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Welcome to Neurals beginners guide to AI. This long-running series should provide you with a very basic understanding of what AI is, what it can do, and how it works.

In addition to the article youre currently reading, the guide contains articles on (in order published)neural networks,computer vision,natural language processing,algorithms,artificial general intelligence,the difference between video game AI and real AI,the difference between human and machine intelligence,and ethics.

In this edition of the guide, well take a glance at global AI policy.

The US, China, Russia, and Europe each approach artificial intelligence development and regulation differently. In the coming years it will be important for everyone to understand what those differences can mean for our safety and privacy.

Artificial intelligence has traditionally been swept in with other technologies when it comes to policy and regulation.

That worked well in the days when algorithm-based tech was mostly used for data processing and crunching numbers. But the deep learning explosion that began around 2014 changed everything.

In the years since, weve seen the inception and mass adoption of privacy-smashing technologies such as virtual assistants, facial recognition, and online trackers.

Just a decade ago our biggest privacy concerns, as citizens, involved worrying about the government tracking us through our cell phone signals or snooping on our email.

Today, we know that AI trackers are following our every move online. Cameras record everything we do in public, even in our own neighborhoods, and there were at least 40 million smart speakers sold in Q4 of 2020 alone.

Regulators and government entities around the world are trying to catch up to the technology and implement polices that make sense for their particular brand of governance.

In the US, theres little in the way of regulation. In fact the US government is highly invested in many AI technologies the global community considers problematic. It develops lethal autonomous weapons (LAWS), its policies allow law enforcement officers to use facial recognition and internet crawlers without oversight, and there are no rules or laws prohibiting snake oil predictive AI services.

In Russia, the official policy is one of democratizing AI research by pooling data. A preview of the nations first AI policy draft indicates Russia plans to develop tools that allow its citizens to control and anonymize their own data.

However, the Russian government has also been connected to adversarial AI ops targeting governments and civilians around the globe. Its difficult to discern what rules Russias private sector will face when it comes to privacy and AI.

And, to the best of our knowledge, theres no declassified data on Russias military policies when it comes to the use of AI. The best we can do is speculate based on past reports and statements made by the countrys current leader, Vladmir Putin.

Putin, speaking to Russian students in 2017, said whoever becomes the leader in this sphere will become the ruler of the world.

China, on the other hand, has been relatively transparent about its AI programs. In 2017 China released the worlds first robust AI policy plan incorporating modern deep learning technologies and predicted future machine learning tech.

The PRC intends on being the global leader in AI technology by 2030. Its program to achieve this goal includes massive investments from the private sector, academia, and the government.

US military leaders believe Chinas military policies concerning AI are aimed at the development of LAWS that dont require a human in the loop.

Europes vision for AI policy is a bit different. Where the US, China, and Russia appear focused on the military and global competitive-financial aspects of AI, the EU is defining and crafting policies that put privacy and citizen-safety at the forefront.

In this respect, the EU currently seeks to limit facial recognition and other data-gathering technologies and to ensure citizens are explicitly informed when a product or service records their information.

Predicting the future of AI policy is a tricky matter. Not only do we have to take into account how each nation currently approaches development and regulation, but we have to try to imagine how AI technology itself will advance in each country.

Lets start with the EU:

In Russia, of course, things are different:

Moving to China, the futures a bit easier to predict:

And that just brings us to the US:

At the end of the day, its impossible to make strong predictions because politicians around the globe are still generally ignorant when it comes to the reality of modern AI and the most-likely scenarios for the future.

Technology policy is often a reactionary discipline: countries tend to regulate things only after theyve proven problematic. And, we dont know what major events or breakthroughs could prompt radical policy change for any given nation.

In 2021, the field of artificial intelligence is at an inflection point. Were between eurekas, waiting on autonomy to come of age, and hoping that our world leaders can come to a safe accord concerning LAWS and international privacy regulations.

