The concept of Joint All Domain Command-and-Control (JADC2) remains a nascent one, with clear doctrines yet to be defined and tested. However, no matter how these are shaped it is apparent that two key requirements must be addressed: speed of action, and the ability to process and analyze vast volumes of complex data that could not have been perceived of in the past.

The capabilities inherent in fifth generation aircraft, such as the F-35, exemplify the data management challenges that advanced systems bring and which must be addressed in multi-domain operations. The aircraft is as much a flying sensor as it is a combat platform, and the diversity and volume of data that it can collect places a significant burden on militaries if they are to benefit from it in a meaningful way. When factoring in the speed at which a conflict with a near-peer will be conducted and that it will extend beyond the traditional domains, there is a genuine risk that commanders could be overwhelmed by the data that needs to be processed in order to affect a winning outcome.

While significantly scaling up manpower could be one solution, the complexity of the data and speed of action required necessitates a step-change in capabilities in the command and control domain. It is here that potentially game-changing benefits can be brought through leveraging artificial intelligence.

JADC2 demands a comprehensive, dynamic, and near-real time common operating picture (COP) and AI can certainly aid in speeding-up decision making and defining parameters. AI can automate filtering and configuration based on prior experience. Beyond this, however, it promises the ability to examine command decisions and learn what should be done to achieve mission goals, automatically proposing and ranking actions.

Central to the utility of AI will be the availability of robust data, and the successful application of machine learning (ML) will be dependent on this. Machine learning has already proven its worth in anomaly detection and track correlation, taking this to the next level it will also be able to provide early warning of enemy actions. AI has the potential to recognize when an adversary is preparing their forces for a particular action and in a particular area, for example, by analyzing troop movements, aircraft sorties, and training activity. The technology could, in theory, automatically alert commanders, propose a course of action, and ultimately task units. The application of natural language understanding could even enable intelligence reports to be generated from disparate data.

AI also has a clear application in supporting resource-to-task management, such as in composing an air tasking order. Understanding which assets are available and best placed to complete a task is a significant challenge in a theatre-wide conflict, if AI can reach across all domains it will be able to appraise commanders of the most suitable resources to employ, including those that might not have been apparent with manpower alone. AI will also be able to quickly alert commanders and even automatically adjust orders as a mission unfolds or new intelligence emerges, for example, in editing an air tasking order to optimize the deployment of assets.

The utility of AI in enabling JADC2 is apparent. What is less clear, however, is how the best AI capabilities should be developed and fielded to ensure maximum affect across all services and domains. The need for a man in the loop is essential and the application of AI does not imply a change to autonomous systems and robotic warfare, but AI support must be regarded as trustworthy by operators and commanders.

A cohesive approach is essential in developing AI for JADC2 and services must consider themselves to be customers and suppliers of one another. Capabilities cannot be developed in silos. If services are not cognizant of the needs of a combined force there will inevitably be capability gaps and disconnects in the command structure and processes. This challenge is complicated further when considering the nature of operations, where the coalition is the norm.

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The design of the core C2 systems employed for JADC2 is also a key consideration. The need for information sharing at speed and the ability to draw on a wide range of sources are crucial. Inherent in their design must be open architectures that enable new applications to be quickly developed and integrated, along with seamless interoperation between forces. Standards-driven designs are a must and it is essential that systems are not stovepiped and can reach not only across services, but the theatre of operation as a whole. Security issues will exist but must be overcome and not constitute a barrier for information and data sharing between domains.

Ensuring that partners have the requisite AI capabilities and access to relevant data is another hurdle. While disparities in capabilities is not a new issue, in the context of JADC2 where speed of action will be critical this is magnified.

There are many technological, doctrinal, and operational factors to consider in the implementation of AI, what is clear, however, is that the technology promises the ability to greatly shorten the OODA loop and bring a step change in C2 functionality. In a conflict with a near peer, AI will be a necessity rather than a luxury.

Retired Maj. Gen. Henrik Rboe Dam is the former head of the Royal Danish Air Force and the air domain adviser at Systematic.

Originally posted here:
Central to meeting the complexities of JADC2? Artificial intelligence - C4ISRNet

With the advancements in deep learning, the recent years have seen a humongous growth of artificial intelligence (AI) applications and services, traversing from personal assistant to recommendation systems to video/audio surveillance. All the more as of late, with the expansion of mobile computing and Internet of Things (IoT), billions of mobile and IoT gadgets are connected with the Internet, creating zillions of bytes of information at the network edge.

