UNFPA urges international support to deal with gender-based violence in HaitiCaley Thistle boss Scott Kellacher confirmed injured defender Remi Savage could be fit to feature in Saturday’s League One trip to Montrose. The centre-half suffered a back injury late in the 2-0 Scottish Cup defeat at Cove Rangers at the weekend and is a doubt for the Links Park tussle. Before the cup tie, administration-hit Inverness beat Cove and Alloa Athletic in successive league matches as they seek to eat into the 10-point gap between themselves and eighth-placed Annan Athletic. With several players carrying injuries, having former Newcastle and Liverpool youth defender Savage available would be a big boost. Several players have 50-50 chances At his weekly pre-match press conference, Kellacher said: “We’ve had a few boys injured of late and two or three are 50-50 at the moment. We’ll wait and see “Remi Savage did a little bit of light running today, which was a boost. Keith Bray also did some running work (as he recovers from a shoulder injury). “We’ll see how they get on in terms of any reaction they might have, do a bit more work with them on Friday and see if they are in the picture for Saturday. “As much as we want them back, we can’t take chances in rushing them given the circumstances we’re in and the size of squad we have. “It is important we look after them and try to do the right thing by them. “The boys know that as well. I know they are trying to push themselves a wee bit, but we have to be sensible with them.” Also on the injury list are defenders Connall Ewan and Lewis Nicolson, with on-loan Dundee midfielder Charlie Reilly and defender Jake Davidson expected to be longer-term absentees. ‘Capable of beating anyone’ So far in League One, Cove Rangers have had the best running sequence of victories when they defeated Alloa, Queen of the South, Montrose and Dumbarton in October into November. Inverness are looking for nine points from nine as they look to move up the table. Kellacher said: “If you can put two or three wins together, the league changes every week. “Stenhousemuir are top of the league now, but it was Cove and it was Alloa recently. “All of the teams are more than capable of beating any of the other teams. “We’re capable of beating anyone on our day and that’s why we need to make sure we’re at it every week.” ICT ‘need to be at their best’ to win Former Ross County attacker Stewart Petrie has just celebrated eight years in charge of Montrose and it was ICT in August and Kellacher expects another tense affair. He added: “They have some very good players at Montrose. Stewart has done a great job. “It is a really hard place to go, and our boys are aware of that. “We played them down there last year (in the relegation play-off) and we remember how hard the game was (a 0-0 draw before a 1-0 home win). “We know we will have to be at our best to get something from the game and we’ll go down there and be as positive as we can.” Manner of victory won’t matter – boss This will be Kellacher’s sixth match in charge , and Inverness are showing signs of positive play paying off. He said: “It has been good more recently to see us get our rewards when we are in control of games, especially the two games against Cove and Alloa in the league. “Whatever it takes to win a game of football, whether it is an ugly, or a great performance, that’s where we’re at right now. “As long as we win the game of football, that’s what is most important.” Chase continues to catch Annan A controversial late penalty award for Cove, which Declan Glass scored from, riled Inverness last week in their Scottish Cup tie and the Missing out on the chance of a potential money-spinning fourth-round tie hurt, but Kellacher explained the focus has shifted. He said: “We were absolutely gutted last week with the way the game ended, but that’s in the past now. “Our focus is the league. We know what’s at stake and what we need to do in the next few weeks and months.” Dumbarton, who are also in administration and were also hit with a 15-point penalty, drew 2-2 with Alloa in midweek, On Saturday, the , while the team Inverness are hunting, Annan, are at home to fourth-placed Arbroath.
