Ivy Grant, SVP of Strategy & Operations, Twilio June 13, 20255 Min Readpeshkova via alamy stockAsk technologists and enterprise leaders what they hope AI will deliver, and most will land on some iteration of the "T" word: transformation. No surprise, AI and its “cooler than you” cousin, generative AI (GenAI), have been hyped nonstop for the past 24 months. But therein lies the problem. Many organizations are rushing to implement AI without a grasp on the return on investment (ROI), leading to high spend and low impact. Without anchoring AI to clear friction points and acceleration opportunities, companies invite fatigue, anxiety and competitive risk. Two-thirds of C-suite execs say GenAI has created tension and division within their organizations; nearly half say it’s “tearing their company apart.” Most (71%) report adoption challenges; more than a third call it a massive disappointment. While AI's potential is irrefutable, companies need to reject the narrative of AI as a standalone strategy or transformational savior. Its true power is as a catalyst to amplify what already works and surface what could. Here are three principles to make that happen. 1. Start with friction, not function Many enterprises struggle with where to start when integrating AI. My advice: Start where the pain is greatest. Identify the processes that create the most friction and work backward from there. AI is a tool, not a solution. By mapping real pain points to AI use cases, you can hone investments to the ripest fruit rather than simply where it hangs at the lowest. Related:For example, one of our top sources of customer pain was troubleshooting undeliverable messages, which forced users to sift through error code documentation. To solve this, an AI assistant was introduced to detect anomalies, explain causes in natural language, and guide customers toward resolution. We achieved a 97% real-time resolution rate through a blend of conversational AI and live support. Most companies have long-standing friction points that support teams routinely explain. Or that you’ve developed organizational calluses over; problems considered “just the cost of doing business.” GenAI allows leaders to revisit these areas and reimagine what’s possible. 2. The need for (dual) speed We hear stories of leaders pushing an “all or nothing” version of AI transformation: Use AI to cut functional headcount or die. Rather than leading with a “stick” through wholesale transformation mandates or threats to budgets, we must recognize AI implementation as a fundamental culture change. Just as you wouldn't expect to transform your company culture overnight by edict, it's unreasonable to expect something different from your AI transformation. Related:Some leaders have a tendency to move faster than the innovation ability or comfort level of their people. Most functional leads aren’t obstinate in their slow adoption of AI tools, their long-held beliefs to run a process or to assess risks. We hired these leaders for their decades of experience in “what good looks like” and deep expertise in incremental improvements; then we expect them to suddenly define a futuristic vision that challenges their own beliefs. As executive leaders, we must give grace, space and plenty of “carrots” -- incentives, training, and support resources -- to help them reimagine complex workflows with AI. And, we must recognize that AI has the ability to make progress in ways that may not immediately create cost efficiencies, such as for operational improvements that require data cleansing, deep analytics, forecasting, dynamic pricing, and signal sensing. These aren’t the sexy parts of AI, but they’re the types of issues that require superhuman intelligence and complex problem-solving that AI was made for. 3. A flywheel of acceleration The other transformation that AI should support is creating faster and broader “test and learn” cycles. AI implementation is not a linear process with start here and end there. Organizations that want to leverage AI as a competitive advantage should establish use cases where AI can break down company silos and act as a catalyst to identify the next opportunity. That identifies the next as a flywheel of acceleration. This flywheel builds on accumulated learnings, making small successes into larger wins while avoiding costly AI disasters from rushed implementation. Related:For example, at Twilio we are building a customer intelligence platform that analyzes thousands of conversations to identify patterns and drive insights. If we see multiple customers mention a competitor's pricing, it could signal a take-out campaign. What once took weeks to recognize and escalate can now be done in near real-time and used for highly coordinated activations across marketing, product, sales, and other teams. With every AI acceleration win, we uncover more places to improve hand-offs, activation speed, and business decision-making. That flywheel of innovation is how true AI transformation begins to drive impactful business outcomes. Ideas to Fuel Your AI Strategy Organizations can accelerate their AI implementations through these simple shifts in approach: Revisit your long-standing friction points, both customer-facing and internal, across your organization -- particularly explore the ones you thought were “the cost of doing business” Don’t just look for where AI can reduce manual processes, but find the highly complex problems and start experimenting Support your functional experts with AI-driven training, resources, tools, and incentives to help them challenge their long-held beliefs about what works for the future Treat AI implementation as a cultural change that requires time, experimentation, learning, and carrots (not just sticks) Recognize that transformation starts with a flywheel of acceleration, where each new experiment can lead to the next big discovery The most impactful AI implementations don’t rush transformation; they strategically accelerate core capabilities and unlock new ones to drive measurable change. About the AuthorIvy GrantSVP of Strategy & Operations, Twilio Ivy Grant is Senior Vice President of Strategy & Operations at Twilio where she leads strategic planning, enterprise analytics, M&A Integration and is responsible for driving transformational initiatives that enable Twilio to continuously improve its operations. Prior to Twilio, Ivy’s career has balanced senior roles in strategy consulting at McKinsey & Company, Edelman and PwC with customer-centric operational roles at Walmart, Polo Ralph Lauren and tech startup Eversight Labs. She loves solo international travel, hugging exotic animals and boxing. Ivy has an MBA from NYU’s Stern School of Business and a BS in Applied Economics from Cornell University. See more from Ivy GrantReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

