Daily Archives: April 22, 2022

The European approach to artificial intelligence (AI) will help build a resilient Europe for the Digital Decade where people and businesses can enjoy the benefits of AI. It focuses on 2 areas: excellence in AI and trustworthy AI. The European approach to AI will ensure that any AI improvements are based on rules that safeguard the functioning of markets and the public sector, and peoples safety and fundamental rights.

To help further define its vision for AI, the European Commission developed an AI strategy to go hand in hand with the European approach to AI. The AI strategy proposed measures to streamline research, as well as policy options for AI regulation, which fed into work on the AI package.

The Commission published its AI package in April 2021, proposing new rules and actions to turn Europe into the global hub for trustworthy AI. This package consisted of:

Fostering excellence in AI will strengthen Europes potential to compete globally.

The EU will achieve this by:

The Commission and Member States agreed boost excellence in AI by joiningforces on AI policy and investment. The revised Coordinated Plan on AI outlines a vision to accelerate, act, and align priorities with the current European and global AI landscape and bring AI strategy into action.

Maximising resources and coordinating investments is a critical component of the Commissions AI strategy. Through the Digital Europe and Horizon Europe programmes, the Commission plans to invest 1 billion per year in AI. It will mobilise additional investments from the private sector and the Member States in order to reach an annual investment volume of 20 billion over the course of the digital decade.

The newly adopted Recovery and Resilience Facility makes 134 billion available for digital. This will be a game-changer, allowing Europe to amplify its ambitions and become a global leader in developing cutting-edge, trustworthy AI.

Access to high quality data is an essential factor in building high performance, robust AI systems. Initiatives such as the EU Cybersecurity Strategy, the Digital Services Act and the Digital Markets Act, and the Data Governance Act provide the right infrastructure for building such systems.

Building trustworthy AI will create a safe and innovation-friendly environment for users, developers and deployers.

The Commission has proposed 3 inter-related legal initiatives that will contribute to building trustworthy AI:

The Commission aims to address the risks generated by specific uses of AI through a set of complementary, proportionate and flexible rules. These rules will also provide Europe with a leading role in setting the global gold standard.

This framework gives AI developers, deployers and users the clarity they need by intervening only in those cases that existing national and EU legislations do not cover. The legal framework for AI proposes a clear, easy to understand approach, based on four different levels of risk: unacceptable risk, high risk, limited risk, and minimal risk.

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A European approach to artificial intelligence | Shaping ...

As the opinion rapporteur for the Artificial Intelligence Act in the Committee on Culture and Education (CULT), I will present a proposal for amending the Artificial Intelligence Act in March. The draft focuses on several key areas of artificial intelligence (AI), such as high-risk AI in education, high-risk AI requirements and obligations, AI and fundamental rights as well as prohibited practices and transparency obligations.

The regulation is aiming to create a legal framework that prevents discrimination and prohibits practices that violate fundamental rights or endanger our safety or health.One of the most problematic areas is the use of remote biometric identification systems in public space.

Unfortunately, the use of such systems has increased rapidly, especially by governments and companies to monitor places of gathering, for example. It is incredibly easy for law enforcement authorities to abuse these systems for mass surveillance of citizens. Therefore, the use of remote biometric identification and emotion recognition systems is over the line and must be banned completely.

Moreover, the misuse of technology is concerning. I am worried that countries without a functioning rule of law will use it to persecute journalists and prevent their investigations. It is obviously happening to a certain extent in Poland and Hungary, where governments have used the Pegasus software to track journalists and members of the opposition. How hard will it be for these governments to abuse remote biometric identification, such as facial recognition systems?

It is absolutely necessary to set rules that will prevent governments from abusing AI systems to violate fundamental rights

As far as we know, the Hungarian government has already persecuted journalists in the so-called interest of national security for questioning the governments actions amid the pandemic. Even the Chinese social credit system, which ranks the countrys citizens, is based on the alleged purpose of ensuring security.

It is absolutely necessary to set rules that will prevent governments from abusing AI systems to violate fundamental rights. In October, a majority of the European Parliament voted in favour of a report on the use of AI in criminal law. The vote showed a clear direction for the European Parliament in this matter.

The proposal includes a definition of so-called high-risk AI systems. HR tools that could filter applications, banking systems that evaluate our creditworthiness and predictive control systems all fall under the definition of high-risk because they could easily reproduce bias and have a negative impact on disparity.

With AI being present in education as well, the proposal includes test evaluation and entrance examination systems. Still, this list shall be expanded to include online proctoring systems. However, there is a problem with different interpretations of the GDPR in the case of online proctoring systems, resulting in differences in personal data protection in Amsterdam, Copenhagen and Milan.

According to the Dutch and Danish decisions, there was no conflict between online proctoring systems and the GDPR, but the Italian data protection authority fined and banned further use of these technologies. Currently, universities are investing in new technologies without knowing whether they are authorised to use them or if they are going to be fined.

