#Vision AI technology

[inferencelab]

How Vision AI is Personalizing the Customer Experience

In today’s rapidly evolving digital landscape, customer experience has become a crucial differentiator for businesses. Traditional methods of personalization, such as targeted emails and tailored product recommendations, are now standard practice. However, the advent of Vision AI (Artificial Intelligence) is transforming how businesses interact with customers, offering unprecedented levels of personalization and engagement. Vision AI, which involves the use of machine learning and computer vision to interpret and understand visual data, is enabling businesses to create highly individualized experiences that cater to the unique preferences and behaviors of each customer.

Understanding Vision AI

Vision AI refers to technologies that enable machines to gain high-level understanding from digital images or videos. It encompasses various techniques such as image recognition, object detection, facial recognition, and scene interpretation. By mimicking human vision, Vision AI can analyze visual data in real time, making it a powerful tool for personalizing the customer experience across multiple industries.

Enhancing Retail Experiences

One of the most significant applications of Vision AI in retail is in creating immersive and personalized shopping experiences. Traditional retail has been revolutionized by e-commerce, but Vision AI is bridging the gap between online and offline shopping by offering enhanced customer experiences.

Personalized In-Store Assistance

Vision AI-powered cameras and sensors can track customer movements and behaviors in real time. By analyzing this data, stores can offer personalized assistance and recommendations. For instance, when a customer spends a significant amount of time in a particular section, Vision AI can alert store associates to offer help or suggest related products, enhancing the shopping experience.

Smart Mirrors and Virtual Try-Ons

Smart mirrors equipped with Vision AI allow customers to virtually try on clothes and accessories. These mirrors use augmented reality (AR) to overlay products onto the customer’s reflection, providing a personalized fitting experience without the need for physical trials. This technology not only improves customer satisfaction but also reduces return rates, as customers can make more informed purchasing decisions.

Customer Behavior Analysis

Vision AI can analyze customer behavior patterns to offer personalized promotions and discounts. By understanding which products customers frequently interact with or purchase, retailers can tailor their marketing strategies to individual preferences. This level of personalization can significantly boost customer loyalty and increase sales.

Revolutionizing Online Shopping

E-commerce platforms are leveraging Vision AI to provide highly personalized and engaging online shopping experiences. The ability to analyze visual content in real-time enables online retailers to understand and predict customer preferences more accurately than ever before.

Visual Search

Traditional text-based searches can sometimes be limiting for customers who are unsure how to describe what they are looking for. Vision AI enables visual search functionality, allowing customers to upload images of desired products and find similar items instantly. This feature not only enhances the user experience but also increases the likelihood of conversion by making the search process more intuitive and efficient.

Personalized Recommendations

Vision AI can analyze customers’ visual preferences based on their browsing history and interactions with images and videos. By understanding color preferences, style choices, and visual aesthetics, AI can offer highly personalized product recommendations. For example, if a customer frequently browses floral patterns, the AI can prioritize showing them products that match this preference.

Dynamic Content Customization

E-commerce platforms can use Vision AI to dynamically customize content for each user. By analyzing visual data and user behavior, websites can display personalized banners, advertisements, and product suggestions. This ensures that each customer has a unique and relevant shopping experience, increasing engagement and conversion rates.

Transforming Customer Service

Vision AI is also revolutionizing customer service by enabling more efficient and personalized interactions. The ability to analyze visual data in real-time can significantly enhance the quality of customer support and improve overall satisfaction.

Facial Recognition for Personalized Interactions

Facial recognition technology can identify customers and retrieve their purchase history, preferences, and previous interactions with the brand. This allows customer service representatives to offer highly personalized assistance, addressing the customer by name and providing relevant information quickly. Such personalized interactions can enhance the customer’s perception of the brand and foster loyalty.

Visual Customer Support

Vision AI can be used to provide visual customer support, enabling customers to share images or videos of issues they are facing. Support agents can then analyze this visual data to diagnose problems and offer precise solutions. For example, if a customer is having trouble assembling a product, they can share a video of the issue, and the AI can guide them through the steps to resolve it. This reduces resolution times and improves the overall customer experience.

