Category Archives: Artificial Intelligence

Unusual applications of Artificial Intelligence are less commonly discussed but still has significance

Artificial Intelligence is leading humans towards digital transformation. From weather predictions, stock market crashes, drug discovery, to understanding customer data, better filling systems, artificial intelligence is ruling every industry. Artificial Intelligence is capable of achieving enormous feats in the modern world, but it does sound weird when you hear about an AI system that can test beer or AI perfumers and Chef AI. This article features the top 10 unusual applications of artificial intelligence that are in use today.

With the integration of Artificial Intelligence and a wide array of technological advancements, one can use the power of AI to uncover hidden truths about the Universe, which would otherwise be deemed as impossible. To discuss some of the unusual applications of Artificial Intelligence in exploring the Universe, it was found that the Generative Adversarial Networks can create mappings of the Universe extremely similar to the realistic expectations of what humans have hoped for in their theoretical visions.

IBM research is collaborated with a German Fragrance House Symrise to introduce AI perfumers in the perfume industry. In June, an AI perfume created by a computer system was launched on the Brazilian market. Artificial intelligence involved combined ingredients in a manner unthinkable to most humans. It is one of the best unusual applications of artificial intelligence that are in use today.

San Franciscos Creator uses a combination of AI and robotics for every step of the burger-making process, i.e., grinding beef, frying patties, toasting buns, dispensing condiments, adding tomatoes, onions, and pickles, and even assembling burgers. Meanwhile, MIT students and researchers have built an AI system that generates new pizza recipes. Chef AI is the future of the food industry and also one of the best unusual applications of artificial intelligence that are in use today.

Scientists worked to develop an artificial bee with an incorporated AI system that mimics. The bee drones come with a GPS to locate a specific location and a High-resolution Camera working similar to the eye of a honeybee.

AI helps in better-removing germs and protecting teeth against bacteria that even stay after regular brushing. The scrubbers on AI-Brush are trained for several brushing styles to support thousands of people. Besides, the AI system also comes with a deep learning algorithm that remembers the brushing behavior of a particular user and adopts its nature. After learning, it provides the user with the same brushing behavior based on his usage habit.

Fashion retailers have turned to AI to make their businesses more efficient, replace photoshoots and predict what people will want to buy and wear in the future. AI can map clothes onto peoples bodies these can either be models or potential customers who upload their own photos to an app.

Dont be surprised to know that the hit song you are enjoying will be written with the help of AI. In fact, its already been done: In 2017, Alex Da Kids single, Not Easy, performed with Elle King, X Ambassadors, and Wiz Khalifa, made both the Billboard and iTunes charts. AI for lyrics writing is one of the unusual applications of Artificial Intelligence that are in use today.

Artificial Intelligence can create a video game, music, screenplays, novel and poetry and the next a movie custom-built to the individual viewer. Machine learning and artificial intelligence algorithms can be utilized to create new scripts or write synopsis and characters for movies. It is one of the unusual applications of Artificial Intelligence that are in use today.

Whether you are a firm believer of Astrology, Numerology, or any other similar field, or you dont really care about the existence of such fields, it is fascinating to explore what impact Artificial Intelligence would have on these subjects matters. Alexander Reben, an artist and MIT-trained roboticist, built a system that generates one-line predictions by training a neural network on the messages found in thousands of fortune cookies and thousands of inspirational expressions he scraped off the Internet.

Beauty AI is the new judge of the beauty contest that will perceive you on how they see you and does not judge on how they feel about you. Beauty.AI is an artificial intelligence solution that can perceive human beauty by how healthy someone is neither by age nor by nationality. It is one of the unusual applications of Artificial Intelligence that are in use today.

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Top 10 Unusual Applications of Artificial Intelligence in Use - Analytics Insight

Our world is increasingly technology centric, offering unlimited opportunities with just the click of a button. As a result, artificial intelligence (AI), which aims to create technology with human-like problem-solving and decision-making capabilities, is becoming part of our daily lives, be it through voice controlled personal assistants, smart cars, or automated investing platforms. The use of AI gives rise to an array of societal, economic, and legal issues causing the European Commission to include AI in its digital strategy and propose the first legal framework on AI in an attempt to encourage the uptake of AI and address the risks associated with its uses.

In April 2021 the EU Commission published its draft proposal for a Regulation laying down harmonised rules on Artificial Intelligence (the Proposal). The objectives of the Proposal are to:

(a) facilitate the development of a single market for lawful, safe and trustworthy AI applications and prevent market fragmentation.

(b) guarantee legal certainty to facilitate investment and innovation in AI;

(c) enhance governance and effective enforcement of existing law on fundamental rights and safety requirements applicable to AI systems;

(d) safeguard and respect existing law on fundamental rights and European values;

The Proposal defines AI systems as software that is developed with machine learning, logic-and knowledge-based approaches, and/ or statistical approaches and can, for a given set of human defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with. The Proposal puts forward rules for the development, placement on the market and use of AI systems following a human-centric and risk-based approach. Risk is measured in relation to the purpose of the AI system similar to product safety legislation.

