Mediaboss Marketing

Nothing is built to last, not even the stuff we create to last as long as possible. Everything eventually degrades, especially art, and many people make careers and hobbies out of restoring timeworn items. AI could provide a useful second pair of eyes during the process.

Was Rahman pointed out that machine learning has served a vital role in art restoration by figuring out the most likely missing pieces that need replacing. Consider the exorcism scene in "Invincible;" Machine learning cuts down on the time-consuming, mind-numbing work human restorers have to carry out. To be fair, machine learning is technically different from AI, but it is also a subset of AI, so since we can use machine learning in art restoration, it stands to reason we could use AI, too.

Rahman also stated machine learning helps guide art restorers and is generally more accurate than prior techniques. More importantly, Rahman believes AI programs assigned to art restoration could prevent botched attempts that are the product of human error or when someone's pride exceeds their talent. Rahman cited the disastrous event when a furniture restorer forever disfigured Bartolom Esteban Murillo's Immaculate Conception, but that is far from the only case where an AI could come in handy. After all, someone once tried restoring EliasGarcia Martinez' Ecce Homofresco andaccidentally birthed what is colloquially known as "Monkey Christ."

While a steady hand and preternatural skill are necessary to rekindle the glory of an old painting or sculpture, Rahman believes AI could provide a guiding hand that improves the result's quality, provided the restorer already knows what they're doing.

Read more here:
An AI Ethics Researcher's Take On The Future Of Machine Learning In The Art World - SlashGear

COLUMBUS, Ohio, April 22, 2024 Researchers at Ohio State University are pioneering the field of imageomics. Founded on advancements in machine learning and computer vision, the researchers are using imageomics to explore fundamental questions about biological processes by combining images of living organisms with computer-enabled analysis.

The field was the subject of a presentation by Wei-Lun Chao, an investigator at Ohio State Universitys Imageomics Institute and a distinguished assistant professor, during the annual meeting of the American Association for the Advancement of Science (AAAS). The presentation focused on the fields application for micro- to macro-level problems by turning research questions into computable problems.

Nowadays we have many rapid advances in machine learning and computer vision techniques, said Chao. If we use them appropriately, they could really help scientists solve critical but laborious problems.

Traditional methods for image classification with trait detection require a huge amount of human annotation, but our method doesnt, said Chao. We were inspired to develop our algorithm through how biologists and ecologists look for traits to differentiate various species of biological organisms.

Chao said that one of the most challenging parts of fostering imageomics research is integrating different parts of scientific culture to collect enough data and form novel scientific hypotheses from them. That being said, he is enthusiastic about its potential to allow for the natural world to be seen within multiple fields.

What we really want is for AI to have strong integration with scientific knowledge, and I would say imageomics is a great starting point towards that, he said.

Chaos AAAS presentation, An Imageomics Perspective of Machine Learning and Computer Vision: Micro to Global, was part of the session Imageomics: Powering Machine Learning for Understanding Biological Traits.

Continue reading here:
Imageomics Applies AI and Vision Advancements to Biological Questions - Photonics.com

Grimaldi, D. & Engel, M. S. Evolution of the Insects (Cambridge Univ. Press, 2005).

Deora, T., Gundiah, N. & Sane, S. P. Mechanics of the thorax in flies. J. Exp. Biol. 220, 13821395 (2017).

Article PubMed Google Scholar

Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354377 (2018).

Article ADS Google Scholar

Kramer, M. A. Nonlinear principal component analysis using autoassociative neural networks. AlChE J. 37, 233243 (1991).

Article ADS CAS Google Scholar

Pringle, J. W. S. The excitation and contraction of the flight muscles of insects. J. Physiol. 108, 226232 (1949).

Article CAS PubMed PubMed Central Google Scholar

Josephson, R. K., Malamud, J. G. & Stokes, D. R. Asynchronous muscle: a primer. J. Exp. Biol. 203, 27132722 (2000).