Automated Support with Visual Data

Chatbots and virtual assistants equipped with Vision AI can handle customer queries that involve visual data. For instance, customers can upload images of damaged products, and the AI can assess the damage and process returns or replacements automatically. This level of automation streamlines customer service processes and ensures quick and efficient resolutions.

Elevating the Entertainment Industry

The entertainment industry is another sector where Vision AI is making significant strides in personalizing the customer experience. By analyzing visual data from videos and images, AI can offer tailored content recommendations and enhance user engagement.

Personalized Content Recommendations

Streaming platforms use Vision AI to analyze viewers’ watching habits and preferences. By understanding the visual elements that resonate with each user, such as genre, actors, and cinematography, AI can recommend personalized content. This not only enhances the viewing experience but also keeps users engaged by continuously offering relevant and appealing suggestions.

Interactive and Immersive Experiences

Vision AI is also enabling more interactive and immersive experiences in entertainment. For example, augmented reality (AR) and virtual reality (VR) applications use computer vision to create personalized and engaging experiences. Users can interact with virtual environments tailored to their preferences, making entertainment more immersive and enjoyable.

Enhancing Social Media Engagement

Social media platforms leverage Vision AI to analyze user-generated content and interactions. By understanding the visual preferences and behaviors of users, these platforms can personalize content feeds, advertisements, and recommendations. This ensures that users see content that is most relevant and engaging to them, enhancing their overall experience on the platform.

Improving Healthcare Services

In the healthcare sector, Vision AI is transforming patient care by offering personalized medical services and improving diagnostic accuracy. The ability to analyze visual data in real-time is enabling healthcare providers to deliver more precise and tailored treatments.

Personalized Treatment Plans

Vision AI can analyze medical images such as X-rays, MRIs, and CT scans to identify specific conditions and recommend personalized treatment plans. By comparing visual data from numerous patients, AI can identify patterns and suggest the most effective treatments for individual patients. This level of personalization can significantly improve patient outcomes.

Remote Patient Monitoring

Vision AI is also enhancing remote patient monitoring by analyzing visual data from wearable devices and home monitoring systems. This technology can detect changes in a patient’s condition in real-time and alert healthcare providers to take immediate action. Personalized alerts and recommendations ensure that patients receive timely and appropriate care, even from a distance.

Enhancing Telemedicine

Telemedicine services are benefiting from Vision AI’s ability to analyze visual data during virtual consultations. Doctors can use AI-powered tools to examine patients remotely, ensuring accurate diagnoses and personalized treatment recommendations. This improves the quality of care and makes healthcare more accessible to a broader population.

Conclusion

Vision AI is revolutionizing the way businesses personalize the customer experience across various industries. By leveraging the power of visual data, companies can create highly individualized and engaging interactions that cater to the unique preferences and behaviors of each customer. From retail and e-commerce to customer service, entertainment, and healthcare, Vision AI is enabling businesses to offer more relevant, efficient, and satisfying experiences. As this technology continues to evolve, its potential to transform the customer experience and drive business success will only grow, making it an essential tool for any forward-thinking organization.

Source: https://inferencelabs.blogspot.com/2024/06/how-vision-ai-personalizing-customer-experience.html

[thesunoficarus1]

occasionally if christopher doesn't know how to (or doesn't want to) do his homework, he'll use ai. one night, eddie walks into the room to check on him and looks at his computer and absolutely loses his shit because "that's not natural. turn that off!" its not even necessarily the fact that chris is using it to cheat that bothers him. no, eddie just hates the entire concept of ai.

and christopher, like the teenager he is, is having none of it and is like "it's helping me do my homework, dad." and eddie pulls out like 30 statistics about how ai is NOT helpful, which then evolves into a 10 minute long rant/lecture. "you need help? I will help you! that won't help you!"

he eventually finishes it off by saying this is a "ai free household" and chris "needs to learn to do it by himself" and then he takes the laptop and shuts it like a bitchy main character from a 2000s movie would shut a flip phone.

