Category Archives: Artificial Intelligence
Deep Tech has become one of the most powerful use cases for A.I. in business. Here are 3 keys to making it work – Fortune
In early 2020, when scientists rushed to develop a vaccine to take on the SARS-CoV-2 coronavirus that causes COVID-19, it seemed like a really long shot. The fastest a vaccine had ever previously been developed was for mumps, back in the 1960san effort that took 48 months. Still, just nine months later, in December 2020, the American pharmaceutical giant Pfizer and a German deep-tech startup, BioNTech, had developed the first COVID-19 vaccine, validating the use of the new technology of mRNA-based vaccines.
The first studies on DNA vaccines began 25 years ago, and the science of RNA vaccines too has been evolving for over 15 years. One outcome was mRNA technology, which required the convergence of advances in synthetic biology, nanotechnology, and artificial intelligence, and has transformed the scienceand the businessof vaccines. Pfizer generated nearly$37 billion in salesfrom the COVID-19 vaccine last year, making it one of the most lucrative products in the companys history.
Like Pfizer and Moderna in the pharmaceuticals sector, several corporations in other industriessuch as Tesla inautomobiles,Bayerin agrochemicals,BASFin specialty chemicals,Deerein agriculture machinery, andGoodyearin rubberare relying on deep technologies. Deep Tech, as we call it, is the problem-driven approach to tackling big, hairy, audacious, and wicked challenges by combining new physical technologies, such as advanced material sciences, with sophisticated digital technologies, such as A.I. and soon, quantum computing.
Deep Tech is risingto the fore because of businessspressing need to develop new products faster than before; to develop sustainable products and processes; and to become more future-proof. Deep Tech can generate enormous value and will provide companies with new sources of advantage. In fact, Deep Tech will disrupt incumbents in almost every industry. Thats because the products and processes that will result because of these technologies will be transformational, creating new industries or fundamentally altering existing ones.
The early prototypes of Deep Tech-based products are already available. For instance, the use of drones, 3-D printers, and syn-bio kits is proliferating, while No Code / Low Code tools are making A.I. more accessible. Theyre opening upmore avenuesby which companies can combine emerging technologies and catalyze more innovations. Unsurprisingly, incubators and accelerators have sprung up worldwide to facilitate their development. Not only are more Deep Tech start-ups being set up nowadays, but theyre launching successful innovations faster than before.
Its risky for CEOs of incumbent companies to count on a wait-and-watch strategy. They need to figureout ways to tap into Deep Techs potentialright away before their organizations are disrupted by themjust as digital technologies and start-ups disrupted business not so long ago. Unlike digital disruption, though, the physical-cum-digital nature of Deep Tech provides a golden opportunity for incumbents to shape these technologies evolution and to harness them for their benefit.
Established giants can help Deep Tech start-ups scale their products, which can be especially complex and costly for physical products, by leveraging their expertise in engineering and manufacturing scale-up and by providing market access. And because the incumbents are already at the center of global networks, they can also help navigate government regulations and influence their suppliers and distributors to transition to infrastructure that will support the new processes and products. Doing so will unlock enormous value, as the Pfizer-BioNTech case exemplifies.
Most incumbents will find thatDeep Tech poses two stiff challenges at first. One, it isnt easy to spot or assess the business opportunities that the new technologies will create. Two, its equally tough to develop and deploy Deep Tech-based solutions and applications, which usually requires participating in and catalyzing collective actions with ecosystems. To manage the twin challenges of Deep Tech, CEOs should keep in mind three starting points.
Despite its sophistication, conventional technology forecasting produces linear predictions and siloed thinking; it doesnt account for how technologies change and converge. As a result, most forecasts underestimate the speed at which technologies evolve and when business will be able to use them. Thats why companies should use backcasting, the method outlined by University of WaterloosJohn Robinsonin the late 1980s.
Rather than tracking the development of many technologies, business would do better to start by focusing on the worlds biggest needs and pressing problems, to identify the long-standing frictions and tradeoffs that have prevented it from tackling them until now. Then, they should define a desirable future in which those issues have been resolved, and work back to identify the technologies, and combinations thereof, that will make solutions possible and commercially feasible. Backcasting helps companies come to grips with both short-term and long run technological changes, making it ideal to manage Deep Tech.
The Anglo-American think tankRethink X, for instance, has used a technology disruption framework, predicated on backcasting, to highlight the implications of creating a sustainable world. The analysis suggests that the technological changes under way in the energy, transportation, and food sectors, driven by a combination of just eight emerging technologies, could eliminate over 90% of net greenhouse gas emissions in 15 years time. The same technologies will also make the cost of carbon withdrawal affordable, so more breakthrough technologies may not be needed in the medium term.
When companies evaluate the business opportunities that deep technologies will open up, they should take into account the scope of thechanges they will bring about. It will be determined by the complexity of a technology and the businesss ability to scale solutions based on it. As Arnulf Grubler, the head of the Austria-basedInternational Institute for Applied Systems Analysis, and his co-authorsargued six years ago,new technologies can bring about four levels of change. They can:
1. Improve an existing product. For example, sustainable biodegradable plastic can replace conventional plastic packaging.
2. Improve an existing system. Nanomaterial-infused paints and an A.I.-enabled smart home system can, for instance, dramatically change homes.
3. Transform a system. Developing the ecosystem for hydrogen-powered automobiles, from hydrogen production to refueling stations, could transform urban mobility.
4. Transform a system-of-systems. Creating a purification technology that transforms current water supply and management systems will also alter the working of water-consuming sectors such as agriculture, alcohol, beverages, paper, and sugar.
Figuring out which of the four levels of change is likely to result will help companies better assess market sizes as well as growth trajectories. WhenBCG recently estimatedthe market size of Deep Tech solutions in nine sustainability-related sectors, for example, it found that while technology improvements in existing value chains would generate additional revenues of over $123 billion per annum, those that resulted in systemic changes would generate 20 times more. Or as much as $2.7 trillion a year.
