A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.

The algorithm, devised by a scientist at the U.S. Department of Energys (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop the predictions. Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations, said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. What Im doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law.

Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a serving algorithm, then made accurate predictions of the orbits of other planets in the solar system without using Newtons laws of motion and gravitation. Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data, Qin said. There is no law of physics in the middle.

PPPL physicist Hong Qin in front of images of planetary orbits and computer code. Credit: Elle Starkman / PPPL Office of Communications

The program does not happen upon accurate predictions by accident. Hong taught the program the underlying principle used by nature to determine the dynamics of any physical system, said Joshua Burby, a physicist at the DOEs Los Alamos National Laboratory who earned his Ph.D. at Princeton under Qins mentorship. The payoff is that the network learns the laws of planetary motion after witnessing very few training examples. In other words, his code really learns the laws of physics.

Machine learning is what makes computer programs like Google Translate possible. Google Translate sifts through a vast amount of information to determine how frequently one word in one language has been translated into a word in the other language. In this way, the program can make an accurate translation without actually learning either language.

The process also appears in philosophical thought experiments like John Searles Chinese Room. In that scenario, a person who did not know Chinese could nevertheless translate a Chinese sentence into English or any other language by using a set of instructions, or rules, that would substitute for understanding. The thought experiment raises questions about what, at root, it means to understand anything at all, and whether understanding implies that something else is happening in the mind besides following rules.

Qin was inspired in part by Oxford philosopher Nick Bostroms philosophical thought experiment that the universe is a computer simulation. If that were true, then fundamental physical laws should reveal that the universe consists of individual chunks of space-time, like pixels in a video game. If we live in a simulation, our world has to be discrete, Qin said. The black box technique Qin devised does not require that physicists believe the simulation conjecture literally, though it builds on this idea to create a program that makes accurate physical predictions.

The resulting pixelated view of the world, akin to what is portrayed in the movie The Matrix, is known as a discrete field theory, which views the universe as composed of individual bits and differs from the theories that people normally create. While scientists typically devise overarching concepts of how the physical world behaves, computers just assemble a collection of data points.

Qin and Eric Palmerduca, a graduate student in the Princeton University Program in Plasma Physics, are now developing ways to use discrete field theories to predict the behavior of particles of plasma in fusion experiments conducted by scientists around the world. The most widely used fusion facilities are doughnut-shaped tokamaks that confine the plasma in powerful magnetic fields.

Fusion, the power that drives the sun and stars, combines light elements in the form of plasma the hot, charged state of matter composed of free electrons and atomic nuclei that represents 99% of the visible universe to generate massive amounts of energy. Scientists are seeking to replicate fusion on Earth for a virtually inexhaustible supply of power to generate electricity.

In a magnetic fusion device, the dynamics of plasmas are complexand multi-scale, and the effective governing laws or computational models for a particular physical process that we are interested in are not always clear, Qin said. In these scenarios, we can apply the machine learning technique that I developed to create a discrete field theory and then apply this discrete field theory to understand and predict new experimental observations.

This process opens up questions about the nature of science itself. Dont scientists want to develop physics theories that explain the world, instead of simply amassing data? Arent theories fundamental to physics and necessary to explain and understand phenomena?

I would argue that the ultimate goal of any scientist is prediction, Qin said. You might not necessarily need a law. For example, if I can perfectly predict a planetary orbit, I dont need to know Newtons laws of gravitation and motion. You could argue that by doing so you would understand less than if you knew Newtons laws. In a sense, that is correct. But from a practical point of view, making accurate predictions is not doing anything less.

Machine learning could also open up possibilities for more research. It significantly broadens the scope of problems that you can tackle because all you need to get going is data, Palmerduca said.

The technique could also lead to the development of a traditional physical theory. While in some sense this method precludes the need of such a theory, it can also be viewed as a path toward one, Palmerduca said. When youre trying to deduce a theory, youd like to have as much data at your disposal as possible. If youre given some data, you can use machine learning to fill in gaps in that data or otherwise expand the data set.

