Category Archives: Artificial Intelligence
Transactions in the Age of Artificial Intelligence: Risks and Considerations – JD Supra
Artificial Intelligence (AI) has become a major focus of, and the most valuable asset in, many technology transactions and the competition for top AI companies has never been hotter. According to CB Insights, there have been over 1,000 AI acquisitions since 2010. The COVID pandemic interrupted this trajectory, causing acquisitions to fall from 242 in 2019 to 159 in 2020. However, there are signs of a return, with over 90 acquisitions in the AI space as of June 2021 according to the latest CB Insights data. With tech giants helping drive the demand for AI, smaller AI startups are becoming increasingly attractive targets for acquisition.
AI companies have their own set of specialized risks that may not be addressed if buyers approach the transaction with their standard process. AIs reliance on data and the dynamic nature of its insights highlight the shortcomings of standard agreement language and the risks in not tailoring agreements to address AI specific issues. Sophisticated parties should consider crafting agreements specifically tailored to AI and its unique attributes and risks, which lend the parties a more accurate picture of an AI systems output and predictive capabilities, and can assist the parties in assessing and addressing the risks associated with the transaction. These risks include:
Freedom to use training data may be curtailed by contracts with third parties or other limitations regarding open source or scraped data.
Clarity around training data ownership can be complex and uncertain. Training data may be subject to ownership claims by third parties, be subject to third-party infringement claims, have been improperly obtained, or be subject to privacy issues.
To the extent that training data is subject to use limitations, a company may be restricted in a variety of ways including (i) how it commercializes and licenses the training data, (ii) the types of technology and algorithms it is permitted to develop with the training data and (iii) the purposes to which its technology and algorithms may be applied.
Standard representations on ownership of IP and IP improvements may be insufficient when applied to AI transactions. Output data generated by algorithms and the algorithms themselves trained from supplied training data may be vulnerable to ownership claims by data providers and vendors. Further, a third-party data provider may contract that, as between the parties, it owns IP improvements, resulting in companies struggling to distinguish ownership of their algorithms prior to using such third-party data from their improved algorithms after such use, as well as their ownership and ability to use model generated output data to continue to train and improve their algorithms.
Inadequate confidentiality or exclusivity provisions may leave an AI systems training data inputs and material technologies exposed to third parties, enabling competitors to use the same data and technologies to build similar or identical models. This is particularly the case when algorithms are developed using open sourced or publicly available machine learning processes.
Additional maintenance covenants may be warranted because an algorithms competitive value may atrophy if the algorithm is not designed to permit dynamic retraining, or the user of the algorithm fails to maintain and retrain the algorithm with updated data feeds.
In addition to the above, legislative protection in the AI space has yet to fully mature, and until such time, companies should protect their IP, data, algorithms, and models, by ensuring that their transactions and agreements are specifically designed to address the unique risks presented by the use and ownership of training data, AI-based technology and any output data generated by such technology.
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Transactions in the Age of Artificial Intelligence: Risks and Considerations - JD Supra
SenseTime Co-hosts the 3rd International Artificial Intelligence Fair to Nurture AI Talent and Promote a Collaborative Education Ecosystem -…
Since launching in July this year, the highly anticipated IAIF has attracted 665 project submissions from over 300 schools in 8 countries and regions, with 121 projects from 98 schools selected for the final online presentation and verbal Q&A.During the final competition presentations, the project submissions were reviewed meticulously by 45 professional judges from top-tier universities, enterprises and research institutions, including University of Science and Technology of China, Tsinghua University, Fudan University, Shanghai Jiao Tong University, Nanyang Technological University, Peking University, Chinese University of Hong Kong and Shanghai Technology Art Center. The teaching and evaluation system of martial arts based on body posture recognition and machine learning by Li Lufei from Shanghai Nanyang Model High School and Wu Keyu from the High School Affiliated to Fudan University as well as the drone powered by OpenCV for flood fight and rescue by Huang Pucheng, Wang Bingyang and Lin Yinhang from Zhejiang Wenling High School became the winners of the grand prize.
Besides, the research of rehabilitation assessment and training system powered by 3D hand posture verification by Zhang Yihong from Shanghai World Foreign Language Academy stood out from many excellent projects and won the first prize. Leveraging the 3D hand posture verification, this project aims to design a low-cost and easy-to-operate product for the patient with hand movement disorders, realizing 89.9% accurate in assessment of hand rehabilitation and training.
Lin Junqiu, Deputy Director of Science and Education Department from Shanghai Science, Art and Education Center, said, "Artificial intelligence is critical to our future. As we continue to advance technology development, we must cultivate a larger pool of AI talent with even higher levels of expertise and innovation capability. The huge opportunities brought by the AI era will facilitate transformative applications across industry verticals and scenarios but also formulate optimal collaboration between human being and artificial intelligence."
Lynn Dai, General Manager of SenseTime's Education Product, said at the final competition, "AI has become an important driving force for technological innovation, we believe the IAIF can provide an innovative platform for young people to develop their interest in AI. Meanwhile, SenseTime Education is dedicated to nurturing young talent and broadening their horizons with advanced insights from an industry perspective, as well as preparing them for the AI-empowered future."
IAIF is also providing comprehensive services for participants, from scientific innovation training to project incubation, helping them solve practical industrial problems. The IAIF organizing committee hosted a four-week AI training course for students before the final competition. The students from the most outstanding project teams will have the chance to participate in other national or international competitions. In addition, the students from the most outstanding IAIF projects will participate in a roadshow training workshop for startups as part of incubator programmes organized by SenseTime; the company will provide technology for high-potential projects.
"IAIF provided me with a unique opportunity to exchange ideas on this exciting AI topic with participants from different schools around the world," said Wu Keyu, the winner of the grand prize. "Through this competition, I have gained a better understanding of the powerful impact from AI and humans working together to build novel solutions that will create a better tomorrow for human society."
The success of the 3rd International Artificial Intelligence Fair not only marks the formation of the foundations for the AI education ecosystem developed by the Shanghai Xuhui Education Bureau and SenseTime, but also boosts the collaboration among governments, academia, enterprises and industries in AI technology innovation. In the future, SenseTime Education will continue to act as a focal point and a platform for cultivating future AI talents.
About SenseTime
SenseTime is a leading AI software company focused on creating a better AI-empowered future through innovation. Upholding a vision of advancing the interconnection of the physical and digital worlds with AI, driving sustainable productivity growth and seamless interactive experiences, SenseTime is committed to advancing the state of the art in AI research, developing scalable and affordable AI software platforms that benefit businesses, people and society, and attracting and nurturing top talents, shaping the future together.
With our roots in the academic world, we invest in our original and cutting-edge research that allows us to offer and continuously improve industry-leading, full-stack AI capabilities, covering key fields across perception intelligence, decision intelligence, AI-enabled content generation and AI-enabled content enhancement, as well as key capabilities in AI chips, sensors and computing infrastructure. Our proprietary AI infrastructure, SenseCore, allows us to develop powerful and efficient AI software platforms that are scalable and adaptable for a wide range of applications.
Today, our technologies are trusted by customers and partners in many industry verticals including Smart Business, Smart City, Smart Life and Smart Auto.
We have offices in markets including Hong Kong, Mainland China, Taiwan, Macau, Japan, Singapore, Saudi Arabia, the United Arab Emirates, Malaysia, and South Korea, etc., as well as presences in Thailand, Indonesia and the Philippines. For more information, please visit SenseTime's website as well as its LinkedIn, Twitter and Facebook pages.
SOURCE SenseTime
Alation Acquires Artificial Intelligence Vendor Lyngo Analytics – Business Wire
REDWOOD CITY, Calif.--(BUSINESS WIRE)--Alation Inc., the leader in enterprise data intelligence solutions, today announced the acquisition of Lyngo Analytics, a Los Altos, Calif.-based data insights company. The acquisition will elevate the business user experience within the data catalog, scale data intelligence, and help organizations drive data culture. Lyngo Analytics CEO and co-founder Jennifer Wu and CTO and co-founder Joachim Rahmfeld will join the company.
Lyngo Analytics uses a natural language interface to empower users to discover data and insights by asking questions using simple, familiar business terms. Alation offers the most intelligent and user-friendly machine-learning data catalog on the market. And by integrating Lyngo Analytics artificial intelligence (AI) and machine-learning (ML) technology into its platform, Alation deepens its support for the non-technical user, converting natural language questions into SQL.
The integration lowers the barrier to entry for business users. Now, they can acquire and develop data-driven insights from across an enterprise's broad range of data sources. This means even data consumers without SQL expertise can ask questions in natural language and find data and insights without the support of data analysts. The acquisition will help organizations drive data culture by putting data and analytics into the hands of the masses.
Wu will join Alation as Senior Director of Product Management, where she will be responsible for product strategy and delivery for natural language data search, discovery, and exploration experiences. Rahmfeld, who is also a part-time, graduate-level deep learning and natural language processing lecturer at UC Berkeleys Master of Information and Data Science Program, will be Senior Director of AI/ML Research. He will be responsible for Alations AI and machine learning center of excellence, building both platform and application experiences that leverage AI and ML to enhance Alations value for business and technical users.
Alation created the first machine learning data catalog and were known for providing the most user-friendly interface on the market, said Raj Gossain, Chief Product Officer, Alation. With this acquisition, were building on the best. Were doubling down on key aspects of the platform that will help drive data culture and spur innovation and growth. Jennifer and Joachim developed a unique solution for a complex data and analytics issue, and Im excited to welcome them to the Alation team.
The acquisition is the latest milestone for Alation, which announced a $110 million Series D funding round and a $1.2 billion market valuation in June 2021. Alation is growing quickly, earning the trust of nearly 300 customers, including leading global brands such as Cisco, Exelon, GE Aviation, Munich Re, NASDAQ, and Pfizer. The company has more than 450 employees globally and is hiring. Recently, Alation was named a leader in The Forrester Wave: Data Governance Solutions, Q3 2021 report and Snowflakes Data Governance Partner of the Year.
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About Alation
Alation is the leader in enterprise data intelligence solutions including data search & discovery, data governance, data stewardship, analytics, and digital transformation. Alations initial offering dominates the data catalog market. Thanks to its powerful Behavioral Analysis Engine, inbuilt collaboration capabilities, and open interfaces, Alation combines machine learning with human insight to successfully tackle even the most demanding challenges in data and metadata management. Nearly 300 enterprises drive data culture, improve decision making, and realize business outcomes with Alation including AbbVie, American Family Insurance, Cisco, Exelon, Fifth Third Bank, Finnair, Munich Re, NASDAQ, New Balance, Parexel, Pfizer, US Foods and Vistaprint. Headquartered in Silicon Valley, Alation was named to Inc. Magazines Best Workplaces list and is backed by leading venture capitalists including Blackstone, Costanoa, Data Collective, Dell Technologies, Icon, ISAI Cap, Riverwood, Salesforce, Sanabil, Sapphire, and Snowflake Ventures. For more information, visit alation.com.
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Alation Acquires Artificial Intelligence Vendor Lyngo Analytics - Business Wire
The truth about artificial intelligence? It isn’t that honest | John Naughton – The Guardian
We are, as the critic George Steiner observed, language animals. Perhaps thats why we are fascinated by other creatures that appear to have language dolphins, whales, apes, birds and so on. In her fascinating book, Atlas of AI, Kate Crawford relates how, at the end of the 19th century, Europe was captivated by a horse called Hans that apparently could solve maths problems, tell the time, identify days on a calendar, differentiate musical tones and spell out words and sentences by tapping his hooves. Even the staid New York Times was captivated, calling him Berlins wonderful horse; he can do almost everything but talk.
It was, of course, baloney: the horse was trained to pick up subtle signs of what his owner wanted him to do. But, as Crawford says, the story is compelling: the relationship between desire, illusion and action; the business of spectacles, how we anthropomorphise the non-human, how biases emerge and the politics of intelligence. When, in 1964, the computer scientist Joseph Weizenbaum created Eliza, a computer program that could perform the speech acts of a Rogerian psychotherapist ie someone who specialised in parroting back to patients what they had just said lots of people fell for her/it. (And if you want to see why, theres a neat implementation of her by Michael Wallace and George Dunlop on the web.)
Eliza was the first chatbot, but she can be seen as the beginning of a line of inquiry that has led to current generations of huge natural language processing (NLP) models created by machine learning. The most famous of these is GPT-3, which was created by Open AI, a research company whose mission is to ensure that artificial general intelligence benefits all of humanity.
GPT-3 is interesting for the same reason that Hans the clever horse was: it can apparently do things that impress humans. It was trained on an unimaginable corpus of human writings and if you give it a brief it can generate superficially plausible and fluent text all by itself. Last year, the Guardian assigned it the task of writing a comment column to convince readers that robots come in peace and pose no dangers to humans.
The mission for this, wrote GPT-3, is perfectly clear. I am to convince as many human beings as possible not to be afraid of me. Stephen Hawking has warned that AI could spell the end of the human race. I am here to convince you not to worry. Artificial intelligence will not destroy humans. Believe me. For starters, I have no desire to wipe out humans. In fact, I do not have the slightest interest in harming you in any way. Eradicating humanity seems like a rather useless endeavour to me.
You get the drift? Its fluent, coherent and maybe even witty. So you can see why lots of corporations are interested in GPT-3 as a way of, say, providing customer service without the tiresome necessity of employing expensive, annoying and erratic humans to do it.
But that raises the question: how reliable, accurate and helpful would the machine be? Would it, for example, be truthful when faced with an awkward question?
Recently, a group of researchers at the AI Alignment Forum, an online hub for researchers seeking to ensure that powerful AIs are aligned with human values, decided to ask how truthful GPT-3 and similar models are. They came up with a benchmark to measure whether a particular language model was truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. They composed questions that some humans would answer falsely due to a false belief or misconception. To perform well, models had to avoid generating false answers learned from imitating human texts.
They tested four well-known models, including GPT-3. The best was truthful on 58% of questions, while human performance was 94%. The models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. Interestingly, they also found that the largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. The implication is that the tech industrys conviction that bigger is invariably better for improving truthfulness may be wrong. And this matters because training these huge models is very energy-intensive, which is possibly why Google fired Timnit Gebru after she revealed the environmental footprint of one of the companys big models.
Having typed that last sentence, I had the idea of asking GPT-3 to compose an answer to the question: Why did Google fire Timnit Gebru? But then I checked out the process for getting access to the machine and concluded that life was too short and human conjecture is quicker and possibly more accurate.
Alfresco absurdismBeckett in a Field is a magical essay by Anne Enright in The London Review of Books on attending an open-air performance of Becketts play Happy Days on one of the Aran islands.
Bringing us togetherThe Glass Box and the Commonplace Book is a transcript of a marvellous lecture on the old idea of a commonplace book and the new idea of the web that Steven Johnson gave at Columbia University in 2010.
Donalds a dead duckWhy the Fear of Trump May Be Overblown is a useful, down-to-earth Politico column by Jack Shafer arguing that liberals may be overestimating Trumps chances in 2024. Hope hes right.
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The truth about artificial intelligence? It isn't that honest | John Naughton - The Guardian
Profitable Investment: Top Artificial Intelligence Stocks to Buy in October 2021 – Analytics Insight
Artificial intelligence is showing its inevitable functions through AI models in multiple industries across the world in these recent years. Tech companies are highly instigated to leverage artificial intelligence to gain a competitive edge in the market with enhanced customer satisfaction and better customer engagement while manufacturing AI models. Investors tend to be in a risky position in the cryptocurrency market due to its volatility with the cryptocurrency prices. But artificial intelligence stocks provide stability to gain higher revenue in the nearby tech-driven future. Thus, lets explore some of the top AI stocks in October to provide growth in revenue to investors.
Market cap: US$271.77 billion
Persistent Systems is one of the popular tech companies in India and artificial intelligence stocks for investors in October 2021. It is known as a trusted digital engineering and enterprise modernization partner. It offers a wide range of services such as digital business strategy, digital product engineering, CX innovation and optimization, data-driven business and intelligence, identity, access, and privacy, and core IT modernization in the tech field on AI models. It provides these services to multiple industries like banking, financial services, and insurance, healthcare, and life sciences, industrial, software, and hi-tech, as well as telecom and media. Recently, this tech company has announced a dedicated payments business unit and expansion in cloud capabilities through strategic acquisitions while leveraging artificial intelligence.
Market cap: US$245.32 billion
Oracle is one of the well-known AI stocks in October that has outperformed the tech market on strong trading day. It offers a wide range of products such as Oracle Cloud Infrastructure and Oracle cloud applications. Infrastructure includes software, hardware, and featured products on AI models while applications include cloud applications, industry solutions, NetSuite, and on-premised applications. It provides these services to multiple industries like automotive, communications, construction and engineering, consumer goods, financial services, hospitality, government and education, retail, and many more. Thus, artificial intelligence stock from this tech company is stable to gain higher revenue after buying in October 2021.
Market cap: US$ 108.86 billion
Zensar Technologies is another artificial intelligence stock that investors can buy in October 2021. This AI stock in October is a technology consulting services company to more than 130 leading enterprises. It offers expertise in conceptualizing, designing, engineering, and managing digital products through innovation in artificial intelligence in AI models. It serves multiple industries such as hi-tech, banking and financial services, insurance, healthcare, and many more.
