Category Archives: Artificial Intelligence
For US and allies, prepping for AI warfare starts with the data – C4ISRNet
WASHINGTON The U.S. and allies are using a new forum started by the Pentagons top artificial intelligence office to work toward developing AI systems that can connect in the future to help them fight better together.
The Partnership for Defense, started by the Joint Artificial Intelligence Center last September, is laying the groundwork for future AI-enabled joint war-fighting capabilities that will need to connect to each other for the U.S. and its allies to effectively fight as a coalition.
One day, the countries could collaborate on other AI-backed efforts, such as sharing data from sensors that track how machines run to predict when maintenance is needed before parts fail, possibly during a mission when theres no time to lose for repairs or replacements. Or the allies could use AI for data about shipping and supply movements to improve logistics efficiency.
The end goal is for the allied nations to be ready to cooperate easily on AI-driven projects in the future.
But first, the U.S. and partner countries must start at a basic level of readying data for artificial intelligence, viewing the information as a war-fighting resource. That starts with keeping and storing all of the facts and figures that AI needs to work.
The U.S. and its allies messed up in not using data or looking at data over the last several decades as a resource, said Stephanie Culberson, head of international AI policy at the JAIC. For instance, if we were to go to war again in Afghanistan, would we have all the data that we pulled in the last 20 years? You can probably guess the answer to that.
The partnership came from smaller discussions that the JAIC had with like-minded nations. After several interactions, it became clear that the nations struggled with the same challenges around scaling AI efforts, educating and training the workforce on AI, and overcoming internal cultures resistant to technological change, Culberson said.
We started to realize that many of us are grappling with the same hard problems in implementing AI into our defense organizations, Culberson said. Instead of staying within those siloes on our own, I thought, Well, why dont we pull together some of the strongest nations that are really focused on this in their defense sector and do this together?
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Thus far, the partnership includes defense representatives from Australia, Canada, Denmark, Estonia, Finland, France, Israel, Japan, Norway, the Republic of Korea, Sweden and the United Kingdom. The group has twice gathered to identify common challenges, and meetings are expected three times a year.
The Partnership for Defense is not working on codevelopment of AI systems, rather its focused on preparing allied militaries to be AI-ready, as Culberson puts it.
We decided to talk about building blocks that we all need to work through that are massive undertakings for ministries of defense, Culberson said. For instance, how are we handing data? For the most part, not very well.
The meetings are different than typical international conversations with foreign militaries, which can be rigid, Culberson said. The partnership meetings encourage open dialogue, including roundtable discussions and TED Talk-style presentations describing how ministries tackle challenges and analysis of case studies for lessons learned.
In the next two years of the partnership, Culberson said that she really wants to have a solid foundation for AI-readiness, developing a way to assess whether members have achieved that readiness. In a few years, she said, the countries could consider codeveloping a data aggregation capability.
This is how we do interoperability as well, Culberson said. We dont want to get too far down the path of everyones doing their own thing in their siloes, and then we look up and next time we need to go to war together, or even humanitarian assistance or any of those types of things where we might use our militaries together, nothing is interoperable.
The JAICs role on the international stage
Since its inception about two years ago, the JAICs mission has been to help the Pentagons internal components adopt artificial intelligence, through its national mission initiatives or by delivering services. Adding international engagement to its portfolio also serves that mission.
I see it has kind of the same thing actually for international: to help enable key allies and partners, which at the end of the day is going to make our war fighter more ready to have ready allies at their side, Culberson said.
U.S. military services are starting to try to include allies and partners as they develop their joint war-fighting systems, such as the Air Forces Advanced Battle Management System or the Armys Project Convergence.
Those service-led programs, which will rely heavily on artificial intelligence, are how the services plan to connect sensors and shooters for future battles. Work with allies now will ease challenges plugging them together later.
In this broader strategic competition between the U.S. and China, as it continues to evolve, the Defense Department will need these avenues for partnerships, said Megan Lamberth, research associate at the Center for a New American Security. It allows for increased interoperability between partner militaries, and it gives countries access to broader, more robust shared datasets.
The partnership could lead to talent-sharing programs that would benefit the Pentagon, Lamberth added, particularly given workforce shortages in AI professionals.
The Partnership for Defense has an open door to adding more allies, Culberson said. While other nations have expressed interest, members plan to set admission standards before expanding.
I dont want it just to be the U.S. projecting, which is often I think expected when we have multilateral conversations like this, Culberson said. Instead I want it to be truly a forum where like-minded allies can come together and share and learn.