Reference: Machine learning and serving of discrete field theories by Hong Qin, 9 November 2020, Scientific Reports.DOI: 10.1038/s41598-020-76301-0

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New Machine Learning Theory Raises Questions About the Very Nature of Science - SciTechDaily

The expectation is that in 2021, artificial intelligence and machine learning technologies will continue to become more mainstream. Businesses that havent traditionally viewed themselves as candidates for AI applications will embrace these technologies.

A great story of machine learning being used in an industry that is not known for its technology investments is the story of Makoto Koike. Using Googles TensorFlow, Makoto initially developed a cucumber sorting system using pictures that he took of the cucumbers. With that small step, a machine learning cucumber sorting system was born.

Getting started with AI and machine learning is becoming increasingly accessible for organizations of all sizes. Technology-as-a-service companies including Microsoft, AWS and Google all have offerings that will get most organizations started on their AI and machine learning journeys. These technologies can be used to automate and streamline manual business processes that have historically been resource-intensive.

An article on forbes.com claims that, as business leaders continue to refine their processes to support the new normal of the Covid-19 pandemic, they should be considering where these technologies might help reduce manual, resource-intensive or paper-based processes. Any manual process should be fair game for review for automation possibilities.

Photo source: Dreamstime.com

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Using AI and Machine Learning will increase in horti industry - hortidaily.com

Machine Learning in Medicine Market Comprehensive Study is an expert and top to bottom investigation on the momentum condition of the worldwide Machine Learning in Medicine industry with an attention on the Global market. The report gives key insights available status of the Machine Learning in Medicine producers and is an important wellspring of direction and course for organizations and people keen on the business. By and large, the report gives an inside and out understanding of 2021-2028 worldwide Machine Learning in Medicine Market covering extremely significant parameters.

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Market Drivers The Rise in the Number ofPatients Opting For Medical Insurance & Increasein Premium Costs The Surge in the Geriatric Population with Chronic Diseases

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Key Points Covered in the Table of Content:Chapter 1 to explain Introduction, market review, market risk and opportunities, market driving force, product scope of Machine Learning in Medicine Market;Chapter 2 to inspect the leading manufacturers (Cost Structure, Raw Material) with sales Analysis, revenue Analysis, and price Analysis of Machine Learning in Medicine Market;Chapter 3 to show the focused circumstance among the best producers, with deals, income, and Machine Learning in Medicine market share 2021;Chapter 4 to display the regional analysis of Global Machine Learning in Medicine Market with revenue and sales of an industry, from 2021to 2028;Chapter 5, 6, 7 to analyze the key countries (United States,China,Europe,Japan,Korea&Taiwan), with sales, revenue and market share in key regions;Chapter 8 and 9 to exhibit International and Regional Marketing Type Analysis, Supply Chain Analysis, Trade Type Analysis;Chapter 10 and 11 to analyze the market by product type and application/end users (industry sales, share, and growth rate) from2021 to 2028Chapter 12 to show Machine Learning in Medicine Market forecast by regions, forecast by type and forecast by application with revenue and sales, from 2021 to 2028;Chapter 13, 14 & 15 to specify Research Findings and Conclusion, Appendix, methodology and data source of Machine Learning in Medicine market buyers, merchants, dealers, sales channel.

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Machine Learning in Medicine Market 2021 to Perceive Biggest Trend and Opportunity by 2028 KSU | The Sentinel Newspaper - KSU | The Sentinel...

For the past few years, JPMorgan has been busy building out its machine learning capability underDaryush Laqab, its San Francisco-based head of AI platform product management, who was hired from Google in 2019. Last time we looked, the bank seemed to be paying salaries of $160-$170k to new joiners onLaqab's team.

If that sounds appealing, you might want to watch the video below so that you know what you're getting into. Recorded at the AWS re:Invent conferencein December, it's only just made it to you YouTube. The video is flagged as a day in the life of JPMorgan's machine learning data scientists, butLaqab arguably does a better of job of highlighting some of the constraints data professionals at allbanks have to work under.

"There are some barriers to smooth data science at JPMorgan," he explains - a bank is not the same as a large technology firm.

For example, data scientists at JPMorgan have to check data is authorized for use, saysLaqab: "They need to go to a process to log that use and make surethat they have the adequate approvals for that intent in terms of use."