Driven by this pattern, there is a pressing need to push the AI frontiers to the network edge in order to completely release the potential of the edge big data. To satisfy this need, edge computing, an emerging paradigm that pushes computing undertakings and services from the network core to the network edge, has been generally perceived as a promising arrangement. The resulting new interdiscipline, edge AI or edge intelligence (EI), is starting to get an enormous amount of interest.

In any case, research on EI is still in its earliest stages, and a devoted scene for trading the ongoing advances of EI is exceptionally wanted by both the computer system and AI people group. The dissemination of EI doesnt mean, clearly, that there wont be a future for a centralized CI (Cloud Intelligence). The orchestrated utilization of Edge and Cloud virtual assets, truth be told, is required to make a continuum of intelligent capacities and functions over all the Cloudifed foundations. This is one of the significant challenges for a fruitful deployment of a successful and future-proof 5G.

Given the expanding markets and expanding service and application demands put on computational data and power, there are a few factors and advantages driving the development of edge computing. In view of the moving needs of dependable, adaptable and contextual data, a lot of the data is moving locally to on-device processing, bringing about improved performance and response time (in under a couple of milliseconds), lower latency, higher power effectiveness, improved security since information is held on the device and cost savings as data-center transports are minimized.

Probably the greatest advantage of edge computing is the capacity to make sure about real-time results for time-sensitive needs. Much of the time, sensor information can be gathered, analyzed, and communicated immediately, without sending the information to a time-sensitive cloud center. Scalability across different edge devices to help speed local decision-making is fundamental. The ability to give immediate and dependable information builds certainty, increases customer engagement, and, in many cases, saves lives. Simply think about all of the businesses, home security, aviation, car, smart cities, health care in which the immediate understanding of diagnostics and equipment performance is critical.

Indeed, recent advances in AI may have an extensive effect in various subfields of ongoing networking. For example, traffic prediction and characterization are two of the most contemplated uses of AI in the networking field. DL is likewise offering promising solutions for proficient resource management and network adoption therefore improving, even today, network system performance (e.g., traffic scheduling, routing and TCP congestion control). Another region where EI could bring performance advantages is a productive resource management and network adaption. Example issues to address traffic scheduling, routing, and TCP congestion control.

Then again, today it is somewhat challenging to structure a real-time framework with overwhelming computation loads and big data. This is where EC enters the scene. An orchestrated execution of AI methods in the computing assets in the cloud as well as at the edge, where most information is produced, will help towards this path. In addition, gathering and filtering a lot of information that contain both network profiles and performance measurements is still extremely crucial and that question turns out to be much progressively costly while considering the need of data labelling. Indeed, even these bottlenecks could be confronted by empowering EI ecosystems equipped for drawing in win-win collaborations between Network/Service Providers, OTTs, Technology Providers, Integrators and Users.

A further dimension is that a network embedded pervasive intelligence (Cloud Computing integrated with Edge Intelligence in the network nodes and smarter-and-smarter terminals) could likewise prepare to utilize the accomplishments of the developing distributed ledger technologies and platforms.

Edge computing gives an option in contrast to the long-distance transfer of data between connected devices and remote cloud servers. With a database management system on the edge devices, organizations can accomplish prompt knowledge and control and DBMS performance wipes out the reliance on latency, data rate, and bandwidth. It also lessens threats through a comprehensive security approach. Edge computing gives an environment to deal with the whole cybersecurity endeavors of the intelligent edge and the wise cloud. Binding together management systems can give intelligent threat protection.

It maintains compliance regulations entities like the General Data Protection Regulation (GDPR) that oversee the utilization of private information. Companies that dont comply risk through a significant expense. Edge computing offers various controls that can assist companies with ensuring private data and accomplish GDPR compliance.

Innovative organizations, for example, Amazon, Google, Apple, BMW, Volkswagen, Tesla, Airbus, Fraunhofer, Vodafone, Deutsche Telekom, Ericsson, and Harting are presently embracing and supporting their wagers for AI at the edge. Some of these organizations are shaping trade associations, for example, the European Edge Computing Consortium (EECC), to help educate and persuade small, medium-sized, and large enterprises to drive the adoption of edge computing within manufacturing and other industrial markets.

Excerpt from:
The Future of Artificial Intelligence: Edge Intelligence - Analytics Insight

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Markets in IVD" report has been added to ResearchAndMarkets.com's offering.

This report examines selected AI-based initiatives, collaborations, and tests in various in vitro diagnostic (IVD) market segments.

