How CIOs are working on the Data Act As required by current regulations for private healthcare, elderly healthcare management company Karol Strutture Sanitarie collects patient data in their medical records, allowing them to use it even after hospitalization. The data is, in fact, recorded by medical devices, remains in the logs, and is shared with the suppliers or manufacturers of these devices. “The Data Act impacts data sharing,” says Massimo Anselmo, its director of information systems. “An important aspect is, for example, our ability to use patient data for research purposes after anonymizing them, in line with GDPR. The Data Act helps us because it defines more clearly how to use this data, and we’re currently trying to understand if, compared to the past, there’s more data we can make available to patients. So not only the results of a diagnostic test, but specifications of the machine used. Most of the medical machines are owned by us, but with the Data Act, we’ll always have a relationship with the manufacturer to analyze the logs and verify their correct functioning or schedule maintenance. I also foresee an intervention on contracts with suppliers, together with the legal office, and on rental machines to control which data are shared and for how long.” The impact on Karol’s data governance won’t be a major upheaval either, adds Anselmo. “I’ll have to work, above all, on monitoring data traffic and protecting communications, while isolating some data and regulation of access,” he says. source

By Theresa Schliep ( December 5, 2024, 7:44 PM EST) — The U.S. Department of Justice is examining the role foreign countries might be playing in funding patent litigation in the U.S., the Government Accountability Office said in a report released Thursday exploring the benefits and pitfalls of the proliferation of third-party intellectual property litigation financing…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Cost remains a primary concern of enterprise AI usage and it’s a challenge that AWS is tackling head-on. At the AWS:reinvent 2024 conference today, the cloud giant announced HyperPod Task Governance, a sophisticated solution targeting one of the most expensive inefficiencies in enterprise AI operations: underutilized GPU resources. According to AWS, HyperPod Task Governance can increase AI accelerator utilization, helping enterprises to optimize AI costs and producing potentially significant savings. “This innovation helps you maximize computer resource utilization by automating the prioritization and management of these Gen AI tasks, reducing the cost by up to 40%,” said Swami Sivasubramanian, VP of AI and Data at AWS. End GPU idle time As organizations rapidly scale their AI initiatives, many are discovering a costly paradox. Despite heavy investments in GPU infrastructure to power various AI workloads, including training, fine tuning and inference, these expensive computing resources frequently sit idle. Enterprise leaders report surprisingly low utilization rates across their AI projects, even as teams compete for computing resources. As it turns out, it’s actually a challenge that AWS itself faced. “Internally, we had this kind of problem as we were scaling up more than a year ago, and we built a system that takes into account the consumption needs of these accelerators,” Sivasubramanian told VentureBeat. “I talked to many of our customers, CIOs and CEOs, they said we want exactly that; we want it as part of Sagemaker and that’s what we are launching.” Swami said that once the system was deployed AWS’ AI accelerator utilization went through the roof with utilization rates rising over 90% How HyperPod Task Governance works The SageMaker Hyperpod technology was first announced at the re:invent 2023 conference. SageMaker HyperPod is built to handle the complexity of training large models with billions or tens of billions of parameters, which requires managing large clusters of machine learning accelerators. HyperPod Task Governance adds a new layer of control to SageMaker Hyperpod by introducing intelligent resource allocation across different AI workloads. The system recognizes that different AI tasks have varying demand patterns throughout the day. For instance, inference workloads typically peak during business hours when applications see the most use, while training and experimentation can be scheduled during off-peak hours. The system provides enterprises with real-time insights into project utilization, team resource consumption, and compute needs. It enables organizations to effectively load balance their GPU resources across different teams and projects, ensuring that expensive AI infrastructure never sits idle. AWS wants to make sure enterprises don’t leave money on the table Sivasubramanian highlighted the critical importance of AI cost management during his keynote address. As an example, he said that if an organization has allocated a thousand AI accelerators deployed not all are utilized consistently over a 24 hour period. During the day, they are heavily used for inference, but at night, a large portion of these costly resources are sitting idle when the inference demand might be very low.  “We live in a world where compute resources are finite and expensive and it can be difficult to maximize utilization and efficiently allocate resources, which is typically done through spreadsheets and calendars,” he said. ” Now, without a strategic approach to resource allocation, you’re not only missing opportunities, but you’re also leaving money on the table.” source

