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

New York, March 09, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Mobile Artificial Intelligence Market 2020-2024" - https://www.reportlinker.com/p05873481/?utm_source=GNW 26 bn during 2020-2024, progressing at a CAGR of 29% during the forecast period. Our reports on global mobile artificial intelligence market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors. The report offers an up-to-date analysis regarding the current global market scenario, latest trends and drivers, and the overall market environment. The market is driven by increasing use of ai chip-enabled mobile devices.In addition, introduction of new chips is anticipated to boost the growth of the global mobile artificial intelligence market as well.

Market Segmentation The global mobile artificial intelligence market is segmented as below: Application: Smartphone

Camera

Automotive

Robotics

Others

Geographic Segmentation: APAC

Europe

MEA

North America

South America

Key Trends for global mobile artificial intelligence market growth This study identifies introduction of new chips as the prime reasons driving the global mobile artificial intelligence market growth during the next few years.

Prominent vendors in global mobile artificial intelligence market We provide a detailed analysis of around 25 vendors operating in the global mobile artificial intelligence market , including some of the vendors such as Alphabet Inc., Apple Inc., Huawei Investment & Holding Co. Ltd., Imagination Technologies Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., NVIDIA Corp., Qualcomm Inc. and Samsung Electronics Co. Ltd. . The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.Read the full report: https://www.reportlinker.com/p05873481/?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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The Global Mobile Artificial Intelligence Market is expected to grow by USD 13.26 bn during 2020-2024, progressing at a CAGR of 29% during the...

Forget robot teachers, adaptive intelligent tutors and smart essay marking software these are not the future of artificial intelligence in education but merely a step along the way.

The real power that AI brings to education is connecting our learning intelligently to make us smarter in the way we understand ourselves, the world and how we teach and learn.

For the first time we will be able to extend, develop and measure the complexity of human intelligence an intellect that is more sophisticated than any AI. This will revolutionise the way we think about human intelligence.

We take much of our intelligence for granted. For example, when travelling to an unfamiliar country, I recognise a slight anxiety when ordering food in a foreign language and feel the pleasure when my meal arrives as requested. It Is only when we attempt to automate these kinds of activities that we realise how much intelligence they require.

Such a future will not be easy or uncontroversial. We need to confront the possible harm that such a pervasive, connected intelligence infrastructure could permit when misused or abused.

However, if we get the ethics right, the intelligence infrastructure will power our learning needs, both with and without technology. Just as electricity invisibly powers lighting, computers and the internet, so it shall be for AI in education.

For example, secondary school students explain to a friend how much they understand about photosynthesis. The way they articulate their explanation can be captured and analysed, and each student offered an immersive augmented reality experience that targets their misconceptions.

The future is the use of AI to build the intelligence infrastructure to radically reform the way we value our own human intelligence

The analysis of each students performance is available to the teacher, who can encourage them to listen to a recording of their original explanation and identify corrections. Students can then predict how well they are now explaining photosynthesis and the accuracy of their predictions could be used to stimulate conversations between student and teacher.

We will be able to tap into, evaluate and galvanise our meta-intelligence: the ability to probe, reflect upon, control and understand our intelligence. We will be able to gauge our ability to deal with complex situations to differentiate our human intelligence from that of AI as we build the social relationships that are the foundation of civil society.

How do we build this intelligence infrastructure for education? Through the integration of big data about human behaviour, deep learning algorithms and our own intelligence to interpret what the algorithms tell us. We must leverage the science that has helped us to understand how humans learn, as well as the science that has helped us build machines that learn.

For example, explaining and articulating our developing knowledge makes reflection and metacognition possible so that we can examine and monitor our learning processes. Metacognition in turn helps us to understand things more deeply.

The implications are significant. We can collect and analyse huge amounts of data about how we move, what we say and how we speak, where we look, what problems we can and cannot solve and which questions we can answer.

The processing and AI-enabled analysis of multimodal data such as this will highlight more about our progress than how much better we understand science, maths, history or foreign languages.

It will show us how well we work with other people, how resilient, self-aware, motivated and self-effective we are. Sound ethical frameworks, regulation and education about AI are essential if we are to minimise the risks and reap the benefits.

Embrace todays educational AI systems judiciously. Use them to learn as much as possible about AI. But remember that todays AI is merely the start. The future is the use of AI to build the intelligence infrastructure to radically reform the way we value our own human intelligence.

Rose Luckin is a UCL professor, co-founder of the Institute for Ethical AI in Education, and author of Machine Learning and Human Intelligence: the future of education in the 21st century

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AI in education will help us understand how we think - Financial Times