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Artificial Intelligence Is Coming After Writers. How Will That Fair? – Analytics Insight

Artificial Intelligence Is Coming After Writers. How Will That Fair?

Can you imagine reading a news report written by a robot? Would you read a novel written by artificial intelligence? It might just be possible by the way artificial intelligence is advancing.

Lets take Grammarly as an example. It can already form better or equally-proper sentences like humans. This AI predictive text technology is already used in phones and email applications and much of everyday writing that humans do might soon be done by AI.

According to Gartner, by 2022, AI and similar technology will automate the production of 30% of all content found on the internet. Astonishingly, some texts like opinion articles and scripts are already being written by AI. MSN, a news website dismissed 50 freelance news editors and replaced them with AI-driven bots. Freelance sports writers at 30 Swedish news sites were replaced by automated sports news robot systems. Based on this, literacy, in todays world, means interacting with rapidly advancing AI. In schools, todays children should no longer be taught just writing. Instead, writing should include skills that go beyond the capabilities of artificial intelligence.

In 2019, a New Yorker magazine experimented to see if Open AIs (an IT company) natural language generator GPT-2 could write an article in the New Yorker magazines unique style. But this experiment had limited success because the AI generator made many errors. In 2020, GPT-3s new version which was trained with more data wrote an article for The Guardian newspaper. The headline said A robot wrote this entire article. Are you scared yet, human?

This article was much improved from the previous one and leaves a question mark on the future of journalism.

The day is far when robots will mimic human nature to its maximum of 100%. School curriculum needs to make developments based on what AI cannot do, especially when it comes to creativity. It has been observed that AI writing has a voice but no soul. According to New Yorkers John Seabrook, human writers give, color, personality, and emotion to writing by bending the rules. Students, therefore, should be encouraged to break the rules of writing, something that an AI cannot do as machines are trained on a finite amount of data to predict and replicate, not to improvise.

AI is not yet as complex as the human mind. Humans can write humor and satire. We know words can have multiple emotions attached to them. A reader can make a judgment between good and bad writing, a writing that has empathy, perception, and insight. Humans possess sophistication versus an AI machine.

According to the Institute For The Future, social intelligence is an essential skill for the future generation. Social intelligence is the ability to connect to others in a deep and direct way. It requires adaptive thinking, cross-culture compatibility, and virtual collaboration. These skills are in stark contrast to what an AI bot can do, at least, as of now. Creativity should be fostered with machines and not only by machines. While we cant turn away from the reality that artificial intelligence is here to stay, kids should be taught skills that are greater than just writing to coexist in a workforce with AI robots.

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Unfortunately, Commercial AI is Failing. Here’s Why. – Analytics Insight

Unfortunately, Commercial AI is Failing. Heres Why.

What happens when a product fails to justify its hype? It fails. In the 90s, interest was booming and many companies wanted to take advantage, but could not. Only a few survived, and when the dot-com bust happened, those companies had to shut down due to lack of effort. In the mid-2000s, cloud computing was the hot topic. Several companies tried to gain momentum but failed because they could not move their data to the cloud.

Commercial AI products are not booming as expected. This is leading to disappointments not only to the artificial intelligence developers but also to the industry and businesses who wanted to employ these products. This is also known as AI fatigue, when a product is unable to deliver the results as promised by its hype, informational, and sometimes, misinformation. For example, when companies were developing a chatbot for Facebooks Messenger, they observed a 70% failure rate in handling user requests. According to a research report by McKinsey Global Institute, 45% of work activities can be automated, of which 80% is enabled by machine learning. Companies in sectors like manufacturing and health care have captured less than 30% of the potential from their data.

One of the reasons why AI products fail to make an impact on a commercial scale is the lack of deep learning. Deep Learning is a subset of AI. Most often, it is used to classify data problems that involve finding data patterns. But many in the AI industry have found it challenging to build artificial intelligence products with deep learning. This issue can be tackled by producing scalable AI products.