They also have to deal with the legacy infrastructureissue: "We are a large organization, we have a lot of legacy infrastructure," says Laqab. "Like any other legacy infrastructure, it is built over time,it is patched over time. These are tightly integrated,so moving part or all of that infrastructure to public cloud,replacing rule base engines with AI/ML based engines.All of that takes time and brings inertia to the innovation."

JPMorgan's size and complexity is another source of inertia as multiple business lines in multiple regulated entities in different regulated environments need to be considered. "Making sure that those regulatory obligationsare taken care of, again, slows down data science at times," saysLaqab.

And then there are more specific regulations such as those concerning model governance. At JPMorgan, a machine learning model can't go straight into a production environment."It needs to go through a model review and a model governance process," says Laqab. "- To make sure we have another set of eyes that looksat how that model was created, how that model was developed..." And then there are software governance issues too.

Despite all these hindrances, JPMorgan has already productionized AI models and built an 'Omni AI ecosystem,'which Laqab heads,to help employees to identify and ingest minimum viable data so that they canbuild models faster. Laqab saysthe bank saved $150m in expenses in 2019 as a result. JPMorgan's AI researchers are now working on everything fromFAQ bots and chat bots, to NLP search models for the bank'sown content, pattern recognition in equities markets and email processing. - The breadth of work on offer is considerable. "We play in every market that is out there," saysLaqab,

The bank has also learned that the best way to structure its AI team is to split people into data scientists who train and create models and machine learning engineers who operationalize models, saysLaqab. - Before you apply, you might want to consider which you'd rather be.

Photo by NeONBRAND on Unsplash

Have a confidential story, tip, or comment youd like to share? Contact:sbutcher@efinancialcareers.comin the first instance. Whatsapp/Signal/Telegram also available. Bear with us if you leave a comment at the bottom of this article: all our comments are moderated by human beings. Sometimes these humans might be asleep, or away from their desks, so it may take a while for your comment to appear. Eventually it will unless its offensive or libelous (in which case it wont.)

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The head of JPMorgan's machine learning platform explained what it's like to work there - eFinancialCareers

It is common for patients with psychosis or depression to experience symptoms of both conditions which has meant that traditionally, mental health diagnoses have been given for a primary illness with secondary symptoms of the other.

Making an accurate diagnosis often poses difficulties to mental health clinicians and diagnoses often do not accurately reflect the complexity of individual experience or neurobiology. For example, a patient being diagnosed with psychosis will often have depression regarded as a secondary condition, with more focus on the psychosis symptoms, such as hallucinations or delusions; this has implications on treatment decisions for patients.

A team at the University of Birminghams Institute for Mental Health and Centre for Human Brain Health, along with researchers at the European Union-funded PRONIA consortium, explored the possibility of using machine learning to create extremely accurate models of pure forms of both illnesses and using these models to investigate the diagnostic accuracy of a cohort of patients with mixed symptoms. The results of this study have been published in Schizophrenia Bulletin.

Paris Alexandros Lalousis, lead author, explains that the majority of patients have co-morbidities, so people with psychosis also have depressive symptoms and vice versa That presents a big challenge for clinicians in terms of diagnosing and then delivering treatments that are designed for patients without co-morbidity. Its not that patients are misdiagnosed, but the current diagnostic categories we have do not accurately reflect the clinical and neurobiological reality.

The researchers analysed questionnaire responses and detailed clinical interviews, as well as data from structural magnetic resonance imaging from a cohort of 300 patients taking part in the study. From this group of patients, they identified small subgroups of patients, who could be classified as suffering either from psychosis without any symptoms of depression, or from depression without any psychotic symptoms.

With the goal of developing a precise disease profile for each patient and testing it against their diagnosis to see how accurate it was, the research team was able to identify machine learning models of pure depression, and pure psychosis by using the collected data. They were then able to use machine learning methods to apply these models to patients with symptoms of both illnesses.

The team discovered that patients with depression as a primary illness were more likely to have accurate mental health diagnoses, whereas patients with psychosis with depression had symptoms which most frequently leaned towards the depression dimension. This may suggest that depression plays a greater part in the illness than had previously been thought.

Lalousis added: There is a pressing need for better treatments for psychosis and depression, conditions which constitute a major mental health challenge worldwide. Our study highlights the need for clinicians to understand better the complex neurobiology of these conditions, and the role of co-morbid symptoms; in particular considering carefully the role that depression is playing in the illness.

