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When subject matter experts help train AI algorithms to detect and categorize certain data patterns that reflect how language is actually used in their part of the health industry, this natural language processing (NLP) enables the algorithm to isolate meaningful data. This helps decision makers find the information they need to make informed care or business decisions quickly.

Healthcare payers

For healthcare payers, this NLP capability can take the form of a virtual agent using conversational AI to help connect health plan members with personalized answers at scale. View the resource.

Government health and human service professionals

For government health and human service professionals, a case worker can use AI solutions to quickly mine case notes for key concepts and concerns to support an individual's care.

Clinical operations and data managers

Clinical operations and data managers executing clinical trials can use AI functionality to accelerate searches and validation of medical coding, which can help reduce the cycle time to start, amend, and manage clinical studies.

Continued here:
What is artificial intelligence in healthcare? | IBM

Artificial Intelligence, more commonly known as AI, is slowly creeping into our daily lives, and we do not even suspect it one bit. It is not making huge decisions for you. Still, it might be influencing the more minor decisions and the past week, jumping from one social platform to another. How many ads did you come across? One can say that there are too many to count. Who precisely is controlling all these ads that are specifically targeted to you?

Artificial Intelligence (AI) now accounts for the significant spending in ad revenue this year. The figures locked in at a shocking $370 billion, and they are only expected to increase in the upcoming years, according to a report released by GroupM. The particular report also dives into the influence of Artificial Intelligence AI-enabled media influence over ad spending in the coming years. It is predicted that ad spending, specifically that of media, will reach almost $1.3 trillion. It is either this or more than 90% of all media spending. It is not expected to happen over a decade or so but in just a few short years. The forecast might come true by 2032, according to the report.

The report also dives into other sectors other than AI enabled media Ad spending. It considers the mediums that will be used to project Ads to its targeted customers. From the graphs they put out for the general public, one can easily observe that digital TV is at the lowest of all the mediums. During the next ten years, companies will be less likely to be advertising on the said medium.

For now, some factors are not being considered by the forecast report, such as chatbots that are handled by Artificial Intelligence and their impact in the coming years. One thing is for sure; Artificial Intelligence is taking over the Ad industry.

Read next:Zero Party Data on the Rise as Brands Adjust to the New Normal

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Artificial Intelligence Takes Over The Media Ad Industry - Digital Information World

With the Artificial Intelligence Act (AI Act), we have again crossed the Rubicon. The die has been cast, there is no way back. We are setting standards for another industry that until now has been left mostly on its own, that has important social functions, and that is of central importance in the global tech rivalry. The European electorate was and still is quite united in demanding rules for digital players while maintaining easy digital access and a competitiveness for all things digital.

With the AI Act and other legislation currently under way in such fields as cybersecurity, data, crypto and chips, the European Union is finalizing what it began with the General Data Privacy Regulation (GDPR), the Digital Services Act (DSA) and the Digital Markets Act (DMA). It will surely not be the last time digital policy is undertaken in Brussels, and updates to these regulations are partly already necessary. But hopefully soon we will be able to say that we have dealt with the most pressing digital issues. This was the promise we gave to European citizens shocked by scandals, cyber-attacks and anti-democratic malfeasance.

I am certain that this regulation, along with the changes that we will propose in the coming months in the ITRE Committee, will enhance the spread of an important new technology while ensuring its safety, which should always be our main goal

As the Industry, Research and Energy (ITRE) Committee rapporteur, I welcome the European Commissions proposal on an AI Act. Maintaining the right balance between freedom and supervision, it will bolster trust in the European AI industry. I am certain that this regulation, along with the changes that we will propose in the coming months in the ITRE Committee, will enhance the spread of an important new technology while ensuring its safety, which should always be our main goal.

Unfortunately, some are focusing on prohibiting AI by fear mongering. When I asked [Wikipedia whistleblower Frances] Haugen at her brave testimony, she was very clear: we dont need bans, we need transparency and clear guidelines. No responsible political group wants to let these potentially powerful systems be used without strong safeguards. But prohibiting technology seldom works as anticipated. There are better ways to deal with this, and that is what the AI Act is doing, to a large extent.

As mentioned, there is much to appreciate in the proposal. First and foremost, the risk-based approach that calls for the prohibition of certain practices, specific requirements for high-risk AI systems, harmonised transparency rules for AI systems intended to interact with natural persons, and rules on market monitoring and surveillance would allow the development of AI systems in line with European values.

