What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.

When most people hear the term artificial intelligence, the first thing they usually think of is robots. That's because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.

As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optimal character recognition are no longer considered to embody artificial intelligence, since this function is now taken for granted as an inherent computer function.

AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based in mathematics, computer science, linguistics, psychology,and more.

Algorithms often play a very important part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame strong artificial intelligence.

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for dosing drugs and different treatment in patients, and for surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account depositsall of which help a bank's fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making supply, demand, and pricing of securities easier to estimate.

Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, it answers it for you.

Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.

Since its beginning, artificial intelligence has come under scrutiny from scientists and the public alike. One common theme is the idea that machines will become so highly developed that humans will not be able to keep up and they will take off on their own, redesigning themselves at an exponential rate.

Another is that machines can hack into people's privacy and even be weaponized.Other arguments debate the ethics of artificial intelligence and whether intelligent systems such as robots should be treated with the same rights as humans.

Self-driving cars have been fairly controversial as their machines tend to be designed for the lowest possible risk and the least casualties. If presented with a scenario of colliding with one person or another at the same time, these cars would calculate the option that would cause the least amount of damage.

Another contentious issue many people have with artificial intelligence is how it may affect human employment. With many industries looking to automate certain jobs through the use of intelligent machinery, there is a concern that people would be pushed out of the workforce. Self-driving cars may remove the need for taxis and car-share programs, while manufacturers may easily replace human labor with machines, making people's skills more obsolete.

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Artificial Intelligence (AI) Definition

What do we need to consider about a future where artificial intelligence (AI) and tech have transformed the way we live? That was exactly what we pondered when I recently spoke with Jamie Susskind, barrister, speaker and award-winning author of Future Politics: Living Together in a World Transformed by Tech.

Whats The Impact Of Artificial Intelligence And Technology On Society

Trends That Are Changing Civilization

Technology is changing society. Digitization is challenging the way we live. These changes create conveniences and ways of problem-solving that were never possible before. Along with the positives, there are also challenges that need to be overcome.

Here are three trends that are taking us to a phase of civilization thats quite different than anything thats come before.

1.Increasingly capable systems

We already live in a world where non-human systems can do things that previously only humans could do. In some cases, these non-human systems can do tasks even better than we can. Artificial intelligence can now mimic human speech, translate languages, diagnose cancers, draft legal documents and play games (and even beat human competitors). Were already in a society where systems can accomplish tasks we didnt believe would be possible in our time. The capabilities of non-human systems will continue to expand.

2.Systems become more ubiquitous

This line between online and offline, real space and cyberspace is one that will become less important and less meaningful as time goes on. Systems are becoming more capable and more integrated into the world around us, Susskind explained. It used to be very easy to distinguish between technology and non-technology. Today and increasingly in the future, technology will be dispersed in the world around us in objects and artifacts that we never previously thought of as technology such as smart homes with smart appliances and in public spaces in smart cities dense with sensors.

3.Increasingly quantified society

We generate more data now every couple of hours than we did from the dawn of time to 2003. What that means is that when that data is caught, captured and sorted those who own it and control it have an insight into our lived experience beyond anything that anyone in the past could ever have dreamed of into what we think, what we care about, how we feel, where we go, what we buy, who we speak to, what we say, what we do on any given day, who we associate with. We leave a trail of these things which offers a window into our soul both individually and collectively that dwarfs anything that the philosophers or the kings or the priests of the past could have dreamed of, Susskind explained.

These three trends are accelerating, and it seems highly unlikely that we as humans are going to be unchanged in the way we live together as a result of them. Weve never had to live alongside such powerful non-human systems. Weve never known what its like to be surrounded by technology thats never switched off. Weve never been in a world where our lives are datified to such as extent. In his book, Future Politics, Susskind examines these changes and proposes what we might need to do, theorize and think about regarding these changes as a society.

The Digital is Political

The digital is political. Instead of looking at these technologies as consumers or capitalists we need to look at them as citizens. Those who control and own the most powerful digital systems in the future will increasingly have a great deal of control over the rest of us, Susskind predicts.

