Imagine losing your ability to speak or move, yet still having so much to say. For Brad G. Smith, this became his reality after being diagnosed with ALS, a rare and progressive disease that attacks the nerves controlling voluntary muscle movement. But thanks to a groundbreaking Neuralink brain implant, Smith is now able to communicate with the world using only his thoughts. Join The FREE CyberGuy Report: Get my expert tech tips, critical security alerts and exclusive deals -- plus instant access to my free Ultimate Scam Survival Guide when you sign up! Before receiving the Neuralink implant, Smith relied on eye-tracking technology to communicate.

A majority of small businesses are using artificial intelligence and finding out it can save time and money. So, you want to start making money using AI but you're not trying to build Skynet or learn 15 coding languages first? Good, because neither am I. You don't need to become the next Sam Altman or have a Ph.D. in machine learning to turn artificial intelligence into real income. What you do need is curiosity, a dash of creativity, and the right prompts.

At the Red Hat Summit and Ansible Fest in Boston this month, much of the hype and overpromising about generative AI took a back seat to conversations about how organizations can actually build and deploy AI for their own business using their own data. Of course, this is a Red Hat Summit, and there was plenty of focus on core topics like open source, with the release of Red Hat Enterprise Linux 10, and automation and management with Ansible. But like everything nowadays, AI took up a lot of the attention at the conference, but at least much of it was refreshingly and critically practical. Also: 96% of IT pros say AI agents are a security risk, but they're deploying them anyway Rather than the more hyped AI-areas such as AI assistants, which a recent Aberdeen/ZDNet poll found to be of limited interest to a majority of users, most of the sessions and even major announcements were focused on technologies and strategies that business can use today to help them get the most out of AI while leveraging their own data in a secure and efficient manner. For example, there was a great deal of focus on inferencing, the process of running an AI model with new data to make predictions or decisions. Announcements on technologies such as vLLM and llm-d provide improved scaling and deployment options that simplify the complexities of inferencing while spreading compute loads.

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs.

There is little debate that AI will revolutionize working practices, but there is less agreement about the best way to exploit this transformation. While 90% of CIOs are piloting AI or investing in small or large-scale developments, over two-thirds (67%) haven't seen measurable ROI, according to the recently released Nash Squared/Harvey Nash Digital Leadership Report. "Leaders know the technology, but they're struggling with its application in the business to create value," Nash Squared CIO Ankur Anand told ZDNET during a conversation about the key points emerging from the leadership survey. So, how can business leaders overcome this struggle? Four business leaders provide their best-practice tips for using AI to solve big business problems.

Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare. The accuracy, robustness, and fairness of diagnostic algorithms depend on the data (and its quality) used to train and evaluate the models. MI datasets used to be proprietary, but have become increasingly available to the public, including on community-contributed platforms (CCPs) like Kaggle or HuggingFace. While open data is important to enhance the redistribution of data's public value, we find that the current CCP governance model fails to uphold the quality needed and recommended practices for sharing, documenting, and evaluating datasets. In this paper, we conduct an analysis of publicly available machine learning datasets on CCPs, discussing datasets' context, and identifying limitations and gaps in the current CCP landscape. We highlight differences between MI and computer vision datasets, particularly in the potentially harmful downstream effects from poor adoption of recommended dataset management practices. We compare the analyzed datasets across several dimensions, including data sharing, data documentation, and maintenance. We find vague licenses, lack of persistent identifiers and storage, duplicates, and missing metadata, with differences between the platforms. Our research contributes to efforts in responsible data curation and AI algorithms for healthcare.

Melania Trump announced on Friday that she is releasing an AI audiobook version of her memoir, Melania. In an X post, the first lady welcomed followers into "a new era in publishing" and announced that an audiobook featuring an AI-generated version of her voice will be released in the ElevenReader app. "I am honored to bring you Melania -- The AI Audiobook -- narrated entirely using artificial intelligence in my own voice. Let the future of publishing begin." The First Lady's book, Melania, was published in October 2024, and it's part memoir, part coffee table book.

"We're excited to become a clinical-stage biotech company; it's exciting from an AI drug discovery standpoint," says Absci founder and CEO Sean McClain. Artificial intelligence has been working its way into the drug development process for years now, but with little to show so far in revamping the notoriously burdensome process. While drugs are being developed using AI in a variety of ways, no drugs developed completely by AI, from start to finish, have so far made it over the finish line of regulatory approval. For that reason, every attempt by an AI drug to get approval is a landmark of sorts. Tuesday, drug development startup Absci, based in Vancouver, Washington, announced such a landmark, the beginning of a Phase I clinical trial for a therapy it built from scratch using generative AI to treat irritable bowel disease.

AI agent deployments will grow 327% during the next two years. Chief human resources officers (CHROs) plan to expand their digital labor in the next two years, investing in AI agents to increase productivity, according to the latest Salesforce global research. By 2030, 80% of CHROs believe most companies will have humans and AI agents working together. Almost nine of every 10 CHROs will focus on integrating AI agents into the workforce. By 2027, CHROs anticipate 327% growth in agent AI adoption, from 15% in 2025 to 64% in 2027.

And, as AI gets more advanced, so do the resurrections. Most recently, Stacey Wales used AI to generate a video of her late brother, Christopher Pelkey, to address the courtroom at the sentencing hearing for the man who killed him in a road rage incident in Chandler, Arizona. According to NPR, its the first time AI has ever been used in this way. "He doesn't get a say. He doesn't get a chance to speak," Wales told NPR, referring to her brother.