trying to get the system to understand what you want. The fact is, IVR isn't great for anyone. It's not great for customers because it's confusing and frustrating to use.

Marie Kratz, Professor at ESSEC Business School and Director of ESSEC CREAR (Center of Research in Econo-finance and Actuarial sciences on Risk), shares her thoughts about how artificial intelligence will re-shape the actuarial profession. We've all experienced those minor, sometimes major, mishaps in life – your bike gets stolen, you have a car accident, you drop your computer or, worse still, you wake up one morning to find the living room swamped in 3 feet of muddy floodwater. And we have all witnessed the drama of natural catastrophe on the news. The camera covers the victims, some considering themselves lucky, others overcome by the emotion of losing their hard-earned belongings or loved ones – all of them in wait for the complications that will come, not least the financial ones. This is where the insurance company comes in.

"As more and more everyday communications take place over email, lots of people have complained about how hard it is to read and respond to every message. This is because they actually read and respond to all their messages." Autopilot replied to email as if it were actually you. It included an authenticity control panel to adjust for "tone, typo propensity, and preferred punctuation." Autopilot was launched as an April Fools' prank.

This is because deep-learning neural networks are a "black box": they consist of millions of connections between nodes that are fine-tuned in opaque, subtle ways as data is fed in. When a deep-learning network produces an output (e.g., the decision to stop or not to stop at a yellow light), that output cannot be traced to a particular sequence in the AV's software; rather, it is an emergent outcome of the overall system. Experts call this problem "lack of interpretability." As well as deep learning networks may perform at driving 99.9% of the time, this lack of interpretability becomes a real concern on those rare occasions when an AV makes the wrong decision and causes an accident. In those situations, humans have no way to explain what went wrong and no way to troubleshoot the error.

Here are four case studies on how machines can perform tasks that previously required human intelligence across various industries. The rise of artificial intelligence is the great story of our time. Leaving the laboratory after decades in the making, artificial intelligence, or AI, is infusing itself into many aspects our daily lives – from homes and phones to cars and offices. Machines are now able to perform tasks that previously required human intelligence across various industries. Insurance, once perceived as highly resistant to change, has now accelerated the race for innovation.

Google is betting big on its dominance in machine learning and artificial intelligence to break into the cloud market, a message that was the underlying theme on the first day of the technology giant's cloud conference that began here on Wednesday. The company also made a slew of announcements strengthening its place as the leader in machine learning and artificial intelligence platforms. "We put $30 billion in the Google Cloud Platform," said Eric Schmidt, chairman of Google's parent company Alphabet. He added that big data, or large volumes of information that are analysed to reveal patterns through machine learning and artificial intelligence, "is so powerful that nation states will fight over it." Google announced customers such as HSBC, Colgate-Palmolive, The Home Depot, SAP, Disney, Verizon and eBay, most of which have large data sets to the tune of billions of records.

Some artificial intelligence products have become so advanced in online discussion moderation that they will no longer be confused by colloquial language, neologisms or spelling mistakes. AI is able to take on routine human tasks, but cannot fully replace human intelligence. Online discussions are abound with hate speech and off-topic comments, causing massive headaches for media companies. Legislation requires that illegal messages are removed, and users are more content if they can avoid becoming the target of inappropriate insults. The volumes of comments posted on discussion forums and below news articles can be staggering, and their proper moderation may sometimes require infeasible amounts of manpower.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software--the pattern, colour and texture-- without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in ecommerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

For songwriters, the subject of artificial intelligence is an especially fraught one: Will technology that learns by watching and listening to us enhance human creativity or replace it? These initiatives, emphasizing the collaborative possibilities inherent in AI, insist that the former is possible -- from using Watson technology to inspire composers to uber-personalizing already existing tunes. THINK OF IT AS... IBM engineer Janani Mukundan calls Watson Beat a "creative assistant": It listens to existing music and scans social media chatter to find trends that could inform new work. RECENT WIN: Alex Da Kid used Watson Beat to write "Not Easy," which peaked at No. 48 on Billboard's Rock Airplay chart in 2016. A tool that can tweak an existing style, then adapt it to a new composition, drawing on the vast melody and harmony database of the Sony laboratory.

Dr. Ayanna Howard (1972 –) has some impressive credentials. She is a noted expert in the area of Artificial Intelligence. She is often referred to as an "old school Blerd" (Black Nerd). Her motivation to pursue a career in the sciences was fueled by watching TV shows such as, The Bionic Woman, Star Trek, and Wonder Woman" as a child. Howard has worked as a roboticist and Motorola Foundation Professor at Georgia Tech's Institute for Robotics and Intelligent Machines.