In this study we have shown how using sophisticated machine learning algorithms, which take into account clinical, neurocognitive, and neurobiological factors can aid our understanding of the complexity of mental illness. In the future, we think machine learning could become a critical tool for accurate diagnosis. We have a real opportunity to develop data-driven diagnostic methods this is an area in which mental health is keeping pace with physical health and its really important that we keep up that momentum.

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Mental health diagnoses and the role of machine learning - Health Europa

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By Emily Newton

The Internet of Things (IoT) and machine learning are two of the most disruptive technologies in business today. Separately, both of these innovations can bring remarkable benefits to any company. Together, they can transform your business entirely.

The intersection of IoT devices and machine learning is a natural progression. Machine learning needs large pools of relevant data to work at its best, and the IoT can supply it. As adoption of both soars, companies should start using them in conjunction.

Here are five ways the IoT and machine learning can improve operations in any business.

Around 25% of businesses today use IoT devices, and this figure will keep climbing. As companies implement more of these sensors, they add places where they can gather data. Machine learning algorithms can then analyze this data to find inefficiencies in the workplace.

Looking at various workplace data, a machine learning program could see where a company spends an unusually high amount of time. It could then suggest a new workflow that would reduce the effort employees expend in that area. Business leaders may not have ever realized this was a problem area without machine learning.

Machine learning programs are skilled at making connections between data points that humans may miss. They can also make predictions 20 times earlier than traditional tools and do so with more accuracy. With IoT devices feeding them more data, theyll only become faster and more accurate.

Machine learning and the IoT can also automate routine tasks. Business process automation (BPA) leverages AI to handle a range of administrative tasks, so workers dont have to. As IoT devices feed more data into these programs, they become even more effective.

Over time, technology like this has contributed to a 40% productivity increase in some industries. Automating and streamlining tasks like scheduling and record-keeping frees employees to focus on other, value-adding work. BPAs potential doesnt stop there, either.

BPA can automate more than straightforward data manipulation tasks. It can talk to customers, plan and schedule events, run marketing campaigns and more. With more comprehensive IoT implementation, it would have access to more areas, becoming even more versatile.

One of the most promising areas for IoT implementation is in the supply chain. IoT sensors in vehicles or shipping containers can provide companies with critical information like real-time location data or product quality. This data alone improves supply chain visibility, but paired with machine learning, it could transform your business.

Machine learning programs can take this real-time data from IoT sensors and put it into action. It could predict possible disruptions and warn workers so they can respond accordingly. These predictive analytics could save companies the all-too-familiar headache of supply chain delays.

UPS Orion tool is the gold standard for what machine learning can do for supply chains. The system has saved the shipping giant 10 million gallons of fuel a year by adjusting routes on the fly based on traffic and weather data.

If a company cant understand the vulnerabilities it faces, business leaders cant make fully informed decisions. IoT devices can provide the data businesses need to get a better understanding of these risks. Machine learning can take it a step further and find points of concern in this data that humans could miss.

IoT devices can gather data about the workplace or customers that machine learning programs then process. For example, Progressive has made more than 1.7 trillion observations about its customers driving habits through Snapshot, an IoT tracking device. These analytics help the company adjust clients insurance rates based on the dangers their driving presents.

Business risks arent the only hazards the Internet of Things and machine learning can predict. IoT air quality sensors could alert businesses when to change HVAC filters to protect employee health. Similarly, machine learning cybersecurity programs could sense when hackers are trying to infiltrate a companys network.

Another way the IoT and machine learning could transform your business is by eliminating waste. Data from IoT sensors can reveal where the company could be using more resources than it needs. Machine learning algorithms can then analyze this data to suggest ways to improve.

One of the most common culprits of waste in businesses is energy. Thanks to various inefficiencies, 68% of power in America ends up wasted. IoT sensors can measure where this waste is happening, and with machine learning, adjust to stop it.

Machine learning algorithms in conjunction with IoT devices could restrict energy use, so processes only use what they need. Alternatively, they could suggest new workflows or procedures that would be less wasteful. While many of these steps may seem small, they add up to substantial savings.