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For US and allies, prepping for AI warfare starts with the data - C4ISRNet
Nucleus Biologics Launches Artificial Intelligence Research (NB-AIR): The World’s First AI Platform for Media Configuration That Gives Control Back To…
SAN DIEGO, Feb. 9, 2021 /PRNewswire/ --Nucleus Biologics, The Cell Performance Company, today announced the availability of NB-AIR, the world's first Artificial Intelligence Research platform for optimizing cell culture formulations for cell and gene therapies. Leveraging cutting-edge AI algorithms, the system empowers scientists to create optimized formulas based on meta-analysis of peer-reviewed articles. AI guided formulations will allow scientists to improve the performance of their cell therapy and shorten the time to get these lifesaving therapies into patients faster.
Cell and gene therapy in 2019 was a $1 Billion market and is estimated to be growing at 36% CAGR.Most cell therapy companies struggle to achieve reproducible potency in their cell therapies.The media used for in vitro cell growth have a documented impact on cell quality and hence therapeutic efficacy.Until now, scientists have had to rely on major suppliers who sell proprietary media formulations.These black box formulations limit the scientist's ability to chemically modify their media. This slows down discovery and introduces supply chain risk.Until now, no tool existed that allowed scientists to research and select components and formulations based on current published knowledge on conditions that impact cell performance.
"This is an industry transforming tool. Imagine being able to take months of research and reduce it to minutes through the power of machine learning. We are democratizing formulations enabling scientists to tap into the collective knowledge of their peers, become experts quickly and own their media formulation." said David Sheehan, Founder, President and CEO of Nucleus Biologics. "Our vision is that we can create a constantly evolving technology ecosystem that allows therapy providers to create intellectual property that improves cell performance and reduces development time."
Initially targeted for developers of cell therapies, NB-AIR speeds formulation development by providing peer-tested compounds and formula recommendations based on cell type and critical quality attributes. It is directly connected to NB-Lux, a cloud-based ordering and tracking portal, to allow online ordering of lot sizes from 2L to 2000L, allowing media scaled from bench to bioreactor. Further, changing even one component in your media can improve therapeutic yields, phenotype, and efficacy. Giving scientists the tools to optimize at a component level and control their media will help speed the time from discovery to cure.
Media Contact:Michael Morgan[emailprotected], (858) 251-2010
About Nucleus BiologicsNucleus Biologics, The Cell Performance Company, is the leading provider of custom cell-growth media, tools, and technologies for cell and gene therapy. Their mission is to speed the time from scientific discovery to cure by delivering innovative, transparent and cGMP products and services with the goal of disrupting the market and eliminating antiquated practices and products. Ultimately, Nucleus Biologics strives to create a new paradigm that serves both scientists and clinicians, while reducing the environmental footprint of cell culture. http://www.nucleusbiologics.com
SOURCE Nucleus Biologics
How Artificial Intelligence Is Transforming the Textile Industry – IoT For All
As the demand for products such as fitness trackers and wearable technology increases, so does the need for smart textile and smart apparel. According to a recent market report, the global elegant textile market size is expected to reach USD 5.55 billion by 2025.
The rise of new technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed the once labor-intensive textile industry. Computerized machinery is now found in most textile factories, and these machines are far more efficient at creating specific designs on a massive volume than human workers.
New smart apparel products are being created every day. By implementing AI along with technologies such as Bluetooth Low Energy (BLE), edge computing, and cloud data, smart textiles can monitor and communicate the wearers information, including biometric data such as blood pressure, heart rate, perspiration, temperature, and more.
This article will examine how AI is impacting the textile industry, some new use cases, and why ultra-low-power (ULP) technologies are a must to fully unleash AI at the endpoints.
For textile manufacturers, AI is reshaping their entire production process and the way they conduct business. AI can access and collect historical and real-time operational data, providing insights that can improve operational efficiency. When you have a clear view of your operations, it is easier to tweak processes to magnify human workers capabilities.
Whether it is product cost, textile production, quality control, just-in-time manufacturing, data collection, or computer integrated manufacturing, AI leaves an imprint on every part of the process. Some commonly integrated AI applications for textile production include defect detection, pattern inspection, and color matching.
The use of AI has enabled smart apparel, or smart clothes that leverage IoT and electronic sensors to create a better user experience. By leveraging these technologies, smart clothes can offer a more comfortable experience and a more healthcare-focused experience. Below, we will examine some of these new possibilities in the textile industry.
Much like how fitness trackers can help their users live a healthier and more attentive lifestyle, smart apparel combined with electronic sensing technology can do the same. However, since your clothes have a larger area of contact with your body than something like a smartwatch, smart apparel can potentially provide more types of physiological signal measurements.
Smart clothing can enable continuous monitoring of important biometrics, such as our heart rate. With long-term monitoring more feasible, physicians can better identify or diagnose potential cardiac diseases. Smart clothing helps patients collect complete and comprehensive heart-related data, track long-term heart disease, and enhance the detection and diagnosis of heart issues through regular monitoring over an extended period.