CIOs and business leaders know they’re sitting on a goldmine of business data. And while traditional tools such as business intelligence platforms and statistical analysis software can effectively surface insights from the collated data resources, doing so quickly, in real-time and at scale remains an unsolved challenge.Enterprise AI, when deployed responsibly and at scale, can turn these bottlenecks into opportunities. Acting quickly on data, even ‘live’ (during a customer interaction, for example), is one of the technology’s abilities, as is scalability: AI can process large amounts of information from disparate sources almost as easily as it can summarize a one-page spreadsheet.But deploying an AI solution in the modern enterprise isn’t simple. It takes structure, trust and the right talent. Along with the practical implementation challenges, using AI brings its own challenges, such as data governance, the need to impose guardrails on AI responses and training data, and persistent staffing issues.We met with Rani Radhakrishnan, PwC Principal, Technology Managed Services – AI, Data Analytics and Insights, to talk candidly about what’s working — and what’s holding back CIOs in their AI journey. We spoke ahead of her speaking engagement at TechEx AI & Big Data Expo North America, June 4 and 5, at the Santa Clara Convention Center.Rani is especially attuned to some of the governance, data privacy and sovereignty issues that face enterprises, having spent many years in her career working with numerous clients in the health sector — an area where issues like privacy, data oversight and above all data accuracy are make-or-break aspects of technology deployments.“It’s not enough to just have a prompt engineer or a Python developer. … You still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.” —Rani Radhakrishnan, PwCFrom support to strategy: shifting expectations for AIRani said that there’s a growing enthusiasm from PwC’s clients for AI-powered managed services that can provide both business insights in every sector, and for the technology to be used more proactively, in so-called agentic roles where agents can independently act on data and user input; where autonomous AI agents can take action based on interactions with humans, access to data resources and automation.For example, PwC’s agent OS is a modular AI platform that connects systems and scales intelligent agents into workflows, many times faster than traditional computing methods. It’s an example of how PwC responds to the demand for AI from its clients, many of whom see the potential of this new technology, but lack the in-house expertise and staff to act on their needs.Depending on the sector of the organization, the interest in AI can come from many different places in the business. Proactive monitoring of physical or digital systems; predictive maintenance in manufacturing or engineering; or cost efficiencies won by automation in complex, customer-facing environments, are just a few examples.But regardless of where AI can bring value, most companies don’t yet have in-house the range of skills and people necessary for effective AI deployment — or at least, deployments that achieve ROI and don’t come with significant risk.“It’s not enough to just have a prompt engineer or a Python developer,” Rani said. “You’ve got to put all of these together in a very structured manner, and you still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.”Cleaning house: the data challenge behind AIRani says that effective AI implementations need a mix of technical skills — data engineering, data science, prompt engineering — in combination with an organization’s domain expertise. Internal domain expertise can define the right outcomes, and technical staff can cover the responsible AI practices, like data collation and governance, and confirm that AI systems work responsibly and within company guidelines.“In order to get the most value out of AI, an organization has to get the underlying data right,” she said. “I don’t know of a single company that says its data is in great shape … you’ve got to get it into the right structure and normalize it properly so you can query, analyze, and annotate it and identify emerging trends.”Part of the work enterprises have to put in for effective AI use is the observation for and correction of bias — in both output of AI systems and in the analysis of potential bias inherent in training and operational data.It’s important that as part of the underlying architecture of AI systems, teams apply stringent data sanitization, normalization, and data annotation processes. The latter requires “a lot of human effort,” Rani said, and the skilled personnel required are among the new breed of data professionals that are beginning to emerge.If data and personnel challenges can be overcome, then the feedback loop makes the possible outcomes from generative AI really valuable, Rani said. “Now you have an opportunity with AI prompts to go back and refine the answer that you get. And that’s what makes it so unique and so valuable because now you’re training the model to answer the questions the way you want them answered.”For CIOs, the shift isn’t just about tech enablement. It’s about integrating AI into enterprise architecture, aligning with business strategy, and managing the governance risks that come with scale. CIOs are becoming AI stewards — architecting not just systems, but trust and transformation.ConclusionIt’s only been a few years since AI emerged from its roots in academic computer science research, so it’s understandable that today’s enterprise organizations are, to a certain extent, feeling their way towards realizing AI’s potential.But a new playbook is emerging — one that helps CIOs access the value held in their data reserves, in business strategy, operational improvement, customer-facing experiences and a dozen more areas of the business.As a company that’s steeped in experience with clients large and small from all over the world, PwC is one of the leading choices that decision-makers turn to, to begin or rationalize and direct their existing AI journeys.Explore how PwC is helping CIOs embed AI into core operations, and see Rani’s latest insights at the June TechEx AI & Big Data Expo North America.(Image source: “Network Rack” by one individual is licensed under CC BY-SA 2.0.)