Technologies exert power. They contain rules that the rest of us must follow and those who write the rules increasingly have a degree of power.In our society there are two major benefactors of technology and who wield this power: governing bodies that can use technologies and surveillance for enforcement of rules and large corporations, specifically tech companies or companies who use a lot of tech and are increasingly writing the rules we must abide by (think the 280-character limit on Twitter).

By gathering data about our preferences, browsing history and more other people have power over us. They know what makes us tick and they know our carrots and sticks. In the example of Cambridge Analytica and the 2016 U.S. presidential campaign, the company had a couple of thousand data points about 200 million Americans. This enabled them to project an image of a candidate that was tailored to the preferences and prejudices and biases at an individual level.

Bottom line: The more data that is gathered about us, the easier it is for others to persuade, influence and manipulate us. In addition, just knowing that data is being gathered about us is likely to change our behavior. Many people dont understand the level of surveillance thats already going on. As more people become more cognizant of the fact that were always being watched, Susskind believes that people will start changing their behavior. This is a kind of power itself, albeit subtle but important.

Technology Enables Perception Control and Power

We currently rely on third parties to tell us what is going on in the world and those third parties are more often than not mediated by digital technology. When we get our news from a newsfeed were at the mercy of those technologies who decide which very small slice of reality were going to be presented. We must acknowledge that those who own and control the technologies that filter our perception of the world are very, very powerful because they shape our innermost feelings and our soul as well as our collective understanding of what matters.

Power cascades onto other simple political concepts like democracy. How we deliberate online changes the democratic process. There are also questions of freedom. What does it mean to live in a world when rules are set often not by states but private companies and often in ways that arent liberty maximizing?

Thinks about justice. Whats it going to be like living in a world where your access to important things like jobs, insurance or credit might well be mediated by algorithms which are themselves not necessarily as fair as morality or the law would like them to be? As an example, there have been face recognition systems that dont see people of color because they were trained on datasets of white people. Similarly, voice recognition systems can struggle to understand voices with accents. Previously these kinds of problems were seen as engineering problems or corporate problems, but Susskind sees them as political problems.

Call for Clarity

While individuals have the power to improve digital hygiene, one person by their individual actions doesnt have the power to sway these issues. These are problems that can only be solved through collective means and mechanisms. Susskind believes if you want the rules of the game to be changed for everyone, then law, legislation and regulation are the only way to do it.

Its a call to action at a level which will make some people uncomfortable particularly in the United States because some are skeptical about the state trying to correct issues that are thrown up by private ordering but I think is necessary, Susskind shared.

Although some are reticent to trust governments to establish regulations and boundaries for technology, Susskind believes we must have some faith in politics if were going to make sure we dont live in a world where were not fundamentally buffeted around by forces that are effectively invisible and out of our control because they are concentrated in private hands.

Tech companies are led by humans who have the pursuit of profits as a goal in a capitalist society. While there is nothing wrong with that, we designed political systems to hold them to account for when they slip up. Thats precisely why the political steps we take are critical in a world transformed by technology.

For more on AI and technology trends, see Bernard Marrs book Artificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problems and his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.

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Whats The Impact Of Artificial Intelligence And Technology On Society - Forbes

Artificial intelligence, or AI, often brings to mind hospitality robots and self-driving cars -- the shiny, flashy machines effortlessly performing everyday tasks with superhuman speed and efficiency. Yet, peek under the hood, and youll find the real magic. Its here that AI has a different meaning as the DNA of data that fills in the gaps.

The sentient pieces of metal might dominate popular imagination, but in todays world, AI is more likely to be an obscured yet essential building block of any business. Most organizations understand the importance of using data to reduce costs and serve clients and customers effectively, but what happens when the data is too voluminous to understand? AI steps in to help turn what would otherwise be an unconnected rabble of data into meaningful insights into every facet of the business.

From computer code to big data to AI to business results

Ideas for computer code existed in the human mind for hundreds of years. But it wasnt until the 20th century that hardware caught up, allowing a spate of programming languages to manipulate the new machines and create the big data condition that is the modern world.

