Ten Famous Quotes About Artificial Intelligence

One cannot emphasize enough the influence AI has had on the daily lives of people. While laypeople can get into an endless discourse on the benefits of this technology, the views of well-known researchers and personalities in this industry lend these discussions some credibility.

These views are diverse and some have made headlines for their hypocrisy; some have even caused panic.

Below, we look at some of the quotes by significant figures when it comes to artificial intelligence:

As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.

Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, and we will have multiplied the intelligence the human biological machine intelligence of our civilization a billion-fold.

The development of full artificial intelligence could spell the end of the human race.It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldnt compete, and would be superseded.

It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers They would be able to converse with each other to sharpen their wits. At some stage, therefore, we should have to expect the machines to take control.

AI doesnt have to be evil to destroy humanity if AI has a goal and humanity just happens to come in the way, it will destroy humanity as a matter of course without even thinking about it, no hard feelings.

The real question is, when will we draft an artificial intelligence bill of rights? What will that consist of? And who will get to decide that?

We have seen AI providing conversation and comfort to the lonely; we have also seen AI engaging in racial discrimination. Yet the biggest harm that AI is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with AI is vastly larger than before. As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive.

Robots are not going to replace humans, they are going to make their jobs much more humane. Difficult, demeaning, demanding, dangerous, dull these are the jobs robots will be taking.

If we do it right, we might be able to evolve a form of work that taps into our uniquely human capabilities and restores our humanity. The ultimate paradox is that this technology may become a powerful catalyst that we need to reclaim our humanity.

The coming era of Artificial Intelligence will not be the era of war, but be the era of deep compassion, non-violence, and love.

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Ten Famous Quotes About Artificial Intelligence

Artificial Intelligence Enables Rapid COVID-19 Lung …

For most patients who have died of COVID-19, the pandemic disease caused by a novel coronavirus, the ultimate cause of death was pneumonia, a condition in which inflammation and fluid buildup make it difficult to breathe. Severe pneumonia often requires lengthy hospital stays in intensive care units and assistance breathing with ventilators medical devices now in high demand in some cities grappling with a surge of COVID-19 cases.

To quickly detect pneumonia and therefore better distinguish between COVID-19 patients likely to need more supportive care in the hospital and those who could be followed closely at home UC San Diego Health radiologists and other physicians are now using artificial intelligence (AI) to augment lung imaging analysis in a clinical research study enabled by Amazon Web Services (AWS).

Chest X-rays from a patient with COVID-19 pneumonia, original x-ray (left) and AI-for-pneumonia result (right). Patient has a pacemaker device and an enlarged heart, which indicates that the AI algorithm is powerful enough to work even when the patient has underlying health issues.

The new AI capability has so far provided UC San Diego Health physicians with unique insights into more than 2,000 images. In one case, a patient in the Emergency Department who did not have any symptoms of COVID-19 underwent a chest X-ray for other reasons. Yet the AI readout of the X-ray indicated signs of early pneumonia, which was later confirmed by a radiologist. As a result, the patient was tested for COVID-19 and found to be positive for the illness.

We would not have had reason to treat that patient as a suspected COVID-19 case or test for it, if it werent for the AI, said Christopher Longhurst, MD, chief information officer and associate chief medical officer for UC San Diego Health. While still investigational, the system is already affecting clinical management of patients.

The new capability got its start several months ago when Albert Hsiao, MD, PhD, associate professor of radiology at University of California San Diego School of Medicine and radiologist at UC San Diego Health, and his team developed a machine learning algorithm that allows radiologists to use AI to enhance their own abilities to spot pneumonia on chest X-rays. Trained with 22,000 notations by human radiologists, the algorithm overlays X-rays with color-coded maps that indicate pneumonia probability.

Pneumonia can be subtle, especially if its not your average bacterial pneumonia, and if we could identify those patients early, before you can even detect it with a stethoscope, we might be better positioned to treat those at highest risk for severe disease and death, Hsiao said.