Looker_Studio - stock.adobe.com News Analysis of job vacancies shows earnings boost for AI skills Even when parts of a job are being automated, those who know how to work with artificial intelligence tools can expect higher salaries By Cliff Saran, Managing Editor Published: 03 Jun 2025 7:00 UK workers with skills in artificial intelligence (AI) appear to earn 11% more on average, even in sectors where AI is automating parts of their existing job functions. Workers in sectors exposed to AI, where the technology can be deployed for some tasks, are more productive and command higher salaries, according to PwC’s 2025 Global AI Jobs Barometer. The study, which was based on an analysis of almost one billion job adverts, found that wages are rising twice as fast in industries most exposed to AI. From a skills perspective, PwC reported that AI is changing the skills required of job applicants. According to PwC, to succeed in the workplace, candidates are more likely to need experience in using AI tools and the ability to demonstrate critical thinking and collaboration. Phillippa O’Connor, chief people officer at PwC UK, noted that while degrees are still important for many jobs, a reduction in degree requirements suggests employers are looking at a broader range of measures to assess skills and potential. In occupations most exposed to AI, PwC noted that the skills sought by employers are changing 59% faster than in occupations least exposed to AI. “AI is reshaping the jobs market – lowering barriers to entry in some areas, while raising the bar on the skills required in others,” O’Connor added. Those with the right AI skills are being rewarded with higher salaries. In fact, PwC found that wages are growing twice as fast in AI-exposed industries. This includes jobs that are classed as “automatable”, which means they contain some tasks that can readily be automated. The highest premiums are attached to occupations requiring AI skills, with an average premium in 2024 of 11% for UK workers in these roles.   AI is reshaping the jobs market – lowering barriers to entry in some areas, while raising the bar on the skills required in others Phillippa O’Connor PwC UK PwC’s analysis shows that sectors exposed to AI experience three times higher growth in the revenue generated by each employee. It also reported that growth in revenue per employee for AI-exposed industries surged when large language models (LLMs) such as generative AI (GenAI) became mainstream. Revenue growth per employee has nearly quadrupled in industries most exposed to AI, such as software, rising from 7% between 2018 and 2022, to 27% between 2018 and 2024. In contrast, revenue growth per employee in industries least exposed to AI, such as mining and hospitality, fell slightly, from 10% between 2018 and 2022, to 9% between 2018 and 2024. However, since 2018, job postings for occupations with greater exposure to AI have grown at a slower pace than those with lower exposure – and this gap is widening. Umang Paw, chief technology officer (CTO) at PwC UK, said: “There are still many unknowns about AI’s potential. AI can provide stardust to those ready to adapt, but risks leaving others behind.” Paw believes there needs to be a concerted effort to expand access to technology and training to ensure the benefits of AI are widely shared. “In the intelligence age, the fusion of AI with technologies like real-time data analytics – and businesses broadening their products and services – will create new industries and fresh job opportunities,” Paw added. Read more about AI skills AWS addresses the skills barrier holding back enterprises: The AWS Summit in London saw the public cloud giant appoint itself to take on the task of skilling up hundreds of thousands of UK people in using AI technologies. Could generative AI help to fill the skills gap in engineering: The role of artificial intelligence and machine learning in society continues to be hotly debated as the tools promise to revolutionise our lives, but how will they affect the engineering sector? In The Current Issue: UK government outlines plan to surveil migrants with eVisa data Why we must reform the Computer Misuse Act: A cyber pro speaks out Download Current Issue What to expect from Aera Technology AeraHUB 25 – CW Developer Network NTT IOWN all-photonics ‘saves Princess Miku’ from dragon – CW Developer Network View All Blogs

Virtual reality (VR) is a game changer, and it’s here to stay. No longer a tech novelty or niche hobby, VR is rapidly shifting how we design, create, and share everything from cars and buildings to games and films.Around the world, industries of all sorts are realizing VR’s potential and creating exciting new career paths for tomorrow’s workforce. In fact, PWC estimates that 23 million jobs will involve AR and VR globally by 2030, augmenting the many roles already available today.As use cases expand, so does demand for VR talent. However, unequal access to equipment and educator training leaves many institutions struggling to prepare students for the VR boom’s emergent careers.This year, Unity and Meta Immersive Learning partnered to help educators and institutions overcome these challenges. Read on to learn more.Virtual reality is predicted to grow 170% globally in the next 10 years, but educators and students eager to dive into VR today can often find it hard to get