The proposal by the European Commission, however, does not go far enough in helping companies compete in return for the many obligations expected from them. This applies especially to start-ups and SMEs Europes most competitive and desired companies and therefore undermines the legitimacy and relevance of the AI Act. We need to provide companies with clearer guidelines, simpler tools and more efficient resources to cope with regulation and to innovate.

I therefore will work to enhance measures supporting innovation, especially those helping start-ups and SMEs. I am especially worried that the current state of the regulatory sandboxes is too cumbersome, which defeats the purpose of this highly important tool in developing AI that works on the ground.

In addition, I will try to provide a clear and more concise definition of an artificial intelligence system with an emphasis on establishing clear oversight on how to change this definition in the future. Next, I want to set high but realistic standards for cybersecurity and data that allow for the best mix of safety and usability. Finally, I want to future-proof the AI Act. This means better linkages to the other parts of digital policy, to the green transition and to the international stage, as well as anticipating possible changes in the AI industry, AI technology and the power of AI.

I will try to provide a clear and more concise definition of an artificial intelligence system with an emphasis on establishing clear oversight on how to change this definition in the future

As we all know, actions have implications, and we need to be aware of those. Digital policy is as much politics as it is policy. Even if some see it that way, digital policy surely is not just a technocratic fix.

Therefore, we need to see beyond the AI Act to consider how this policy impacts our important relationship to the United States, how it will affect our neighbourhood, especially the many internal and international conflicts, and how it could be a way to mend or sever our relations to China.

International digital rules could at the same time bridge this current climate of mistrust with our rivals as well as forge a new alliance with democracies around the world. The AI Act together with the Data Act and other regulations and policies could help foster a democratic market and forum that would be our strongest defence against creeping nationalism and unfairness.

Finally, we should not make a mistake that the EU has made again and again: writing a law is important but implementing and enforcing it will be key. This means that the AI Act needs to be more than a just well-written piece of legislation: it requires a long-term commitment from the Member States, the Commission and the international community.

Excerpt from:
The best way to regulate artificial intelligence? The EU's AI Act - The Parliament Magazine

Summary: Using MEG data, a new AI algorithm called AI-MIND is able to assess dementia risk and the potential effectiveness of treatments for depression, researchers say.

Source: Aalto University

The human brain consists of some 86 billion neurons, nerve cells that process and convey information through electrical nerve impulses.

Thats why measuring neural electrical activity is often the best way to study the brain, says Hanna Renvall. She is Aalto University and HUS Helsinki University Hospital Assistant Professor in Translational Brain Imaging and heads the HUS BioMag Laboratory.

Electroencephalography, or EEG, is the most used brain imaging technique in the world. Renvalls favorite, however, is magnetoencephalography or MEG, which measures the magnetic fields generated by the brains electrical activity.

MEG signals are easier to interpret than EEG because the skull and other tissues dont distort magnetic fields as much. This is precisely what makes the technique so great, Renvall explains.

MEG can locate the active part of the brain with much greater accuracy, at times achieving millimeter-scale precision.

An MEG device looks a lot like bonnet hairdryers found in hair salons. The SQUID sensors that perform the measurements are concealed and effectively insulated inside the bonnet because they only function at truly freezing temperatures, close to absolute zero.

The worlds first whole-head MEG device was built by a company that emerged from Helsinki University of Technologys Low Temperature Laboratoryand is now the leading equipment manufacturer in this field.

MEG plays a major role in the European Unions new AI-Mind project, whose Finnish contributors are Aalto and HUS. The goal of the 14-million project is to learn ways to identify those patients, whose dementia could be delayed or even prevented.

For this to happen, neuroscience and neurotechnology need help from artificial intelligence experts.

Fingerprinting the brain

Dementia is a broad-reaching neural function disorder that significantly erodes the sufferers ability to cope with everyday life. Some 10 million people are afflicted in Europe, and as the population ages this number is growing. The most common illness that causes dementia is Alzheimers disease, which is diagnosed in 7080% of dementia patients.

Researchers believe that communication between neurons begins to deteriorate well before the initial clinical symptoms of dementia present themselves. This can be seen in MEG dataif you know what to look for.

MEG is at its strongest when measuring the brains response to stimuli like speech and touch that occur at specific moments and are repetitive.

Interpreting resting-state measurements is considerably more complex.

Thats why the AI-Mind project uses a tool referred to as the fingerprint of the brain. It was created when Renvall and Professor Riitta Salmelin and her colleagues began to investigate whether MEG measurements could detect a persons genotype.