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A beginner's guide to AI: Policy - The Next Web

One of Rembrandts finest works, Militia Company of District II under the Command of Captain Frans Banninck Cocq (better known as The Night Watch) from 1642, is a prime representation of Dutch Golden Age painting. But the painting was greatly disfigured after the artists death, when it was moved from its original location at the Arquebusiers Guild Hall to Amsterdams City Hall in 1715. City officials wanted to place it in a gallery between two doors, but the painting was too big to fit. Instead of finding another location, they cut large panels from the sides as well as some sections from the top and bottom. The fragments were lost after removal.

Now, centuries later, the painting has been made complete through the use of artificial intelligence. The Rijksmuseum in the Netherlands has owned The Night Watch since it opened in 1885 and considers it one of the best-known paintings in its collection. In 2019, the museum embarked on a multi-year, multi-million-dollar restoration project, referred to as Operation Night Watch, to recover the painting. The effort marks the 26th restoration of the work over the span of its history.

In the beginning, restoring The Night Watch to its original size hadnt been considered until the eminent Rembrandt scholar Erst van der Wetering suggested it in a letter to the museum, noting that the composition would change dramatically. The museum tapped its senior scientist, Rob Erdmann, to head the effort using three primary tools: the remaining preserved section of the original painting, a 17th-century copy of the original painting attributed to Gerrit Lundens that had been made before the cuts, and AI technology.

About the decision to use AI to reconstruct the missing pieces instead of commissioning an artist to repaint the work, Erdmann told ARTnews, Theres nothing wrong with having an artist recreate [the missing pieces] by looking at the small copy, but then wed see the hand of the artist there. Instead, we wanted to see if we could do this without the hand of an artist. That meant turning to artificial intelligence.

AI was used to solve a set of specific problems, the first of which was that the copy made by Lundens is one-fifth the size of the original, which measures almost 12 feet in length. The other issue was that Lundens painted in a different style than Rembrandt, which raised the question of how the missing pieces could be restored to an approximation of how Rembrandt would have painted them. Erdmann created three separate neural networks, a type of machine learning technology that trains computers to learn how to do specific tasks to address the problems.

The first [neural network] was responsible for identifying shared details. It found more than 10,000 details in common between The Night Watch and Lundenss copy. For the second, Erdmann said, Once you have all of these details, everything had to be warped into place, essentially by tinkering with the pieces by scoot[ing one part] a little bit to the left and making another section of the painting 2 percent bigger, and rotat[ing another] by four degrees. This way all the details would be perfectly aligned to serve as inputs to the third and final stage. Thats when we sent the third neural network to art school.

Erdmann made a test for the neural network, similar to flashcards, by splitting up the painting into thousands of tiles and placing matching tiles from both the original and the copy side-by-side. The AI then had to create an approximation of those tiles in the style of Rembrandt. Erdmann graded the approximationsand if it painted in the style of Lundens, it failed. After the program ran millions of times, the AI was ready to reproduce tiles from the Lundens copy in the style of Rembrandt.

The AIs reproduction was printed onto canvas and lightly varnished, and then the reproduced panels were attached to the frame of The Night Watch over top the fragmented original. The reconstructed panels do not touch Rembrandts original painting and will be taken down in three months out of respect for the Old Master. It already felt to me like it was quite bold to put these computer reconstructions next to Rembrandt, Erdmann said.

As for the original painting by Rembrandt, it may receive conservation treatment depending on the conclusions of the research being conducted as part of Operation Night Watch. The painting has sustained damaged that may warrant additional interventions. In 1975, the painting was slashed several times, and, in 1990, it was splashed with acid.

The reconstructed painting went on view at the Rijksmuseum on Wednesday and will remain into September.

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Artificial Intelligence Restores Mutilated Rembrandt Painting The Night Watch - ARTnews

By Justin Bercich, Head of AI, Lucinity

Financial crime has thrived during the pandemic. It seems obvious that the increase in digital banking, as people were forced to stay inside for months on end, would correlate with a sharp rise in money laundering (ML) and other nefarious activity, as criminals exploited new attack surfaces and the global uncertainty caused by the pandemic.

But, when you consider that fines for money-laundering violations have catapulted by 80% since 2019, you begin to realise just how serious and widespread the situation is. Consequently, the US Government is making strides to re-write its anti-money laundering (AML) rulebook, having enacted its first major piece of AML legislation since 2004 earlier this year.New secretary of the treasury Janet Yellen, with her decades of financial regulation experience, adds further credence to the fact the AML sector is primed for more significant reform in the coming months and years.

