FAIR GPT is a first virtual consultant in ChatGPT designed to help researchers and organizations make their data and metadata compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. It provides guidance on metadata improvement, dataset organization, and repository selection. To ensure accuracy, FAIR GPT uses external APIs to assess dataset FAIRness, retrieve controlled vocabularies, and recommend repositories, minimizing hallucination and improving precision. It also assists in creating documentation (data and software management plans, README files, and codebooks), and selecting proper licenses. This paper describes its features, applications, and limitations.

Blind people are often called to contribute image data to datasets for AI innovation with the hope for future accessibility and inclusion. Yet, the visual inspection of the contributed images is inaccessible. To this day, we lack mechanisms for data inspection and control that are accessible to the blind community. To address this gap, we engage 10 blind participants in a scenario where they wear smartglasses and collect image data using an AI-infused application in their homes. We also engineer a design probe, a novel data access interface called AccessShare, and conduct a co-design study to discuss participants' needs, preferences, and ideas on consent, data inspection, and control. Our findings reveal the impact of interactive informed consent and the complementary role of data inspection systems such as AccessShare in facilitating communication between data stewards and blind data contributors. We discuss how key insights can guide future informed consent and data control to promote inclusive and responsible data practices in AI.

In his seminal 2017 blog post, The Downfall of the Data Engineer, Maxime Beauchemin wrote that the data engineer had the worst seat at the table. Data technology and teams have changed tremendously since that time, and now the Preset CEO and creator of Apache Airflow and Apache Superset has a brighter outlook on the future of the profession. I have also seen what was once a thankless position turn into a strategic driver of company value as data expanded beyond dashboards to machine learning models, customer-facing applications, and systems of record. So, if the data engineer no longer has the worst seat at the table, who then on the data team has inherited this unfortunate title? When you infer some of Maxime's original criteria–tedious tasks, low recognition, a lack of authority, and victim of operational creep–the data steward becomes the obvious choice.

The Pentagon is creating a new position -- the chief digital and artificial intelligence officer. Intended to be the successor to the Defense Department's Joint Artificial Intelligence Center, the new office will better align data, analytics, digital solutions and AI efforts across the department, reflecting a "shift in organizational concept," according to Deputy Secretary of Defense Kathleen Hicks. The department's Chief Information Officer John Sherman has been named as the acting chief digital and artificial intelligence officer, while the search for permanent leader continues -- targeted for June 1 hire or earlier. Whomever takes on the permanent role will face an enormous task: establishing foundational analytics goals for the Defense Department, helping it better define AI for its enterprise operations, and providing best practices for building out tools, processes and reporting to drive toward those goals. Some distinct skills and strategic actions will be needed not only for the new director but for the estimated 200 to 300 individuals who will work under this office.

From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.

Fortanix Inc., a data-first multicloud security company, today introduced Confidential AI, a new software and infrastructure subscription service promising users the secure use of private data without compromising privacy and compliance. AI modeling relies on accurate complete data sets. Because of privacy laws, data teams instead often use educated assumptions to make AI models as strong as possible. The development of AI applications can be hindered by the inability to use highly sensitive, private data for AI modeling. Fortanix utilizes Intel SGX secure enclaves on Microsoft Azure confidential computing infrastructure to provide trusted execution environments, making AI models more accurate.

From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.

Organizations are becoming more analytically inclined, automation is rampant, and business users are empowered to accomplish more at a greater scale than they previously could. Nonetheless, there's another side to the pervasive deployment of cognitive computing technologies throughout the data ecosystem, particularly in terms of the mounting ease, accessibility, and utility of advanced analytics. The increasing demand for predictive insight--and the data required to facilitate it--has very real repercussions in terms of data privacy and regulatory compliance which, if not properly addressed, can restrict AI's use for organizations. Many firms are attempting to balance the data demands for AI with what Privacera SVP of Marketing Piet Loubser termed the "let's stay out of trouble side of things. As much as we think externally of regulations from on top, the majority of organizations have much more stringent things going on inside their four walls."

Data is the new strategic asset, the biggest business asset today. Data is to today's digital economy what electricity was to the industrial economy. Organizations that understand the value of their data have been excited about the prospects of leveraging artificial intelligence (AI) and machine learning (ML) for smarter insights. They have invested in AI and ML tools and technologies, but have yet to see quantifiable benefits from their investments. Others are reluctant to even start, with a combination of skepticism, lack of expertise, and lack of confidence in the reliability of their datasets holding them back.

In his keynote at the recent AWS re:Invent conference, Amazon vice president and chief technology officer Werner Vogels said that the cloud had created a "egalitarian" computing environment where everyone has access to the same compute, storage, and analytics, and that the real differentiator for enterprises will be the data they generate, and more importantly, the value the enterprises derive from that data. For Rob Thomas, general manager of IBM Analytics, data is the focus. The company is putting considerable muscle behind data analytics, machine learning, and what it calls more generally cognitive computing, much of it based on its Watson technology. That includes the Watson Data Platform and its Data Catalog, Data Refinery and Analytics Engine. But when it comes to data analytics, Thomas takes what's been called an "attitude before aptitude" approach, with the idea being that enterprises need to create a "culture of data" before they can take full advantage of analytics. They need to have in place a belief that data and facts are what's important when making business decisions rather than instinct, beliefs and what's been done in the past. And it's an approach that's got to come from the top and become part of how the business operates.