View data as a shared asset. A modern data architecture needs to eliminate departmental data silos and give all stakeholders a complete view of the company: 360 degrees of customer insights and the ability to correlate valuable data signals from all business functions, like manufacturing and logistics. Provide user interfaces for consuming data. Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs. Data must be able to freely move to and from data warehouses, data lakes, and data marts, and interfaces must make it easy for users to consume that data. Ensure security and access controls. Modern data architectures must be designed for security, and they must support data policies and access controls directly on the raw data, not in a web of downstream data stores and applications. Establish a common vocabulary. Shared data assets, such as product catalogs, fiscal calendar dimensions, and KPI definitions, require a common vocabulary to help avoid disputes during analysis. Curate the data. Invest in core functions that perform data curation such as modeling important relationships, cleansing raw data, and curating key dimensions and measures. Optimize data flows for agility. Limit the times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility. Data architecture components A modern data architecture consists of the following components, according to IT consulting firm BMC: Data pipelines. A data pipeline is the process in which data is collected, moved, and refined. It includes data collection, refinement, storage, analysis, and delivery. Cloud storage. Not all data architectures leverage cloud storage, but many modern data architectures use public, private, or hybrid clouds to provide agility. Cloud computing. In addition to using cloud for storage, many modern data architectures make use of cloud computing to analyze and manage data. Application programming interfaces. Modern data architectures use APIs to make it easy to expose and share data. AI and machine learning models. AI and ML are used to automate systems for tasks such as data collection and labeling. At the same time, modern data architectures can help organizations unlock the ability to leverage AI and ML at scale. Data streaming. Data streaming is data flowing continuously from a source to a destination for processing and analysis in real-time or near real-time. Container orchestration. A container orchestration system, such as open-source Kubernetes, is often used to automate software deployment, scaling, and management. Real-time analytics. The goal of many modern data architectures is to deliver real-time analytics — the ability to perform analytics on new data as it arrives in the environment. Data architecture vs. data modeling According to Data Management Book of Knowledge (DMBOK 2), data architecture defines the blueprint for managing data assets as aligning with organizational strategy to establish strategic data requirements and designs to meet those requirements. On the other hand, DMBOK 2 defines data modeling as, “the process of discovering, analyzing, representing, and communicating data requirements in a precise form called the data model.” While both data architecture and data modeling seek to bridge the gap between business goals and technology, data architecture is about the macro view that seeks to understand and support the relationships between an organization’s functions, technology, and data types. Data modeling takes a more focused view of specific systems or business cases. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The AI research community continues to find new ways to improve large language models (LLMs), the latest being a new architecture introduced by scientists at Meta and the University of Washington. Their technique, Byte latent transformer (BLT), could be the next important paradigm for making LLMs more versatile and scalable. BLT solves one of the longstanding problems of LLMs that operate at byte level as opposed to tokens. BLT can open the way for new models that can process raw data, are robust to changes and don’t rely on fixed vocabularies. Tokens vs bytes Most LLMs are trained based on a static set of tokens, predefined groups of byte sequences. During inference, a tokenizer breaks the input sequence down into tokens before passing it to the LLM. This makes the models more efficient in using compute resources but also creates biases that can degrade the model’s performance when faced with tokens not included in the vocabulary. For example, many leading language models can become slow and more costly when faced with languages that have a small representation on the web because their words were not included in the model’s token vocabulary. Misspelled words can also cause the model to tokenize the input incorrectly. And tokenized models can struggle with character-level tasks, such as manipulating sequences. Moreover, modifying the vocabulary requires the model to be retrained. And expanding the token vocabulary can require architectural changes to the model to accommodate the added complexity. Alternatively, LLMs can be trained directly on single bytes, which can solve many of the abovementioned problems. However, byte-level LLMs are prohibitively costly to train at scale and can’t handle very long sequences, which is why tokenization remains an essential part of current LLMs. Byte latent transformer (BLT) Byte latent transformer (BLT) is a tokenizer-free architecture that learns directly from raw bytes and matches the performance of tokenization-based models. To solve the inefficiencies of other byte-level LLMs, BLT uses a dynamic method that groups bytes based on the level of information they contain. “Central to our architecture is the idea that models should dynamically allocate compute where it is needed,” the researchers write.  