Committee

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications. “In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems,” Anand says. “As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration.” That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI system has already told a customer. The challenge is not simply access to data, but the absence of a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems into a common understanding. Traditional CX architecture was built for linear, human-driven routing, not for managing real-time data flows between autonomous AI systems, data lakes, and human workers. “Today’s operational complexity is no longer about adding more intelligence,” he adds. “It is about coordinating the existing intelligence across the enterprise, so the enterprise customer never feels the friction of those internal silos. That requires a shared context layer that allows AI systems, applications, and people to operate from the same understanding of the customer and the business.” Why orchestration is replacing automation as the top CX priority As that coordination problem grows, Anand says the strategic priority inside enterprises is shifting from automation to orchestration. “Automation solves individual tasks, whereas orchestration connects them into end-to-end outcomes,” Anand says. “The next evolution is context-aware orchestration, where AI agents, applications, and human workers operate using a shared understanding of customers, processes, and business intent rather than isolated system records.” As organizations accumulate more bots, agents, and AI tools, managing them grows exponentially more complex. Anand says the competitive advantage now sits less in deploying automation and more in how intelligently systems hand off work, collaborate, and escalate. The trap of bolting AI onto legacy systems Companies that simply place a voice AI agent in front of an existing system are repeating the same old mistake. Instead of improving the experience, they end up recreating the deterministic phone menus AI was supposed to replace. The real benefit of AI is the scale, speed, and orchestration it provides. Anand points to a wave of consolidation across the industry, as established contact center providers acquire AI-native firms to close capability gaps and strengthen their customer experience offerings. The broader industry shift reflects a growing recognition that enterprises need more than channels and automation; they need an intelligence layer capable of orchestrating AI, people, data, and workflows across the business. The goal across industries is to make AI the connective layer between customers, employees, and enterprise systems. To achieve that, organizations increasingly need a common enterprise ontology: a shared business vocabulary that aligns customer data, products, policies, SOPs, transactions, and workflows across otherwise disconnected platforms. Tata Communications’ solution is the Interaction Fabric, an orchestration layer that unifies contact center, messaging, collaboration, AI, and customer data while coordinating AI agents, channels, and enterprise systems in real time. Underpinning that orchestration is a context-driven architecture that continuously connects identities, conversations, transactions, and operational data so interactions retain continuity across channels and touchpoints. That means AI and agents can move across voice, WhatsApp, chat, email, and CRM workflows without losing customer context. Identity, intent, and AI-driven insight flow continuously across channels instead of remaining trapped in disconnected applications. The next phase of orchestration is not simply coordinating tasks across systems, but coordinating them through a shared understanding of the enterprise. Context graphs, built on enterprise ontologies, create that common understanding by connecting customers, interactions, products, policies, decisions, and outcomes across organizational silos. This allows AI agents and human workers to operate from the same source of context, driving more accurate decisions, seamless handoffs, and consistent customer experiences. But synchronizing customer intent, conversation history, enterprise data, and AI decision-making across channels only works without lag. Legacy networks not designed for modern data frequency create what Anand calls data gravity, producing latency and inconsistent journeys as users switch channels. “The underlying network needs to be engineered to be as agile as the AI systems running on top of it,” he explains. “Interactions stay synchronous and technology itself becomes invisible, leaving only an experience that feels effortless.” Making AI a better partner for human agents Effective shared visibility between human agents and AI systems starts with the agent experience rather than any single technology. The most effective implementations allow both the AI and human agent to operate from the same contextual understanding of the customer, ensuring that information gathered in one interaction can inform the next regardless of channel or system. Automated call summaries, real-time sentiment analysis, and AI-powered assistance provide agents with instant, actionable insights and suggested next steps directly within their workflow. That allows AI to handle routine, high-volume tasks such as password resets, delivery tracking, and account updates, while human agents focus on interactions requiring judgment and empathy. “If a customer is facing a sudden crisis like a fraudulent transaction, the AI can instantly block the card, but it cannot provide the emotional comfort and delicate communication needed in that moment of panic,” Anand says. “The answer to the dilemma is intelligent orchestration, rather than a choice between systems.” In practice, AI handles the immediate technical transaction, while real-time sentiment analysis recognizes the customer’s distress and routes the call to a human expert. The objective is to orchestrate AI and human agents together so efficiency never comes at the cost of brand trust and loyalty. Building a unified CX architecture Moving from fragmented experimentation to coordinated orchestration requires both technical and organizational change, Anand says, beginning with consolidating data and fragmented point solutions onto a unified, cloud-first platform. “IT and CX teams need to work