Without the IoT and machine learning, businesses cant reach their full potential. These technologies enable savings companies couldnt achieve otherwise. As they advance, theyll only become more effective.

The Internet of Things and machine learning are reshaping the business world. Those that dont take advantage of them now could soon fall behind.

Emily Newton is the Editor-in-Chief of Revolutionized, a magazine exploring how innovations change our world. She has over 3 years experience writing articles in the industrial and tech sectors.

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5 Ways the IoT and Machine Learning Improve Operations - BOSS Magazine

Longmont, CO, Feb. 09, 2021 (GLOBE NEWSWIRE) -- Parascript, which provides document analysis software processing for over 100 billion documents each year, announced today the Smart-Force (SFORCE) and Parascript partnership to provide a digital workforce that augments operations by combining cognitive Robotic Process Automation (RPA) technology with customers current investments for high scalability, improved accuracy and an enhanced customer experience in Mexico and across Latin America.

Partnering with Smart-Force means we get to help solve some of the greatest digital transformation challenges in Intelligent Document Processing instead of just the low-hanging fruit. Smart-Force is forward-thinking and committed to futureproofing their customers processes, even with hard-to-automate, unstructured documents where the application of techniques such as NLP is often required, said Greg Council, Vice President of Marketing and Product Management at Parascript. Smart-Force leverages bots to genuinely collaborate with staff so that the staff no longer have to spend all their time on finding information, and performing data entry and verification, even for the most complex multi-page documents that you see in lending and insurance.

Smart-Force specializes in digital transformation by identifying processes in need of automation and implementing RPA to improve those processes so that they run faster without errors. SFORCE routinely enables increased productivity, improves customer satisfaction, and improves staff morale through leveraging the technology of Automation Anywhere, Inc., a leader in RPA, and now Parascript Intelligent Document Processing.

As intelligent automation technology becomes more ubiquitous, it has created opportunities for organizations to ignite their staff towards new ways of working freeing up time from the manual tasks to focus on creative, strategic projects, what humans are meant to do, said Griffin Pickard, Director of Technology Alliance Program at Automation Anywhere. By creating an alliance with Parascript and Smart-Force, we have enabled customers to advance their automation strategy by leveraging ML and accelerate end-to-end business processes.

Our focus at SFORCE is on RPA with Machine Learning to transform how customers are doing things. We dont replace; we compliment the technology investments of our customers to improve how they are working, said Alejandro Castrejn, Founder of SFORCE. We make processes faster, more efficient and augment their staff capabilities. In terms of RPA processes that focus on complex document-based information, we havent seen anything approach what Parascript can do.

We found that Parascript does a lot more than other IDP providers. Our customers need a point-to-point RPA solution. Where Parascript software becomes essential is in extracting and verifying data from complex documents such as legal contracts. Manual data entry and review produces a lot of errors and takes time, said Barbara Mair, Partner at SFORCE. Using Parascript software, we can significantly accelerate contract execution, customer onboarding and many other processes without introducing errors.

The ability to process simple to very complex documents such as unstructured contracts and policies within RPA leveraging FormXtra.AI represents real opportunities for digital transformation across the enterprise. FormXtra.AI and its Smart Learning allow for easy configuration, and by training the systems on client-specific data, the automation is rapidly deployed with the ability to adapt to new information introduced in dynamic production environments.

About SFORCE, S.A. de C.V.

SFORCE offers services that allow customers to adopt digital transformation at whatever pace the organization needs. SFORCE is dedicated to helping customers get the most out of their existing investments in technology. SFORCE provides point-to-point solutions that combine existing technologies with next generation technology, which allows customers to transform operations, dramatically increase efficiency as well as automate manual tasks that are rote and error-prone, so that staff can focus on high-value activities that significantly increase revenue. From exploring process automation to planning a disruptive change that ensures high levels of automation, our team of specialists helps design and implement the automation of processes for digital transformation. Visit SFORCE.

About Parascript

Parascript software, driven by data science and powered by machine learning, configures and optimizes itself to automate simple and complex document-oriented tasks such as document classification, document separation and data entry for payments, lending and AP/AR processes. Every year, over 100 billion documents involved in banking, insurance, and government are processed by Parascript software. Parascript offers its technology both as software products and as software-enabled services to our partners. Visit Parascript.