Few companies already have in-house all the technologies and capabilities they need to deploy Deep Tech. They must gain thesupport of technology-related ecosystems, which extend from academics and university departments to investors and governments, to develop those competencies. The types of linkages that will result will depend on the business opportunity as well as the ecosystems maturity.
Several kinds of collaborations are likely to form. Some incumbents will, obviously, join hands with start-ups to develop new products or processes, as Bayer did in 2017, setting up ajoint venturewithGinkgo Bioworks to synthesize microbes that will allow plants to produce their own fertilizers.Others will orchestrate systemic changes, which is whatHyundai Motor Groupis trying to do in the field of mobility by working with several Deep Tech startups. Still others may focus on nurturing deep technologies to maturity themselves, akin to theefforts of SwedensSSAB(formerly Swedish Steel), Vattenfal, and Finlands LKAB to scale a sustainable steel-making process in which fossil-free electricity and green hydrogen replace coking coal.
***
A deep technology was impossible yesterday, is barely feasible today, and may soon become so pervasive and impactful that it will be difficult to remember life without it, points out Michigan State Universitys Joshua Siegel. The future will likely belong to companies that dont just track Deep Tech, but invest in its development and drive its adoption by engaging with ecosystems, forcing rivals to play the losing strategy of catch up.
ReadotherFortunecolumns by Franois Candelon.
Franois Candelonisa managing director and senior partner at BCG and global director of the BCG Henderson Institute.Maxime Courtauxis a project leader at BCG and ambassador at the BCG Henderson Institute.Antoine Gourevitch is a managing director and senior partner at BCG.John Paschkewitz is a partner and associate director at BCG.Vinit Patelis a project leader at BCG and ambassador at the BCG Henderson Institute.
Some companies featured in this column are past or current clients of BCG.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs ofFortune.
Sign up for theFortune Features email list so you dont miss our biggest features, exclusive interviews, and investigations.
Continue reading here:
Deep Tech has become one of the most powerful use cases for A.I. in business. Here are 3 keys to making it work - Fortune
Global Artificial Intelligence in Healthcare Diagnosis Market Research Report 2022: Rising Adoption of Healthcare Artificial Intelligence in Research…
DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Healthcare Diagnosis Market Research Report by Technology, Component, Application, End User, Region - Global Forecast to 2026 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.
The Global Artificial Intelligence in Healthcare Diagnosis Market size was estimated at USD 2,318.98 million in 2020, USD 2,725.72 million in 2021, and is projected to grow at a Compound Annual Growth Rate (CAGR) of 17.81% to reach USD 6,202.67 million by 2026.
Market Segmentation:
This research report categorizes the Artificial Intelligence in Healthcare Diagnosis to forecast the revenues and analyze the trends in each of the following sub-markets:
Competitive Strategic Window:
The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.
FPNV Positioning Matrix:
The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Healthcare Diagnosis Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.
Market Share Analysis:
The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.
Market Dynamics
Drivers
Restraints
Opportunities
Challenges
Key Topics Covered:
1. Preface
2. Research Methodology
3. Executive Summary
4. Market Overview
5. Market Insights
6. Artificial Intelligence in Healthcare Diagnosis Market, by Technology
7. Artificial Intelligence in Healthcare Diagnosis Market, by Component
8. Artificial Intelligence in Healthcare Diagnosis Market, by Application
9. Artificial Intelligence in Healthcare Diagnosis Market, by End User
10. Americas Artificial Intelligence in Healthcare Diagnosis Market
11. Asia-Pacific Artificial Intelligence in Healthcare Diagnosis Market
12. Europe, Middle East & Africa Artificial Intelligence in Healthcare Diagnosis Market
13. Competitive Landscape
14. Company Usability Profiles
15. Appendix
Companies Mentioned
For more information about this report visit https://www.researchandmarkets.com/r/vgkht7
Originally posted here:
Global Artificial Intelligence in Healthcare Diagnosis Market Research Report 2022: Rising Adoption of Healthcare Artificial Intelligence in Research...
Global Artificial Intelligence of Things Solutions Market Report 2022: AIoT Market will Reach $83.6 Billion by 2027, Growing at 39.1% CAGR -…
DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence of Things Solutions by AIoT Market Applications and Services in and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.
This AIoT market report provides an analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2022 through 2027. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.
While it is no secret that AI is rapidly becoming integrated into many aspects of ICT, many do not understand the full extent of how it will transform communications, applications, content, and commerce. For example, the use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective smart city solutions in terms of decision-making.
The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision-making. IoT adds value to AI through connectivity, signaling, and data exchange.
AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks.APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.
While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.
Industry adoption for AIoT is gaining momentum. By way of example, Advantech partnered with Momenta Ventures to launch the AIoT Ecosystem Fund, a venture capital fund with a target of $50 million USD and a focus on the digital industry. KC Liu, CEO of Advantech, stated: "Advantech is committed to enabling an intelligent planet. This starts at the industrial edge with early innovators in energy, manufacturing, smart spaces and supply chain management."
The company launched Advantech Industrial Wireless solutions with Qualcomm, NXP, DEKRA, and E Ink. "We provide AIW industrial grade wireless modules and wireless design-in services to embedded customers. This one-stop shopping service helps customers acquire leading wireless enabled AIoT products and reduce their time to market," said Andy Lin, Advantech Senior ProductManager.
Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service systems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.
We see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, andSaaS managed service offerings. Recent years have witnessed rapid growth for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy service industries will lead the way.
As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT-supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.
The use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective decision-making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.
In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.
The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $5.21B by 2027. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. We see machine learning in edge computing as the key to realizing the full potential of IoT analytics.