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Everything seemed to be going Ohio State’s way in the leadup to Saturday’s clash with bitter rival Michigan. The No. 2-ranked Buckeyes had a healthy roster and were hosting a Michigan team without star tight end Colston Loveland and cornerback Will Johnson — arguably the Wolverines’ top players on offense and defense, respectively. But despite Michigan being depleted from a talent standpoint and the Buckeyes one win away from locking up a sport in the College Football Playoffs, the Wolverines rode an inspired defensive effort to a huge upset of their archrivals, which could have thrown a wrench in OSU’s CFP hopes. Though Ohio State quarterback Will Howard remaining confident the Buckeyes would still get into the 12-team CFP field, the senior signal-caller shouldered a lot of the blame for OSU’s fourth straight loss to Michigan, publicly apologizing to his teammates for not leading them to victory. “I don't know if I have the answer to that at this moment,” Howard said of what he planned to tell his teammates, via Chase Brown of 11 Warriors. “Man, I still love this team. I still love this university... I'm sorry... I'm blessed to have the opportunity to be a Buckeye. I'm sorry I couldn't get this one done.” Adam Cairns/Columbus Dispatch / USA TODAY NETWORK via Imagn Images Howard played his worst game of the season against the Wolverines, finishing with just 175 yards, a touchdown and two interceptions — his first multi-interception game as a Buckeye — and he averaged a lowly 5.3 yards per pass attempt. He also completed a season-low 57.6% of his passes with his lowest passer rating of the year as well. On Ohio State’s final drive, Howard completed just 1 of his 5 passes for one yard. He completed 6 of his final 14 passes for 50 yards in the second half.Shutting down CeeDee Lamb should result in domino effect for Dallas Cowboys starting this Sunday vs Eagles
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More One of Google’s latest experimental models, Gemini-Exp-1206, shows the potential to alleviate one of the most grueling aspects of any analyst’s job: getting their data and visualizations to sync up perfectly and provide a compelling narrative, without having to work all night . Investment analysts, junior bankers, and members of consulting teams aspiring for partnership positions take their roles knowing that long hours , weekends, and pulling the occasional all-nighter could give them an inside edge on a promotion. What burns so much of their time is getting advanced data analysis done while also creating visualizations that reinforce a compelling storyline . Making this more challenging is that every banking, fintech and consulting firm, like JP Morgan, McKinsey and PwC, has unique formats and conventions for data analysis and visualization. VentureBeat interviewed members of internal project teams whose employers had hired these firms and assigned them to the project. Employees working on consultant-led teams said producing visuals that condense and consolidate the massive amount of data is a persistent challenge. One said it was common for consultant teams to work overnight and do a minimum of three to four iterations of a presentation’s visualizations before settling on one and getting it ready for board-level updates. A compelling use case for test-driving Google’s latest model The process analysts rely on to create presentations that support a storyline with solid visualizations and graphics has so many manual steps and repetitions that it proved a compelling use case for testing Google’s latest model. In launching the model earlier in December, Google’s Patrick Kane wrote , “Whether you’re tackling complex coding challenges, solving mathematical problems for school or personal projects, or providing detailed, multistep instructions to craft a tailored business plan, Gemini-Exp-1206 will help you navigate complex tasks with greater ease.” Google noted the model’s improved performance in more complex tasks, including math reasoning, coding, and following a series of instructions. VentureBeat took Google’s Exp-1206 model for a thorough test drive this week. We created and tested over 50 Python scripts in an attempt to automate and integrate analysis and intuitive, easily understood visualizations that could simplify the complex data being analyzed. Given how hyperscalers are dominant in news cycles today, our specific goal was to create an analysis of a given technology market while also creating supporting tables and advanced graphics. Through over 50 different iterations of verified Python scripts, our findings included: Pushing Exp-1206 toward complex, layered tasks VentureBeat’s goal was to see how far the model could be pushed in terms of complexity and layered tasks. Its performance in creating, running, editing and fine-tuning 50 different Python scripts showed how quickly the model attempts to pick up on nuances in code and react immediately. The model flexes and adapts based on prompt history. The result of running Python code created with Exp-1206 in Google Colab showed that the nuanced granularity extended into shading and translucency of layers in an eight-point spider graph that was designed to show how six hyperscaler competitors compare. The eight attributes we asked Exp-1206 to identify across all hyperscalers and to anchor the spider graph stayed consistent, while graphical representations varied. Battle of the hyperscalers We chose the following hyperscalers to compare in our test: Alibaba Cloud, Amazon