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The Recommendations Regarding Data Protection in the Field of Artificial Intelligence – JD Supra

The Recommendations on Data Protection in the Field of Artificial Intelligence (the "Recommendations") was published by the Turkish Personal Data Protection Authority (the "DPA")1 on its website on 15 September 2021.

The scope of the Recommendations address the Developers, Manufacturers, Service Providers and Decision Makers in accordance with the Law on the Protection of Personal Data numbered 6698 and its secondary legislation (the "Law"). This is the first time that DPA has published a document regarding data protection regarding AI-based applications.

The Recommendations consist of three parts, namely: (i) general recommendations; (ii) the recommendations for developers; manufacturers and service providers and (iii) recommendations for decision makers.

Under the Recommendations the term Artificial Intelligence (the "AI") is defined as the human-specific abilities to be analysed and passed to machines. The AI focuses on creating algorithms and computer software, which can think, interpret and make decisions as humans.

The Recommendations put forward the definitions of Developer, Manufacturer and Service Provider but do not define the Decision Maker. Considering the European Union documents on the issue, we believe that Decision Maker corresponds to the legislative organ and policy makers.

Further while the Developers are introduced as any real persons or legal entities developing content or application for the AI systems whereas the Manufacturers are real persons or legal entities who produce any products such as software and hardware systems that constitutes these systems.

Service Providers are defined as any real people or legal entities who offer a product and/or service using the AI based systems, data collection systems, software or devices under the Recommendations.

Under the General Recommendations section, the importance of protecting fundamental rights and freedoms of real persons whose data are being processed (the "Data Subject") in the process of developing and applying the AIs is emphasized.

In this context, the right to protection of human dignity should be respected and the principles of "compliance with the law, fairness, proportionality, transparency, accuracy and accuracy of personal data, specific and limited purpose of the use of personal data" should be a basis for the AI developments relying on the processing of personal data and data collection.

Considering the individual and social effects of the data processing activity conducted by the AI, the Data Subject should have the control over. The Recommendations include further guidelines on the issue for everyone working in the field.

Regarding AI developments relying on the processing of personal data;

While developing and applying artificial intelligence technologies, if reaching to the same result is possible without processing personal data, the data should be processed by anonymization3.

Pursuant to the Recommendations, an approach that complies with national and international regulations which respects data privacy should be adopted in AI-oriented designs. In addition, Data Subject's rights regarding their personal data arising from both national and international regulations should be preserved.

Together with these, the points below are stressed in the scope of the Recommendations:

This section includes advices for Decision Makers who are working in the field of personal data protection.

Pursuant to the Recommendations, the Decisions Makers should:

This is the first time that DPA has published recommendations regarding AI-based applications. Since EU has been focusing on AI based works heavily, we believe the Recommendations published by DPA is a result of getting under this influence. While considering the DPA's Recommendations, the principles of human agency and oversight; privacy and data governance; transparency; diversity, non-discrimination and fairness and accountability should also be taken into account.

Although the recommendations presented are general and each of them are different matters of debate, this document signals the AI ethics as a rising toping that we can expect to hear more.

Click here to download 'The Recommendations Regarding Data Protection in the Field of Artificial Intelligence' PDF in Turkish.

1 https://www.kvkk.gov.tr/Icerik/7048/Yapay-Zeka-Alaninda-Kisisel-Verilerin-Korunmasina-Dair-Tavsiyeler2 According to the Article 6 of the Law, "personal data relating to the race, ethnic origin, political opinion, philosophical belief, religion, religious sect or other belief, appearance, membership to associations, foundations or trade-unions, data concerning health, sexual life, criminal convictions and security measures, and the biometric and genetic data" are deemed to be special categories of personal data.3 According to the Article 3 of the Law, anonymization is defined as "rendering personal data impossible to link with an identified or identifiable natural person, even through matching them with other data".

Selin Kaledelen (Associate), Elif Engin (Legal Intern)and Deniz Alkan (Summer Intern) of GKC Partners authored this publication.

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UGA receives grant to study turfgrass water conservation using artificial intelligence – The Albany Herald

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Artificial intelligence may help reveal the next animal-to-human virus threat – Study Finds

GLASGOW, Scotland Artificial intelligence may be the best hope humans have for finding the next virus jumping from animals to humans before it becomes a pandemic. Scientists from the University of Glasgow say a form of AI which analyzes viral genomes could predict and possibly stop the next pathogen which is ready to jump from other species into humans like COVID-19.

