The sociology professor is suitably comfortable with AI helpers that he creates his own - it's their inventors' motives and unregulated environment he argues we should be concerned about I f much of the discussion of AI risk conjures doomsday scenarios of hyper-intelligent bots brandishing nuclear codes, perhaps we should be thinking closer to home. In his urgent, humane book, sociologist James Muldoon urges us to pay more attention to our deepening emotional entanglements with AI, and how profit-hungry tech companies might exploit them. A research associate at the Oxford Internet Institute who has previously written about the exploited workers whose labour makes AI possible, Muldoon now takes us into the uncanny terrain of human-AI relationships, meeting the people for whom chatbots aren't merely assistants, but friends, romantic partners, therapists, even avatars of the dead. To some, the idea of falling in love with an AI chatbot, or confiding your deepest secrets to one, might seem mystifying and more than a little creepy. But Muldoon refuses to belittle those seeking intimacy in "synthetic personas".

AI companions like Replika are designed to engage in intimate exchanges, but people use general-purpose chatbots for sex talk too, despite their stricter content moderation policies. Now new research shows that not all chatbots are equally willing to talk dirty. DeepSeek is the easiest to convince. But other AI chatbots can be enticed too. Huiqian Lai, a PhD student at Syracuse University, found vast differences in how mainstream models process sexual queries, from steadfast rejection to performative refusal followed by the requested sexually explicit content.

Unless you've been on a retreat in some far-flung location with no internet access for the past few months, chances are you're well aware of how much hype and fear there's been around ChatGPT, the artificial intelligence (AI) chatbot developed by OpenAI. Maybe you've seen articles about academics and teachers worrying that it'll make cheating easier than ever. On the other side of the coin, you might have seen the articles evangelising all of ChatGPT's potential applications. Alternatively, you may have been tickled by some of the more esoteric examples of people using the tool. One user, for example, got it to write an instruction guide for removing peanut butter sandwiches from a VCR in the style of the King James Bible.

We've joined the AI-hype bandwagon here at the Seattle Search Network. We live and breathe search engine marketing and jump at every Google hiccup, even if we've been okay ignoring Bing all these years. Maybe that's about to change. From random thoughts to early experiments with the tools, there's no shortage of differences of opinion, but that's what keeps us on our toes as digital marketers. We'd love to hear what you think.

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. Over the past two weeks, emotions have run high around the evolution and use of emotion artificial intelligence (AI), which includes technologies such as voice-based emotion analysis and computer vision-based facial expression detection. Video conferencing platform Zoom came under fire after saying it might soon include emotion AI features in its sales-targeted products. A nonprofit advocacy group, Fight for the Future, published an open letter to the company: It said Zoom's possible offering would be a "major breach of user trust," is "inherently biased," and "a marketing gimmick." Meanwhile, Intel and Classroom Technologies are working on tools that use AI to detect the mood of children in virtual classrooms.

Unlike many valuable resources, real-time data is both abundant and growing rapidly. But it also needs to be handled with great care. That was one of the key takeaways from an online workshop produced by Science Business' Data Rules group, which explored what the rapid growth in real-time data means for artificial intelligence (AI). Real-time data is increasingly feeding machine learning systems that then adjust the algorithms they use to make decisions, such as which news item to display on your screen or which product to recommend. "With AI, especially, you want to make sure that the data that you have is consistent, replicable and also valid," noted Chris Atherton, senior research engagement officer at GÉANT, who described how his organisation transmits data captured by the European Space Agency's satellites to researchers across the world.

For over 20 years, Deloitte Consulting Chief Cloud Strategy Officer and Gigaom Research Analyst David Linthicum has witnessed and documented the evolution of cloud computing. While the first generation of cloud computing provided capacity on demand for computing, storage, and networking, the second generation that exists today includes technologies that do not typically exist on-premise, such as machine learning-based systems, serverless computing, and AI. "Probably five years ago, the cloud kind of crossed the chasm where they have better technology than we do on premise – whether it's security, governance, management, or monitoring," Linthicum explains. Despite this, some organizations still choose to invest largely in legacy, on-premise systems, while balancing this with the cloud, creating a hybrid cloud setup. "We're getting to security, we're getting to governance, we're getting management monitoring, which has this duality of roles where they're able to monitor systems that we own, monitor systems and manage service providers and CoLos as well as in the public cloud. Linthicum adds that being overly concerned about the cloud's control aspect is another reason to set aside the idea of moving to the cloud. For over 20 years, Deloitte Consulting Chief Cloud Strategy Officer and Gigaom Research Analyst David Linthicum has witnessed and documented the evolution of cloud computing. While the first generation of cloud computing provided capacity on demand for computing, storage, and networking, the second generation that exists today includes technologies that do not typically exist on-premise, such as machine learning-based systems, serverless computing, and AI. "Probably five years ago, the cloud kind of crossed the chasm where they have better technology than we do on premise – whether it's security, governance, management, or monitoring," Linthicum explains. Despite this, some organizations still choose to invest largely in legacy, on-premise systems, while balancing this with the cloud, creating a hybrid cloud setup. "We're getting to security, we're getting to governance, we're getting management monitoring, which has this duality of roles where they're able to monitor systems that we own, monitor systems and manage service providers and CoLos as well as in the public cloud.

This will likely lead to major advances in life sciences and medicine, accelerating efforts to understand the building blocks of life and enabling quicker and more sophisticated drug discovery. Most of the planet now knows to their cost how the unique shape of the spike proteins in the SARS-CoV-2 virus are key to its ability to invade our cells, and also to the vaccines developed to combat its deadly progress.

In The Last of Us Part II, no one's hands are clean. In The Last of Us Part II, no one's hands are clean. For years now, some of the best, wildest, most moving or revealing stories we've been telling ourselves have come not from books, movies or TV, but from video games. So we're running an occasional series, Reading The Game, in which we take a look at some of these games from a literary perspective. Warning: If you haven't played The Last of Us Part II yet, there are some spoilers ahead.

BB: While there are caveats, the rewards significantly outweigh the risks. There are many underserved individuals, businesses, and micro-borrowers with little or no credit that could benefit from alternative data. If an applicant doesn't have good credit, or any credit at all, lenders can use MLAI techniques to paint a richer portrait about the borrower's reliability using nontraditional factors like e-commerce histories, phone bills, and purchasing records. MLAI can open up the credit market, measure patterns, and fill in the data gaps, giving lenders a more holistic view of an applicant. Alternative data doesn't have to be "creepy" data and doesn't have to be social media data.