The traditional retail industry is undergoing a significant reinvention and upgrade as more and more brick and mortar stores boost business by adopting e-commerce platforms powered by cutting-edge tech. The recent rapid development and deployment of AI technologies such as machine learning, computer vision and reinforcement learning have enabled new e-commerce products and solutions for various scenarios and strengthened the retail value chain. Alibaba's Taobao and Tmall, Amazon, JD.com); or on a brand's own official web stores (e.g. Thanks to recent advancements in AI and digital technologies, operating costs for e-commerce have been reduced, enabling more retailers to realize e-commerce transformations. The 2018 global retail e-commerce market amounted to US$2.8 trillion and is expected to grow 75 percent to US$4.9 trillion by 2021.

Bill Stasior, the longtime former Apple executive overseeing its Siri digital assistant, has joined Microsoft, continuing a reshuffling of artificial intelligence leadership at big tech companies. Starting this month, Stasior, whose move to Microsoft hasn't been previously reported, will become a corporate vice president of technology, reporting to Chief Technology Officer Kevin Scott. Stasior referred questions about his new job to a Microsoft spokesperson, who confirmed Stasior's hiring, adding that "he will work to help align technology strategies across the company." At Microsoft, Stasior will lead an artificial intelligence group, a person familiar with the matter said. The shift underscores the intense demand for AI leadership inside the largest tech companies, all of which are jockeying for position against each other.

Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems. In this work, they make use of the popular Transformer model to capture sequential signals in user behaviour in online shopping, in order to perform next click prediction. Recommender systems often make use of a 2-stage paradigm of retrieval and ranking, and Alibaba's approach is no different. The retrieval step used at Alibaba consists of selecting, with high recall, a subset of a million relevant candidate items from the entire item set (which is of course much larger than a million possible items), and the ranking step consists of ranking these candidates with high precision.

Nowadays, it hardly seems a day or week goes by without taking the name of a company which joins the rank of organizations using their own virtual assistants. From news agencies to normal stores, each is getting driven by some kind of artificial intelligence. So, it is not surprising to see the baking sector whose traditional call centers are getting replaced by AI assistants. Despite the obviousness of the trend, the introduction of robotics in banking industry will have a great financial effect in other fields of apps. This is what expert analyst of the banking sector feel.

Coaching is an important aspect of our daily lives as it helps in offering guidance and support to individuals. Coaches can engage clients online through video calls in micro-learning sessions and gamification which is effective and offers clients adequate content. Artificial Intelligence coaching works the same way as a traditional coach will have because kit can help with information and guidance that you are seeking and setting clear and convincing targets. It can also help one in the development of action plans and offer support in taking action as well as holding one accountable. AI coaching may appear like a futuristic thing but the reality is it is here as the AI technology continues to mature it seems like everything be possible.

In 2009, Netflix was sued for releasing movie ratings data from half a million subscribers who were identified only by unique ID numbers. The video streaming service divulged this "anonymized" information to the public as part of its Netflix Prize contest, in which participants were asked to use the data to develop a better content recommendation algorithm. But researchers from the University of Texas showed that as few as six movie ratings could be used to identify users. A closet lesbian sued Netflix, saying her anonymity was compromised. The lawsuit was settled in 2010. The Netflix case reveals a problem about which the public is just starting to learn, but that data analysts and computer scientists have known for years. In anonymized datasets where distinguishing characteristics of a person such as name and address have been deleted, even a handful of seemingly innocuous information can lead to identification.

Humans can communicate a range of nonverbal emotions, from terrified shrieks to exasperated groans. Voice inflections and cues can communicate subtle feelings, from ecstasy to agony, arousal and disgust. Even when simply speaking, the human voice is stuffed with meaning, and a lot of potential value if you're a company collecting personal data. Now, researchers at the Imperial College London have used AI to mask the emotional cues in users' voices when they're speaking to internet-connected voice assistants. The idea is to put a "layer" between the user and the cloud their data is uploaded to by automatically converting emotional speech into "normal" speech.

Enthusiasts predicted the plan would relieve the pressure on hard-pressed GPs. Critics saw it as a sign of creeping privatisation and a data-protection disaster in waiting. Reactions to news last month that Amazon's voice-controlled digital assistant Alexa was to begin using NHS website information to answer health queries were many and varied. US-based healthcare tech analysts say the deal is just the latest of a series of recent moves that together reveal an audacious, long-term strategy on the part of Amazon. From its entry into the lucrative prescription drugs market and development of AI tools to analyse patient records, to Alexa apps that manage diabetes and data-driven experiments on how to cut medical bills, the $900bn global giant's determination to make the digital disruption of healthcare a central part of its future business model is becoming increasingly clear.

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so. As a software engineer, I can claim that any piece of software has A.I. due to an algorithm that responds based on pre-defined multi-faceted input or user behavior.