SCOPE

The scope of application of the Proposal is broad and will have extraterritorial effect. The Proposal is intended to apply to (i) providers placing on the market or putting into service AI systems in the EU, irrespective of whether they are established within the EU or in a third country; (ii) users of AI systems located within the EU; and (iii) providers and users of AI systems located outside of the EU, where the output produced by the system is used in the EU.

AI systems used exclusively for military purposes are expressly excluded from the scope of the Proposal together with those used by public authorities or international organisations in a third country to the extent such use is in the framework of international agreements for law enforcement and judicial cooperation with the EU or individual Member States.

PROHIBITED ARTIFICIAL INTELLIGENCE PRACTICES

The Proposal lists several AI system types which are considered to create an unacceptable level of risk and are prohibited. These include:

(a) AI systems that deploy subliminal techniques beyond a persons consciousness to materially distort a persons behaviour in a manner that causes or is likely to cause physical or psychological harm;

(b) AI systems that exploit any of the vulnerabilities of a specific group of persons in order to materially distort the behaviour of a person pertaining to that group in a manner that causes or is likely to cause physical or psychological harm;

(c) AI systems placed on the market or put into service by public authorities or on their behalf for the evaluation or classification of the trustworthiness of natural persons over a certain period of time based on their social behaviour or known or predicted personal or personality characteristics, with the social score leading to detrimental or unfavourable treatment of persons either in social contexts which are unrelated to the contexts in which the data was originally generated or collected and/or disproportionate to their social behaviour or its gravity;

(d) the use of real-time remote biometric identification systems in publicly accessible spaces for the purpose of law enforcement, unless and in as far as such use is strictly necessary for reasons such as targeted search for specific potential victims of crime, prevention of specific, substantial and imminent threat of a terrorist attack, detection, identification and prosecution of a perpetrator or suspect of a criminal offence in relation to whom a European arrest warrant is in place.

HIGH-RISK SYSTEMS

AI systems identified as high-risk are only permitted subject to compliance with mandatory requirements and a conformity assessment. High-risk AI systems include:

(a) AI systems intended to be used for the real-time and post remote biometric identification of natural persons;

(b) AI systems intended to be used for the purpose of determining access or assigning natural persons to educational and vocational training institutions;

(c) AI systems intended to be used for recruitment or selection of natural persons, notably for advertising vacancies, screening or filtering applications, evaluating candidates in the course of interviews or tests;

(d) AI systems intended to be used by public authorities or on behalf of public authorities to evaluate the eligibility of natural persons for public assistance benefits and services, as well as to grant, reduce, revoke, or reclaim such benefits and services;

(e) AI systems intended to be used by law enforcement authorities as polygraphs and similar tools or to detect the emotional state of a natural person;

(f) AI systems intended to assist a judicial authority in researching and interpreting facts and the law and in applying the law to a concrete set of facts.

High-risk AI systems will give rise to obligations relating to data quality and governance, documentation and record keeping, conformity assessment, transparency and provision of information to users, human oversight, robustness, accuracy and security.

The Proposal envisages an EU-wide database for stand-alone high risk AI systems with fundamental rights implications to be operated by the European Commission and provided with data by the providers of the AI systems prior to placing them on the market or otherwise putting them into service.

Providers of high risk AI systems will be required to establish and document a post-market monitoring system and to collect, document and analyse data on the performance of the AI system and report serious incidents and malfunctioning to the market surveillance authorities.

LOW RISK AND MINIMAL RISK SYSTEMS

AI systems which do not qualify as prohibited or high-risk systems are not subject to AI specific requirements other than disclosing the use of AI to the users. However, they could choose to adopt a code of conduct with a view to increasing their AI systems trustworthiness.

ENFORCEMENT AND SANCTIONS

The Proposal provides for a two-tier AI governance system at European Union and national level. At Union level, it is envisaged that an Artificial Intelligence Board composed of representatives of the Member States and the Commission will be created to facilitate harmonised implementation and cooperation. At national level, each Member State will be expected to designate one or more national competent authorities and a national supervisory authority.

It is envisaged that non-compliance of an AI system with requirements or obligations provided for in the Proposal will result in, among other sanctions, administrative fines. The Proposal sets out three sets of maximum thresholds for administrative fines that may be imposed for relevant infringements ranging from the higher of 30m or 6% of the total worldwide annual turnover of the offender to the higher of 10m or 2% of the worldwide annual turnover of the offender.

