"I'll split 50% royalties on any successful AI generated song that uses my voice. "Feel free to use my voice without penalty. I have no label and no legal bindings." The musician's declaration comes in the wake of streaming platforms removing an AI-generated song using simulated voices of Drake and The Weeknd. Universal Music Group (UMG), which represents both artists, called for the purge after "Heart on My Sleeve" garnered over 15 million listens on TikTok and 600,000 on Spotify.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! A recent survey from McKinsey showed that 56% of respondents reported AI adoption in at least one function, up from 50% the year prior, with the three most common use cases focused on service-operations optimization, AI-based enhancement of products, and contact-center automation. Businesses are committing huge amounts of money to AI initiatives. According to Appen's 2021 State of AI report, AI budgets increased 55% year-over-year, reflecting a shift from an experimental project mindset to an expectation of business benefits and ROI.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. A recent survey from McKinsey showed that 56% of respondents reported AI adoption in at least one function, up from 50% the year prior, with the three most common use cases focused on service-operations optimization, AI-based enhancement of products, and contact-center automation. Businesses are committing huge amounts of money to AI initiatives. According to Appen's 2021 State of AI report, AI budgets increased 55% year-over-year, reflecting a shift from an experimental project mindset to an expectation of business benefits and ROI. One reason this shift is happening now is that many businesses have built expert data science teams and matured their understanding of the discipline.

Even though they have been around for years, the phrase "MLPerf benchmarks" holds little meaning to most people outside of the AI developer community. However, this community-driven benchmark suite, which measures performance of a broad range of machine learning (ML) tasks, is quickly becoming the gold standard for the fair and unbiased assessment of accelerated computing solutions for machine learning training, inference, and high performance computing (HPC). The era of MLPerf is here, and everyone should be paying attention. Organizations across every industry are racing to take advantage of AI and machine learning to improve their businesses. According to Karl Freund, founder and principal analyst at Cambrian AI Research, businesses should expect that customer demand for AI-accelerated outcomes will continue to grow.

Over the past two years, we've seen the release of many books that provide deep insights about the fundamental concepts, technical process, and applications of artificial intelligence. This list highlights books authored by renowned computer scientists and practitioners who are entrenched in the AI industry. No matter you are a researcher, an engineer, or a business professional in the AI/ML domain, your are bound to find a few interesting books to add to your reading list this year! In this book, professors at New York University Gary Marcus and Ernest Davis explain the technological and theoretical gap between creating successful AI which is constrained to a fixed set of rules (or a fixed environment), and creating successful AI which can effectively interact with the complexities and intricacies of an open world. This book is for researchers and entrepreneurs who want to make practical predictions on the immediate future of AI. Gary Marcus is a Professor of Psychology and Neural Science and CEO of Robust.AI, and Ernest Davis is a Professor of Computer Science.

Having been privileged to witness the evolution of the data science and artificial intelligence (AI) scene in the Middle East for the past 10 years and having spoken at one of the first big data events in Dubai back in 2013, it is clear to me that there are considerable opportunities for AI in this vibrant region. Recently, I got the chance to present on the top 10 AI challenges of companies in the Gulf Cooperation Council (GCC) region, at Virtual Executive Boardroom: Key Insights on Becoming a Data-Driven Enterprise, which took place at DigiConnect (UAE) and was delivered to top C-level executives and senior data managers from the most relevant companies in the GCC region. In this post, I will not get into each of these ten challenges. However, I will focus on a common issue that came up as a top priority for them in a quick live poll during the session: The issue of trusting decisions made by AI. Interpreting deep learning networks takes place in a tough playground, so making AI interpretable serves one specific goal, and that is to trust the decisions made by AI models.

Nearly half of CIOs say they now employ artificial intelligence (AI) or intend to within the next 12 months. But how to make AI a core IT competency still eludes most organizations. Boards of directors, CEOs and customers want to use AI to power real improvements in customer and employee experience. Promoting positive habits to get the best value from the technology is an important way to make it more commonplace. "The idea that your habits are intentional is key to understanding how to address AI in this modern capacity," said Whit Andrews, Distinguished VP Analyst, during his session at virtual Gartner IT Symposium/Xpo 2020 . "Think about these habits in a way that is strategic; it'll make a huge difference."

Nearly half of CIOs say they now employ AI or intend to within the next 12 months. But how to make AI a core IT competency still eludes most organizations. Boards of directors, CEOs and customers want to use AI to power real improvements in customer and employee experience. Promoting positive habits to get the best value from the technology is an important way to make it more commonplace. "The idea that your habits are intentional is key to understanding how to address AI in this modern capacity," said Whit Andrews, Distinguished VP Analyst, during his session at virtual Gartner IT Symposium/Xpo 2020 . "Think about these habits in a way that is strategic; it'll make a huge difference."

Sponsored Spending on artificial intelligence systems will grow from $37.5bn worldwide in 2019 to $97.9bn in 2023, according to IDC. And use cases cover everything from ERP, manufacturing software and content management to automated customer service agents, threat intelligence and fraud investigation. The number of smaller data centres in operation is also likely to increase, IDC analysts say. This will enable enterprises to navigate local data protection regulation and host AI workloads in edge computing environments to optimise application performance. However, respondents to Gartner's 2019 CIO Agenda survey indicate that data scope and quality continue to be major challenges to AI adoption.

Ironically, people are more likely to trust AI when they don't know it's being used. AI is proving itself superior to human intelligence in an expanding number of fields. That is, except when people know AI is being used. Yes, because in certain human-centric sectors, the performance of artificial intelligence starts to drop off if people are apprised of the involvement of an intelligent machine. In fact, human resistance would seem to be the achilles heel of artificial intelligence, since for all the recent advances of AI technology this resistance is preventing AI from doing its job in areas where human contact and interaction would normally play a central role. This message was brought home most recently by a study published in Marketing Science on September 20, titled "The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases."