Category Archives: Ai

Written by Brandi Vincent Jul 29, 2022 | FEDSCOOP

Naval Surface Force, U.S. Pacific Fleet has reached the final phase of drafting its first-ever, overarching data and artificial intelligence strategy and implementation plan and aims to share those resources more broadly before this summer ends, a key task force leader told FedScoop on Friday.

Naval Surface Force organizations perform a variety of administrative, maintenance, workforce and operational training functions and help equip and staff Navy warships before they are deployed to the respective fleet commands for military missions. As one of the Navys largest enterprises, the organizations capture, produce and rely on massive amounts of data.

About a year ago, the Navy created Task Force Hopper to produce a complex digital infrastructure and cultural transformation to ultimately drive AI-enabled capabilities across the surface force.

We understand that AI is the most powerful decision engine for the readiness and sustainment of our ships and for warfighting and we wanted to see, as an enterprise, how we can best accelerate this effort. This is about AI adaptation, Task Force Hopper Director Capt. Pete Kim told FedScoop in an interview.

Kim has served in that role since the task forces inception, and also leads the Surface Analytics Group.

To him and his team, the task force represents a broad enterprise approach about hiring the right digital talent, making sure our teams have the right development platforms for data excellent exploitation and creating new processes for the force.

As such a sprawling enterprise, the surface force has many projects and initiatives within the Navy and in collaboration with academia and industry, resulting in siloed but duplicative efforts and gaps in transparency, among other issues.

We all know that AI is totally dependent on high-quality and accurate data. We believe that data management is the most important discipline for our era, and thats what we wanted to focus on. So, one of our first initiatives was to draft a data AI strategy and implementation plan for the force to establish that structure, Kim explained.

That in-the-making document will be unclassified, but the audience is really the surface enterprise, he added, suggesting that only a summary may be publicly disseminated.

Right now, it is in the final stages of drafting. We hope to push that out to the enterprise here in the next few weeks, the director said.

Broadly, the strategy and accompanying plan will detail why the Naval Surface Force is taking the approach that it is, and include directions for how officials will support the chosen framework.

It really focuses on making data AI-ready across the board and so it gets into data management, data governance, digital talent, ensuring that weve got clearly defined use cases, Kim noted.

The strategy anchors on what he deemed a federated model with a number of different supporting organizations.

Its about central data governance, with a central data catalog and then having these decentralized analytic and AI development nodes at different places in the enterprise, where people know the data the best, Kim said.

While nodes at one military location, or associated with one team, might focus on maintenance, others could concentrate on staffing or other needs. Kim added that the nodes will hone in on different categories of AI, depending on the use case and what products and models were trying to build.

Offering two examples of nodes aligned with readiness, he pointed to the Surface Analytics Group that he runs, which assists with the force generation of about 168 warships, and a separate group that zeroes in on operational safety and risk indicators.

Readiness is our focus here at the [Naval Surface Force]. Program offices and warfare centers that are, lets just say, focused on lethality or warfighting, we expect those organizations to have nodes working on their specific areas and use cases, Kim said. Then the follow on here is, again, that common development environment where we as an enterprise have transparency on all the different projects that are going on and then we can leverage each others works.

Once the strategy and implementation plan are released, next steps will prioritize empowering each of the AI nodes.

These nodes are going to support all the priority projects. So, its going to be about, Hey, do you have the right tools in this development environment? Do you have the right digital talent to move out on this? Are there different datasets that you need that the rest of the enterprise can help you out with? So, well really focus in on the select nodes to support those priority projects, Kim said.

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Naval Surface Force plan to accelerate AI adoption expected 'in the next few weeks' - FedScoop

The NHS is to use artificial intelligence to detect, screen and treat people at risk of hepatitis C under plans to eradicate the disease by 2030.

Hepatitis C often does not have any noticeable symptoms until the liver has been severely damaged, which means thousands of people are living with the infection known as the silent killer without realising it.

Left untreated, it can cause life-threatening damage to the liver over years. But with modern treatments now available, it is possible to cure the infection.

Now health chiefs are launching a hi-tech screening programme in England in a fresh drive to identify thousands of people unaware they have the virus.

The scheme, due to begin in the next few weeks, aims to help people living with hepatitis C get a life-saving diagnosis and access to treatment before it is too late.