A drop to zero in a chart can be abrupt, but sometimes, that’s what you want. On the other hand, there will be times when you won’t want to draw attention to a zero. When you don’t want to display zero values, you have a few choices for hiding or otherwise managing those zeros. In this tutorial, I’ll review a few methods for handling zero values that offer quick but limited results with minimal effort. Depending on how much charting you do, you might find more than one of these methods helpful. Following along For this demonstration, I’m using Microsoft 365 Desktop on a Windows 11 64-bit system, but you can also use earlier versions of Excel. Excel for the web supports most of these techniques. SEE: Google Workspace vs. Microsoft 365: A side-by-side analysis w/checklist (TechRepublic Premium) You can follow along more closely by downloading our demonstration file. If you work through the instructions using our demonstration workbook file, undo each solution before you start the next. You can do this by simply closing the file and reopening it without saving it. Exploring the sample dataset The example below shows the data and initial charts that we’ll update throughout this article. The pie and single-line charts reflect the data in column B for Vendor 1. The other two charts have three data series: Vendor 1, Vendor 2, and Vendor 3. The Minimum column returns the minimum value for each month, so April, Ma, and July return zero for the minimum value. This setup simplifies all the examples we’ll be reviewing in this guide. We’ll use four chart types to review the inherent behaviors that come with charting zero. Right now, the charts display zero values by default in each chart type. Pie chart By default, the pie chart, shown below, charts the zero, but you can’t see it. If you turn on data labels, you will see the zero listed. There are seven slices but eight items in the legend. The pie chart plots zero by default. Line chart The below example shows the line chart’s default behavior, which drops the one to zero on the X-axis. The line chart plots zero by default, which can be a bit abrupt. Stacked bar chart Excel plots four stacks for the months without a zero value in the stacked bar chart shown below. The months with a zero display only two values because the Minimum column also returns zero for those months, so the chart is actually plotting two zeros for each month. Readers might be a bit confused by what they’re seeing. The stacked bar chart plots zero values. Multiple-line chart This multiple-line chart below is messy; enlarging it doesn’t improve its readability. Although you can’t see all of the lines, they’re there. The values are so close that some lines obscure the others, which is misleading. Zero values in a multiple-line chart can add to the chaos. Your results may vary depending on Excel’s default settings and theme colors. Now that you know the example data, let’s review a few methods for suppressing the zero values in our example charts. Some will work with limited results, and others won’t work at all. Removing and formatting zero There’s more than one way to suppress zero values in a chart, but none work the same consistently for all charts. Manual removals of zero To begin with, you might try removing zero values altogether if it’s a literal zero and not the result of a formula. By removing, I mean simply deleting all zero values from the dataset. Unfortunately, this simplest approach doesn’t always work as expected. Pie chart The pie chart doesn’t chart the blank cell, but the legend still displays the category label, as shown below. Removing the zero values from the dataset changed nothing. Removing zero values won’t help the pie chart. Stacked bar chart The stacked bar responds interestingly. It doesn’t chart the zero values, but because the zeros are gone, the MIN() functions in the Minimum column are now all non-zero values and chart accordingly. Removing the zero values changes the formula’s results, which can have unintended results. Line and multiple-line charts Neither line chart handles the missing zeros well, but the multiple-line chart is hopeless. The line chart has a gap between the two months, which definitely looks odd. Removing zero values leaves a gap, which probably won’t be what you want. The multiple-line chart is deceptive. The Vendor 1 series appears wrong, but you will see the markers if you click it. It’s there but obscured by other lines; even doubling its size does nothing to improve its readability. This multiple-line chart seems to hide data. If you removed zero values in the sheet during this phase, re-enter them before continuing to our next example. Or, close the demonstration file without saving your changes and reopen it. More about Software Unchecking worksheet display options You can also hide zeros by unchecking the worksheet display option called Show A Zero In Cells That Have Zero Value. Here’s how: Click the File tab and choose Options. You might have to click More first. Choose Advanced in the left pane. In the Display Options For This Worksheet section, choose the right sheet from the drop-down menu. (This is a sheet-level property.) Uncheck the Show A Zero In Cells That Have Zero Value option. Click OK. This option doesn’t change anything. The zero values still exist — you can see them in the Formula bar. However, Excel won’t display them; thus, this method has no impact. The charts treat the zero values as if they’re still there because they are. Excel for the web doesn’t allow access to this setting. We’ve found that unchecking this setting offers no advantage. I include this step in our tutorial to prevent you from wasting your time on this technique yourself. Setting a custom format Before you try this next formatting option, reset the Advanced option that you disabled in our previous

