If your phone takes amazing photos, chances are its camera has been augmented by artificial intelligence embedded in the operating system. Now videos are getting the same treatment.

In recent years, smartphone makers have been gradually transforming their cameras into devices that capture data for AI processing beyond what the lens and sensor pick up in a single shot. That effectively turns a smartphone into a professional camera on auto mode and lowers the bar of capturing compelling images and videos.

In an era of TikTok and vlogging, theres a huge demand to easily produce professional-looking videos on the go. Like still images, videos shot on smartphones rely not just on the lens and sensor but also on enhancement algorithms. To some extent, those lines of codes are more critical than the hardware, argued Andreas Lifvendahl, founder and chief executive of Swedish company Imint, whose software now enhances video production in roughly 250 million devices most of which come from Chinese manufacturers.

[Smartphone makers] source different kinds of camera solutions motion sensors, gyroscopes, and so on. But the real differentiator, I would say, is more on the software side, Lifvendahl told TechCrunch over the phone.

Imint started life in 2007 as a spin-off academic research team from Uppsala University in Sweden. It spent the first few years building software for aerial surveillance, just as many cutting-edge innovations that find their first clients in the defense market. In 2013, Lifvendahl saw the coming of widespread smartphone adaptation and a huge opportunity to bring the same technology used in defense drones into the handsets in peoples pockets.

Smartphone companies were investing a lot in camera technology and that was a clever move, he recalled. It was very hard to find features with a direct relationship to consumers in daily use, and the camera was one of those because people wanted to document their life.

But they were missing the point by focusing on megapixels and still images. Consumers wanted to express themselves in a nice fashion of using videos, the founder added.

Source: Imints video enhancement software, Vidhance

The next February, the Swedish founder attended Mobile World Congress in Barcelona to gauge vendor interest. Many exhibitors were, unsurprisingly, Chinese phone makers scouring the conference for partners. They were immediately intrigued by Imints solution, and Lifvendahl returned home to set about tweaking his software for smartphones.

Ive never met this sort of open attitude to have a look so quickly, a clear signal that something is happening here with smartphones and cameras, and especially videos, Lifvendahl said.

Vidhance, Imints enhancement software suite mainly for Android, was soon released. These days, it can enhance precision, reduce motion, track moving objects, auto-correct horizon, reduce noise, and strengthen other aspects of a video in real-time all through deep learning.

In search of growth capital, the founder took the startup public on the Stockholm Stock Exchange at the end of 2015. The next year, Imint landed its first major account with Huawei, the Chinese telecoms equipment giant that was playing aggressive catch-up on smartphones at the time.

It was a turning point for us because once we could work with Huawei, all the other guys thought, Okay, these guys know what they are doing,' the founder recalled. And from there, we just grew and grew.

The hyper-competitive nature of Chinese phone makers means they are easily sold on new technology that can help them stand out. The flipside is the intensity that comes with competition. The Chinese tech industry is both well-respected and notorious for its fast pace. Slow movers can be crushed in a matter of a few months.

In some aspects, its very U.S.-like. Its very straight to the point and very opportunistic, Lifvendahl reflected on his experience with Chinese clients. You can get an offer even in the first or second meeting, like, Okay, this is interesting, if you can show that this works in our next product launch, which is due in three months. Would you set up a contract now?'

Thats a good side, he continued.The drawback for a Swedish company is the demand they have on suppliers. They want us to go on-site and offer support, and thats hard for a small Swedish company. So we need to be really efficient, making good tools and have good support systems.

The fast pace also permeates into the phone makers development cycle, which is not always good for innovation, suggested Lifvendahl. They are reacting to market trends, not thinking ahead of the curve what Apple excels in or conducting adequate market research.

Despite all the scrambling inside, Lifvendahl said he was surprised that Chinese manufacturers could get such high-quality phones out.

They can launch one flagship, maybe take a weekend break, and then next Monday they are rushing for the next project, which is going to be released in three months. So theres really no time to plan or prepare. You just dive into a project, so there would be a lot of loose ends that need to be tied up in four or five weeks. You are trying to tie hundreds of different pieces together with fifty different suppliers.

Imint is one of those companies that thrive by finding a tough-to-crack niche. Competition certainly exists, often coming from large Japanese and Chinese companies. But theres always a market for a smaller player who focuses on one thing and does it very well. The founder compares his company to a little niche boutique in the corner, the hi-fi store with expensive speakers. His competitors, on the other hand, are the Walmarts with thick catalogs of imaging software.

About three-quarters of Imints revenues come from licensing its proprietary software that does these tricks. Some clients pay royalties on the number of devices shipped that use Vidhance, while others opt for a flat annual fee. The rest of the income comes from licensing its development tools or SDK, and maintenance fees.

Imint now supplies its software to 20 clients around the world, including the Chinese big-four of Huawei, Xiaomi, Oppo and Vivo as well as chip giants like Qualcomm and Mediatek. ByteDance also has a deal to bake Imints software into Smartisan, which sold its core technology to the TikTok parent last year. Imint is beginning to look beyond handsets into other devices that can benefit from high-quality footage, from action cameras, consumer drones, through to body cameras for law enforcement.

So far, the Swedish company has been immune from the U.S.-China trade tensions, but Lifvendahl worried as the two superpowers move towards technological self-reliance, outsiders like itself will have a harder time entering the two respective markets.

We are in a small, neutral country but also are a small company, so were not a strategic threat to anyone. We come in and help solve a puzzle, assured the founder.

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Imint is the Swedish firm that gives Chinese smartphones an edge in video production - TechCrunch

The University of Florida on Tuesday announced a $70 million partnership that will bring artificial intelligence to the forefront of the schools technology programs and introduce the topic more broadly to the student body.

