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Flexible expressions could lift 3D-generated faces out of the uncanny valley

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3D-rendered faces are a big part of any major movie or game now, but the task of capturing and animating them in a natural way can be a tough one. Disney Research is working on ways to smooth out this process, among them a machine learning tool that makes it much easier to generate and manipulate 3D faces without dipping into the uncanny valley.

Of course this technology has come a long way from the wooden expressions and limited details of earlier days. High-resolution, convincing 3D faces can be animated quickly and well, but the subtleties of human expression are not just limitless in variety, they’re very easy to get wrong.

Think of how someone’s entire face changes when they smile — it’s different for everyone, but there are enough similarities that we fancy we can tell when someone is “really” smiling or just faking it. How can you achieve that level of detail in an artificial face?

Existing “linear” models simplify the subtlety of expression, making “happiness” or “anger” minutely adjustable, but at the cost of accuracy — they can’t express every possible face, but can easily result in impossible faces. Newer neural models learn complexity from watching the interconnectedness of expressions, but like other such models their workings are obscure and difficult to control, and perhaps not generalizable beyond the faces they learned from. They don’t enable the level of control an artist working on a movie or game needs, or result in faces that (humans are remarkably good at detecting this) are just off somehow.

A team at Disney Research proposes a new model with the best of both worlds — what it calls a “semantic deep face model.” Without getting into the exact technical execution, the basic improvement is that it’s a neural model that learns how a facial expression affects the whole face, but is not specific to a single face — and moreover is nonlinear, allowing flexibility in how expressions interact with a face’s geometry and each other.

Think of it this way: A linear model lets you take an expression (a smile, or kiss, say) from 0-100 on any 3D face, but the results may be unrealistic. A neural model lets you take a learned expression from 0-100 realistically, but only on the face it learned it from. This model can take an expression from 0-100 smoothly on any 3D face. That’s something of an over-simplification, but you get the idea.

Image Credits: Disney Research

The results are powerful: You could generate a thousand faces with different shapes and tones, and then animate all of them with the same expressions without any extra work. Think how that could result in diverse CG crowds you can summon with a couple clicks, or characters in games that have realistic facial expressions regardless of whether they were hand-crafted or not.

It’s not a silver bullet, and it’s only part of a huge set of improvements artists and engineers are making in the various industries where this technology is employed — markerless face tracking, better skin deformation, realistic eye movements and dozens more areas of interest are also important parts of this process.

The Disney Research paper was presented at the International Conference on 3D Vision; you can read the full thing here.

Source: https://techcrunch.com/2020/11/25/flexible-expressions-could-lift-3d-generated-faces-out-of-the-uncanny-valley/

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How do the different OCR solutions compare in 2021?

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Back in the day, only a handful of companies considered document digitization and data-entry automation as a priority. Fast forward to today, only a handful of organizations do not consider document digitization and data-entry automation as a priority. The COVID-19 pandemic has reordered organizational priorities in unthinkable ways.

The thesis that document digitization will reap rich dividends isn’t without merit. From our experience at Nanonets, businesses across the spectrum, from around the world, from early-stage to the Fortune 500 have all benefited from the use of data-entry automation.

Consider the invisible inefficiencies in your manual data entry and document management processes that eat up your margins. And top it with the dollars lost through a hundred different holes like manual errors, slow turnaround times, and man-hour overheads.

You get the drift.

So how far have the technological advances in document reading, understanding, and automation come?

We were in for a huge surprise when our Machine Learning team screamed breakthroughs in Learning Models that improved extraction accuracy to a whopping 95%. I get it. It’s hard to fathom that computers would be this capable one day. But we’re living in the times, in 2020, and let’s dive in to see the many different OCR based data extraction solutions that are available and how they stack up against each other.

The true north for Nanonets has been about ever-improving accuracy, speed, and usability. In this post, let’s look at other competing solutions through the same lens and see how they compare.

Given that Nanonets competes head to head with some of the compared solutions this wasn’t an easy post. But we’ve tried to be sure to strip out any bias and be as objective as possible here.

OCR Software - Speed Vs Accuracy
OCR Software – Speed Vs Accuracy

Nanonets:

Nanonets stands out as the only solution in the market with an on-premise solution.

Accuracy: Nanonets is the real winner when it comes to accuracy at a whopping 95%+ and improving. This literally eliminates the need for human intervention making it the real automation in the true sense.

