In recent years, the use of Artificial Intelligence (AI) has been widely changed many aspects of our lives. For example, retailers understand consumer behaviour by analysing customer data through AI, and video game companies create immersive games with AI to enhance gaming experience. Education too has great potential to utilise AI for enhancing the quality of education by streamlining learning and teaching procedures. What Is Artificial Intelligence (AI)? Artificial Intelligence is the intelligence demonstrated by machines, in contrast to the human intelligence.

The phrase "opinion leader" became popular after Paul Lazarsfeld and Elihu Katz wrote about it in their 1955 book Personal Influence: The Part Played by People in the Flow of Mass Communications. In their book, the authors describe opinion leaders as influential people who further circulate media messages through face-to-face communication within their own personal networks. In today's pharmaceutical landscape, KOLs (Key Opinion Leaders) are primarily valued for their level of influence. Pharma companies leverage the influence of expert physicians and researchers in order to recruit patients for clinical trials and influence the adoption of drugs at regional, national, and global scales. KOLs are so important that they cost pharma companies just under one-third of their total marketing expenditures.

Microsoft might just be the hipsters of the AI chatbot world – they were dabbling in call-making chatbots long before Google made them mainstream cool. Earlier this month, Google made waves when CEO Sundar Pichai demoed Duplex, a new Google Assistant feature that can make routine voice calls on a user's behalf. Yesterday, Microsoft CEO Satya Nadella demoed a social chatbot called Xiaoice (pronounced "Shao-ice") at an AI event in London. Turns out, Xiaoice has been having two-way verbal conversations with users in China since August. In fact, the bot has spoken to 600,000 people already, according to a blog post by Harry Shum, Google's Executive Vice President of Artificial Intelligence and Research.

Keeko is just 45 centimeters tall, or one-foot seven inches, and weighs only 45 kilograms, roughly 99 pounds. Gliding across the room to the amazement of starry-eyed five-year-olds, it rolls its head and tells the transfixed children "remember to wash your hands before you eat." They all giggle and rush to cuddle the diminutive AI robot with the cutesy, cartoon character voice, and stare with utter bewilderment. In nursery schools across China, Keeko models are being brought in as teaching aids to engage and stimulate young and impressionable minds. With the assistance of her "little helper," Yang Huizhen showed her class the importance of recycling.

In the 1970 science fiction film "Colossus: The Forbin Project," the United States decides to turn over control of its strategic arsenal to Colossus, a massive supercomputer. Almost immediately it becomes clear that, as its creator Dr. Charles Forbin says, "Colossus is built even better than we thought." In fact, it's a self-aware artificial intelligence -- quickly discovering that the Soviets have also activated an almost identical system and joining up with it to take over the planet. Along the way, Colossus nukes a Russian oil complex and a U.S missile base to enforce its control. Now, instead of two human superpowers threatening nuclear Armageddon, humanity's continued survival is at the mercy (or mercy's AI equivalent) of a supercomputer.

Even though one of the main goals of digital marketing is to serve customers the right message at the right time, we all know what it's like to be chased around the internet by an ad that's completely irrelevant or just plain annoying. And that's because deciding when and where to deliver that message, not to mention the labor that goes into creating the message in the first place, has long involved human guesswork. Granted, those guesses often come after rigorous testing, but those best tests are limited to the often slow-moving process human analysis. Artificial intelligence has already transformed everything from the IT department to the customer service experience, and now, machine learning is on track to completely change the ways we think about ad creative. A recent study by Adlucent found that 46% of consumers say that their ideal online experience would involve free access to websites that served only relevant ads, with 58% reporting that personalized content improves their perception of a brand.

LONDON – Tokyo has topped a list of the world's most innovative cities, leapfrogging London and New York after embracing the "globe-shaking trends of robotics and 3-D manufacturing." The Japanese capital has risen rapidly since entering the top 10 of the Innovation Cities Index three years ago behind Paris, and was one of three Asian cities to feature this year. "What really surprised us this year was the resurgence of Tokyo, moving up to eclipse rival cities like Boston," said Christopher Hire, director of commercial data provider 2thinknow, which published the annual ranking on Friday. "They showed clear direction by embracing smart technology change to lead innovation and leadership in what we have identified as the twin long-term globe-shaking trends of robotics and 3-D manufacturing." Singapore, Sydney and Seoul also ranked highly in the index, which judges cities on 162 indicators including web censorship, wealth distribution and the potential for green businesses.

Here's how it's related to artificial intelligence, how it works and why it matters. Deep learning is a subset of machine learning, which itself falls within the field of artificial intelligence. Artificial intelligence is the study of how to build machines capable of carrying out tasks that would typically require human intelligence. That rather loose definition means that AI encompasses many fields of research, from genetic algorithms to expert systems, and provides scope for arguments over what constitutes AI. Within the field of AI research, machine learning has enjoyed remarkable success in recent years -- allowing computers to surpass or come close to matching human performance in areas ranging from facial recognition to speech and language recognition. Machine learning is the process of teaching a computer to carry out a task, rather than programming it how to carry that task out step by step. At the end of training, a machine-learning system will be able to make accurate predictions when given data.

Increasingly, it's being applied to conservation; a coalition of researchers earlier this year developed a machine learning algorithm that can identify and describe wildlife. And in a blog post this week, Microsoft highlighted a Santa Cruz-based startup -- Conservation Metrics -- that's leveraging AI to keep tabs on African savanna elephants. Conservation Metrics, a recipient of Microsoft's AI for Earth grant program, is using algorithms to analyze a corpus from Cornell University Lab of Ornithology's Elephant Listening Project, which collects data from acoustic sensors embedded throughout Nouabalé-Ndoki National Park and adjacent logging areas in the Republic of Congo. Its system isolates elephant calls -- the low-frequency rumbling sounds they use to communicate with one another -- from the recordings and derives insights, like population size and herd movement. It's precise enough to identify individual animals that can't be seen from the air, according to Conservation Metrics CEO Matthew McKown.

Bringing IoT to a factory floor is as much about robots as it is about any other class of device. Meanwhile, robots are more attractive in their own right because labor costs are rising globally. Although robotics for automation generally focuses on movement and manipulations, IoT and robotics involves a world of devices in the field -- devices that depend on electronic sensors and software. "A lot of companies have outsourced for low-cost labor, but labor in China is going up in cost by 15% a year," said Jim Lawton, COO at Rethink Robotics Inc., a company that focuses on designing robots that work well with humans. In fact, he noted, almost every manufacturing company he has spoken with in recent years complains about the lack of labor; temporary help firms are hired to bring in workers and by lunch time, half of them are gone, he said.