The joint effort with the California-based company NVIDIA will result in the hiring of 100 new faculty and touch every UF graduate with at least one class exposing them to artificial intelligence concepts, the university said. It also will give UF the fastest artificial intelligence supercomputer in higher education, officials said.

The discipline is a branch of computer science that has brought the world products like self-driving cars, food delivery robots and computers that engage humans in a game of chess. Artificial intelligence makes it possible for machines to perform human-like tasks by learning from experience and making adjustments.

Gov. Ron DeSantis, who was present for the Zoom announcement, said the initiative would attract students to UF from across the nation.

I think its going to help the state of Florida modernize our workforce, he said. I think well have an added boost as we seek to become an economic powerhouse and internally as we bounce back from the pandemic.

Provost Joseph Glover said more than 600 faculty and staff members have been involved so far in conversations on how to incorporate the subject of artificial intelligence into their disciplines.

We believe this is so important to our future and our students futures that every student who graduates from the University of Florida, no matter what their major is, should have the opportunity to acquire the knowledge, the training and the tools of artificial intelligence and data science, Glover said. This is not only important to our students but the nation.

Glover said the partnership will help catapult the University of Florida to the forefront of AI and create the next generation of AI-enabled workforce in this country.

The announcement laid out the financial details of the partnership: a $25 million gift from Chris Malachowsky, co-founder of NVIDIA and an alumnus of UFs Herbert Wertheim College of Engineering; $25 million in hardware, software, training and services from NVIDIA; and a $20 million investment from UF.

The supercomputer is expected to be installed by November. Its an upgrade from the existing UF supercomputer known as HiPerGator. Malachowsky said the machine will be among the worlds top 10 supercomputers, capable of processing vast amounts of data that can be used across many fields, including climate research and medicine.

In explaining why the company brought the partnership to Florida, Malachowsky described the state as a living laboratory for some of the really big problems of today for society, with its long coastline, large population, varied demographics and a workforce in need of upscaling to deal with the growing digital divide.

UF is in the process of infusing artificial intelligence into its curricula, Glover said. And, while not everyone will be expected to become a programmer, everyone will have exposure to it and the opportunity to be certified in that area, he said.

Certifications will include courses on ethics, bias and how artificial intelligence applies to certain disciplines. By spring, Glover said, he expects a survey course to be offered, and by next summer the school may offer more specific boot camps in artificial intelligence.

Cammy Abernathy, dean of the College of Engineering, said artificial intelligence is already being applied across disciplines to finding solutions for diabetes and as a means to combat climate change and red tide. Broadening access to the topic can lead to a more equitable society, she said.

Philosophers, lawyers and computer scientists are working together to ensure that AI does not further enforce the biases that are present in our society and does not intrude on the privacy and rights of the public, Abernathy said. These issues of fairness and access are important, so much so that they will be at the heart of this initiative.

Glover said the university will work to provide access to businesses, the K-12 education system, historically black colleges and universities and state and community colleges.

The population in Florida will have opportunities to train on this, Glover said.

Malachowsky said he hopes wealthy alumni in each state consider investing in artificial intelligence at their universities.

Rep. Michael T. McCaul, co-chair of the AI Caucus, said in a statement released by the university that the announcement serves as a model.

Computing power is at the heart of artificial intelligence, and this investment in the fastest AI supercomputer in academia will ensure that the U.S. stays on the cutting edge of research and development, he said.

Mori Hosseini, a member of UFs Board of Trustees and former chair of the Board of Governors, said Tuesdays announcement made him wish he could go back to school.

We are all witnessing a significant moment in history for the state of Florida and the University of Florida, he said.

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UF students, get used to this topic: artificial intelligence - Tampa Bay Times

Express News Service

BHUBANESWAR: In its bid to ensure greater fiscal accountability, effective resources management and increased productivity, the state government is set to go for integration of artificial intelligence (AI) in the financial auditing mechanism for its organisations in a big way.

The Directorate of Local Fund Audit (DLFA) has started work on an AI project that is aimed at reshaping organisational accountability and bringing about greater trust on Government institutions. It is already seeking advisory services from experts for the successful implementation of the technology infusion in local fund audit automation.

The Government has appointed Professor (Information Systems) of XIMB Sanjay Mohapatra as Advisor to the AI implementation committee of DLFA. He will assist LFA to develop a comprehensive strategy to analyze processes and develop implementation plans for AI-enabled automation and advise on available options and capabilities along with skilling of officials As per the project, the audit of organisations will be conducted using various components of AI like machine learning, deep learning, natural language processing and computer vision.

Combined with other AI components, the technology can classify documents according to type, extract relevant information and perform analysis. The auditors can detect fraud by creating sophisticated machine learning-based modules. The audit can be further transformed by deep learning, which can analyze unstructured data. Data from financial statements can be extracted to calculate proposed materiality based on a range of benchmarks.

AI will help auditors optimise their time, enabling their ability to judge and analyze deeper set of data besides making it possible for them to work smarter and better, said an official involved with the project.The State has 13,606 auditee institutions that are subjected to audit by the LFA organisation. The DLFA, a statutory audit organisation, functioning under the administrative control of Finance department has been undertaking audit of Government, grant-in-aid institutions, Panchayatiraj institutions, Urban Local Bodies, Development Authorities and Universities besides schools, colleges and endowments.

Odisha to use AI in organisation audit in big way

Since auditors face innumerable challenges while evaluating thousands of documents and undertaking investigations on ambiguous transactions, use of AI will enhance their service and accuracy.

After skilling of officials with basic and applied concept of AI in local fund audit, the project will be implemented on a pilot basis before making it universal, the official added.