Select Report Findings:
Key Topics Covered:
1.0 Executive Summary
2.0 Introduction
2.1 Defining AIoT
2.2 AI in IoT vs. AIoT
2.3 Artificial General Intelligence
2.4 IoT Network and Functional Structure
2.5 Ambient Intelligence and Smart Lifestyles
2.6 Economic and Social Impact
2.7 Enterprise Adoption and Investment
2.8 Market Drivers and Opportunities
2.9 Market Restraints and Challenges
2.10 AIoT Value Chain
2.10.1 Device Manufacturers
2.10.2 Equipment Manufacturers
2.10.3 Platform Providers
2.10.4 Software and Service Providers
2.10.5 User Communities
3.0 AIoT Technology and Market
3.1 AIoT Market
3.1.1 Equipment and Component
3.1.2 Cloud Equipment and Deployment
3.1.3 3D Sensing Technology
3.1.4 Software and Data Analytics
3.1.5 AIoT Platforms
3.1.6 Deployment and Services
3.2 AIoT Sub-Markets
3.2.1 Supporting Device and Connected Objects
3.2.2 IoT Data as a Service
3.2.3 AI Decisions as a Service
3.2.4 APIs and Interoperability
3.2.5 Smart Objects
3.2.6 Smart City Considerations
3.2.7 Industrial Transformation
3.2.8 Cognitive Computing and Computer Vision
3.2.9 Consumer Appliances
3.2.10 Domain-Specific Network Considerations
3.2.11 3D Sensing Applications
3.2.12 Predictive 3D Design
3.3 AIoT Supporting Technologies
3.3.1 Cognitive Computing
3.3.2 Computer Vision
3.3.3 Machine Learning Capabilities and APIs
3.3.4 Neural Networks
3.3.5 Context-Aware Processing
3.4 AIoT Enabling Technologies and Solutions
3.4.1 Edge Computing
3.4.2 Blockchain Networks
3.4.3 Cloud Technologies
3.4.4 5G Technologies
3.4.5 Digital Twin Technology and Solutions
3.4.6 Smart Machines
3.4.7 Cloud Robotics
3.4.8 Predictive Analytics and Real-Time Processing
3.4.8.1 All-Flash Array
3.4.8.2 Real-Time Operating Systems
3.4.9 Post Event Processing
3.4.10 Haptic Technology
4.0 AIoT Applications Analysis
4.1 Device Accessibility and Security
4.2 Gesture Control and Facial Recognition
4.3 Home Automation
4.4 Wearable Device
4.5 Fleet Management
4.6 Intelligent Robots
4.7 Augmented Reality Market
4.8 Drone Traffic Monitoring
4.9 Real-time Public Safety
4.10 Yield Monitoring and Soil Monitoring Market
4.11 HCM Operation
5.0 Analysis of Important AIoT Companies
5.1 Sharp
5.2 SAS
5.3 DT42
5.4 Chania Tech Giants: Baidu, Alibaba, and Tencent
5.4.1 Baidu
5.4.2 Alibaba
5.4.3 Tencent
5.5 Xiaomi Technology
5.6 NVidia
5.7 Intel Corporation
5.8 Qualcomm
5.9 Innodisk
5.10 Gopher Protocol
5.11 Micron Technology
Read the original here:
Global Artificial Intelligence of Things Solutions Market Report 2022: AIoT Market will Reach $83.6 Billion by 2027, Growing at 39.1% CAGR -...
Artificial Intelligence Regulation Updates: China, EU, and U.S – The National Law Review
Wednesday, August 3, 2022
Artificial Intelligence (AI) systems are poised to drastically alter the way businesses and governments operate on a global scale, with significant changes already under way. This technology has manifested itself in multiple forms including natural language processing, machine learning, and autonomous systems, but with the proper inputs can be leveraged to make predictions, recommendations, and even decisions.
Accordingly,enterprises are increasingly embracing this dynamic technology. A2022 global study by IBMfound that 77% of companies are either currently using AI or exploring AI for future use, creating value by increasing productivity through automation, improved decision-making, and enhanced customer experience. Further, according to a2021 PwC studythe COVID-19 pandemic increased the pace of AI adoption for 52% of companies as they sought to mitigate the crises impact on workforce planning, supply chain resilience, and demand projection.
For these many businesses investing significant resources into AI, it is critical to understand the current and proposed legal frameworks regulating this novel technology. Specifically for businesses operating globally, the task of ensuring that their AI technology complies with applicable regulations will be complicated by the differing standards that are emerging from China, the European Union (EU), and the U.S.
China has taken the lead in moving AI regulations past the proposal stage. In March 2022, China passed aregulationgoverning companies use of algorithms in online recommendation systems, requiring that such services are moral, ethical, accountable, transparent, and disseminate positive energy. The regulation mandates companies notify users when an AI algorithm is playing a role in determining which information to display to them and give users the option to opt out of being targeted. Additionally, the regulation prohibits algorithms that use personal data to offer different prices to consumers. We expect these themes to manifest themselves in AI regulations throughout the world as they develop.
Meanwhile in the EU, the European Commission has published an overarchingregulatory framework proposaltitled the Artificial Intelligence Act which would have a much broader scope than Chinas enacted regulation. The proposal focuses on the risks created by AI, with applications sorted into categories of minimal risk, limited risk, high risk, or unacceptable risk. Depending on an applications designated risk level, there will be corresponding government action or obligations. So far, the proposed obligations focus on enhancing the security, transparency, and accountability of AI applications through human oversight and ongoing monitoring. Specifically, companies will be required to register stand-alone high-risk AI systems, such as remote biometric identification systems, in an EU database. If the proposed regulation is passed, the earliest date for compliance would be the second half of 2024 with potential fines for noncompliance ranging from 2-6% of a companys annual revenue.
Additionally, the previously enacted EU General Data Protection Regulation (GDPR) already carries implications for AI technology.Article 22prohibits decisions based on solely automated processes that produce legal consequences or similar effects for individuals unless the program gains the users explicit consent or meets other requirements.
In the United States there has been a fragmented approach to AI regulation thus far, with states enacting their own patchwork AI laws. Many of the enacted regulations focus on establishing various commissions to determine how state agencies can utilize AI technology and to study AIs potential impacts on the workforce and consumers. Common pending state initiatives go a step further and would regulate AI systems accountability and transparency when they process and make decisions based on consumer data.