Weve recently reached a point where our devices generate mind-blowing amounts of data. According to a recent Forbes article, the amount of newly created data in 2020 was predicted to reach 35 zettabytes (or 35 trillion gigabytes). Two years ago, we were at 33 zettabytes, leading IDC to predict that in 2025, 175 zettabytes (or 175 trillion gigabytes) of new data will be created worldwide. Its far too much for any human worker or team of workers to process. Instead, organizations rely on artificial intelligence to aggregate, analyze and assess data -- something that happens across industries.

Banking, for example, benefits from AI tremendously. At HDFC Bank in India, machine learning, a subset of AI, analyzes demographic, geographic and other data for thin loan applications. This enables the banks human analysts to quickly identify the best applicants and manage the companys risk.

In content management, AI has dozens of use cases, assisting departments seemingly as different as marketing, editorial and enterprise search. Finding, reading and recommending articles is an age-old process of getting the news that today owes itself to the collaboration of custom-built AI algorithms and teams of up to 20 employees.

Data is the foundation, and AI is the DNA

For all its power, AI is nothing without data. An algorithms ability to locate patterns and offer suggestions is contingent on whatever it can draw from raw data. Yet, as data proliferates, it runs the risk of becoming chaotic and unruly. Data today isnt just strings of numbers, letters and symbols. It could be thousands of characters representing human-based speech. So, how does one make sense of it all?

Natural language processing (NLP), a subfield of computer science that relies heavily on machine learning, works at the nexus of computers and natural language. Its one example of a cognitive technology that can quickly analyze large, unstructured data sets -- such as medical data, contracts and legal literature -- to elicit trends and discover solutions to complex problems. Its a time-consuming task that a human would struggle to complete, but it can now be done in seconds.

Another example is translation. A person starting from scratch might spend years (if not a lifetime) learning to translate a piece of work from English to Japanese. A machine learning-based algorithm can now do it instantaneously as you type.

Its in these ways that AI proves itself as the DNA of data. Data alone cant automatically solve a problem. A database containing every word in every language in the world would be useless without an interpreter to fill in the gaps.

One study showed that object transplanting trips up deep facial recognition AI, concluding that the double-take, an instinctive human gesture that helps us identify when things might not be what they seem, continues to elude smart algorithms. AIs evolution as the DNA of data cannot be dismissed, and sometimes the merger of human and machine is uncanny.

Conclusion

To solve a classic machine learning problem -- correctly identifying handwritten numbers -- Caltech researchers developed an artificial neural network made out of DNA. If we have the capacity to program AI into synthetic biomolecular circuits, companies should be able to use AI to better serve their customers.

By integrating technological back ends with middleware and filling the gaps with AI, applications become flexible, ground-up platforms where this happens. Built with speed and agility, they can cut the number of steps in a process from 20 to four, while evolving with customer demand.

An enterprise corollary concept is an intelligent data hub, a centralized architecture for managing data about various parties, places and things. As the data grows and becomes increasingly complex, the hub keeps them easily relatable with its layer of artificial intelligence, governing them while serving them up to real-time business users.

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Artificial Intelligence: The DNA Of Data That Fills The Gap - Forbes

CSS Corp is enabling leading mapping companies to accelerate POI extraction from geotagged images through AI-ML led automation

While navigating using a mobiledevice, we often select a source and a destination that are well-known andfrequently visited places, such as hotels, apartment complexes, touristattractions, corporate offices, etc. In mapping parlance, these places are calledpoints of interest (POI). Most location-based applications and services needaccurate POI data to serve their users effectively. Among several ways tocapture POI data, extracting it from geotagged images is one of the mostpopular. Geotagged images contain geographical metadata like latitude,longitude, and place names, etc.

However,mapping companies often find it challenging to get detailed POI data generated from geotaggedimages accurately. Fierce competition in this field has created a demand forhigh quality and freshness of POI data. It necessitates using efficientprocesses that bring reality to the maps in real-time or as soon as possible.