If an AI is both accurate and powerful, it is known as scalable AI. In this context, powerful refers to AIs ability to adapt itself to any business model. For example, a medical imaging AI should work in different clinical settings and for patients worldwide. Silicon Valley investor Andreessen Horowitz, who worked with a range of AI companies wrote in his recent article about the lack of scalable AI. It is becoming a challenge in the AI industry to make a program scalable for commercial use, once its out of the lab. If we look deeper, the problem is not with AI, though. The problem lies in the way these AI applications are made for commercial use. On paper, it has a different perspective than when it is put to use.

To build scalable AI for commercial use, the industry needs to shift its focus from data quantity and AI accuracy to data quality, diversity, powerfulness, and knowledge about the industry to fix the problem.

Scalable AI cannot work with poor quality data. It affects both the accuracy and powerfulness of AI. Even a 1% error in data can impact AI accuracy. As a practice, AI practitioners must clean the data. Having effective data cleaning methods to improve data quality is a significant factor to build a robust scalable AI.

A globally diverse dataset is essential for testing and validating the AI. As scalable AI should be robust and powerful for it to function universally, the need for additional investment and efforts to take a global approach has arisen. In healthcare, AI can be biased to a certain section of people or clinics. Healthcare problems are global, hence it is crucial to take this approach. But collecting global data is complicated. The easiest way for AI companies is to collect data from one or more clinics to have a large dataset, preferably from a prestigious clinic that has larger patient data.

Why is scalable AI important? Because people have started doubting the credibility of AI in the commercial space. An industry transformation is needed for companies to believe in AI and employ them. This means a big change in the companys organizational DNA. AI companies that will succeed in making commercial AI will be the ones that focus on creating scalable AI for business learning and business functions. This will harness the transformative power of AI.

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Tech for Good: Artificial Intelligence Applications that will Improve the Environment and Healthcare in the EU – GlobeNewswire

Stockholm, Sweden, Feb. 10, 2021 (GLOBE NEWSWIRE) -- Logical Clocks announces three new research projects part of the European Union (EU) Horizon 2020 research and innovation programme that will benefit from Hopsworks artificial intelligence (AI) capabilities to scale deep learning and enhance research focused on understanding environmental changes and improving healthcare in Europe. Hopsworks is the worlds first and most advanced managed Feature Store with an end-to-end AI platform for the development and operation of AI applications at scale.

The European Union leads the world when it comes to leveraging AI for the benefit of the environment and public health comments Jim Dowling, CEO at Logical Clocks and Associate Professor at KTH Royal Institute of Technology in Sweden. Research is part of our DNA and we are proud to be one of the few AI companies leading projects that will ultimately enhance quality of life, not just in the European Union, but around the world.

AI Applications to Improve HealthcareThe Human Exposome Assessment Platform (HEAP) is building a research platform, leveraging AI capabilities, to reveal the influence of environmental factors on human health, such as the link between airborne particles and predisposition to late-onset disease such as cancer. The project received 11 million funding from the European Union for 11 partners from 6 European countries to combine machine learning with computational statistics and develop powerful statistical modelling tools. With Hopsworks metadata mechanisms, which makes large volumes of data easily searchable, accessible and shareable, the HEAP platform will not only unlock new insights but it will also facilitate sharing data in a secure environment, becoming an open resource for the research communities as well as policy-makers across the world.

AI Applications to Predict Climate ChangesThe DeepCube project tackles, through AI, urgent problems caused by climate change in Europe and the whole Mediterranean region, such as forecasting of localized extreme drought and deadly heat impacts in Africa. The project is part of a consortium formed by 9 organizations from 6 European countries that will combine cutting-edge technologies, such as the Hopsworks platform for machine learning, the Earth System Data Cube, and an advanced visualization tool, to extract meaningful information from a large volume of data and to develop data-driven AI models. Funded with 4 million million by the European Union, the project will develop AI applications by extracting extract data from the Copernicus Earth Observation programme which already produces annually more than 3 petabytes of free, open and high quality data from satellites and from non-conventional data sources, such as social network data, industry-specific data, and sensor data.