Artificial Intelligence Markets in IVD contains the following important data points:

The past few years have seen extraordinary advances in artificial intelligence (AI) in clinical medicine. More products have been cleared for clinical use, more new research-use-only applications have come to market and many more are in development.

In recent years, diagnostics companies - in collaboration with AI companies - have begun implementing increasingly sophisticated machine learning techniques to improve the power of data analysis for patient care. The goal is to use developed algorithms to standardize and aid interpretation of test data by any medical professional irrespective of expertise. This way AI technology can assist pathologists, laboratorians, and clinicians in complex decision-making.

Digital pathology products and diabetes management devices were the first to come to market with data interpretation applications. The last few years have seen the use of AI interpretation apps extended to a broader range of products including microbiology, disease genetics, and cancer precision medicine.

This report will review some of the AI-linked tests and test services that have come to market and others that are in development in some of the following market segments:

Applications of AI are evolving that predict outcomes such as diagnosis, death, or hospital readmission; that improve upon standard risk assessment tools; that elucidate factors that contribute to disease progression; or that advance personalized medicine by predicting a patient's response to treatment. AI tools are in use and in development to review data and to uncover patterns in the data that can be used to improve analyses and uncover inefficiencies. Many enterprises are joining this effort.

The following are among the companies and institutions whose innovations are featured in Artificial Intelligence Markets in IVD:

Key Topics Covered

Chapter 1: Executive Summary

Chapter 2: Artificial Intelligence In Diagnostics Markets

Chapter 3: Market Analysis: Artificial Intelligence in Diagnostics

For more information about this report visit https://www.researchandmarkets.com/r/bqxzvs.

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Artificial Intelligence Markets in IVD, 2019-2024 - Initiatives, Collaborations and Tests - ResearchAndMarkets.com - Business Wire

The future lies in Artificial Intelligence (AI). The technology that stands to change the world, promising an unprecedented and prosperous future. AI has long been at the vanguard of a list of emerging technologies, including big data, robotics and IoT (Internet of Things). Many believe that its role as a technological innovator will only improve in the days to come.

In the world of domain names, the extension that is most commonly associated with AI is, no surprise, .AI. It is slowly gaining popularity, which is evident by the online presence of several artificial intelligence companies taking advantage of .ai domain names. So, is .AI here to stay, or just a passing phase to fade away with time?

First, What is AI?

Artificial intelligence, or AI, is a way of programming computers to think, make decisions, and process data without additional input from human users. AI has existed in rudimentary forms for many years, but as the technology improves, many believe that AI that surpasses human intelligence is right around the corner. Such an achievement would be equivalent to milestones like the invention of writing, agriculture, or the industrial revolution. Many futurists predict that by 2050, this technology will provide considerable benefit to our daily lives.

At present, AI is in an interesting state. Like with the introduction of PCs in the early 1980s or the Internet evolution from the early 1990s, artificial intelligence is prevalent and well-known, but not very well understood by the general public. Much of the public perception of AI is thanks to eminent Silicon Valley executives like Elon Musk of Tesla/SpaceX, who has started widespread AI integration and is open about its potential consequences.

With that in mind, the section below describes some real world AI applications:

AI in Banking: The banking sector has been deploying AI at a rapid pace. For example, it incorporated data analytics techniques to reduce illegal transactions. In fact, even outside of banking, AI is one of the best possible security enhancement solutions available to several business sectors, including retail and finances.

AI in Agriculture: Many organizations that deal in automation and robotics have helped farmers find more efficient ways of protecting their crops from weeds. In addition, a Berlin-based agricultural tech start-up called PEAT, has developed an application called Plantix that detects the potential defects and nutrient deficiencies found in the soil through images.

AI in Healthcare: Healthcare facilities and medical care centers often rely on AI to save human lives. For instance, an organization called Cambio HealthCare developed its clinical decision support system that can help prevent strokes in patients while giving the physician a warning that there's a patient at risk of having a heart stroke.

AI in autonomous vehicles: Self-driving cars have been the poster child of the AI industry for quite a long time. The development of autonomous cars will revolutionize the transportation system. The best example would be Tesla's self-driving car, which uses AI for computer vision, image detection, and deep learning. The result is cars which can automatically detect objects and drive around without any human interaction.

AI in Chatbots: Virtual assistants are becoming a regular thing now. Many home appliances can now be controlled by Siri, Cortana, or Alexa, and are gaining popularity because of their easy to use features. Amazon's Echo is perhaps the one of the great examples where words are put into action by Artificial Intelligence. Now, you can tune into your favorite music, set your alarm, or search the internet, all with one voice command.