Dubai Police has always been at the forefront of innovation, embracing technology to enhance the safety, security, and well-being of the city’s residents and visitors. As part of its ongoing digital transformation, the force is launching a series of initiatives that integrate smart technology, artificial intelligence (AI), and robotics into its operations. Among the most groundbreaking of these projects is the announcement of the Middle East’s first floating Smart Police Station (SPS), set to go live by the end of 2026. A vision for smarter policing This floating SPS is part of an ambitious AED 2 billion initiative announced by His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai. The initiative, which includes specialized police training, improved employee well-being through housing projects, and enhanced security measures, is designed to elevate Dubai Police’s operations and ensure the safety of its citizens in a rapidly changing world. Lieutenant Colonel Faisal Al Tamimi, Director of the Assets and Facilities Department at Dubai Police, described the project as a transformative leap forward in police services. The floating SPS will offer a wide range of advanced services at sea, meeting the needs of yacht and boat owners, as well as water sports enthusiasts. The station is designed to ensure faster, easier access to police services, aligning with the broader vision of making Dubai the “World’s Smartest and Happiest City.” source

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Google DeepMind researchers have built an AI weather forecasting tool that makes faster and more accurate predictions than the best system available today. Dubbed GenCast, the new model outperformed the ENS forecast, widely regarded as the world leader, 97% of the time for predictions up to 15 days in advance. It was tested on over 1,320 weather scenarios, including tropical cyclones and heatwaves. “Outperforming ENS marks something of an inflection point in the advance of AI for weather prediction,” Ilan Price, a research scientist at Google DeepMind, told the Guardian. “At least in the short term, these models are going to accompany and be alongside existing, traditional approaches.” GenCast is a diffusion machine learning model, similar to those used in generative AI for tasks like image or text creation. However, it’s uniquely adapted for weather prediction, trained on four decades of data from the European Centre for Medium-Range Weather Forecasts (ECMWF) — the agency behind ENS.  The 💜 of EU tech The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now! During the experiments, researchers asked GenCast to generate a forecast for 2019. They then compared the results to the actual weather during that year as well as ENS’ predictions.   GenCast creates an ensemble of 50+ different predictions, each showing a possible future scenario. This data helps authorities prepare for extreme weather events like hurricanes or wind farm operators better predict power output days in advance.  The fancy name for this technique is probabilistic ensemble forecasting. It’s already the gold standard in traditional forecast systems. However, GenCast is taking things up a notch. The system can spit out predictions in far less time: 8 minutes, compared to hours for traditional models.  That’s because models like ENS run on massive supercomputers that have to crunch through millions of equations to make a prediction. By contrast, GenCast runs on a single Google Cloud TPU, a chip designed for machine learning. That’s because the AI has been trained, it’s “learnt” the data — it doesn’t have to go through it every single time it needs to make a forecast.  GenCast improves upon Deepmind’s GraphCast model unveiled last year. Other tech firms are also developing their own AI weather forecasters. Nvidia released FourCastNet in 2022, while Huawei launched its Pangu-Weather model in 2023.  So will AI replace traditional forecasting soon? Probably not. Models like GenCast still rely on data from traditional weather systems and models to train and calibrate their predictions. However, AI can certainly enhance current methods. “The greatest value comes from a hybrid approach, combining human assessment, traditional physics-based models and AI-based weather forecasting,” Steven Ramsdale, chief forecaster at the UK’s Met Office, told the Financial Times.   source

Every organization wants to optimize operational processes while keeping costs low. That’s why artificial intelligence has exploded onto the global stage in recent years. Companies see the promise of powerful automation tools, data suggestion and response systems, and the generative capabilities of platforms like OpenAI’s ChatGPT, Google’s Gemini, or Anthropic’s Claude and want to add them to their ever-growing toolbelt.   In the construction industry, we’re seeing clients lean on AI tools to summarize and track punch list items as they complete projects, brainstorm ideas for request for information (RFI) drafts, and other review and analytical tasks that significantly speed up the process from initial design to final build. AI is an immense boon, and I recommend that any company looking to improve the quality of its offerings implement AI where it can.  However, I also ask these organizations a simple question: “Will the data you have right now provide the results you’re looking for?”  Improving data quality isn’t as flashy as choosing the latest large language model, but it’s critical to AI’s efficacy — ensuring that the information you get is helpful for your business and customers. It requires a foundational shift in how your business treats its data, from understanding the link between data and results to internalizing that information and transforming your business with data-centric policies and processes in mind.  