HR tools that could filter applications, banking systems that evaluate our creditworthiness and predictive control systems all fall under the definition of high-risk because they could easily reproduce bias and have a negative impact on disparity

In my opinion, technologies used for students personalised education should be included in the high-risk category as well. In this case, incorrect usage can negatively affect a students future.

In addition to education, the CULT committee focuses on the media sector, where AI systems can be easily misused to spread disinformation. As a result, the functioning of democracy and society may be in danger.

When incorrectly deployed, AI systems that recommend content and learn from our responses can systematically display content which form so-called rabbit holes of disinformation. This increases hatred and the polarisation of society and has a negative impact on democratic functioning.

We need to set clear rules that will not be easy to circumvent. Currently, I am working on a draft legislative opinion which will be presented in the CULT committee in March. I will do my best to fill all the gaps that I have identified.

The Council is also working on its position. A common compromise presented by the Slovenian presidency was found, for example, in the extension of social scoring from public authorities to private companies as well.

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How to amend the Artificial Intelligence Act to avoid the misuse of high-risk AI systems - The Parliament Magazine

In a new survey of retail executives, Symphony RetailAIfound that 82% of them are focusing on data-driven demand forecasting and nearly two thirds (61%) are prioritizing data management in their supply chain.

While there is strong agreement that data is key, the embrace of technologies to achieve those goals is somewhat behind sentiment. Only 13% of retail execs polled think they outperform their peers, while 87% say that their supply chain performance lags or is equal to competing businesses.

Symphony RetailAI's research, conducted with partner Incisiv, also sought to uncover retailers use of AI and machine learning. A high number of 87% of respondents said they have not yet taken meaningful steps to embrace AI and many of them are stalling for a variety of reasons. Barriers include poor data quality, an inability to integrate data from several sources and a general lack of confidence in AI.

The gap between intent and progress underscores the opportunity for retailers to use AI to enhance demand forecasting and supply chain management, according to Symphony RetailAI's experts. As new threats loom and other economic factors create supply chain unpredictability, these results highlight the need to future-proof grocery supply chains to handle unexpected disruptions, declared Troy Prothero, the companys SVP, product management, supply chain solutions. The importance of using data, including AI-driven demand forecasting, to gain a competitive supply chain advantage isnt going away, so organizations that prioritize new ways of using data for decision-making will be better positioned to succeed.

Added Gaurav Pant, chief insights officer for Incisiv: Our research with Symphony RetailAI sheds light on the critical need for retailers to use AI to break down silos and utilize as much organizational data as possible.

These and other insights on grocery forecasting will be shared in a Progressive Grocer webinaron April 27:"The New Rules of Grocery Demand Forecasting:Exclusive research reveals supply chain priorities and pain points."

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Industry Executives Share Real Insights on Artificial Intelligence - Progressive Grocer

Gradient AI Sponsors Webinar to Explore the Promise and Challenges of AI in the Insurance Industry

April 21, 2022--(BUSINESS WIRE)--Gradient AI

WHAT: Business Insurance is hosting a webinar "How Artificial Intelligence is Transforming the Insurance Industry." Sponsored by Gradient AI, a leading provider of proven artificial intelligence (AI) solutions for the insurance industry, this webinar will cover real-world use cases, and explore AIs powerful benefits enabling attendees to gain an actionable understanding of AIs potential and its value to their business.

WHEN: April 26, 20221:00 PM - 2:00 PM EDT/10:00 AM -11:00 AM PDT

WHO: Featured Speakers include:

Builders Insurances Mark Gromek, Chief Marketing and Underwriting Office

Florida State Universitys Dr. Patricia Born Midyette, Eminent Scholar in Risk

CCMSIs S. F. "Skip" Brechtel, Jr., FCAS, MAAA, Executive VP and CIO

WHY ATTEND: As digital transformation has disrupted many industries, AI is poised to do the same for insurance enterprises. Attendees will learn:

How to use AI to gain a competitive advantage and generate improved business outcomes such as improved key operational metrics

How AI can increase the efficiency and accuracy of underwriting and claims operations

The challenges and opportunities facing the next generation of insurance professionals

WHERE: Learn more and register here.

Tweet this: How Artificial Intelligence is Transforming the Insurance Industry Webinar: April 26, 2022, 1:00 pm EDT https://register.gotowebinar.com/register/5324033635572250381?source=GradientAI #AI #insurance #insurtech

About Gradient AI:

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprised of tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs, and large self-insureds across all major lines of insurance. By using Gradient AIs solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit https://www.gradientai.com.

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View source version on businesswire.com: https://www.businesswire.com/news/home/20220421005382/en/

Contacts

Elyse Familantelysef@resultspr.net 978-376-5446

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MEDIA ALERT: Business Insurance to Host Webinar "How Artificial Intelligence is Transforming the Insurance Industry" - Yahoo Finance

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