Unlike tokenized models, BLT has no fixed vocabulary. Instead, it maps arbitrary groups of bytes into patches using entropy measures. BLT does this dynamic patching through a novel architecture with three transformer blocks: two small byte-level encoder/decoder models and a large “latent global transformer.” BLT architecture (source: arXiv) The encoder and decoder are lightweight models. The encoder takes in raw input bytes and creates the patch representations that are fed to the global transformer. At the other end, the local decoder takes the batch representations processed by the global transformer and decodes them into raw bytes. The latent global transformer is the model’s main workhorse. It takes in the patch representations generated by the encoder and predicts the next patch in the sequence. When processed by the decoder, this patch is unpacked into one or several bytes. The global transformer accounts for the largest share of compute resources during training and inference. Therefore, the patching mechanism determines how the global transformer is used and can help control the amount of compute used for different portions of the input and output. BLT redefines the tradeoff between vocabulary size and compute requirements. In standard LLMs, increasing the size of the vocabulary means larger tokens on average, which can reduce the number of steps required to process a sequence. However, it will also require larger dimensions in the projection layers inside the transformer, which itself consumes more resources.  In contrast, BLT can balance compute resources based on the complexity of the data instead of the vocabulary size. For example, the ending of most words is easy to predict and requires fewer resources. On the other hand, predicting the first byte of a new word or the first word of a sentence requires more compute cycles. “BLT unlocks a new dimension for scaling, allowing simultaneous increases in model and patch size within a fixed inference budget,” the researchers write. “This new paradigm becomes advantageous for compute regimes commonly encountered in practical settings.” BLT in action The researchers conducted experiments with BLT and classic transformers on models of different scales, running from 400 million to 8 billion parameters. According to the authors, this is “the first flop-controlled scaling study of byte-level models up to 8B parameters and 4T training bytes, showing that we can train a model end-to-end at scale from bytes without fixed-vocabulary tokenization.” Their findings show that when controlled for the amount of compute resources allocated to training, BLT matches the performance of Llama 3 while using up to 50% fewer FLOPs at inference. This efficiency comes from the model’s dynamic patching, which results in longer groups of bytes, saving compute that can be reallocated to grow the size of the global latent transformer. “To the best of our knowledge, BLT is the first byte-level Transformer architecture to achieve matching scaling trends with BPE-based models at compute optimal regimes,” the researchers write. Beyond efficiency, BLT models proved to be more robust to noisy inputs compared to tokenizer-based models. They had enhanced character-level understanding abilities and also showed improved performance on tasks such as character manipulation and low-resource machine translation. According to the researchers, the ability of BLT to directly process raw bytes as opposed to tokens “provides significant improvements in modeling the long tail of the data,” which means the models are better at working with patterns that don’t appear often in the training corpus. This is still the beginning of what could be a new standard for creating language models. The researchers note that existing transformer libraries and codebases are designed to be highly efficient for tokenizer-based transformer architectures. This means that BLT still has room to benefit from software and hardware optimizations. source

ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Salesforce unveiled a major upgrade to its artificial intelligence platform on Tuesday, introducing technology that enables AI agents to perform deeper reasoning and take more autonomous actions across enterprise workflows — part of what the company’s CEO frames as an ambitious push into “digital labor.” The San Francisco software giant’s Agentforce 2.0 represents a significant evolution in how AI assistants operate within businesses, moving beyond simple chatbots to AI agents that can understand complex requests, access relevant company data, and independently complete multi-step tasks. “We’re creating a new industry,” said Marc Benioff, Salesforce’s chief executive, at a press conference announcing the release. “This isn’t just about managing and sharing information and data anymore. We’re a digital labor provider.” How Atlas Reasoning Engine powers next-generation enterprise AI The upgraded platform introduces what Salesforce calls the Atlas Reasoning Engine, which enables AI agents to engage in more sophisticated analysis and decision-making. Unlike traditional AI assistants that provide quick responses based on pattern matching, Atlas employs “System 2” reasoning — a more deliberative approach inspired by psychologist Daniel Kahneman’s research on human thought processes. “The reasoning engine should be one of the first factors enterprise organizations consider when comparing digital labor options,” said Claire Cheng, Ph.D., VP of machine learning and engineering at Salesforce. Early results appear promising. In testing, Agentforce 2.0 achieved a 33% improvement in answer accuracy compared to DIY AI solutions, while doubling response relevance, according to Salesforce. The company has already deployed the technology internally. At help.salesforce.com, AI agents now handle 83% of customer support queries independently, with human escalations dropping by 50% since implementation two weeks ago. “Suddenly, as a CEO, I’m not just managing human beings, but I’m also managing agents,” said Benioff. “There is an authentic agentic layer around the