What is an .AI domain?

.AI is the country-code Top Level Domain (ccTLD) for Anguilla, which is a small British Overseas Territory in the Caribbean. However, a massive amount of businesses that have nothing to do with Anguilla have registered .AI domain names. In fact, the .AI domain extension has become so integrated with the world of artificial intelligence that it sometimes seems that it was specifically designed for the industry.

To backtrack a little, the .AI extension was created in 1995. However, it didn't see widespread use until after 2009, when .ai registration was made available to businesses and individuals outside Anguilla. This is where AI companies stepped in to capitalize on the extension.

Currently, .AI domain name extensions are being used by almost every organization dealing in AI, or any other segment of AI-like robotics, deep learning, or machine learning.

Why should I opt for .AI domain?

The .ai domain name is snappy, easy to remember, and at the same time is prompt in showcasing to the world your passion and enthusiasm for innovation. Right now, the best choice for startups and artificial intelligence companies with a view to change the world using technology are at the top of the list of potential buyers for .ai domains.

Why is .ai domain considered as an investment?

Firstly, the price of the .AI domain is generally lower than that of a .COM domain. Furthermore, due to the age and massive popularity of .COM, almost all the good and memorable .COM names are already taken, and even if the right one still exists, it is most likely very expensive or not for sale. Unlike .COM domain names, the .AI domain is relatively "new," so finding a relevant name should be far easier. At this moment, AI domain names are huge in the startup sphere, so registering a domain name for a considerable price now might prove fruitful after a few years. If you want to secure your .AI domain, make sure to do so at an accredited registrar, such as 101domain.

Thoughts about its SEO ranking

Today, when typing the word "AI" in a search engine, users probably don't have the small Caribbean island in their minds. Rather, they are seeking news related to the hot new tech on the block, artificial intelligence. The presence of the .AI domain name extension adds an extra value, fetching a high SEO ranking.

Is this right for me?

The thought of artificial intelligence automatically comes to mind whenever AI is heard, making this domain name ideal for organizations in this highly innovative field. It is most suitable for academics and tech companies who want to present their outstanding and unique ideas and research about AI.

The list below describes services using the .AI domain name for throwing light on their area of functioning.

Service personalization:

Machine learning is an aspect of AI that provides businesses with supreme quality customer care. For example, ebo.ai is paving the path for AI-powered customer care systems, having the ability to assemble customer service data, natural language processing, and machine learning algorithms which continuously goes through a learning process by interacting with customers.

Business operations:

Many aspects of business management, especially aspects that are monotonous or rudimentary, can also be handled by the right AI. This is in reference to proteus.ai, which has helmed the creation of deep learning machinery, which can be used to sort information and documents at a rapid pace with greater accuracy and less error.

Healthcare improvements:

Healthcare-related enterprises like onwardhealth.ai are deploying deep learning technologies for pattern recognition, and are helping medical practitioners and health care specialists monitor and process different types of medical treatment data. Deep learning algorithms are meant to detect the subtle patterns that correspond to disease profiles. Additionally, the healthcare industry is also utilizing deep learning in ways never thought before. This includes primary stage disease detection, finding new medicines, and opening new avenues for repurposing already established and tested drugs for usage on a different disease.

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Tesla : Artificial Intelligence - the .AI domain is on the rise - marketscreener.com

A new COO, CTO, and chief innovation officer will help the Toronto-based company transform into a next-generation AI company

Internet of Things Inc () (OTCMKTS:INOTF) announced Tuesday that itsmanagement team will be spearheaded by four industry veterans to transform the company into a next-generation artificial intelligence (AI) technology developer and services company.

The company said that Darryl Smith, who is the current chief technology officer at Internet of Things, will take on the role of CTO at AI Labs Inc, the product development arm of the company. Smith has worked in information technology for over two decades and has created mobile road weather sensors and road weather products, leveraging the latest AI techniques. He has an engineering degree in computer systems.

In addition, accomplished electronics engineer Malcolm Rook has been tapped as AI Labs new chief innovation officer. Over a span of fifty years, Rook has developed ultra-low noise microwave receivers for military and weather radar systems. He has also developed electronic systems for many areas of application including asset tracking in hospitals, retail environments, and remote monitoring.