Yet, despite the positives and promises of technological innovation in the AML space, there still remains great debate and scepticism about the ethics and viability of incorporating artificial intelligence (AI) and machine learning deeply into banks and the broader financial ecosystem. What are the opportunities and limitations of AI, and how can we ensure its application remains ethical for all?

Human AI A banks newest investigator

While AI isnt a new asset in the fight against financial crime, Human AI is a ground-breaking application that has the potential to drastically improve compliance programs among forward-thinking banks. Human AI is all about bringing together the best tools and capabilities of people and machines. Together, human and machine help one another unearth important insights and intelligence at the exact point when key decisions need to be made forming the perfect money laundering front-line investigator and drastically improve productivity in AML.

The most powerful aspect of Human AI is that its a self-fulfilling cycle. Insights are fed back into the machine learning model, so that both human and technology improve. After all, the more the technology improves, the more the human trusts it. As we gain trust in technology we feed more relevant human-led insights back into the machine, ultimately resulting in a flowing stream of synergies that strengthens the Human-AI nexus, therefore empowering users and improving our collective defenses against financial crime. That is Human AI.

An example of this in action is Graph Data Science (GDS) an approach that is capable of finding hidden relationships in financial transaction networks. The objective of money launderers is to hide in plain sight, while AML systems are trying to uncover the hidden connections between a seemingly normal person/entity and a nefarious criminal network. GDS helps uncover these links, instead of relying on a human to manually trawl through a jungle of isolated spreadsheets with thousands of fields.

Human AI brings us all together

Whats more, a better understanding of AI doesnt just benefit the banks and financial institutions wielding its power on the frontline, it also strengthens the relationship between bank and regulator. Regulatorus need to understand why a decision has been made by AI in order to determine its efficacy and with Human AI becoming more accessible and transparent (and, therefore, human), banks can ensure machine-powered decisions are repeatable, understandable, and explainable.

This is otherwise known as Explainable AI, meaning investigators, customers, or any user of an AI system have the ability to see and interact with data that is logical, explainable and human. Not only does this help build a bridge of trust between humans and machines, but also between banks and regulators, ultimately leading to better systems of learning that help improve one another over time.

This collaborative attitude should also be extended to the regulatory sandbox, a virtual playground where fintechs and banks can test innovative AML solutions in a realistic and controlled environment overseen by the regulators. This prevents brands from rushing new products into the market without the proper due diligence and regulatory frameworks in place.

Known as Sandbox 2.0, this approach represents the future of policy making, giving fintechs the autonomy to trial cutting-edge Human AI solutions that tick all the regulatory boxes, and ultimately result in more sophisticated and effective weapons in the fight against financial crime and money laundering.

Overhyped or underused? The limitations of AI

Anti-money laundering technology has, in many ways, been our last line of defence against financial crime in recent years a dam that is ready to burst at any moment. Banks and regulators are desperately trying to keep pace with the increasing sophistication of financial criminals and money launderers. New methods for concealing illicit activity come to surface every month, and technological innovation is struggling to keep up.

This is compounded by our need to react quicker than ever before to new threats. This leaves almost no room for error, and often not enough time to exercise due diligence and ethical considerations. Too often, new AI and machine learning technologies are prematurely hurried out into the market, almost like rushing soldiers to the front line without proper training.

Increasing scepticism around AI is understandable, given the marketing bonanza of AI as a panacea to growth. Banks that respect the opportunities and limitations of AI will use the technology to focus more on efficiency gains and optimization, allowing AI algorithms to learn and grow organically, before looking to extract deeper intelligence used to driverevenue growth. It is a wider business lesson that can easily be applied to AI adoption: banks must learn their environment, capabilities, and limitations beforemastering a task.

What banks must also remember is that AI experimentation comes withdiminishing returns. They should focus on executing strategic, production-readyAI micro-projects in parallel with human teams to deliver actionable insights and value. At the same time, this technology can be trained to learn from interactions with their human colleagues.

