MIT researchers utilized specially trained generative AI models to create a system that can complete the shape of hidden 3D objects, like the ones pictured. Credit: Courtesy of the researchers.
By Adam Zewe
MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items.
Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot’s ability to reliably grasp and manipulate objects that are blocked from view.
This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.
The researchers also introduced an expanded system that uses generative AI to accurately reconstruct an entire room, including all the furniture. The system utilizes wireless signals sent from one stationary radar, which reflect off humans moving in the space.
This overcomes one key challenge of many existing methods, which require a wireless sensor to be mounted on a mobile robot to scan the environment. And unlike some popular camera-based techniques, their method preserves the privacy of people in the environment.
These innovations could enable warehouse robots to verify packed items before shipping, eliminating waste from product returns. They could also allow smart home robots to understand someone’s location in a room, improving the safety and efficiency of human-robot interaction.
“What we’ve done now is develop generative AI models that help us understand wireless reflections. This opens up a lot of interesting new applications, but technically it is also a qualitative leap in capabilities, from being able to fill in gaps we were not able to see before to being able to interpret reflections and reconstruct entire scenes,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of two papers on these techniques. “We are using AI to finally unlock wireless vision.”
Adib is joined on the first paper by lead author and research assistant Laura Dodds; as well as research assistants Maisy Lam, Waleed Akbar, and Yibo Cheng; and on the second paper by lead author and former postdoc Kaichen Zhou; Dodds; and research assistant Sayed Saad Afzal. Both papers will be presented at the IEEE Conference on Computer Vision and Pattern Recognition.
Surmounting specularity
The Adib Group previously demonstrated the use of millimeter wave (mmWave) signals to create accurate reconstructions of 3D objects that are hidden from view, like a lost wallet buried under a pile.
These waves, which are the same type of signals used in Wi-Fi, can pass through common obstructions like drywall, plastic, and cardboard, and reflect off hidden objects.
But mmWaves usually reflect in a specular manner, which means a wave reflects in a single direction after striking a surface. So large portions of the surface will reflect signals away from the mmWave sensor, making those areas effectively invisible.
“When we want to reconstruct an object, we are only able to see the top surface and we can’t see any of the bottom or sides,” Dodds explains.
The researchers previously used principles from physics to interpret reflected signals, but this limits the accuracy of the reconstructed 3D shape.
In the new papers, they overcame that limitation by using a generative AI model to fill in parts that are missing from a partial reconstruction.
“But the challenge then becomes: How do you train these models to fill in these gaps?” Adib says.
Usually, researchers use extremely large datasets to train a generative AI model, which is one reason models like Claude and Llama exhibit such impressive performance. But no mmWave datasets are large enough for training.
Instead, the researchers adapted the images in large computer vision datasets to mimic the properties in mmWave reflections.
“We were simulating the property of specularity and the noise we get from these reflections so we can apply existing datasets to our domain. It would have taken years for us to collect enough new data to do this,” Lam says.
The researchers embed the physics of mmWave reflections directly into these adapted data, creating a synthetic dataset they use to teach a generative AI model to perform plausible shape reconstructions.
The complete system, called Wave-Former, proposes a set of potential object surfaces based on mmWave reflections, feeds them to the generative AI model to complete the shape, and then refines the surfaces until it achieves a full reconstruction.
Wave-Former was able to generate faithful reconstructions of about 70 everyday objects, such as cans, boxes, utensils, and fruit, boosting accuracy by nearly 20 percent over state-of-the-art baselines. The objects were hidden behind or under cardboard, wood, drywall, plastic, and fabric.
The team also built an expanded system that fully reconstructs entire indoor scenes by leveraging wireless signal reflections off humans moving in a room. Credit: Courtesy of the researchers.
Seeing “ghosts”
The team used this same approach to build an expanded system that fully reconstructs entire indoor scenes by leveraging mmWave reflections off humans moving in a room.
Human motion generates multipath reflections. Some mmWaves reflect off the human, then reflect again off a wall or object, and then arrive back at the sensor, Dodds explains.
