Category Archives: Artificial Intelligence

Alexa! What is Artificial Intelligence? Artificial Intelligence is the study of devices that perceive the environment and take action that maximize their chance of success at some goal, more commonly is used to describe when machines are seen as mimicking human cognitive functions such as learning and problem solving. Well, isnt that convenient.

Artificial intelligence (AI), a radical concept developed by computer scientists in the 1950s, has tremendous applications in our daily lives today. It has come to play a crucial role in manufacturing, healthcare, finance, marketing and more. Companies and people worldwide are relying on machine learning, robotics and artificial intelligence to improve their products, processes and customer experience. Whether its the cars we drive, computers that predict the weather, toys that learn to interact with children, or writing computer codes, AI is changing the way we live, work and play.

Artificial intelligence is based on the principles of human intelligence and is defined as the way in which machines imitate human intelligence to perform simple and complex tasks. Artificial intelligence aims to learn human cognitive abilities and eventually surpass it. Currently, most AI models rely on deep neural networks, inspired by the human brain, which takes in a lot of raw data and expected results and learns the relationship between the two. Once trained it can apply this knowledge to new data and predict the result. More advanced methods use Deep reinforcement learning, where the model learns to make decisions by trial and error. In 2017, AlphaGo based on Deep reinforcement learning beat Ke Jie, the number one ranked player in the world at the time in board game Go. The self-taught AlphaGo Zero achieved a 1000 victory against the early competitive version of AlphaGo, and is the best Go player in the world.

Artificial intelligence has given computers the skills and capabilities never seen before. These advancements have helped us to understand more about diseases and how we can treat them better, for instance last year when the Google attention-based neural network AlphaFold 2 demonstrated a result that would have taken decades for humans to reach. AlphaFold 2 was able to determine the 3D structure of a protein with an accuracy rivaling crystallography, the gold standard for convincingly modelling proteins. But unlike crystallography, which takes months to return results, AlphaFold 2 can model proteins in hours. In healthcare, advanced AI systems are helping doctors diagnose and treat diseases in patients faster and more accurately and perform robotic surgeries which are more accurate and efficient. In Fact, the biggest problem that humanity is facing today, the Covid crisis, was first alerted to us by the Candian AI Platform BlueDot on the new years eve. AI has led to the rise of Self-driving vehicles and drones and weve seen how they have fueled the chatbots to give customers a wholesome experience.

Artificial intelligence is a central premise of disruptive change in the Fourth Industrial Revolution (Industry 4.0), a revolution that challenges our notions of what it means to be human and is more transformative than any other industrial revolution we experienced. Simply put, artificial intelligence is learning to make decisions faster than humans. This is enabling us for the first time to bring automation in the service sector as computers get more comfortable in speaking, writing, reading and recognising more patterns. AI has created a number of tools that allow people to rethink how we integrate information, analyze data and use the results to improve decision-making.

Most of the AI we talked about is called Artificial Narrow Intelligence (ANI), machines which are extremely good at doing one task but cannot extend their capabilities to other tasks. ANI has already surpassed humans in terms of accuracy and speed in many tasks, and it is getting better fast. The next stage in the evolution of AI is Artificial General Intelligence (AGI). With AGI we could develop machines capable of understanding the world as well as any human, and with the same capacity to learn how to carry out a huge range of tasks.

In 2020, OpenAI developed GPT-3, a language model capable of performing numerous diverse tasks without specific training. While GPT-3 is not an example of AGI, it is considered by some to be too advanced to classify as a narrow AI system.

The two driving forces for exponential AI development have been data, more accessible than ever due to the internet, and cheap computation power. By 2030, we would have enough computation power that the idea of a machine capable of human intelligence would not seem impossible anymore.

AI is poised to have a profound impact on society, subsequently we also need to start thinking about all the implications. Today, technology is improving at a faster rate than our education and legal system can keep up. Regulations have to be put in place in what data is accessible to machines and what AI machines are allowed to do. Research has to be focused in directions where AI is helpful for humanity. Most of the dangerous, repeatable and strenuous jobs will be done by AI powered robots. What will this mean for us, for humanity? This will bring back jobs that require creativity, human presence and social and emotional intelligence and subjective decision making. Jobs of the future will require more collaboration as the computational work will be taken up by a program. Who knows, AI might be the trigger that brings back human touch in its true form.

As a final thought, a segment of this article was generated by AI. Can you tell the difference?