A leading mapping and location dataplatform provider was under immense pressure to scale their services andcapture the market share rapidly. A critical component of their services was seamlessPOI extraction from field-collected geotagged images while maintaining qualityand accuracy benchmarks. Done manually, this process can be tedious andtime-consuming. CSS Corp was able to support them through rapid deployment oftrained resources at scale, empowered with an assisted automation approach,that accelerated the time-to-market for their services. It leveraged itsproprietary Geo.Intelli system which uses artificial intelligence for automatedextraction of POI data from geotagged images, resulting in faster and efficientprocessing.

Geo.Intelli is a smart GIS system thatautomates the geotag extraction for POI location from images and leverages NLPto check the completeness of POI or address name. Its AI-ML based APIs automaticallyextract the relevant data from images, perform a quality check on images, andreject images that are blurred, non-geocoded, or in invalid format. To ensurehigh accuracy, the system automatically cross-validates the extracted data withreference source data like area, city, latitude, longitude, and ZIP Code. Italso allows for multiple POI addition from a single image.

Certain assisted automation processesin the system leverage agents expertise and oversight to deliver high-qualityresults, for example, automated image analyzer with configurable fieldattributes, automated text extraction for additional information, and automatedtranslation/ transliteration processes.

The system also enhances the teamsproductivity with integrated editingand review workflows and progress dashboards for instant reviews and analytics.Once the system extracts the POI, various users like agents, team lead, and QA canedit, review and perform quality checks within the system as per the workflowsset and see the project performance on customized dashboards. Geo.Intelli givesusers multiple export options to download final POI files in the desiredformat.

The system continuously learnsfrom its data and updates its algorithms to get better with every dataextraction. Today, it can extract English text from images with an accuracy ofover 94%. Speaking of benefits to the client, the ability to make faster decisions on key addressattributes accelerated the teams productivity by 25%. Automated quality checkson key fields safeguarded data quality and reduced editing scope by 60%.Overall, the combination of CSS Corps Geo.Intelli system and a highlyskilled team improved the efficiency of POI processing by 22%, enabling theclient to scale their services faster than the competition.

With location-based services andapplications becoming essential for every aspect of businesses, having completeand accurate real-time data in maps becomes crucial. Stiff competition to thetop is prompting location services providers to look for ways to optimize andaccelerate their processes by leveraging AI and automation. CSS CorpsGeo.Intelli is a GIS automation solution that is designed to enable leadingmapping and location players to uplift their user experience with betternavigation while also creating more business opportunities for them.

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Leveraging artificial intelligence to automate data extraction from geotagged images - Geospatial World

KENNESAW, Ga.--(BUSINESS WIRE)--Today at MODEX 2020, KNAPP, a leading supplier of intralogistics systems, and Covariant, a leading AI Robotics company, announced a partnership to deploy and bring to market advanced AI Robotics solutions. KNAPP and Covariant have already developed several solutions together. The Pick-It-Easy Robot powered by Covariant AI, is designed for high performance single-piece picking applications and is currently operating in production at several customer sites in North America and Europe, including at Obeta, a German electrical supply wholesaler outside Berlin.

Artificial intelligence will be a defining feature of the warehouse of the future, impacting all aspects of operations and fundamentally changing how business is done, said Jusuf Buzimkic, SVP Engineering at KNAPP. We looked at every solution on the market, and Covariant was the clear winner. Our partnership with Covariant will enable us to deliver cutting-edge artificial intelligence technology to our customers, providing a major leg up in an increasingly competitive world.

Deploying AI Robotics solutions in customer environments is extremely challenging, said Pieter Abbeel, President, Chief Scientist and co-founder of Covariant. To be successful, you need to combine AI software with robotics hardware components, then make sure they integrate into a customers warehouse, which has dozens of other systems running. Its a complex process that requires that every piece of technology is seamlessly integrated. KNAPPs reputation, scale, and 50+ years of experience delivering innovative logistics technology makes them an ideal partner to deploy our AI Robotics technology to customer environments.