started.One reason for this is the wide disparity in institutional funding. VR relies on hardware and software that can be prohibitively expensive to acquire. At a time when many secondary and post-secondary institutions are systemically underfunded, students from lower-income backgrounds who attend these schools are less likely to have access to the tools needed to develop their VR skills.Securing the right equipment is only half the battle. In order to provide high-quality VR education, instructors also need access to training and resources so they can teach with confidence. VR is a new frontier for many educators just as it is for their students, so resources need to cater to both types of learners to achieve the best outcomes.To address these barriers, Unity and Meta Immersive Learning worked together to design the Create with VR Grant program, which aims to reduce the VR learning gap by:1. Increasing access to the hardware and software needed to create and consume VR content2. Increasing educator preparation and offering resources for teaching VR creationVR headset distributionThis spring, Meta Quest 2 VR headsets were made available to secondary and post-secondary education institutions that applied to the grant program. Applications were selected based on the applicant institutions’ current and future plans for teaching VR, as well as the demographic makeup of their student bodies and their unique challenges related to accessing funding.Educator trainingIn concert with the distribution of VR headsets, the Create with VR for Educators professional development training opened for registration in April 2022, offering a crash course in technical skills and pedagogical approaches for teaching VR effectively.School representatives, educators, IT administrators, and lab leaders of all skill levels were encouraged to sign up for the training, which consists of live, online sessions supplemented by self-paced learning and virtual office hours.“The Create with VR Grant bridges the digital divide, affording meaningful interactions and experiences with VR for our students. Whether they are creators, gamers, or engaged in immersive learning, the Create with VR Grant allows us to integrate VR/AR/XR lessons across disciplines and programs. This access builds technical literacy and inspires innovation. We are excited about what is now possible.” – Jenny Hanson, Director, Film & New Media and Online and Blended Learning Pedagogies, Augsburg UniversityBy jointly addressing access to hardware and educator preparation, the grant program aims to put high-quality VR education within reach for teachers and students who might otherwise miss out.Seven months after the partnership announcement, the Create with VR Grant program has already helped thousands of learners and educators take their first steps into the exciting world of virtual reality creation – and we’re just getting started. As of November 2022, the program has:Distributed more than 5,300 Meta Quest 2 VR headsets to more than 300 education institutions across the United States, the majority of which teach real-time 3D creation and cater predominantly to underserved students.Provided more than 10,700 students with access to essential VR hardware.Welcomed more than 3,000 secondary and post-secondary educators from around the world into the Create with VR for Educators training program.“The Create with VR program made a tremendous difference in how we approach teaching VR. We used to share one headset in a class of 20 students. Now, each of our students gets to keep a headset for the duration of a semester. This means more opportunities for full immersion in VR and more time spent on creating meaningful experiences.” – Wojciech Lorenc, Chair, Mass Communication Department, Sam Houston State UniversityUnity and Meta are proud to be contributing to a VR-ready workforce. With the right combination of tools, professional development, and teaching content, educators will be able to successfully build their curricula and prepare themselves to empower tomorrow’s creators.“We’re incredibly humbled and excited to hear about the impact that the Create with VR Grant program has had on schools and communities around the country. It has always been our mission to equip educators with the tools needed to prepare students to compete in the job market of tomorrow, and we wholeheartedly believe that when given opportunities like this, we’ll see students flourish.”– Jessica Lindl, Vice President, Social Impact, Unity“My students used to be thrilled to surf on YouTube – now they make videos. They were happy to play Fruit Ninja on their laptops – now they make their own games and animations. They love VR, but never contemplated being able to create in 3D or virtual worlds until now.” – Darlene Bowman, Founder, AusomeTech IndustriesVR and AR have the potential to add $1.5 trillion to the global economy by 2030, and today’s learners deserve access to the training they’ll need to reap the benefits of the immersive technology boom. We encourage you to join Unity and Meta on our shared mission to make VR education accessible to all.Ready to get started? Sign in to Unity Learn to access on-demand Create with VR for Educators sessions that will help you prepare to teach, and dive into our community on Discord to connect with fellow educators leveraging Unity for VR around the world.