CONCLUSION

The Proposal is still at the early stages of the European legislative process. Its human-centric, risk-based approach and obligations relating to record keeping, transparency and reporting as well as the introduction of significant administrative fines evoke parallels with the European General Data Protection Regulation. The definition of an AI system as well as the approach to determining the category of risk of an AI system have already been criticised as requiring extensive analysis and having the potential to add significant compliance costs for users and providers. It remains to be seen whether the Proposal will meet with the support of the European Parliament and of the Council and whether it will be successful in setting a new global standard for AI.

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Proposed EU Framework on Artificial Intelligence - everything you need to know - Lexology

Artificial intelligence can be used to identify the secrets of the universe and understand the big bang

Artificial intelligence has entirely transformed many areas of our daily lives, both in the professional and personal aspects. Starting from healthcare to transport, several industrial tasks are now being carried out by computers or advanced robots in a swifter and more efficient fashion. These machines can carry out more dangerous and possibly hazardous jobs that are there in the industry, with minimum errors. The robots can enter unbreathable environments, such as areas that require deep-sea diving, making ceratin processes much safer and faster. All in all, AI has successfully improved the way we perceive our professional and personal environments. Recently, AI researchers have also discovered that integrating machine learning and neural networks can help uncover and clear up several secrets of the big bang theory, the deep mysteries of the universe.

In recent years, Artificial Intelligence has become much more accessible than what humans perceive. With the development of powerful tools like deep learning, it has become quite easier and seamless to teach computers to perform tasks without being explicitly programmed. Deep learning has transformed fields such as speech recognition, NLP, computer vision, and other technologies. Researchers and scientists can now use this technology to study the universe, and develop algorithms from the data gathered through space telescopes to better understand the formation of galaxies.

Big bang can be efficiently studied with the help of high-performance computers and highly complex computer simulations whose results are quite difficult to evaluate. Integrating neural networks can tie the mathematical prospects of space physics with logic and unravel several secrets. Neural networks are used especially for image recognition. Besides, with the help of such neural networks, it becomes possible to make predictions about the systems. These deep learning models will help astronomers see the concepts and technologies behind the incidents taking place in the universe. Furthermore, a hybrid convolutional neural network model, based on deep residual networks is presented. With the help of Artificial Intelligence, scientists can also explore the mapping of the universe and identify distant objects in it.

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Can Artificial Intelligence Unravel the Mysteries of the Big Bang Theory? - Analytics Insight

GAO's managing director Cathleen Berrick: 'Business as usual for DoD is really a losing proposition'

WASHINGTON The U.S. Government Accountability Office in a new report says the Defense Department has to be better prepared to respond to Chinas advances in space, cyberwarfare and artificial intelligence.

Successful preparation for strategic competition with China will depend on continuing efforts to increase U.S. combat credibility and enhance conventional deterrence that can help prevent conflict, protect U.S. interestsand assure allies, GAO said in the report titled Challenges Facing DoD in Strategic Competition with China.

The three-page summary GAO published Feb. 15 is the unclassified version of a much more extensive report that is classified.

Going forward, said GAO, the U.S. defense Department should be preparedto maintain supply chains, gather intelligence, and responsibly leverage emerging space, cyber, and AI technologies in response to potential threats.

The watchdog agency suggested that Congress will need to pay close attention to DoDs efforts in these areas and whether DoD takes timely actions.

Cathleen Berrick, GAOs managing director of defense capabilities and management, said DoD has taken some steps to invest and innovate in response to Chinas leaps in cybersecurity, space and the use of artificial intelligence. But more could be done, she said.

With regard to space, GAO in the report listed a number of recommendations it has issued in the last several years such as the need for DoD to revamp its satellite-based communications architecture and ground-based systems for the command and control of satellites. These are actions that may better position DoD to address the challenges with China but DOD has not yet implemented.

Recent reports that a Chinese satellite actually grabbed another Chinese satellite and pulled it out of its orbit really demonstrates a significant leap in anti-satellite capabilities, said Berrick.

Space is very important because DoD, of course, relies on its space based capabilities for communications, for navigation and targeting, and also for intelligence collection. And China knows this and is actively developing systems that could counter these capabilities.

I think its important for Congress and DoD to continue to have this China pacing threat reality at the forefront of their thinking because business as usual for DoD is really a losing proposition, Berrick said. This means theyre going to have to figure out how to adapt everything the department does away from the current industrial age approach to something more suitable for the information age.

Berrick said it would not be an understatement to say that strategic competition with China is unlike any other challenge DoD has faced.

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GAO: DoD has to step up efforts in space, cyber and artificial intelligence to compete with China - SpaceNews

A new opportunity for WPI students to bolster their understanding of diversity, equity, and inclusion in Artificial Intelligence (AI) is coming to WPI. Through AI4ALL, a national program supported by Melinda Gates Pivotal Ventures, students will be able to take a series of classes on the ethics of AI, collaborate with industry partners, and gain access to AI4ALLs alumni network, to pursue internships and jobs. Students will also receive a certificate upon completion of the program.