Related:Defining an AI Governance Policy How Data Quality Affects AI Results  The quality of the data you use determines the output you get from AI models. Without high-quality data, you’re effectively causing your business to leave productivity gains (and potential profits) on the table. Even the most powerful AI algorithms can only do so much if your data is inaccurate or inconsistent. Twenty-five percent of the highest-quality data isn’t available for public use so, to ensure the best possible results, it is essential to prioritize quality organization-specific data when training AI models.   When investigating the quality of your organization’s data, keep an eye out for the following critical flaws that can lead to poor AI results:  Incomplete data. Records lack critical information, or spreadsheets are missing data values throughout. If this data doesn’t exist, your analytical tools won’t have the comprehensive insight they need to provide actionable results.   Inconsistent data. Different regional methods for calculating and storing data may create incompatibilities when collating data into a single repository. These misalignments can lead to confusion in data processing and contribute to errors in output.   Duplicate data. Multiple databases exist to store the same information, creating data clutter that is difficult to sift through. Not only does this increase the storage required to maintain this data, but it also raises the cost of processing it through AI, significantly reducing operational efficiency.  Delays in data production. Inefficient processes increase the lead time between data gathering, cleaning, and usage, making the information you have obsolete before you can leverage it to benefit your business.   Related:Preparing for AI-Augmented Software Engineering How Data-Mature Organizations Approach Data Quality  Once you understand what the potential flaws in your data might look like, you can correct them. In my experience, organizations that address the root causes of their data quality problems are poised to get the most out of AI integrations. The most successful of them share four common characteristics:  1. They view their data as an asset  Analysts expect global data storage to reach 200 zettabytes by 2025. Every organization will store and process data as a part of day-to-day operations. However, the ones that get the best results from their AI models understand that data isn’t just something that takes up digital space — it’s an asset to be grown and cultivated with a steady hand.  Related:Have We Gone Too Far With AI in Software Development? That means managing data isn’t just a problem for your IT department to deal with. It needs buy-in from key stakeholders, preferably by building it into your organization’s structure at the executive level. Doing so will help you develop more effective solutions tailored to your organization’s unique needs. It will also help you leverage this data throughout the business to improve your product offerings, up to and including any AI models you use.  Automated data collection is a critical means for maximizing the value of data. Manual input can be erroneous, time-consuming, and limited in scope. Most companies that need accurate information for decision making do this effectively by establishing as many guardrails as possible such as suggestions, likely values, third party information, and standard drop-down lists.  2. They build standardized processes  First-party data is a key market differentiator. When properly sourced and vetted, high-quality data can give you a competitive advantage with insight no other organization can access. As a result, your data needs to be gathered from trusted sources and processed uniformly so it’s accessible throughout the organization and secured against cyberattacks.   Standardized processes help organizations achieve these goals. They ensure consistency and accuracy regardless of how data is ingested, which department gathers it, or who uses it. They also help to break down silos and improve intra-organizational accessibility, which is crucial for developing a genuinely unified and holistic approach to data storage. Build these processes into data gathering, training, and usage protocols, and enforce them across the organization to see better results.  3. They install comprehensive data governance policies  AI models need to be trained on data to provide accurate results. However, this data can be exposed (whether unintentionally or through malicious activity) without effective data governance policies in place.   These policies dictate how data can be used, whether it can be exposed to AI models for training or excluded in part or in whole. They also help your models align with required industry and governmental security and privacy regulations. Improving and enforcing your organization’s data governance policies will enhance its security stance while improving the overall quality of its output.  4. They invest in employee training  Less than half of executives believe their leaders have the knowledge to use AI safely and effectively. Investing

“I think acceptance is at the heart of all these things,” he says, “but after that, you have to make some decisions.” Schadler says CIOs can take steps to lessen the discomfort (and risk) of such situations — first by recognizing they’re not alone. “Bring in those rational voices to bolster your own ability to shape the investments you’re going to make,” Schadler says, adding that privacy, risk, and security officers are often partners here. So are third parties, such as service providers and consulting firms, because they typically bring a wealth of experiences and lessons-learned. “Bring them in as advisors to you and the business, to step into the conversation and to problem-solve,” Schadler says. And learn to stretch as an executive. “Change your comfort calibration to take on risk that doesn’t violate corporate principles but that stretches your team’s ability to execute,” he advises. 