[nando161mando]

CAPTCHAs tech companies exploiting free labor to train AI vision for defense contractors military drones and autonomous weapons

[fragile-practice]

Week in Review

October 23rd-29th

Welcome to Fragile Practice, where I attempt to make something of value out of stuff I have to read.

My future plan is to do longer-form original pieces on interesting topics or trends. For now, I'm going to make the weekly reviews habitual and see if I have any time left.

Technology

OpenAI forms team to study ‘catastrophic’ AI risks, including nuclear threats - Tech Crunch; Kyle Wiggers

OpenAI launched a new research team called AI Safety and Security to investigate the potential harms of artificial intelligence focused on AI alignment, AI robustness, AI governance, and AI ethics.

Note: Same energy as “cigarette company funds medical research into smoking risks”.

Artists Allege Meta’s AI Data Deletion Request Process Is a ‘Fake PR Stunt’ - Wired; Kate Knibbs

Artists who participated in Meta’s Artificial Intelligence Artist Residency Program accused the company of failing to honor their data deletion requests and claim that Meta used their personal data to train its AI models without their consent.

Note: Someday we will stop being surprised that corporate activities without obvious profit motive are all fake PR stunts.

GM and Honda ditch plan to build cheaper electric vehicles - The Verge; Andrew J. Hawkins

General Motors and Honda cancel their joint venture to develop and produce cheaper electric vehicles for the US market, citing the chip shortage, rising costs of battery materials, and the changing market conditions.

Note: What are the odds this isn’t related to the 7 billion dollars the US government announced to create hydrogen hubs.

'AI divide' across the US leaves economists concerned - The Register; Thomas Claburn

A new study by economists from Harvard University and MIT reveals a significant gap in AI adoption and innovation across different regions in the US.

The study finds that AI usage is highest in California's Silicon Valley and the San Francisco Bay Area, but was also noted in Nashville, San Antonio, Las Vegas, New Orleans, San Diego, and Tampa, as well as Riverside, Louisville, Columbus, Austin, and Atlanta.

Nvidia to Challenge Intel With Arm-Based Processors for PCs - Bloomberg; Ian King

Nvidia is using Arm technology to develop CPUs that would challenge Intel processors in PCs, and which could go on sale as soon as 2025.

Note: I am far from an NVIDIA fan, but I’m stoked for any amount of new competition in the CPU space.

New tool lets artists fight AI image bots by hiding corrupt data in plain sight - Engadget; Sarah Fielding

A team at the University of Chicago created Nightshade, a tool that lets artists fight AI image bots by adding undetectable pixels into an image that can alter how a machine-learning model produces content and what that finished product looks like.

Nightshade is intended to protect artists work and has been tested on both Stable Diffusion and an in-house AI built by the researchers.

IBM's NorthPole chip runs AI-based image recognition 22 times faster than current chips - Tech Xplore; Bob Yirka

NorthPole combines the processing module and the data it uses in a two-dimensional array of memory blocks and interconnected CPUs, and is reportedly inspired by the human brain.

NorthPole can currently only run specialized AI processes and not training processes or large language models, but the researchers plan to test connecting multiple chips together to overcome this limitation.

Apple’s $130 Thunderbolt 4 cable could be worth it, as seen in X-ray CT scans - Ars Technica; Kevin Purdy

Note: These scans are super cool. And make me feel somewhat better about insisting on quality cables. A+.

The Shifting Web

On-by-default video calls come to X, disable to retain your sanity - The Register; Brandon Vigliarolo

Video and audio calling is limited to anyone you follow or who is in your address book, if you granted X permission to comb through it.

Calling other users also requires that they’ve sent at least one direct message to you before.

Only premium users can place calls, but everyone can receive them.

Google Search Boss Says Company Invests to Avoid Becoming ‘Roadkill’ - The New York Times; Nico Grant

Google’s senior vice president overseeing search said that he sees a world of threats that could humble his company at any moment.