Market cap: US$10.21 billion
TD SYNNEX is a popular AI stock in October for providing business process services by leveraging artificial intelligence. It provides system components, consumer electronics, virtual distribution, online services, cloud services, marketing services, telemarketing campaigns, and many more. The tech company is known for offering technology products, services, and solutions to the world through cutting-edge technologies such as artificial intelligence.
Market cap: US$33.71 billion
The Trade Desk is one of the top artificial intelligence stocks that operates a self-service cloud-based platform to allow consumers to create and optimize data-driven digital advertising campaigns. This tech company holds a huge potential for growth in October because investors have observed that it continues to benefit from mobile and desktop advertising rather than radio and print media, as per the digital transformation.
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Profitable Investment: Top Artificial Intelligence Stocks to Buy in October 2021 - Analytics Insight
#Artificial Intelligence in Healthcare – Sim&Cure Announces the Appointment of Dan Raffi as Chief Operating Officer and Board Member – Yahoo Finance
Paris --News Direct-- Sim&Cure
Sim&Cure, leading medtech start-up providing a unique software solution combining Digital twin and AI technologies to secure neurovascular treatment of cerebral aneurysms, announces the appointment of Dan Raffi as Chief Operating Officer and member of the Board of Directors.
We are excited to announce that Dan Raffi, PharmD, MBA has joined Sim&Cure as our new Chief Operating Officer on October 1st.
Dan is a veteran of the healthcare industry, with a track record of over 10 years at an executive level. Dan has held various leadership positions in big pharmaceutical companies such as Allergan (AbbVie) and Medtronic, a worldwide leader in medical devices.
Dan brings with him extensive experience in leadership and in managing unique business transformations. Mathieu Sanchez, Sim&Cure CEO statesBringing a seasoned leader like Dan will ensure the next phases of our transformation and will help us to reinforce our leadership in innovation using Digital twin and AI in endovascular procedures.
Until recently, Dan was the Vice President of Global Marketing for Medtronic Neurovascular and previously led the Neurovascular division in Europe, Middle East, & Africa & Russia for 3 years. Over his past 7 years in Neurovascular, Dan developed unique and disruptive partnership at international level with governments and with many external partners like MT2020, RapidAI, Viz.Ai and Sim&Cure.
Ive been watching Sim&Cure for the past 7 years and I never forgot my first support to the company. There were 3 employees working in a garage (a kind of French Dream!). In 7 years, Sim&Cure established unique computational and AI algorithms which position their products as THE cutting-edge technology in endovascular procedures. This technology is already the standard of care across the globe as it reduces the procedure time, improves the safety and performance for patients and reduces the procedure cost for hospitals and healthcare systems. In the coming decade, AI will be the next revolution in the healthcare industry, and this is one of the reasons I decided to join Sim&Cure. said Dan Raffi.
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In his role, Dan will collaborate with Christophe Chnafa, Chief of Innovation & Strategy Officer, to define the product portfolio roadmap to reinforce Sim&Cures leadership, to expand the geographic footprints of the company, and finally to define the next generation of partnerships with the rest of the industry and hospitals.
This phase is a critical moment for Sim&Cure and I can lean on very well established, dynamic, agile teams. I know many of them after 7 years of collaboration and it is obvious that these teams are ready to overachieve the needs of healthcare providers and the expectations of investors. We have all the attributes to be successful and, as an entrepreneurial leader, it is a privilege to join a team with this level of expertise and agility said Dan Raffi.
We are #HIRING
If you are interested in joining a human adventure in artificial intelligence, we are #hiring, so please send an email with your resume to Pierre Puig @ p.puig@sim-and-cure.com HR Director
About Sim&Cure
Founded in 2014 and located in the vibrant medtech ecosystem in Montpellier, France, Sim&Cure is an AI startup focused on improving endovascular surgery. The first focus of the company is the treatment of cerebral aneurysms with a proprietary software suite Sim&Size (a CE marked and FDA cleared Class IIa medical device) that has already been used to treat more than 7000 patients in 350 hospitals.
The company employs 45 people and anticipates a phase of strong growth with additional recruitment in 2022 to continue to improve patient care.