More recently, Hsiaos team applied this AI approach to 10 chest X-rays, published in medical journals, from five patients treated in China and the United States for COVID-19. The algorithm consistently localized areas of pneumonia, despite the fact that the images were taken at several different hospitals, and varied considerably in technique, contrast and resolution. The details are published in the Journal of Thoracic Imaging.

Now, enabled by donated service credits provided by the AWS Diagnostic Development Initiative and the efforts of UC San Diego Healths Clinical Research IT team, Hsiaos AI method has been deployed across UC San Diego Health in a clinical research study that allows any physician or radiologist to get an initial estimate regarding a patients likelihood of having pneumonia within minutes, at point-of-care.

AWS has partnered with us on multiple projects in the past, said Michael Hogarth, MD, professor of biomedical informatics at UC San Diego School of Medicine and clinical research information officer at UC San Diego Health. Once COVID-19 became a crisis, AWS reached out to us and asked if there was anything they could do to help. My mind immediately went to a presentation Id seen Albert give on their initial AI tests for pneumonia. AWS helped our Clinical Research IT team get the study up and running system-wide in just 10 days.

According to Hsiao, chest X-rays are cheaper, the equipment is more portable and easier to clean, and results are returned more quickly than many other diagnostics. Polymerase chain reaction-based clinical diagnostic tests for the virus that causes COVID-19 can take several days to return results in some regions of the U.S.

Albert Hsiao, MD, PhD, associate professor of radiology at UC San Diego School of Medicine and radiologist at UC San Diego Health, and team developed a machine learning algorithm that allows radiologists to use AI to enhance their own abilities to spot pneumonia on chest X-rays.

Thats where imaging can play an important role. We can quickly triage patients to the appropriate level of care, even before a COVID-19 diagnosis is officially confirmed, Hsiao said.

To be clear, UC San Diego Health experts emphasize they are not diagnosing COVID-19 itself by lung imaging. Pneumonia can be caused by several different types of bacteria and viruses. In addition, use of Hsiaos AI algorithm is still considered investigational. Although these images are available for use by clinicians, patient care is still guided by formal interpretation from human radiologists.

As we prepare for a potential surge in patients with COVID-19, its not just patient rooms and supplies that may become limited, but also physician and staff capacity, Longhurst said. So its tremendously helpful to have tools that allow physicians who are not as experienced as radiologists in reading X-rays to get a quick idea of what theyre looking at, especially frontline emergency and hospital-based physicians.

Next, the UC San Diego Health team hopes to expand the AI-powered study for detecting pneumonia to the University of Californias four other academic medical centers.

As an academic medical center, were always looking for ways to bring innovations to the bedside, Longhurst said. Although we need more studies to evaluate the effectiveness of this algorithm and improve its accuracy as we see more patients, what were seeing so far is evidence that this approach could be a powerful tool for health care providers to provide more reliable, early diagnoses of COVID-19 and other infections.

Hsiaos Journal of Thoracic Imaging study was co-authored by Brian Hurt, MD, and Seth Kligerman, MD, of the Department of Radiology, UC San Diego School of Medicine, and funded in part by the National Institutes of Health (T32 Institutional National Research Service Award), NVIDIA Corporation (GPU grant) and American Roentgen Ray Society.

Disclosure: Albert Hsiao also receives grant support from GE Healthcare and Bayer AG, and is a founder, shareholder, consultant and receives income for Arterys, Inc. Brian Hurt provides consulting services to Arterys, Inc. and IBM.

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Best Master’s Degrees in Artificial Intelligence 2020

Enrolling in a masters program after obtaining a bachelors degree can give students advanced knowledge in subjects that enhance their careers. This degree may qualify students as experts in their chosen fields and make them eligible for a wider selection of high-paying jobs than if they only have a bachelors degree.What is a Master in Artificial Intelligence? It is a program Read more

Enrolling in a masters program after obtaining a bachelors degree can give students advanced knowledge in subjects that enhance their careers. This degree may qualify students as experts in their chosen fields and make them eligible for a wider selection of high-paying jobs than if they only have a bachelors degree.