The exact origins of COVID-19 are still unclear. However, most scientists agree that at some point SARS-CoV-2 jumped from an animal (like bats) to humans. While COVIDs outbreak is bringing the threat of animal-to-human disease transmission to the forefront of the conversation, the reality is that many infectious diseases in recent years originated within an animal before crossing over. Researchers say this is why identifying new high-risk zoonotic viruses before they have a chance to spread is so important.

Its no easy feat identifying animal viruses potentially capable of infecting humans. Estimates show there are 1.67 million animal viruses out there, but only a small portion are capable of infecting humans. So, in order to create AI models capable of using viral genome sequences, researchers put together a dataset of 861 virus species from 36 families.

From that point, the team constructed machine learning models which assigned a human infection probability score for each virus based on patterns in their genomes. Researchers used the top performing AI model to analyze patterns in the predicted zoonotic potential of additional virus genomes from various species.

That process led researchers to conclude that viral genomes may have generalizable features that preadapt these viruses to infect humans. Study authors then created more machine learning models capable of identifying specific viruses likely to infect humans via viral genomes.

While this work is very promising, the team concedes that their models do have limitations. They add using AI is just the first step in terms of identifying animal-based viruses which can pass to humans. Researchers say any viruses the models red flag should be subject to further lab tests.

Moreover, just because an animal virus may be able to infect human beings, that doesnt necessarily mean the virus will actually prove especially harmful, or particularly contagious for that matter.

Our findings show that the zoonotic potential of viruses can be inferred to a surprisingly large extent from their genome sequence. By highlighting viruses with the greatest potential to become zoonotic, genome-based ranking allows further ecological and virological characterization to be targeted more effectively, the researchers write in a media release.

These findings add a crucial piece to the already surprising amount of information that we can extract from the genetic sequence of viruses using AI techniques, adds study co-author Simon Babayan.

A genomic sequence is typically the first, and often only, information we have on newly-discovered viruses, and the more information we can extract from it, the sooner we might identify the virus origins and the zoonotic risk it may pose. As more viruses are characterized, the more effective our machine learning models will become at identifying the rare viruses that ought to be closely monitored and prioritized for preemptive vaccine development.

The study appears in the journal PLoS Biology.

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Artificial Intelligence in Manufacturing Industry is Expected to Reach US$ 11.5 Bn by 2027 – GlobeNewswire

PLEASANTON CA, Sept. 30, 2021 (GLOBE NEWSWIRE) -- The latest study titled Global Artificial Intelligence in Manufacturing Market Ecosystem By Components; By Deployment; By Technology; By Application; By Device; By Region; By End Users (Logistics, Healthcare, Automotive, Retail, BFSI, Defence, Aerospace, Oil & Gas, Others) Forecast by 2027 published by AllTheResearch, features an analysis of the current and future scenario of the global Artificial Intelligence (AI) in Manufacturing Market.

The Global Artificial Intelligence (AI) in Manufacturing Market was valued at USD 2.1 Bn in 2020 and is expected to reach USD 11.5 Bn by 2027, with a growing CAGR of 27.2% during the forecast period.

The Artificial Intelligence in manufacturing market is forecasted to grow at a high rate owing to the accelerating innovations in industrial IoT and automation.

The manufacturing industry is expected to be among the market leader in the artificial intelligence market. Further, the manufacturing industry is also expected to display the fastest growth during the forecast period due to rapid digital transformation to promote smart solutions in factories, logistics and management. The manufacturing industry is expected to generate an excess of 2,000 Pb of data every year, which is far more than industries such as BFSI, retail, and aerospace & defense, among others.

Request for sample copy of the report including ToC, Tables, and Figures with detailed informationathttps://www.alltheresearch.com/sample-request/381

Artificial Intelligence (AI) in Manufacturing Market Report Overview:

The report overview includes studying the market scope, leading players like Google, Amazon, Microsoft Corporation, IBM Corporation, Intel, etc., market segments and sub-segments, market analysis by type, application, geography.The report covers Leading Countries and analyzes the potential of the global Artificial Intelligence in Manufacturing industry, providing statistical information about market dynamics, growth factors, major challenges, PEST analysis, and market entry strategy Analysis, opportunities, and forecasts. The biggest highlight of the report is to provide companies in the industry with a strategic analysis of the impact of COVID-19.