Following the COVID-19 outbreak, consumers have emphasized healthcare and medical attention in their wearable products, which is now extending to smart apparel. Clothes embedded with BLE technology can feel, sense, and regulate data, and the development of fabric-based sensors should only improve the overall wearing experience.
Artificial Intelligence isnt the only technology driving forward the textile industry. Cloud data, edge compute, accurate sensors, and ultra-low-power technologies are also necessary components. Especially for smart clothes that rely on BLE and IoT technologies, a long-lasting energy source from their embedded battery must provide a satisfactory and useful consumer experience.
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How Artificial Intelligence Is Transforming the Textile Industry - IoT For All
Artificial Intelligence gets major boost with Amsterdam University of Applied Sciences new centre of expertise – Science Business
The Amsterdam University of Applied Sciences (AUAS) is the first knowledge institution in the Netherlands to create a Centre of Expertise on Applied Artificial Intelligence, together with partners from the business community and the public sector. This will give a major boost to knowledge on applied Artificial Intelligence (AI) in the Amsterdam region. A unique feature of this Centre of Expertise is that students from all degree programmes at the AUAS can learn through labs how to apply AI in their fields of study; from nursing and ICT to teacher training.
Currently, AI algorithms, which are really self-learning systems, support us in things like navigation on the web or with chatbots. More and more possibilities are being developed, and this has an impact on all fields and on what employees need to be able to do. In the Netherlands, there is therefore an urgent need for more knowledge and talent in relation to AI, particularly from SMEs and the public sector.
As the largest knowledge institution in the Amsterdam region, the AUAS is responding to this need by establishing a Centre of Expertise on Applied Artificial Intelligence to help businesses and public organisations in this transition. The AUAS is working on this with several partners, including AI Technology for People, knowledge institutions in Amsterdam, the City of Amsterdam, Amsterdam Data Science and the Amsterdam Economic Board.All professions are changing
The AUAS is taking a broad approach with the new CoE: the aim is for all AUAS students to acquire the latest skills and knowledge in order to apply AI in their own fields. To do this, 7 labs have been created (in fields including retail, media, healthcare and education), which also have links with the sectors themselves.
By doing so, the AUAS wants to contribute to more knowledge about AI in the region, while also better preparing students themselves for the future, as many professions will change due to the new applications. Whereas an accountant can currently distinguish himself by being good at mathematics, for example, this will soon be an ideal task for AI. Healthcare professionals are likely to use AI support in the future in to predict certain conditions better. Business owners will have to be able to interpret and apply AI, to respond to further digitalisation. And in education, AI can help lecturers by looking at how pupils or students learn, for example. The AUAS has set up a lab for this too.
Ethical aspects
The Centre of Expertise will pay extra attention to the ethical aspects of AI. Because algorithms can exclude certain information, which may not always be desirable for users. Through the Responsible AI Lab, the AUAS will explore how to design inclusive and responsible AI solutions.
Finally, the AUAS will investigate the impact of AI on the professional field and society. The aim is to achieve inclusive, responsible AI, geared towards the user. Among other things, this will result in tools, instruments and training courses for businesses, municipal authorities and public organisations. In this way, the AUAS will become an important player in AI, as the university of applied sciences focuses on practical application in addition to the theoretical knowledge developed by research universities.
ABOUT CENTRES OF EXPERTISE (COES)
The first Centres of Expertise were launched in 2011 as an important innovation in vocational and higher professional education. They work in co-creation with the business community, public organisations and citizens on social solutions that have a positive impact. Based on current issues in the Amsterdam region, the AUAS has clustered its research and education in several CoEs, with each theme linked to a social or metropolitan theme that is important to Amsterdam.
In the seven faculty labs of the Centre of Expertise Applied AI work will be carried out on innovation within the various areas of application in co-creation with education, research, the business community and civil society organisations.
Impact on education
Innovation in education is an important priority area of the CoE. The faculty labs are linked to one or more degree programmes. Students and faculty are involved through projects, minors and Masters programmes on digitalisation and AI. Take the minors in Legal Tech (through the Legal Tech Lab of the same name) and FinTech (through the Finance Lab), for example. Or the Masters programme in Digital Driven Business (through the Centre for Market Insights).
The CoE has taken the initiative to develop a professional Masters degree in Applied Artificial Intelligence, which it aims to offer from September 2022. In addition, a methodology is being developed to help all degree programmes become AI-ready. The CoE has already started offering introductory AI training courses to lecturers to build expertise within its own organisation.
Do you have questions or would you like to work together? Then send an email to [emailprotected]
More information can be found at: http://www.amsterdamuas.com/ai
This article was first published on 9 February by AUAS.