More than 100 sibling pairs took part in the study that sat subjects in an MEG, first for a couple of minutes with their eyes closed and then for a couple of minutes with their eyes open. They also submitted blood samples for a simple genetic analysis.

When researchers compared the graphs and genetic markers, they noticed that, even though there was substantial variance between individuals, siblings graphs were similar.

Next, Aalto University Artificial Intelligence Professor Samuel Kaskis group tested whether a computer could learn to identify graph sections that were as similar as possible between siblings while also being maximally different when compared to other test subjects.

The machine did itand more, surprisingly.

It learned to distinguish the individual perfectly based on just the graphs, irrespective of whether the imaging had been performed with the test subjects eyes open or closed, Hanna Renvall says.

For humans, graphs taken with eyes closed or open look very different, but the machine could identify their individual features. Were extremely excited about this brain fingerprinting and are now thinking about how we could teach the machine to recognize neural network deterioration in a similar manner.

Risk screening in one week

A large share of dementia patients are diagnosed only after the disorder has already progressed, which explains why treatments tend to focus on managing late-stage symptoms.

Earlier research has, however, demonstrated that many patients experience cognitive deterioration, such as memory and thought disorders, for years before their diagnosis.

One objective of the AI-Mind project is to learn ways to screen individuals with a significantly higher risk of developing memory disorders in the next few years from the larger group of those suffering from mild cognitive deterioration.

Researchers plan to image 1,000 people from around Europe who are deemed at risk of developing memory disorders and analyze how their neural signals differ from people free from cognitive deterioration. AI will then couple their brain imaging data with cognitive test results and genetic biomarkers.

Researchers believe this method could identify a heightened dementia risk in as little as a week.

If people know about their risk in time, it can have a dramatic motivating effect, says Renvall, who has years of experience of treating patients as a neurologist.

Lifestyle changes like a healthier diet, exercise, treating cardiovascular diseases and cognitive rehabilitation can significantly slow the progression of memory disorders.

Better managing risk factors can give the patient many more good years, which is tremendously meaningful for individuals, their loved ones and society, as well, Renvall says.

Identifying at-risk individuals will also be key when the first drugs that slow disease progression come on the market, perhaps in the next few years. Renvall says it will be a momentous event, as the medicinal treatment of memory disorders has not seen any substantial progress in the last two decades.

The new pharmaceuticals will not suit everybody, however.

These drugs are quite powerful, as are their side effectsthats why we need to identify the people who can benefit from them the most, Renvall emphasizes.

Zapping the brain

Brain activity involves electric currents, which generate magnetic fields that can be measured from outside the skull.

The process also works in the other direction, the principle on whichtranscranial magnetic stimulation(TMS) is based. In TMS treatments, a coil is placed on the head to produce a powerful magnetic field that reaches the brain through skin and bone, without losing strength. Themagnetic fieldpulse causes a short, weak electric field in the brain that affects neuron activity.

It sounds wild, but its completely safe, says Professor of Applied Physics Risto Ilmoniemi, who has been developing and using TMS for decades.

The strength of the electric field is comparable to the brains own electric fields. The patient feels the stimulation, which is delivered in pulses, as light taps on their skin.

Magnetic stimulation is used to treatsevere depressionand neuropathic pain. At least 200 million people around the world suffer from severe depression, while neuropathic pain is prevalent among spinal injury patients, diabetics and multiple sclerosis sufferers. Pharmaceuticals provide adequate relief to only half of all depression patients; this share is just 30% in the case of neuropathic pain sufferers.

How frequently pulses are given is based on the illness being treated. For depression, inter-neuron communication is stimulated with high-frequency pulse series, while less frequent pulses calm patients neurons for neuropathic pain relief.

Stimulation is administered to the part of the brain where, according to the latest medical science, the neurons tied to the illness being treated are located.

About half of treated patients receive significant relief from magnetic stimulation. Ilmoniemi believes this could be much higherwith more coils and the help of algorithms.

One-note clanger to concert virtuoso

In 2018, the ConnectToBrain research project headed by Ilmoniemi was granted 10 million in European Research Council Synergy funding, the first time that synergy funds were awarded to a project steered by a Finnish university. Top experts in the field from Germany and Italy are also involved.

The goal of the project is to radically improve magnetic stimulation in two ways: by building a magnetic stimulation device with up to 50 coils and by developing algorithms to automatically control the stimulation in real time, based on EEG feedback.

Ilmoniemi looks to the world of music for a comparison.

The difference between the new technology and the old is analogous to a concert pianist playing two-handed, continuously fine-tuning their performance based on what they hear, rather than hitting a single key while wearing hearing protection.