Associate Professor of Computer Science Rodica Neamtu spearheaded the effort to bring AI4ALL to WPI and will lead the program. AI4ALL seeks to help students become different kinds of leaders, says Neamtu. Leaders who are not just knowledgeable scientists invested in the work, but also have a deep understanding of the social and ethical implications of their work.

Applications for the program open in late March, with the first module scheduled to begin in fall 2022. Information sessions will be held leading up to the application deadline.

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Nationally Recognized Diversity Equity and Inclusion in Artificial Intelligence Program Coming to WPI - WPI News

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Sonantic startup engineers taught artificial intelligence incredible speech realism thanks to the introduction of very important changes in the program. The technology is already being used in games.

The technology that imitates the human voice was shown on February 14, teaching how to confess one's love. It is simply impossible to distinguish it from a real person.

Demonstration of the work of artificial intelligence: video

The artificial voice in the video sounds quite natural and very realistic, especially the sigh and laughter that fit perfectly into her speech. That's why it's so surprising when she suddenly confesses: I'm not real. I was never born. And I'll never die. Because I don't exist.

Remind. Startup Sonantic announced the creation of a neural network to generate speech with imitation of human emotions in 2020. For two years, the developers have made great progress. In the first commercials, IS's voice did not sound very natural, and the speech was accompanied by distortion. The company is already cooperating with game studios. Her work was used by Obsidian Entertainment to generate the voices of some minor characters in The Outer Worlds. In August 2021, a startup used technology to replicate the voice of actor Val Kilmer, who lost the ability to speak as a result of laryngeal cancer.

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The realism of artificial intelligence has reached a new level - The Times Hub

Artificial intelligence has been with us for decades -- just throw on a movie if you don't believe it.

Even though A.I. may feel like a newer phenomenon, the groundwork of these technologies are more dated than you'd think. The English mathematician Alan Turing, considered by some as the father of modern computer science, started questioning machine intelligence in 1950. Those questions resulted in the Turing Test, which gauges a machine's capacity to give the impression of"thinking" like a human.

The concept of A.I. can feel nebulous, but it doesn't fall under just one umbrella. From smart assistants and robotics to self-driving cars, A.I. manifests in different forms...some more clear than others. Spoiler alert! Here are 10 movies in chronological order that can help you visualize A.I.:

1. Metropolis (1927)

German directorFritz Lang's classicMetropolis showcases one of the earliest depictions of A.I. in film, with the robot, Maria, transformed intothe likeness of a woman. The movie takes place in an industrial city called Metropolis that is strikingly divided by class, where Robot Maria wreaks havoc across the city.

2. 2001: A Space Odyssey (1968)

Stanley Kubrick's 2001 is notable for its early depictionof A.I. and is yet another cautionary tale in which technology takes a turn for the worse. A handful of scientists are aboard a spacecraft headed to Jupiter where a supercomputer, HAL(IBM to the cynical), runs most of the spaceship's operations. After HAL makes a mistake and tries to attribute it to human error, the supercomputer fights back when those aboard the ship attempt to disconnect it.

3. Blade Runner (1982)and Blade Runner 2049 (2017)

The original Blade Runner (1982) featured Harrison Ford hunting down "replicants,"or humanoids powered by A.I., which are almost indistinguishable from humans. In Blade Runner2049 (2017), Ryan Gosling's character, Officer K, lives with an A.I. hologram, Joi. So at least we're getting along better with our bots.

4. The Terminator (1984)

The Terminator's plot focuses on a man-made artificial intelligence network referred to as Skynet -- despite Skynet being created for military purposes, the system ends up plotting to kill mankind. Arnold Schwarzenegger launched his acting career out ofhis role as the Terminator, a time-traveling cyborg killer that masquerades as a human. The film probes the question -- and consequences -- of what happens when robots start thinking for themselves.

5. The Matrix Series (1999-2021)

Keanu Reeves stars in this cult classic as Thomas Anderson/Neo, a computer programmer by day and hacker by night who uncovers the truth behind the simulation known as "the Matrix." The simulated reality is a product of artificially intelligent programs that enslaved the human race. Human beings are kept asleep in "pods," where they unwittingly participate in the simulated reality of the Matrix while their bodies are used to harvest energy.

6. I, Robot (2004)

Thissci-fiflickstarring Will Smith takes place in 2035 in a society where robots with human-like featuresserve humankind. An artificial intelligent supercomputer, dubbed VIKI (which stands for Virtual Interactive Kinetic Intelligence), is one to watch, especially once a programming bug goes awry. The defect in VIKI's programming leads the supercomputer to believe that the robots must take charge in order to protect mankind from itself.