Article CAS PubMed Google Scholar

Gau, J. et al. Bridging two insect flight modes in evolution, physiology and robophysics. Nature 622, 767774 (2023).

Article ADS CAS PubMed PubMed Central Google Scholar

Boettiger, E. G. & Furshpan, E. The mechanics of flight movements in diptera. Biol. Bull. 102, 200211 (1952).

Article Google Scholar

Pringle, J. W. S. Insect Flight (Cambridge Univ. Press, 1957).

Miyan, J. A. & Ewing, A. W. How Diptera move their wings: a re-examination of the wing base articulation and muscle systems concerned with flight. Phil. Trans. R. Soc. B 311, 271302 (1985).

ADS Google Scholar

Wisser, A. Wing beat of Calliphora erythrocephala: turning axis and gearbox of the wing base (Insecta, Diptera). Zoomorph. 107, 359369 (1988).

Article Google Scholar

Ennos, R. A. A comparative study of the flight mechanism of diptera. J. Exp. Biol. 127, 355372 (1987).

Article Google Scholar

Dickinson, M. H. & Tu, M. S. The function of dipteran flight muscle. Comp. Biochem. Physiol. A 116, 223238 (1997).

Article Google Scholar

Nalbach, G. The gear change mechanism of the blowfly (Calliphora erythrocephala) in tethered flight. J. Comp. Physiol. A 165, 321331 (1989).

Article Google Scholar

Walker, S. M., Thomas, A. L. R. & Taylor, G. K. Operation of the alula as an indicator of gear change in hoverflies. J. R. Soc. Inter. 9, 11941207 (2011).

Article Google Scholar

Walker, S. M. et al. In vivo time-resolved microtomography reveals the mechanics of the blowfly flight motor. PLoS Biol. 12, e1001823 (2014).

Article PubMed PubMed Central Google Scholar

Wisser, A. & Nachtigall, W. Functional-morphological investigations on the flight muscles and their insertion points in the blowfly Calliphora erythrocephala (Insecta, Diptera). Zoomorph. 104, 188195 (1984).

Article Google Scholar

Heide, G. Funktion der nicht-fibrillaren Flugmuskeln von Calliphora. I. Lage Insertionsstellen und Innervierungsmuster der Muskeln. Zool. Jahrb., Abt. allg. Zool. Physiol. Tiere 76, 8798 (1971).

Google Scholar

Fabian, B., Schneeberg, K. & Beutel, R. G. Comparative thoracic anatomy of the wild type and wingless (wg1cn1) mutant of Drosophila melanogaster (Diptera). Arth. Struct. Dev. 45, 611636 (2016).

Article Google Scholar

Tu, M. & Dickinson, M. Modulation of negative work output from a steering muscle of the blowfly Calliphora vicina. J. Exp. Biol. 192, 207224 (1994).

Article CAS PubMed Google Scholar

Tu, M. S. & Dickinson, M. H. The control of wing kinematics by two steering muscles of the blowfly (Calliphora vicina). J. Comp. Physiol. A 178, 813830 (1996).

Article CAS PubMed Google Scholar

Muijres, F. T., Iwasaki, N. A., Elzinga, M. J., Melis, J. M. & Dickinson, M. H. Flies compensate for unilateral wing damage through modular adjustments of wing and body kinematics. Interface Focus 7, 20160103 (2017).

Article PubMed PubMed Central Google Scholar

OSullivan, A. et al. Multifunctional wing motor control of song and flight. Curr. Biol. 28, 27052717.e4 (2018).

Article PubMed Google Scholar

Azevedo, A. et al. Tools for comprehensive reconstruction and analysis of Drosophila motor circuits. Preprint at BioRxiv https://doi.org/10.1101/2022.12.15.520299 (2022).

Donovan, E. R. et al. Muscle activation patterns and motoranatomy of Annas hummingbirds Calypte anna and zebra finches Taeniopygia guttata. Physiol. Biochem. Zool. 86, 2746 (2013).