Conversational artificial intelligence is centered on enabling large language models (LLMs) to engage in dynamic interactions where user needs are revealed progressively. These systems are widely deployed in tools that assist with coding, writing, and research by interpreting and responding to natural language instructions. The aspiration is for these models to flexibly adjust to changing user inputs over multiple turns, adapting their understanding with each new piece of information. This contrasts with static, single-turn responses and highlights a major design goal: sustaining contextual coherence and delivering accurate outcomes in extended dialogues. A persistent problem in conversational AI is the model’s inability to handle user instructions distributed across multiple conversation turns. Rather than receiving all necessary information simultaneously, LLMs must extract and integrate key details incrementally. However, when the task is not specified upfront, models tend to make early assumptions about what is being asked and attempt final solutions prematurely. This leads to errors that persist through the conversation, as the models often stick to their earlier interpretations. The result is that once an LLM makes a misstep in understanding, it struggles to recover, resulting in incomplete or misguided answers. Most current tools evaluate LLMs using single-turn, fully-specified prompts, where all task requirements are presented in one go. Even in research claiming multi-turn analysis, the conversations are typically episodic, treated as isolated subtasks rather than an evolving flow. These evaluations fail to account for how models behave when the information is fragmented and context must be actively constructed from multiple exchanges. Consequently, evaluations often miss the core difficulty models face: integrating underspecified inputs over several conversational turns without explicit direction. Researchers from Microsoft Research and Salesforce Research introduced a simulation setup that mimics how users reveal information in real conversations. Their “sharded simulation” method takes complete instructions from high-quality benchmarks and splits them into smaller, logically connected parts or “shards.” Each shard delivers a single element of the original instruction, which is then revealed sequentially over multiple turns. This simulates the progressive disclosure of information that happens in practice. The setup includes a simulated user powered by an LLM that decides which shard to reveal next and reformulates it naturally to fit the ongoing context. This setup also uses classification mechanisms to evaluate whether the assistant’s responses attempt a solution or require clarification, further refining the simulation of genuine interaction. The technology developed simulates five types of conversations, including single-turn full instructions and multiple multi-turn setups. In SHARDED simulations, LLMs received instructions one shard at a time, forcing them to wait before proposing a complete answer. This setup evaluated 15 LLMs across six generation tasks: coding, SQL queries, API actions, math problems, data-to-text descriptions, and document summaries. Each task drew from established datasets such as GSM8K, Spider, and ToTTo. For every LLM and instruction, 10 simulations were conducted, totaling over 200,000 simulations. Aptitude, unreliability, and average performance were computed using a percentile-based scoring system, allowing direct comparison of best and worst-case outcomes per model. Across all tasks and models, a consistent decline in performance was observed in the SHARDED setting. On average, performance dropped from 90% in single-turn to 65% in multi-turn scenarios—a 25-point decline. The main cause was not reduced capability but a dramatic rise in unreliability. While aptitude dropped by 16%, unreliability increased by 112%, revealing that models varied wildly in how they performed when information was presented gradually. For example, even top-performing models like GPT-4.1 and Gemini 2.5 Pro exhibited 30-40% average degradations. Additional compute at generation time or lowering randomness (temperature settings) offered only minor improvements in consistency. This research clarifies that even state-of-the-art LLMs are not yet equipped to manage complex conversations where task requirements unfold gradually. The sharded simulation methodology effectively exposes how models falter in adapting to evolving instructions, highlighting the urgent need to improve reliability in multi-turn settings. Enhancing the ability of LLMs to process incomplete instructions over time is essential for real-world applications where conversations are naturally unstructured and incremental. Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Salesforce AI Releases BLIP3-o: A Fully Open-Source Unified Multimodal Model Built with CLIP Embeddings and Flow Matching for Image Understanding and GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/Georgia Tech and Stanford Researchers Introduce MLE-Dojo: A Gym-Style Framework Designed for Training, Evaluating, and Benchmarking Autonomous Machine Learning Engineering (MLE) AgentsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Investigates Test-Time Scaling of English-Centric RLMs for Enhanced Multilingual Reasoning and Domain GeneralizationNikhilhttps://www.marktechpost.com/author/nikhil0980/PwC Releases Executive Guide on Agentic AI: A Strategic Blueprint for Deploying Autonomous Multi-Agent Systems in the Enterprise

Multimodal modeling focuses on building systems to understand and generate content across visual and textual formats. These models are designed to interpret visual scenes and produce new images using natural language prompts. With growing interest in bridging vision and language, researchers are working toward integrating image recognition and image generation capabilities into a unified system. This approach eliminates the need for separate pipelines and opens the path to more coherent and intelligent interactions across modalities. A key challenge in this field is to develop architectures that handle both understanding and generation without compromising the quality of either. Models need to grasp complex visual concepts and produce high-quality images matching user prompts. The difficulty lies in identifying suitable picture representations and training procedures that support both tasks. This problem becomes more evident when the same model is expected to interpret detailed text descriptions and generate visually accurate