Originally posted here:

Odisha to use artificial intelligence in organisation audit in big way - The New Indian Express

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It should have been artificial intelligences moment in the sun. With billions of dollars of investment in recent years, AI has been touted as a solution to every conceivable problem. So when the COVID-19 pandemic arrived, a multitude of AI models were immediately put to work.

Some hunted for new compounds that could be used to develop a vaccine, or attempted to improve diagnosis. Some tracked the evolution of the disease, or generated predictions for patient outcomes. Some modelled the number of cases expected given different policy choices, or tracked similarities and differences between regions.

The results, to date, have been largely disappointing. Very few of these projects have had any operational impact hardly living up to the hype or the billions in investment. At the same time, the pandemic highlighted the fragility of many AI models. From entertainment recommendation systems to fraud detection and inventory management the crisis has seen AI systems go awry as they struggled to adapt to sudden collective shifts in behaviour.

Also read: How AI is helping reopen factory floors safely in a pandemic

The unlikely hero emerging from the ashes of this pandemic is instead the crowd. Crowds of scientists around the world sharing data and insights faster than ever before. Crowds of local makers manufacturing PPE for hospitals failed by supply chains. Crowds of ordinary people organising through mutual aid groups to look after each other.

COVID-19 has reminded us of just how quickly humans can adapt existing knowledge, skills and behaviours to entirely new situations something that highly-specialised AI systems just cant do. At least yet.

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We now face the daunting challenge of recovering from the worst economic contraction on record, with societys fault lines and inequalities more visible than ever. At the same time, another crisis climate change looms on the horizon.

At Nesta, we believe that the solution to these complex problems is to bring together the distinct capabilities of both crowd intelligence and machine intelligence to create new systems of collective intelligence.

In 2019, we funded 12 experiments to help advance knowledge on how new combinations of machine and crowd intelligence could help solve pressing social issues. We have much to learn from the findings as we begin the task of rebuilding from the devastation of COVID-19.

In one of the experiments, researchers from the Istituto di Scienze e Tecnologie della Cognizione in Rome studied the use of an AI system designed to reduce social biases in collective decision-making. The AI, which held back information from the group members on what others thought early on, encouraged participants to spend more time evaluating the options by themselves.

The system succeeded in reducing the tendency of people to follow the herd by failing to hear diverse or minority views, or challenge assumptions all of which are criticisms that have been levelled at the British governments scientific advisory committees throughout the pandemic.

In another experiment, the AI Lab at Brussels University asked people to delegate decisions to AI agents they could choose to represent them. They found that participants were more likely to choose their agents with long-term collective goals in mind, rather than short-term goals that maximised individual benefit.

Making personal sacrifices for the common good is something that humans usually struggle with, though the British public did surprise scientists with its willingness to adopt new social-distancing behaviours to halt COVID-19. As countries around the world attempt to kickstart their flagging economies, will people be similarly willing to act for the common good and accept the trade-offs needed to cut carbon emissions, too?

COVID-19 may have knocked Brexit off the front pages for the last few months, but the UKs democracy will be tested in the coming months by the need to steer a divided nation through tough choices in the wake of Britains departure from the EU and an economic recession.

In a third experiment, a technology company called Unanimous AI partnered with Imperial College, London to run an experiment on a new way of voting, using AI algorithms inspired by swarms of bees. Their swarming approach allows participants to see consensus emerging during the decision-making process and converge on a decision together in real-time helping people to find collectively acceptable solutions. People were consistently happier with the results generated through this method of voting than those produced by majority vote.

In each of these experiments, weve glimpsed what could be possible if we get the relationship between AI and crowd intelligence right. Weve also seen how widely held assumptions about the negative effects of artificial intelligence have been challenged. When used carefully, perhaps AI could lead to longer-term thinking and help us confront, rather than entrench, social biases.

Alongside our partners, the Omidyar Network, Wellcome, Cloudera Foundation and UNDP, we are investing in growing the field of collective-intelligence design. As efforts to rebuild our societies after coronavirus begin, were calling on others to join us. We need academic institutions to set up dedicated research programmes, more collaboration between disciplines, and investors to launch large-scale funding opportunities for collective intelligence R&D focused on social impact. Our list of recommendations is the best place to get started.

In the meantime, well continue to experiment with novel combinations of crowd and machine intelligence, including launching the next round of our grants programme this autumn. The world is changing fast and its time for the direction of AI development to change, too.

Kathy Peach, Head of the Centre for Collective Intelligence Design, Nesta

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Also read: Power consumption can explode with increasing use of artificial intelligence

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Covid could have been AIs moment in sun. But it isnt as flexible as humans yet - ThePrint

This year is likely to be remembered for the COVID-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters 10 times more than previous competitors is surprisingly powerful.

The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential. GPT-3 can converse at a conceptual level, translate language, answer email, perform (some) programming tasks, help with medical diagnoses and, perhaps someday, serve as a therapist. It can write poetry, dialogue and stories with a surprising degree of sophistication, and it is generally good at common sense a typical failing for many automated response systems. You can even ask it questions about God.

Imagine a Siri-like voice-activated assistant that actually did your intended bidding. It also has the potential to outperform Google for many search queries, which could give rise to a highly profitable company.

GPT-3 does not try to pass the Turing test by being indistinguishable from a human in its responses. Rather, it is built for generality and depth, even though that means it will serve up bad answers to many queries, at least in its current state. As a general philosophical principle, it accepts that being weird sometimes is a necessary part of being smart. In any case, like so many other technologies, GPT-3 has the potential to rapidly improve.

It is not difficult to imagine a wide variety of GPT-3 spinoffs, or companies built around auxiliary services, or industry task forces to improve the less accurate aspects of GPT-3. Unlike some innovations, it could conceivably generate an entire ecosystem.