Web Services (AWS), Digital Realty, Equinix, Google Cloud Platform (GCP), Huawei, IBM Cloud, Meta Platforms (Facebook), Microsoft Azure, NTT Global Data Centers, Oracle Cloud, and Tencent Cloud. Next, we wrote an 11-step prompt of over 450 words. The goal was to see how well Exp-1206 can handle sequential logic and not lose its place in a complex multistep process. (You can read the prompt in the appendix at the end of this article.) We next submitted the prompt in Google AI Studio , selecting the Gemini Experimental 1206 model, as shown in the figure below. Next, we copied the code into Google Colab and saved it into a Jupyter notebook (Hyperscaler Comparison – Gemini Experimental 1206.ipynb), then ran the Python script. The script ran flawlessly and created three files (denoted with the red arrows in the upper left). Hyperscaler comparative analysis and a graphic — in less than a minute The first series of instructions in the prompt asked Exp-1206 to create a Python script that would compare 12 different hyperscalers by their product name, unique features and differentiators, and data center locations. Below is how the Excel file that was requested in the script turned out. It took less than a minute to format the spreadsheet to shrink it to fit in the columns. The next series of commands asked for a table of the top six hyperscalers compared across the top of a page and the spider graph below. Exp-1206 chose on its own to represent the data in HTML format, creating the page below. The final sequence of prompt commands centered on creating a spider graph to compare the top six hyperscalers. We tasked Exp-1206 with selecting the eight criteria for the comparison and completing the plot. That series of commands was translated into Python, and the model created the file and provided it in the Google Colab session. A model purpose-built to save analysts’ time VentureBeat has learned that in their daily work, analysts are continuing to create, share and fine-tune libraries of prompts for specific AI models with the goal of streamlining reporting, analysis and visualization across their teams. Teams assigned to large-scale consulting projects need to consider how models like Gemini-Exp-1206 can vastly improve productivity and alleviate the need for 60-hour-plus work weeks and the occasional all-nighter. A series of automated prompts can do the exploratory work of looking at relationships in data, enabling analysts to produce visuals with much greater certainty without having to spend an inordinate amount of time getting there. Appendix: Google Gemini Experimental 1206 Prompt Test Write a Python script to analyze the following hyperscalers who have announced a Global Infrastructure and Data Center Presence for their platforms and create a table comparing them that captures the significant differences in each approach in Global Infrastructure and Data Center Presence. Have the first column of the table be the company name, the second column be the names of each of the company’s hyperscalers that have Global Infrastructure and Data Center Presence, the third column be what makes their hyperscalers unique and a deep dive into the most differentiated features, and the fourth column be locations of data centers for each hyperscaler to the city, state and country level. Include all 12 hyperscalers in the Excel file. Don’t web scrape. Produce an Excel file of the result and format the text in the Excel file so it is clear of any brackets ({}), quote marks (‘), double asterisks (**) and any HTML code to improve readability. Name the Excel file, Gemini_Experimental_1206_test.xlsx. Next, create a table that is three columns wide and seven columns deep. The first column is titled Hyperscaler, the second Unique Features & Differentiators, and the third, Infrastructure and Data Center Locations. Bold the titles of the columns and center them. Bold the titles of the hyperscalers too. Double check to make sure text within each cell of this table wraps around and doesn’t cross into the next cell. Adjust the height of each row to make sure all text can fit in its intended cell. This table compares Amazon Web Services (AWS), Google Cloud Platform (GCP), IBM Cloud, Meta Platforms (Facebook), Microsoft Azure, and Oracle Cloud. Center the table at the top of the page of output. Next, take Amazon Web Services (AWS), Google Cloud Platform (GCP), IBM Cloud, Meta Platforms (Facebook), Microsoft Azure, and Oracle Cloud and define the eight most differentiating aspects of the group. Use those eight differentiating aspects to create a spider graph that compares these six hyperscalers. Create a single large spider graph that clearly shows the differences in these six hyperscalers, using different colors to improve its readability and the ability to see the outlines or footprints of different hyperscalers. Be sure to title the analysis, What Most Differentiates Hyperscalers, December 2024. Make sure the legend is completely visible and not on top of the graphic. Add the spider graphic at the bottom of the page. Center the spider graphic under the table on the page of output. These are the hyperscalers to include in the Python script: Alibaba Cloud, Amazon Web Services (AWS), Digital Realty, Equinix, Google Cloud Platform (GCP), Huawei, IBM Cloud, Meta Platforms (Facebook), Microsoft Azure, NTT Global Data Centers, Oracle Cloud, Tencent Cloud. If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here . An error occured.The Latest: Police search for man who killed UnitedHealthcare CEO, new photos of suspect released