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Parascript and SFORCE Partner to Leverage Machine Learning Eliminating Barriers to Automation - GlobeNewswire

A recommendation engine is a class of machine learning algorithm that suggests products, services, information to users based on analysis of data. Robust recommendation systems are the key differentiator in the operations of big companies like Netflix, Amazon, and Byte Dance (TikTok parent) etc.

Alok Menthe, Data Scientist at Ericsson, gave an informative talk on building Custom recommendation engines for real-world problems at the Machine Learning Developers Summit (MLDS) 2021. Whenever a niche business problem comes in, it has complicated intertwined ways of working. Standard ML techniques may be inadequate and might not serve the customers purpose. That is where the need for a custom-made engine comes in. We were also faced with such a problem with our service network unit at Ericsson, he said.

Menthe said the unit wanted to implement a recommendation system to provide suggestions for assignment workflow a model to delegate the incoming projects to the most appropriate team or resource pool

Credit: Alok Menthe

There were three kinds of data available:

Pool definition data: It relates to the composition of a particular resource poolthe number of people, their competence, and other metadata.

Historical demand data: This kind of data helps in establishing a relationship between the feature demand and a particular resource pool.

Transactional data: It is used for operational purposes.

Menthe said building a custom recommendation system in this context involves the following steps:

Credit: Alok Menthe

After building our model, the most difficult part was feature engineering, which is imperative for building an efficient system. Among the two major modules classification and clusteringwe faced challenges with respect to the latter. We had only categorical information making it difficult to find distances within the objects. We went out of the box to see if we can do any special encoding for the data. We adopted data encoding techniques and frequency-based encoding in this regard, said Menthe.

Clustering module: For this module, initially the team implemented K-modes and agglomerative. However, the results were far from perfect, prompting the team to consider the good-old K-means algorithm. For evaluation purposes, it was done manually with the help of subject matter experts.

The final model had 700 resource pools condensed to 15 pool clusters.

Classification module: For this module, three kinds of algorithm iterations were usedRandom Forest, Artificial Neural Network, XGBoost. Classification accuracy was used as an evaluation metric. Finally, upon 50,00,000 training records, this module demonstrated an accuracy of 71 percent.

Menthe said this recommendation model is monitored on a fortnightly basis by validating the suggested pools against the allocated pools for project demands:

The model has proved to be successful on three fronts:

Menthe summarised the three major takeaways from this project in his concluding remarks: the need to preserve business nuances in ML solutions; thinking beyond standard ML approaches; and understanding that there is no silver bullet ML solution.

I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.

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There Is No Silver Bullet Machine Learning Solution - Analytics India Magazine

AI and machine-learning advances have made it possible to produce fake videos and photos that seem real, commonly known as deepfakes. Deepfake content is exploding in popularity.[i] In Star Wars: The Rise of Skywalker, for instance, a visage of Carrie Fischer graced the screen, generated through artificial intelligence models trained on historic footage. Using thousands of hours of interviews with Salvador Dali, the Dali Museum of Florida created an interactive exhibit featuring the artist.[ii] For Game of Thrones fans miffed over plot holes in the season finale, Jon Snow can be seen profusely apologizing in a deepfake video that looks all too real.[iii]

Deepfake technologyhow does it work? From a technical perspective, deepfakes (also referred to as synthetic media) are made from artificial intelligence and machine-learning models trained on data sets of real photos or videos. These trained algorithms then produce altered media that looks and sounds just like the real deal. Behind the scenes, generative adversarial networks (GANs) power deepfake creation.[iv] With GANs, two AI algorithms are pitted against one another: one creates the forgery while the other tries to detect it, teaching itself along the way. The more data is fed into GANs, the more believable the deepfake will be. Researchers at academic institutions such as MIT, Carnegie Mellon, and Stanford University, as well as large Fortune 500 corporations, are experimenting with deepfake technology.[v] Yet deepfakes are not solely the province of technical universities or AI product development groups. Anybody with an internet connection can download publicly available deepfake software and crank out content.[vi]