7. WALL-E (2008)

Disney Pixar's WALL-E follows a robot of the same namewhose main role is to compact garbage on a trash-ridden Earth. But after spending centuries alone, WALL-E evolves into a sentient piece of machinery who turns out to be very lonely. The movie takes place in2805 and follows WALL-E and another robot, named Eve, who's job is toanalyzeif a planet is habitable for humans.

8. Tron Legacy (2010)

The Tron universe is filled to the brim with A.I. given that it takes place in a virtual world, known as "the Grid." The movie's protagonist, Sam, finds himself accidentally uploaded to the Grid, where he embarks on an adventure that leads him face-to-face with algorithms and computer programs.The Grid is protected by programs such as Tron, but corrupt A.I. programs surface as well throughout the virtual network.

9. Her (2013)

Joaquin Phoenix plays Theodore Twombly, a professional letter writer going through a divorce. To help himself cope, Theodore picks up a new operating system with advanced A.I. features. He selects a female voice for the OS, naming the device Samantha (voiced by Scarlett Johansson), but it proves to have smart capabilities of itsown. Or is it, her own?Theodore spends a lot of time talking with Samantha, eventually falling in love. The film traces their budding relationship and confronts the notion of sentience and A.I.

10. Ex-Machina (2014)

After winning a contest at his workplace, programmer Caleb Smith meets his company's CEO, Nathan Bateman. Nathan reveals to Caleb that he's created a robot with artificial intelligence capabilities. Caleb's task? Assess if the feminine humanoid robot, Ava, is able to show signs of intelligent human-like behavior: in other words, pass the Turing Test. Ava has a human-like face and physique, but her "limbs" are composed of metal and electrical wiring. It's later revealed that other characters aren't exactly human, either.

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The Top 10 Movies to Help You Envision Artificial Intelligence - Inc.

Artificial intelligence (AI) and machine learning (ML) seem to have piqued the interest of automated data collection providers. While web scraping has been around for some time, AI/ML implementations have appeared in the line of sight of providers only recently.

Aleksandras ulenko, Product Owner at Oxylabs.io, who has been working with these solutions for several years, shares his insights on the importance of artificial intelligence, machine learning, and web scraping.

BN: How has the implementation of AI/ML solutions changed the way you approach development?

AS: AI/ML has an interesting work-payoff ratio. Good models can sometimes take months to write and develop. Until then, you dont really have anything. Dedicated scrapers or parsers, on the other hand, can take up to a day or two. When you have an ML model, however, maintaining it takes a lot less time for the amount of work it covers.

So, theres always a choice. You can build dedicated scrapers and parsers, which will take significant amounts of time and effort to maintain once they start stacking up. The other choice is to have "nothing" for a significant amount of time, but a brilliant solution later on, which will save you tons of time and effort.

Theres some theoretical point where developing custom solutions is no longer worth it. Unfortunately, theres no mathematical formula to arrive at the correct answer. You have to make a decision when all the repetitive tasks are just too much of a hog on resources.

BN: Have these solutions had a visible impact on the deliverability and overall viability of the project?

AS: Getting started with machine learning is tough, though. Its still, comparatively speaking, a niche specialization. In other words, you wont find many developers that dabble in ML, and knowing how hard it can be to find one for any discipline, its definitely a tough river to cross.

Yet, if the business approach to scraping is based on a long-term vision, ML will definitely come in handy sometime down the road. Every good vision has scaling in it and with scaling comes repetitive tasks. These are best handled with machine learning.

Our awesome achievement we call Adaptive Parser is a great example. It was once almost unthinkable that a machine learning model could be of such high benefit. Now the solution can deliver parsed results from a multitude of e-commerce product pages, irrespective of the changes between them or any that happen over time. Such a solution is completely irreplaceable.

BN: In a previous interview, youve mentioned the importance of making things more user-friendly for web scraping solutions. Is there any particular reason you would recommend moving development towards no-code implementations?

AS: Even companies that have large IT departments may have issues with integration. Developers are almost always busy. Taking time out of their schedules for integration purposes is tough. Most end-users of the data Scraper APIs, after all, arent tech-savvy.

Additionally, the departments that would need scraping the most such as marketing, data analytics, etc., might not have enough sway in deciding the roadmaps of developers. As such, even relatively small hurdles can become impactful enough. Scrapers should now be developed with a non-tech user in mind.

There should be plenty of visuals that allow for a simplified construction of workflows with a dashboard thats used to deliver information clearly. Scraping is becoming something done by everyone.

BN: What do you think lies in the future of scraping? Will websites become increasingly protective of their data, or will they eventually forego most anti-scraping sentiment?

AS: There are two of the answers I can give. One is "more of the same". Surely, a boring one, but its inevitable. Delving deeper into scaling and proliferation of web scraping isnt as fun as the next question -- the legal context.

Currently, it seems as if our position in the industry isnt perfectly decided. Case law forms the basis of how we think and approach web scraping. Yet, it all might change on a whim. Were closely monitoring the developments due to the inherent fragility of the situation.