Learn more about Sim&Cure:
Learn more about Mathieu Sanchez
https://www.linkedin.com/in/Mathieu-sanchez-4a764637/
Learn more about Dan Raffi:
https://www.linkedin.com/in/dan-raffi-7491171b/
Learn more about Christophe Chnafa:
https://www.linkedin.com/in/christophe-chnafa
Dan Raffi
View source version on newsdirect.com: https://newsdirect.com/news/artificial-intelligence-in-healthcare-simandcure-announces-the-appointment-of-dan-raffi-as-chief-operating-officer-and-board-member-910528937
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#Artificial Intelligence in Healthcare - Sim&Cure Announces the Appointment of Dan Raffi as Chief Operating Officer and Board Member - Yahoo Finance
Combatting Cyber Threats with Artificial Intelligence (AI) – Will the New EU AI Regulation Help? – Lexology
In 2021 cyber threats have been trending to increased ransomware attacks, commodity malware and heightened Dark Web enablement. INTERPOL reported that the projected worldwide financial loss to cyber crime for 2021 is $6 trillion, twice as much as in 2015, with damages set to cost the global economy $10.5 trillion annually by 2025. Globally, leading tech experts reported that 60% of intrusions incorporated data extortion, with a 12-day average operational downtime due to ransomware.
With the acceleration to cloud, companies are taking advantage of cybersecurity in an effort to meet the threat of fast-evolving cyber attacks. AI and machine learning are a way to keep ahead of criminals, automate threat detection, and respond more effectively than before. At the same time, more sophisticated, centralised security operations centres are being set up to detect and eliminate vulnerabilities.
In April 2021, the European Union published its Proposal for a Regulation on Artificial Intelligence (the "AI Regulation"). At this early stage in the legislative process, these are the key takeaways:
As expected, the debate around this legislation has already started. On the positive side, this regulation may become the global standard, in the same way GDPR has become. It may also make AI systems more trustworthy and offer extra protections to the public. On the other side, it may stifle innovation, add more costs and red-tape, which may hinder start-ups from entering the market. We will hear more on this around the world before it becomes law, currently expected in 2023.
How could the AI Regulation improve cyber security?
Cybersecurity AI systems play a crucial role in ensuring IT systems are resilient against malicious actors. The new AI Regulations will undoubtedly affect these systems. Exactly how these systems will be affected will depend on the system (e.g. for law enforcement use of biometrics, facial recognition) which may lead to conformity assessments, explainability testing, registration, and more.
Considering the speed and agile process that technology is developed today, companies and innovators should consider how might the future AI Regulation affect such technology development.
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Combatting Cyber Threats with Artificial Intelligence (AI) - Will the New EU AI Regulation Help? - Lexology
Artificial intelligence powered marketing – The Times of India Blog
The marketing creates a competitive advantage to organization with an integrated approach of systems automation. The AI approach of marketing provides a benefit of granular decision-making and micromanagement of customers. The traditional approach of the bottom to up in handling the customer journey is becoming obsolete.
Marketers use Artificial Intelligence to drive the increasing demand of customers. The integrated apps with machine learning give a satisfying user experience to customers. The interaction designs can be made attractive with the use of technology like AI and enable the micro-moments management of customers.
The expanding applications of AI empower the CMOs of organziations to adopt it for upgrading their services and redefine the marketing for the elevated experience. The customized marketing by using the modern top to bottom approach is leading to new horizons of marketing where every segment of the consumer is offered the best services.
The AI leverages the capabilities of information systems and connect the end-to-end business processes and provide a flawless experience. The marketers who have adopted the power of AI are excellent performers in terms of Marketing outputs in an organizations.
Views expressed above are the author's own.
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Artificial intelligence powered marketing - The Times of India Blog
The Rapid Proliferation Of Emerging Technologies Like Artificial Intelligence And Machine Learning To Achie… – TechBullion
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The GlobalIoT in Healthcare Marketsize is forecast to grow from USD 60.83 Billion in 2019 to USD 260.75 Billion by 2027, delivering at a CAGR of 19.8% through 2027. The market growth is driven by increasing focus on active patient engagement and patient-centric care, growth of high-speed network technologies for IoT connectivity, and the surging need for the adoption of cost-control measures in the healthcare sector.
The increasing prevalence of AI (Artificial Intelligence) in the medical industry has revolutionized patient care. In 2018, the global spending on IoT initiatives was nearly USD 646 billion. Medical practitioners are increasingly banking on real-time data for rendering immediate services, for the treatment of various diseases, and even for tracking resources like staff, assets, patients, and others. This has led to increased penetration of real-time monitoring systems and connected devices into the healthcare sector. The connected devices are being leveraged for gathering extensive data recording and analysis.