On a national level, the U.S. Congress enacted theNational AI Initiative Actin January 2021, creating theNational AI Initiativethat provides an overarching framework to strengthen and coordinate AI research, development, demonstration, and education activities across all U.S. Departments and Agencies . . . . The Act created new offices and task forces aimed at implementing a national AI strategy, implicating a multitude of U.S. administrative agencies including the Federal Trade Commission (FTC), Department of Defense, Department of Agriculture, Department of Education, and the Department of Health and Human Services.
Pending national legislation includes theAlgorithmic Accountability Act of 2022, which was introduced in both houses of Congress in February 2022. In response to reports that AI systems can lead to biased and discriminatory outcomes, the proposed Act would direct the FTC to create regulations that mandate covered entities, including businesses meeting certain criteria, to perform impact assessments when using automated decision-making processes. This would specifically include those derived from AI or machine learning.
While the FTC has not promulgated AI-specific regulations, this technology is on the agencys radar. In April 2021 the FTC issued amemowhich apprised companies that using AI that produces discriminatory outcomes equates to a violation of Section 5 of the FTC Act, which prohibits unfair or deceptive practices. And the FTC may soon take this warning a step fartherin June 2022 theagency indicatedthat it will submit an Advanced Notice of Preliminary Rulemaking to ensure that algorithmic decision-making does not result in harmful discrimination with the public comment period ending in August 2022. The FTC also recently issued areportto Congress discussing how AI may be used to combat online harms, ranging from scams, deep fakes, and opioid sales, but advised against over-reliance on these tools, citing the technologys susceptibility to producing inaccurate, biased, and discriminatory outcomes.
Companies should carefully discern whether other non-AI specific regulations could subject them to potential liability for their use of AI technology. For example, the U.S. Equal Employment Opportunity Commission (EEOC) put forthguidancein May 2022 warning companies that their use of algorithmic decision-making tools to assess job applicants and employees could violate the Americans with Disabilities Act by, in part, intentionally or unintentionally screening out individuals with disabilities. Further analysis of the EEOCs guidance can be foundhere.
Many other U.S. agencies and offices are beginning to delve into the fray of AI. In November 2021, the White House Office of Science and Technology Policysolicited engagementfrom stakeholders across industries in an effort to develop a Bill of Rights for an Automated Society. Such a Bill of Rights could cover topics like AIs role in the criminal justice system, equal opportunities, consumer rights, and the healthcare system. Additionally, the National Institute of Standards and Technology (NIST), which falls under the U.S. Department of Commerce, is engaging with stakeholders todevelopa voluntary risk management framework for trustworthy AI systems. The output of this project may be analogous to the EUs proposed regulatory framework, but in a voluntary format.
The overall theme of enacted and pending AI regulations globally is maintaining the accountability, transparency, and fairness of AI. For companies leveraging AI technology, ensuring that their systems remain compliant with the various regulations intended to achieve these goals could be difficult and costly. Two aspects of AIs decision-making process make oversight particularly demanding:
Opaquenesswhere users can control data inputs and view outputs, but are often unable to explain how and with which data points the system made a decision.
Frequent adaptationwhere processes evolve over time as the system learns.
Therefore, it is important for regulators to avoid overburdening businesses to ensure that stakeholders may still leverage AI technologies great benefits in a cost-effective manner. The U.S. has the opportunity to observe the outcomes of the current regulatory action from China and the EU to determine whether their approaches strike a favorable balance. However, the U.S. should potentially accelerate its promulgation of similar laws so that it can play a role in setting the global tone for AI regulatory standards.
Thank you to co-author Lara Coole, a summer associate in Foley & Lardners Jacksonville office, for her contributions to this post.
View original post here:
Artificial Intelligence Regulation Updates: China, EU, and U.S - The National Law Review
Business leaders commemorate anniversary of EqualAI and its new leadership role on the National Artificial Intelligence Advisory Committee – PR…
EqualAI's Miriam Vogel leads committee advising the President and National AI Initiative Office on a range of issues related to artificial intelligence
NEW YORK, Aug. 2, 2022 /PRNewswire/ -- LivePerson(Nasdaq: LPSN), a global leader in customer engagement solutions, joined business leaders in congratulating EqualAIon four years of progress fighting unconscious bias in AI.
EqualAI is an independent nonprofit organization and movement founded in 2018 to reduce unconscious bias in the development and use of artificial intelligence. It is supported by corporate members from the tech industry.
In addition to launching impactful initiatives including the EqualAI Pledgeand EqualAI Badge Program for Responsible AI Governance the organization's president, Miriam Vogel, was recently appointed as Chair of the National Artificial Intelligence Advisory Committee(NAIAC), which advises the US President and National AI Initiative Office on a range of issues related to artificial intelligence.
The NAIAC was established by the US Department of Commerce and consists of leaders with a broad and interdisciplinary range of AI-relevant expertise across academia, nonprofits, civil society, and the private sector.
LivePerson founder and CEO Rob LoCascio said, "In just four years, EqualAI has made an incredible impact on the trajectory of artificial intelligence, bending it toward more responsible and ethical outcomes for all. At LivePerson, we're proud to have played a key role investing in and spearheading these efforts as a founding member of EqualAI. As we celebrate Miriam Vogel's appointment to the NAIAC, we encourage organizations of all kinds to take the EqualAI Pledge and undertake the EqualAI Badge Program to make tangible steps toward responsible AI governance."
Arianna Huffington, founder and CEO of Thrive Global and a founding member of EqualAI, added, "From raising awareness to designing frameworks and initiatives that fight bias, EqualAI has pushed policymakers and business leaders to do more and do better when it comes to artificial intelligence. As AI continues to reshape our daily lives, it's more critical than ever that we come together to ensure it helps, not hurts, the well-being of all of our communities."