KNAPP implemented its first Pick-It-Easy Robot seven years ago in Europe and has been actively developing and refining robotic and vision system technology since that time. The new Pick-It-Easy Robot powered by Covariant is now deployed, field proven and ready to use. According to Jusuf Buzimkic, It can handle unlimited SKU types and works on challenging objects including polybags, banded-apparel, transparent objects and blister packs. It also learns to pick new objects its never seen before and improves over time. The system can easily integrate into warehouses and facilities in the e-commerce, retail, electronics, cosmetics, food, pharmaceuticals and healthcare industries; and a formidable advantage when leveraging the sequencing and versatility of KNAPPs OSR ShuttleTM EVO.

About Covariant

Covariant is building the Covariant Brain: universal AI that allows robots to see, reason and act on the world around them. Founded in 2017 by the worlds top AI researchers and roboticists from UC Berkeley and OpenAI, Covariant is bringing the latest artificial intelligence research breakthroughs to the biggest industry opportunities. The company is headquartered in Berkeley, CA. For more information, visit covariant.ai.

About KNAPP

KNAPP is an internationally operating company and is one of the world market leaders in warehouse logistics and automation with over 4,000 employees worldwide. As a solutions provider, KNAPP provides one-stop, custom-designed intralogistics solutions in health care, retail, apparel, food, manufacturing and ecommerce sectors. Our clients experience results that are flexible, resource efficient, ergonomic and self-learning. The companys North American headquarters are in Atlanta, GA. For more information, visit http://www.knapp.com.

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KNAPP and Covariant Introduce the Pick-it-Easy Robot, Powered by Artificial Intelligence (AI) to North American Market - Business Wire

A Torah scroll. Photo: RabbiSacks.org.

Long before the invention of self-driving cars and robotics, Jews conceived the idea of man-made life.

Artificial intelligence (AI) is becoming so advanced now that it is conceivable that machines could not only replace humans for most jobs, but actually develop intelligence higher than that of humans. Machines will be capable of designing machines.

This development is a danger as great as any challenge we face today. Humans ceding control of life to machines is a threat to all humankind. This is not science fiction; it is real, and it is imminent.

There are currently no global rules on the development of AI, and no ethical standards or restraints. And there is no organized effort or political movement to demand that ethical and moral standards be applied to research in this field.

March 9, 2020 7:35 am

Jewish sources could not have foreseen AI, but they did perceive a level of life between the fully-developed human and the animals the Golem.

There are many different Talmudic and Kabbalistic interpretations of the Golem. One suggests that God created Adam as well as the Golem; another tradition has it that Adam was a Golem before God breathed life into him and gave him a soul. All seem to agree that the Golem represents a being that is limited, unfinished, and incomplete.

In a later incarnation, the Golem takes on an ominous aspect, as it escapes its creator and terrorizes the community. This version inspired writers, including Mary Shelley, who wrote the famous novel Frankenstein.

According to this legend, the Golem is a giant created by a rabbi who inscribed the word EMET (Hebrew for truth) on his forehead, which gave him life. The giant becomes invincible and uncontrollable. In a desperate attempt to restore order, the rabbi finds a way to remove the first letter of the word EMET, leaving the letters MET (death) and the Golem dies.

The point of the story is that once the monster gets out of control, only its creator can find a way to disable it.

In recent history, during the proliferation of nuclear weapons following World War II, when civilization itself was in peril, one of the creators of the nuclear bomb, Robert Oppenheimer, alerted the world to the danger and worked to have its use restricted though the Nuclear Non-Proliferation Treaty. Where is the Robert Oppenheimer of today? Who will disable todays monster about to devour us?

In todays secular society, who will raise the topic of morality and responsibility? Lord Jonathan Sacks, former Chief Rabbi of the UK, reminds us that religion deals with the moral limits of power. Just because we can do something, doesnt mean that we should: We have the power but not the permission; we have the ability but not the right.

In the Garden of Eden, Adam and Eve could partake of absolutely anything except the fruit of one tree, interestingly called the Tree of Knowledge. The modern, scientific mind rejects the idea that any knowledge is off limits, but even in Paradise, there is forbidden fruit.

Paul Socken (PhD, University of Toronto) was on the faculty of the University of Waterloo, Canada for 37 years and is currently Distinguished Professor Emeritus. He is a former Chairman of the Department of French Studies and the author of 10 books. He is also the founder of the Jewish Studies program at Waterloo.