AI Applications to Improve Food Security and Navigation SafetyThe ExtremeEarth project focuses on the most concerning issues of food security, such as water availability for irrigation of vegetation growth for the former. Currently 20 percent of the agricultural areas of the world are irrigated, producing 40 percent of the global food. The project is also dedicated to developing near real-time automated sea mapping, positively impacting the maritime sea navigation and safety, thus improving the life of 4 million people living in the Arctic. Currently, sea ice information is available either as ice charts or as satellite data, a practice that requires time consuming expert analysis to produce and, consequently, leads to less frequent updates than desired. With support of 11 organizations, the project is implementing state-of-the-art technologies such as Hopeworks Deep Learning and big data processing of massive amounts of data. ExtremeEarth received 6 million funding from the European Union and it is generating key insights for the development of sustainable practices with high significant financial impacts.

The Hopsworks platform will play a major role in going beyond the current state of the state-of-the-art of AI technologies, especially when addressing large volumes of data and scale-out deep learning, while remaining open source. We will continue to make Hopsworks available for free to researchers across the world to bring answers to problems that concern all of us, comments Dowling.

About Logical ClocksLogical Clocks was founded by the team that created and continues to drive Hopsworks Feature Store, the worlds first and most complete feature store with an end-to-end machine learning platform. With offices in Stockholm, London and Palo Alto, Logical Clocks aims to simplify the process of refining data into intelligence at scale.

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Post-COVID-19, Artificial Intelligence and Being Relevant – ThinkAdvisor

(Image: Thinkstock)

There has never been more demand for ways to translate strategy into action and action into outcomes.

The way companies sell, and consumers buy, has dramatically changed; especially post COVID-19.

(Related:Ways to Stress Test Your Business for Coronavirus Impact)

Bold changes are needed to support those changes.

Technologies to support the new virtual selling process are and should continuously be changing.

Onboarding has to be quicker and more effective.

Getting agents and advisors into production sooner has to be scalable, cost-effective and skill-development-sustainable, with development assets and systems playing a more critical role.

We have to have a method, system, and platform to help agents and advisors:

How do we do all this, on a budget and while seamlessly implementing these changes?

Thats where Im at right now, in the research and planning phase.

Its amazing how many companies are now marketing their services to the sales industry. Some seem useful, others seem like Johnny come lately, and a couple Im going to try.

Because a lot of what I do has been virtual for some time, Im always looking for a way to use services that combine or package some of the things I do anyway, like email marketing, creating landing pages, and making outbound calls.

Part of what I do is to help sales managers, agents and marketing teams improve their conversion rates by becoming more relevant to all customer interactions. Ive been looking for an AI-enabled platform to store my material, scripts, email templates and rebuttals, in a way that makes the conversation with prospects more relevant, so agents can close more.

I doubt getting email marketing, creating landing pages and making outbound calls in one platform is possible. Even if I could eliminate the cost of just one of these (I know Mailchimp can do the email and landing pages) Id consider it; I like to split test things, anyway.

It would be nice if an AI platform could deliver my content, in real time, during a call or presentation, to provide the right message at the right time for the salesperson.

(I know, Im a dreamer, but, if an 18-wheeler can be driven across the country using AI am I really asking too much?)

I recently came across a platform by a company called Sales Talk Technology, a platform with sales intelligence, sales content, and analytics in one place.

They use the right words workflows built-in,customization available, automate sales tasks,seamless integration into someones current CRM, etc.

But can I upload my content, customize it for markets agents/advisors work, could SalesTalks AI make my scripts, rebuttals, and templates available in real time, so agents and advisors could use them to improve their conversion rates, by becoming more relevant in all customer interactions, in real time?

I dont know, but that system is one of three Im going to try; Im telling you so, if you have any ideas, you might comment and let us all become aware of COVID-19-inspired innovation.

I know this much: We all have to make it possible for agents and advisors to be more engaging with prospects, at the right time with the right message, right?

If it was only possible. Stay tuned Ill let you know what I find out.

Connect with ThinkAdvisor Life/Health onFacebook,LinkedInandTwitter.

Lloyd Lofton is the founder of Power Behind the Sales and the author ofThe Salesheros Guide To Handling Objections.