These secondary reflections create so-called “ghost signals,” which are reflected copies of the original signal that change location as a human moves. These ghost signals are usually discarded as noise, but they also hold information about the layout of the room.
“By analyzing how these reflections change over time, we can start to get a coarse understanding of the environment around us. But trying to directly interpret these signals is going to be limited in accuracy and resolution.” Dodds says.
They used a similar training method to teach a generative AI model to interpret those coarse scene reconstructions and understand the behavior of multipath mmWave reflections. This model fills in the gaps, refining the initial reconstruction until it completes the scene.
They tested their scene reconstruction system, called RISE, using more than 100 human trajectories captured by a single mmWave radar. On average, RISE generated reconstructions that were about twice as precise than existing techniques.
In the future, the researchers want to improve the granularity and detail in their reconstructions. They also want to build large foundation models for wireless signals, like the foundation models GPT, Claude, and Gemini for language and vision, which could open new applications.
This work is supported, in part, by the National Science Foundation (NSF), the MIT Media Lab, and Amazon.
This analysis was produced by an AI financial research system. All data is sourced exclusively from publicly available filings, earnings transcripts, government data, and free financial aggregators — no proprietary data, paid research, or institutional tools are used. Every figure cited can be independently verified by the reader using the sources listed at the end...
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This analysis was produced by an AI financial research system. All data is sourced exclusively from publicly available filings, earnings transcripts, government data, and free financial aggregators — no proprietary data, paid research, or institutional tools are used. Every figure cited can be independently verified by the reader using the sources listed at the end...
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In the latest in our series of interviews meeting the AAAI/SIGAI Doctoral Consortium participants, we caught up with Aniket Roy to find out more about his research on generative models for computer vision tasks.
I recently completed my PhD in Computer Science at Johns Hopkins University, where I worked under the supervision of Bloomberg Distinguished Professor Rama Chellappa. My research primarily focused on developing methods for resource-constrained image generation and visual understanding. In particular, I explored how modern generative models can be adapted to operate efficiently while maintaining strong performance.
During my PhD, I worked broadly at the intersection of generative AI, multimodal learning, and few-shot learning. Much of my work involved designing techniques that enable models to learn new concepts or perform complex visual tasks with limited data or computational resources. This included research on diffusion models, personalized image generation, and multimodal representation learning. Overall, my work aims to make advanced vision and generative AI systems more adaptable, efficient, and practical for real-world applications.
During my PhD, my research broadly focused on improving the adaptability, efficiency, and quality of modern generative models for computer vision tasks. The rapid progress in generative AI–particularly diffusion models and vision–language models–has created new opportunities to address long-standing challenges such as data scarcity, controllable generation, and personalized image synthesis. My work aimed to develop methods that allow these large models to adapt effectively with limited data and computational resources while maintaining high visual fidelity.
One line of my research addressed learning in data-constrained settings. For example, I proposed FeLMi, a few-shot learning framework that leverages uncertainty-guided hard mixup strategies to improve robustness and generalization when only a small number of labeled samples are available. Building on this idea of improving training data quality, I also developed Cap2Aug, which introduces caption-guided multimodal augmentation. This approach uses textual descriptions to guide synthetic image generation, improving visual diversity while reducing the domain gap between real and generated data.
Overview of Cap2Aug.
Another aspect of my research focused on improving the perceptual quality of images generated by diffusion models. In this direction, I proposed DiffNat, a plug-and-play regularization method based on the kurtosis-concentration property observed in natural images. By incorporating this principle into diffusion models through a KC loss, the generated images exhibit more natural texture statistics and improved perceptual realism, which also benefits downstream vision tasks.
A major part of my work explored personalization and efficient adaptation of large generative models. I introduced DuoLoRA, a parameter-efficient framework for composing low-rank adapters that enables fine-grained control over content and style without requiring full retraining of the base model. I further extended personalization to zero-shot settings using a training-free textual inversion approach that allows arbitrary objects to be customized directly during generation. Finally, I proposed MultiLFG, a frequency-guided multi-LoRA composition framework that uses wavelet-domain representations and timestep-aware weighting to enable accurate and training-free fusion of multiple concepts in diffusion models.