There is a notable buzz about GPT-3 in the tech community. One user in the U.K. tweeted: "I just got access to gpt-3 and I can't stop smiling, i am so excited." Venture capitalist Paul Graham noted coyly: "Hackers are fascinated by GPT-3. To everyone else it seems a toy. Pattern seem familiar to anyone?" Venture capitalist and AI expert Daniel Gross referred to GPT-3 as "a landmark moment in the field of AI."

I am not a tech person, so there is plenty about GPT-3 I do not understand. Still, reading even a bit about it fills me with thoughts of the many possible uses.

It is noteworthy that GPT-3 came from OpenAI rather than from one of the more dominant tech companies, such as Alphabet/Google, Facebook or Amazon. It is sometimes suggested that the very largest companies have too much market power but in this case, a relatively young and less capitalized upstart is leading the way. (OpenAI was founded only in late 2015 and is run by Sam Altman).

GPT-3 is also a sign of the underlying health and dynamism of the Bay Area tech world, and thus of the U.S. economy. The innovation came to the U.S. before China and reflects the power of decentralized institutions.

Like all innovations, GPT-3 involves some dangers. For instance, if prompted by descriptive ethnic or racial words, it can come up with unappetizing responses. One can also imagine that a more advanced version of GPT-3 would be a powerful surveillance engine for written text and transcribed conversations. Furthermore, it is not an obvious plus if you can train your software to impersonate you over email. Imagine a world where you never know who you are really talking to "Is this a verified email conversation?" Still, the hope is that protective mechanisms can at least limit some of these problems.

We have not quite entered the era where "Skynet goes live," to cite the famous movie phrase about an AI taking over (and destroying) the world. But artificial intelligence does seem to have taken a major leap forward. In an otherwise grim year, this is a welcome and hopeful development. Oh, and if you would like to read more, here is an article about GPT-3 written by GPT-3.

Tyler Cowen is a Bloomberg Opinion columnist. He is a professor of economics at George Mason University and writes for the blog Marginal Revolution. His books include "Big Business: A Love Letter to an American Anti-Hero."

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Artificial intelligence is the hope 2020 needs | Commentary | Seattle Times - Walla Walla Union-Bulletin

WASHINGTON - New software developed by BAE Systems could help the Department of Defense build confidence in decisions and intelligence produced by machine learning algorithms, the company claims.

BAE Systems said it recently delivered its new MindfuL software program to the Defense Advanced Research Projects Agency in a July 14 announcement. Developed in collaboration with the Massachusetts Institute of Technologys Computer Science and Artificial Intelligence Laboratory, the software is designed to increase transparency in machine learning systemsartificial intelligence algorithms that learn and change over time as they are fed ever more databy auditing them to provide insights about how it reached its decisions.

The technology that underpins machine learning and artificial intelligence applications is rapidly advancing, and now its time to ensure these systems can be integrated, utilized, and ultimately trusted in the field, said Chris Eisenbies, product line director of the cmpanys Autonomy, Control, and Estimation group. The MindfuL system stores relevant data in order to compare the current environment to past experiences and deliver findings that are easy to understand.

While machine learning algorithms show promise for DoD systems, determining how much users can trust their output remains a challenge. Intelligence officials have repeatedly noted that analysts cannot rely on black box artificial intelligence systems that simply produce a decision or piece of intelligencethey need to understand how the system came to that decision and what unseen biases (in the training data or otherwise) might be influencing that decision.

MindfuL is designed to help address that gap by providing more context around those outputs. For instance, the company says its program will issue statements such as The machine learning system has navigated obstacles in sunny, dry environments 1,000 times and completed the task with greater than 99 percent accuracy under similar conditions; or The machine learning system has only navigated obstacles in rain 100 times with 80 percent accuracy in similar conditions; manual override recommended. Those types of statements can help users evaluate how much confidence they should place in any individual decision produced by the system.

This is the first release of the MindfuL software as part of a $5 million, three-year contract under DARPAs Competency-Aware Machine Learning (CAML) program. BAE Systems plans to demonstrate their software in both simulation and in prototype hardware later this year.

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Could this software help users trust machine learning decisions? - C4ISRNet

Artificial intelligence (AI) is at the top of the buzzword bingo reel in the world of tech, and for good reason. Were seemingly shifting from the era of businesses (and the public) talking about AI and marvelling at its mysterious power, to wondering how it can be used to best tackle real-world challenges day to day.

That said, with the fine-tuning of the technology comes increasing attempts to exploit some of its frailties. So just how will the world harness, advance and protect AI technology within the year to come? Here are few of the more edge-case applications of AI taking place.

Advances in deep-learning and AI continue to make deepfakes more realistic. This technology has already proven itself dangerous in the wrong hands; many predict that deepfakes could provide a dangerous new medium for information warfare, helping to spread misinformation or fake news. The majority of its use, however, is in the creation of non-consensual pornography which most frequently targets celebrities, owed to large amounts of data samples in the public domain.Deepfake technology has also been used in highly-sophisticated phishing campaigns.

Beyond illicit ingenuity in shady corners of cyberspace, the fundamental technology is proving itself a valuable tool in a few other disparate places. Gartners Andrew Frank called the technology a potential asset to enterprises in personalized content production: Businesses that utilize mass personalization need to up their game on the volume and variety of content that they can produce, and GANs [Generative Adversarial Network] simulated data can help.

Last year, a video featuring David Beckham speaking in nine different languages for a Malaria No More campaign was released. The content was a result of video manipulation algorithms and represented how the technology can be used for a positive outcome reaching a multitude of different audiences quickly with accessible, localized content in an engaging medium.

Meanwhile, a UK-based autonomous vehicle software company has developed deepfake technology that is able to generate thousands of photo-realistic images in minutes, which helps it train autonomous driving systems in lifelike scenarios, meaning the vehicle makers can accelerate the training of systems when off the road.