Theres a possibility that companies will realize the value of their data and start selling it on third-party marketplaces. It would reduce the value of web scraping as a whole as you could simply acquire what you need for a small price. Most businesses, after all, need the data and the insights, not web scraping. Its a means to an end.

Theres a lot of potential in the grand vision of Web 3.0 -- the initiative to make the whole Web interconnected and machine-readable. If this vision came to life, the whole data gathering landscape would be vastly transformed: the Web would become much easier to explore and organize, parsing would become a thing of the past, and webmasters would get used to the idea of their data being consumed by non-human actors.

Finally, I think user-friendliness will be the focus in the future. I dont mean just the no-code part of scraping. A large part of getting data is exploration -- finding where and how its stored and getting to it. Customers will often formulate an abstract request and developers will follow up with methods to acquire what is needed.

In the future, I expect, the exploration phase will be much simpler. Maybe well be able to take the abstract requests and turn them into something actionable through an interface. In the end, web scraping is breaking away from its shell of being something code-ridden or hard to understand and evolving into a daily activity for everyone.

Photo Credit: Photon photo/Shutterstock

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Tying Artificial intelligence and web scraping together [Q&A] - BetaNews

In April last year, the European Commission published its ambitious proposal to regulate Artificial Intelligence. The regulation was meant to be the first of its kind, but the progress has been slow so far due to the file's technical, political and juridical complexity.

Meanwhile, the EU lost its first-mover advantage as other jurisdictions like China and Brazil have managed to pass their legislation first. As the proposal is entering a crucial year, it is high time to take stock of the state of play, the ongoing policy discussions, notably around data, and potential implications for businesses.

For the European Parliament, delays have been mainly due to more than six months of political disputes between lawmakers over who was to take the lead in the file. The result was a co-lead between the centrists and the center-left, sidelining the conservative European People's Party.

Members of European Parliament are now trying to make up for lost time. The first draft of the report is planned for April, with discussions on amendments throughout the summer. The intention is to reach a compromise by September and hold the final vote in November.

The timeline seems particularly ambitious since co-leads involve double the number of people, inevitably slowing down the process. The question will be to what extent the co-rapporteurs will remain aligned on the critical political issues as the center-right will try to lure the liberals into more business-friendly rules.

Meanwhile, the EU Council made some progress on the file, however, limited by its highly technical nature. It is telling that even national governments, which have significantly more resources than MEPs, struggle to understand the new rules' full implications.

Slovenia, which led the diplomatic talks for the second half of 2021, aimed to develop a compromise for 15 articles, but only covered the first seven. With the beginning of the French presidency in January, the file is expected to move faster as Paris aims to provide a full compromise by April.

As the policy discussions made some progress in the EU Council, several sticking points emerged. The very definition of AI systems is problematic, as European governments distinguish them from traditional software programs or statistical methods.

The diplomats also added a new category for "general purpose" AI, such as synthetic data packages or language models. However, there is still no clear understanding of whether the responsibility should be attributed upstream, to the producer, or downstream, to the provider.

The use of real-time biometric recognition systems has primarily monopolized the public debate, as the commission's proposal falls short of a total ban for some crucial exceptions, notably terrorist attacks and kidnapping. In October, lawmakers adopted a resolution pushing for a complete ban, echoing the argument made by civil society that these exceptions provide a dangerous slippery slope.

By contrast, facial recognition technologies are increasingly common in Europe. A majority of member states wants to keep or even expand the exceptions to border control, with Germany so far relatively isolated in calling for a total ban.

"The European Commission did propose a set of criteria for updating the list of high-risk applications. However, it did not provide a justification for the existing list, which might mean that any update might be extremely difficult to justify," Lilian Edwards, a professor at Newcastle University, said.

Put differently, since the reasoning behind the lists of prohibited or high-risk AI uses are largely value-based, they are likely to remain heatedly debated points point through the whole legislative process.

For instance, the Future of Life Institute has been arguing for a broader definition of manipulation, which might profoundly impact the advertising sector and the way online platforms currently operate.

A dividing line that is likely to emerge systematically in the debate is the tension between the innovation needs of the industry, as some member states already stressed, and ensuring consumer protection in the broadest sense, including the use of personal data.

This underlying tension is best illustrated in the ongoing discussion for the report of the parliamentary committee on Artificial Intelligence in a Digital Age, which are progressing in parallel to the AI Act.

In his initial draft, conservative MEP Axel Voss attacked the General Data Protection Regulation, presenting AI as part of a technological race where Europe risks becoming China's "economic colony" if it did not relax its privacy rules.

The report faced backlash from left-to-center policymakers, who saw it as an attempt to water down the EU's hard-fought data protection law. For progressive MEPs, data-hungry algorithms fed with vast amounts of personal data might not be desirable, and they draw a parallel with their activism in trying to curb personalized advertising.