Article PubMed Google Scholar

Bashivan, P., Kar, K. & DiCarlo, J. J. Neural population control via deep image synthesis. Science 364, eaav9436 (2019).

Article CAS PubMed Google Scholar

Lindsay, T., Sustar, A. & Dickinson, M. The function and organization of the motor system controlling flight maneuvers in flies. Curr. Biol. 27, 345358 (2017).

Article CAS PubMed Google Scholar

Reiser, M. B. & Dickinson, M. H. A modular display system for insect behavioral neuroscience. J. Neurosci. Meth. 167, 127139 (2008).

Article Google Scholar

Albawi, S., Mohammed, T. A. & Al-Zawi, S. Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) 16 https://doi.org/10.1109/ICEngTechnol.2017.8308186 (2017).

Kennedy, J. & Eberhart, R. Particle swarm optimization. In Proc. ICNN95International Conference on Neural Networks Vol. 4, 19421948 (1995).

Dana, H. et al. High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nat. Methods 16, 649657 (2019).

Article CAS PubMed Google Scholar

Muijres, F. T., Elzinga, M. J., Melis, J. M. & Dickinson, M. H. Flies evade looming targets by executing rapid visually directed banked turns. Science 344, 172177 (2014).

Article ADS CAS PubMed Google Scholar

Gordon, S. & Dickinson, M. H. Role of calcium in the regulation of mechanical power in insect flight. Proc. Natl Acad. Sci. USA 103, 43114315 (2006).

Article ADS CAS PubMed PubMed Central Google Scholar

Nachtigall, W. & Wilson, D. M. Neuro-muscular control of dipteran flight. J. Exp. Biol. 47, 7797 (1967).

Article CAS PubMed Google Scholar

Heide, G. & Gtz, K. G. Optomotor control of course and altitude in Drosophila melanogaster is correlated with distinct activities of at least three pairs of flight steering muscles. J. Exp. Biol. 199, 17111726 (1996).

Article CAS PubMed Google Scholar

Balint, C. N. & Dickinson, M. H. The correlation between wing kinematics and steering muscle activity in the blowfly Calliphora vicina. J. Exp. Biol. 204, 42134226 (2001).

Article CAS PubMed Google Scholar

Elzinga, M. J., Dickson, W. B. & Dickinson, M. H. The influence of sensory delay on the yaw dynamics of a flapping insect. J. R. Soc. Interface 9, 16851696 (2012).

Article PubMed Google Scholar

Dickinson, M. H., Lehmann, F.-O. & Sane, S. P. Wing rotation and the aerodynamic basis of insect flight. Science 284, 19541960 (1999).

Article CAS PubMed Google Scholar

Lehmann, F. O. & Dickinson, M. H. The changes in power requirements and muscle efficiency during elevated force production in the fruit fly Drosophila melanogaster. J. Exp. Biol. 200, 11331143 (1997).

Article CAS PubMed Google Scholar

Lucia, S., Ttulea-Codrean, A., Schoppmeyer, C. & Engell, S. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Eng. Pract. 60, 5162 (2017).

Article Google Scholar

Cheng, B., Fry, S. N., Huang, Q. & Deng, X. Aerodynamic damping during rapid flight maneuvers in the fruit fly Drosophila. J. Exp. Biol. 213, 602612 (2010).

Article CAS PubMed Google Scholar

Collett, T. S. & Land, M. F. Visual control of flight behaviour in the hoverfly, Syritta pipiens L. J. Comp. Physiol. 99, 166 (1975).

Article Google Scholar

Muijres, F. T., Elzinga, M. J., Iwasaki, N. A. & Dickinson, M. H. Body saccades of Drosophila consist of stereotyped banked turns. J. Exp. Biol. 218, 864875 (2015).