Speed: The on-premise dockers offered by Nanonets provides unbeatable speed while the cloud-based solution also leads others by a mile.

Usability: Nanonets’ solution is document agnostic and can extract from any language offering complete customization in an intuitive easy to use interface. The results keep getting better as more and more documents get trained.

ABBYY:

ABBYY is one of the earliest players in the industry and uses the template approach.

Accuracy: ABBYY does a reasonable job when extracting information from specific document listed types while the accuracy is markedly low when it comes to unstructured documents and other document types.

Speed: ABBYY only offers specific templates and trying to use them for non-standard documents may affect the speed considerably.

Usability: Users have said that using ABBYY has been severely limiting with only a handful of document types supported out of the box. Another area where users have had issues with is about getting an organized consolidated report with ABBYY only giving output for documents individually.

Docparser:

Docparser is one of the newer players like Nanonets in the document & information extraction space.

Accuracy: Docparser’s parsing accuracy comes close when the documents are at least semi-structured while it does give up when the documents are non-standard and unstructured.

Speed: You can get your information extracted fairly quickly if you have a standard document at hand but the speed nosedives when you have to deal with non-standard documents.

Usability: We’ve heard from users that while some features are straightforward to use it is a struggle when they start using features to create custom parsing rule etc as those steps aren’t very intuitive.

Unshackle from manual data entry with OCR Software
Unshackle from manual data entry with OCR Software

Now that you know the benefits of using an AI-powered OCR solution for information extraction don’t let the operational inefficiencies hold you back. Give it a spin and unshackle your team from process bottlenecks and laborious manual data-entry efforts.

Start using Nanonets for Automation

Try out the model or request a demo today!

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Source: https://nanonets.com/blog/how-do-the-different-ocr-solutions-compare-in-2021/

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AI clocks first-known ‘binary sextuply-eclipsing sextuple star system’. Another AI will be along shortly to tell us how to pronounce that properly

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Astronomers have discovered the first-known “sextuply-eclipsing sextuple star system,” after a neural network flagged it up in data collected by NASA’s Transiting Exoplanet Survey Satellite (TESS).

The star system, codenamed TIC 168789840, is an oddball compared to its peers. Not only does it contain six suns, they’re split into three pairs of eclipsing binary stars. That means the suns in each pair, to an observer, pass directly in front of one another in their orbits. The stars in each pair are gravitationally bound to each other and to every other sun in their system, meaning each pair circles around each other, and around a common center of mass.

To get an idea of how this is structured, consider each star pair to be labeled A, B, and C. The A pair circle one another every 1.6 days, and C every 1.3 days. The A and C system completes a full orbit around their galactic center in a little under four years.

The remaining pair, B, is much further away, and is described as an outer binary. The B suns revolve around one another every 8.22 days, and it takes them about two thousand years to run a lap around the system’s common center of mass, according to a paper due to appear in The Astrophysical Journal detailing these findings. If that’s all a little mind-boggling, here’s a rough sketch of the system’s structure taken from the paper:

stars

The system’s orbital mechanics … Click to enlarge

An alien living on a hypothetical planet orbiting one of the inner quadruple stars would see four very bright suns in the sky and another two dimmer ones further away. These stars would periodically disappear, as they eclipsed one another. The chances of anyone observing this, however, are pretty slim to none as it doesn’t look like there are any exoplanets in TIC 168789840.

Finding the first sextuply-eclipsing sextuple star system with machine learning

NASA’s TESS telescope gathers a massive amount of data. Instead of manually poring over tens of millions of objects, scientists instead feed the data into machine-learning algorithms designed to highlight the most interesting examples for further examination.

Brian Powell, first author of the study and a data scientist at NASA’s High Energy Astrophysics Science Archive Research Center, trained a classifier to spot eclipsing binary systems.

The neural network looks for the characteristic dip in an object’s light curve, caused when one star passes in front of the other. It assigns a score on the likelihood it’s identified a eclipsing binary systems: ones rated above 0.9 on a scale up to 1.0 is considered a strong candidate.

tess

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The computer-vision model that performs all this is made up of a layer of approximately 5.5 million parameters, and was taught using more than 40,000 training examples on a cluster of eight Nvidia V100 GPUs for approximately two days.