Alibaba’s Qwen team has released Qwen3.8-Flash-Next, an open-weight multimodal Mixture-of-Experts model built for cost per token. The checkpoint pairs a 125B backbone with a 51B N-gram embedding table and a 4B multi-token prediction module. Only 6B parameters activate per token. The team positions it as an early preview of the architecture that will underpin Qwen4, the same role Qwen3-Next played for Qwen3.5. Four changes carry the release: a Gated DeltaNet and Qwen Sparse Attention hybrid, Gated Residual, N-gram Embedding, and the Muon optimizer. Qwen team reports training cost at roughly one-ninth that of Qwen3.7-Plus. Is it deployable? Yes but not on a workstation. The FP8 checkpoint is 172.78 GiB and the BF16 checkpoint is 335.28 GiB. Per vLLM recipes, TP2 is the minimum validated FP8 configuration on GB300 and TP4 is recommended. On an 8×H200 node, use TEP8; plain TP8 is incompatible with the checkpoint’s 128-wide quantization blocks. Sparse activation cuts compute, not storage. https://qwen.ai/blog?id=qwen3.8-flash-next What is actually new Qwen3.8-Flash-Next pairs a 125B main model with 51B N-gram embedding parameters and a 4B multi-token prediction module, totaling 180B on disk. Only 6B parameters activate per token. Four changes drive this: Hybrid attention (GDN + QSA): Three of every four layers use Gated DeltaNet, a linear-attention layer that compresses history into a fixed-size recurrent state. The fourth layer runs Qwen Sparse Attention (QSA), which uses a lightweight indexer to select context at micro-block granularity rather than per token. The layer layout is 12 × (3 × GDN → 1 × QSA) across 48 layers, with a QSA budget of 512 blocks or 2048 tokens. Gated Residual: The residual stream widens into 4 parallel branches, with an element-wise read gate and a per-branch scalar write gate, at bottleneck rank 320. N-gram Embedding: A 20,000,000-entry bigram/trigram table at layer 2 adds capacity through deterministic lookups. It can be offloaded to host memory with asynchronous prefetch — though offload currently runs only on NVIDIA devices. Training recipe.:The Muon optimizer is applied alongside AdamW to specific weight categories, with batch-size warmup eliminated and scaling laws refitted. The MoE layer carries 512 experts, activating 10 routed plus 1 shared, at expert intermediate dimension 640. Benchmarks Qwen reports 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 81.0 on SWE-bench Multilingual, and 91.9 on LiveCodeBench v6. On agentic tasks it posts 73.9 on CoWorkBench, 55.7 on JobBench, and 73.5 on Toolathlon Verified. Multimodal results include 84.5 on AndroidWorld, 76.6 on LVBench, 88.5 on RealWorldQA, and 95.7 on MathVision with code interpreter. The model does not lead everywhere. Claude Opus 4.6 (Max) takes HLE at 40.0 against Qwen’s 35.9, and DeepSeek-V4-Flash-0731 leads NL2Repo-Bench at 54.2 versus 48.1. Frontier reasoning remains the gap. Efficiency Qwen states training cost roughly 1/9 that of Qwen3.7-Plus. On serving, the announcement cites QSA kernel speedups of up to 7.6× prefill and 4.9× decode at 1M tokens, while the SGLang cookbook and vLLM recipes cite 10.2× and 6.6×. Treat the range as vendor-reported until independently measured. Qwen also reports 8.6× the prefill throughput of Qwen3.7-Plus at a 90% prefix-cache hit rate. Context is 262,144 tokens natively, extensible to 1,000,000 with YaRN. Running it The model serves through vLLM, SGLang, TokenSpeed, transformers serve, and llama.cpp for GGUF quants. Fine-tuning is supported via Unsloth, Swift, and LLaMA-Factory. It already powers the “Standard” mode on QwenWork and works with Qwen Code. Thinking mode is on by default, with reasoning_effort at xhigh, medium, or low. Qwen recommends temperature 1.0 and top_p 0.95 for thinking mode, and temperature 0.7 with top_p 0.80 for instruct mode. Key Takeaways 125B backbone + 51B N-gram embeddings + 4B MTP, with only 6B parameters active per token. Three of four layers use Gated DeltaNet; the fourth runs Qwen Sparse Attention at micro-block granularity. Trained at roughly 1/9 the cost of Qwen3.7-Plus, with 262K native context extensible to 1M via YaRN. FP8 weights are 172.78 GiB, so self-hosting needs a multi-GPU node, not a workstation. Licensed under qwen-community-1.0, not Apache-2.0 — verify terms before commercial use. Check out the GitHub Page, HF Model Card and Technical Details. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Alibaba’s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture appeared first on MarkTechPost.

