Earlier this week, Paul McCartney sent the music-nerd internet ablaze with some news: Artificial intelligence had helped resurrect a bit of John Lennon's voice for a new Beatles song, more than four decades after his death. The song is set for release later this year and comes from vocals Lennon recorded on an old demo. "We were able to take John's voice and get it pure through this AI," McCartney told BBC Radio 4, "so then we could mix the record, as you would normally do." The reaction this elicited on WIRED Slack channels was somewhere between "cool" and "gross." Using AI to resurrect Lennon for a new song has its appeal, but given the recent ethical questions around using the technology to make fake songs from artists like Drake and The Weeknd, it also feels icky.

Technology can be a topic difficult to understand and make predictions on, even for those with a strong technical background and perceived expertise. From Ethernet's creator Robert Metcalfe's 1995 prediction that the internet would "catastrophically collapse" by the next year to Intel's prediction that 3D TV was the future, it is clear that predicting tech trends is a difficult endeavor. No matter how hard predicting the future of technology is, every new technology that creates disruption will go through this cycle. Most recently, we have gone through multiple hype cycles with innovations like blockchain, cryptocurrency, the metaverse, VR, and now, AI. Every single of these technologies has captivated not only the public but also developers and investors, blurring the line between facts and fiction.

According to the Gartner hype cycle, the least amount of time a product takes to hit the'plateau' of expectations is two years. The hot chatbot has shattered all records of a product lifecycle, going through all stages of the cycle within 3 months. Launched in November-end last year, ChatGPT has already been through the innovation trigger, inflated expectations, disillusionment, enlightenment, and is now reaching a mature period of measured expectations, leading to industry adoption. A contributing factor to this might be the bot's meteoric growth, which scaled to 10 million users within 40 days. For contrast, Instagram took almost a year to reach the same milestone.

I hope that you enjoy the latest AI news, insights, and the Web3 section at the end! See the outlook and impact. It isn't ready to assume human qualities that emphasize empathy, ethics, and morality. Even experts don't know what it will mean: Behind the headlines and memes a fundamental revolution is underway – with profound social, artistic, economic, and technological implications. Engage directly with top 20 thought leaders in AI/ML from Google, IBM, AWS, Samsung, Oxford Brookes University, Volkswagen, SAP, Mercedes Benz, and more!

When analysts evaluate the maturity of AI, the first step is to parse out the many technologies that fall under the AI umbrella. Natural language processing, RPA, machine learning and deep learning have all found individual use cases across industries within the past few years. "2020 is the year that AI is going to enter the mainstream of enterprise adoption," said Jack Fritz, a principal in Deloitte Consulting LLP's Technology, Media, and Telecommunications practice. "It's already integrated into a lot of enterprise applications like ERP, CRM." In a survey of 1,100 AI adopters, Deloitte found that about 70% are using machine learning and around half of them were deploying deep learning.

There's no shortage of promises when it comes to AI. Some say it will solve all issues while others warn it will result in the end of the world once we know it. Both positions regularly play out in Hollywood plotlines like Westworld, Carbon Black, Minority Report, Her, and Ex Machina. Those stories are compelling because they require us as creators and consumers of AI technology to decide whether we trust an AI system or, more correctly, trust what the system is doing with the information it has been given. This content was produced by Nuance.

There's no shortage of promises when it comes to AI. Some say it will solve all problems while others warn it will bring about the end of the world as we know it. Both positions regularly play out in Hollywood plotlines like Westworld, Carbon Black, Minority Report, Her, and Ex Machina. Those stories are compelling because they require us as creators and consumers of AI technology to decide whether we trust an AI system or, more precisely, trust what the system is doing with the information it has been given. This content was produced by Nuance.

Retailers are expected to spend 7.3 billion dollars on AI annually by 2022, according to a CapGemini Research Institute report. This investment is largely motivated by companies' interest in improving customer experience across all engagement points, including marketing, buying, and after-sales service. Eugenio Cassiano is the chief innovation officer for the SAP Customer Experience organization. He talked about three ways AI can deliver great customer experiences for retailers and other types of organizations. According to Cassiano, conversational AI is moving into the mainstream.

There has recently been an explosion of cynicism in the Twitterverse (and generally in the tech community) about machine learning and AI. This was taken up a notch after Facebook's F8 conference earlier this week and the splashy launch of their bot platform on Messenger. When you dig in, many of the companies claiming to be leveraging machine learning are doing nothing of the sort. It's easy to find companies that are building weak ML-driven user experiences, turning off early adopters. Others are building products centered around gimmicky ML features that won't create any sort of long term barrier to competitors, frustrating would-be investors.

There has recently been an explosion of cynicism in the Twitterverse (and generally in the tech community) about machine learning and AI. This was taken up a notch after Facebook's F8 conference earlier this week and the splashy launch of their bot platform on Messenger. When you dig in, many of the companies claiming to be leveraging machine learning are doing nothing of the sort. It's easy to find companies that are building weak ML-driven user experiences, turning off early adopters. Others are building products centered around gimmicky ML features that won't create any sort of long term barrier to competitors, frustrating would-be investors.