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Artificial Intelligence gets major boost with Amsterdam University of Applied Sciences new centre of expertise - Science Business
Artificial Intelligence (AI) in Healthcare Market Competitive landscape and Key Vendors, Forecast by 2025 KSU | The Sentinel Newspaper – KSU | The…
According to the findings of this business intelligence study, the demand forartificial intelligence in healthcare sectoracross the globe will increase at an exuberant CAGR during the forecast period of 2017 to 2025. This report has been developed by healthcare IT professionals and aspires to serve as a credible business tool for targeted audiences such as healthcare software vendors, chipset companies, technology providers, doctors and hospitals, software solution providers, artificial intelligence system providers, and venture capitalist.
The report includes comprehensive and figurative assessment of the demand potential of various market segments, analyzes various impacting factors including trends, drivers, and obstructions, and takes stock of the demand that can be expected out of different countries and regions. The report also contains a featured chapter on the competitive landscape.
Artificial Intelligence (AI) in Healthcare Market: Trends and Opportunities
Greater new possibilities with big data, ability of AI to enhance patient care, strong imbalance between the pool of patients and healthcare professionals, and possibilities of reducing medical costs are some of the key factors expected to augment the demand for AI in the healthcare sector. Additionally, growing importance of precision medicine, increasing number of cross-industry collaborations, consistent inflow of venture capital investments, and increasing geriatric population are some of the other factors that are expected to reflect positively over this market.
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On the other hand, reluctance of medical practitioners in adopting new technologies, strong lack of a preset and universal regulatory guidelines, lack of curated healthcare data, and concerns of data privacy are curtailing the market from attaining higher grounds.
Technology-wise, the artificial intelligence (AI) in healthcare market can be segmented into querying method, deep learning, context aware processing, and natural language processing, whereas application-wise, artificial intelligence (AI) in healthcare marketcan be bifurcated into wearables, virtual assistant, research and drug discovery, in-patient care and hospital management, medical imaging and diagnosis, precision medicine, lifestyle management and monitoring, and patient data and risk analysis.
Artificial Intelligence (AI) in Healthcare Market: Regional Analysis
The developed country of the U.S., which readily adopts new technology and houses a number of pioneering companies, is expected to maintain North America are the region with maximum demand potential, with little but significant demand added by Canada. While the European region is another key region for the vendors of artificial intelligence (AI) in healthcare market, emerging economies of Japan, South Korea, China, and India are expected to provide for decent demand over the course of the aforementioned forecast period.
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The EIRS quadrant framework in the report sums up our wide spectrum of data-driven research and advisory for CXOs to help them make better decisions for their businesses and stay as leaders.
Below is a snapshot of these quadrants.
1. Customer Experience Map
The study offers an in-depth assessment of various customers journeys pertinent to the market and its segments. It offers various customer impressions about the products and service use. The analysis takes a closer look at their pain points and fears across various customer touchpoints. The consultation and business intelligence solutions will help interested stakeholders, including CXOs, define customer experience maps tailored to their needs. This will help them aim at boosting customer engagement with their brands.
2. Insights and Tools
The various insights in the study are based on elaborate cycles of primary and secondary research the analysts engage with during the course of research. The analysts and expert advisors at TMR adopt industry-wide, quantitative customer insights tools and market projection methodologies to arrive at results, which makes them reliable. The study not just offers estimations and projections, but also an uncluttered evaluation of these figures on the market dynamics. These insights merge data-driven research framework with qualitative consultations for business owners, CXOs, policy makers, and investors. The insights will also help their customers overcome their fears.
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Artificial Intelligence (AI) in Healthcare Market Competitive landscape and Key Vendors, Forecast by 2025 KSU | The Sentinel Newspaper - KSU | The...
Ethical Concerns Associated with Artificial Intelligence (AI) – Brunel University News
18 Feb 2021, 16:00 - 17:15
Online event
The Centre for Artificial Intelligence Seminar Series examines the emerging challenges and opportunities surrounding AI.
Ethical Concerns Associated with Artificial Intelligence(AI)
by Dov Greenbaum, Professor of Law
Director at Zvi Meitar Institute for Legal Implications of Emerging Technologies,
IDC Herzliya
Host: Dr Cristina Sisu, Lecturer in Genomic Data Analytics, Brunel University London
Dov Greenbaumhas undergraduate degrees in economics and biology from Yeshiva University and a doctorate in bioinformatics/genetics, a data science field, from Yale University. Dov also has a law degree with a focus on intellectual property from the University of California, Berkeley. Subsequent to his degrees, Dov had two postdoctoral appointments, one at Stanford University and one at ETH Zurich, both synthesized science, technology, law and society. Dov practiced law for two years at a large multinational law firm in Silicon Valley where he was involved in complex high-stakes civil litigation. Dov continued his practice in Israel in the area of patenting of hitech innovations, including biotech, complex algorithms, robotics and missile defense. Currently, Dov is the director of the Zvi Meitar Institute, an academic institute at the Interdisciplinary Center in Herzliya (IDC). The Institute is broadly interested in the ethical, social and legal concerns arising from new and emerging technologies.