The Financial Times also reported on a growing divide between traditional computer-generated graphics which are often expensive and time-consuming and the recent rise in deepfake tech, while Disney used deepfake technology to include the young version of Harrison Ford as Han Solo in the recent Star Wars films.

Facial recognition is enabling convenience, whether its a quick passport check-in process at the airport (remember those?) or the swanky facial software in newer phone models. But AIs use in facial recognition extends now to surveillance, security, and law enforcement. At best, it can cut through some of the noise of traditional policing. At worst, its susceptible to some of its own in-built biases, with recorded instances of systems trained on misrepresentative datasets leading to gender and ethnicity biases.

Facial recognition has been dragged to the fore of discussion, following its use at BLM protests and the wrongful arrest of Robert Julian-Borchak Williams at the hand of faulty AI algorithms earlier this year. A number of large tech firms, including Amazon and IBM,have withdrawn their technology from use by law enforcement.

AI has a long way to go to match the expertise of our human brains when it comes to recognizing faces. These things on the front of us are complex and changeable; algorithms can be easily confused. Theres a roadmap of hope for the format, though, thanks to further advances in deep-learning. As an AI machine matches two faces correctly or incorrectly, it remembers the steps and creates a network of connections, picking up past patterns and repeating them or altering them slightly.

Facial recognitions controversies have furthered discussions around ethical AI, allowing us to clearly understand the tangible impact of misrepresentative datasets in training AI models, which are equally worrying in other applications and use cases, such as recruitment.As the technology is deployed into more and more areas in the world around us, its dependability, neutrality and compliance with existing laws becomes all the more critical.

With every promising advance in technology comes another challenge, and a recent CBInsights paper warns of AIs role in the rise of new-age hacks.

Sydney-based researchers Skylight Cyber reported finding an inherent bias in an AI model developed by cybersecurity firm Cylance, and were able to create a universal bypass that allowed malware to go undetected. They were able to understand how the AI model works, the features it uses to reach decisions, and create tools to fool it time and again. Theres also the potential for a new crop of hackers and malware to poison data corrupting AI algorithms and disrupting the usual detection of malicious/normal network behaviour. This problematic level of manipulation doesnt do a lot for the plaudits that many cybersecurity firms give to products that use AI.

AI is also being used by the attackers themselves. In March last year, scammers were thought to have leveraged AI to impersonate the voice of a business executive at a UK-based energy business, requesting from an employee the successful transfer of hundreds and thousands of dollars to a fraudulent account.More recently, its emerged that these concerns are valid, and not a whole lot of sophistication is required to pull them off. As seen in the case of Katie Jones a fake LinkedIn account used to spy and phish information from her connections an AI-generated image was enough to dupe unsuspecting businessmen into connecting and potentially sharing sensitive information.

Meanwhile, some believe AI-driven malware could be years away if on the horizon at all but IBM has researched how existing AI models can be combined with current malware techniques to create challenging new breeds in a project dubbed DeepLocker. Comparing its potential capabilities to a sniper attack as opposed to traditional malwares spray and pray approach, IBM said DeepLocker was designed for stealth: It flies under the radar, avoiding detection until the precise moment it recognizes a specific target.

Theres no end to innovation when it comes to cybercrime, and we seem set for some sophisticated, disruptive activity to emerge from the murkier shadows of AI.

Automated machine learning, or AutoML (a term coined by Google), reduces or completely removes the need for skilled data scientists to build machine learning models. Instead, these systems allow users to provide training data as an input, and receive a machine learning model as an output.

AutoML software companies may take a few different approaches. One approach is to take the data and train every kind of model, picking the one that works best. Another is to build one or more models that combine the others, which sometimes give better results. Businesses ranging from motor vehicles to data management, analytics and translation are seeking refined machine learning models through the use of AutoML. With a marked shortage of AI experts, this technology will help democratise the tech and cut down computing costs.

Despite its name, AutoML has so far relied a lot on human input to code instructions and programs that tell a computer what to do. Users then still have to code and tune algorithms to serve as building blocks for the machine to get started. There are pre-made algorithms that beginners can use, but its not quite automatic.

Google computer scientists believe they have come up with a new AutoML method that can generate the best possible algorithm for a specific function, without human intervention. The new method is dubbed AutoML-Zero, which works by continuously trying algorithms against different tasks, and improving upon them using a process of elimination, much like Darwinian evolution.

AI and machine learning may be streamlining processes, but they are doing so at some cost to the environment.

AI is computationally intensive (it uses a whole load of energy), which explains why a lot of its advances have been top-down. As more companies look to cut costs and utilize AI, the spotlight will fall on the development and maintenance of energy-efficient AI devices, and tools that can be used to turn the tide by pointing AI expertise towards large-scale energy management.

Artificial Intelligence also has a role in augmenting energy efficiency. Tech giants are using systems that can gather data from sensors every five minutes, and use algorithms to predict how different combinations of actions will positively or negatively affect energy use.

In 2018, Chinas data centers produced 99 million metric tons of carbon dioxide (thats equivalent to 21 million cars on the road). Worldwide, data centers consume 3 to 5 percent of total global electricity, and that will continue to rise as we rely more on cloud-bases services. Savvy to the need to go green, tech giants are now employing AI systems that can gather data from sensors every five minutes, and use algorithms to predict how different combinations of actions will positively or negatively affect energy use. AI tools can also spot issues with cooling systems before they happen, avoiding costly shutdowns and outages for cloud customers.

From low power AI processors in edge technologies to large scale renewable energy solutions (thats AI dictating the angle of solar panels, and predicting wind power output based on weather forecasts), there are positive moves happening as we enter the 2020s. More green-conscious, AI-intensive tech firms are popping all the time, and we look forward to seeing how they navigate the double-edged sword of energy-guzzling AI being used to mitigate the guzzling of energy.