Views expressed above are the author's own.

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Importance of Artificial Intelligence and its impact on humanity - The Times of India Blog

We have enjoyed the power of technology in the past few decades, and we saw it progress. From the gadgets that we use daily to make our life more convenient to the medical field and healthcare, we have been enjoying the technology of artificial intelligence to make things easier.

AI in healthcare benefits both medical practitioners and patients alike. Lets dive in on how were using this and how we can use it in the future.

RELATED: AI Applications for Clinical Trials Increase, Refining Endpoints, Quantifying Pain, & More

The future of healthcare is here as we are using artificial intelligence in diagnostics and treatment. It could only mean that we can expect the advancements in technology in this field to rise further and faster.

Here are some examples of how we apply artificial intelligence in healthcare:

Medical practitioners use AI in healthcare from the smallest scale to the biggest and most crucial ones, such as dealing with high-risk diseases. On a small scale, patients use telehealth thru computers and mobile devices.

There are telehealth tools used for documentation, recording metrics, and process of information. These are commonly used from home.

Especially in times like these, going out of your home can be a threat because of the pandemic. Telemedicine is one of the best options, especially for those who need immediate care.

Doctors and physicians use AI on patients to detect early signs of stroke, cancer, neurological, or cardiovascular disorders by recording algorithms. This way, the computer can see the trends and activity of a persons organs to catch and cure a potential disease before it can pose a threat.

IBM recently partnered with Pfizer to develop an AI machine that can detect the early onset of Alzheimers disease in a person. The test evaluates cognitive impairment in various neurological disorders, including stroke and Alzheimers disease.

In addition to helping with diagnosis and prevention, AI can also be used by physicians as their assistants when dealing with patients.

A study revealed that physicians spend almost half of their work time dealing with data in Electronic Health Records (EHR). Primary care physicians can focus on dealing with the patients more since computers can now take notes for them, analyze discussions with the patients, and enter the necessary information into the EHRs.

In addition to this, science now uses voice recognition and speech dictation to make clinical tasks possible through natural language processing. It is a process where the computer catches the commands given by a person and converts that into data.

In relationship with the use of EHR, AI can help treat patients through personalized medicine. With all the records stored in the computer, the computer can identify large quantities of data to identify treatment options instantly based on a patients background.

The precise and quick process of drug development and clinical trials are now possible because of AI.

Computers and artificial intelligence can help clinicians work efficiently and lead to more precise diagnoses at the clinical level.

Valence Discovery recently used machine learning and artificial intelligence in their healthcare institution for molecular property prediction and multiparameter optimization for preclinical drug discovery to Charles Rivers patients.

RELATED: AI in Biopharma: Deep Genomics and BioMarin Forge Pact; InterVenn Raises $34 Million

Wearable technology like smartwatches or even smartphones can detect oxygen levels, heart rate, and even violent falls.

These devices can even directly call emergency if it reaches critical level making these smart devices a reliable way to prevent serious conditions.

Most useful to dermatologists or ophthalmologists, using a smartphone to take selfies for diagnostic is being used to treat and examine patients, especially in this day and age.

With the popularity of phone-in check-ups during this pandemic, using this technology for clinical improvements and diagnosis can be considered a step-up for healthcare using technology.

Inserting smart device capabilities on hospital machines and devices can help doctors detect an early sign of a patients critical condition through algorithms and patterns.

When were talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on the aggregation of that data is not something that a human can do very well, Executive Director of the MGH & BWH Center for Clinical Data Science Mark Michalski, MD said in an interview.

How can technology help a person deal with pain, you ask? Artificial intelligence combined with virtual reality is being used as pain management tools by some companies.

Clinics and hospitals can create simulated realities to distract patients from their pain and even an opioid crisis.

Johnson & Johnson Reality Program is the first company to do this and is expected to become a trend and be used by other clinics or hospitals.

As a prediction by medical experts, obtaining tissues and other radiology tools will be improved through AI.

If non-invasive tools like x-rays, MRI machines, and CT scans are for internal visibility of the body, and biopsies are created to collect tissue samples from organs, the future, with the development of using AI technology, can do these things without being invasive or cause any harm from patients.

We want to bring together the diagnostic imaging team with the surgeon or interventional radiologist and the pathologist, MD Brigham & Womens Hospital Director of Image-Guided Neurosurgery Alexandra Golby said in an interview. That coming together of different teams and aligning goals is a big challenge.