Article PubMed Google Scholar

Syme, D. A. & Josephson, R. K. How to build fast muscles: synchronous and asynchronous designs. Integr. Comp. Biol. 42, 762770 (2002).

Article PubMed Google Scholar

Snodgrass, R. E. Principles of Insect Morphology (Cornell Univ. Press, 2018).

Williams, C. M. & Williams, M. V. The flight muscles of Drosophila repleta. J. Morphol. 72, 589599 (1943).

Article Google Scholar

Wootton, R. The geometry and mechanics of insect wing deformations in flight: a modelling approach. Insects 11, 446 (2020).

Article PubMed PubMed Central Google Scholar

Lerch, S. et al. Resilin matrix distribution, variability and function in Drosophila. BMC Biol. 18, 195 (2020).

Article CAS PubMed PubMed Central Google Scholar

Weis-Fogh, T. A rubber-like protein in insect cuticle. J. Exp. Biol. 37, 889907 (1960).

Article CAS Google Scholar

Weis-Fogh, T. Energetics of hovering flight in hummingbirds and in Drosophila. J. Exp. Biol. 56, 79104 (1972).

Article Google Scholar

Ellington, C. P. The aerodynamics of hovering insect flight. VI. Lift and power requirements. Phil. Trans. R. Soc. B 305, 145181 (1984).

ADS Google Scholar

Alexander, R. M. & Bennet-Clark, H. C. Storage of elastic strain energy in muscle and other tissues. Nature 265, 114117 (1977).

Article ADS CAS PubMed Google Scholar

Mronz, M. & Lehmann, F.-O. The free-flight response of Drosophila to motion of the visual environment. J. Exp. Biol. 211, 20262045 (2008).

Article PubMed Google Scholar

More here:
Machine learning reveals the control mechanics of an insect wing hinge - Nature.com

Enter the world of ML development services, a land where everything is in constant change due to technological advancements and data-driven innovation solutions.

In recent years, ML has become a groundbreaking technology that revolutionized various sectors such as health care, finances and transportation among others. The demand for ML development services has been growing at an extremely fast pace due to the rise of digitization that is taking place in various companies and doesnt seem like it will reduce any time soon. However, what is the future of machine learning in this fast-growing field? In this post, we will analyze the newest tendencies and make some forecasts on how ML development companies may change our world in a few years. Prepare for an adventurous journey into the world of existing technologies and their future possibilities!

First, we will address the described tendencies and forecasts without going into deeper details regarding why machine learning is gaining popularity in todays digital reality. This usefulness can be credited to the unmatched capacity to process vast tracts of data and make inferences or choices devoid of software. The advent of big data brought some enormous opportunities and challenges, high on the list of which is my favorite technology machine learning (ML). Importantly, it has already disrupted sectors such as healthcare services and finance industries especially when artificial intelligence is applied. Nevertheless, other applications of this technology are almost limitless to various areas and beyond; thus displaying the broad range of influence that transformative machine learning has.

Recently, there has been a significant increase in cloud-based machine learning capabilities. Most vendors, enterprises or individuals will find these platforms to be cost-effective means of deploying ML-based applications. Cloud-based solutions for the development of ML have three main benefits scalability, availability and automation. They provide an opportunity for developers to apply complex ML models and do not distract attention from important infrastructure details. In addition, the ML cloud platforms contain many tools and APIs for pre built models that result in development speed faster. The industry-wide adoption of ML-oriented products has determined the development of cloud-based platforms where solutions based on machine learning can be constructed. Because technology is developing every single day, we can assume that in future these platforms are going to be more complicated and provide developers with better choices of options as well as skills for AI.

With the above great leaps in machine learning for developers, there have been increasing conversations surrounding one field and it is interpretability. In other words, producing outputs is not enough for AI; the developers and users must come to grips with how those results were arrived at or what factors are involved. It is especially important for such areas as healthcare or finance since decisions made by AI models can influence significantly there. As a result, there is an elevated need for the generation of models that are easily transparent and interpretable to the needs shown. This is such a key achievement in ensuring that Artificial Intelligence becomes reliable and answerable to everything it offers.