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Artificial intelligence is being used to help divorcing couples divide assets and develop a parenting plan for their children. The technology has the potential to make family law matters cheaper and less stressful. University of Newcastle researchers Professor Tania Sourdin and Dr Bin Li have examined this technology, including the apps Amica, Adieu and Penda. Their work was part of a major research project on justice apps at Newcastle Law School. The Newcastle academics say complex family law cases can "cost each party more than $200,000". "There is a need for cheaper, smarter dispute resolution options in the family law area," said Professor Sourdin, who is Dean of the university's law school. Asked if the apps could improve family law, Professor Sourdin said: "Yes, for some people an app can help". "In a way it may make it easier because the time frames might be shorter. "Also not having to talk with your former partner can be helpful where there are no children." Some apps also support "easy referral to counselling, mediation and other services, which can be very useful". "Often people who are self represented need support and there is evidence that a lot of people have difficulty finalising arrangements without a lawyer," she said. Apps can help reduce legal costs while ensuring that people have access to a lawyer when needed. The coronavirus pandemic has put a spotlight on relationships, amid reports that lockdown and job losses has led to more strain between couples and separations. The use of apps to settle family law disputes seem suited to the times. "It is where society is heading. Many people want to access the justice system from their home 24-7," Professor Sourdin said. "Apps can help with this and also provide referral to professionals when needed." Dr Li said the trend towards such apps was "much clearer" in the pandemic. The federal government is supporting apps with artificial intelligence to "empower separating couples to resolve their family law disputes online". Attorney-General Christian Porter issued a press release last month about the Amica app. National Legal Aid developed Amica with $3 million in federal funding. This app is suitable for couples whose relationship is "relatively amicable". "Amica uses artificial-intelligence technology to suggest the split of assets," Mr Porter said. He added that Amica considers a couple's circumstances, agreements reached by couples in similar situations and how courts generally handle disputes of the same nature. "The tool can also assist parents to develop a parenting plan for their children." The Morrison government wants to improve the family law system to make it "faster, simpler, cheaper and much less stressful for separating couples and their children". The government believes Amica will help couples resolve disputes between themselves and avoid court. The app is aimed at reducing legal bills for separating couples and pressure on family law courts. Dr Li, a lecturer at the university's law school, said there had been "extensive discussion and debate on the reform of the family law system in Australia". He said the apps could "alleviate the burden of courts and the load on judges". Professor Sourdin said the apps "need to be carefully developed". "There are concerns they may not function well and that the data used to power the AI [artificial intelligence] is deficient," she said. "There are also real issues about how effective justice apps can be where there is a lack of agreement about what the issues are or what evidence is correct. "Law can be very complex and requires contextual understandings." There are also concerns about digital literacy and access to technology. However, Professor Sourdin was surprised when their review of the Adieu justice app showed users were older than expected. "For example 41 per cent of the 800 or so people who had used the Adieu app had a relationship of more than 15 years," she said. Dr Li said there was also concern about data and privacy protection. "What if the data collected by apps are hijacked and used by an unauthorised third party?", he said. Nevertheless, they say apps in the justice sector can have many benefits. Professor Sourdin said justice apps could be used to "help people with their legal rights". "The DoNotPay app that is used in the US is a good example. This app can help people with simple matters - from parking fines to travel refunds," she said.

https://nnimgt-a.akamaihd.net/transform/v1/crop/frm/3AijacentBN9GedHCvcASxG/3a380c63-19c6-4322-9665-f43828eb021a.jpg/r48_0_4729_2645_w1200_h678_fmax.jpg

Artificial intelligence is being used to help divorcing couples divide assets and develop a parenting plan for their children.

The technology has the potential to make family law matters cheaper and less stressful.

University of Newcastle researchers Professor Tania Sourdin and Dr Bin Li have examined this technology, including the apps Amica, Adieu and Penda.

Their work was part of a major research project on justice apps at Newcastle Law School.

The Newcastle academics say complex family law cases can "cost each party more than $200,000".

"There is a need for cheaper, smarter dispute resolution options in the family law area," said Professor Sourdin, who is Dean of the university's law school.

Asked if the apps could improve family law, Professor Sourdin said: "Yes, for some people an app can help".

"In a way it may make it easier because the time frames might be shorter.

"Also not having to talk with your former partner can be helpful where there are no children."

Some apps also support "easy referral to counselling, mediation and other services, which can be very useful".

"Often people who are self represented need support and there is evidence that a lot of people have difficulty finalising arrangements without a lawyer," she said.

Apps can help reduce legal costs while ensuring that people have access to a lawyer when needed.

The coronavirus pandemic has put a spotlight on relationships, amid reports that lockdown and job losses has led to more strain between couples and separations.

The use of apps to settle family law disputes seem suited to the times.

"It is where society is heading. Many people want to access the justice system from their home 24-7," Professor Sourdin said.

"Apps can help with this and also provide referral to professionals when needed."

Dr Li said the trend towards such apps was "much clearer" in the pandemic.

The federal government is supporting apps with artificial intelligence to "empower separating couples to resolve their family law disputes online".

Attorney-General Christian Porter issued a press release last month about the Amica app.

National Legal Aid developed Amica with $3 million in federal funding.

This app is suitable for couples whose relationship is "relatively amicable".

"Amica uses artificial-intelligence technology to suggest the split of assets," Mr Porter said.

He added that Amica considers a couple's circumstances, agreements reached by couples in similar situations and how courts generally handle disputes of the same nature.

"The tool can also assist parents to develop a parenting plan for their children."

The Morrison government wants to improve the family law system to make it "faster, simpler, cheaper and much less stressful for separating couples and their children".