Deepfake risks and abuse. Deepfakes are not always fun and games. Deepfake videos can phish employees to reveal credentials or confidential information, e-commerce platforms may face deepfake circumvention of authentication technologies for purposes of fraud, and intellectual property owners may find their properties featured in videos without authorization. For consumer-facing online platforms, certain actors may attempt to leverage deepfakes to spread misinformation. Another well-documented and unfortunate abuse of deepfake technology is for purposes of revenge pornography.[vii]

In response, online platforms and consumer-facing companies have begun enforcing limitations on the use of deepfake media. Twitter, for example, announced a new policy within the last year to prohibit users from sharing synthetic or manipulated media that are likely to cause harm. Per its policy, Twitter reserves the right to apply a label or warning to Tweets containing such media.[viii] Reddit also updated its policies to ban content that impersonates individuals or entities in a misleading or deceptive manner (while still permitting satire and parody).[ix] Others have followed. Yet social media and online platforms are not the only industries concerned with deepfakes. Companies across industry sectors, including financial and healthcare, face growing rates of identity theft and imposter scams in government services, online shopping, and credit bureaus as deepfake media proliferates.[x]

Deepfake legal claims and litigation risks. We are seeing legal claims and litigation relating to deepfakes across multiple vectors:

1. Claims brought by those who object to their appearance in deepfakes. Victims of deepfake media sometimes pursue tort law claims for false light, invasion of privacy, defamation, and intentional infliction of emotional distress. At a high level, these overlapping tort claims typically require the person harmed by the deepfake to prove that the deepfake creator published something that gives a false or misleading impression of the subject person in a manner that (a) damages the subjects reputation, (b) would be highly offensive to a reasonable person, or (c) causes mental anguish or suffering. As more companies begin to implement countermeasures, the lack of sufficient safeguards against misleading deepfakes may give rise to a negligence claim. Companies could face negligence claims for failure to detect deepfakes, either alongside the deepfake creator or alone if the creator is unknown or unreachable.

2. Product liability issues related to deepfakes on platforms. Section 230 of the Communications Decency Act shields online companies from claims arising from user content published on the companys platform or website. The law typically bars defamation and similar tort claims. But e-commerce companies can also use Section 230 to dismiss product liability and breach of warranty claims where the underlying allegations focus on a third-party sellers representation (such as a product description or express warranty). Businesses sued for product liability or other tort claims should look to assert Section 230 immunity as a defense where the alleged harm stems from a deepfake video posted by a user. Note, however, the immunity may be lost where the host platform performs editorial functions with respect to the published content at issue. As a result, it is important for businesses to implement clear policies addressing harmful deepfake videos that broadly apply to all users and avoid wading into influencing a specific users content.

3. Claims from consumers who suffer account compromise due to deepfakes. Multiple claims may arise where cyber criminals leverage deepfakes to compromise consumer credentials for various financial, online service, or other accounts. The California Consumer Privacy Act (CCPA), for instance, provides consumers with a private right of action to bring claims against businesses that violate the duty to implement and maintain reasonable security procedures and practices.[xi] Plaintiffs may also bring claims for negligence, invasion of privacy claims under common law or certain state constitutions, and state unfair competition or false advertising statutes (e.g., Californias Unfair Competition Law and Consumers Legal Remedies Act).

4. Claims available to platforms enforcing Terms of Use prohibitions of certain kinds of deepfakes. Online content platforms may be able to enforce prohibitions on abusive or malicious deepfakes through claims involving breach of contract and potential violations of the Computer Fraud and Abuse Act (CFAA), among others. These claims may turn on nuanced issues around what conduct constitutes exceeding authorized access under the CFAA, or Terms of Use assent and enforceability of particular provisions.

5. Claims related to state statutes limiting deepfakes. As malicious deepfakes proliferate, several states such as California, Texas, and Virginia have enacted statutes prohibiting their use to interfere with elections or criminalizing pornographic deepfake revenge video distribution.[xii] More such statutes are pending.

Practical tips for companies managing deepfake risks. While every company and situation is unique, companies dealing with deepfakes on their platforms, or as a potential threat vector for information security attacks, can consider several practical avenues to manage risks:

While the future of deepfakes is uncertain, it is apparent that the underlying AI and machine-learning technology is very real and here to staypresenting both risks and opportunity for organizations across industries.

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The Collision of AI's Machine Learning and Manipulation: Deepfake Litigation Risks to Companies from a Product Liability, Privacy, and Cyber...