platform today. It’s not some vision fantasy in the future idea, it’s what is happening right now.” Digital labor: The key to solving global workforce challenges Salesforce’s push into “digital labor” comes amid growing labor shortages across industries. With birth rates declining and companies struggling to fill positions, Benioff sees AI agents as a crucial solution for business growth. “To unlock GDP growth, we need breakthrough technology. We have to become a digital labor provider,” he said. “This is the new horizon for business — this idea that a door has opened and business will never be the same.” The technology is already finding real-world applications. The Adecco Group, a global staffing firm, is using Agentforce to process millions of resumes and match candidates to opportunities. Digital tablet maker reMarkable deployed it for customer service, while accounting firm 1-800 Accountant expects to deflect 65% of incoming service requests using AI agents. Behind the tech: The innovation powering Salesforce’s AI revolution Under the hood, Agentforce 2.0 introduces several technical advances. The Atlas Reasoning Engine creates detailed semantic understanding of company data and processes, enabling more contextual responses. “We’re able to associate each data component with contextual metadata information, which allows us to find the mapping between data and the corresponding semantic meaning,” explained Silvio Savarese, who leads Salesforce’s AI research. “This enables much more relevant, much more aligned responses to user queries.” The platform also introduces enhanced integration with Slack, Salesforce’s workplace messaging platform, allowing employees to work alongside AI agents directly in their communication flows. “If you want these agents to be used, to be engaged with, and you want them to get better over time, having them where people are already working is critical,” said Rob Seaman, who oversees Slack integration. Looking ahead, Salesforce envisions expanding into physical robotics, with Benioff announcing plans for a “robot force partner program” to connect physical robots with the company’s AI agent platform. Trust, security, and the future: Navigating AI’s enterprise integration For Salesforce, the stakes of this initiative are significant. While the company expects $38 billion in revenue this year from its traditional software business, Benioff believes the digital labor market represents a multi-trillion dollar opportunity. However, challenges remain, particularly around trust and security. Salesforce emphasizes its “trust layer” that prevents toxic content and maintains data privacy, while giving customers control over how agents operate within their organizations. “These things act as a user — they don’t have God permissions or admin permissions,” noted Seaman. “We don’t create any holes for the AI to see things that it should not be able to.” As businesses grapple with persistent labor shortages and productivity challenges, Salesforce is betting that AI agents will become an essential part of the modern workforce. The company’s vision suggests a future where human employees work alongside AI agents that can handle increasingly complex tasks — fundamentally changing how businesses operate and scale. “This is the beginning of the beginning,” said Benioff. “When you’re at the beginning of the beginning, you see these little things, and then you try to extrapolate what this is going to be. This is an incredible moment.” source

00:00 Hi everybody, welcome to DEMO, the show where companies come in and they show us their latest products and platforms. Today, I’m joined by Neil Miller, he is the Director of Product Marketing at Kissflow. Welcome to the show, Neil. So tell us a little bit about what Kissflow is, and then what are you going to show us here today? 00:14So Kissflow is a no-code, low-code, application platform that really spreads the power of application development across the enterprise. 00:21Generally, who is this designed for? Is it designed for product developers, or is it other people within the company that might not be a developer? 00:30Yes, so Kissflow is a unique product in that it’s really built for both groups, so it can handle the people that like the high-powered, low-code developers who really want to build a lot of things out — they don’t need a lot of certifications or anything. It’s a platform they can get on to build things very quickly and easily. But then the other side, you also have junior developers, business analysts and specifically process owners, who want to build their own thing themselves. The platform is easy enough for them to use that they can build their own applications too. 00:56Do you subscribe to the whole citizen developer movement that we’re seeing in the space, because of skill shortages and things like that? 01:04Absolutely. Citizen development is one of the main themes we like to play into. Now our platform is very governed, so IT admins have the ability to govern the entire platform and know what’s going on in there. But at the same time, we believe that people who actually run these processes probably know best what’s there. So as long as you give them the right tools, and you give them enough training, they’re able to build what they need. 01:25Because I guess if I was in IT, the biggest fear would be like, “Wait, you’re we’re giving power to who?” Because the last thing you want is me programming something, with very little design or development skills. So why should companies care about Kissflow versus maybe some of the other platforms out there, or some of the other processes that go on. 01:42Like I said, Kissflow’s unique thing is that one, it’s just super easy to use. You’re going to see it in the demo here a second. The layout, the feel of it, is just really geared towards someone who knows what they want but maybe doesn’t have the development skills about it. There’s a lot of things we won’t show today that show the power of the low-code side of it as well, so that even a higher-powered developer can come in and really do what they want to do, but that’s one thing that really makes it easy, is just the ease of use and be able to generate things quickly. So even somebody who’s in the IT department that just wants to build a lot of apps very quickly to get things out of their backlog, they can really easily build dozens of apps on this. 02:16If companies didn’t have this, would they just be relying on developers to do a lot of the requests that are coming in? Does this offload some of those processes to people that usually don’t have those skills? 