Overview of DuoLoRA.
Overall, my research contributes toward building generative systems that are more efficient, adaptable, and controllable, enabling high-quality image generation and understanding even in data-limited or resource-constrained scenarios.
One project that I found particularly interesting during my PhD is DiffNat, which was published in TMLR 2025. Diffusion models have become the backbone of many modern generative AI systems and have achieved impressive results in generating and editing realistic images. However, improving the perceptual quality and naturalness of generated images remains an important challenge.
Overview of DiffNat.
In this work, we introduced a simple but effective regularization technique called the kurtosis concentration (KC) loss, which can be integrated into standard diffusion model pipelines as a plug-and-play component. The idea was inspired by a statistical property of natural images: when an image is decomposed into different band-pass filtered versions–for example using the Discrete Wavelet Transform–the kurtosis values across these frequency bands tend to be relatively consistent. In contrast, generated images often show large discrepancies across these bands. Our method reduces the gap between the highest and lowest kurtosis values across the frequency components, encouraging the generated images to follow more natural image statistics.
In addition, we introduced a condition-agnostic perceptual guidance strategy during inference that further improves image fidelity without requiring additional training signals. We evaluated the approach across several diverse tasks, including personalized few-shot finetuning with text guidance, unconditional image generation, image super-resolution, and blind face restoration. Across these tasks, incorporating the KC loss and perceptual guidance consistently improved perceptual quality, measured through metrics such as FID and MUSIQ, as well as through human evaluation.
What I particularly liked about this project is that it connects classical image statistics with modern diffusion models. It shows that relatively simple statistical insights about natural images can still play a powerful role in improving large generative models.
During my PhD, I discovered that I genuinely enjoy the process of research–especially the moment when an intuition or idea turns out to work in practice. That process of exploring new ideas and pushing the boundaries of what we know is something I find very motivating.
To continue pursuing this, I will be joining NEC Laboratories America as a Research Scientist. In this role, I hope to build on my PhD work by developing new methods for generative models and exploring how these models can interact with broader multimodal systems. In particular, I am interested in advancing research at the intersection of generative models, vision–language–action models, and embodied AI. More broadly, my goal is to contribute to the development of intelligent systems that can understand, generate, and interact with the visual world more effectively, while also continuing to push forward the scientific understanding of these models.
My interest in computer vision and machine learning started during my undergraduate studies, when I took courses in signal processing and image processing. I found those subjects particularly fascinating because they allowed you to experiment with algorithms and immediately see their effects on images. That visual and intuitive aspect made the field very engaging, and it helped me appreciate how mathematical concepts can directly translate into meaningful visual results.
At the same time, I was also curious about how the human brain processes visual information—how we are able to recognize objects, understand scenes, and interpret complex visual signals so effortlessly. That curiosity led me to wonder whether we could design computational models that mimic aspects of human perception and enable machines to understand visual data in a similar way.
A major influence during this time was my professor, Dr. Kuntal Ghosh, who encouraged me to think more deeply about these problems and approach them with a scientific mindset. His mentorship played an important role in shaping my interest in research. Since then, that curiosity about visual perception and intelligent systems has continued to drive my work in computer vision and machine learning.
Unfortunately, I was not able to attend the AAAI Doctoral Consortium in person due to visa-related issues. However, a colleague kindly helped present my poster on my behalf during the event. Even though I could not be there physically, I was very encouraged by the response my work received. Several researchers reached out to me after seeing the poster, and we had some very insightful discussions about the ideas and potential future directions of the research. In that sense, I still found the experience quite rewarding. The Doctoral Consortium is a great platform for sharing early-stage ideas, receiving feedback from the community, and connecting with other researchers working on related problems. I appreciated the opportunity to engage with people who were interested in the work, and those interactions helped spark new perspectives and collaborations.