outputs based on them. It requires alignment of semantic understanding and pixel-level synthesis. Previous approaches have generally used Variational Autoencoders (VAEs) or CLIP-based encoders to represent images. VAEs are efficient for reconstruction but encode lower-level features, often leading to less informative representations. CLIP-based encoders provide high-level semantic embeddings by learning from large-scale image-text pairs. However, CLIP was not built for image reconstruction, making it challenging to use for generation unless paired with models like diffusion decoders. In terms of training, Mean Squared Error (MSE) is widely used for simplicity but tends to produce deterministic outputs. To improve generation diversity and quality, researchers have turned to Flow Matching, which introduces controlled stochasticity and better models the continuous nature of image features. Researchers from Salesforce Research, in collaboration with the University of Maryland and several academic institutions, introduced BLIP3-o, a family of unified multimodal models. The model adopts a dual-stage training strategy where image understanding is learned first, followed by image generation. The proposed system leverages CLIP embeddings to represent images and integrates them with a diffusion transformer to synthesize new visual outputs. Unlike previous joint training methods, the sequential approach maintains the strength of each task independently. The diffusion module is trained while keeping the autoregressive backbone frozen, avoiding task interference. To improve alignment and visual fidelity, the team also curated BLIP3o-60k, a high-quality instruction-tuning dataset created by prompting GPT-4o across varied visual categories, including scenes, objects, gestures, and text. They developed two model versions: an 8-billion parameter model trained with proprietary and public data, and a 4-billion version using only open-source data. The image generation pipeline of BLIP3-o is built on Qwen2.5-VL large language models. Prompts are processed to produce visual features refined through a Flow Matching diffusion transformer. This transformer is based on the Lumina-Next architecture, optimized for speed and quality with 3D rotary position embedding and grouped-query attention. The model encodes each image into 64 fixed-length semantic vectors, regardless of resolution, which supports compact storage and efficient decoding. The research team used a large-scale dataset of 25 million images from sources like CC12M, SA-1B, and JourneyDB to train the models. They extended it with 30 million proprietary samples for the 8B model. They also included 60k instruction-tuning samples covering challenging prompts such as complex gestures and landmarks, generated via GPT-4o. In terms of performance, BLIP3-o demonstrated top scores across multiple benchmarks. The 8B model achieved a GenEval score of 0.84 for image generation alignment and a WISE score of 0.62 for reasoning ability. Image understanding scored 1682.6 on MME-Perception, 647.1 on MME-Cognition, 50.6 on MMMU, and 83.1 on both VQAv2 and TextVQA datasets. A human evaluation comparing BLIP3-o 8B with Janus Pro 7B showed that BLIP3-o was preferred 50.4% of the time for visual quality and 51.5% for prompt alignment. These results are supported by statistically significant p-values (5.05e-06 and 1.16e-05), indicating the superiority of BLIP3-o in subjective quality assessments. This research outlines a clear solution to the dual challenge of image understanding and generation. CLIP embeddings, Flow Matching, and a sequential training strategy demonstrate how the problem can be approached methodically. The BLIP3-o model delivers state-of-the-art results and introduces an efficient and open approach to unified multimodal modeling. Check out the Paper, GitHub Page and Model on Hugging Face. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Georgia Tech and Stanford Researchers Introduce MLE-Dojo: A Gym-Style Framework Designed for Training, Evaluating, and Benchmarking Autonomous Machine Learning Engineering (MLE) AgentsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Investigates Test-Time Scaling of English-Centric RLMs for Enhanced Multilingual Reasoning and Domain GeneralizationNikhilhttps://www.marktechpost.com/author/nikhil0980/PwC Releases Executive Guide on Agentic AI: A Strategic Blueprint for Deploying Autonomous Multi-Agent Systems in the EnterpriseNikhilhttps://www.marktechpost.com/author/nikhil0980/Multimodal AI Needs More Than Modality Support: Researchers Propose General-Level and General-Bench to Evaluate True Synergy in Generalist Models

Machine learning engineering (MLE) involves developing, tuning, and deploying machine learning systems that require iterative experimentation, model optimization, and robust handling of data pipelines. As model complexity increases, so do the challenges associated with orchestrating end-to-end workflows efficiently. Researchers have explored the automation of MLE tasks using AI agents to handle these demands. Large Language Models (LLMs), particularly those with strong coding and problem-solving abilities, have shown potential to enhance this process significantly. Their role in automating structured workflows is now being tested through rigorous benchmarks and environments tailored to emulate real-world MLE scenarios. A primary hurdle in automating machine learning engineering lies in the work’s inherently iterative and feedback-driven nature. Tasks such as hyperparameter tuning, model debugging, and data preprocessing cannot be resolved in one step; they require repeated modifications and evaluations. Traditional evaluation tools for AI models often rely on static datasets and do not allow for real-time error feedback or interactive problem-solving. This limitation prevents LLM agents from learning through trial and error, an essential component for mastering engineering tasks that evolve or require multiple attempts for success. Earlier tools to evaluate LLMs in engineering or coding tasks have mostly focused on individual subtasks or isolated challenges. These include tools like MLAgentBench and DSBench, which rely on narrow test cases sourced from Kaggle competitions or synthetic datasets. While they cover more than basic tasks, they do not enable agents to perform code execution, debugging, or results interpretation in a live setting. Other environments, like SWE-Gym, focus exclusively on software engineering and lack support for machine learning-specific workflows. These limitations have slowed the creation of