The NHS will identify people who may have the virus by using AI to scan health records for a number of key risk factors, such as historical blood transfusions or an HIV diagnosis.

Anyone identified through the new screening process will be invited for a review by their GP and, if appropriate, further screening for hepatitis C. Those who test positive for the virus will be offered treatment available after NHS England struck a deal with three major pharmaceutical companies.

Prof Graham Foster, national clinical chair for NHS Englands hepatitis C elimination programmes, said the scheme marks a significant step forward in the fight to eliminate the virus before 2030. It will use new software to identify and test patients most at risk from the virus potentially saving thousands of lives, he added.

Hepatitis C can be a fatal disease which affects tens of thousands across the country but with unlimited access to NHS treatments, innovative patient finding initiatives such as this one we will continue to boost the life chances of thousands of patients by catching the virus even earlier.

Hepatitis C deaths have fallen by over a third in five years. Recent data shows the number of deaths from the virus has decreased by 35%, from 482 in 2015 to 314 in 2020 in England. New cases have also fallen from 129,000 in 2015 to 81,000 in 2020, according to the UK Health Security Agency.

Hepatitis C is usually spread through blood-to-blood contact. It can be spread by sharing unsterilised needles particularly needles used to inject recreational drugs.

The actor Pamela Anderson contracted hepatitis C while married to the musician Tommy Lee, who had a history of drug abuse, when the couple shared a tattoo needle. Anderson, 55, was cured after taking antivirals.

Experts say the recent fall in deaths is largely down to earlier detection of the virus coupled with improved access to treatments.

Rachel Halford, the chief executive of the Hepatitis C Trust, said: Thanks to the brilliant advances we have seen in hepatitis C treatment in recent years we have a real opportunity to eliminate the virus as a public health concern in the next few years. However, in order to do so we need to make progress in finding those living with an undiagnosed infection and refer them into treatment.

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That is why the announcement of this new screening programme is such welcome news. Primary care is where we are most likely to find those who have been living with an undiagnosed infection for many years.

There has been brilliant work to expand testing in a wide range of settings in recent years but we have not yet seen the advances we need to see in primary care. The rollout of this screening programme is therefore another crucial step towards achieving elimination.

NHS staff are also visiting at-risk communities in specially equipped trucks to test for the virus and carry out liver health checks with portable scans to detect liver damage.

The health service is aiming to wipe out the virus in England before the global goal of 2030 set by the World Health Organization.

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Exclusive: NHS to use AI to identify people at higher risk of hepatitis C - The Guardian

Tesla and Drive.ai are top key players in the Intellgent Driving market according to a new Global Intellgent Driving Market report. The report gives an overview of the 2021 growth of Intellgent Driving and how that growth has significant changes from the previous year as our global economy recovers from the impacts of Covid-19.

The report looks at business models and marketing strategies used by key market players such as Tesla and Drive.ai and how they are able to stay competitive while accelerating their business growth in the market.

It gives several market scenarios and recommendations for solutions to help these companies to stay ahead of their competitors. It also provides insights into how the Covid-19 pandemic impacted the market.

Additionally, it highlights the trends that either influenced or challenged the market during the pandemic.

The report identifies existing opportunities and strategies that a company can use to increase its competitive edge.

Tesla, Drive.ai, and Mobileye are just three of the top players market identified by the report. The Intellgent Driving market types are divided into two categories: autonomous vehicles and autonomous systems.

For Autonomous vehicles, the two types of applications include passenger vehicles and commercial vehicles.

The full list is as follows:

Many people forget that Tesla isnt just an automaker but its also a technology company. On September 30, 2022, Tesla will hold its second AI Day and theres a chance we could see the Optimus Bot prototype.

During the Q2 2022 earnings call, Elon Musk spoke briefly about AI Day.

Were hosting our AI Day in a few months.

I think people will be amazed at what were able to show off on AI Day. So basically, theres a tremendous amount to look forward to in the second half of this year.

While attending Teslas first AI Day in person last year, I watched Ganesh Venkataramanan, Teslas senior director of Autopilot hardware and the leader of the Dojo project.

Venkataramanan pointed out the insatiable demand for speed and capacity for neural network training.

Then he shared Teslas goal which is to achieve the best artificial intelligence training performance while supporting the larger and more complex models while also being both power efficient and cost effective.