Outside of research, I’m a big fan of music and stand-up comedy, and I really enjoy traveling whenever I get the chance. Exploring new places, cultures, and perspectives is something I find refreshing—it’s a great way to recharge and stay curious about the world beyond work. I also enjoy writing poetic satire from time to time, and I occasionally perform it. It’s a fun creative outlet that allows me to mix humor and storytelling, which is quite different from the analytical nature of the research work I usually do.
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Aniket is currently a Research Scientist at NEC Labs America. He obtained his PhD from the Computer Science dept at Johns Hopkins University under the guidance of Bloomberg Distinguished Professor Prof. Rama Chellappa. Prior to that, he did a Master’s from Indian Institute of Technology Kharagpur. He was recognized with the Best Paper Award at IWDW 2016 and the Markose Thomas Memorial Award for the best research paper at the Master’s level. During PhD, he explored domains of few-shot learning, multimodal learning, diffusion models, LLMs, LoRA merging with publications in leading venues such as NeurIPS, ICCV, TMLR, WACV, CVPR and also 3 US patents filed. During his PhD, he also gained industrial experience through multiple internships in Amazon, Qualcomm, MERL, and SRI International. He was awarded as an Amazon Fellow (2023-24) at JHU and selected to participate in ICCV’25 and AAAI’26 doctoral consortium. |
When your website goes down, you know it immediately. Alerts fire, users complain, revenue may stop. When your AI agents fail, none of that happens. They keep responding. They just respond wrong.
Agents can appear fully operational while hallucinating policy details, losing conversation context mid-session, or burning through token budgets until rate limits shut them down.
Zero-downtime for AI agents isn’t the same as infrastructure uptime. It means preserving behavioral continuity, controlling costs, and maintaining decision quality through every deployment, update, and scaling event. This post is for the teams responsible for making that happen.
Key takeaways
Your web services either respond or they don’t. Databases either accept queries or they fail. But your AI agents don’t work that way. They remember context across a conversation, produce different outputs for identical inputs, make multi-step decisions where latency compounds, and consume real budget with every token processed.
“Working” and “failing” aren’t binary for agents. That’s what makes them hard to monitor and harder to deploy safely.
System uptime is binary: Infrastructure responds, endpoints return 200s, and logs show activity.
Functional uptime is what matters. Your agent produces accurate, timely, and cost-effective outputs that users can trust.
The difference plays out like this:
“Up and running” is not the same as “working as intended.” For enterprise AI, only the latter counts.
Traditional software throws errors. AI agents don’t — they produce confidently wrong answers instead. Because large language models (LLMs) are non-deterministic, failures surface as subtly degraded outputs, not 500 errors. Users can’t tell the difference between a model limitation and a deployment problem, which means trust erodes before anyone on your team knows something is wrong.
Deployment strategies for agents must detect behavioral degradation, not just error rates. Traditional DevOps wasn’t built for systems that degrade instead of crash.
Real zero-downtime for enterprise AI agents requires managing three distinct tiers — each entering the lifecycle at a different stage, each with different owners:
Most teams have tier one covered. The gaps that break production agents live in tiers two and three.
Infrastructure availability is necessary, but insufficient for agent reliability. This tier belongs to your platform, cloud, and infrastructure teams: the people keeping compute, networking, and storage operational.
Perfect infrastructure uptime guarantees only one thing: the possibility of agent success.
Traditional SLAs matter, but they stop short for agent workloads.
CPU utilization, network throughput, and disk I/O tell you nothing about whether your agent is hallucinating, exceeding token budgets, or returning incomplete responses.
Infrastructure health and agent health are not the same metric.
Kubernetes, scheduling, and resource isolation carry more weight for AI workloads than traditional applications. GPU contention degrades response quality. Cold starts interrupt conversation flow. Inconsistent runtime environments introduce subtle behavioral changes that users experience as unreliability.
When your sales assistant suddenly changes its tone or reasoning approach because of underlying infrastructure changes, that’s functional downtime, despite what your uptime dashboard may say.