At first, TIC 168789840 didn’t seem so odd. “The neural network was trained to look for the feature of the eclipse in the light curve with no concern as to periodicity,” Powell told The Register.

“Therefore, to the neural net, an eclipsing binary is no different than an eclipsing sextuple, both of them would likely have an output near 1.0.”

Upon closer inspection, however, the scientists were shocked when they realized they had discovered the first-known triplet eclipsing binary system. Each star locked in their pairs are very similar to one another in terms of mass, radius, and temperature.

“The fact that all three binaries show eclipses allows us to determine the radii and relative temperatures of each star. This, together with measurement of the radial velocities, allows us to determine the masses of the stars. Having this much information on a multiple star system of this order is quite rare,” Powell added.

There are 17 or so known sextuple star systems, though TIC 168789840 is the first structure where the sextuple suns are also binary eclipsing stars. Scientists hope that studying all its structural and physical properties will unlock mysteries of how multiple star systems are born. ®

Source: https://go.theregister.com/feed/www.theregister.com/2021/01/26/sextuple_star_system/

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Governance: Companies mature in their use of AI know that it needs guardrails

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Quality governance ensures responsible data models and AI execution, as well as helps the data models stay true to the business objectives.

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Image: Getty Images/iStockphoto

More about artificial intelligence

The fundamentals of traditional IT governance have focused on service-level agreements like uptime and response time, and also on oversight of areas such as security and data privacy. The beauty of these goals is that they are concrete and easy to understand. This makes them attainable with minimal confusion if an organization is committed to getting the job done.

SEE: TechRepublic Premium editorial calendar: IT policies, checklists, toolkits, and research for download (TechRepublic Premium)

Unfortunately, governance becomes a much less-definable task in the world of artificial intelligence (AI), and a premature one for many organizations.

“This can come down to the level of AI maturity that a company is at,” said Scott Zoldi, chief analytics officer at FICO. “Companies are in a variety of stages of the AI lifecycle, from exploring use cases and hiring staff, to building the models, and having a couple of instances deployed but not widely across the organization. Model governance comes into play when companies are mature in their use of AI technology, are invested in it, and realize that AI’s predictive and business value should be accompanied by guardrails.”

Because AI is more opaque than enterprise IT environments, AI requires a governance strategy that asks questions of architectures and that requires architectures to be more transparent,” Zoldi said.

SEE: 3 steps for better data modeling with IT and data science (TechRepublic)

Achieving transparency in AI governance begins with being able explain in plain language the technology behind AI and how it operates to board members, senior management, end users, and non-AI IT staff. Questions that AI practitioners should be able to answer should include but not be limited to, how data is prepared and taken into AI systems, which data is being taken in and why, and how the AI operates on the data to return answers to the questions that the business is asking. AI practitioners should also explain how both data and what you ask of it continuously change over time as business and other conditions change.

This is a pathway to ensuring responsible data models and AI execution, and also a way to ensure that the data models that a company develops for its AI stay true to its business objectives.

One central AI governance challenge is ensuring that the data and the AI operating on it are as bias-free as possible.

“AI governance is a board-level responsibility to mitigate pressures from regulators and advocacy groups,” Zoldi said. “Boards of directors should care about AI governance because AI technology makes decisions that profoundly affect everyone. Will a borrower be invisibly discriminated against and denied a loan? Will a patient’s disease be incorrectly diagnosed, or a citizen unjustly arrested for a crime he did not commit?  

How to achieve AI fairness

The increasing magnitude of AI’s life-altering decisions underscores the urgency with which AI fairness and bias should be ushered onto boards’ agendas.”

SEE: Equitable tech: AI-enabled platform to reduce bias in datasets released  (TechRepublic)

Zoldi said that to eliminate bias, boards must understand and enforce auditable, immutable AI model governance based on four classic tenets of corporate governance: accountability, fairness, transparency, and responsibility. He believes this can be achieved if organizations focus their AI governance on ethical, efficient, and explainable AI.

Ethical AI ensures that models operate without bias toward a protected group, and are used only in areas where we have confidence in the decisions the models generate. These issues have strong business implications; models that make biased decisions against protected groups aren’t just wrong, they are illegal.

Efficient AI helps AI make the leap from the development lab to making decisions in production that can be accepted with confidence. Otherwise, an inordinate amount of time and resources are invested in models that don’t deliver real-world business value. 