02:26So a few different things. One is that they would either just be custom developing things. Second is they might be using a different low-code platform, but typically, you’re only going to be building two or three applications a year on those really big platforms that are out there. Kissflow is more like, “Hey, give us a few dozen that we’re going to get done in a year.” Then the citizen developers, the other thing they would be using would just be spreadsheets, Excel or something like that, to build what they need. So this is their option to actually get something that’s an improvement on Excel. 02:53So let’s go into the demo. Tell us what you’re going to show us. 03:00What you see here is the basics of a process. So we have a few different modules in Kissflow, but the process builder is one of the easiest to understand, and the one that generates the most interest for most people. So we start off with a form, which is a little bit interesting. Most people are going to say, OK, build us a data table or something like that. But we actually want to start with a form, because that’s where the process owner is usually going to start. They know what they want to do. In this situation, we’re looking at asset disposal requests. So this is, again, something maybe your admin team is going to use, or maybe somebody else in the IT department is going to use, but it’s really something that the person who is building this knows what they need, and they just want to be able to bring that in. So the form is very, very simple. It’s all drag and drop. We have tons of form fields that are available here on the left, all sorts of things, from very simple things to very complicated. We also have some AI features here. So if you want to, just based on the name asset disposal request, you’re going to be able to generate types of fields you might need to know. So if somebody’s building it for the first time, they’re going to be able to bring those in. They can do anything here, building out sections, you’re also able to refer back to fields. We have remote lookup, so you can go to databases outside of Kissflow and bring those things in. Every field has the ability to change the settings, to add validations, add visibility requirements, stylize it the way you want, and even add these custom events, which is more the low-code part of what’s going on. So the field, the form is one of the things that is most

Earlier this month, we wrapped up Forrester’s India Predictions 2025 event. I look forward to this event every year, and this year was no exception. This year’s event for India saw record turnout, with over 350 participants across three cities in India including marketing and tech leaders. The conversations were insightful and engaging, setting the stage for discussing the predictions we had lined up. Here is a summary of the key marketing predictions for India for 2025. Prediction 1: CMOs and CSOs will aim to reorganize in 2025, but half will fail to fix what ails them. We predict that CMOs and CSOs will aim to reorganize their processes and teams, but half of these efforts will fail to address underlying issues. Forrester’s Q4 2023 Demand Marketing Organizational Design And Process Survey uncovered that many organizations are embarking on transformation projects, change management, and AI-driven disruptions to drive growth. Despite these efforts, only 12% of marketing leaders believe that their current organizational design will help them meet revenue targets over the next year. This lack of confidence will drive more reorganization efforts in 2025. Organizations may attempt to address competency gaps by quick moves such as moving partner ecosystem marketing under the CMO, swapping revenue development reps between sales and marketing, or rebranding revenue operations under a “go-to-market” title, but superficial changes won’t suffice. Instead, the focus should be on resetting strategy and planning around customers, developing shared KPIs for marketing and sales teams, fixing broken revenue processes, improving operational effectiveness, building stakeholder trust, and enhancing talent to blend human and machine competencies. Prediction 2: Generative AI will drive B2B buyers to consider more vendors in the purchase cycle. We predict that generative AI (genAI) will drive 50% of B2B buyers to consider five or more providers for large purchases but will still shrink buying cycles. GenAI has been adopted faster than any technology in history, significantly changing B2B buying behavior. Forrester’s Buyers’ Journey Survey, 2024, revealed that B2B buyers are now spoiled for choice, with genAI aiding in more thorough research during the sourcing and provider evaluation process. A survey of nearly 600 Asia Pacific purchase influencers involved in B2B purchases of USD$1 million found that 91% of business buyers using or planning to use genAI reported achieving better business outcomes. Additionally, 65% of buyers considered more than one provider, with one-third considering five or more vendors. GenAI is also compressing the buying cycle, with 65% of buyers who are using genAI to inform their purchases reporting quicker decision-making. Marketers must respond to this compressed sales cycle by reaching buyers before they enter an active sales cycle, focusing on their