This tier moves beyond machines running to models and orchestration functioning correctly together. It belongs to the ML platform, AgentOps, and MLOps teams. Latency, throughput, and orchestration integrity are the availability metrics that matter here.
Enterprise AI agents rarely rely on a single model. Orchestration chains route requests, apply reasoning, select tools, and blend responses, often across multiple specialized models per request.
Updating any single component risks breaking the entire chain. Your deployment strategy must treat multi-model updates as a unit, not independent versioning. If your reasoning model updates but your routing model doesn’t, the behavioral inconsistencies that follow won’t surface in traditional monitoring until users are already affected.
Budget overruns create hidden downtime. When an agent hits token caps mid-month, it’s functionally unavailable, regardless of what infrastructure metrics show.
Latency compounds the same way. A 500 ms slowdown across five sequential reasoning calls produces a 2.5-second user-visible delay — enough to degrade the experience, not enough to trigger an alert. Traditional availability metrics don’t account for this stacking effect. Yours need to.
Standard deployment approaches assume clean version separation, deterministic outputs, and reliable rollback to known-good states. None of those assumptions hold for enterprise AI agents.
Blue-green, canary, and rolling updates weren’t designed for stateful, non-deterministic systems with token-based economics. Each requires meaningful adaptation before it’s safe for agent deployments.
This tier is what users actually experience. It’s owned by AI product teams and agent developers, and measured through task completion, accuracy, cost per interaction, and user trust. It’s where the business value of your AI investment is realized or lost.
Losing context qualifies as functional downtime.
When a customer explains their problem to your support agent, and it then loses that context mid-conversation during a deployment rollout, that’s functional downtime — regardless of what system metrics report. Session affinity, memory persistence, and handoff continuity are availability requirements, not nice-to-haves.
Agents must survive updates mid-conversation. That demands session management that traditional applications simply don’t require.
Enterprise agents depend on external APIs, databases, and internal tools. Schema or contract changes can break agent functionality without triggering any alerts.
A minor update to your product catalog API structure can render your sales agent useless without touching a line of agent code. Versioned tool contracts and graceful degradation aren’t optional. They’re availability requirements.
Subtle prompt changes, token usage shifts, or orchestration tweaks can alter agent behavior in ways that don’t show up in metrics but are immediately apparent to users.
Deployment processes must validate behavioral consistency, not just code execution. Agent correctness requires continuous monitoring, not a one-time check at release.
Traditional deployment patterns aren’t wrong. They’re just incomplete without agent-specific adaptations.
Blue-green deployments for agents require session migration, sticky routing, and warm-up procedures that account for model loading time and cold-start penalties. Running parallel environments doubles token consumption during transition periods — a meaningful cost at enterprise scale.
Most importantly, behavioral validation must happen before cutover. Does the new environment produce equivalent responses? Does it maintain conversation context? Does it respect the same token budget constraints? These checks matter more than traditional health checks.
Even small canary traffic percentages — 1% to 5% — incur significant token costs at enterprise scale. A problematic canary stuck in reasoning loops can consume disproportionate resources before anyone notices.
Effective canary strategies for agents require output comparison and token tracking alongside traditional error rate monitoring. Success metrics must include correctness and cost efficiency, not just error rates.
Rolling updates are incompatible with most stateful enterprise agents. They create mixed-version environments that produce inconsistent behavior across multi-turn conversations.
When a user starts a conversation with version A and continues with the new version B mid-rollout, reasoning shifts — even subtly. Context handling differences between versions cause repeated questions, missing information, and broken conversation flow. That’s functional downtime, even if the service never technically went offline.
For most enterprise agents, full environment swaps with careful session handling are the only safe option.
For AI agents, observability is about agent behavior: what the agent is doing, why, and whether it’s doing it correctly. It’s the foundation of deployment safety and zero-downtime operations.
No single metric captures agent health. You need correlated visibility across correctness, cost, and latency — because each can move independently in ways that matter.
When accuracy improves but token consumption doubles, that’s a deployment decision. When latency stays flat but correctness degrades, that’s a regression. Individual metrics won’t surface either. Correlated observability will.