Google Maps is getting new AI-powered search updates, an enhanced navigation interface and more - Tech Crunch; Aisha Malik

Note: These AI recommender systems are going to be incredibly valuable advertising space. It is interesting that Apple decided to compete with Google in maps but not in basic search, but has so far not placed ads in the search results.

Reddit finally takes its API war where it belongs: to AI companies - Ars Technica; Scharon Harding

Reddit met with generative AI companies to negotiate a deal for being paid for its data, and may block crawlers if no deal is made soon.

Note: Google searches for info on Reddit often seem more effective than searching Reddit itself.  If they are unable to make a deal, and Reddit follows through, it will be a legitimate loss for discoverability but also an incredibly interesting experiment to see what Reddit is like without Google.

Bandcamp’s Entire Union Bargaining Team Was Laid Off - 404 Media; Emanuel Maiberg

Bandcamp’s new owner (Songtradr) offered jobs to just half of existing employees, with cuts disproportionately hitting union leaders. Every member of the union’s eight-person bargaining team was laid off, and 40 of the union's 67 members lost their jobs.

Songtradr spokesperson Lindsay Nahmiache claimed that the firm didn’t have access to union membership information.

Note: This just sucks. Bandcamp is rad, and it’s hard to imagine it continuing to be rad after this. I wonder if Epic had ideas for BC that didn’t work out.

Surveillance & Digital Privacy

Mozilla Launches Annual Digital Privacy 'Creep-o-Meter'. This Year's Status:  'Very Creepy' - Slashdot

Mozilla gave the current state of digital privacy a 75.6/100, with 100 being the creepiest.

They measured security features, data collection, and data sharing practices of over 500 gadgets, apps, and cars to come up with their score.

Every car Mozilla tested failed to meet their privacy and security standards.

Note: It would be great if even one auto brand would take privacy seriously.

EPIC Testifies in Support of Massachusetts Data Privacy and Protection Act -Electronic Privacy Information Center (EPIC)

Massachusetts version of ADPPA.

Note: While it may warm my dead heart to see any online privacy protections in law, scrambling to do so in response to generative AI is unlikely to protect Americans in any meaningful way from the surveillance driven form of capitalism we’ve all been living under for decades.

Complex Spy Platform StripedFly Bites 1M Victims - Dark Reading

StripedFly is a complex platform disguised as a cryptominer and evaded detection for six years by using a custom version of EternalBlue exploit, a built-in Tor network tunnel, and trusted services like GitLab, GitHub, and Bitbucket to communicate with C2 servers and update its functionality.

iPhones have been exposing your unique MAC despite Apple's promises otherwise - Ars Technica

A privacy feature which claimed to hide the Wi-Fi MAC address of iOS devices when joining a network was broken since iOS 14, and was finally patched in 17.1, released on Wednesday.

Note: I imagine this bug was reported a while ago, but wasn’t publically reported until the fix was released as a term of apple’s bug bounty program.

What the !#@% is a Passkey? - Electronic Frontier Foundation

Note: I welcome our passkey overlords.

[d0nutzgg]

Analyzing An Ataxic Dysarthria Patient's Speech with Computer Vision and Audio Processing

Hey everyone, so as you know I have been doing research on patients like myself who have Ataxic Dysarthria and other neurological speech disorders related to diseases and conditions that affect the brain. I was analyzing this file

with a few programs that I have written.

The findings are very informative and I am excited that I am able to explain this to my Tumblr following as I feel it not only promotes awareness but provides an understanding of what we go through with Ataxic Dysarthria.

Analysis of the audio file with an Intonation Visualizer I built

As you can tell this uses a heatmap to visualize loudness and softness of a speaker's voice. I used it to analyze the file and I found some really interesting and telling signs of Ataxic Dysarthria

At 0-1 seconds it is mostly pretty quiet (which is normal because it is harder for patients with AD to start their speaking off. You can notice that around 1-3 seconds it gets louder, and then when she speaks its clearer and louder than the patients voice. However the AD makes the patients speech constantly rise and fall in loudness from around -3 to 0 decibels most of the audio when the patient is speaking. The variation though between 0 and -3 varies quickly though which is a common characteristic in AD

The combination of the constant rising and falling in loudness and intonation as well as problems getting sentences started is one of the things that makes it so hard for people to understand those with Ataxic Dysarthria.