Even as a teenager, Laurie Stach ’06 says, she had a “crazy ambition to take on the world and solve problems.” At MIT, she realized she wasn’t the only one. “Adults always see these youth who are good at math and science and say, ‘You’re going to do great things—someday,’” says Stach. “Meanwhile, traditional education isn’t preparing them.” That’s why in 2012 Stach founded LaunchX, a for-profit entrepreneurship program that helps high school students build practical skills through an intensive summer program in which they start real companies. Stach says that students form teams, identify a problem, and build an actual product or service that they then take to market—often generating preorders or even revenue before the four-week program ends. Raised in Texas and the Carolinas, Stach majored in mechanical engineering at MIT and earned her MBA from Harvard Business School in 2011. She ran her first summer program on MIT’s campus with 30 students in 2013 and says “it kind of snowballed.” LaunchX now serves about 500 students per year across both in-person and online programs. Read more at www.technologyreview.com/alumni-profiles.

For Rupert Young ’95, SM ’95, his career in data science and cybersecurity began when his grandfather gifted him thousands of stamps: He built intricate databases to catalogue them, displaying the “precise eye” for detail and nuance that his MIT application essay said would make him a good engineer. Young is now chief product officer of ­MaxMind, whose work with IP-address location data and fraud prevention has made it a go-to resource. Streaming companies, security vendors, merchants, and ad-network providers use its signature tool, GeoIP, to determine how and where users are accessing services. This information makes it possible to ensure retail sites are using the correct currency, flag suspicious bank logins, and more. Young’s passion for engineering was shaped by internships, and he has paid it forward by volunteering at his children’s California high school and trying to keep up with what “the next generation of engineers are tinkering with.” The next generation of product innovation at MaxMind excites him too. “Working with my team to try to find patterns in data and solve challenging problems—to me, there’s no greater joy,” he says. Read more at www.technologyreview.com/alumni-profiles.

When Don Yansen ’63 arrived at the MIT AgeLab for a study on technology in care­giving for older adults, he didn’t plan to launch another company. But when he heard participants talk about how hard modern devices can be to use, he decided to develop an alternative. Yansen, a serial entrepreneur who retired to care for his wife, earned an MIT degree in electrical engineering and a master’s in physics from Northeastern. “What I took from MIT was a good education and the feeling I could solve difficult problems,” he says. One such problem: Many older adults “can’t even use a modern cell phone,” he says, “and very few people can use something like an Apple Watch.” So Kiwi Health, which he founded in October 2024, sells a wristband controlled primarily by voice. Wearers can set reminders, make calls, send texts, and share health data, all without navigating screens. AI helps interpret these commands, accounting for the way voices may change with age. The device also measures health metrics and automatically alerts caregivers to falls.  Yansen’s hope, he says, is that it can “help seniors live the best life that they can.” Read more at www.technologyreview.com/alumni-profiles.

Oral and forehead thermometers may not accurately capture a person’s core body temperature, and the few ingestible temperature sensors on the market are so big they are hard to swallow and risk obstructing the GI tract. But MIT engineers created one that can send continuous temperature updates at a size of just six by four millimeters. To create it, the researchers built a circuit that can fit on a one-square-­millimeter silicon chip and designed an oscillator based on leakage current—the small current that flows through a circuit when it’s off. Its frequency varies depending on the temperature of the chip’s surroundings. The circuit detects temperature within 0.01 °C and can be powered with a 1.55-volt coin cell battery. A strategy known as backscattering further cuts energy consumption, allowing most of the power requirements to be outsourced to an antenna outside the body. It emits an ultra-high-frequency radio wave, which is modulated by an antenna within the sensor and sent back to the external one. By interpreting the changes in the radio wave, the external antenna can calculate the temperature value. The result is “the smallest ingestible capsule that we have seen for temperature-­sensing paradigms,” says Saransh Sharma, a former MIT postdoc now at the University of Cambridge, who is lead author of a paper on the work.  The researchers envision using it to monitor infection, observe patients during and after anesthesia, track fevers in children, help mark ovulation, and more. It could also be used to monitor athletes, soldiers, or anyone else exposed to extreme temperatures.  “A sensor like this gives us the ability to monitor infections and identify them early,” says Giovanni Traverso, an associate professor of mechanical engineering and one of the paper’s senior authors, along with MIT provost Anantha Chandrakasan. “That’s very relevant, particularly for at-risk populations like people who are immunosuppressed.” Ultimately, he hopes, it could replace other types of thermometers for everyone.