By the time users report agent issues, trust is already eroding. Proactive observability is what prevents that.
Effective observability tracks semantic drift in responses, flags changes in reasoning paths, and detects when agents access tools or data sources outside defined boundaries. These signals let you catch regressions before they reach users, not after.
Agent failures aren’t just technical problems — they erode trust, create compliance exposure, and put your AI strategy at risk.
Fixing that means treating deployment as an agent-first discipline: tiered monitoring across infrastructure, orchestration, and behavior; deployment strategies built for statefulness and token economics; and observability that catches drift before users do.
The DataRobot Agent Workforce Platform addresses these challenges in one place — with agent-specific observability, governance across every layer, and the operational controls enterprises need to deploy and update agents safely at scale.
Learn whyAI leaders turn to DataRobot’s Agent Workforce Platform to keep agents reliable in production.
Traditional uptime only tells you whether infrastructure responds. AI agents can appear healthy while producing incorrect answers, losing conversation state, or failing mid-workflow due to cost or latency issues, all of which are functional downtime for users.
System uptime measures whether services are reachable. Functional uptime measures whether agents behave correctly, maintain context, respond within acceptable latency, and operate within budget. Enterprise AI success depends on the latter.
LLMs are non-deterministic and degrade gradually. Instead of throwing errors, agents produce subtly worse outputs, inconsistent reasoning, or incomplete responses, making failures harder to detect and more damaging to trust.
Traditional rolling updates often break stateful agents. Blue-green and canary deployments can work, but only when adapted for session continuity, behavioral validation, token economics, and multi-model orchestration dependencies.
Teams need agent-specific observability, behavioral validation during deployments, cost-aware health signals, and governance across infrastructure, orchestration, and application layers. DataRobot’s Agent Workforce Platform provides these capabilities in one control plane, keeping agents reliable through updates, scaling, and change.
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For years, the technology industry has operated under the shadow of a single, green-tinted giant. NVIDIA, through a combination of visionary leadership and the early realization that GPUs were the secret sauce for parallel processing, effectively “owned” the AI market […]
The post The Sleeping Giant Wakes: Why AMD’s MLPerf Breakthrough Signals the Beginning of the End for NVIDIA’s AI Monopoly appeared first on TechSpective.
Easily the most prominent trend that emerged in AI writing in Q1 2026 is that major businesses are all-in when it comes to bringing the tech on-board.
The only problem: Rank and file employees haven’t gotten the memo.
A new study from Boston Consulting Group, for example, found that 94% of CEOs surveyed are committed to staying invested in AI — no matter how long it takes to metastasize in their organizations.
And a survey from AI consulting firm Section found that 41% of execs say AI is saving them eight hours-a-week on routine tasks.
But a new poll from Gallup simultaneously found that for all its glories, AI is only being used by 12% of workers on a daily basis.
Given that most of that 12% probably represents creative pros who are using AI writing daily to handle marketing, reports or legal work, that leaves maybe 2% of the everyday workforce that has actually embraced AI in a meaningful way.
Alarmed, some employers , like Bausch + Lomb, have resorted to bullying staff into adopting AI, threatening to withhold bonuses — or worse, indicating that without AI chops, employees’ days are numbered.
But the real solution may lie in businesses redoubling their efforts to offer highly effective training programs, which ensure workers deeply grasp how to use the new tech.
Observes Wall Street Journal writer Christopher Mims: “There is a huge gap between what AI can already do today and what most people are actually doing with it.”
Here’s detail on the key stories in Q1 2026 that revealed the AI adoption challenge – along with other significant developments in AI’s ongoing evolution:
*ChatGPT Now Clocking 900 Million Weekly Users: It’s official: 900 million people are now flocking to ChatGPT each week for AI-powered writing, answers, thinking and more.
Most of those people use the free version of ChatGPT, while about 50 million users access the AI via a paid subscription, according to writer Aisha Malik.