Researchers have already used a two-coil device to demonstrate that an algorithm can steer stimulation in the right direction ten times faster than even the most experienced expert. This is just the beginning.

A five-coil device completed last year covers an area of ten square centimeters of cortex at a time. A 50-coil system would cover both cerebral hemispheres.

Building this kind of device involves many technical challenges. Getting all these coils to fit around the head is no easy task, nor is safely producing the strong currents required.

Even once these issues are resolved, the hardest question remains: how can we treat the brain in the best possible way?

What kind of information does the algorithm need? What data should instruct its learning? It is an enormous challenge for us and our collaborators, Ilmoniemi says thoughtfully.

The project aims to build one magnetic stimulation device for Aalto, another for the University of Tbingen in Germany and a third for the University of Chieti-Pescara in Italy. The researchers hope that, in the future, there will be thousands of such devices in operation around the world.

The more patient data is accumulated, the better the algorithms can learn and the more effective the treatments will become.

Quantum optics sensors could revolutionize how we read neural signals

Professor Lauri Parkkonens working group is developing a new kind of MEG device that adapts to the head size and shape and utilizes sensors based onquantum optics. Unlike the SQUID sensors currently employed in MEG, they do not need to be encased in a thick layer of insulation, enabling measurements to be taken closer to the scalp surface. This makes it easier to perform precise measurements on children and babies especially.

The work has progressed at a brisk pace and yielded promising results: measurements made with optical sensors are already approaching the spatial accuracy of measurements made inside the cranium.

Parkkonen believes that a MEG system based on optical sensors could also be somewhat cheaper and more compact and thus easier to place than traditional devices; such a MEG system could utilize a person-sized magnetic shield instead of a large shielded room as the conventional MEG systems do.

This would bring it into reach of more researchers and hospitals.

Author: Minna HlttSource: Aalto UniversityContact: Minna Hltt Aalto UniversityImage: The image is in the public domain

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Artificial Intelligence to Assess Dementia Risk and Enhance the Effectiveness of Depression Treatments - Neuroscience News

The last year was a busy one for Russias military and civilian artificial intelligence efforts. Moscow poured money into research and development, and Russias civil society debated the countrys place in the larger AI ecosystem. But Vladimir Putins invasion of Ukraine in February and the resulting sanctions have brought several of those efforts to a haltand thrown into question just how many of its AI advancements Russia will be able to salvage and continue.

Ever since Putin extolled the development of robotic combat systems in the new State Armaments Program in 2020, the Russian Ministry of Defense has been hyper-focused on AI. We have learned more about the Russian militarys focus on AI in the past year thanks to several public revelations.

But talk of AI has been muted since the Russian invasion of Ukraine. Apart from the widespread use of UAVs for reconnaissance and target acquisition and a single display of a mine-clearing robotall of which are remote-controlledthere is no overt evidence of Russian AI in C4ISR or decision-making among the Russian military forces, other than a single public deepfake attempt to discredit the Ukrainian government. That does not mean AI isnt used, considering how Ukrainians are now utilizing artificial intelligence in data analysisbut there is a notable absence of larger discussion about this technology in open-source Russian media.

The gap between Russian military aspirations for high-tech warfare of the future and the actual conduct of war today is becoming clear. In January 2021, Colonel-General Vladimir Zarudnitsky, the head of the Military Academy of the Russian Armed Forces General Staff, wrote that the development and use of unmanned and autonomous military systems, the robotization of all spheres of armed conflict, and the development of AI for robotics will have the greatest medium-term effect on the Russian armed forces ability to meet their future challenges. Other MOD military experts also debated the impact of these emerging technologies on the Russian military and future balance of forces. Russia continued to upgrade and replace Soviet-made systems, part of the MODs drive from digitization (weapons with modern information technologies for C4ISR) to intellectualization (widespread implementation of AI capable of performing human-like creative thinking functions). These and other developments were covered in detail during Russias Army-2021 conference, with AI as a key element in C4ISR at the tactical and strategic levels.

Meanwhile, Russian military developers and researchers worked on multiple AI-enabled robotics projects, including the Marker concept unmanned ground vehicle and its autonomous operation in groups and with UAVs.

Toward the end of 2021, the state agency responsible for exporting Russian military technology even announced plans to offer unmanned aviation, robotics, and high-tech products with artificial intelligence elements to potential customers this year. The agency emphasized the equipment is geared toward defensive, border protection, and counter-terrorism capabilities.