The Centre for Artificial Intelligence Seminar Series examines the emerging challenges and opportunities surrounding AI.
Ethical Concerns Associated with Artificial Intelligence(AI)
by Dov Greenbaum, Professor of Law
Director at Zvi Meitar Institute for Legal Implications of Emerging Technologies,
IDC Herzliya
Host: Dr Cristina Sisu, Lecturer in Genomic Data Analytics, Brunel University London
Dov Greenbaumhas undergraduate degrees in economics and biology from Yeshiva University and a doctorate in bioinformatics/genetics, a data science field, from Yale University. Dov also has a law degree with a focus on intellectual property from the University of California, Berkeley. Subsequent to his degrees, Dov had two postdoctoral appointments, one at Stanford University and one at ETH Zurich, both synthesized science, technology, law and society. Dov practiced law for two years at a large multinational law firm in Silicon Valley where he was involved in complex high-stakes civil litigation. Dov continued his practice in Israel in the area of patenting of hitech innovations, including biotech, complex algorithms, robotics and missile defense. Currently, Dov is the director of the Zvi Meitar Institute, an academic institute at the Interdisciplinary Center in Herzliya (IDC). The Institute is broadly interested in the ethical, social and legal concerns arising from new and emerging technologies.
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Ethical Concerns Associated with Artificial Intelligence (AI) - Brunel University News
Artificial Intelligence Challenge: These are the finalists for the 2021 KUKA Innovation Award – MarketScale
Innovations centered on the megatrend of artificial intelligence (AI): the finalists for the 2021 KUKA Innovation Award have been selected. Five teams convinced the international jury with their robotics concepts on the topic of AI. A monetary prize of 20,000 euro awaits the winner.
AI is becoming increasingly important for industry and the combination with robotics, in particular, is opening up completely new possibilities and applications.
An international jury of experts evaluated the concepts for the Artificial Intelligence Challenge and selected five finalists. The teams are provided with a KUKA robot free of charge and are being trained and coached by KUKA experts throughout the competition. The five finalist teams now have until the virtual Hannover Messe in April to implement their ideas. They will present their applications to a large specialist audience at the international industrial trade fair before the jury chooses the winner of the renowned competition during the fair.
These are the finalists of the Artificial Intelligence Challenge:
Implicit knowledge instead of complex programming codes: the goal of the team from theBrandenburg University of Technology Cottbus-Senftenbergis intelligent robot programming based on manual manufacturing sequences. The individual process steps are recorded by means of innovative data gloves and reproduced on the industrial robot using an AI-based self-learning system. The operator is freed from the need to formulate explicitly what the task is and how the robot has to perform it. Instead, the implicit knowledge of the operator during the manual manufacturing process is accessed. A corresponding skill sequence is automatically generated with this information, and the robot carries out its task without the need to write a single line of code.
Humans can often easily explore closed spaces with their hands and pick up objects without even looking. The application by the international team of researchers fromthe Indian Institute of Science and the U.S. Massachusetts Institute of Technologyaims to bring such capabilities to robots. The goal is for robots to explore, recognize and pick up objects in vision-denied environments using the sense of touch. To this end, the BlindGrasp team is designing a novel gripper with tactile sensing capabilities that gathers the contact and proximity information. This data, coupled with the force-sensing capabilities of KUKAs lightweight robot LBR iiwa, is used by a machine learning agent to learn motion policies and thus safely explore the environment and pick up objects.
Team Chorrobot
The goal of Chorrobot fromBelgiums Katholieke Universiteit Leuven and Flanders Make@KU Leuvenis to leverage artificial intelligence in order to enhance the productivity of car manufacturers as well as small and medium-sized enterprises by facilitating and expediting the deployment of bimanual robot manipulation tasks. The concept enables users without extensive expertise in robotics to demonstrate some aspects of the task and to intuitively specify other aspects via a graphical user interface. This approach facilitates the commissioning of challenging bimanual tasks including fixtureless assembly operations that involve non-rigid and non-fixed elements as well as bimanual inspection operations in unstructured environments.
Particularly during the COVID-19 pandemic, collaborative robots could help to reduce human-to-human interaction. However, configuring these machines for a set of given tasks still requires a great effort. The team from theA*STAR Institute for Infocomm Research in Singaporeis developing a programming-free approach that leverages the latest developments in AI capabilities. The technology enables more natural and safer human-robot collaboration. This allows the robot to support operators, especially in a high-mix low-volume manufacturing environment. The concept from Team CHRIS is comprised of intuitive object and task teaching, activity understanding as well as multimodal perception (vision, touch and speech) and reasoning.