We thought about how to build this and we came up with a distributed compute architecture. After all, all the training computers are distributed computers in one form or the other.

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Tesla, Drive.AI top key players in the Intellgent Driving market - Teslarati

Shared micromobility companies have been adopting startlingly advanced new tech to correct for the thing that cities hate most sidewalk riding. Some companies, like Bird, Neuron and Superpedestrian, have relied on hyperaccurate GPS systems to determine if a rider is riding inappropriately. Others, like Lime, have started integrating camera-based computer vision systems that rely on AI and machine learning to accurately detect where a rider is.

The latter camp has largely leaned on the innovations of Drover AI, a Los Angelesbased startup that has tested and sold its attachable IoT module to the likes of Spin, Voi, Helbiz, Beam and Fenix to help operators improve scooter safety and, most importantly, win city permits.

Drover, which was founded in May 2020, closed out a $5.4 million Series A Wednesday. The startup will use the funds to continue building on the next generation of PathPilot, Drovers IoT module that contains a camera and a compute system that analyzes visual data and issues commands directly to the scooter. Depending on the citys needs, the scooters will either make noises to alert a rider that theyre driving on the sidewalk or slow them down. The new version, called PathPilot Lite, will do much of the same, except it will be more integrated, better and cheaper, says Drovers co-founder and chief business officer Alex Nesic.

Drover has modules on over 5,000 vehicles with orders for over 15,000 more that the company needs to deliver by the end of the year, according to Nesic.

Getting those next-gen modules into production will require hiring a few more minds across the engineering and project management side of things, as well as in government relations and communications over in Europe to aid Drovers expansion across the pond.

Nesic said Drover also intends to hire a software engineer to help build out the data dashboards the company offers to micromobility partners.

Drovers operator-facing beta dashboard that breaks trips down by infrastructure used. Image Credits: Drover AI

We have a beta dashboard that shows a color-coded version of what trips look like broken down by infrastructure, how much time each vehicle spends on each section, and in aggregate across an entire fleet, Nesic told TechCrunch. We have a parking validation dashboard where for any one of your vehicles deployed in the city, you can see where the ride ended, what our AI scored the parking job as along with a photo. So all these tools were offering our operator customers that they could build themselves based on the data were already sharing with them, but they just dont have the bandwidth, so these customer-facing tools are a value add.

Drover also sells its data to cities and is exploring the use of distributed cameras moving through cities to build out a suite of tools that could potentially provide a city-facing dashboard that displays information like the state of infrastructure or bike lane violations, which is a pet project of Nesics.

Our system can tell you, for example, the rider was on the bike lane for 20% of the time, 30% of the time on the sidewalk and the rest in the street, said Nesic. That can inform a lot of policy decisions on where to put bike lanes or whether the bike lanes youve invested in are working.

Drover has been receiving interest from transportation agencies like Transport for London, as well as insurance companies that want this kind of granular data to understand how new mobility modes are being used in the infrastructure.

There are some who say the future of micromobility is really in owned vehicles, rather than shared. If thats the case, it stands to reason that shared micromobility sets the trends that future private scooters will have to live by. Advanced rider assistance systems are becoming table stakes for operators moving forward who want to win cities, and Nesic thinks policy for private vehicles might soon follow in fact, hes hoping it does.

Part of the money we raised is gonna go towards exploring other integrations further up the supply chain with vehicle manufacturers and IoT manufacturers, said Nesic. The real goal is to drive the cost down, and if we can, already have our tech be a feature set of the next gen IoT that has compute capability already, and then were just licensing Drovers AI, which is equipped to handle different infrastructure across the globe.

But thats way down the line. For the short-term, Drover is still hyperfocused on expanding off the backs of shared scooter companies that are increasingly hearing demand for this kind of tech from cities.

Drovers Series A was led by Vektor Partners, a VC firm focusing on the future of mobility. The firm recently raised a 125 million fund for sustainable mobility, which is where Drovers recent raise came from.

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Drover AI is using computer vision to keep scooter riders off sidewalks - TechCrunch

Despite loud and sustained protests from Instagram users, Meta chief Mark Zuckerberg says the changes that have rolled out to the social media platform are not only going to remain, theyre going to intensify.