core target audience, and optimizing their generative presence. Prediction 3: AI coworkers in marketing will become commonplace. We predict that AI coworkers will emerge as valued team members in two out of five organizations but won’t affect marketing departments’ headcount in 2025. AI may eventually reduce the human marketing function, but this won’t happen in 2025. While AI-powered assistants are becoming smarter, marketers still don’t fully trust them (29% of genAI decision-makers say that lack of trust in AI is a significant barrier to adoption). Investment in B2B conversation automation solutions continues to grow, with 55% of global B2B marketing leaders planning to increase spending on this technology. This will expand further in 2025 to support use cases requiring real-time contextual insights and output to fuel marketing and sales processes. As AI-powered chatbots and assistants evolve from experiments to essential components of the B2B martech stack, they will become trusted coworkers working alongside humans, supporting a wide range of use cases across the growth engine, engaging prospects and customers in conversations across channels, and automating back-end tasks with greater autonomy. It is crucial for organizations to get started with agentic AI solutions to drive greater efficiency and effectiveness in process workflows. If you would like to discuss how your company can benefit from these predictions and build a more effective marketing organization, feel free to reach out by contacting us. source

Marketing campaign templates help you add structure and clarity to upcoming campaigns. They can also help ensure each campaign aligns with big-picture goals and contains a full plan on how to execute, track, and measure everything. Some campaigns are quick one-off emails while others last weeks or months and encompass multiple marketing channels. This post covers the best free templates for various tools and situations, from complex campaigns using project management tools to simpler approaches using a spreadsheet. Top marketing campaign templates compared Every marketing campaign template in this guide is 100% free to use. Some work with standard tools you might already have, like Excel, Google Sheets, or Google Docs, while others require you to sign up for a dedicated tool. Costs aside, these are the notable differences. Best for Template type Built in collaboration Complexity Estimated setup monday.com Best overall Project management tool Yes Medium 30-60 min ClickUp High-volume content creation Project management tool Yes High 2+ hours Wrike Marketing agencies Project management tool Yes High 3+ hours Asana Small teams Project management tool Yes Low 30 min Notion Campaign brief management Online document editor Limited Medium 30-45 min Confluence Identifying deliverables Online document editor Limited Low 30-60 min Smartsheet Budget tracking Spreadsheet No Low 20 min HubSpot Keeping it simple Document No Low 5 min monday.com: Manage campaigns, projects, and tasks in one Monday.com is a great project management tool that lets you plan campaigns and manage all of the work in the same place. You can even use it to track other types of projects, your budget, digital assets, content, and more. With built-in project management features, you’ll be able to easily create tasks, assign ownership to your team, and stay on top of everything. More monday.com coverage: monday.com Review | Trello vs monday.com | Asana vs monday.com.  monday.com makes it easy to plan, track, and execute campaigns alongside other types of projects in one platform. Image: monday.com One of the more unique aspects of this marketing campaign template is that you can import campaigns you’ve started in a spreadsheet. This makes it a great option if you’ve previously managed everything in Excel or Google Sheets and want an upgrade. The template also includes a timeline view for each campaign, automations that move tasks through the workflow, and team collaboration features — commenting, @ mentions, and prioritization capabilities to name a few. One of the biggest benefits is that you can use it to manage multiple campaigns. There are a few different ways you can set it up and you can add additional trackers in the same tool, giving you ultimate flexibility if you’re willing to customize a bit on your own. ClickUp: A high-volume marketing campaign template ClickUp also checks all the boxes for campaign planning and execution within a project management solution. It offers one of the most generous free plans with up to five spaces, unlimited users, and unlimited tasks. Beyond the free plan, it becomes more and more similar to monday.com. Both are just as capable of campaign management scale — many people use personal preference to decide between the two. More ClickUp coverage: ClickUp review | Asana vs ClickUp | ClickUp vs Notion.  You can use this ClickUp template to plan and manage all of your campaigns simultaneously. Image: clickup.com Although ClickUp will work just fine for simple campaign management, this template is more advanced, and better suited for planning and execution at scale. It’s not difficult to use but it does have a lot of functionality built in, allowing for easy management of dozens of concurrent campaigns and projects across an entire marketing department. The template comes with seven project views, 11 custom fields, built-in budget tracking, and automated task dependency warnings. Once you copy the template into your account, you’ll be able to define channel-specific objectives, map out entire campaigns, assign them to teams or individuals, set deadlings, and prioritize work however you’d like. But the best part is that you can track progress against one campaign, several campaigns, or all of them at once. Wrike: A marketing campaign template for agencies Wrike is a popular project management tool that’s the perfect blend of simplicity and power. It’s easy to scale, which makes it an