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Artificial Intelligence: The Forbidden Fruit of the 21st Century - Algemeiner

Report Includes: - An overview of the global market of artificial intelligence (AI) and a detailed review of how AI is being applied in fighting Alzheimer disease. - Introduction to Alzheimers and main medical issues.

New York, March 09, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence (AI) in Alzheimers Applications" - https://www.reportlinker.com/p05873500/?utm_source=GNW - Description of AI tools applications in the diagnosis, therapy, R&D and health management of Alzheimers - Information on types of complex algorithms developed for Alzheimers - Coverage of major issues related to the utilization of AI for diagnosis and treatment of Alzheimers - A look at the current and emerging trends in AI as it relates to Alzheimers disease - Discussion on recent achievements and innovations within the industry

Summary Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines aimed at reproducing wholly or in part the intelligent behavior of human beings. These machines include computers, sensors, robots, and hypersmart devices.

As shown in the figure below, the ultimate purpose of artificial intelligence is to create smart machines that, through the steps of learning, reasoning, and self-correcting, will eventually be able to make decisions, solve problems, and act as human beings.Read the full report: https://www.reportlinker.com/p05873500/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Artificial Intelligence (AI) in Alzheimers Applications - Yahoo Finance

It is manifest the growing interest of mankind in disruptive themes as Artificial Intelligence (AI). As we have been analyzing, this theme has increased its significance as the inventions reach new and inspiring outcomes. This article intends to analyze if there have been a growing tendency on patent applications related to AI in the European Patents Office or if, besides all the euphoria, we are still far away from a technological boom particulary inventive. Throughout the article, we will analyze some graphics and charts so we can draw some conclusions about the technological advance involving AI.

Before we proceed, we need to pay attention to our research methodology which was based on the following topics:

Search in the Espacenet patent database of European patent applications containing at least one subgroup of the Cooperative Patent Classification (CPC) mentioned in chart 1 and published from 2010 to 2018;

Table 1:CPC subgroups that refers to Artificial Intelligence

Data retract allowed to check an exponencial raise on the European patent applications number since the year 2000, having its peak been observed on 2016, according to the Figure 1 below.

Figure 1: European patent applications for the subgroups of selected CPCs trends

Although, it is expectable that the number from 2017 and 2018 reaches a superior quantity, due to the fact that there are still applications in secrecy that were not made public through its publication.

Between 2010 and 2018 were requested 2026 patent applications related to AI. From this total, 57 were refused, 208 granted, and 1666 are still pending decision (see picture 2 below).

Picure 2: Current stage of European patent applications, published from 2010 to 2018, for the selected CPC subgroups

It may be verified that exists a high quantity of pending applications, which is justifiable, for the evident growing of applications on the years of 2015/16. It could also be find that the average time for the applicants to be informed of the intention to grant its application is 1475 days, approximately 4 years (Table 3).

Table 3: Time interval for the beginning of the substantive exame and for the communication of concession intention

It would not be surprising even if big multinationals dominate the quantity of applications related to AI. According to Figure 2 below, Qualcomm has on its portfolio 113 applications, followed by Google (if we put together LLC with INC) and INTEL with 99 applications. Curiously, Apple does not figure on top 30, unlike Samsung and Huawei.

Figure 2: Main applicants on the European Patent Office for the selected CPC subgroups

From the collected sample, it is manifest that there has been an increase of the AI related patent applications. Although the numbers are not astronomically surprising, it is possible to verify that exists a tendency for them to multiply. The main technological players continue to bet on this inventive area, so it is presumable that in a medium-long term there will be huge disputs involving IP assets related to Artificial Intelligence.

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Patents related to Artificial Intelligence in the European Patents Office - Inventa International

MORGANTOWN The use of artificial intelligence could be a game-changer in the field of health care, and these technologies are being developed and refined in the Mountain State at West Virginia University.

Artificial intelligence refers to a computer system that can perform tasks that typically require human intelligence.