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For US and allies, prepping for AI warfare starts with the data – C4ISRNet

WASHINGTON The U.S. and allies are using a new forum started by the Pentagons top artificial intelligence office to work toward developing AI systems that can connect in the future to help them fight better together.

The Partnership for Defense, started by the Joint Artificial Intelligence Center last September, is laying the groundwork for future AI-enabled joint war-fighting capabilities that will need to connect to each other for the U.S. and its allies to effectively fight as a coalition.

One day, the countries could collaborate on other AI-backed efforts, such as sharing data from sensors that track how machines run to predict when maintenance is needed before parts fail, possibly during a mission when theres no time to lose for repairs or replacements. Or the allies could use AI for data about shipping and supply movements to improve logistics efficiency.

The end goal is for the allied nations to be ready to cooperate easily on AI-driven projects in the future.

But first, the U.S. and partner countries must start at a basic level of readying data for artificial intelligence, viewing the information as a war-fighting resource. That starts with keeping and storing all of the facts and figures that AI needs to work.

The U.S. and its allies messed up in not using data or looking at data over the last several decades as a resource, said Stephanie Culberson, head of international AI policy at the JAIC. For instance, if we were to go to war again in Afghanistan, would we have all the data that we pulled in the last 20 years? You can probably guess the answer to that.

The partnership came from smaller discussions that the JAIC had with like-minded nations. After several interactions, it became clear that the nations struggled with the same challenges around scaling AI efforts, educating and training the workforce on AI, and overcoming internal cultures resistant to technological change, Culberson said.

We started to realize that many of us are grappling with the same hard problems in implementing AI into our defense organizations, Culberson said. Instead of staying within those siloes on our own, I thought, Well, why dont we pull together some of the strongest nations that are really focused on this in their defense sector and do this together?

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Thus far, the partnership includes defense representatives from Australia, Canada, Denmark, Estonia, Finland, France, Israel, Japan, Norway, the Republic of Korea, Sweden and the United Kingdom. The group has twice gathered to identify common challenges, and meetings are expected three times a year.

The Partnership for Defense is not working on codevelopment of AI systems, rather its focused on preparing allied militaries to be AI-ready, as Culberson puts it.

We decided to talk about building blocks that we all need to work through that are massive undertakings for ministries of defense, Culberson said. For instance, how are we handing data? For the most part, not very well.

The meetings are different than typical international conversations with foreign militaries, which can be rigid, Culberson said. The partnership meetings encourage open dialogue, including roundtable discussions and TED Talk-style presentations describing how ministries tackle challenges and analysis of case studies for lessons learned.

In the next two years of the partnership, Culberson said that she really wants to have a solid foundation for AI-readiness, developing a way to assess whether members have achieved that readiness. In a few years, she said, the countries could consider codeveloping a data aggregation capability.

This is how we do interoperability as well, Culberson said. We dont want to get too far down the path of everyones doing their own thing in their siloes, and then we look up and next time we need to go to war together, or even humanitarian assistance or any of those types of things where we might use our militaries together, nothing is interoperable.

The JAICs role on the international stage

Since its inception about two years ago, the JAICs mission has been to help the Pentagons internal components adopt artificial intelligence, through its national mission initiatives or by delivering services. Adding international engagement to its portfolio also serves that mission.

I see it has kind of the same thing actually for international: to help enable key allies and partners, which at the end of the day is going to make our war fighter more ready to have ready allies at their side, Culberson said.

U.S. military services are starting to try to include allies and partners as they develop their joint war-fighting systems, such as the Air Forces Advanced Battle Management System or the Armys Project Convergence.

Those service-led programs, which will rely heavily on artificial intelligence, are how the services plan to connect sensors and shooters for future battles. Work with allies now will ease challenges plugging them together later.

In this broader strategic competition between the U.S. and China, as it continues to evolve, the Defense Department will need these avenues for partnerships, said Megan Lamberth, research associate at the Center for a New American Security. It allows for increased interoperability between partner militaries, and it gives countries access to broader, more robust shared datasets.