excellent choice for agencies looking for a better way to manage client campaigns from a single workspace. Wrike’s free forever plan is a good place to start and feel out the platform. But for agencies that want to onboard clients, leverage automations, and set up an approval flow, you’ll need to upgrade to a paid plan. More on Wrike: Wrike Review | Wrike vs Smartsheet | Wrike vs Asana. Break down client’s marketing campaigns into manageable tasks with Wrike. Image: Wrike.com Staying organized when planning, launching, and tracking marketing campaigns for multiple clients can quickly become overwhelming. Members of your team are likely working on tasks for multiple clients at the same time, and the lines between each project can start to blur if you’re not careful. Wrike’s marketing campaign template simplifies all of this. It’s built specifically for agencies, so you can create separate workspaces for all of your clients. From there, Wrike makes it easy to manage each campaign separately while still being able to roll everything up into a single view. My favorite part of this template is that you can create a custom request form. Clients can easily submit it and a new task will be instantly added to the system. If you’d like, these inbound requests can even be assigned to a specific person with time-based due dates automatically. Wrike also lets you create custom dashboards and reports so you can view real-time metrics for marketing campaigns. These reports are easy to share, so you can pass along progress to your clients to keep them looped in. The main drawback of this template is it’s complexity. Since you’ll be managing different spaces for each client, it can take time to set up. It can also take time for your

How Latinos describe machismo is linked to their views on whether it is a good or bad thing and whether they say they behave in a way that is consistent with it. In general, Hispanics who have a negative perception of machismo are less likely to say they act in a way they consider consistent with the term.   Most Latinos who have heard of ‘machismo’ view it as a bad thing Nearly three-in-four Latinos (73%) who have heard of machismo say it is a very or somewhat bad thing among Latinos. Meanwhile, 7% say it is a very or somewhat good thing and 18% say it is neither. By Latinos’ descriptions of ‘machismo’ Nine-in-ten Latinos view the term machismo as the belief that men are superior to or better than women say it is a very or somewhat bad thing. Equal shares of those who describe machismo as acting in a dominating or aggressive way (88%) – or as the belief that men and women should have certain roles in society based on their gender (88%) – say it is a bad thing. Smaller shares of those who describe it in other ways say machismo is bad thing among Latinos: 61% who define machismo as acting strong, tough or unemotional say it is a bad thing. 53% who define machismo as acting with emphasized or prideful masculinity say it is a bad thing. And about a third who define it this way (32%) say it is neither a good nor bad thing. Roughly four-in-ten Latinos who define machismo as acting confident, chivalrous or protective (43%) see it negatively, while 23% see it positively. About one-third say it is neither positive nor negative (31%).   By gender Overall, 79% of Hispanic women and 66% of Hispanic men who have heard of machismo say it is a bad thing. While how Hispanics view machismo is generally linked to how they define it, there are some differences by gender among those who describe it the same way: 63% of Hispanic women who define machismo as acting with emphasized or prideful masculinity say it is a very or somewhat bad thing. A smaller share of Hispanic men who define it the same way (46%) say this. About 1 in 5 Latinos aware of ‘machismo’ say they behave in a way consistent with it Roughly one-in-five Latino adults (22%) who have heard of machismo say they act in a way that personifies the term. This includes 16% who say they sometimes act this way and 6% who say they extremely or very often do. By Latinos’ descriptions of ‘machismo’ Whether Latinos say they display machismo behavior is linked to how they define the concept. Among Latinos who define machismo as acting confident, chivalrous or protective, 35% say they often or sometimes act in a way that can be considered consistent with machismo. And 30% who define it as acting with emphasized or prideful masculinity say they act this way at least sometimes. Meanwhile, Latinos who describe machismo in other ways are less likely to say they often or sometimes act in a way that represents machismo. 16% who define it as acting dominating or aggressive say they behave in a way that is representative of machismo. 14% who define it as the belief that men are superior to or better than women say they act in a way representative of machismo. By gender Among those who are aware of the term machismo, Hispanic men (28%) are more likely than Hispanic women (17%) to say they act in a way that personifies the term. The shares who say they act this way still differ among men and women who agree on what the term means. Hispanic men who define machismo as acting with emphasized or prideful masculinity are much more likely than Hispanic women who define it this way to say they exhibit machismo behavior (45% vs. 10%). Hispanic men who describe machismo as acting strong, tough or unemotional are more likely than Hispanic women who use this definition to say their behavior at least sometimes exemplifies machismo (39% vs. 20%).   Meanwhile, similar shares of Hispanic men and women who define machismo as the belief that men are superior to women say they act in a way that is consistent with the term. (While machismo is often associated with men, both men and women can display machismo behavior.) For more details on Hispanics’ views of and experiences with machismo, including by age and political party, refer to the appendix. source