The COVID-19 pandemic and social distancing are increasing the reliance on remote work. However, the impact of online tools for the construction industry is limited. Team CRC from theChair for Individualized Production / RWTH Aachen University & Robots in Architecture Researchis therefore integrating automation technology into online collaboration. Cloud Remote Control enables users to run robots, monitor processes and adapt tool paths from the comfort of their home or international office. This increases accessibility to worldwide robotic production, adding layers of Industrie 4.0 device communication and artificial intelligence to path planning. In this way, Cloud Remote Control empowers teams to remain safely at a distance while still collaborating closely on automated construction.
KUKA launched the innovation competition in 2014, focusing on different technologies each year. The goal of the award is to promote and expedite the transfer of technology from research to industry. All information about the competition as well as the topics and winners in recent years can be foundhere.
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Artificial Intelligence Challenge: These are the finalists for the 2021 KUKA Innovation Award - MarketScale
$11.2 Billion Artificial Intelligence in Agriculture Markets – Global Industry Analysis and Growth Forecast to 2030 – PRNewswire
DUBLIN, Feb. 11, 2021 /PRNewswire/ -- The "Artificial Intelligence in Agriculture Market Research Report: By Type, Technology, Application - Global Industry Analysis and Growth Forecast to 2030" report has been added to ResearchAndMarkets.com's offering.
The revenue generated in the global AI in agriculture market share is expected to increase to $11,200.1 million in 2030 from $671.6 million in 2019, at a 30.5% CAGR during 2020-2030
Service, based on type, is projected to be the faster-growing category during the forecast period. With an increasing number of farmers wanting to implement AI in their practices, the demand for training and equipment installation and maintenance services is also rising.
The highest CAGR, under the application segment of the AI in the agriculture market, would be experienced by the drone analytics division. With the surging requirement for high-quality crops by the continuously growing population, heavy investments are being put in agricultural drones. The demand for such devices is rising rapidly in China and the U.S., which is driving the advance of the drone analytics division.
The most important factor leading to the growth of the AI in the agriculture market is the increasing demand for food. The United Nations Department of Social and Economic Affairs (UN-DESA) claims that the worldwide population would rise from 7.7 billion currently to 8.6 billion by 2030. Additionally, with the changing consumption pattern of the populace, increasing disposable income, and high rate of urbanization, the demand for agricultural products is burgeoning. Due to this, the agrarian community is pursuing an increase in the farms' productivity, by leveraging AI.
Developing regions are expected to offer ample opportunities to the players in the AI in the agriculture market in the coming years. In emerging economies such as Brazil, India, and South Africa, the usage of AI in the agricultural domain is quite low; however, with the governments in these countries extending their support for the adoption of advanced technologies to grow crops, market players can hope to augment their revenue substantially here. For instance, the Maharashtra government began a partnership with the World Economic Forum in January 2019, to use drones for collecting insights on farmlands.
Software is expected to witness the fastest advance in the AI in the agriculture market, on the basis of product type, in the coming years. This is attributed to the fact that the use of AI for smart greenhouse management, soil management, and livestock monitoring necessitates advanced software to control and operate the complex devices and instruments. In 2019, machine learning was the largest technology category in the market, as farmers are rapidly adopting it to augment their yield, by combining data technologies with advanced agricultural science.
On a geographical basis, Europe and North America dominated the AI in the agriculture market in 2019, with a combined revenue share of around 70.0%. During the forecast period, the highest CAGR would be witnessed in Asia-Pacific (APAC), as the developing countries in the region, including China, India, Thailand, and Indonesia, are rapidly integrating agricultural robots, drone analytics, precision farming, and other advanced techniques to raise the productivity of farms.
Owing to the presence of numerous leading players, such as Microsoft Corporation, IBM Corporation, Deere & Company, Bayer AG, AgEagle Aerial Systems Inc., A.A.A Taranis Visual Ltd., Raven Industries, AGCO Corporation, Trimble Inc., Ag Leader Technology, Gamaya SA, Google LLC, and Granular Inc., the global AI in agriculture market is quite competitive.