Since the invasion, things have changed. Russias defense-industrial complexespecially military high-tech and AI research and developmentmay be affected by the international sanctions and cascading effects of Russia being cut off from semi-conductor and microprocessor imports.

Throughout 2021, the Russian government was pushing for the adoption of its AI civilian initiatives across the country, such as nationwide hackathons aimed at different age groups with the aim of making artificial intelligence familiar at home, work, and school. The government also pushed for the digital transformation of science and higher education, emphasizing the development of AI, big data, and the internet of things.

Russian academic AI R&D efforts drove predictive analytics; development of chat bots that process text and voice messages and resolve user issues without human intervention; and technologies for working with biometric data. Russias development of facial recognition technology continued apace, with key efforts implemented across Moscow and other large cities. AI as a key image recognition and data analytical tool was used in many medical projects and efforts dealing with large data sets.

Russian government officials noted their countrys efforts in promoting the ethics of artificial intelligence, and expressed confidence in Russias continued participation in this UN-sponsored work. The Russian Council for the Development of the Digital Economy has officially called for a ban on artificial intelligence algorithms that discriminate against people.

Russias Ministry of Economic Development was asked to "create a mechanism for assessing the humanitarian impact of the consequences of the introduction of such [AI] technologies, including in the provision of state and municipal services to citizens," and to prepare a "road map" for effective regulation, use, and implementation. According to the council, citizens should be able to appeal AI decisions digitally, and such a complaint should only be considered by a human. The council also proposed developing legal mechanisms to compensate for damage caused as a result of AI use.

In October, Russias leading information and communications companies adopted the National Code of Ethics in the Field of AI; the code was recommended for all participants in the AI market, including government, business, Russian and foreign developers. Among the basic principles in the code are a human-centered approach to the development of this technology and the safety of working with data.

AI workforce development was spelled out as a key requirement when the government officially unveiled the national AI roadmap in 2019. A 2021 government poll that tried to gauge the level of confidence in the governments AI efforts showed that only about 64 percent of domestic AI specialists were satisfied with the working conditions in Russia.

The survey reflected the microcosm of AI research, development, testing, and evaluation in Russialots of government activity and different efforts that did not automatically translate into a productive ecosystem conducive for developing AI, some major efforts notwithstanding.

Among some of the reasons in 2021 that Russia was lagging behind in the development of artificial intelligence technologies were the personnel shortage and the weakness of the venture capital market. The civilian developer community also noted the low penetration of Russian products into foreign markets, dependence on imports, slow introduction of products into business and government bodies, and a weak connection between AI theory and practice.

Russias likely plans to concentrate on these areas in 2022 were revised or put on hold once Russia invaded Ukraine. The sudden pull-out of major IT and high-tech companies from Russia, coupled with a rapid brain drain of Russias IT workers, and the ever-expanding high-tech sanctions against the Russian state may hobble domestic AI research and development for years to come. While the Russian government is trying to prop up its AI and high-tech industry with subsidies, funding, and legislative support, the impact of the above-mentioned consequences may be too much for the still-growing and evolving Russian AI ecosystem. That does not mean AI research and development will stopon the contrary, many 2021 trends, efforts, and inventions are being implemented into the Russian economy and society in 2022, and there are domestic high-tech companies and public-private partnerships which are trying to fill the void left by the departed global IT majors. But the effects of the invasion will be felt in the AI ecosystem for a long time, especially with so many IT workers leaving the country, either because of the massive impact on the high-tech economy, or because they disagree with the war, or both.

One of the most-felt sanctions aftereffects has been the severing of international cooperation on AI among Russian universities and research instructions, which earlier was enshrined as one of the most important drivers for domestic AI R&D, and reinforced by support from the Kremlin. For most high-tech institutions around the world, the impact of civilian destruction across Ukraine by the Russian military greatly outweighs the need to engage Russia on AI. At the same time, much of the Russian military AI R&D took place in a siloed environmentin many cases behind a classified firewall and without significant public-private cooperationso its hard to estimate just how sanctions will affect Russian military AI efforts.

While many in Russia now look to China as a substitute for departed global commercial relationships and products, its not clear if Beijing could fully replace the software and hardware products and services that left Russian markets at this point.

Recent events may not stop Russian civilians and military experts from discussing how AI influences the conduct of war and peacebut the practical implementation of these deliberations may become increasingly more difficult for a country under global high-tech isolation.

Samuel Bendett is an Adjunct Senior Fellow at the Center for a New American Security and an Adviser at the CNA Corporation.

Link:
Russia's Artificial Intelligence Boom May Not Survive the War - Defense One