What is a Master in Artificial Intelligence? It is a program designed to give students a deeper understanding of technology and how to apply logic to create artificial intelligence. Students may study mechanics such as engineering and robotics while delving into courses that explore principles of logic, programming, and intelligence. Each course typically gives students in-depth knowledge of an aspect of machine learning and teaches them to create and program unique projects for the artificial intelligence field.

Many skills may be developed during a masters program that could lead to high-paying jobs and career advancements in the future. Students may develop critical-thinking and technology skills that help them excel in their career field, and they may also learn crucial problem-solving abilities.

Because it can take anywhere from one to three years to finish a masters degree, the cost varies drastically. Every school is different and has its own set tuition fees, and the location of the classes could also affect the price.

The field of artificial intelligence is relatively new and exciting, and a number of interesting careers are available to those with the right degree. Some students may choose to work as software engineers, while others prefer careers as analysts. Many find positions as developers. Jobs working with machine learning applications may also be available, and new career opportunities continue to appear as the market expands.

Balancing personal lives with education is a challenge for many students. A number of universities have begun offering online classes to give students the flexibility they need to succeed. To find a university that meets your needs, search for your program below and contact directly the admission office of the school of your choice by filling in the lead form.

Other options within this field of study:

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An understanding of AIs limitations is starting to sink in – The Economist

Jun 11th 2020

IT WILL BE as if the world had created a second China, made not of billions of people and millions of factories, but of algorithms and humming computers. PwC, a professional-services firm, predicts that artificial intelligence (AI) will add $16trn to the global economy by 2030. The total of all activityfrom banks and biotech to shops and constructionin the worlds second-largest economy was just $13trn in 2018.

PwCs claim is no outlier. Rival prognosticators at McKinsey put the figure at $13trn. Others go for qualitative drama, rather than quantitative. Sundar Pichai, Googles boss, has described developments in AI as more profound than fire or electricity. Other forecasts see similarly large changes, but less happy ones. Clever computers capable of doing the jobs of radiologists, lorry drivers or warehouse workers might cause a wave of unemployment.

Yet lately doubts have been creeping in about whether todays AI technology is really as world-changing as it seems. It is running up against limits of one kind or another, and has failed to deliver on some of its proponents more grandiose promises.

There is no question that AIor, to be precise, machine learning, one of its sub-fieldshas made much progress. Computers have become dramatically better at many things they previously struggled with. The excitement began to build in academia in the early 2010s, when new machine-learning techniques led to rapid improvements in tasks such as recognising pictures and manipulating language. From there it spread to business, starting with the internet giants. With vast computing resources and oceans of data, they were well placed to adopt the technology. Modern AI techniques now power search engines and voice assistants, suggest email replies, power the facial-recognition systems that unlock smartphones and police national borders, and underpin the algorithms that try to identify unwelcome posts on social media.

Perhaps the highest-profile display of the technologys potential came in 2016, when a system built by DeepMind, a London-based AI firm owned by Alphabet, Googles corporate parent, beat one of the worlds best players at Go, an ancient Asian board game. The match was watched by tens of millions; the breakthrough came years, even decades, earlier than AI gurus had expected.

As Mr Pichais comparison with electricity and fire suggests, machine learning is a general-purpose technologyone capable of affecting entire economies. It excels at recognising patterns in data, and that is useful everywhere. Ornithologists use it to classify birdsong; astronomers to hunt for planets in glimmers of starlight; banks to assess credit risk and prevent fraud. In the Netherlands, the authorities use it to monitor social-welfare payments. In China AI-powered facial recognition lets customers buy groceriesand helps run the repressive mass-surveillance system the country has built in Xinjiang, a Muslim-majority region.