Key Topics Covered:
Chapter 1. Research Background1.1 Research Objectives1.2 Market Definition1.3 Research Scope1.4 Key Stakeholders
Chapter 2. Research Methodology2.1 Secondary Research2.2 Primary Research2.3 Market Size Estimation2.4 Data Triangulation2.5 Assumptions for the Study
Chapter 3. Executive Summary
Chapter 4. Introduction4.1 Definition of Market Segments4.1.1 By Type4.1.1.1 Product4.1.1.1.1 Hardware4.1.1.1.2 Software4.1.1.2 Service4.1.1.2.1 Professional4.1.1.2.2 Managed4.1.2 By Technology4.1.2.1 Machine Learning4.1.2.2 Computer Vision4.1.2.3 Predictive Analytics4.1.3 By Application4.1.3.1 Agricultural Robots4.1.3.2 Precision Farming4.1.3.3 Drone Analytics4.1.3.4 Livestock Monitoring4.1.3.5 Others4.2 Value Chain Analysis4.3 Market Dynamics4.3.1 Trends4.3.1.1 Increasing use of robotics in agriculture4.3.1.2 Increasing use of smart sensors in agriculture4.3.2 Drivers4.3.2.1 Growing demand for agricultural production4.3.2.2 Rising adoption of internet of things (IoT)4.3.2.3 Increasing demand for monitoring of livestock4.3.2.4 Increasing demand for drones in agricultural farms4.3.2.5 Impact analysis of drivers on market forecast4.3.3 Restraints4.3.3.1 Lack of awareness and high cost of AI solutions4.3.3.2 Impact analysis of restraints on market forecast4.3.4 Opportunities4.3.4.1 Growth opportunities from developing countries4.3.4.2 AI powered chatbots4.4 Porter's Five Forces Analysis
Chapter 5. Global Market Size and Forecast5.1 By Type5.1.1 By Product5.1.2 By Service5.2 By Technology5.3 By Application5.4 By Region
Chapter 6. North America Market Size and Forecast
Chapter 7. Europe Market Size and Forecast
Chapter 8. APAC Market Size and Forecast
Chapter 9. LATAM Market Size and Forecast
Chapter 10. MEA Market Size and Forecast
Chapter 11. Competitive Landscape11.1 Analysis of Key Players in the Market11.2 List of Key Players and Their Offerings11.3 Competitive Benchmarking of Key Players11.4 Global Strategic Developments of Key Players11.4.1 Mergers and Acquisitions11.4.2 Product Launches11.4.3 Partnerships
Chapter 12. Company Profiles
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$11.2 Billion Artificial Intelligence in Agriculture Markets - Global Industry Analysis and Growth Forecast to 2030 - PRNewswire
Dyno Therapeutics Announces Publication in Nature Biotechnology Demonstrating Use of Artificial Intelligence to Generate Unprecedented Diversity of…
CAMBRIDGE, Mass.--(BUSINESS WIRE)--Dyno Therapeutics, a biotech company applying artificial intelligence (AI) to gene therapy, today announced a publication in Nature Biotechnology that demonstrates the use of artificial intelligence to generate an unprecedented diversity of adeno-associated virus (AAV) capsids towards identifying functional variants capable of evading the immune system, a factor that is critical to enabling all patients to benefit from gene therapies. The research was conducted in collaboration with Google Research, Harvards Wyss Institute for Biologically Inspired Engineering and the Harvard Medical School laboratory of George M. Church, Ph.D., a Dyno scientific co-founder. The publication is entitled Deep diversification of an AAV capsid protein by machine learning.
It is estimated that up to 50-70% of the human population have pre-existing immunity to natural forms of the AAV vectors currently being using to deliver gene therapies. This immunity renders a large portion of patients ineligible to receive gene therapies which rely upon these capsids as the vector for delivery. Overcoming the challenge of pre-existing immunity to AAV vectors is therefore a major goal for the gene therapy field.
The approach described in the Nature Biotechnology paper opens a radically new frontier in capsid design. Our study clearly demonstrates the potential of machine learning to guide the design of diverse and functional sequence variants, far beyond what exists in nature, said Eric Kelsic, Ph.D., Dynos CEO and co-founder. We continue to expand and apply the power of artificial intelligence to design vectors that can not only overcome the problem of pre-existing immunity but also address the need for more effective and selective tissue targeting. At Dyno, we are making rapid progress to design novel AAV vectors that overcome the limitations of current vectors, improving treatments for more patients and expanding the number of diseases treatable with gene therapies.
The Nature Biotechnology paper describes the rapid production of a large library of distinct AAV capsid variants designed by machine learning models. Nearly 60% of the variants produced were determined to be viable, a significant increase over the typical yield of <1% using random mutagenesis, a standard method of generating diversity.
The more we change the AAV vector from how it looks naturally, the more likely we are to overcome the problem of pre-existing immunity, added Sam Sinai, Ph.D., Dyno co-founder and Machine Learning Team Lead. Key to solving this problem, however, is also ensuring that capsid variants remain viable for packaging the DNA payload. With conventional methods, this diversification is time- and resource-intensive, and results in a very low yield of viable capsids. In contrast, our approach allows us to rapidly unlock the full potential diversity of AAV capsids to develop improved gene therapies for a much larger number of patients.