If we want the imaging to give us information that we presently get from tissue samples, then were going to have to be able to achieve very close registration so that the ground truth for any given pixel is known.

Technology and artificial intelligence in healthcare have been very vital in its progress. It has been a major help with drug discovery and the recognition of diseases.

As researchers continue to discover new technology, the medical field, doctors, and patients will benefit from its advancements.

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Artificial Intelligence In Healthcare And How It's Transforming The Industry - BioSpace

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

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

Companies everywhere are pouring resources into artificial intelligence (A.I.) and machine learning (ML) initiatives. Many technologists believe that apps smartened with A.I. and ML tools will eventually offer better customer personalization; managers hope that A.I. will lead to better data analysis, which in turn will power better business strategies.

But which industries are actually hiring A.I. specialists? If you answer that question, it might give you a better idea of where those resources are being deployed. Fortunately,CompTIAs latest Tech Jobs Reportoffers a breakdown of A.I. hiring, using data from Burning Glass, which collects and analyzes millions of job postings from across the country. Check it out:

Perhaps its no surprise that manufacturing tops this list; after all, manufacturers have been steadily automating their production processes for years, and it stands to reason that they would turn to A.I. and ML to streamline things even more. In theory, A.I. will also help manufacturers do everythingfrom reducing downtime to improving supply chainsalthough it may take some time to get the models right.

The presence of healthcare, banking, and public administration likewise seem logical.These three industries have the money to invest in A.I. and ML right now and have the greatest opportunity to see the investment pay off, fast, Gus Walker, director of product at Veritone, an A.I. tech company based in Costa Mesa, California,told Dicelate last year.That being said, the pandemic has caused industries hit the hardest to take a step back and look at how they can leverage AI and ML to rebuild or adjust in the new normal.

Compared to overall tech hiring, the number of A.I.-related job postings is still relatively small. Right now, mastering and deploying A.I. and machine learning is something of a specialist industry; but as these technologies become more commodified, and companies develop tools that allow more employees to integrate A.I. and ML into their projects, the number of job postings for A.I. and ML positions could increase over the next several years. Indeed, one IDC report from 2020 found three-quarters of commercial enterprise applications could lean on A.I. in some way by2021.

Its also worth examining where all that A.I. hiring is taking place; its interesting that Washington DC tops this particular list, with New York City a close second; Silicon Valley and Seattle, the nations other big tech hubs, are somewhat further behind, at least for the moment. Washington DC is notable not only for federal government hiring, but the growing presence of companies such as Amazon that hunger for talent skilled in artificial intelligence:

Jobs that leverage artificial intelligence are potentially lucrative, with a current median salary (according to Burning Glass)of $105,000. Its also a skill-set thatmore technologists may need to become familiar with, especially managers and executives.A.I. is not going to replace managers but managers that use A.I. will replace those that do not, Rob Thomas, senior vice president of IBMscloudand data platform,recently told CNBC. If you mention A.I. or ML on your resume and applications, make sure you know your stuff before the job interview; chances are good youll be tested on it.

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Which Industries are Hiring AI and Machine Learning Roles? - Dice Insights

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

Healthcare Industry Leverages Artificial Intelligence

With the continuous evolvement of Artificial Intelligence, the world is being benefited to the utmost level, as the applications of Artificial Intelligence is unremitting. This technology can be operated in any sector of industry, including the healthcare industry.The advancement of technology and the AI (Artificial Intelligence), as a part of modern technology have resulted in the formation of a digital macrocosm. Artificial Intelligence, to be precise, is a programming where, there is a duplication of human intelligence incorporated in the machines and it works and acts like a human.

Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare, were found together over half a century. The healthcare industries use Natural Language Process to categorise certain data patterns.Natural Language Process is the process of giving a computer, the ability to understand text and spoken words just like the same way human beings can. In the healthcare sector, it gives the effect to the clinical decision support. The natural language process uses algorithms that can mimic like human responses to conversation and queries. This NLP, just like a human can take the form of simulated mediator using algorithms to connect to the health plan members.

Artificial Intelligence can be used by the clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical trainings. In simple words medical coding is transmitting medial data about a patient into alphanumeric code.

Clinical Decisions All the healthcare sectors are overwhelmed with gigantic volumes of growing responsibility and health data. Machine learning technologies as a part of Artificial Intelligence, can be applied to the electronic health records, with the help of this the clinical professionals can hunt for proper, error-free, confirmation-based statistics that has been cured by medical professionals. Further, Natural Language Process just like the chatbots, can be used for everyday conversation where it allows the users to type questions as if they are questioning a medical professional and receive fast and unfailing answers.