In an earnings call Wednesday, Zuckerberg said the photo-sharing app will double the amount of A.I.-recommended content it shows by the end of next year.

One of the main transformations in our business right now is that social feeds are going from being driven primarily by the people and accounts you follow to increasingly also being driven by A.I. recommending content that youll find interesting from across Facebook or Instagram, even if you dont follow those creators, he said.

At present, about 15% of the content in a persons Facebook or Instagram feed is recommended by the companys A.I. Thats reflected in posts from people, groups, or accounts users dont follow. By the end of next year, Zuckerberg said, those numbers will double.

The basis for the increase is increased engagement, he says, which increases monetization.

Social content from people you know is going to remain an important part of the experience and some of our most differentiated content, he said. But increasingly, well also be able to supplement that with other interesting content from across our networks.

The push for this AI content, which is currently best reflected in the number of Reels that appear on Facebook and Instagram pages, comes as TikTok threatens Metas dominance in the social media space. That was reflected in Metas earnings Wednesday as the company reported the first revenue decline in its history as a public company.

The push is working, though. Meta says it has seen a 30% increase in the amount of time people are spending with Reels.

The cost of that success, though, is customer and ambassador goodwill. Facebook suffered a backlash from Instagram power user Kylie Jenner earlier this week, who shared a post that read stop trying to be TikTokI just want to see cute photos of my friends. Sister Kim Kardashian, who has 326 million Instagram followers, soon followed suit.

That caught the attention of Instagram CEO Adam Mosseri, who posted avideoonTwitterto address the criticisms, pledging Were going to continue to support photos. Its part of our heritage.

That said, I need to be honest, he added. I do believe that more and more of Instagram is going to become video over time. We see this even if we change nothing.

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Mark Zuckerberg ignores objections, says Instagram will show twice as much A.I.-recommended content by end of 2023 - Fortune

HOUSTON, TX, July 31, 2022 /24-7PressRelease/ The AI Innovation Consortium is collaborating with the University of Houston (UH), NVIDIA, and TechnipFMC to advance the creation of industrial metaverse applications as part of the digital oilfield laboratory initiative. This collaboration brings together end-users, technology providers, and domain experts to build commercially relevant use cases and accelerator tools and break through informational silos. These resources ultimately enable an interconnected world with an industrial metaverse.

Adam Berg, Manager of Learning Solutions at TechnipFMC, has been working with the UH College of Technology and the AI Innovation Consortium to test and pilot a successful augmented reality program to train and manage upstream resources. TechnipFMC, a leading engineering and services company, is a driving force for the augmented, immersive, and virtual reality program known as X-Reality or the XR program.

The purpose of the XR Network is to bring together the knowledge, tools, and people necessary to leverage XR technologies, to achieve business goals, and to transform industry environments. Our work with the college and the consortium has enabled us to move beyond concept to pilot and implementation, added Berg.

Berg and his team have received some of the digital oilfield labs foundational tools in the form of digital twins of assets and natural language processing and AI-enhanced computer vision pipelines. These tools have helped bridge the gap between the real world and the metaverse, making it possible to create augmented reality environments for training, repair, and quality control without many of the manual complexities.

This industrial metaverse initiative is part of the University of Houston College of Technologys digital oilfield laboratory to bring together faculty and students with AI Innovation Consortium partners such as TechnipFMC and NVIDIA to explore the development of commercially relevant digital solutions. These activities involve some of the largest manufacturers and oil and gas companies in the world. The goal of the consortium is to use technologies such as artificial intelligence, augmented reality, and digital twins to create and build foundational tools for training solutions with unlimited scalability for enterprise.

Building on top of NVIDIA accelerated computing platforms including NVIDIA DGX systems, the NVIDIA Jetson edge AI platform, and NVIDIA OVX, consortium members have access to software platforms like NVIDIA Omniverse to build and accelerate the evolution of the metaverse.

With the help of NVIDIAs Global Energy Director, Marc Spieler, the initiative is bringing these tools to end-users like Berg and others.

Our work with the AI Innovation Consortium and its ecosystem of partners and members is enabling the critical collaboration required between academic research, technology, and industry to scale artificial intelligence to achieve measurable outcomes in industries like energy and manufacturing. Using NVIDIA platforms like Omniverse and Modulus for digital twins and physically accurate simulations will accelerate and streamline returns on digital investments by reducing downtime and safety incidents, said Spieler.