This research builds upon previous work published in Science in which a complete landscape of single mutations around the AAV2 capsid was generated followed by evaluation of the functional properties important for in vivo delivery. In parallel with these works, Dyno has established collaborations with leading gene therapy companies Novartis, Sarepta Therapeutics, Roche and Spark Therapeutics to develop next-generation AAV gene therapy vectors with a goal of expanding the utility of gene therapies for ophthalmic, muscle, central nervous system (CNS) and liver diseases.
About CapsidMap for Designing Optimized 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 efficiency, immunity, payload size, and manufacturing challenges. CapsidMap uses artificial intelligence (AI) technology to engineer 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 design of novel capsids optimized for gene therapy.
About Dyno Therapeutics
Dyno Therapeutics is a pioneer in applying artificial intelligence (AI) and quantitative high-throughput in vivo experiments to gene therapy. The companys proprietary CapsidMap platform rapidly discovers and systematically optimizes Adeno-Associated Virus (AAV) capsid vectors that significantly outperform current approaches for in vivo gene delivery, thereby expanding 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 Announces Publication in Nature Biotechnology Demonstrating Use of Artificial Intelligence to Generate Unprecedented Diversity of...
Genethon and WhiteLab Genomics Join Forces to Enhance Gene Therapy Through Artificial Intelligence – Business Wire
PARIS--(BUSINESS WIRE)--WhiteLab Genomics, a specialist in artificial intelligence applied to gene and cell therapies, has signed a partnership agreement with Genethon, a pioneering research center in the field of gene therapy.The alliance will harness the power of artificial intelligence to accelerate development of innovative gene therapies.
As part of this partnership, Genethon teams will use WhiteLab Genomics CatalystTM platform to develop new capsids, or vectors, which are essential components for gene therapy products.
While several gene therapy products have already obtained market authorization for the treatment of rare and common diseases, which demonstrates the efficacy of this approach for conditions considered to be incurable, development of these complex therapies continues to face major scientific and technical hurdles. Many vectors used in gene therapy are derivatives of adeno-associated viruses (AAV). Their use has limitations: natural immunization of 30% to 40% of the population and difficulty targeting a specific tissue. As a result, extremely large quantities of vectors are necessary. In this context, the use of artificial intelligence solutions stands out as a deciding factor to overcome these obstacles and produce optimized vectors that better target the relevant tissues, thus making it possible to inject smaller quantities of product while maximizing the effect of the therapy.
Turning to AI for faster development of optimized vectors
Genethons teams will use WhiteLab Genomics CatalystTM platform to accelerate select research programs.
Thanks to its Machine Learning algorithms, the WhiteLab Genomics platform will help researchers develop next-generation gene therapy vectors, with a view to enhancing their precision with regard to the tissues to be treated while reducing their immunogenic qualities.
The tools developed by WhiteLab will make it possible for us to review thousands of sequences and devise new and innovative combinations. We aim to develop a new generation of more specific AAV vectors, contributing to the emergence of original treatments for neuromuscular disorders, said Dr. Ronzitti, who is managing the collaboration for Genethon.
We are thrilled to be working together with worldwide trailblazers and experts in the area of gene therapy, stated David Del Bourgo, CEO and co-founder of WhiteLab Genomics. France is a prime source of innovation in this field, and we look forward to helping research teams, in France and abroad, to make practical use of these extremely complex biological datasets, while also providing assistance to accelerate the development of optimized products.
About White Lab Genomics
Founded in 2019 by David Del Bourgo, Julien Cottineau and Lucia Cinque, WhiteLab Genomics is a French start-up specializing in artificial intelligence solutions dedicated to biotherapies, such as gene and cell therapies. The companys proprietary technology allows for multi-parameter analysis of complex biological data to optimize these treatments while reducing development costs. WhiteLab Genomics provides this unique technology to its clients via the Catalyst platform, available in SaaS mode. Today, the start-up has locations at the Evry Gnopole Frances first biocluster and at Station F. WhiteLab Genomics was recently ranked among Station Fs Future 40, an index of the 40 most promising companies within Europes largest start-up incubator. https://www.whitelabgx.com
About Genethon
Created by AFM-Telethon, Genethon is a non-profit research and development center dedicated to creating gene therapy products for rare diseases, from initial research to clinical validation. Genethon has several programs underway, in clinical, pre-clinical and research phases, to treat rare muscular, blood, immune system and liver disorders. Today, a product incorporating technologies developed thanks to Genethons pioneering research is available on the market in the United States, Europe and Japan to treat spinal muscular atrophy. Ten other products created through Genethon R&D, alone or in collaboration with partners, are at the clinical trial stage, while many more are slated to begin clinical trials in 2021 and 2022. Genethon.fr
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Genethon and WhiteLab Genomics Join Forces to Enhance Gene Therapy Through Artificial Intelligence - Business Wire