Key Findings:

Any Questions/Queries or need help? Speak with our analyst at https://www.alltheresearch.com/speak-to-analyst/381

The key players operating in the AI in manufacturing market are: IBM Corporation, Google Inc., Amazon.com Inc., Microsoft Corporation, Intel, General Electric (GE) Company, Nvidia, Siemens, Cisco Systems, Oracle Corporation, Alphabet Inc., SparkCognition Inc., Mitsubishi Electric, Micron Technology, Rockwell Automation, Sight Machine, Aquant Inc., Progress Software Corporation, Aibrain, General Vision Inc., SAP, Vicarious, Ubtech Robotics, Rethink Robotics, Flutura Decision Sciences & Analytics, Bright Machines, and More

Global AI in manufacturing market is expected to propel at a significant rate during the forecast period owing to the extensive application of artificial intelligence technology in varied industries such as automobile, energy and power, pharmaceuticals, and food & beverages.

The Global Artificial Intelligence in Manufacturing Market Segmentation:

For more Customization, Contact us athttps://www.alltheresearch.com/customization/381

Regional Analysis of Artificial Intelligence in Manufacturing Market:

In terms of geography, the Asia Pacific region was accounted to hold the largest market share in 2020 and is anticipated to grow at a significant pace throughout the forecast period. The dominance is majorly attributed to the growing manufacturing plants in developed and developing countries such as India, South Korea, China, and Japan. For instance, in May 2018, the South Korean Government announced its plan to invest 2.2 trillion South Korean Won for AI research till 2022. The Ministry of Information and Communication along with the Ministry of Education, Science and Technology established the artificial intelligence R&D strategy called National Strategy for Artificial Intelligence. The investment is expected to create various developments and advantages in the AI in manufacturing Market.

Moreover, the outbreak of covid-19 pandemic has disrupted the supply chain for various industries and also the manufacturing process in several countries including in APAC region. However, China was successful in preventing destructions to human health in Wuhan by implementing a lockdown. With decreasing risk to human health in Wuhan, the manufacturing activities are normalizing in China, thus boosting the AI in manufacturing Market.

Key Highlights of the Table of Contents:

Main Source of The Content: https://www.alltheresearch.com/report/381/artificial-intelligence-in-manufacturing-market-ecosystem

Key Coverage and Benefits:

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The report also provides an in-depth analysis of recent news developments and investments

View Recent Published Press-release by AllTheResearch on Artificial Intelligence in Manufacturing Market at

https://www.alltheresearch.com/press-release/ai-in-manufacturing-market-will-growing-at-a-cagr-27-2-by-2027-driven-by-advance-technologies

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Why automation, artificial intelligence and machine learning are becoming increasingly critical for SOC operations – Security Magazine

Why automation, artificial intelligence and machine learning are becoming increasingly critical for SOC operations This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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Researchers will create super-sophisticated artificial intelligence by ‘copying’ the human brain to a chip – ModularPhonesForum

AI is already an integral part of everyday technology, but there are still some barriers in the way of truly advanced AI. Samsung is now working on an exciting new approach that gives associations to science fiction stories.

The South Korean technology giant, in collaboration with researchers from Harvard University in the United States of America, He has a project in progress Which involves copying the human brain onto a computer chip thus bringing AI many steps forward.

The approach involves using so-called reverse engineering on the human brain to get to the bottom of how it is assembled, to simulate the architecture and operation of a computer chip.

The method is described in technical detail in a research document published by the journal Science temper nature (requires payment).

Admittedly, the idea itself isnt entirely new, and there are other AI concepts that draw inspiration from the way the brain works, but in this case its about more direct modeling of the brain.

This is a demanding job because the human brain is made up of an extremely wide network of about a hundred billion neurons and a thousand times more synapses, called synapses. In this regard, Samsung and the research team are planning to use what they themselves refer to as a revolutionary device the nanoelectrode array.

These nanoelectrodes should be able to detect electrical signals from brain cells with very high sensitivity, and use them to map where neurons connect to each other and how strong the connections are. In this way, an overview of the structure used to be recreated in a piece is configured.

Of course, this is not an ordinary computer chip, but a so-called neural chip that uses a different type of Samsungs 3D memory architecture. Neuroprocessing is also being worked on by many other actors, It was mentioned in previous articles here on digi.no (subscription required).

