I joined Spotify in 2008 to focus on machine learning and music recommendations. It's easy to forget, but Spotify's key differentiator back then was the low-latency playback. People would say that it felt like they had the music on their own hard drive. In 2009 after a crazy amount of negotiation the music labels agreed to try it out as an experiment. Music distribution is a trivial problem now.

Artificial intelligence has an unimaginable potential. Within the next couple of years, it will revolutionize every area of our life, including medicine. I am fully convinced that it will redesign healthcare completely – and for the better. Let's take a look at the promising solutions it offers. There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.

Prescriptive analysis is the best way to see how to make a sale or encourage a customer in the future. Recommendation systems are the another name for prescriptive analysis. Customer activity is used to recommend items and improve conversion in the digital store. The history of previous purchases and interests are used to recommend new products. To make the recommendation we employ both descriptive and predictive analysis several times.

What could possibly go wrong? Elon Musk's AI set to try and learn the art of human conversation ...

Discussing the broad impact of AI on the enterprise, Jon points out that we are already seeing AI in action, if we know where to look. "Some say AI has been hidden away for a long time, but actually it hasn't. There are already AI applications in place in our normal daily activity. This year we have seen IBM Watson, Microsoft's Cortana and other personal assistant technology raise their marketing approach, yet Google Search and chatbots have been in use for some time. Part of what The AI Summit demonstrated was the extraordinary level of application and uptake that is already happening".

Apple, Google, Facebook, and Microsoft are worldwide technology powerhouses, but when it comes to the adoption of virtual digital assistants (VDAs) like Siri, Google Assistant, and Cortana, scale only takes you so far. In this particular business, players who successfully cater to the nuances of individual countries will conquer the global VDA market. The same principle will apply to enterprises looking to automate customer interactions like customer service and e-commerce with enterprise VDAs. The challenges facing VDA providers were brought to light recently by the plight of Jibo, the crowdfunded smart home VDA robot that received pre-orders from consumers in 47 countries. On August 9, the company announced that product rollouts would be limited to the United States and Canada only, and that all orders for Jibo outside those markets will be refunded.

But once Feldman was hired to write the personality of a chatbot for Kasisto, a startup that focuses on artificial intelligence software for banks, she became vocal about the importance of taking gender out of the identity equation. Under her watch, myKai, the bot she was hired to craft a personality for, would be neither female nor male. Feldman's boss at Kasisto, Dror Oren, says the work the team has done with the bot made him more outspoken about the need for equality in tech than he'd have imagined going into the project, and he's a self-proclaimed feminist to begin with. Now, he's hyperaware of the differences between the personality of Kai and overly feminine answers inside similar products made by most large tech companies. Kasisto is on to something.

Bell started working at Intel in 1998. She brought her anthropological research and fieldwork techniques to the world of microprocessors, wearables and artificial intelligence. Over the years, her formal role has evolved from director of user experience at Intel's research lab to VP of corporate strategy. But regardless of the titles, her work has remained firmly focused on studying the patterns and complexities of human behavior across cultures. In her self-proclaimed role as a "full-time anthropologist and part-time futurist," she examines the meaning of "intelligence" within the context of machines, while she continues to trace its cultural impact on humans and their relationships. At a time when robotic helpers and virtual assistants are starting to infiltrate our personal lives, the need to assess the implications of this new kind of interaction feels more pertinent than ever. I recently called Bell to talk about the social impact of building relationships with our machines and the ways in which the story of AI is deeply connected to the history of human culture. In what ways does the study of human societies and cultures drive technological innovation? And how does that translate into your work at Intel?

Which is the best iPhone voice control technology? And which is the best Mac voice control? Which mobile platform - or speaker setup - offers the best voice control technology: Siri, Cortana, Google Now or Amazon Echo's Alexa? Siri has made a lot of progress in the last couple of years, developing into an impressive digital assistant that can handle all sorts of tasks on your iOS devices, on the latest Apple TV and - once macOS Sierra launches in the autumn - on Mac as well. This last step is long overdue: Siri's absence on the Mac has been a glaring omission for years, especially as Microsoft has had its own Cortana voice-tech running on Windows PCs since the launch of Windows 10 last year.

From Siri to Alexa, voice interfaces are becoming increasingly common, but for all their recent advances, they often struggle with one of the most basic characteristics of human speech: accents. The problem is so prevalent that computer scientists have identified the existence of a "machine voice," a standardized way of speaking that individuals with accents adopt in the hope of being understood. Researchers even warn about the existence of a "speech divide" that ostracizes individuals whose accents differ from those the machines have been trained on. As is often the case with technology built on large data sets, the problem begins with the input. If you only train your interface using a narrow selection of voices, then it won't know how to respond to accents that fall outside of its frame of reference.