AIs heralds say further transformations are still to come, for better and for worse. In 2016 Geoffrey Hinton, a computer scientist who has made fundamental contributions to modern AI, remarked that its quite obvious that we should stop training radiologists, on the grounds that computers will soon be able to do everything they do, only cheaper and faster. Developers of self-driving cars, meanwhile, predict that robotaxis will revolutionise transport. Eric Schmidt, a former chairman of Google (and a former board member of The Economists parent company) hopes that AI could accelerate research, helping human scientists keep up with a deluge of papers and data.

In January a group of researchers published a paper in Cell describing an AI system that had predicted antibacterial function from molecular structure. Of 100 candidate molecules selected by the system for further analysis, one proved to be a potent new antibiotic. The covid-19 pandemic has thrust such medical applications firmly into the spotlight. An AI firm called BlueDot claims it spotted signs of a novel virus in reports from Chinese hospitals as early as December. Researchers have been scrambling to try to apply AI to everything from drug discovery to interpreting medical scans and predicting how the virus might evolve.

This is not the first wave of AI-related excitement (see timeline in next article). The field began in the mid-1950s when researchers hoped that building human-level intelligence would take a few yearsa couple of decades at most. That early optimism had fizzled by the 1970s. A second wave began in the 1980s. Once again the fields grandest promises went unmet. As reality replaced the hype, the booms gave way to painful busts known as AI winters. Research funding dried up, and the fields reputation suffered.

Many of the grandest claims made about AI have once again failed to become reality

Modern AI technology has been far more successful. Billions of people use it every day, mostly without noticing, inside their smartphones and internet services. Yet despite this success, the fact remains that many of the grandest claims made about AI have once again failed to become reality, and confidence is wavering as researchers start to wonder whether the technology has hit a wall. Self-driving cars have become more capable, but remain perpetually on the cusp of being safe enough to deploy on everyday streets. Efforts to incorporate AI into medical diagnosis are, similarly, taking longer than expected: despite Dr Hintons prediction, there remains a global shortage of human radiologists.

Surveying the field of medical AI in 2019, Eric Topol, a cardiologist and AI enthusiast, wrote that the state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care. Despite a plethora of ideas, covid-19 is mostly being fought with old weapons that are already to hand. Contacttracing has been done with shoe leather and telephone calls. Clinical trials focus on existing drugs. Plastic screens and paint on the pavement enforce low-tech distancing advice.

The same consultants who predict that AI will have a world-altering impact also report that real managers in real companies are finding AI hard to implement, and that enthusiasm for it is cooling. Svetlana Sicular of Gartner, a research firm, says that 2020 could be the year AI falls onto the downslope of her firms well-publicised hype cycle. Investors are beginning to wake up to bandwagon-jumping: a survey of European AI startups by MMC, a venture-capital fund, found that 40% did not seem to be using any AI at all. I think theres definitely a strong element of investor marketing, says one analyst delicately.

This Technology Quarterly will investigate why enthusiasm is stalling. It will argue that although modern AI techniques are powerful, they are also limited, and they can be troublesome and difficult to deploy. Those hoping to make use of AIs potential must confront two sets of problems.

The first is practical. The machine-learning revolution has been built on three things: improved algorithms, more powerful computers on which to run them, andthanks to the gradual digitisation of societymore data from which they can learn. Yet data are not always readily available. It is hard to use AI to monitor covid-19 transmission without a comprehensive database of everyones movements, for instance. Even when data do exist, they can contain hidden assumptions that can trip the unwary. The newest AI systems demand for computing power can be expensive. Large organisations always take time to integrate new technologies: think of electricity in the 20th century or the cloud in the 21st. None of this necessarily reduces AIs potential, but it has the effect of slowing its adoption.

The second set of problems runs deeper, and concerns the algorithms themselves. Machine learning uses thousands or millions of examples to train a software model (the structure of which is loosely based on the neural architecture of the brain). The resulting systems can do some tasks, such as recognising images or speech, far more reliably than those programmed the traditional way with hand-crafted rules, but they are not intelligent in the way that most people understand the term. They are powerful pattern-recognition tools, but lack many cognitive abilities that biological brains take for granted. They struggle with reasoning, generalising from the rules they discover, and with the general-purpose savoir faire that researchers, for want of a more precise description, dub common sense. The result is an artificial idiot savant that can excel at well-bounded tasks, but can get things very wrong if faced with unexpected input.