The second method I used is using a line graph (plotted) that gives an example of the rate of speech and elongated syllables of the patient.

As you can see I primarily used the Google Speech Recognition library to transcribe and count the syllables using Pyphen via "hyphenated" (elongated) words in the speech of the patient. This isn't the most effective method but it worked well for this example and here is the results plotted out using Matplotlib:

As you can see when they started talking at first there was a rise from the softer speech, as the voice of the patient got louder, they were speaking faster (common for those with AD / and HD) my hypothesis (and personal experience) is that this is how we try to get our words out where we can be understood by "forcing" out words resulting in a rise and fall of syllables / rate of speech that we see at the first part. The other spikes typically happen when she speaks but there is another spike at the end which you can see as well when the patient tries to force more words out.

This research already indicates a pretty clear pattern what is going on in the patients speech. As they try to force out words, their speech gets faster and thus gets louder as they try to communicate.

I hope this has been informative for those who don't know much about speech pathology or neurological diseases. I know it's already showing a lot of exciting progress and I am continuing to develop scripts to further research on this subject so maybe we can all understand neurological speech disorders better.

As I said, I will be posting my research and findings as I go. Thank you for following me and keeping up with my posts!

[tyrelpinnegar]

An Assistive Device Concept:

A seeing-eye AI. A glasses-mounted stereoscopic camera that’s wired to a smartphone in your pocket. It uses machine vision to pipe a description of what it sees directly into your ears, and natural language processing that allows you to request further clarification.

Also it’s open-source. Because if you create an assistive device and don’t open-source it you’re a monster.

[naya-mishra]

This article highlights the key difference between Machine Learning and Artificial Intelligence based on approach, learning, application, output, complexity, etc.

[aiphilosophy]

Ai taking over the world

Artificial intelligence (AI) has been a hot topic in recent years, with many experts predicting that it will have a significant impact on the future of humanity. Some people have even gone as far as to suggest that AI could eventually "take over the world."

While it's true that AI has the potential to be incredibly powerful, the idea that it will take over the world is more science fiction than science fact. There are several reasons why this is unlikely to happen.

First, it's important to remember that AI is simply a tool. It doesn't have its own goals or motivations. It can only do what it's programmed to do. This means that any potential negative effects of AI would be the result of human decisions, not the technology itself.

Second, there are many organizations and individuals working to ensure that AI is developed and used ethically. Researchers, policymakers, and industry leaders are all working to establish guidelines and best practices for the development and use of AI.

Third, it's important to remember that AI is not a monolithic technology. There are many different types of AI, and each has its own strengths and weaknesses. For example, machine learning and deep learning are used to analyze data and make predictions, while natural language processing is used to understand and respond to human language.

Ultimately, while it's true that AI has the potential to change the world in many ways, it's unlikely that it will take over the world. As long as we continue to develop and use AI responsibly, we can reap the benefits of this powerful technology while minimizing any potential negative effects.

In summary, it's a myth that AI will take over the world. AI is a tool, and it's the humans who operate and regulate its use. There are several organizations and individuals working to ensure that AI is developed and used ethically. It's important to remember that AI is not a monolithic technology, and each has its own strengths and weaknesses. Therefore, we must use AI responsibly to reap the benefits of this powerful technology while minimizing any potential negative effects

[techdriveplay]

What Is the Future of Robotics in Everyday Life?

As technology continues to evolve at a rapid pace, many are asking, what is the future of robotics in everyday life? From automated vacuum cleaners to advanced AI assistants, robotics is steadily becoming an integral part of our daily routines. The blending of artificial intelligence with mechanical engineering is opening doors to possibilities that seemed like science fiction just a decade…

[ai-innova7ions]

Simplify Art & Design with Leonardo's AI Tools!