READ:Internet of Things developing ThermalPass fever-detection device for public spaces during coronavirus pandemic

Meanwhile, Robert Klein, who has over two decades of experience in operations and corporate development will be the companys new chief operating officer. He has worked in the technology, telecom, and the consumer-packaged goods industries. Klein has been instrumental in the acquisition and integration of more than twenty companies over his career.

Seasoned media communications and government relations strategist Thomas Park rounds out the new appointments. He will be vice president of government relations and regulatory affairs at Internet of Things. He has held senior positions in government working under ministers John Baird and Janet Ecker. He was also the former spokesperson for the Ontario Energy Board.

Internet of Things CEO Michael Lende said Internet of Things was focused on making the company a nexus for the best talent in the industry.

We are in the business of solving complex challenges that our clients face every day and these strategic additions to our leadership are a testament to our focus on prioritizing the success of our clients, as we continue to disrupt global vertical industries where our machine learning-based products and proprietary data-sets are much-needed solutions," added Lende in a statement.

Internet of Things recentlyannounced the development of ThermalPass, a new fever detection system designed for public spaces during the coronavirus (COVID-19) pandemic. The device instantly screens for higher-than-normal body temperatures with an accuracy of 0.36 degrees Fahrenheit, thus identifying possible carriers of the coronavirus. If ThermalPass machines are stationed at the entrances of high-traffic public locations, they can potentially help reduce the risk of further spread.

Prototypes are currently in advanced development for pre-commercial testing this month, and the company expects to launch the product in June.

Contact the author Uttara Choudhury at [emailprotected]

Follow her on Twitter: @UttaraProactive

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Internet of Things boosts leadership team to advance artificial intelligence technology and fuel growth - Proactive Investors USA & Canada

If the NBA resumes its 2019-20 season this summer or fall, games will likely occur without fans.

But the Mavericks are still focused on the potential need to revamp their American Airlines Center gameday experience amid the COVID-19 coronavirus pandemic.

During an interview Monday with ESPN 103.3 KESN-FM, Mavericks owner Mark Cuban said members of the organization have recently discussed changes to operations and technologies in the AAC if fans can return in limited capacity before treatments and preventions for the coronavirus are created.

The AAC norm may start with staggered fan arrival.

The Mavericks could request fans sign up for an arrival time at a specific parking spot, Cuban said, where theyll then receive a predetermined path to walk to their gate. Upon passing through AAC security, a guide could lead fans to their seats, separated from other guests.

We may do that almost like Disneyland, do it like theres a procession and you have people guiding you to your seat, Cuban said. Or the example I use is more like a haunted house where you wait in line and you go through the haunted house, but youre not allowed to touch anything, and everybody just is guided to their seats at the right time.

It may take a little bit longer for everybody to get into their seats to start the game, but well accommodate that and go from there.

Cuban said the Mavericks have considered requesting advance information from fans to understand whom they can sit near -- those theyve already been quarantining with -- and how to space different groups.

The organization has also discussed new accessories, such as individual sneeze guards or things that surround your whole neck, or lightweight hoodies so if somebody two rows behind you sneezes, youre not freaked out, Cuban said.

To prevent the spread of the coronavirus via contaminated surfaces, Cuban highlighted potential artificial intelligence, such as a service tool similar to Amazons Alexa, to allow fans a way to verbally request needs, rather than moving around and touching items.

Theres just so many things that were trying to deal with. Theres a lot of natural [decontamination] and sterilization tools that we can use to keep the arena clean, Cuban said. Theres all these things that have to be considered, and were trying to put together a list now.

Well be wrong about a lot of things, and things will zig when we zag, but thats the way it works.

Find more Mavericks stories from The Dallas Morning News here.

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Sneeze guards, artificial intelligence and more: The Mavs are discussing new measures if fans return to AAC - The Dallas Morning News

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Dyno Therapeutics, Inc., a biotechnology company applying artificial intelligence (AI) to gene therapy, today announced the companys launch from stealth mode with its proprietary platform, CapsidMap. The platform enables the design of novel Adeno-Associated Virus (AAV) vectors that significantly improve upon current approaches to gene therapy and expand the scope of accessible diseases. Through its R&D and collaborations with biopharmaceutical companies, Dyno has active programs focused on novel gene therapy vectors for ophthalmic, muscle, central nervous system (CNS), and liver diseases. The company could potentially receive well over $2 billion in upfront payments, research support, option fees, as well as pre-clinical, clinical, regulatory and sales milestones under its collaboration agreements.