What this means is that an AI system is expected to learn, just like a human being, from its past experience. It is expected to be able to adjust its behavior to changes, like changes in an environment or changes in certain conditions, and obviously to changes in the inputs that are given Based on this, it can make certain intelligent decisions, said Dr. Donald Adjeroh, professor and associate chair at the WVU Statler College of Engineerings Lane Department of Computer Science and Electrical Engineering.

Artificial intelligence systems can analyze huge amounts of data very quickly and identify patterns that may not be obvious to a human.

This means analyzing tremendous amounts of data in a short period of time to recognize patterns that may lead to quicker diagnosis, more personalized treatments or identification of risk factors for disease.

Data is anonymized to protect patient privacy, Adjeroh said.

While technology helps us function better, we need to now leverage this technology and machine learning to help improve our day-to-day lives and improve our overall health and wellness for all populations, and use this technology to help predict disease earlier, said Dr. Ali Rezai, executive chair of the WVU Rockefeller Neuroscience Institute.

Although artificial intelligence technology is not new, the amount and availability of data for analysis has increased dramatically, Adjeroh said.

At the WVU Heart and Vascular Institute, artificial intelligence is already being used to take measurements from ultrasound images. These tasks are completed not only more quickly through artificial intelligence systems, but the measurements are more standardized, with less variability and more precision in the measurements, according to Dr. Partho Sengupta, Abnash C. Jain chair, chief of cardiology and director of the Center for Cardiac Innovation at the WVU Heart and Vascular Institute.

Adjeroh is also leading a collaboration between the WVU Statler College of Engineering, the WVU Heart and Vascular Institute, West Virginia State University and three campuses of the University of Arkansas System to work on a $4 million project to study AI technology in cardiovascular health funded by the National Science Foundation, according to a press release from the university.

This research includes analysis of data from cardiac imaging technologies like ultrasound and electrocardiograms to find indicators of disease or increased risk for development of disease.

At the WVU Rockefeller Neuroscience Institute, researchers and providers are developing wearable technologies, including rings and watches, and machine learning analytics that have applications for dementia and Alzheimers; addiction; athletics; military; aging; and chronic pain, according to Rezai.

Data collected through wearable devices can help improve understanding of both population and individual health and wellness, he said.

Artificial intelligence can help physicians understand what testing may be needed in order to develop a blueprint for personalized treatment of disease, to halt addiction cravings before they happen, or to implement early interventions that can slow or stop the development of disease.

For all conditions, if you know earlier you may be predisposed, or you come in earlier, then you can change the ways of your day-to-day functioning, Rezai said. Its good that were doing this in West Virginia and trying to help the population of West Virginia by bringing high- technology innovations here, leveraging technology to improve population, functioning, health and wellness, and resilience.

In addition to providing for improvements in care, the technologies free up time that physicians and mid-level providers can spend with patients, Sengupta said.

If youre just looking at the screens and not taking care of the patient, not developing a human relationship, you cannot have a patient-doctor relationship develop, Sengupta said. Machine learning is not replacing physicians or people. It is bringing back the joy of doing medicine into our field. Each one of us went into the clinical medicine world because we like to see our patients and understand their problems, bring back solutions and treat them.

Staff Writer JoAnn Snoderly can be reached at 304-626-1445, by email at jsnoderly@theet.com or on Twitter at @JoAnnSnoderly.

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WVU leading the way in development of artificial intelligence technologies for health care - WV News

Clinical research is a branch of healthcare which determines the safety and efficacy of medicines, devices, diagnostic products and treatment regimens intended for human use.

Whenever there are any new device diagnostic products that need to be launched in the market or any new condition has to be treated with already existing medication, it needs to be checked for the safety and efficacy at the dose that needs to be administered.

A medicine or devices or diagnostic process undergoes the following phases in clinical research:

a) Preclinical:

In this phase, the drug is tested in non-humans. In this evaluation of the efficacy, toxicity and pharmacokinetics are made.b) Phase 0:

In this phase, a small number of healthy volunteers such as around 10 people are tested for the pharmacokinetic parameters. The dose for the healthy volunteer is calculated based on the pre-clinical trials.