Leonardo AI is transforming the creative industry with its cutting-edge platform that enhances workflows through advanced machine learning, natural language processing, and computer vision. Artists and designers can create high-quality images and videos using a dynamic user-friendly interface that offers full creative control.

The platform automates time-consuming tasks, inspiring new creative possibilities while allowing us to experiment with various styles and customized models for precise results. With robust tools like image generation, canvas editing, and universal upscaling, Leonardo AI becomes an essential asset for both beginners and professionals alike.

#LeonardoAI

#DigitalCreativity

#Neturbiz Enterprises - AI Innovations

[thedevmaster-tdm]

STOP Using Fake Human Faces in AI

[jcmarchi]

A fast and flexible approach to help doctors annotate medical scans

New Post has been published on https://thedigitalinsider.com/a-fast-and-flexible-approach-to-help-doctors-annotate-medical-scans/

A fast and flexible approach to help doctors annotate medical scans

To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. It can be a struggle to decipher where one structure (like a tumor) ends and another begins. 

When trained to understand the boundaries of biological structures, AI systems can segment (or delineate) regions of interest that doctors and biomedical workers want to monitor for diseases and other abnormalities. Instead of losing precious time tracing anatomy by hand across many images, an artificial assistant could do that for them.

The catch? Researchers and clinicians must label countless images to train their AI system before it can accurately segment. For example, you’d need to annotate the cerebral cortex in numerous MRI scans to train a supervised model to understand how the cortex’s shape can vary in different brains.

Sidestepping such tedious data collection, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts General Hospital (MGH), and Harvard Medical School have developed the interactive “ScribblePrompt” framework: a flexible tool that can help rapidly segment any medical image, even types it hasn’t seen before. 

Instead of having humans mark up each picture manually, the team simulated how users would annotate over 50,000 scans, including MRIs, ultrasounds, and photographs, across structures in the eyes, cells, brains, bones, skin, and more. To label all those scans, the team used algorithms to simulate how humans would scribble and click on different regions in medical images. In addition to commonly labeled regions, the team also used superpixel algorithms, which find parts of the image with similar values, to identify potential new regions of interest to medical researchers and train ScribblePrompt to segment them. This synthetic data prepared ScribblePrompt to handle real-world segmentation requests from users.

“AI has significant potential in analyzing images and other high-dimensional data to help humans do things more productively,” says MIT PhD student Hallee Wong SM ’22, the lead author on a new paper about ScribblePrompt and a CSAIL affiliate. “We want to augment, not replace, the efforts of medical workers through an interactive system. ScribblePrompt is a simple model with the efficiency to help doctors focus on the more interesting parts of their analysis. It’s faster and more accurate than comparable interactive segmentation methods, reducing annotation time by 28 percent compared to Meta’s Segment Anything Model (SAM) framework, for example.”

ScribblePrompt’s interface is simple: Users can scribble across the rough area they’d like segmented, or click on it, and the tool will highlight the entire structure or background as requested. For example, you can click on individual veins within a retinal (eye) scan. ScribblePrompt can also mark up a structure given a bounding box.

Then, the tool can make corrections based on the user’s feedback. If you wanted to highlight a kidney in an ultrasound, you could use a bounding box, and then scribble in additional parts of the structure if ScribblePrompt missed any edges. If you wanted to edit your segment, you could use a “negative scribble” to exclude certain regions.

These self-correcting, interactive capabilities made ScribblePrompt the preferred tool among neuroimaging researchers at MGH in a user study. 93.8 percent of these users favored the MIT approach over the SAM baseline in improving its segments in response to scribble corrections. As for click-based edits, 87.5 percent of the medical researchers preferred ScribblePrompt.