The partnership could lead to talent-sharing programs that would benefit the Pentagon, Lamberth added, particularly given workforce shortages in AI professionals.

The Partnership for Defense has an open door to adding more allies, Culberson said. While other nations have expressed interest, members plan to set admission standards before expanding.

I dont want it just to be the U.S. projecting, which is often I think expected when we have multilateral conversations like this, Culberson said. Instead I want it to be truly a forum where like-minded allies can come together and share and learn.

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Nucleus Biologics Launches Artificial Intelligence Research (NB-AIR): The World’s First AI Platform for Media Configuration That Gives Control Back To…

SAN DIEGO, Feb. 9, 2021 /PRNewswire/ --Nucleus Biologics, The Cell Performance Company, today announced the availability of NB-AIR, the world's first Artificial Intelligence Research platform for optimizing cell culture formulations for cell and gene therapies. Leveraging cutting-edge AI algorithms, the system empowers scientists to create optimized formulas based on meta-analysis of peer-reviewed articles. AI guided formulations will allow scientists to improve the performance of their cell therapy and shorten the time to get these lifesaving therapies into patients faster.

Cell and gene therapy in 2019 was a $1 Billion market and is estimated to be growing at 36% CAGR.Most cell therapy companies struggle to achieve reproducible potency in their cell therapies.The media used for in vitro cell growth have a documented impact on cell quality and hence therapeutic efficacy.Until now, scientists have had to rely on major suppliers who sell proprietary media formulations.These black box formulations limit the scientist's ability to chemically modify their media. This slows down discovery and introduces supply chain risk.Until now, no tool existed that allowed scientists to research and select components and formulations based on current published knowledge on conditions that impact cell performance.

"This is an industry transforming tool. Imagine being able to take months of research and reduce it to minutes through the power of machine learning. We are democratizing formulations enabling scientists to tap into the collective knowledge of their peers, become experts quickly and own their media formulation." said David Sheehan, Founder, President and CEO of Nucleus Biologics. "Our vision is that we can create a constantly evolving technology ecosystem that allows therapy providers to create intellectual property that improves cell performance and reduces development time."

Initially targeted for developers of cell therapies, NB-AIR speeds formulation development by providing peer-tested compounds and formula recommendations based on cell type and critical quality attributes. It is directly connected to NB-Lux, a cloud-based ordering and tracking portal, to allow online ordering of lot sizes from 2L to 2000L, allowing media scaled from bench to bioreactor. Further, changing even one component in your media can improve therapeutic yields, phenotype, and efficacy. Giving scientists the tools to optimize at a component level and control their media will help speed the time from discovery to cure.

Media Contact:Michael Morgan[emailprotected], (858) 251-2010

About Nucleus BiologicsNucleus Biologics, The Cell Performance Company, is the leading provider of custom cell-growth media, tools, and technologies for cell and gene therapy. Their mission is to speed the time from scientific discovery to cure by delivering innovative, transparent and cGMP products and services with the goal of disrupting the market and eliminating antiquated practices and products. Ultimately, Nucleus Biologics strives to create a new paradigm that serves both scientists and clinicians, while reducing the environmental footprint of cell culture. http://www.nucleusbiologics.com

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How Artificial Intelligence Is Transforming the Textile Industry – IoT For All

As the demand for products such as fitness trackers and wearable technology increases, so does the need for smart textile and smart apparel. According to a recent market report, the global elegant textile market size is expected to reach USD 5.55 billion by 2025.

The rise of new technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed the once labor-intensive textile industry. Computerized machinery is now found in most textile factories, and these machines are far more efficient at creating specific designs on a massive volume than human workers.

New smart apparel products are being created every day. By implementing AI along with technologies such as Bluetooth Low Energy (BLE), edge computing, and cloud data, smart textiles can monitor and communicate the wearers information, including biometric data such as blood pressure, heart rate, perspiration, temperature, and more.

This article will examine how AI is impacting the textile industry, some new use cases, and why ultra-low-power (ULP) technologies are a must to fully unleash AI at the endpoints.