versatile, high-performing MLE agents that can handle real-time project complexities. Researchers from Georgia Institute of Technology and Stanford University have introduced MLE-Dojo, a framework with an interactive environment that connects LLM agents with real-world machine learning tasks derived from over 200 Kaggle competitions. This framework supports tabular data analysis, computer vision, natural language processing, and time-series forecasting challenges. Research introduced MLE-Dojo to allow agents to write, execute, and revise code in a sandboxed, feedback-rich setting. The goal was to replicate the interactive cycles that human engineers follow, enabling structured learning for agents. The environment includes pre-installed dependencies, evaluation metrics, and supports supervised fine-tuning and reinforcement learning strategies. MLE-Dojo’s structure consists of modular components that support a wide range of MLE challenges. Each task runs within its own Docker container, isolating it for safety and reproducibility. Agents interact with the environment through a Partially Observable Markov Decision Process, receiving observations, performing actions, and gaining rewards based on performance. The environment supports five primary action types: requesting task information, validating code, executing code, retrieving interaction history, and resetting the environment. It also provides a detailed observation space that includes datasets, execution results, and error messages. The agent receives structured feedback after every interaction, allowing for step-wise improvement. This modular setup helps maintain interoperability and simplifies adding new tasks to the system. The evaluation included eight frontier LLMs—Gemini-2.5-Pro, DeepSeek-r1, o3-mini, GPT-4o, GPT-4o-mini, Gemini-2.0-Pro, Gemini-2.0-Flash, and DeepSeek-v3—across four core machine learning domains. Gemini-2.5-Pro achieved the highest Elo rating of 1257, followed by DeepSeek-r1 at 1137 and o3-mini at 1108. Regarding HumanRank, Gemini-2.5-Pro led with 61.95%, indicating its superior performance over human benchmarks. Models like GPT-4o-mini executed code only 20% of the time, adopting conservative strategies, while o3-mini performed executions in over 90% of the cases. The average failure rate for Gemini-2.5-Pro remained the lowest across validation and execution phases, reinforcing its robustness. Among domains, computer vision posed the greatest challenge, with most models scoring under 60 in HumanRank. Reasoning models generally produced longer outputs and maintained stronger performance consistency across iterations. The research highlights the difficulty of applying LLMs to full machine learning workflows. It outlines a comprehensive solution in MLE-Dojo that enables learning through interaction, not just completion. MLE-Dojo sets a new standard for training and evaluating autonomous MLE agents by simulating engineering environments more accurately. Check out theTwitter and don’t forget to join our 90k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Investigates Test-Time Scaling of English-Centric RLMs for Enhanced Multilingual Reasoning and Domain GeneralizationNikhilhttps://www.marktechpost.com/author/nikhil0980/PwC Releases Executive Guide on Agentic AI: A Strategic Blueprint for Deploying Autonomous Multi-Agent Systems in the EnterpriseNikhilhttps://www.marktechpost.com/author/nikhil0980/Multimodal AI Needs More Than Modality Support: Researchers Propose General-Level and General-Bench to Evaluate True Synergy in Generalist ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Effective State-Size (ESS): A Metric to Quantify Memory Utilization in Sequence Models for Performance Optimization

In its latest executive guide, “Agentic AI – The New Frontier in GenAI,” PwC presents a strategic approach for what it defines as the next pivotal evolution in enterprise automation: Agentic Artificial Intelligence. These systems, capable of autonomous decision-making and context-aware interactions, are poised to reconfigure how organizations operate—shifting from traditional software models to orchestrated AI-driven services. From Automation to Autonomous Intelligence Agentic AI is not just another AI trend—it marks a foundational shift. Unlike conventional systems that require human input for each decision point, agentic AI systems operate independently to achieve predefined goals. Drawing on multimodal data (text, audio, images), they reason, plan, adapt, and learn continuously in dynamic environments. PwC identifies six defining capabilities of agentic AI: Autonomy in decision-making Goal-driven behavior aligned with organizational outcomes Environmental interaction to adapt in real time Learning capabilities through reinforcement and historical data Workflow orchestration across complex business functions Multi-agent communication to coordinate actions within distributed systems This architecture enables enterprise-grade systems that go beyond single-task automation to orchestrate entire processes with human-like intelligence and accountability. Closing the Gaps of Traditional AI Approaches The report contrasts agentic AI with earlier generations of chatbots and RAG-based systems. Traditional rule-based bots suffer from rigidity, while retrieval-augmented systems often lack contextual understanding across long interactions. Agentic AI surpasses both by maintaining dialogue memory, reasoning across systems (e.g., CRM, ERP, IVR), and dynamically solving customer issues. PwC envisions micro-agents—each optimized for tasks like inquiry resolution, sentiment analysis, or escalation—coordinated by a central orchestrator to deliver coherent, responsive service experiences. Demonstrated Impact Across Sectors PwC’s guide is grounded in practical use cases spanning industries: JPMorgan Chase has automated legal document analysis via its COiN platform, saving over 360,000 manual review hours annually. Siemens leverages agentic AI for predictive maintenance, improving uptime and cutting maintenance costs by 20%. Amazon uses multimodal agentic models to deliver personalized recommendations, contributing to a 35% increase in sales and improved retention. These examples demonstrate how agentic systems can optimize decision-making, streamline operations, and enhance customer engagement across functions—from finance and healthcare to logistics and retail. A Paradigm Shift: Service-as-a-Software One of the report’s most thought-provoking insights is the rise of service-as-a-software—a departure from traditional licensing models. In this paradigm, organizations