Without another breakthrough, these drawbacks put fundamental limits on what AI can and cannot do. Self-driving cars, which must navigate an ever-changing world, are already delayed, and may never arrive at all. Systems that deal with language, like chatbots and personal assistants, are built on statistical approaches that generate a shallow appearance of understanding, without the reality. That will limit how useful they can become. Existential worries about clever computers making radiologists or lorry drivers obsoletelet alone, as some doom-mongers suggest, posing a threat to humanitys survivalseem overblown. Predictions of a Chinese-economy-worth of extra GDP look implausible.

Todays AI summer is different from previous ones. It is brighter and warmer, because the technology has been so widely deployed. Another full-blown winter is unlikely. But an autumnal breeze is picking up.

This article appeared in the Technology Quarterly section of the print edition under the headline "Reality check"

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An understanding of AIs limitations is starting to sink in - The Economist

New TBRC Report Shows Artificial Intelligence Implementation is Being Boosted by the Coronavirus Pandemic – PRNewswire

LONDON, June 12, 2020 /PRNewswire/ -- Artificial Intelligence is a fast-growing market that has found its way in many industries due to its various applications. The Global Market Model predicts the global artificial intelligence market to grow from $28.42 billion in 2019 to $40.74 billion in 2020 at a compound annual growth rate (CAGR) of 43.39%. The growth is mainly due to the COVID-19 health emergency across the globe that has led to a new wave of transformative technologies including the revolutionary Artificial Intelligence technology (for example - smart machines and robots) emerging as a possible solution to contain the epidemic. The Artificial Intelligence market, fueled by growing investments in the technology and its many application uses, is expected to reach $99.94 billion in 2023 at CAGR of 34.86%.

Request A Free Sample Of The Artificial Intelligence Market Report: https://www.thebusinessresearchcompany.com/sample.aspx?id=3160&type=smp

As an example of the increasing use of Artificial Intelligence (AI) tools caused by the coronavirus outbreak, India's Employee Provident Fund Organization (EPFO) is now implementing AI technology in its claim settlement system. Due to the pandemic that has left many people unemployed or furloughed, the number of employee provident fund (EPF) withdrawals in India shot up substantially, leaving the EPFO overburdened with processing and settling claims. To overcome this, Artificial Intelligence (AI) technology is being used. More than half of COVID-19 related claims are now being settled autonomously, thus significantly reducing the burden on manpower.

There were about 3.375 million claims settled in April-May 2019; this number increased to a total of 3.602 million claims settled in April-May 2020 due to the financial crisis brought on by the coronavirus. Despite staff shortages, the claim settlement period has gone down from 10 days to 3 days with the help of AI.i

The Global Market Model is the world's most comprehensive database of integrated market information available. The ten-year forecasts in the Global Market Model are updated in real time to reflect the latest market realities, which is a huge advantage over static, report-based platforms.

The model is based on the consumption of goods and services in monetary terms (nominal growth), and therefore differ from GDP forecasts published by many leading institutions such as the World Bank and IMF.

i https://www.livemint.com/money/personal-finance/epf-withdrawal-epfo-launches-ai-tool-to-settle-claims-11591764868373.html

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COVID-19 Impact and Recovery Analysis- Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024 | Growing Adoption of Cloud Based Solutions to…

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence-as-a-service (AIaaS) market and it is poised to grow by USD 15.14 billion during 2020-2024, progressing at a CAGR of over 48% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Request for Technavio's latest reports on directly and indirectly impacted markets. Market estimates include pre- and post-COVID-19 impact on the Artificial Intelligence-as-a-Service (AIaaS) Market Download free sample report

The market is concentrated, and the degree of concentration will accelerate during the forecast period. Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

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Growing adoption of cloud based solutions has been instrumental in driving the growth of the market.