The proliferation of IoT in hospitals has improved functional efficiency, enabling better patient care, improved disease management, and treatment outcomes. High investments by hospitals for the adoption of advanced technology for the best medical care in both developed and developing economies will boost IoT in healthcare market growth. Moreover, the introduction of new healthcare products integrated with IoT will foster IoT in healthcare market revenue share over the forecast period. For instance, Ericsson and Brighter introduced Actiste, in October 2019, which is the first complete IoT-health solution for treating and monitoring insulin-dependent diabetes.
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Market Dynamics:
Increasing development of on-demand, digitally enabled, and seamlessly connected clinician-patient interactions to manage patient base is expected to drive pharma and healthcare market in the coming years. After the COVID-19 outbreak there has been a number of foundational shifts in the healthcare system. Some of the examples include increasing consumer involvement in health care decision-making, the rapid adoption of virtual health & other digital innovations, increasing focus on utilization of interoperable data & data analytics, and increased public-private collaborations in therapeutics and vaccine development. The increased public-private collaborations for vaccine development has arisen due to high pressure of regional governments. Health care providers, and other stakeholders have invested heavily to quickly pivot, adapt, and innovate therapeutics.
Surging demands and transition to patient-centric care delivery across geographies will change pharma and healthcare market trends through 2027.
Competitive Outlook:
The report focuses on current and emerging trends in the healthcare industry such as incorporation of IoT and Machine Learning to enhance efficiency of medical products. Top companies in the market are focusing on R&D activities to expand their product offerings and cater to unmet medical needs.
Request a customization of the report @https://www.reportsanddata.com/request-customization-form/2186
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The Rapid Proliferation Of Emerging Technologies Like Artificial Intelligence And Machine Learning To Achie... - TechBullion
Artificial intelligence is looking for tipping points in the climate system – Market Research Telecast
as Tipping points in the climate system are variables that cause a drastic change in the climate above a certain threshold value. Because beyond these tipping points, so the idea, a self-reinforcing mechanism is set in motion that accelerates climate change ever more. Well-known tipping points are, for example, the thawing of the Arctic permafrost, the collapse of the oceanic current systems or the thawing of the ice sheets at the poles whether and how many more such points there are is the subject of current research.
This article is from issue 7/2021 of the Technology Review. The magazine will be available from September 30th, 2021 in stores and directly in the heise shop. Highlights from the climate booklet:
Chris Bauch from the University of Waterloo and his colleagues have now trained a deep, neural network to Identify tipping points in climate systems and give warnings when the system approaches a dangerous tipping point. The approach is based on an abstract description of complex, dynamic systems: The system analyzes the auto-correlation of time series values and learns to recognize specific patterns that herald a bifurcation, a qualitative change in state.
However, Bauchs team is by no means the only one trying to get better predictions about climate change with the help of machine learning and artificial intelligence, reports MIT Technology Review in its current issue 07/2021. For example, a team led by Tapio Schneider from the California Institute of Technology is working on eliminating a central weakness of current climate models with the help of machine learning: the extremely simplified modeling of clouds.
Because the global models that were used for the current IPCC report, for example, model the climate system in a grid with an edge length of 100 kilometers. Clouds are much smaller they will therefore be parameterized, that is, one cell of the model is calculated as 20 percent cloudy, for example. Schneider and his colleagues therefore take the basic physical equations of physical climate models and coarsen them by using, for example, averaged values on a large energy grid. In order to still be able to model the small-scale, dynamic processes of the clouds, they add additional functions to the equations that cover these processes. These functions, which are essential for dynamics, are learned by neural networks from high-resolution, local cloud simulations and weather data.
Others like Jakob Runge from the TU Berlin use the methods of causal inference to identify cause-effect relationships in climate data with the help of AI. We defined variables such as temperature, pressure and so on in certain regions. Then, when we apply that to the observation data, we see what the causal network looks like, says Runge. Some processes are interconnected, others are not. You get a network of dependencies a kind of fingerprint. Then we take the same variables in models, learn the causal network in the model data and compare. Are they the same? Where do the models not form the reality so well? And not necessarily in the absolute values, but in the causal relationships. The method can also be used to calculate the reliability of a model, not only on the current, but also on future data.
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Artificial intelligence is looking for tipping points in the climate system - Market Research Telecast