But technology cant triumph alone

Application of AI and machine learning is now being used across most major aspects of the financial ecosystem, areas that have traditionally been people-focussed, such as issuing new products, performing compliance functions, and customer service. This requires an augmentation of thinking, where human and AI work alongside one another to achieve a common goal, rather than just throwing an algorithm at the problem.

But of course, we must recognise that this technology cant win the fight in isolation. This isnt the time to keep our cards close to our chests the benefits of AI against financial crime and ML must be made accessible to everyone affected.

Data must be tracked across all vendors and along the entire supply chain, from payments processors to direct integrations. And, the AI technology being used to enable near-real time information sharing must go both ways: from bank to regulator and back again. Only then suspicious activity can be analysed effectively, meaning everyone can trust the success of AI.

Over the next few years, the potential of Human AI will be brought to life. Building trust between one another is crucial to addressing blackbox concerns, along with consistent training of AI and machines to become more human in their output, which will ultimately make all our lives more fulfilling.

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Banking on AI: The Opportunities and Limitations of Artificial Intelligence in the Fight Against Financial Crime and Money Laundering - International...

We frequently put robotics and artificial intelligence together, but they are two separate fields. The robotics and artificial intelligence industries are some of the largest markets in the tech space today. Almost every industry in the world is adopting these technologies to boost growth and increase customer engagement.

According to reports, the global robotics market is expected to grow up to US$158.21 billion, between the period 2018 to 2025, at a CAGR of 19.11%. This growth is connected to the increasing adoption of artificial intelligence and robotics technology. Between 2020 to 2025, the market will grow at a CAGR of 25.38%.

During the pandemic, the demand for robotics technology has increased drastically. The medical field is deploying surgical robots to fight against Covid-19. Robots are helping healthcare professionals and patients by delivering food and medications, measuring the vitals, and aiding social distancing.

The automation industry is also using robotics technology to drive growth and transformation. Other industries like food, defense, manufacturing, retail, and others are also deploying robotics.

According to the reports, the global AI market is expected to grow from US$58.3 billion in 2021 to US$309.6 billion by 2026. Among the many factors that will drive the growth in the artificial intelligence market, the Covid-19 pandemic is the chief reason.

The pandemic has encouraged new applications and technological advancements in the market. Industries like healthcare, food, and manufacturing are increasingly adopting AI technologies to promote efficiency in business operations. Big tech companies like Microsoft, IBM, and Google are deploying AI to facilitate drug development, remote communication between patients and healthcare providers, and other services. AI-powered machines are also helping educators to track students performances, bridging the gaps in teaching techniques, and automating laborious administrative tasks.

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How to Invest in Robotics and Artificial Intelligence - Analytics Insight

LOS ANGELES, June 28, 2021 (GLOBE NEWSWIRE) -- RadNet, Inc. (NASDAQ: RDNT), a national leader in providing high-quality, cost-effective diagnostic imaging services through a network of fully-owned and operated outpatient imaging centers, today announced that Greg Sorensen, President of RadNets DeepHealth artificial intelligence division, will be hosting a call with Jefferies equity research analysts, Brian Tanquilut and Anthony Petrone, on Wednesday, June 30, 2021 at 1:00 p.m. Eastern Time.

In order to register for the live webcast or its archived replay, you may click https://centurylink.cwebcast.com/ses/e3r1OUZgWmWARn1YIvFMPQ~~

Details for RadNets and Jefferies Discussion:

About RadNet, Inc. RadNet, Inc. is the leading national provider of freestanding, fixed-site diagnostic imaging services and related information technology solutions (including artificial intelligence) in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 346 owned and/or operated outpatient imaging centers. RadNet's markets include California, Maryland, Delaware, New Jersey, New York, Florida and Arizona. Together with affiliated radiologists, and inclusive of full-time and per diem employees and technicians, RadNet has a total of approximately 8,300 employees. For more information, visit http://www.radnet.com.

Contact:RadNet, Inc.Mark Stolper, Executive Vice President and Chief Financial Officer310-445-2928

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RadNet, Inc. and Jefferies to Host a Teach-In on Artificial Intelligence within Diagnostic Imaging - GlobeNewswire