The business need for integration with other growing technologies is because technology continues to evolve at the rate of exponential function. Scalable development is supported by artificial intelligence solutions for machines in different remote locations as we can see the increased popularity among manufacturers through Industrial Internet manufacturing and distribution. By integrating the above technologies it becomes possible to develop new competencies, improved decision making as well enhanced customer service. However, in the modern market, it is no longer possible to perceive these emerging technologies as standalone elements but more so as a constituent of the technology within which they operate. Integration strategy will result in development by a business or the adoption of some other software that is there and they would eventually benefit from this because it makes things much easier for them.

https://www.thewatchtower.com/blogs_on/supervised-machine-learning-its-advantages

Increased demand for personalized and customized ML solutions: With more companies embracing the use of machine learning to have an upper hand, the demand for specially tailored solutions shall grow. This will hence demand that machine learning development services like N-ix.com customize their solutions according to the specific needs and preferences of each client. Advancements in natural language processing (NLP): However, NPL has certainly come a long way and it continues to organize new language machines increasingly with effectiveness. With further advancements that lie ahead, NLP will evolve to even higher levels offering more advanced conversational AI and text analysis in the future.

Continued focus on ethics: However, as AI technologies continue their blend into different sectors of human life and activities in general, there will be an increased interest regarding the ethical development and deployment principles related to these emerging systems. The concern for these companies that provide the standards and guidelines will be for the government to model their operations by strict ethical practices to establish trust with clients as a well-behaved entity. In conclusion, machine learning development services have no limit to their possibilities in the future. Technological progress and wider adoption of AI solutions will surely keep the development in the field actively progressing, turning ML into a sphere with no boundaries for growth and innovation. Machine learning has a transforming effect on the world that is happening right under our noses, and it is quite thrilling for business owners as well as developers.

The trend of ML development services has tremendously changed. With the emergence of big data as a rapidly advancing trend and increasing demands for intelligent software, developers need to change their direction rather fast. Currently, ML algorithms are developed for application in various sectors such as medical care services or the financial sphere and other areas. Given that firms are increasingly embracing the creative development of approaches geared towards the promotion and support for complete production value, as well as other client relations enhancing such a trend is bound to be here with us. It is also clear that, as the demand for ML development services rises, there will be an increased number of innovative solutions to offer businesses a competitive edge. While much about ML remains unknown, there is no denying that such technologies have the potential to reform our lives and business operations.

Original post:
The Future of ML Development Services: Trends and Predictions - FinSMEs

Published 04-18-24

Submitted by Lenovo

Optimizing and accelerating image processing with AI helps conservation experts safeguard seabird nesting sites on Robinson Crusoe Island.

Around the world, biodiversity is under threat. We are now in what many scientists call the sixth mass extinctionand over the last century, hundreds of species of plants and animals have been lost forever.

Island ecosystems can be particularly vulnerable to human activity. On Robinson Crusoe Island in the South Pacific Ocean, native seabirds such as the pink-footed shearwater are easy prey for an invasive species: the South American coati. Introduced to the island by humans almost a century ago, coatis are housecat-sized mammals in the same family as racoons, which hunt for shearwaters in their nesting sites throughout the island.

Protecting island ecosystems

Leading the fight against invasive species on Robinson Crusoe Island is Island Conservation: an international non-profit organization that restores island ecosystems to benefit wildlife, oceans, and communities. For many years, Island Conservation has been working side by side with island residents to help protect threatened and endangered species.

For Island Conservation, physically removing invasive coatis from shearwater nesting sites is only part of the challenge. To track coati activity, the organization also carefully monitors shearwater nesting sites using more than 70 remote camera traps.