In most cases, this phase is skipped and phase I is conducted directly.c) Phase I:

This phase is conducted to check the safety of the drug. This is conducted in healthy volunteers ranging from 20-100 people. It involves testing multiple doses to calculate the apt dosage for the efficacy in patients.d)Phase II:

This phase is conducted in around 100 to 300 patients in different parts of the country to involve all types of sampling pools with a dosage based on the phase I trial. This phase of the study is conducted to assess the efficacy and side effects of the devices or drugs.e) Phase III:

This phase is conducted in a large population of patients from different parts of the country around 300 to 3000 patients in order to study the efficacy, safety and effectiveness of the drug or device. Once the drug passes through this phase, it is eligible for a marketing license.f) Phase IV or post-marketing study:

This involves the study of how well the drug performs in the market after being launched which is in terms of efficacy and safety.

Clinical research is a hub of huge amounts of data related to the drug performance, efficacy in each patient, adverse events produced in different scenarios in each patient, etc. Thus clinical research leads to huge data of different variables for the analysis using artificial intelligence.

Other than the above mentioned broad scope of AI and ML in clinical research, some of the in-depth spheres of clinical research where AI and ML plays an important role are as follows:-

As AI and ML can help in the prediction of appropriate dosage and design required for the drug to pass the clinical trial phases, the same can be incorporated in the protocol designing which would help the manufacturing companies to reduce cost and provide a better medication or treatment to the patients at a faster rate.

One of the case studies of cognizant of how AI helped in fast-tracking the cancer drug development is as follows:-

One of the major clients of the cognizant that required full range of cancer treatments including acute myeloid leukemia (AML), needed a method for more quickly and accurately processing the massive amounts of data emerging from their own trials, from available research, and from the Cancer Cell Line Encyclopedia (CCLE).

Using a variety of data science tools and techniques, the cognizant team was able to build an automated solution that made the identification of optimal doses for drugs dramatically faster.

Hence, with the full drug development process taking from ten to eighteen years and costing $40,000 to $50,000 per patient, the data science solution could trim up to four years from the process and offers savings of as much as 10% of total costs.

Traditionally monitoring of 100% source data verification was performed in clinical research by the CRO team. As this is a cost consuming and time-consuming process, the new ICH-GCP guidelines have introduced a lean approach to clinical monitoring. This involves monitoring on the basis of risk or Risk-Based Monitoring (RBM).

FDA defines RBM as, This guidance assists sponsor of clinical investigations in developing risk-based monitoring strategies and plans for investigational studies of medical products, including human drug, biological products, medical devices, and combinations thereof. The overarching goal of this guidance is to enhance human subject protection and the quality of clinical trial data by focusing on sponsor oversight on the most important aspects of study conduct and reporting.

Data science tools and techniques can help to integrate data from various systems, and effectively analyze and track the issues and risks in a timely manner which might be overseen by humans.

Site selection having the population pool as required by the protocol is one of the biggest challenges faced by the CRO. This can be overcome by AI and ML tools that identify and suggest the sites based on the highest recruitment potential and using appropriate recruitment strategies. This involves mapping patient populations and proactively targeting sites with high predicted potential to deliver the most patients.

Identifying patients and recruitment are one of the crucial issues faced by most of the CRO which leads to crossing the initially accepted study guidelines. This happens mainly because the patient pool is tracked and recruited during the study. Due to medical conditions and other events, the patient might get dropped out before the study completes. This dropout rate can be reduced by AI as it can help in reducing the population heterogeneity during the enrollment phase itself. By analyzing the medical history and the protocol requirements, the data science tools can predict whether the patient would complete the study endpoints.

To ensure drug safety, a huge amount of structured and unstructured data has to be analyzed. Hence, AI and ML technologies could address many of the challenges faced and provide new insights into drug safety.

Artificial intelligence can hence play a vital role in each stage of the phases and help the manufacturers to reduce the cost of clinical research. A better treatment is also possible by analyzing the huge data produced during each stage from the available repositories. This can also help to provide a better design of the study.

This article is presented by AIM Expert Network (AEN), an invite-only thought leadership platform for tech experts. Check your eligibility.

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Clinical Research And The Importance Of Artificial Intelligence - Analytics India Magazine