Model cards report quality under server-class, full-precision conditions. Those numbers rarely predict how the same model behaves on a phone. This week, Liquid AI released Pipette. It is an open-source platform for benchmarking foundation models on edge devices, built in partnership with Artificial Analysis as an independent methodology validator. Pipette treats on-device behavior as a property of the deployed system, not the model in isolation. Its unit of measurement is a full configuration: model + quantization + runtime + device. The launch dataset covers five on-device performance metrics across more than 1,000 model × quantization × runtime × device × context configurations, spanning 30+ models, llama.cpp builds for macOS, iOS, Windows and Android, and context lengths from 256 to 8,192 tokens. Initial verified results come from a MacBook Pro with M5 Max, an iPhone 17 Pro and a Galaxy S26 Ultra. The practical claim is testable: two 350M models at the same quantization on the same phone retain 78.4% and 33.8% of decode throughput at 4,096 tokens. Is it deployable? Yes, Pipette ships as Apache 2.0 infrastructure (pipette-mgmt, pipette-clients, pipette-scores), a public results dataset, a hosted dashboard, and native iOS and Android benchmark apps. Nothing is waitlisted. Publication of community-submitted results is still in beta. Which companies: Any team shipping a model onto hardware it does not own. Solo developers and seed-stage startups can use the dashboard and apps without infrastructure. Mid-market product teams can run the clients across an internal device fleet. Large OEMs, chip vendors and enterprises can operate the whole pipeline behind their own firewall. Industries: Consumer electronics and smartphone OEMs, automotive, industrial and robotics, healthcare devices, financial services, defense — anywhere latency, privacy or connectivity forces inference onto the device. Applications: Model and quantization selection before a sprint commits; SoC and hardware procurement validation; regression testing when a runtime, OS or driver updates; context-length capacity planning; independent verification of vendor performance claims. What Liquid AI shipped Liquid AI released Pipette in partnership with Artificial Analysis, an independent validator that reviewed and verified the methodology. The premise is narrow and useful: on-device behavior is a property of the deployed system, not of the model in isolation. The launch dataset covers five on-device performance metrics across more than 1,000 model × quantization × runtime × device × context configurations. It spans 30+ models, multiple quantization formats, llama.cpp builds for macOS, iOS, Windows and Android, and context lengths from 256 to 8,192 tokens. Initial published results come from a MacBook Pro with M5 Max, an iPhone 17 Pro and a Galaxy S26 Ultra, with AMD Ryzen AI Max+ 395 and Radeon 8060S results listed as coming soon. In Pipette, the unit of measurement is a deployment configuration: model + quantization + runtime + device. A benchmark then defines the metric and token shape, producing a latency, throughput or memory result. Quality is tracked separately on IFBench, GPQA Diamond and MATH-500. Those quality scores currently come from llama.cpp evaluation runs on NVIDIA H100 80GB reference systems, then get matched to on-device runs sharing the same model and quantization — a quality number shown next to phone throughput was not produced on the phone. Why the deployment context changes the answer Four published comparisons show how far a configuration can move a decision: Context scaling can diverge at identical parameter counts. At Q4_K_M on Galaxy S26 Ultra, Granite-4.0-H-350M retains 78.4% of its decode throughput from 256 to 4,096 input tokens, while Granite-4.0-350M retains only 33.8%. Sparse activation buys speed, not memory. At 2,048 input tokens on the same phone, LFM2.5-8B-A1B decodes 2.4x faster than Qwen3.5-4B and 2.6x faster than Ministral-3-3B-Instruct-2512. It activates 1.5B of 8.5B parameters per token, yet still peaks at 5.29 GiB because all expert weights occupy memory. Speed and quality do not co-locate. On iPhone 17 Pro at Q4_K_M, MiniCPM5-1B completes a 2,048-in / 256-out workload in 3.47 seconds versus 4.12 seconds for LFM2.5-1.2B-Instruct, a 15.8% reduction in elapsed time. On the same artifacts, LFM scores 9.0 points higher on MATH-500. Near-identical system profiles can hide task-level reversals. At Q4_K_M and 2,048 input tokens on M5 Max, Granite-4.1-8B and Ministral-3-8B-Instruct-2512 differ by 2.4% in decode throughput and 1.2% in peak RAM. Granite leads IFBench by 7.3 points; Ministral leads GPQA Diamond by 14.0 points. How the measurements are produced Performance runs follow a published methodology: fixed token shapes, greedy decoding, a discarded warm-up, five measured repetitions and readiness gating. Before each timed repetition, a platform-specific check verifies thermal and load conditions; failing runs are not published. Evaluations use a separate protocol with deterministic, model-blind scoring, and pipette-scores never sees generation provenance. Every submission records benchmark version, token shape, model artifact, quantization, runtime version and settings, and device hardware and OS. Interactive explainer Key Takeaways Pipette benchmarks configurations, not models: model + quantization + runtime + device. Apache 2.0 stack, 1,000+ configurations, 30+ models, three verified devices at launch. Quality evals run on H100 references and are matched to on-device performance, not measured on-device. Identical parameter counts can differ 78.4% vs 33.8% in context-scaling retention. Check out the Technical Details and Leaderboard. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Liquid AI Open-Sources Pipette: A Reproducible Benchmarking Suite That Measures On-Device Models, Quantization, Runtime and Hardware Together appeared first on MarkTechPost.