In what can only be labeled as a very encouraging trend, jobs and projects abound for tech professionals wanting to use their skills and expertise to try and make our planet and climate well again. These opportunities fall under the umbrella category of climate technology and involve full-time careers, part-time jobs, and volunteer opportunities. One of the fastest-growing industries in the world, climate tech — and its companion area of nature tech — require a wide range of skills to help solve significant environmental problems. In especially high demand are IT pros with software development, data science and machine learning skills. Projects needing the most IT job and skills help Climate tech professionals can lend their skills and talents in a wide range of areas, explains Kanani Breckenridge, CEO and “headhuntress” at Kismet Search. She works with commercially focused companies developing technologies to support and boost projects and products that impact multiple sectors within greentech. In the U.S., common projects for climate tech professionals are related to EV infrastructure (solar, wind, and nuclear projects), smart grids, and corporate carbon tracking analytics — which is fueled in a large part by government subsidies and funding, Breckenridge explains. In Europe and some other regions, the priority is often with projects related to smart cities, circular economies, and renewable energy integration. Water management projects are more dominant in water-scarce regions, Breckenridge says. She notes, however, that the green sector has a lot of overlap globally as climate and sustainability goals become increasingly universal. While crucial, if organizations are only monitoring environmental metrics, they are missing critical pieces of a comprehensive environmental, social, and governance (ESG) program and are unable to fully understand their impacts. IDC’s Sustainability Readiness Survey 2024 shows that the top 2 areas of ESG/sustainability-related investment for organizations are IT infrastructure efficiency assessments and investments (cited by 41.9% of survey respondents) and circular economy implementations (40.2%). Industries and sectors where the jobs are most plentiful Organizations hiring IT professionals in the climate and greentech space include renewable energy companies (such as solar and wind providers), greentech and agritech startups developing innovative sustainability solutions, and corporate sustainability teams in large organizations focused on reducing carbon footprints, Breckenridge says. Government agencies and nonprofits also seek IT talent for environmental data analysis and policy development. Additionally, nuclear power companies and energy infrastructure firms are hiring to optimize and secure energy systems, while smart city developers need IoT and AI specialists to build sustainable and connected urban environments, Breckenridge explains. In the climate and green sector, IT pros are the backbone of innovation across multiple areas, Breckenridge says. Some of the most common IT needs per specific sector within the broader climate technology space, according to Breckenridge, are: “Renewable energy companies need cloud engineers and data scientists to make smart grids work and integrate renewables like wind and solar.” “Agritech firms are hiring IoT and AI experts to streamline farming — think smart irrigation and predictive crop analytics.” “In the EV and battery space, software engineers and product managers are driving the build-out of connected charging networks and improving battery life.” “Greentech startups and corporate sustainability teams are bringing in AI talent to track carbon emissions and cut waste.” “Government agencies and nonprofits are looking for data scientists and engineers to help with climate modeling and environmental impact analysis.” Breckenridge points out that cybersecurity professionals are also important across all sectors because “securing all this critical infrastructure is just as important as building it.” The tech professionals most in demand The most in-demand technical skills in the climate and greentech sector revolve around cloud computing, data analytics, IoT, and cybersecurity — each playing a critical role in driving sustainable innovation, Breckenridge explains. She breaks down the skills demands as follows: Cloud architects/engineers: Cloud platforms skills with AWS, Microsoft Azure, and Google Cloud are essential for managing the massive datasets generated by renewable energy grids, smart cities, and sustainability projects. IT professionals with expertise in cloud architecture and optimization are needed to ensure these systems are scalable, efficient, and capable of real-time environmental monitoring, Breckenridge says. Data scientists and AI/ML engineers: These skills are in high demand, since large-scale data analytics that drive decision-making are also key to efforts related to sustainability, Breckenridge explains. Skills in Python, R, TensorFlow, and Apache Spark enable professionals to build predictive models for energy usage, optimize resource allocation, and analyze environmental impacts. This is where machine learning algorithms become indispensable for tasks such as predicting energy loads or modeling climate patterns. Companies that are also typically associated with more legacy/non-climate-friendly energy such as oil and gas and mining are also utilizing analytics to optimize efficiencies. Edge device (IoT) engineers: Those skilled in protocols such as MQTT and LoRaWAN, and tools such as Azure IoT and Google IoT Core, are building connected devices that manage everything from smart grids to water conservation, Breckenridge says. These systems collect real-time data to optimize energy distribution, reduce waste, and monitor environmental conditions, making IoT a core piece within smart and sustainable infrastructure. Cybersecurity engineering specialists: With all this digital innovation comes a growing need