In addition to LoCascio and Huffington, EqualAI's leadership includes Karyn Temple, Senior EVP and Global General Counsel at Motion Picture Association; Monica Greenberg, EVP, Corporate Development, Strategic Alliances and General Counsel at LivePerson; Susan Gonzales, CEO of AIandYou; and Reggie Townsend, Director of Data Ethics at SAS. LivePerson, Verizon, and SAS support EqualAI through corporate membership.
To learn more about reducing bias in artificial intelligence, visit EqualAI's websiteand LivePerson's blog.
About LivePerson, Inc.
LivePerson (NASDAQ: LPSN) is a global leader in customer engagement solutions. We create AI-powered digital experiences that feel Curiously Human. Our customers including leading brands like HSBC, Orange, and GM Financial have conversations with millions of consumers as personally as they would with one. Our Conversational Cloud platform powers nearly a billion conversational interactions every month, providing a uniquely rich data set to build connections that reduce costs, increase revenue, and are anything but artificial. Fast Company named us the #1 Most Innovative AI Company in the world. To talk with us or our Conversational AI, please visit liveperson.com.
About EqualAI
EqualAIis a nonprofit organization that was created to reduce unconscious bias in the development and use of artificial intelligence (AI). AI is transforming our society enabling important and exciting developments that were unimaginable just a few years ago. With these immense benefits comes significant responsibility. Together with leaders and experts across industry, academia, technology, and government, EqualAI is developing standards and tools to increase awareness and reduce bias, as well as identifying regulatory and legislative solutions.
Contact:Mike Tague[emailprotected]
SOURCE LivePerson, Inc.
Artificial Intelligence is the Future of the Banking Industry Are You Prepared for It? – International Banker
By Pritham Shetty, Consulting Director, Propel
Our world is moving at a fast pace. Though banks originally built their foundations to be run solely by humans, the time has come forartificial intelligence in the banking industry. In 2020, the global AI banking market was valued at $3.88 billion, andit is projected to reach $64.03 billion by the end of the decade,with a compound annual growth rate of 32.6%. However, when it comes to implementing even the best strategies, theapplication of artificial intelligence in the banking industryis susceptible to weak core tech and poor data backbones.
By my count, there were 20,000 new banking regulatory requirements created in 2015 alone. Chances are your business wont find a one-size-fits-all solution to dealing with this. The next-best option is to be nimble. You need to be able to break down the business process into small chunks. By doing so, you can come up with digital strategies that work with new and existing regulations.
AIcan take you a long way in this process, but you must know how to harness its power. Take originating home loans, for instance. This can be an important, sometimes tedious, process for the loan seeker and bank. With an AI solution, loan origination can happen quicker and be more beneficial to both parties.
As the world of banking moves toward AI, it is integral to note that the crucial working element for AI is data. The trick to using that data is to understand how to leverage it best for your business value. Data with no direction wont lead to progress, nor will it lead to the proper deployment of your AI. That is one of the top reasons it isso challenging to implement AI in banks there has to be a plan.
Even if you come up with a poor strategy, those mistakes can be course-corrected over time. It takes some time and effort, but it is doable. If you home in on how customer information can be used, you can utilize AI for banking services in a way that is scalable and actionable. Once you understand how to use the data you collect, you can develop technical solutions that work with each other, identify specific needs, and build data pipelines that will lead you down the road to AI.
How is artificial intelligence changing the banking sector?
Due to the increasingly digital world, customers have more access to their banking information than ever. Of course, this can lead to other problems. Because there is so much access to data, there are also prime opportunities for fraudulent activities, and this is one example ofhow AI is changing the banking sector. With AI, you can train systems to learn, understand, and recognize when these activities happen. In fact, there was a5% decrease in record exposure from 2020 to 2021.
AI also safeguards against data theft or abuse. Not only can AI recognize breaches from outside sources, but it can also recognize internal threats. Once an AI system is trained, it can identify these problems and even offer solutions to them. For instance, a customer support call center can have traffic directed by AI to assuage an influx of calls during high-volume fluctuations.
Another great example of this is the development ofconversational AI platforms. The ubiquity of social media and other online platforms can be used to tailor customer experiences directly led by AI. By using the data gathered from all sources, AI can greatly improve the customer experience overall.
For example, a loan might take anywhere from seven to 45 days to be granted. But with AI, the process can be expedited not only for the customer, but also the bank. By using AI in a situation such as this, your bank can assess the risk it is taking on by servicing loans. It can also make the process faster by performing underwriting, document scanning, and other manual processes previously associated with data collection. On top of all that, AI can gather and analyze data about your customers behaviors throughout their banking lives.
In the past, so much of this work was done solely by people. Although automation has certainly helped speed up and simplify tasks, it is used for tedium and doesnt have the complexity of AI. AI saves time and money by freeing up your employees to do other processes and provides valuable insights to your customers. And customers can budget better and have a clearer idea of where their money is going.
Even the most traditional banks will want to adopt AI to save time and money and allow employees more opportunities to have positive one-on-one relationships with customers. Look no further than fintech companies such as Credijusto, Nubank, and Monzo that have digitized traditional banking services through the power of cutting-edge tech.
Are you ready to put AI to work for your business?
Today, its not a question ofhow AI is impacting financial services. Now, its about how to implement it. That all starts with you. You must ask the right questions: What are your goals for implementing AI? Do you want to improve your internal processes? Simply provide a better customer service experience? If so, how should you implement AI for your banking services? Start with these strategies:
By making realistic short-term goals, you set yourself up for future success. These are the solutions that will be the building blocks for the type of AI everyone will aspire to use.
You want to ensure that you know how you currently use data and how you plan on using it in the future. Again, this sets your organization up for success in the long run. If you dont have the right practices now, you certainly wont going forward.
As you implement AI into your banking practices, you should know how exactlyyou generate data. Then, you must understand how you interpret it. What is the best use for it? After that, you can make decisions that will be scalable, useful, and seamless.
Technology has not only made the world around us move faster, but also better in so many ways. Traditional institutions such as banks might be slow to adopt, but weve already seenhow artificial intelligence is changing the banking sector. By taking the proper steps, you could be moving right along with it into the future.