The government believes Amica will help couples resolve disputes between themselves and avoid court.

The app is aimed at reducing legal bills for separating couples and pressure on family law courts.

Dr Li, a lecturer at the university's law school, said there had been "extensive discussion and debate on the reform of the family law system in Australia".

He said the apps could "alleviate the burden of courts and the load on judges".

Professor Sourdin said the apps "need to be carefully developed".

"There are concerns they may not function well and that the data used to power the AI [artificial intelligence] is deficient," she said.

"There are also real issues about how effective justice apps can be where there is a lack of agreement about what the issues are or what evidence is correct.

"Law can be very complex and requires contextual understandings."

There are also concerns about digital literacy and access to technology.

However, Professor Sourdin was surprised when their review of the Adieu justice app showed users were older than expected.

"For example 41 per cent of the 800 or so people who had used the Adieu app had a relationship of more than 15 years," she said.

Dr Li said there was also concern about data and privacy protection.

"What if the data collected by apps are hijacked and used by an unauthorised third party?", he said.

Nevertheless, they say apps in the justice sector can have many benefits.

Professor Sourdin said justice apps could be used to "help people with their legal rights".

"The DoNotPay app that is used in the US is a good example. This app can help people with simple matters - from parking fines to travel refunds," she said.

Continued here:

How artificial intelligence is being used for divorce and separations with apps like Amica, Adieu and Penda - Newcastle Herald

The global pandemic is pushing the healthcare system even harder to find ways to help hospitals efficiently address cost and streamline operations. From managing healthcare billing and the insurance process to providing a faster diagnosis of a serious disease, artificial intelligence (AI) has the potential to completely change how hospitals operate. MedTech Intelligence recently discussed some of the areas of impact with Jim McGowan, head of product at ElectrifAI.

MedTech Intelligence: How is AI helping hospitals manage healthcare bills and the insurance process?

Jim McGowan: The original areas within a hospital where AI created efficiency were in registration and insurance processing, most notably in revenue cycle management (RCM). RCM was envisioned as a seamless process across patient appointment and registration; claim coding and submission; payment reconciliation; and appeals. Over time these solutions grew so complex that parallel industries around Pay and Chase emerged, in which providers needed incremental support to capture all their revenue. With margins in the low single digits each dollar counts.

These RCM systems are rule based, which is antiquated AI technology. [Our] RevCaptureAi solution combats the limitations of these traditional revenue cycles with the dynamic intelligence of artificial intelligence (AI) and machine learning (ML) that track, analyze and generate insights about your missed charges. In a billion-dollar health system, just 1% of missed total charges adds up to $10 million in lost revenue. This is the opportunity.

Both providers and payers are implementing chatbots to more efficiently engage with patients/members by automating common support topics like confirming eligibility, getting claims/payment status, scheduling appointments and more. Machine learning is in the early stages of adoption. ElectrifAi has used machine learning to capture missed codes on hospital bills for [more than] five years, and building practical solutions to AI problems for [more than] 15 [years].

ElectrifAIs CEO Edward Scott discusses artificial intelligence and machine learning during the coronavirus crisis in Beating COVID-19 Is a Team SportMTI: How is the technology streamlining medication management? What is its role in managing procedures?

McGowan: Medication errors are still a significant issue in hospitals. EMR solutions were implemented to improve workflow and data capture for a complete patient view. These solutions have reduced adverse drug events (ADEs). Technology has been used to create many checks-and-balances within hospitals, which requires a double-check and scan of a barcode for each patient and medication to validate the drug was prescribed by a physician. There is continued work needed to capture the full patient history as these solutions are hospital system specific, do not include interoperability with the PBM data, and do not share with other hospital systems. Ultimately, a more complete patient system of record may be necessary to ensure that each system connects to each other to share data.

One of the areas where AI in healthcare has shown the most promise is in diagnostics, which can ultimately be leveraged in operating and emergency room settings. Right now, early diagnosis is one of the most important factors in the ultimate outcome of a patients care. AI deep-learning algorithms are being used to shave down the time it takes to diagnose serious illnesses. Our PulmoAi X-ray solution is an example of a tool that amplifies the work of radiologists, who leverage AI to triage cases as emergency rooms and ICUs overflow.AI is being used within healthcare for evidence-based recommendations. AI algorithms ingest collected vitals, lab results, medication orders and comorbidities and produce smarter triage tools.

We have seen growth in digital applications for mental health and virtual assistants to answer patient questions. As telehealth grows, I would not be surprised if the virtual assistants handle increasingly large volumes of questions, significantly greater than live operators. These bots are becoming much more important as the front-end to a telehealth call.

AI and Robotics for laser eye surgery and orthopedic surgeries are growing. AI-based visualizations are exploding in the market. AI is attempting to enter every facet of healthcare.

MTI: What factors should technology developers consider when designing AI solutions for hospitals?

McGowan: There are a number of important factors: Regulatory concerns, community demographics, fitting into existing workflows, technical proficiency of both the hospital personnel and consumers.

Healthcare is a highly regulated industry. HIPAA balances portability with privacy. This is for a very good reason, but has a lot of side effects, like complicating marketing efforts. You cant send an email to a patient telling her its okay to get the hip surgery she canceled when COVID-19 struck, because you cant guarantee someone else wont read it. If you send someone a reminder about their diabetes medication and are too specific in the email, what happens when that email is opened by someone other than the specific patient? Solutions that require you to log into a website to view the information was the evolution during the 2010s and continued to evolve with the growth in depth and sophistication of the mobile app solutions. Inappropriate sharing of data, even within a family, can create legal liability that hampers more specific and appropriate messaging.