"Which algorithms do we train with vast amounts of personal data? Likely those that automatically classify, profile or identify people based on their personal details often with huge consequences and risks of discrimination or even manipulation. Do we really want to be using those, let alone 'leading' their development?" MEP Kim van Sparrentak said.

However, the need to find a balance with data protection has also been underlined by Bojana Bellamy, president of the Centre for Information Policy Leadership, who notes how some fundamental principles of the GDPR would be in contradiction with the AI regulation.

In particular, a core principle of the GDPR is data minimization, namely that only the personal data strictly needed for completing a specific task is processed and should not be retained for longer than necessary. Conversely, the more AI-powered tools receive data, the more robust and accurate they become, leading (at least in theory) to a fairer and non-biased outcome.

For Bojana, this tension is due to a lack of a holistic strategy in the EU's hectic digital agenda, arguing that policymakers should follow a more result-oriented approach to what they are trying to achieve. These contradicting notions might fall on the industry practitioners, which might be requested to square a fair and unbiased system while also minimizing the amount of personal data collected.

The draft AI law includes a series of obligations for system providers, namely the organizations that make the AI applications available on the market or put them into services. These obligations will need to be operationalized, for instance, what it means to have a "fair" system, to what length should "transparency" go and how is "robustness" defined.

In other words, providers will have to put a system in place to manage risks and ensure compliance with support from their suppliers. For instance, a supplier of training data would need to detail how the data was selected and obtained, how it was categorized and the methodology used to ensure representativeness.

In this regard, the AI Act explicitly refers to harmonized standards that industry practitioners must develop to exchange information to make the process cost-efficient. For example, the Global Digital Foundation, a digital policy network, is already working on an industry coalition to create a relevant framework and toolset to share information consistently across the value chain.

In this context, European businesses fear that if the EU's privacy rules are not effectively incorporated in the international standards, they could be put at a competitive disadvantage. The European Tech Alliance, a coalition of EU-born heavyweights such as Spotify and Zalando, voiced concerns that the initial proposal did not include an assessment for training dataset collected in third countries that might use data collected via practices at odds with the GDPR.

Adopting industry standards creates a presumption of conformity, minimizing the risk and costs for compliance. These incentives are so strong that harmonized standards tend to become universally adopted by industry practitioners, as the cost for departing from them become prohibitive. Academics have defined standardization as the "real rulemaking" of the AI regulation.

"The regulatory approach of the AI Act, i.e. standards compliance, is not a guarantee of low barriers for the SMEs. On the contrary, standards compliance is often perceived by SMEs as a costly exercise due to expensive conformity assessment that needs to be carried out by third parties," Sebastiano Toffaletti, secretary-general of the European DIGITAL SME Alliance, said.

By contrast, European businesses that are not strictly "digital" but that could embed AI-powered tools into their daily operations see the AI Act as a way to bring legal clarity and ensure consumer trust.

"The key question is to understand how can we build a sense of trust as a business and how can we translate it to our customers," Nozha Boujemaa, global vice president for digital ethics and responsible AI at IKEA, said.

Photo by Michael Dziedzic on Unsplash

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Inside the EU's rocky path to regulate artificial intelligence - International Association of Privacy Professionals

image:INL researchers perform experiments using the Temporal Analysis of Products (TAP). view more

Credit: Idaho National Laboratory

If you follow the directionsin a cake recipe, you expect to end up with a nice fluffy cake.In Idaho Falls,though, the elevation can affecttheseresults.When baked goods dont turn outas expected, the troubleshooting begins.This happens in chemistry,too.Chemistsmustbeable to account for how subtle changes or additions may affect the outcome for better or worse.

Chemists maketheir version ofrecipes, known as reactions,to create specific materials.These materialsare essential ingredients to an array of products found in healthcare, farming, vehicles andother everyday productsfrom diapers to diesel.When chemists develop new materials, they rely on information from previous experiments and predictions based onpriorknowledge ofhowdifferent starting materials interact with others and behave underspecificconditions.There are a lot of assumptions, guesswork and experimentation in designing reactions using traditional methods.New computational methods like machine learning can help scientists better understand complex processes like chemical reactions.While it can be challenging forhumans topick outpatternshiddenwithin the data from many different experiments, computers excel at this task.

Machine learning isan advancedcomputational toolwhereprogrammers givecomputerslots ofdata andminimalinstructions about how to interpret it. Instead of incorporatinghuman bias into the analysis, the computer isonly instructed to pull out what it finds to be important from the data. This could be an image of a cat (if the input is all the photos on the internet) orinformation about how a chemical reactionproceeds through a series ofsteps, as is thecasefora set of machine learning experiments that are ongoing at Idaho National Laboratory.