For textile manufacturers, AI is reshaping their entire production process and the way they conduct business. AI can access and collect historical and real-time operational data, providing insights that can improve operational efficiency. When you have a clear view of your operations, it is easier to tweak processes to magnify human workers capabilities.

Whether it is product cost, textile production, quality control, just-in-time manufacturing, data collection, or computer integrated manufacturing, AI leaves an imprint on every part of the process. Some commonly integrated AI applications for textile production include defect detection, pattern inspection, and color matching.

The use of AI has enabled smart apparel, or smart clothes that leverage IoT and electronic sensors to create a better user experience. By leveraging these technologies, smart clothes can offer a more comfortable experience and a more healthcare-focused experience. Below, we will examine some of these new possibilities in the textile industry.

Much like how fitness trackers can help their users live a healthier and more attentive lifestyle, smart apparel combined with electronic sensing technology can do the same. However, since your clothes have a larger area of contact with your body than something like a smartwatch, smart apparel can potentially provide more types of physiological signal measurements.

Smart clothing can enable continuous monitoring of important biometrics, such as our heart rate. With long-term monitoring more feasible, physicians can better identify or diagnose potential cardiac diseases. Smart clothing helps patients collect complete and comprehensive heart-related data, track long-term heart disease, and enhance the detection and diagnosis of heart issues through regular monitoring over an extended period.

Following the COVID-19 outbreak, consumers have emphasized healthcare and medical attention in their wearable products, which is now extending to smart apparel. Clothes embedded with BLE technology can feel, sense, and regulate data, and the development of fabric-based sensors should only improve the overall wearing experience.

Artificial Intelligence isnt the only technology driving forward the textile industry. Cloud data, edge compute, accurate sensors, and ultra-low-power technologies are also necessary components. Especially for smart clothes that rely on BLE and IoT technologies, a long-lasting energy source from their embedded battery must provide a satisfactory and useful consumer experience.

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Artificial Intelligence gets major boost with Amsterdam University of Applied Sciences new centre of expertise – Science Business

The Amsterdam University of Applied Sciences (AUAS) is the first knowledge institution in the Netherlands to create a Centre of Expertise on Applied Artificial Intelligence, together with partners from the business community and the public sector. This will give a major boost to knowledge on applied Artificial Intelligence (AI) in the Amsterdam region. A unique feature of this Centre of Expertise is that students from all degree programmes at the AUAS can learn through labs how to apply AI in their fields of study; from nursing and ICT to teacher training.

Currently, AI algorithms, which are really self-learning systems, support us in things like navigation on the web or with chatbots. More and more possibilities are being developed, and this has an impact on all fields and on what employees need to be able to do. In the Netherlands, there is therefore an urgent need for more knowledge and talent in relation to AI, particularly from SMEs and the public sector.

As the largest knowledge institution in the Amsterdam region, the AUAS is responding to this need by establishing a Centre of Expertise on Applied Artificial Intelligence to help businesses and public organisations in this transition. The AUAS is working on this with several partners, including AI Technology for People, knowledge institutions in Amsterdam, the City of Amsterdam, Amsterdam Data Science and the Amsterdam Economic Board.All professions are changing

The AUAS is taking a broad approach with the new CoE: the aim is for all AUAS students to acquire the latest skills and knowledge in order to apply AI in their own fields. To do this, 7 labs have been created (in fields including retail, media, healthcare and education), which also have links with the sectors themselves.

By doing so, the AUAS wants to contribute to more knowledge about AI in the region, while also better preparing students themselves for the future, as many professions will change due to the new applications. Whereas an accountant can currently distinguish himself by being good at mathematics, for example, this will soon be an ideal task for AI. Healthcare professionals are likely to use AI support in the future in to predict certain conditions better. Business owners will have to be able to interpret and apply AI, to respond to further digitalisation. And in education, AI can help lecturers by looking at how pupils or students learn, for example. The AUAS has set up a lab for this too.

Ethical aspects

The Centre of Expertise will pay extra attention to the ethical aspects of AI. Because algorithms can exclude certain information, which may not always be desirable for users. Through the Responsible AI Lab, the AUAS will explore how to design inclusive and responsible AI solutions.