8. Collaboration remains elusive Despite the need for cooperation and collaboration to succeed, Kellie Romack, chief digital information officer at ServiceNow, says, “There are still too many people working in silos.” “When I talk to industry peers, silos are one of the biggest challenges and typically the main reason things go off course. They happen when people try to move fast without taking time to connect the dots, or big initiatives get planned and funded through individual departments without collaboration,” she explains. CIOs can counter that by serving “as the connective fiber for transformation.” “We need to know everything about the business units we serve, so we can have the big-picture view and be that unifier,” she says. “For example, it’s important for CIOs to work with their CFO to understand the finance and budgeting roadmap, determine where tech can solve business problems, and identify other stakeholders who should play a role and give critical input.” source

Voters in the November 2024 election were about equally likely to vote in person on Election Day (34%), vote early in person (32%) or use an absentee or mail-in ballot (35%). Election Day voting Roughly a third of voters (34%) report having cast their ballot on Election Day, a smaller share than the 44% of 2022 midterm voters who did this, but substantially more than the 27% who did so in 2020, in the midst of the coronavirus pandemic. While the share of voters casting ballots in person on Election Day has steadily declined over the last two decades, it was the method used by a majority of voters until 2018. Early in-person voting Early in-person voting hit a high point this year: 32% of voters report having cast their ballots this way, up from 27% in 2020 and 21% in 2022. Absentee and mail-in voting The share who voted by absentee or mail-in ballot in this election is identical to the share of voters who voted by this method in the 2022 midterms. In 2020, 46% of voters voted by absentee or mail-in ballot. Republicans continue to be more likely than Democrats to vote on Election Day, less likely to vote by mail. But rising shares in both parties voted early in person. 39% of Republican voters say they cast their vote in person on Election Day, compared with 28% of Democratic voters. In contrast, 44% of Democratic voters say they voted by mail or absentee, compared with 26% of Republican voters. 35% of Republican voters and 28% of Democratic voters report having voted early in person this year. Some voters switched vote methods this year An overwhelming majority of voters who had voted in elections prior to 2024 (87%) report having used a voting method that was familiar to them in this election. Yet about 13% used a new method of voting this November: 27% of early in-person voters say this was the first time they voted in person before Election Day. 9% of absentee or mail voters say this was their first time voting by absentee or mail-in ballot. 2% of voters who voted in person on Election Day say this was their first time doing so. Most voters say it was easy to vote in the election An overwhelming majority of voters (94%) say it was easy to vote in the election this November. About eight-in-ten (79%) describe voting as very easy, while 15% say it was somewhat easy. Just 6% of voters say voting was somewhat (5%) or very (2%) difficult. Similar shares of voters who backed Donald Trump (95%) and Kamala Harris (93%) say that voting was easy. In 2020, 93% of Trump voters and 95% of Joe Biden voters said it was easy for them to cast their ballots. Most in-person voters had little or no wait to vote Roughly seven-in-ten voters who voted in person (72%) – either on Election Day or earlier – say they waited less than 10 minutes to vote, including 42% who report not waiting at all. About three-in-ten in-person voters (28%) waited at least 10 minutes to vote, including 11% who waited for more than 30 minutes and 4% who waited more than an hour. Voters report shorter wait times this year than in 2020. Wait times of demographic groups Race and ethnicity Black, White, Hispanic and Asian in-person voters report similar wait times this year. In 2020, Black in-person voters reported waiting somewhat longer to vote than White or Hispanic in-person voters. Age Older and younger in-person voters reported similar wait times to cast their ballots. Community type As was the case four years ago, urban and suburban in-person voters had to wait somewhat longer to vote on average than in-person voters living in rural communities. Urban and suburban in-person voters are each about 10 percentage points more likely than those in rural areas to have waited more than 10 minutes. Vote method Those who voted in person before Election Day waited somewhat longer than those who voted on Election Day: 33% of early in-person voters waited more than 10 minutes, compared with 24% of Election Day in-person voters. Candidate preference In-person voters who backed Trump and those who backed Harris report nearly identical wait times. By contrast, in 2020 in-person Biden voters reported waiting longer to vote than Trump voters. Nonvoters’ views of the election Among those eligible to vote who say they did not cast a ballot, 42% say they wish they had voted while 57% say they do not. These shares are similar to other recent presidential elections: 45% of nonvoters said they wished they had voted following the 2020 election, and 44% said this postelection in 2016. Nonvoters point to a number of reasons for their decisions not to vote: 35% say thinking their vote would not make a difference was a major reason why they did not vote. 31% say that not liking politics was a major reason. 18% say it was that they are not registered or not eligible to vote. 17% say a major reason was that they did not care about the outcome. 15% say voting was inconvenient. 8% say a major reason was they forgot to vote. source