At the lab,researchersworking with the innovative Temporal Analysis of Products (TAP)reactorsystemaretryingto improveunderstanding of chemical reactions by studying the role of catalysts,whicharecomponentsthat can be added toamixture of chemicals to alter thereactionprocess.Oftencatalystsspeed up thereaction,but they can do other things,too. In baking and brewing,enzymesact as catalyststo speed up fermentationandbreakdown sugars in wheat (glucose) into alcohol and carbon dioxide,which creates the bubbles that make bread riseand beer foam.

In the laboratory,perfectinga new catalystcan be expensive, time-consuming and even dangerous.According toINLresearcher Ross Kunz, Understanding how and why a specific catalyst behavesin a reaction is theholygrail ofreaction chemistry.To help find it,scientists arecombiningmachine learningwith a wealth of new sensor datafrom the TAP reactorsystem.

The TAP reactor system uses an array of microsensors to examine the different componentsof a reaction in realtime.For the simplestcatalytic reaction,the system captures8uniquemeasurementsin each of 5,000timepointsthat make up the experiment.Assembling the timepoints into a single data set provides 165,000 measurements foroneexperiment on a very simple catalyst.Scientiststhenuse the datatopredict what is happening in the reaction at a specific timeand how different reaction steps work together in a larger chemical reaction network.Traditional analysis methods canbarelyscratch the surfaceofsuch a large quantity of datafor a simple catalyst, let alonethe many more measurements thatare produced by acomplex one.

Machine learning methods can take theTAP dataanalysis further. Using a type of machine learning called explainableartificial intelligence, orAI,theteam caneducatethe computer about known properties of thereactionsstarting materialsand the physics that govern these types of reactions, a process called training.The computer can apply thistrainingand the patterns that it detects in the experimental data to better describe theconditions inareactionacross time.The team hopes that theexplainable AI method will produce adescription of the reaction that can be used toaccuratelymodelthe processes that occur during theTAP experiment.

In most AI experiments, a computer is given almost no trainingon the physicsand simply detects patterns in the data based upon what it can identify,similar tohow a baby might react to seeing something completely new.By contrast,the value of explainable AI lies in the fact that humanscan understand the assumptions and information that lead to the computers conclusions.This human-level understanding can make it easier for scientists to verify predictions and detect flaws and biases in the reaction description produced by explainable AI.

Implementing explainable AIis not as simple or straightforward as it might sound.With support from the Department of Energys Advanced Manufacturing office, theINLteam has spent two years preparing theTAPdata for machine learning,developing andimplementingthe machinelearning program, andvalidating the results for a common catalyst in a simple reaction that occursinthe car you driveeveryday. This reaction,the transformation of carbon monoxideinto carbon dioxide,occurs ina carscatalytic converter andrelies onplatinumasthe catalyst. Since this reaction is well studied,researcherscan checkhow well the results of the explainable AI experiments match known observations.

In April 2021, the INL team published their results validating the explainable AI method with the platinum catalyst in the article Data driven reaction mechanism estimation via transient kinetics and machine learninginChemical Engineering Journal.Now that the team has validated the approach, they are examining TAP data frommore complex industrialcatalystsused in the manufacture of smallmolecules like ethylene, propylene and ammonia. They are also working with collaborators at Georgia Institute of Technologyto applythemathematical models that result from themachine learningexperiments tocomputersimulationscalled digital twins. This type of simulation allows the scientists topredict what will happen if they change an aspectof the reaction. When a digital twin is based on avery accurate model of a reaction, researcherscanbe confident in itspredictions.

Bygivingthe digital twinthe taskto simulate a modification to a reaction or new type of catalyst, researchers can avoid doing physical experiments for modifications that are likely to lead to poor results or unsafe conditions. Instead,the digital twin simulation can savetime and moneyby testing thousands of conditions,while researchers can testonly a handful of the mostpromising conditions in the physical laboratory.

Plus, this machine learning approach can produce newer and more accurate modelsfor each new catalyst and reaction condition testedwith the TAP reactorsystem.In turn, applying these models to digital twin simulations gives researchers the predictive power to pick the best catalysts and conditions to test next in the TAP reaction. As a result, each roundof testing, model development and simulationproducesa greater understanding of how a reactionworksand howtoimprove it.

These toolsarethe foundation of a new paradigm incatalyst science but alsopave the way for radical new approaches inchemical manufacturing,said Rebecca Fushimi, who leads the project team.

About Idaho National LaboratoryBattelle Energy Alliance manages INL for the U.S. Department of Energys Office of Nuclear Energy. INL is the nations center for nuclear energy research and development,and alsoperforms research in each of DOEs strategic goal areas: energy, national security, science and the environment. For more information, visitwww.inl.gov. Follow us on social media:Twitter,Facebook,InstagramandLinkedIn.

Chemical Engineering Journal

Data driven reaction mechanism estimation via transient kinetics and machine learning

18-Apr-2021

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Learning to improve chemical reactions with artificial intelligence - EurekAlert