Finally, the AUAS will investigate the impact of AI on the professional field and society. The aim is to achieve inclusive, responsible AI, geared towards the user. Among other things, this will result in tools, instruments and training courses for businesses, municipal authorities and public organisations. In this way, the AUAS will become an important player in AI, as the university of applied sciences focuses on practical application in addition to the theoretical knowledge developed by research universities.

ABOUT CENTRES OF EXPERTISE (COES)

The first Centres of Expertise were launched in 2011 as an important innovation in vocational and higher professional education. They work in co-creation with the business community, public organisations and citizens on social solutions that have a positive impact. Based on current issues in the Amsterdam region, the AUAS has clustered its research and education in several CoEs, with each theme linked to a social or metropolitan theme that is important to Amsterdam.

In the seven faculty labs of the Centre of Expertise Applied AI work will be carried out on innovation within the various areas of application in co-creation with education, research, the business community and civil society organisations.

Impact on education

Innovation in education is an important priority area of the CoE. The faculty labs are linked to one or more degree programmes. Students and faculty are involved through projects, minors and Masters programmes on digitalisation and AI. Take the minors in Legal Tech (through the Legal Tech Lab of the same name) and FinTech (through the Finance Lab), for example. Or the Masters programme in Digital Driven Business (through the Centre for Market Insights).

The CoE has taken the initiative to develop a professional Masters degree in Applied Artificial Intelligence, which it aims to offer from September 2022. In addition, a methodology is being developed to help all degree programmes become AI-ready. The CoE has already started offering introductory AI training courses to lecturers to build expertise within its own organisation.

Do you have questions or would you like to work together? Then send an email to [emailprotected]

More information can be found at: http://www.amsterdamuas.com/ai

This article was first published on 9 February by AUAS.

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Ethical Concerns Associated with Artificial Intelligence (AI) – Brunel University News

18 Feb 2021, 16:00 - 17:15

Online event

The Centre for Artificial Intelligence Seminar Series examines the emerging challenges and opportunities surrounding AI.

Ethical Concerns Associated with Artificial Intelligence(AI)

by Dov Greenbaum, Professor of Law

Director at Zvi Meitar Institute for Legal Implications of Emerging Technologies,

IDC Herzliya

Host: Dr Cristina Sisu, Lecturer in Genomic Data Analytics, Brunel University London

Dov Greenbaumhas undergraduate degrees in economics and biology from Yeshiva University and a doctorate in bioinformatics/genetics, a data science field, from Yale University. Dov also has a law degree with a focus on intellectual property from the University of California, Berkeley. Subsequent to his degrees, Dov had two postdoctoral appointments, one at Stanford University and one at ETH Zurich, both synthesized science, technology, law and society. Dov practiced law for two years at a large multinational law firm in Silicon Valley where he was involved in complex high-stakes civil litigation. Dov continued his practice in Israel in the area of patenting of hitech innovations, including biotech, complex algorithms, robotics and missile defense. Currently, Dov is the director of the Zvi Meitar Institute, an academic institute at the Interdisciplinary Center in Herzliya (IDC). The Institute is broadly interested in the ethical, social and legal concerns arising from new and emerging technologies.

The Centre for Artificial Intelligence Seminar Series examines the emerging challenges and opportunities surrounding AI.

Ethical Concerns Associated with Artificial Intelligence(AI)

by Dov Greenbaum, Professor of Law

Director at Zvi Meitar Institute for Legal Implications of Emerging Technologies,

IDC Herzliya

Host: Dr Cristina Sisu, Lecturer in Genomic Data Analytics, Brunel University London

Dov Greenbaumhas undergraduate degrees in economics and biology from Yeshiva University and a doctorate in bioinformatics/genetics, a data science field, from Yale University. Dov also has a law degree with a focus on intellectual property from the University of California, Berkeley. Subsequent to his degrees, Dov had two postdoctoral appointments, one at Stanford University and one at ETH Zurich, both synthesized science, technology, law and society. Dov practiced law for two years at a large multinational law firm in Silicon Valley where he was involved in complex high-stakes civil litigation. Dov continued his practice in Israel in the area of patenting of hitech innovations, including biotech, complex algorithms, robotics and missile defense. Currently, Dov is the director of the Zvi Meitar Institute, an academic institute at the Interdisciplinary Center in Herzliya (IDC). The Institute is broadly interested in the ethical, social and legal concerns arising from new and emerging technologies.