See original here:
Artificial Intelligence is the Future of the Banking Industry Are You Prepared for It? - International Banker
CSforALL Urges Greater Focus on AI and Data Science – Government Technology
(TNS) If you're not in the know, artificial intelligence and data science may sound like especially nerdy subsets of the already pocket-protector infused field of computer science.
But anyone who is serious about expanding computer science educationa list that includes Fortune 500 company CEOs and policymakers on both sides of the aisleshould be thinking carefully about emphasizing AI, in which machines are trained to perform tasks that simulate some of what the human brain can do, and data science, in which students learn to record, store, and analyze data.
That means making sure kids have access to well-designed resources to learn those subjects, bolstering professional development for those who teach them, exposing career counselors to information about how to help students pursue jobs in those fields, and much more.
Leigh Ann DeLyser, CSforALL's co-founder and executive director, spoke with Education Week about some big picture ideas around the push for a greater focus on AI and data science within computer science education. Here are some key takeaways from that conversation.
Teaching computer scienceincluding AI and data sciencecan help the next generation grapple with big societal problems.
"Our world is complex and messy and full of big problems," DeLyser said. AI and data science are fast- growing areas when it comes to employment, but "they're also the fastest-growing tools that are being used by business people, nonprofits, and governments every single day. No matter what you do in life, if you want to tackle the big problems we have in the world, you're going to need to understand these things and how they can be used, even if you're not the programmer who is writing the code that makes them go."
Students from all different backgrounds must get grounding in computer science.
It's especially important to increase socioeconomic, racial, and gender diversity in the field.
"Research shows that teams that have different backgrounds are better problem solvers, because they think about problems from different ways," DeLyser said. "When everybody comes with the same perspective, you tend to miss out on some of the ideas or the big challenges that pop up along the way. ... We [want to] give equal access, no matter what ZIP code [students] grow up in, to those high-paying careers and opportunities later in their life."
There are already good models of how to teach AI and data-science.
It's possible to see school districts already experimenting with how to do this well, if you know where to look, DeLyser said. "Often, we frame [computer science access] as a deficit narrative. There's nothing happening in education, or education is failing."
But that's not the case, she added. For instance, the large Gwinnett County school district outside Atlanta, is getting ready to open a high school that will focus on artificial intelligence. And in Bentonville, Ark., where Walmart is headquartered, local high school students interning with the company get a first-hand look at how the retail giant uses AI to configure store layouts, with an eye towards maximizing profit.
It's never too early to start teaching artificial intelligence.
Believe it or not, kids as young as kindergarten or even preschool can become familiar with the basics of AI, DeLyser said.
"AI is pattern recognition. One of the most important pre skills for algebra and math development for kids in kindergarten, and even preschool, is pattern recognition. 'This is a circle, this is a square,'" DeLyser said. Teaching AI is "having them take that learning that they're doing for the pattern recognition just one step further. It's like, OK, 'I'm going to teach you, you're going to teach a friend. Now I'm going to teach a computer.' It's not that far off from the work that they're already doing."
2022 Education Week (Bethesda, Md.). Distributed by Tribune Content Agency, LLC.
Read more:
CSforALL Urges Greater Focus on AI and Data Science - Government Technology
Artificial Intelligence Chipsets Market Is Booming with Progressive Trends and Exciting Opportunities by 2028| IBM Corp. (U.S.), Microsoft Corp….
The rising headway in the innovation and adoption of AI is ad libbing the buyer administrations is the vital variable for driving the development of artificial intelligence chipsets in the market. The rising number of AI applications, helping the processing ability to drive the huge and complex dataset is adding to the Artificial Intelligence Chipsets Market development. The Global Artificial Intelligence Chipsets Market report gives a comprehensive assessment of the market. The report offers an exhaustive examination of key portions, patterns, drivers, limitations, cutthroat scene, and elements that are assuming a significant part in the market.
Artificial intelligence chipset is extraordinarily intended to drive AI errands all the more effectively by depending on the utilization of simple hardware for the low accuracy number-crunching which gives headway in power and space. Artificial brain organizations, AI, and machine vision can all profit from this chipset. Lately, expanded government mediation in the assurance of basic foundation and delicate information has brought about the use of AI (chipsets) in security applications. The extension of AI chipsets in the auto area region is being powered by government backing, especially in the United States. Artificial intelligence chipsets are used for various undertakings, including quantum registering, extortion discovery, risk to the executives, and applications that request continuous information. During the conjecture time frame, these capabilities are supposed to help the Artificial Intelligence Chipsets Market.
Get Sample Copy of this Report: https://www.infinitybusinessinsights.com/request_sample.php?id=877556
The worldwide Artificial Intelligence Chipsets market is expected to grow at a booming CAGR of 2022-2028, rising from USD billion in 2021 to USD billion in 2028. It also shows the importance of the Artificial Intelligence Chipsets market main players in the sector, including their business overviews, financial summaries, and SWOT assessments.
Artificial Intelligence Chipsets Market, By Segmentation:
Artificial Intelligence Chipsets Market segment by Type:
Deep LearningRobot TechnologyDigital Personal AssistantQuerying MethodNatural Language ProcessingContext Aware Processing
Artificial Intelligence Chipsets Market segment by Application:
RetailTransportationAutomationManufacturingOthers
The years examined in this study are the following to estimate the Artificial Intelligence Chipsets market size:
History Year: 2015-2019 Base Year: 2021 Estimated Year: 2022 Forecast Year: 2022 to 2028
Flooding COVID occasions, cautious travel limitations, a concise end in get-together, and breakdowns in the shop connection and standard medication supply have made a liberal part for market progression in 2020 and presently. Whether or not the Covid pandemic issue is settled, dynamic creative exertion in the space might assist with supporting the markets moving diagram somewhat later.