With the support of industry groups like the AI Innovation Consortium, domain experts from across the table are working together toward common goals with barriers of information sharing.

Konrad Konarski, Chairperson of the AI Innovation Consortium added:

We are currently working to build the worlds largest portfolio of digitized metaverse assets and environments for both the oilfield and specific manufacturing sectors. This means a maintenance manager, an operations technology expert, or whoever is responsible for a metaverse technology project will be able to pick up an augmented reality platform or a wearable computer, or simply a smartphone, and seamlessly interconnect their real-world operating environment to and from the metaverse.

The implications of this effort mean that the deployment of augmented reality, additive manufacturing, and digital twin technology platforms and the scalability of these platforms across any organization will be exponentially simpler.

The collective collaboration is also deeply rooted with the support of academic domain experts and professors at the University of Houston College of Technology, including consortium trustee Professor of Practice David Crawley. Professor Crawley, along with several members from the University of Houston College of Technology, has been working with the NVIDIA Isaac robotics platform for enhanced development, simulation, and deployment, as well as with other industrial robotic systems and cloud-based command and control platforms, as part of the AI digital oilfield that directly collaborates as part of the industrial metaverse initiative.

The consortiums academic ecosystem is a critical part of developing the future workforce. This workforce will be capable of using collaborative robot, or cobot, technologies as part of the evolution of industry 5.0, and experienced in developing and deploying artificial intelligence, augmented reality, and digital twin technologies. The joint initiative at our AI digital oilfield lab with the AI Innovation Consortium brings together professors, undergraduates, and postgraduate students on the development of the industrial metaverse, lights out manufacturing, and broader AI and augmented reality application systems and leverages their expertise and aligns academia with industry, said Professor Crawley.

This academic and collaboration footprint also extends to other universities and technology institutes. Among those, PhD student William Aiken from the University of Ottawa in Canada and teams from the MxD smart manufacturing center in Chicago are playing a part in building darkfactory systems with the help of the industrial metaverse.

The mission of the AIIC is to bring together industry leaders across different domains to support a holistic perspective on Artificial Intelligence to build standards and best practices that help drive technology adoption, evolve privacy, data governance, and data biases guiding principles, and effectively align AI evolution with the most relevant industry challenges and objectives now and in the future.

Related Link:https://aiinnovationconsortium.org

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AI Innovation Consortium Accelerates the Evolution of the Industrial Metaverse - Digital Journal

ByHub staff report

The problem with artificial intelligence is that everybody thinks it should do everything perfectly right out of the box, according to Rama Chellappa, an expert in computer vision and machine learning.

"There's no such thing. There are always tradeoffs," said Chellappa, a Bloomberg Distinguished Professor in electrical and computer engineering and biomedical engineering, during a virtual discussion on Thursdaythe latest session in the Johns Hopkins Congressional Briefing series.

AI, Chellappa noted, is built on data, and data is not always perfect or complete. Errors in how data is collected, compiled, or processed will affect the success of any AI tool that relies on it. That is how challenges such as bias creep into autonomous systems.

The process for reaching for fully functional AI, he said, yields new discoveries along the way.

"[AI] is doing good things in a lot of ways, but it is not a perfect technology," he said. "For example, take the autonomous car. We may not have a fully autonomous driving car that works everywhere so you can read your book and drink coffee [while driving]... But the process of getting there has helped us figure out how to design new safety features that have been put in cars, even inexpensive entry-level cars."

The July 28 briefing covered a variety of topics related to AI, including its use in autonomous technologies, how it can help in the design and discovery of new materials, ethical considerations related to AI, the use of AI within health care, and the major challenges that need to be addressed before AI could reach its full potential.

Watch the full briefing here and visit this page to watch other briefings in the series.

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Imperfect AI is to be expected | Hub - The Hub at Johns Hopkins

The world of artificial intelligence is expanding, and a group of AI experts at Northeastern wants to make sure it does so responsibly.

Self-driving cars are hitting the road and others cars. Meanwhile a facial recognition program led to the false arrest of a Black man in Detroit. Although AI has the potential to alter the way we interact with the world, it is a tool made by people and brings with it their biases and limited perspectives. But Cansu Canca, founder and director of the AI Ethics Lab, believes people are also the solution to many of the ethical barriers facing AI technology.