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Artificial Intelligence (AI) in Healthcare Market Competitive landscape and Key Vendors, Forecast by 2025 KSU | The Sentinel Newspaper – KSU | The…

According to the findings of this business intelligence study, the demand forartificial intelligence in healthcare sectoracross the globe will increase at an exuberant CAGR during the forecast period of 2017 to 2025. This report has been developed by healthcare IT professionals and aspires to serve as a credible business tool for targeted audiences such as healthcare software vendors, chipset companies, technology providers, doctors and hospitals, software solution providers, artificial intelligence system providers, and venture capitalist.

The report includes comprehensive and figurative assessment of the demand potential of various market segments, analyzes various impacting factors including trends, drivers, and obstructions, and takes stock of the demand that can be expected out of different countries and regions. The report also contains a featured chapter on the competitive landscape.

Artificial Intelligence (AI) in Healthcare Market: Trends and Opportunities

Greater new possibilities with big data, ability of AI to enhance patient care, strong imbalance between the pool of patients and healthcare professionals, and possibilities of reducing medical costs are some of the key factors expected to augment the demand for AI in the healthcare sector. Additionally, growing importance of precision medicine, increasing number of cross-industry collaborations, consistent inflow of venture capital investments, and increasing geriatric population are some of the other factors that are expected to reflect positively over this market.

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On the other hand, reluctance of medical practitioners in adopting new technologies, strong lack of a preset and universal regulatory guidelines, lack of curated healthcare data, and concerns of data privacy are curtailing the market from attaining higher grounds.

Technology-wise, the artificial intelligence (AI) in healthcare market can be segmented into querying method, deep learning, context aware processing, and natural language processing, whereas application-wise, artificial intelligence (AI) in healthcare marketcan be bifurcated into wearables, virtual assistant, research and drug discovery, in-patient care and hospital management, medical imaging and diagnosis, precision medicine, lifestyle management and monitoring, and patient data and risk analysis.

Artificial Intelligence (AI) in Healthcare Market: Regional Analysis

The developed country of the U.S., which readily adopts new technology and houses a number of pioneering companies, is expected to maintain North America are the region with maximum demand potential, with little but significant demand added by Canada. While the European region is another key region for the vendors of artificial intelligence (AI) in healthcare market, emerging economies of Japan, South Korea, China, and India are expected to provide for decent demand over the course of the aforementioned forecast period.

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The EIRS quadrant framework in the report sums up our wide spectrum of data-driven research and advisory for CXOs to help them make better decisions for their businesses and stay as leaders.

Below is a snapshot of these quadrants.

1. Customer Experience Map

The study offers an in-depth assessment of various customers journeys pertinent to the market and its segments. It offers various customer impressions about the products and service use. The analysis takes a closer look at their pain points and fears across various customer touchpoints. The consultation and business intelligence solutions will help interested stakeholders, including CXOs, define customer experience maps tailored to their needs. This will help them aim at boosting customer engagement with their brands.

2. Insights and Tools

The various insights in the study are based on elaborate cycles of primary and secondary research the analysts engage with during the course of research. The analysts and expert advisors at TMR adopt industry-wide, quantitative customer insights tools and market projection methodologies to arrive at results, which makes them reliable. The study not just offers estimations and projections, but also an uncluttered evaluation of these figures on the market dynamics. These insights merge data-driven research framework with qualitative consultations for business owners, CXOs, policy makers, and investors. The insights will also help their customers overcome their fears.

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Artificial Intelligence (AI) in Healthcare Market Competitive landscape and Key Vendors, Forecast by 2025 KSU | The Sentinel Newspaper - KSU | The...

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