Processing thousands of images a month

The organizations camera traps generate a massive amount of dataaround 140,000 images every monthwhich must be collected and analyzed for signs of coati activity. In the past, the Island Conservation team relied heavily on manual processes to perform this task. To classify 10,000 images would take a trained expert roughly eight hours of non-stop work.

Whats more, manual processing diverted valuable resources away from Island Conservations vital work in the field. The organization knew that there had to be a better way.

Realizing the potential of machine learning

David Will, Head of Innovation at Island Conservation, recalls the challenge: We started experimenting with machine learning [ML] models to accelerate image processing. We were convinced that automation was the way to go, but one of the big challenges was connectivity. Many of the ML solutions we looked at required us to move all of our photos to the cloud for processing. But on Robinson Crusoe Island, we just didnt have a reliable enough internet connection to do that.

As a temporary workaround, Island Conservation saved its camera trap images to SD cards and airmailed them to Santiago de Chile, where they could be uploaded to the cloud for processing. While airmail was the fastest and most frequent link between the island and the mainland, the service only ran once every two weeksand there was a lag of up to three months between a camera trap capturing an image and Island Conservation receiving the analysis.

David Will comments: The time between when we detected an invasive species on a camera and when we were able to respond meant we didnt have enough time to make the kind of decisions we needed to make to prevent extinctions on the island.

Tackling infrastructure challenges

Thats when Lenovo entered the frame. Funded by the Lenovo Work for Humankind initiative with a mission to use technology for good, a global team of 16 volunteers traveled to the island. Using Lenovos smarter technology from devices to software, IT services to servers, the volunteers were able to do their own day jobs while volunteering to help upgrade the islands networking infrastructure: boosting its bandwidth from 1 Mbps to 200 Mbps.

Robinson Crusoe Island is plagued with harsh marine conditions with limited access. They needed a sturdy system that brings compute to the data and allows remote management. The solution was LenovosThinkEdge SE450 with NVIDIA A40 GPUs. The AI-optimized edge server provided a rugged design capable of withstanding extreme conditions while running quietly, allowing it to live comfortably in the new remote workspace. Lenovo worked with Island Conservation to tailor the server to its needs, adding additional graphics cards to increase the AI processing capability per node. We took the supercomputer capability they had in Santiago and brought that into a form factor that is much smaller, says Charles Ferland, Vice President and General Manager of Edge Computing at Lenovo.

The ThinkEdge SE450 eliminated the need for on-site technicians. Unlike a data center, which needs staff on-site, the ThinkEdge server could be monitored and serviced remotely by Lenovo team members. It proved to be the perfect solution. The ThinkEdge server allows for full remote access and management of the device speeding up decisions from a matter of months to days.

David Will comments, Lenovo helped us run both the A40s at the same time immensely speeding up processing, something we previously couldnt do. It has worked tremendously well and almost all of our processing to-date has been done on the ThinkEdge SE450.

Unleashing the power of automation

To automate both the detection and classification of coatis, Lenovo data scientists from the AI Center of Excellence built a custom AI script to detect and separate out the results for coatis and other species from MegaDetectoran open-source object detection model that identifies animals, people, and vehicles in camera trap images. Next, Lenovo data scientists trained an ML model on a custom dataset to give a multi-class classification result for nine species local to Robinson Crusoe Island, including shearwater and coatis.

This two-step GPU-enabled detector-and-classifier pipeline can provide results for 24,000 camera trap images in just one minute. Previously, this would have taken a trained expert twenty hours of laboran astonishing 99.9% time saving. The model achieved 97.5% accuracy on a test dataset with approximately 400 classifications per second. Harnessing the power of NVIDIAs CUDA enabled GPUs allowed us to have a 160x speedup on MegaDetector compared to the previous implementation.

Sachin Gopal Wani, AI Data Scientist at Lenovo, comments: Delivering a solution that is easily interpretable by the user is a crucial part of our AI leadership. I made a custom script that generates outputs compatible with TimeLapsea software the conservationists use worldwide to visualize their results. This enabled much faster visualization for a non-technical end-user without storing additional images. Our solution allows for the results to load with the original images overlapped with classification results, saving terabytes of disk space.