Department of Computer Science

Grade 7:-33,797 - 40,322 per annumFixed Term-Full TimeContract Duration:7 monthsContracted Hours per Week:35Closing Date:13-Mar-2021, 7:59:00 AM

Durham University

Durham University is one of the world's top universities with strengths across the Arts and Humanities, Sciences and Social Sciences. We are home to some of the most talented scholars and researchers from around the world who are tackling global issues and making a difference to people's lives.

The University sits in a beautiful historic city where it shares ownership of a UNESCO World Heritage Site with Durham Cathedral, the greatest Romanesque building in Western Europe. A collegiate University, Durham recruits outstanding students from across the world and offers an unmatched wider student experience.

Less than 3 hours north of London, and an hour and a half south of Edinburgh, County Durham is a region steeped in history and natural beauty. The Durham Dales, including the North Pennines Area of Outstanding Natural Beauty, are home to breathtaking scenery and attractions. Durham offers an excellent choice of city, suburban and rural residential locations. The University provides a range of benefits including pension and childcare benefits and the Universitys Relocation Manager can assist with potential schooling requirements.

Durham University seeks to promote and maintain an inclusive and supportive environment for work and study that assists all members of our University community to reach their full potential. Diversity brings strength and we welcome applications from across the international, national and regional communities that we work with and serve.

The Department

The Department of Computer Science is rapidly expanding. A new building for the department (joint with Mathematical Sciences) has recently opened to house the expanded Department. The current Department has research strengths in (1) algorithms and complexity, (2) computer vision, imaging, and visualisation and (3) high-performance computing, cloud computing, and simulation. We work closely with industry and government departments. Research-led teaching is a key strength of the Department, which came 5th in the Complete University Guide. The department offers BSc and MEng undergraduate degrees and is currently redeveloping its interdisciplinary taught postgraduate degrees. The size of its student cohort has more than trebled in the past five years. The Department has an exceptionally strong External Advisory Board that provides strategic support for developing research and education, consisting of high-profile industrialists and academics.Computer Science is one of the very best UK Computer Science Departments with an outstanding reputation for excellence in teaching, research and employability of our students.

The Role

Postdoctoral Research Associate to work on the AHRC-funded project Visitor Interaction and Machine Curation in the Virtual Liverpool Biennial.

The project looks at virtual art exhibitions that are curated by machines, or even co-curated by humans and machines; and how audiences interact with these exhibitions in the era of online art shows. The project is in close collaboration with the 2020 (now 2021) Liverpool Biennial (http://biennial.com/). The role of the post holder is, along with the PI Leonardo Impett, to implement different strategies of user-machine interaction for virtual art exhibits; and to investigate the interaction behaviour of different types of users with such systems.

Responsibilities:

This post is fixed term until31 August 2021 as the research project is time limited and will end on 31 August 2021.

The post-holder is employed to work on research/a research project which will be led by another colleague. Whilst this means that the post-holder will not be carrying out independent research in his/her own right, the expectation is that they will contribute to the advancement of the project, through the development of their own research ideas/adaptation and development of research protocols.

Successful applicants will, ideally, be in post byFebruary 2021.

How to Apply

For informal enquiries please contactDr Leonardo Impett (leonardo.l.impett@durham.ac.uk).All enquiries will be treated in the strictest confidence.

We prefer to receive applications online via the Durham University Vacancies Site.https://www.dur.ac.uk/jobs/. As part of the application process, you should provide details of 3 (preferably academic/research) referees and the details of your current line manager so that we may seek an employment reference.

Applications are particularly welcome from women and black and minority ethnic candidates, who are under-represented in academic posts in the University.We are committed to equality: if for any reason you have taken a career break or periods of leave that may have impacted on your career path, such as maternity, adoption or parental leave, you may wish to disclose this in your application.The selection committee will recognise that this may have reduced the quantity of your research accordingly.

What to Submit

All applicants are asked to submit:

The Requirements

Essential:

Qualifications

Experience

Skills

Desirable:

Experience

Skills

DBS Requirement:Not Applicable.

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Postdoctoral Research Associate in Digital Humanities and Machine Learning job with DURHAM UNIVERSITY | 246392 - Times Higher Education (THE)