pay not for access to software but for task-specific outcomes delivered by AI agents. For instance, instead of maintaining a support center, a business might deploy autonomous agents like Sierra and only pay per successful customer resolution. This model reduces operational costs, expands scalability, and allows organizations to move incrementally from “copilot” to fully autonomous “autopilot” systems. To implement these systems, enterprises can choose from both commercial and open-source frameworks: LangGraph and CrewAI offer enterprise-grade orchestration with integration support. AutoGen and AutoGPT, on the open-source side, support rapid experimentation with multi-agent architectures. The optimal choice depends on integration needs, IT maturity, and long-term scalability goals. Crafting a Strategic Adoption Roadmap PwC emphasizes that success in deploying agentic AI hinges on aligning AI initiatives with business objectives, securing executive sponsorship, and starting with high-impact pilot programs. Equally crucial is preparing the organization with ethical safeguards, data infrastructure, and cross-functional talent. Agentic AI offers more than automation—it promises intelligent, adaptable systems that learn and optimize autonomously. As enterprises recalibrate their AI strategies, those that move early will not only unlock new efficiencies but also shape the next chapter of digital transformation. Download the Guide here. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. Here’s a brief overview of what we’re building at Marktechpost: ML News Community – r/machinelearningnews (92k+ members) Newsletter– airesearchinsights.com/(30k+ subscribers) miniCON AI Events – minicon.marktechpost.com AI Reports & Magazines – magazine.marktechpost.com AI Dev & Research News – marktechpost.com (1M+ monthly readers) Partner with us NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Multimodal AI Needs More Than Modality Support: Researchers Propose General-Level and General-Bench to Evaluate True Synergy in Generalist ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Effective State-Size (ESS): A Metric to Quantify Memory Utilization in Sequence Models for Performance OptimizationNikhilhttps://www.marktechpost.com/author/nikhil0980/Huawei Introduces Pangu Ultra MoE: A 718B-Parameter Sparse Language Model Trained Efficiently on Ascend NPUs Using Simulation-Driven Architecture and System-Level OptimizationNikhilhttps://www.marktechpost.com/author/nikhil0980/Google Redefines Computer Science R&D: A Hybrid Research Model that Merges Innovation with Scalable Engineering

Comments from NISTA’s Matthew Vickerstaff come as ministers weigh up benefits of relaunching initiative next monthThe government’s new infrastructure advisory body has said ministers would need to “learn from the mistakes” of the past if a new generation of PFI contracts are launched as part of the upcoming infrastructure strategy. Matthew Vickerstaff, deputy chief executive of the The National Infrastructure and Service Transformation Authority (NISTA), said there was still a “constant drumbeat” of construction issues on schools built through private finance initiatives (PFI). Matthew Vickerstaff speaking at the Public Accounts Committee yesterday afternoon Chancellor Rachel Reeves is understood to be considering reinstating a form of private financing to pay for public projects, including social infrastructure schemes such as schools, ahead of the launch of its 10-Year Infrastructure Strategy next month. It would be the first major rollout of PFI in England since 2018, when then chancellor Philip Hammond declared the successor scheme to the original PFI programme as “inflexible and overly complex”. >> See also: PFI: Do the numbers add up? Speaking at a meeting of the Public Accounts Committee in Parliament yesterday, Vickerstaff highlighted issues that had blighted historic PFI schemes where construction risk had been transferred to the private sector. “Just what we’re seeing on school projects, leaking roofs is a consistent, constant drum beat, fire door stopping, acoustics, lighting levels, the ability of classrooms to be operable in a white board environment, problems around leisure centres or sports facilities, contamination of land, latent defects of refurbishments on old buildings creating real problems,” he said. “The dash to get the schools ready for September, I cannot tell you how many PFI schools have that problem, and we need to get the private sector to fix it.” But while Vickerstaff said he was “ambivalent” about a new generation of PFI contracts, he argued contractual arrangements on new schemes could contain less risk for the public purse if the government did decide to opt for this route in its infrastructure strategy. “I would say that compared with 25 years ago, the asset management, the building information systems and computer aided facilities management has vastly improved so we’re dealing with a generation of contracts that would certainly by improved whether it’s public sector or private sector,” he said. “I’m ambivalent but what we need to make sure is that we learn from the mistakes and definitely get them to fix what we’re experiencing in some situations.” Vickerstaff added: “In terms of lessons learned, making sure construction is monitored by a clerk of works and independently certified would be a really important factor moving forward, because construction defects have been a problem because the construction contracts whether it be public sector or private sector have not been well monitored or controlled.” Meanwhile, a new report by PwC has called on the government to explore a new generation of public-private finance in order to address the deficit in infrastructure including schools and healthcare. The research, published today, found “strong market appetite” for a new model of public-private partnerships which could be based on the Mutual Investment Model developed in Wales. PwC corporate finance associate director Dan Whittle said: “There is a strong view that public-private finance has a valuable role to play as a strategic tool to close the UK’s infrastructure gap, particularly at a time when we are constrained by fiscal rules. “There is no need to reinvent the fundamentals of the PPP model. What must continue to evolve is how we implement this model with refined risk allocation to reflect the current appetite of the market, smarter contract management, and a genuine partnership approach.” The government is expected to unveil its infrastructure strategy alongside its spending review in June.