At Dyno, we see a vast opportunity to expand the treatment landscape for gene therapies. The success of gene therapy relies on the ability of vectors to safely and precisely deliver a gene to the intended target cells and tissues, said Eric Kelsic, Ph.D., CEO and Co-founder of Dyno Therapeutics. Our approach addresses the major limitations of naturally occurring AAV vectors and creates optimized, disease-specific vectors for gene therapies with great curative potential. Our portfolio of R&D programs and newly-announced collaborations with leading gene therapy developers reflect the applicability of our AI-powered approach to improve treatments for patients and expand the number of treatable diseases with gene therapies.

Dynos technology platform builds on certain intellectual property developed in the lab of George Church, Ph.D., who is Robert Winthrop Professor of Genetics at Harvard Medical School (HMS) and a Core Faculty member at Harvards Wyss Institute for Biologically Inspired Engineering. Several of the technical breakthroughs that enable Dynos approach to AAV capsid engineering were described in a November 2019 publication in the journal Science, based on work conducted by the companys founders and members of the Church Lab at HMS and the Wyss Institute. Dyno has an exclusive option to enter into a license agreement with Harvard University for this technology. Church is a co-founder of Dyno and Chairman of the companys Scientific Advisory Board.

The CapsidMap platform applies Dynos proprietary artificial intelligence technology to discover and design novel AAV capsids, the cell-targeting protein shell of viral vectors. CapsidMap systematically generates and then evaluates millions of new AAV variants at an unprecedented scale, dramatically accelerating the identification of improved AAV vectors. CapsidMap uses advanced machine learning search algorithms, combined with high-throughput experiments generating massive quantities of in vivo data, to accelerate the creation of superior synthetic AAV capsids.

The company launched in late 2018 with a $9 million financing co-led by Polaris Partners and CRV. Alan Crane, a co-founder of Dyno and Entrepreneur Partner at Polaris Partners, and Dylan Morris, General Partner at CRV, have joined Dynos board of directors, with Alan Crane serving as Dynos Executive Chairman. Dyno does not anticipate the need for additional fundraising at this time based on the significant financial resources made available from collaborations.

Alan Crane stated, We invested in Dyno because we believe that the companys platform represents a paradigm shift in the development of gene therapies. Gene therapies have the potential to cure diseases that are not adequately treated by existing small molecule and antibody therapeutics. As an industry, we often know which genes we want to administer for treatment, but we cant effectively get them to the target tissues and cell types. AAV capsids with improved tropism, immunogenicity, packaging size, and manufacturing features will expand treatments to more patients.

Dyno is building a multi-disciplinary team of experts in business, gene therapy, and machine learning. In addition to Eric Kelsic, George Church, and Alan Crane, the companys founders include Sam Sinai, Ph.D., Lead Machine Learning Scientist, Adrian Veres, Ph.D., Scientific Advisor, and Tomas Bjorklund, Ph.D., a scientific advisor of Dyno who is Associate Professor at Lund University and a leader in AAV capsid engineering.

About CapsidMap for Designing AAV Gene Therapies

By designing capsids that confer improved functional properties to Adeno-Associated Virus (AAV) vectors, Dynos proprietary CapsidMap platform overcomes the limitations of todays gene therapies on the market and in development. Todays treatments are primarily confined to a small number of naturally occurring AAV vectors that are limited by delivery, immunity, packaging size, and manufacturing challenges. CapsidMap uses artificial intelligence (AI) technology for the design of novel capsids, the cell-targeting protein shell of viral vectors. The CapsidMap platform applies leading-edge DNA library synthesis and next generation DNA sequencing to measure in vivo gene delivery properties in high throughput. At the core of CapsidMap are advanced search algorithms leveraging machine learning and Dynos massive quantities of experimental data, that together build a comprehensive map of sequence space and thereby accelerate the discovery and optimization of synthetic AAV capsids.

About Dyno Therapeutics

Dyno Therapeutics is a pioneer in applying artificial intelligence (AI) and quantitative high-throughput in vivo experimentation to gene therapy. The companys proprietary CapsidMap platform is designed to rapidly discover and systematically optimize superior Adeno-Associated Virus (AAV) capsid vectors with delivery properties that significantly improve upon current approaches to gene therapy and expand the range of diseases treatable with gene therapies. Dyno was founded in 2018 by experienced biotech entrepreneurs and leading scientists in the fields of gene therapy and machine learning. The company is located in Cambridge, Massachusetts. Visit http://www.dynotx.com for additional information.

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Dyno Therapeutics Emerges from Stealth to Transform Gene Therapy Using Artificial Intelligence - Business Wire