Adds Malik: “The new weekly active user figure marks a jump of 100 million users from the 800 million that OpenAI reported in October 2025.”
*94% of CEOs All-In on AI: A new study finds that nearly all CEOs surveyed are working to integrate AI into their businesses in 2026 – even if return-on-investment takes a while.
Even more encouraging for AI advocates: On average, those same CEOs plan to invest more than twice as much in AI during 2026 as they did the previous year.
Firms leading the way in AI are using the tech to up-skill and retrain their workforces, according to writer Cliff Saran.
*41% Execs: ‘AI Saves Me Eight Hours-a-Week:’ A new survey finds that 41% of execs using AI are saving at least eight hours a week with the tech.
Even more eye-opening: An additional 33% of execs say they’re saving at least four-to-eight hours a week with AI.
That makes 74% of execs total who say they’re reaping significant productivity gains with AI.
One downside finding of the survey: Employees tend to be less enthused about AI — which many believe can be easily solved with highly targeted training.
*AI as Journalist: At Fortune Magazine, It’s De Rigueur: As many fiction and nonfiction media outlets express outrage over AI-generated content, others are embracing it unabashedly.
Case-in-point: Fortune Magazine, where nearly 20% of all articles are generated in part by AI, according to writer Isabella Simonetti.
Most of those articles are penned – with the help of AI – by journalist Nick Lichtenberg, who has “produced more stories in six months than any of his colleagues at Fortune delivered in a year,” according to Simonetti.
*Only 12% of Workers Use AI Daily: More than three years after the release of the AI that changed the world – ChatGPT– only 12% of workers are using AI on a daily basis.
Observes writer Brandon Vigliarolo: “Frequent AI users are still a tiny minority of overall workers.”
The greatest irony here is that a $20/month ChatGPT subscription, for example, will pay for itself in the workspace, simply with its ability to significantly reduce the amount of time writing emails each day – while elevating that writing to the world-class level.
*Learn AI — Or Forget About that Bonus: Bausch + Lomb’s CEO Brent Saunders has issued a simple ultimatum to employees: Get a clue when it comes to AI, or kiss your bonus goodbye.
Observes writer Francisco Velasquez: “By tying bonuses to (AI) education, Saunders is essentially legislating the end of resistance.
“He also noted that employees risk becoming ‘irrelevant’ should they fall short of implementing AI in their career pursuits.”
*AI Training Now the Chokepoint: Wall Street Journal writer Christopher Mims reports that while AI is plenty smart across a wide spectrum of tasks, too few people know how to use AI well.
Observes Mims: “There is a huge gap between what AI can already do today and what most people are actually doing with it.”
*Slash and Burn: Elon Musk Rebuilding ChatGPT-Competitor xAI from the Ground Up: Completely disenchanted with the performance of xAI – which makes Grok, a key competitor to ChatGPT – CEO Elon Musk has decided to rip it up and start over.
Observes writer Victor Tangermann: “Musk reportedly ordered higher-ups from Tesla and SpaceX — the latter of which xAI was folded into earlier this year — to conduct audits and weed out anybody deemed to be underperforming.”
*Gemini Gets Tighter Integration with Google Workspace Suite: Google is out with a new upgrade to Gemini designed to ensure the ChatGPT competitor is more tightly integrated with Google Docs, Sheets, Slides and Drive.
Observes Yulie Kwon Kim, VP product/workspace: “Today we are re-imagining how people create content.”
Click here for the blow-by-blow that backs-up Kim’s statement.
*China’s Open-Source AI Could Upend U.S. Market: MIT Technology Review is out with a new, in-depth article warning that the rising popularity of AI created by Chinese researchers and companies could scramble U.S. hopes to continue to dominate in AI.
China’s open-source AI software is incredibly attractive to many companies, given that it can be downloaded for free – and custom-tailored or improved by anyone.
Observes writer Caiwei Chen: “If these open-source AI models keep getting better, they will not just offer the cheapest options for people who want access to frontier AI capabilities — they will change where innovation happens and who sets the standards.”
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–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
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