Explainable AI makes sure that companies using AI models can meet a growing list of regulations, starting with GDPR, to be able to explain how the model made its decision, and why.”

SEE: Encourage AI adoption by moving shadow AI into the daylight (TechRepublic)

Some organizations are already tackling these AI governance challenges, while others are just beginning to think about them.

This is why, when putting together an internal team to address governance, a best practice approach is a three-tiered structure that begins with an executive sponsor at the top to champion AI at a corporate level.

“One tier down, executives such as the CAO, CTO, CFO, and head of legal should lead the oversight of AI governance from a policy and process perspective,” Zoldi said. “Finally, at the blocking-and-tackling level, senior practitioners from the various model development and model delivery areas, who work together with AI technology on a daily basis, should hash out how to meet those corporate governance standards.”

 Also see

Source: https://www.techrepublic.com/article/governance-companies-mature-in-their-use-of-ai-know-that-it-needs-guardrails/#ftag=RSS56d97e7

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Gartner: The future of AI is not as rosy as some might think

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A Gartner report predicts that the second-order consequences of widespread AI will have massive societal impacts, to the point of making us unsure if and when we can trust our own eyes.

Vector of a face made of digital particles as symbol of artificial intelligence and machine learning

Image: iStockphoto/Feodora Chiosea

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Gartner has released a series of Predicts 2021 research reports, including one that outlines the serious, wide-reaching ethical and social problems it predicts artificial intelligence (AI) to cause in the next several years. In Predicts 2021: Artificial Intelligence and Its Impact on People and Society, five Gartner analysts report on different predictions it believes will come to fruition by 2025. The report calls particular attention to what it calls second-order consequences of artificial intelligence that arise as unintended results of new technologies.

SEE: TechRepublic Premium editorial calendar: IT policies, checklists, toolkits, and research for download (TechRepublic Premium)

Generative AI, for example, is now able to create amazingly realistic photographs of people and objects that don’t actually exist; Gartner predicts that by 2023, 20% of account takeovers will use deepfakes generated by this type of AI. “AI capabilities that can create and generate hyper-realistic content will have a transformational effect on the extent to which people can trust their own eyes,” the report said.

The report tackles five different predictions for the AI market, and gives recommendations for how businesses can address those challenges and adapt to the future: 

  • By 2025, pretrained AI models will be largely concentrated among 1% of vendors, making responsible use of AI a societal concern
  • In 2023, 20% of successful account takeover attacks will use deepfakes as part of social engineering attacks
  • By 2024, 60% of AI providers will include harm/misuse mitigation as a part of their software
  • By 2025, 10% of governments will avoid privacy and security concerns by using synthetic populations to train AI 
  • By 2025, 75% of workplace conversations will be recorded and analyzed for use in adding organizational value and assessing risk

Each of those analyses is enough to make AI-watchers sit up and take notice, but when combined it creates a picture of a grim future rife with ethical concerns, potential misuse of AI, and loss of privacy in the workplace. 

How businesses can respond 

Concerns over AI’s effect on privacy and truth are sure to be major topics in the coming years if Gartner’s analysts are accurate in their predictions, and successful businesses will need to be ready to adapt quickly to those concerns.

A recurring theme in the report is the establishment of ethics boards at companies that rely on AI, whether as a service or a product. This is mentioned particularly for businesses that plan to record and analyze workplace conversations: Boards with employee representation should be established to ensure fair use of conversations data, Gartner said.

SEE: Natural language processing: A cheat sheet (TechRepublic)

Gartner also recommends that businesses establish criteria for responsible AI consumption and prioritize vendors that “can demonstrate responsible development of AI and clarity in addressing related societal concerns.”

As for security concerns surrounding deepfakes and generative AI, Gartner recommends that organizations should schedule training about deepfakes. “We are now entering a zero-trust world. Nothing can be trusted unless it is certified as authenticated using cryptographic digital signatures,” the report said. 

There’s a lot to digest in this report, from figures saying that the best deepfake detection software will top out at a 50% identification rate in the long term, to the prediction that in 2023 a major US corporation will adopt conversation analysis to determine employee compensation. There’s much to be worried about in these analyses, but potential antidotes are included as well. The full report is available at Gartner, but interested parties will need to pay for access.

Also see

Source: https://www.techrepublic.com/article/gartner-the-future-of-ai-is-not-as-rosy-as-some-might-think/#ftag=RSS56d97e7

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