Health Equity Artificial Intelligence and Machine learning algorithms can be used to reduce bias in this sector by promoting diversities and transparency in data to help in the improvement of health equity.

Medication Detection Artificial Intelligence can be used by the pharma companies, to deal with drug discoveries and thus helping in reducing the time to determine and taking drugs all the way to the market. Machine Learning and Big Data as a part of Artificial Intelligence do have the great prospective to cut down the value of new medications.

Pain Management With the help of Artificial Intelligence and by creating replicated veracities the patients can be easily distracted from their existing cause of pain. Not only this, the AI can also be incorporated for the for the help of narcotic crisis.

System Networked Infirmaries Unlike now, one big hospital curing all kind of diseases can be divided into smaller pivots and spokes, where all these small and big clinics will be connected to a single digital framework. With the help of AI, it can be easy to spot patients who are at risk of deterioration.

Medical Images and Diagnosis The Artificial Intelligence alongside medical coding can go through the images and X-rays of the body to identify the system of the diseases that is to be treated. Further Artificial Intelligence technology with the help of electronic health records is used in healthcare industry that allows the cardiologists to recognize critical cases first and give diagnosis with accuracy and potentially avoiding errors.

Health Record Analysing With the advance of Artificial Intelligence, now it is easy for the patients as well as doctors to collect everyday health data. All the smart watches that help to calculate heart rates are the best example of this technology.

This is just the beginning of Artificial Intelligence in the healthcare industry. Making a start from Natural Language process, Algorithms and medical coding, imaging and diagnosis, there is a long way for the Artificial Intelligence to be capable of innumerable activities and to help medical professionals in making superior decisions. The healthcare industry is now focusing on technological innovation in serving to its patients. The Artificial Intelligence have highly transmuted the healthcare industry, thus resulting in development in patient care.

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Acceleration of Artificial Intelligence in the Healthcare Industry - Analytics Insight

ASAPP is one of the few companies advancing research and development in artificial intelligence

"ASAPP is one of the few companies advancing research and development in artificial intelligence and its application for customer experience. In an environment where customer expectations are rising, ASAPP is helping large enterprises advance digital engagement, real-time voice transcription, speech analytics, and live agent coaching and analytics," said Gustavo Sapoznik, ASAPP founder and CEO. "The Customer Experience (CX) industry is at a crossroads. After years of interactive voice response systems (IVR) and bot investments, customer satisfaction is down, and costs have increased. We apply our AI research to make people in contact centers wildly more productive because existing rules-based technology and architectures limit companies to small improvements that can't bridge the digital transformation opportunity that AI is enabling and delivering."

"ASAPP's growth doubled last year, as we continue to deliver for customers like JetBlue and DISH Network with automation that supports both agents and agent-less interactions with their customers," said Tim Stone, ASAPP's Chief Operating Officer and Chief Financial Officer. "This Series C financing will enable ASAPP to increase investment in ASAPP's AI Native, Customer Experience Performance (CXP) platformthat helps global enterprise customers drive differentiated customer experiences, revenue, cost reductions and automation designed to support employees in customer serviceand sales."

Jason Green, founder of Emergence Capital who joined the ASAPP board recently said: "ASAPP is leading the way with the world's best AI talent and technology to help global enterprises dramatically improve customer experience, reduce costs and drive sales in a trillion-dollar market. By marrying human creativity, intelligence and empathy with artificial intelligence, the company is arming businesses with a next generation of productivity applications that every business at scale will eventually need to adopt."

ASAPP builds AI-Native products to solve problems of massive scale and inefficiency. With theCustomer Experience Performance (CXP) platformcontact centers can create better experiences and handle more customer engagement volume in less time, while consumers enjoy faster resolution to their needs.

About ASAPPASAPP is a research-based artificial intelligence software provider that solves large, complex, data-rich problems with AI-Native technology. Large enterprises use ASAPP to make customer experience teams highly productive and effective by augmenting human activity and automating the world's workflows. The company has offices in New York, Silicon Valley, Buenos Aires, London, and Bozeman. Visithttps://www.asapp.com for more information.

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Artificial Intelligence Platform Provider ASAPP Raises $120 Million to Drive Greater Performance of Customer Experience (CX) Teams - PRNewswire