With these ML capabilities, Island Conservation can filter out images that do not contain invasive species with a high degree of certainty. Using its newly upgraded internet connection, the organization can upload images of coati activity to the cloud, where volunteers on the mainland evaluate the images and send recommendations to the island rapidly.

Using ML, we can expedite image processing, get results in minutes, and cut strategic decision time from three months to a matter of weeks, says David Will. This shorter response time means more birds protected from direct predation and faster population recovery.

Looking to the future

Looking ahead, Island Conservation plans to continue its collaboration with the Lenovo AI Center of Excellence to develop Gen AI to detect other types of invasive species, including another big threat to native fauna: rodents.

With Lenovos support, were now seeing how much easier it is to train our models to detect other invasive species on Robinson Crusoe Island, says David Will. Recently, I set up a test environment to detect a new species. After training the model for just seven hours, we recorded 98% detection accuracyan outstanding result.

As the project scope expands, Island Conservation plans to use more Lenovo ThinkEdge SE450 devices with NVIDIA A40 GPUs for new projects across other islands. Lenovos ThinkEdge portfolio has been optimized for Edge AI inferencing, offering outstanding performance and ruggedization to securely process the data where its created.

Backed by Lenovo and NVIDIA technology, Island Conservation is in a stronger position than ever to protect native species from invasive threats.

David Will says: In many of our projects, we see that more than 30% of the total project cost is spent trying to remove the last 1% of invasives and confirm their absence. With Lenovo, we can make decisions based on hard data, not gut feeling, which means Island Conservation takes on new projects sooner.

Healing our oceans

Island Conservations work with Lenovo on Robinson Crusoe Island will serve as a blueprint for future activities. The team plans to repurpose the AI application to detect different invasive species on different islands around the world from the Caribbean to the South and West Pacific, the Central Indian Ocean, and the Eastern Tropical Pacificwith the aim of saving endangered species, increasing biodiversity, and increasing climate resilience.

In fact, Island Conservation, Re:wild, and Scripps Institution of Oceanography recently launched the Island-Ocean Connection Challenge to bring NGOs, governments, funders, island communities, and individuals together to begin holistically restoring 40 globally significant island-ocean ecosystems by 2030.

Everything is interconnected in what is known as the land-and-sea cycle, says David Will. Healthy oceans depend on healthy islands. Island and marine ecosystem elements cycle into one another, sharing nutrients vital to the plants and animals within them. Indigenous cultures have managed resources this way for centuries. Climate change, ocean degradation, invasive species, and biodiversity loss are causing entire land-sea ecosystems to collapse, and island communities are disproportionately impacted.

The Island-Ocean Connection Challenge marks the dawn of a new era of conservation that breaks down artificial silos and is focused on holistic restoration.

David Will concludes: Our collective effort, supported by Lenovo and NVIDIA, is helping to bridge the digital divide on island communities, so they can harness cutting-edge technology to help restore, rewild, and protect their ecosystems, and dont get further left behind by AI advances.

Get involved today at http://www.jointheiocc.org.

To read the Lenovo case study on Island Conservation, click here. Or to watch the Lenovo case study video, click here.

Lenovo is a US$62 billion revenue global technology powerhouse, ranked #217 in the Fortune Global 500, employing 77,000 people around the world, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the worlds largest PC company by further expanding into growth areas that fuel the advancement of New IT technologies (client, edge, cloud, network, and intelligence) including server, storage, mobile, software, solutions, and services. This transformation together with Lenovos world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992)(ADR: LNVGY). To find out more visit https://www.lenovo.com, and read about the latest news via our StoryHub.

More from Lenovo

Read more:
CSRWire - Island Conservation Harnesses Machine Learning Solutions From Lenovo and NVIDIA To Restore Island ... - CSRwire.com