What the researchers envision is using this approach to develop a new type of memory chip that approximates the brains unique characteristics, characteristics such as low energy consumption, the ability to adapt to different environments, and learning without significant difficulties, not least of which is independence and cognitive abilities.

It remains to be seen whether the piece will become a reality, and the researchers admit that the vision is very ambitious. However, Samsung said it will continue to research the technology, so one fine day well likely see results in one form or another.

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It will use artificial intelligence to predict which virus will be next to move from animal to human – ModularPhonesForum

It is estimated that there are about 1.7 million types of viruses that attack different types of animals. Some of these have or may develop the ability to attack humans. This applies to a few of them, but how does one find out in advance which of them?

This matter was further investigated by British researchers at the University of Glasgow. They hope that in the future, artificial intelligence and machine learning can be used to beat viruses.

What the researchers found is that, to a surprising degree, it appears that the genetics of a virus determine whether or not it can become zoonotic that is, moving from animals to humans.

This is good news, because genetic sequencing is often the first and only source of information when newly discovered viruses emerge. Thus, one has a better chance than before to quickly determine the origin of the virus and to assess the animal risks it may pose.

The more viruses that are tested and characterized in this way, the better the machine learning model will be.

It could be a huge help in identifying rare viruses that should be closely monitored and prioritized for developing a preventative vaccine, said Simon Papian, one of the researchers behind the project. phys.org.

The research team began by collecting a data set of 861 known viruses from a total of 36 different virus families. They then built various machine learning models that determined the likelihood of infecting humans, based on the classification and relationship of known viruses that can infect humans.

Then they used the model that gave the best result, to analyze how likely it is that several viruses in animals could infect humans.

This provides a good basis for further laboratory research, but is still only a step in the right direction. The method the research team is developing, for example, says nothing about how easily the virus is transmitted between humans, or how suitably viruses are to actually causing disease.

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Google is using artificial intelligence to make information more useful – TechSpot

In brief: This week, Google unveiled plans for a way to search that combines images and text to give more context to search queries. The method can use a smartphone's camera in combination with AI, attempting to intuitively refine and expand search results.

At its Search On event this week, Google revealed details about how it plans to use a technology it calls Multitask Unified Model (MUM), which should intelligently figure out what a user is searching for based on images and text, as well as give users more ways to search for things.

While Google didn't give a specific date, its blog post stated the feature should roll out "in the coming months." Users will be able to point at something with a phone camera, tap an icon which Google calls Lens, and ask Google something related to what they're looking at. The blog post theorizes scenarios like taking a picture of a bicycle part you don't know the name of and asking Google how to fix it, or taking a picture of a pattern and trying to find socks with the same pattern.

Google initially introduced MUM back in May where it theorized more scenarios in which the AI might help expand and refine searches. If a user asks about climbing Mt. Fuji for instance, MUM might bring up results with information about the weather, what gear one might need, the mountain's height, and so-on.

A user should also be able to use MUM to take a picture of a piece of equipment or clothing and ask if it's suitable for climbing Mt. Fuji. MUM should additionally be able to deliver information it learns from sources in many different languages other than the one the user searched in.

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This is what companies can do to build more inclusive AI – World Economic Forum

Conversations around responsible artificial intelligence (AI) are heating up as the ethical implications of its use are increasingly felt in our daily lives and society. With AI influencing life-changing decisions around mortgage loans, healthcare, parole and more, an ethical approach to AI development isnt just a nice-to-have its a requirement.

In theory, companies want to produce AI thats inclusive, responsible and ethical both in service of their customers and to maintain their brand reputation; in practice, they often struggle with the specifics.

Creating AI thats inclusive requires a full shift in mindset throughout the entirety of the development process. It involves considering the full weight of every crucial decision in the build process. At a minimum, a full revamp in strategies around data, the AI model (programmes that represent the rules, numbers and any other algorithm-specific data structures required to make predictions for a specific task) and beyond will be needed.

Its the responsibility of the people who build AI solutions to ensure that their AI is inclusive and provides a net-positive benefit to society. To accomplish this, there are several essential steps to take during the AI life cycle:

1. Data: At the data stage, organizations collect, clean, annotate, and validate data for their machine learning models. At this phase of the AI life cycle theres maximum opportunity to incorporate an inclusive approach, as the data serves as the foundation of the model. Here are two factors to consider:

Without representative data, you cant hope to create an inclusive product. Spend the majority of the time on your project making sure youve got the data right or partner with an external data provider who can ensure the data is representative of the group for whom your model is built.