With the AI Ethics Advisory Board, Canca, co-chair of the board and AI ethics lead of the Institute for Experiential AI at Northeastern, and a group of more than 40 experts hope to chart a responsible future for AI.

There are a lot of ethical questions that arise in developing and using AI systems, but also there are a lot of questions regarding how to answer those questions in a structured, organized manner, Canca said. Answering both of those questions requires experts, especially ethics experts and AI experts but also subject matter experts.

The board is one of the first of its kind, and although it is housed in Northeastern, it is made up of multidisciplinary experts from inside and outside the university, with expertise ranging from philosophy to user interface design.

The AI Ethics Advisory Board is meant to figure out: What is the right thing to do in developing or deploying AI systems? Canca said. This is the ethics question. But to answer it we need more than just AI and ethics knowledge.

The boards multidisciplinary approach also involves industry experts like Tamiko Eto, the research compliance, technology risk, privacy and IRB manager for healthcare provider Kaiser Permanente. Eto stressed that whether AI is utilized in healthcare or defense, the impacts need to be analyzed extensively.

The use of AI-enabled tools in healthcare and beyond requires a deep understanding of the potential consequences, Eto said. Any implementation must be evaluated in the context of bias, privacy, fairness, diversity and a variety of other factors, with input from multiple groups with context-specific expertise.

The AI Ethics Advisory Board will function as an external, objective consultant for companies that are grappling with AI ethical questions. When a company contacts the board with a request, it will determine the subject matter experts best suited to tackling that question. Those experts will form a smaller subcommittee that will be tasked with considering the question from all relevant perspectives and then resolving the case.

But the aim is not only to address the concerns of specific companies. Canca and the board members hope to answer broader questions about how AI can be implemented ethically in real-world settings.

The mindset is for truly solving questions, not just managing the question for the client but truly solving the question, and contributing to the progress of the practice Canca said. This is not a review board or a compliance board. Our approach is one, Lets figure the ethical issues and create better technologies. Lets enhance the technology with all these multidisciplinary capabilities that we have, that we can bring on board.'

Its an approach that Ricardo Baeza-Yates, co-chair of the board, director of research for the Institute for Experiential AI and professor of practice in Khoury College of Computer Science, said is necessary in order to tackle the privacy and discrimination issues that are most commonly seen in AI use. Baeza-Yates said the latter is especially concerning, since its not always a simple technical fix.

This sometimes comes from the data but also sometimes comes from the system, Baeza-Yates said. What you are trying to optimize can sometimes be the problem.

Baeza-Yates points to facial recognition programs and e-commerce AI that have profiled people of color and reinforced pre-existing biases and forms of discrimination. But the most well-known ethical problem in current AI use is the self-driving car, which Baeza-Yates likened to the trolley problem, a famous philosophical thought experiment.

We know that self-driving cars will kill less people [than human drivers], for sure, Baeza-Yates said. The problem is that we are saving a lot of people, but also we will kill some people who before were not in danger. Mostly, this will be vulnerable people, women, children, old people that, for example, didnt move so fast like the model expected or the kid moved too fast for the model to expect.

Conversations around the ethical implications of technology like the self-driving car are only starting in companies. For now, AI ethics seems very mysterious to a lot of companies, Canca said, which can lead to confusion and disinterest. With the board, Canca hopes to spark a more meaningful, engaged conversation and put an ethics-based approach at the core of how companies approach the technology moving forward.

We can help them understand the issues they are facing and figure out the problems that they need to solve through a proper knowledge exchange, Canca said. Through advising, We can help them ask the right questions and help them find novel and innovative solutions or mitigations. Companies are getting more and more interested in establishing a responsible AI practice, but its important that they do this efficiently and in a way that fits their organizational structure.

For media inquiries, please contact Shannon Nargi at s.nargi@northeastern.edu or 617-373-5718.

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Northeastern Launches AI Ethics Board to Chart a Responsible Future in AI - Northeastern University

You can find AI that creates new images, but what if you want to fix an old family photo? You might have a no-charge option. Louis Bouchard and PetaPixel have drawn attention to a free tool recently developed by Tencent researchers, GFP-GAN (Generative Facial Prior-Generative Adversarial Network), that can restore damaged and low-resolution portraits. The technology merges info from two AI models to fill in a photo's missing details with realistic detail in a few seconds, all the while maintaining high accuracy and quality.