for cybersecurity engineering skills, especially related to critical infrastructure such as energy grids and EV charging networks, Breckenridge says. As they become increasingly digital, securing these systems is critical for preventing potentially disastrous outages and events. Cybersecurity experts fluent in tools such as those from Palo Alto Networks, Cisco, and Splunk are vital for protecting against cyberattacks and ensuring the resilience of sustainable systems. DevOps and software engineers: To build out infrastructure and applications, DevOps and software engineering skills are critical for making the solutions that drive/support climate efforts and tech, Breckenridge says. Proficiency with tools such as Kubernetes, Docker, and Jenkins enables teams to build and deploy these solutions to make sure these systems can scale. ESG skills becoming a must-have at some organizations As organizations and government agencies face increasing pressure to meet sustainability goals, knowledge of ESG reporting tools is becoming a must-have in many IT

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More On the heels of releasing its new generative AI models, Google updated its Code Assist tools to work with Gemini 2.0 and expanded the external data sources it connects to.  Code Assist will now run on the recently released Gemini 2.0, offering a larger context window to understand bigger code bases from enterprises.  Google will also launch Gemini Code Assist tools in a private preview. The platform will connect to data sources like GitLab, GitHub, Google Docs, Sentry.io, Atlassian and Snyk. This will allow developers and other coders to ask Code Assist for help directly in their IDEs. Previously, Code Assist connected to VS Code and JetBrains.  Google Cloud senior director for product management Ryan J. Salva told VentureBeat in an interview that the idea is to allow coders to add more context to their work without interrupting their flow. Salva said Google will add more partners in the future.  Formerly Duet AI, Code Assist was launched for enterprises in October. As organizations sought ways to streamline coding projects, demand for AI coding platforms like GitHub Copilot grew. Code Assist added enterprise-grade security and legal indemnification when the enterprise option was released.   AI where developers work Salva said connecting Code Assist to other tools developers use provides more context for their work without them having to simultaneously open multiple windows.  “There’s so many other tools that a developer uses in the course of a day,” Salva said. “They might use GitHub or Atlassian Jira or DataDog or Snyk or all these other tools. What we wanted to do is to enable developers to bring in that additional context to their IDE.” Demo of Code Assist Salva said developers just need to open the Code Assist chat window and ask it to summarize the most recent comments for particular issues or the most recent pull requests on repositories, “so that it queries the data source and brings the context back to the IDE and [the] large language model can synthesize it.” AI code assistants were some of the first significant use cases for generative AI, especially after software developers began using ChatGPT to help with coding. Since then, a slew of enterprise-focused coding assistants have been released. GitHub released Copilot Enterprise in February, and Oracle launched its Java and SQL coding assistant. Harness came out with a coding assistant built with Gemini that gives real-time suggestions.  Meanwhile, OpenAI and Anthropic began offering interface features that let coders work directly on their chat platforms. ChatGPT’s Canvas lets users generate and edit code without copying and pasting it elsewhere. OpenAI also added integrations to tools like VS Code, XCode, Terminal and iTerm 2 from the ChatGPT MacOS desktop app. Meanwhile, Anthropic launched Artifacts for Claude so Claude users can generate, edit and run code.  Not Jules Salva pointed out that while Code Assist now supports Gemini 2.0, it remains wholly separate from Jules, the coding tool Google announced during the launch of the new Gemini models.  “Jules is really one of the many experiments to emerge out of the Google Labs team to show how we can use autonomous or semiautonomous agents to automate the process of coding,” Salva said. “You can expect that over time, the experiments that graduate from Google Labs, those same capabilities, might become a part of products like Gemini Code Assist.” He added that his team works closely with the Jules team and is excited to see Jules progress, but Code Assist remains the only generally available enterprise-grade coding tool powered by Gemini.  Salva said early feedback from Code Assist and Jules users shows great interest in Gemini 2.0’s latency improvements.  “When you’re sitting there trying to code and trying to stay in the flow state, you want those kinds of responses to come up in milliseconds. Any moment the developer feels like they’re waiting for the tool is a bad thing, and so we’re getting faster and faster responses out of it,” he said.  Coding assistants will still be crucial to the growth of the generative AI space, but Salva said the next few years may see a change in how companies develop code generation models and applications. Salva pointed to the 2024 Accelerate State of DevOps Report from Google’s DevOps Research and Assessment team, which showed 39% of respondents distrusted AI-generated code and a decline in documentation and delivery quality.  “We have as an industry with AI assistive tools focused largely on throughput productivity improvements and velocity improvements over the course of the last four years,” Salva said. “And as we’re starting to see that that be associated with a drop in overall stability, I suspect here that the conversation in the next year is really going to shift to how are we using AI to improve quality across multiple dimensions.” source