Coming up next are the essential named locales for the Artificial Intelligence Chipsets market: America, North (United States, Canada, and Mexico), Europe (European Union) (Germany, France, United Kingdom, Russia, Italy, and Rest of Europe), Asia-Pacific district (China, Japan, Korea, India, Southeast Asia, and Australia), Americas, South (Brazil, Argentina, Colombia, and Rest of South America), Africa, and the Middle East (Saudi Arabia, UAE, Egypt).
The Key companies profiled in the Artificial Intelligence Chipsets Market: IBM Corp. (U.S.), Microsoft Corp. (U.S.), Google Inc. (U.S.), FinGenius Ltd. (U.K.), NVIDIA Corporation (U.S.), Intel Corporation (U.S.), General Vision, Inc. (U.S.), Numenta, Inc. (U.S.), Sentient Technologies (U.S.), Inbenta Technologies, Inc. (U.S.),
Some of the key questions answered in this report:1. Analysis of Artificial Intelligence Chipsets market (Preceding, present, and future) to calculate the rate of growth and market size.2. Market risk, market opportunities, driving forces.3. New technologies and issues to investigate market dynamics.4. Market Forecast5. Closely evaluate current and rising market segments.
Then, the report describes the Artificial Intelligence Chipsets market division based on various parameters and attributes that are based on geographical distribution, product types, and applications. The market segmentation clarifies further regional distribution for the market, business trends, potential revenue sources, and upcoming market opportunities.
If you need anything more than these then let us know and we will prepare the report according to your requirement.
For More Details On this Artificial Intelligence Chipsets Market Report @:https://www.infinitybusinessinsights.com/request_sample.php?id=877556
Table of Contents: List of Data Sources:Chapter 2. Executive SummaryChapter 3. Industry Outlook3.1. Artificial Intelligence Chipsets Market Industry segmentation3.2. Artificial Intelligence Chipsets Market Industry size and growth prospects, 2015 20263.3. Artificial Intelligence Chipsets Market Industry Value Chain Analysis3.3.1. Vendor landscape3.4. Regulatory Framework3.5. Market Dynamics3.5.1. Market Driver Analysis3.5.2. Market Restraint Analysis3.6. Porters Analysis3.6.1. Threat of New Entrants3.6.2. Bargaining Power of Buyers3.6.3. Bargaining Power of Buyers3.6.4. Threat of Substitutes3.6.5. Internal Rivalry3.7. PESTEL AnalysisChapter 4. Artificial Intelligence Chipsets Market Industry Product OutlookChapter 5. Artificial Intelligence Chipsets Market Industry Application OutlookChapter 6. Artificial Intelligence Chipsets Market Industry Geography Outlook6.1. Artificial Intelligence Chipsets Industry Share, by Geography, 2022 & 20286.2. North America6.2.1. Market 2022 -2028 estimates and forecast, by product6.2.2. Market 2022 -2028, estimates and forecast, by application6.2.3. The U.S.6.2.3.1. Market 2022 -2028 estimates and forecast, by product6.2.3.2. Market 2022 -2028, estimates and forecast, by application6.2.4. Canada6.2.4.1. Market 2022 -2028 estimates and forecast, by product6.2.4.2. Market 2022 -2028, estimates and forecast, by application6.3. Europe6.3.1. Market 2022 -2028 estimates and forecast, by product6.3.2. Market 2022 -2028, estimates and forecast, by application6.3.3. Germany6.3.3.1. Market 2022 -2028 estimates and forecast, by product6.3.3.2. Market 2022 -2028, estimates and forecast, by application6.3.4. the UK6.3.4.1. Market 2022 -2028 estimates and forecast, by product6.3.4.2. Market 2022 -2028, estimates and forecast, by application6.3.5. France6.3.5.1. Market 2022 -2028 estimates and forecast, by product6.3.5.2. Market 2022 -2028, estimates and forecast, by applicationChapter 7. Competitive LandscapeChapter 8. Appendix
About Us:Infinity Business Insights is a market research company that offers market and business research intelligence all around the world. We are specialized in offering the services in various industry verticals to recognize their highest-value chance, address their most analytical challenges, and alter their work.We attain particular and niche demand of the industry while stabilize the quantum of standard with specified time and trace crucial movement at both the domestic and universal levels. The particular products and services provided by Infinity Business Insights cover vital technological, scientific and economic developments in industrial, pharmaceutical and high technology companies.
Contact us:Amit JSales Co-OrdinatorInternational: +1-518-300-3575Email: [emailprotected]Website: https://www.infinitybusinessinsights.comFacebook: https://facebook.com/Infinity-Business-Insights-352172809160429LinkedIn: https://www.linkedin.com/company/infinity-business-insightsTwitter: https://twitter.com/IBInsightsLLP
See the rest here:
Artificial Intelligence Chipsets Market Is Booming with Progressive Trends and Exciting Opportunities by 2028| IBM Corp. (U.S.), Microsoft Corp....
Artificial intelligence making diagnosis more real? – The New Indian Express
Express News Service
HYDERABAD: As more and more wearable technologies such as fitbits, smart watches and wearable monitors are becoming increasingly popular, doctors seem to be the happiest. IoT (Internet of Things) devices are providing healthcare professionals and patients with new and accurate ways to monitor their vitals.
Dr Namrata Rupani, founder and CEO, Capture Life Dental Care, Banjara Hills, welcomes the change and says, One of the best IoT healthcare applications is remote patient monitoring, which can automatically collect health measurements from patients who arent in a healthcare institution, thus removing the need for patients to travel or collect it themselves. An IoT gadget today helps send patient data to a software application that can be accessed by both the doctors and patients.
For instance, IoT sensors on patients could warn doctors if their heart rate is low.That being said, these come with benefits as well as problems.Dr Sudheer Reddy Udumula, founder of Wishealth Lifestyle Homeo Clinics, Ameerpet, says that artificial intelligence and IoT is helping make precise diagnosis a possibility. Statistical and empirical data is helping in better treatment as well as prescription, allowing doctors across the globe to map and tap on these findings, even generations later. Robotic scanning and surgeries have brought in the beauty of technical accuracy and clarity. However, he adds, that doctors have to learn to adapt and be updated about the technology.