The most lucrative use cases of AI until 2025

Image: Statista

2. Model: While perhaps less weighty than the data element, there are critical opportunities during the building of the model through which to incorporate inclusive practices.

Strategizing and delivering on the right objectives (for instance, a KPI that measures bias) will take you a long way toward building a responsible end product.

3. Post-deployment: Some teams feel their work is mostly done after they deploy their model, but the opposite is true: this is only the beginning of the models life cycle. Models need significant maintenance and retraining to stay at the same performance level and this cant be an afterthought: letting performance dip could have serious ethical implications under certain use cases. Incorporate the following best practices as part of your post-deployment infrastructure:

The above isnt an exact blueprint, but offers a starting point for transitioning inclusive AI from a theoretical discussion to an action plan for your organization. If you approach AI creation with an inclusive lens, youll ideally find many additional steps to take throughout the development life cycle. Its a mission-critical endeavour: for AI to work well, it needs to work well for everyone.

Written by

Mark Brayan , Chief Executive Officer, Appen

The views expressed in this article are those of the author alone and not the World Economic Forum.

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IonQ is set to make its public trading debut. Here’s a look at the quantum computing company’s 2021 highlights – Technical.ly DC

This week, College Park, Maryland quantum computing company IonQ is officially going public.

Following a merger with dMY Technology Group Inc. III, which is a special purpose acquisition company based in Las Vegas, the firm will begin trading on the New York Stock Exchange on Friday, Oct. 1. The merger was officially approved on Tuesday by dMY III stockholders.

The company will be trading under the symbol IONQ, and CEO Peter Chapman said it is expected to raise $635 million, with an additional $132 million in outstanding warrants. Of this, $350 million will be raised through private investment in public equity (PIPE) funding from investors including Fidelity Management & Research Company, Silver Lake, Breakthrough Energy Ventures, MSD Partners, Hyundaiand Kia.

Founded in 2015 by University of Maryland College Park professor Dr. Chris Monroe and Duke University professor Dr. Jungsang Kim, IonQ specializes in trapped ion quantum computing. Drawing on two decades of research, the company is working to create more powerful computers than those currently available, and apply the technology to solving foundational problems in new ways.

IonQ first announced plans to go public earlier this year, estimating that the company would be valued at $2 billion when the deal closed. Chapman told Technical.ly that the IPO will make IonQ more competitive in talent recruiting and help it to reach the manufacturing stage with its products, particularly in quantum networking.

This was not actually a liquidity event for us, Chapman said. Most people when they get to an IPO, theyre thinking about how can they cash out there. But there isnt anyone actually cashing out. We just thought of this as a means to an end on how to raise money.

Going forward, Chapman said the company expects to double its 90-person team, which is spread across offices in College Park, Seattle and Boston.

Since it announced the IPO in March, 2021 has been a banner year for IonQ. It has landed partnerships that will help to further explore real-world applications of quantum computing with GE Research, the Fidelity Center for Applied Technology, Goldman Sachs and QCWare, Google, Accenture andSoftbank. It is teaming with theUniversity of Maryland on a new lab in College Park.

When it comes to tech advances, the company launched what it says is the industrys first reconfigurable multicore quantum architecture, as well as designed and launched a chipset known as Evaporated Glass Traps. This year also brought its second research credit program cohort, which offers free credits to academics building novel quantum algorithms (Want to know more about quantums rise out of the lab? Check out our explainer here).

[Going public] will lift all the boats in quantum computing in this sense that we can show that it can be done in quantum now, and thats probably good for the entire industry, Chapman said.

Nir Minerbi, CEO and cofounder of Classiq, a fellow quantum company, agrees, although he thinks theres still more work to be done in the industry.

Organizations understand that the ability to extract true business value from quantum computing grows as more qubits with higher quality are available, said Minerbi in a statement. IonQs funding is good news for the industry and their quantum roadmap is encouraging as well.

As the company moves into the new year, Chapman said IonQ will be expanding into the drug discovery, materials science and battery industries. But, he noted, the possibilities with quantum computing offer plenty of new, yet-to-be-discovered options, as well.

Every day at the company is fun. You have a customer thats doing something that has never been done before, Chapman said. Its a pretty exciting place to be.

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