ScribblePrompt was trained on simulated scribbles and clicks on 54,000 images across 65 datasets, featuring scans of the eyes, thorax, spine, cells, skin, abdominal muscles, neck, brain, bones, teeth, and lesions. The model familiarized itself with 16 types of medical images, including microscopies, CT scans, X-rays, MRIs, ultrasounds, and photographs.

“Many existing methods don’t respond well when users scribble across images because it’s hard to simulate such interactions in training. For ScribblePrompt, we were able to force our model to pay attention to different inputs using our synthetic segmentation tasks,” says Wong. “We wanted to train what’s essentially a foundation model on a lot of diverse data so it would generalize to new types of images and tasks.”

After taking in so much data, the team evaluated ScribblePrompt across 12 new datasets. Although it hadn’t seen these images before, it outperformed four existing methods by segmenting more efficiently and giving more accurate predictions about the exact regions users wanted highlighted.

“​​Segmentation is the most prevalent biomedical image analysis task, performed widely both in routine clinical practice and in research — which leads to it being both very diverse and a crucial, impactful step,” says senior author Adrian Dalca SM ’12, PhD ’16, CSAIL research scientist and assistant professor at MGH and Harvard Medical School. “ScribblePrompt was carefully designed to be practically useful to clinicians and researchers, and hence to substantially make this step much, much faster.”

“The majority of segmentation algorithms that have been developed in image analysis and machine learning are at least to some extent based on our ability to manually annotate images,” says Harvard Medical School professor in radiology and MGH neuroscientist Bruce Fischl, who was not involved in the paper. “The problem is dramatically worse in medical imaging in which our ‘images’ are typically 3D volumes, as human beings have no evolutionary or phenomenological reason to have any competency in annotating 3D images. ScribblePrompt enables manual annotation to be carried out much, much faster and more accurately, by training a network on precisely the types of interactions a human would typically have with an image while manually annotating. The result is an intuitive interface that allows annotators to naturally interact with imaging data with far greater productivity than was previously possible.”

Wong and Dalca wrote the paper with two other CSAIL affiliates: John Guttag, the Dugald C. Jackson Professor of EECS at MIT and CSAIL principal investigator; and MIT PhD student Marianne Rakic SM ’22. Their work was supported, in part, by Quanta Computer Inc., the Eric and Wendy Schmidt Center at the Broad Institute, the Wistron Corp., and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health, with hardware support from the Massachusetts Life Sciences Center.

Wong and her colleagues’ work will be presented at the 2024 European Conference on Computer Vision and was presented as an oral talk at the DCAMI workshop at the Computer Vision and Pattern Recognition Conference earlier this year. They were awarded the Bench-to-Bedside Paper Award at the workshop for ScribblePrompt’s potential clinical impact.

[townpostin]

RVS College of Engineering and Technology Inaugurates AI Skills Lab in Partnership with Dell and Intel

New AI Skills Lab at RVS College of Engineering and Technology, Jamshedpur, aims to enhance digital education and prepare students for future challenges. In a significant step towards innovative education, RVS College of Engineering and Technology, Jamshedpur, has partnered with Dell Technologies and Intel Corporation to inaugurate an advanced AI Skills Lab. JAMSHEDPUR – RVS College of…

[crazydiscostu]

Orbbec Femto Bolt ToF Camera

Techie Tech Tech!

The Orbbec Femto Bolt stands strong as a compact and high-performance device, aimed at meeting the demanding needs of AI developers and those engaged in 3D vision applications. This multi-mode Depth and RGB camera, is equipped with a USB-C connection for power and data, presenting itself as a versatile and cost-effective solution. Its capabilities make it an attractive option for developers…

[third-eyeai]

PPE Monitoring Solution for Leading Automotive Component Manufacturers

Implementing a PPE monitoring solution for an automotive component manufacturer addresses safety challenges such as unsafe incidents and compliance issues. The solution includes CCTV surveillance for real-time monitoring, integration with SAP and Industry 4.0 for data synchronization, and IoT integration to enhance safety measures. It ensures comprehensive checks for helmets, eyeglasses, shoes, and other PPE, effectively mitigating accidents and promoting workplace safety.