Conventional methods fine-tune an existing AI model to restore images by gauging differences between the artificial and real photos. That frequently leads to low-quality results, the scientists said. The new approach uses a pre-trained version of an existing model (NVIDIA's StyleGAN-2) to inform the team's own model at multiple stages during the image generation process. The technique aims to preserve the "identity" of people in a photo, with a particular focus on facial features like eyes and mouths.

You can try a demo of GFP-GAN for free. The creators have also posted their code to let anyone implement the restoration tech in their own projects.

This project is still bound by the limitations of current AI. While it's surprisingly accurate, it's making educated guesses about missing content. The researchers warned that you might see a "slight change of identity" and a lower resolution than you might like. Don't rely on this to print a poster-sized photo of your grandparents, folks. All the same, the work here is promising it hints at a future where you can easily rescue images that would otherwise be lost to the ravages of time.

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

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Free AI tool restores old photos by creating slightly new loved ones - Engadget

UK deep tech AI company, T-DAB.AI, has closed its funding round of 495,000 to accelerate the development of its Edge AI platform.

Funding has been sourced from private angel investors, including Growceanu, and non-equity funding from Innovate UK as part of an industry collaboration with Domin and the University of Bath.

This rounds funds will bring forward more advanced Federated Learning capabilities, automated Edge ML-Ops, and accelerated deployment mechanisms. In addition, funds will support the evolution of the sales and marketing functions of T-DAB.AI.

T-DAB.AIs state-of-the-art Edge AI platform will revolutionise AI for IoT applications. It will enable Edge AI to be implemented across IoT infrastructure without the need to move vast volumes of data to the cloud. Hence increasing security and privacy, reducing expensive data and compute costs, and reducing the need for continuous network connectivity for connected device intelligence.

At the core of the platform is a new paradigm known as Federated Learning. T-DAB.AIs vision is to serve seamless and massively scalable Federated Learning to the full spectrum of Edge compute, all the way down to the chip.

Designed by data scientists, T-DAB.AIs platform enables teams to build or buy machine learning solutions, trained at the Edge, and push models into production and deploy them faster, more privately, and cost efficiently than ever before. Users of the platform can either build their own solutions, or adopt pre-packaged solutions developed by T-DAB.AI and use AI to solve high-value industrial machine intelligence use cases.

This rounds funds will bring forward more advanced Federated Learning capabilities, automated Edge ML-Ops, and accelerated deployment mechanisms. In addition, funds will support the evolution of the sales and marketing functions of T-DAB.AI.

We invested in T-DAB because it is a company made up of an excellent team, with people very well trained in business, technology and sales. They have also developed a solid set of innovative products and services in a technical field applicable in many industries, including production, energy, automotive, HVAC and Smart Building. The solution is already validated, the current sales are excellent and have a very high traction.

Image courtesy of Shutterstock

Ciprian Man, one of the investors and founders of Growceanu: Were hugely excited to have accelerated the next evolution of T-DAB.AI through this funding round. This is a big step towards bringing our revolutionary distributed Edge AI solution, OctaiPipe to the market. Were delighted to have added a fantastic group of investors to our team.

Dr. Eric Topham, CEO & Founder, said: T-DAB.AI aims to reduce the operating expenses for the industrial, energy, and IoT segments through a combination of innovative AI and ML assets and efficient processes and automation. T-dab.ai has a great team and very ambitious plans, that checked my boxes. For these reasons I decided to invest in t-dab.

Sebastian Ivan, MBA, one of the investors, added:This is great news for T-DAB.AI and for manufacturing more generally, to be able to utilise AI at the edge without the need for huge amounts of data collection and analysis is game changing.

Nick Hussey, CEO of The Manufacturer, commented: Ive worked alongside T-DAB.AIs team for over the past year and Ive been impressed with their vision, passion and innovative suite of AI and ML assets theyve developed for industrial, energy and IoT segments. I feel T-DAB.AI is now at a critical juncture after a successful start-up phase. This latest investment round will propel T-DAB.AI into the big-league and Im happy support that journey.

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State-of-the-art Edge AI platform to accelerate UKs AI adoption - The Manufacturer