When building solutions, AI can enable a very quick solution to the above concerns. Tools like robotic process automation (RPA) and chat bots have allowed providers to quickly create solutions that gather patient information and respond with an appropriate response, even in the patients preferred language. These more natural language conversations guide the patient to a choice without being overly and overtly intrusive.Most importantly, AI and ML people really have to deeply understand their craft if they want to influence medical decisions of any kind. Data science is not just technology development. It requires deep understanding of the problem domain being addressed, as well as statistics, inference, and logic. And data science without exceptional data engineering is useless. There is no magic inside the algorithms. If the data is bad, the results will be bad. Weve seen data systems where almost half the data is inaccurate. Let that sink in. Would you go to a doctor if half the facts in their medical books were wrong? AI solutions start with great data engineering.

Id like to talk directly to the C-Suite in the hospitals for a moment.

Lets discuss the elephant in the room: many hospitals are poorly run businesses, with razor thin margins and inadequate spending controls. These are not financially healthy organizations.

This year we saw 42 hospitals file for bankruptcyso far. All have two things in common: They all had revenue capture solutions, and they all couldnt pay their bills.

First, revenue capture doesnt address your problem: you need elective surgeries. Revenue Capture fixes leaks in your billing process. Hospitals dont go bankrupt because their billing process is too leaky. The revenue isnt coming in. The elective surgeries arent there.

Second, the revenue capture programs you do have use rules-based systems, and those dont work when the rules change. COVID-19 changed the rules. You needed a machine-learning based solution. Rules-based systems have been around since the 1950s. The world has moved on. We have a machine learning based revenue capture solution, and not one hospital using it has gone bankrupt. And still, that should not be your priority right nowthats just a part of getting healthy.

You need to restart elective surgeries. You need to manage your finances.

Customer engagement isnt optional for any other business, and it isnt optional for yours. Machine learning can help.

You also need to get control of your spending. Spend analytics is critical. Again, this is not optional for any business, hospital or not. Machine learning can help.

AIespecially machine learninghelps improve the health of the patient, the financial health of the hospital, and ultimately the health of the community. The pandemic should not be a reason to push off these technologiesits the reason you should embrace them today.

Artificial intelligence and machine learning are proving to be meaningful weapons in our arsenal during the coronavirus crisis.

Change is constant, and we continue to evolve.

A recent paper released by Duke University cites the promise of AI, but urges policy changes in order to bring AI-enabled clinical decision software to fruition.

Expanded designs that enable clinicians to leverage data in making healthcare decisions, but privacy challenges remain.

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Tech Q&A: Artificial Intelligence Has Promise of Streamlining Hospital Processes, Diagnostic Tools - MedTech Intelligence

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Global Artificial Intelligence for Automotive Market 2020 Size, Share, Trends, Growth and Outlook with Company Analysis and Forecast to 2026 - Express...

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Major Players Of Automotive Artificial Intelligence Market

Hyundai Motor CompanyInternational Business Machines CorporationUber TechnologiesBayerische Motoren Werke AGTeslaDaimler AGHarman International IndustriesFord Motor CompanyToyota Motor CorporationVolvo Car CorporationMicrosoft CorporationStart-Up EcosystemDidi ChuxingAlphabetAudi AGGeneral Motors CompanyIntel CorporationHonda MotorXilinxQualcomm

This report covers the Types as well as Application data for Automotive Artificial Intelligence Market along with the country level information for the period of 2020-2026

Market Segmented By Types and By its Applications:

Global Automotive Artificial Intelligence Market Segmentation: By Types

HumanMachine InterfaceSemi-autonomous DrivingAutonomous Driving

Global Automotive Artificial Intelligence Market Segmentation: By Applications

Deep LearningMachine LearningContext AwarenessComputer VisionNatural Language Processing

Global Automotive Artificial Intelligence Market Scope and Features

Global Automotive Artificial Intelligence Market Introduction and Overview Includes Automotive Artificial Intelligence market Definition, Market Scope and Market Size Estimation and region-wise Automotive Artificial Intelligence Value and Growth Rate history from 2015-2026,

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Automotive Artificial Intelligence Market Analysis and Forecast by Region Includes Market Value and Consumption Forecast (2014-2026) of Automotive Artificial Intelligence market Of the following region and sub-regions including North America, Europe(Germany, UK, France, Italy, Spain, Russia, Poland), China, Japan, Southeast Asia (Malaysia, Singapore, Philippines, Indonesia, Thailand, Vietnam) the Middle East and Africa(Saudi Arabia, United Arab Emirates, Turkey, Egypt, South Africa, Nigeria), India, South America(Brazil, Mexico, Colombia)

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Table Of Content

1 Automotive Artificial Intelligence Introduction and Market Overview

2 Industry Chain Analysis

3 Global Automotive Artificial Intelligence Value (US$ Mn) and Market Share, Production , Value (US$ Mn) , Growth Rate and Average Price (US$/Ton) analysis by Type (2015-2019E)

4 Automotive Artificial Intelligence Consumption, Market Share and Growth Rate (%) by Application (2015-2019E) by Application

5 Global Automotive Artificial Intelligence Production, Value (US$ Mn) by Region (2015-2019E)

6 Global Automotive Artificial Intelligence Production (K Units), Consumption (K Units), Export (%), Import (%) by Regions (2015-2019E)

7 Global Automotive Artificial Intelligence Market Status by Regions

8 Competitive Landscape Analysis

9 Global Automotive Artificial Intelligence Market Analysis and Forecast by Type and Application

10 Automotive Artificial Intelligence Market Analysis and Forecast by Region

11 New Project Feasibility Analysis

12 Research Finding and Conclusion13 Appendix13.1 Methodology13.2 Research Data Source

Table of Content & Table Of Figures

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Global Automotive Artificial Intelligence Market Analysis by Emerging Trends, Size, Share, Future Growth, Current Statistics, Brand Endorsements and...