The recent cases about people finding low levels of oxygen or other data related to lifestyle diseases on their smartwatches, and proactively rushing to the doctor has reinstated our faith in such devices. They are helping lay people track their bodies without much interference and trips to the doctor, which is welcome, he adds.
Krishna Veer Singh, co-founder of Lissun, a mental wellness app, says, The issue of mental health is more significant than most of us imagine. During the pandemic, the mental health issue highlighted the significance of how digital healthcare is a big step toward filling the mental health gap. Technology is making therapy and other psychological tools available at your convenience anytime and anywhere. With technology, mental health apps are like your own personal pocket assistant, they help fill the mental health gap, making mental health solutions more accessible to all. With a team of experts and in-depth, informative content, apps like ours are a mini healthcare centre online.
He adds that psychological tools such as journalling, flash cards for concepts and tracking, meditations, creative visualisations, breathing techniques, psychological assessments, etc., are all available on such apps, making remote consultations more effective.
Original post:
Artificial intelligence making diagnosis more real? - The New Indian Express
UNAM. Artificial Intelligence And High Technology Laboratory Inaugurated In – Nation World News
UNAM. Artificial Intelligence and High Technology Laboratory inaugurated in Data Science and Artificial Intelligence will generate $5.5 billion in 2030, said Enrique Gray Wichers
It is imperative to obtain a patent in innovation, technology development and digital capability, said Marcelo Ebrard Casabon
Open cooperation leads to shared success, said Cao Jibin
A Letter of Intent was also signed between the National University, SRE and Huawei to promote the development of digital capabilities in Mexico.
In addition, they signed a letter of intent for the Coalition to Promote the Development of Digital Capabilities in Mexico, which aims to support and encourage technological innovation projects with an emphasis on artificial intelligence and that contribute to the solution of social problems. We do.
At the ceremony, the rector thanked the generosity of the Huawei company and the good offices of the Mexican Foreign Ministry for the laboratory. He also underlined UNAMs commitment to being decisively involved in the creation of innovative projects, for which it has established substantial infrastructure in recent years and created a bachelors degree in data science, and courses focused on artificial intelligence. Is.
Its a commitment that we have to embrace with all our might. He highlighted that data production has grown vertically in recent years and it is estimated that the economic impact of these and artificial intelligence on the global economy will be around $5.5 trillion in 2030. Meanwhile, the use of computers will help reduce greenhouse gas emissions by up to four percent.
In his speech, Foreign Minister Marcelo Ebrard said that the letter of intent will allow us to have more researchers, researchers who can develop circuits, algorithms, complex calculation systems that allow us to accelerate the pace. [] If we digitize our society we can have a financial coverage of 100 or 98 per cent, which will greatly increase the growth potential of the economy.
The future of Mexico is nowhere else: either we develop patents in innovation, technological development and digital capability, or we are not going to spend more than 10 thousand dollars per person in our income; This is a clear imperative of Mexico, he insisted.
Meanwhile, Cao Jibin expressed his strong belief that open cooperation leads to shared success, for which he was grateful to be part of the alliance. Together with UNAM, the Ministry of Foreign Affairs, the German Cooperation Agency for Sustainable Development and all members, we will bring benefits to Mexico and the Mexican people.
They recognized UNAMs academic leadership, a benchmark in computing and systems research, as well as its technical capabilities for managing the Artificial Intelligence and High Technology Laboratory with equipment donated by Huawei. He said the involvement of the federal government is essential to create a productive environment that leaves no one behind.
Continuing his message, Gray Vechers also recalled that 64 years ago the National University was a pioneer in Latin America by operating the first computer. Today it has 92,000 computers with Internet connections, a dozen educational and research programs in computing and artificial intelligence.
human capital
At the event held at UNAMs Institute for Research in Applied Mathematics and Systems (IIMAS), and where the first results Artificial Intelligence Forum was held, the National Universitys Secretary of Institutional Development, Patricia Dvila Aranda, celebrated the initiative with constant technological changes. The alliance and its efforts to create greater capacity in the human capital of the countrys universities to meet the digital, economic and social challenges.
By being part of this alliance, UNAM has managed to articulate the partnership of IIMAS, the Institute for Social Research, DGTIC and the Coordination of Linkage and Technology Transfer, to reiterate its commitment to training professionals in the use of innovative technologies. as well as in the application of artificial intelligence and sustainable development for the benefit of the most vulnerable groups in our country, he emphasized.
Meanwhile, Ramses Humberto Mena Chavez, Director of IIMAS, stressed that the initiative fosters the partnership of government, academia and the private sector especially technology experts to educate and build a connected and intelligent world with To monitor simultaneously. Development and proper utilization of digital capabilities.
He also remarked that as part of this strategy in 2021, the first call was issued to submit artificial intelligence research projects within the UNAM-Huawei Innovation Space, the results of which already exist.
Dora Luz Flores Gutierrez, professor and researcher in the Faculty of Engineering, Architecture and Design of the Autonomous University of Baja California, who spoke on behalf of members of the 10 projects supported by the coalition in 2021 that had access to resources from high-performance computing , said that collaboration between academia, the productive sector and the government can yield effective, efficient results for the benefit of all.
Proof of this, he indicated, are projects focused on estimating the ripeness of strawberries or dates through mobile robots and trait identification; for the development of a prototype of a system Web Medical support for the classification of mammographic studies, as well as the development of an adjunctive tool for the identification of drug-resistance of pulmonary tuberculosis, among others.
Also present at the ceremony were from UNAM: the Secretary-General, Leonardo Lomeli Venegas; coordinator of scientific research, William Lee Allardine; and Hector Bentez Prez, general director of computing and information and communication technology (DGTIC), among others.
#UNAMosAccionesContralaCovid19https://covid19comition.unam.mx/
-ooo-
Follow this link:
UNAM. Artificial Intelligence And High Technology Laboratory Inaugurated In - Nation World News