Technavio's custom research reports offer detailed insights on the impact of COVID-19 at an industry level, a regional level, and subsequent supply chain operations. This customized report will also help clients keep up with new product launches in direct & indirect COVID-19 related markets, upcoming vaccines and pipeline analysis, and significant developments in vendor operations and government regulations. https://www.technavio.com/report/report/artificial-intelligence-as-a-service-market-industry-analysis

Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Segmentation

Artificial Intelligence-as-a-Service (AIaaS) Market is segmented as below:

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. The artificial intelligence-as-a-service (AIaaS) market report covers the following areas:

This study identifies the increasing adoption of AI in predictive analysis as one of the prime reasons driving the artificial intelligence-as-a-service (AIaaS) market growth during the next few years.

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Key Highlights

Table of Contents:

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by End-user

Customer Landscape

Geographic Landscape

Drivers, Challenges, and Trends

Vendor Landscape

Vendor Analysis

Appendix

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Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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The key differences between rule-based AI and machine learning – The Next Web

Companies across industries are exploring and implementingartificial intelligence(AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According toMcKinsey, embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth. But with that promise comes challenges.

Computers and machines dont come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they need to carry out tasks like driving a car or diagnosing a disease?

There are multiple ways to achieve AI, and existential to them all is data. Withoutquality data, artificial intelligence is a pipedream. There are two ways data can be manipulatedeither through rules or machine learningto achieve AI, and some best practices to help you choose between the two methods.

Long before AI and machine learning (ML) became mainstream terms outside of the high-tech field, developers were encoding human knowledge into computer systems asrules that get stored in a knowledge base. These rules define all aspects of a task, typically in the form of If statements (if A, then do B, else if X, then do Y).

While the number of rules that have to be written depends on the number of actions you want a system to handle (for example, 20 actions means manually writing and coding at least 20 rules), rules-based systems are generally lower effort, more cost-effective and less risky since these rules wont change or update on their own. However, rules can limit AI capabilities with rigid intelligence that can only do what theyve been written to do.

While a rules-based system could be considered as having fixed intelligence, in contrast, amachine learning systemis adaptive and attempts to simulate human intelligence. There is still a layer of underlying rules, but instead of a human writing a fixed set, the machine has the ability to learn new rules on its own, and discard ones that arent working anymore.

In practice, there are several ways a machine can learn, butsupervised trainingwhen the machine is given data to train onis generally the first step in a machine learning program. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.

The anticipated benefits to AI are high, so the decisions a company makes early in its execution can be critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to achieve.What problems are you trying to solve, or challenges are you trying to meet?

The decision to implement a rules-based or machine learning system will have a long-term impact on how a companys AI program evolves and scales. Here are some best practices to consider when evaluating which approach is right for your organization:

When choosing a rules-based approach makes sense:

The promises of AI are real, but for many organizations, the challenge is where to begin. If you fall into this category, start by determining whether a rules-based or ML method will work best for your organization.

This article was originally published byElana Krasner on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.

Published June 13, 2020 13:00 UTC

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The key differences between rule-based AI and machine learning - The Next Web

How AI Will Transform Healthcare In Developing Regions – AI Daily

With all the buzz surrounding artificial intelligence, it is so important that we roll out our technologies and toolkits to bring global healthcare forward. From surveillance of cases to the progression of mutative strains (perhaps more relevant to viruses), it is no doubt that many parts of the world have leaps and bounds to make in improvement, as highlighted in our COVID-19 pandemic. Even though we are still using age-old methods such as quarantining to this day, it is no doubt that AI could play a fundamental part in our future technologies to combat the next viral arrival. However, there must be a great deal of caution in the ethics of deploying AI toolkits on demographics that vary greatly from those used in the training dataset. How we can get a rapid solution to this conundrum will be paramount in helping developing regions catch up with the west. Furthermore, by investing in global health, we will certainly get a return on investment. By allowing developing regions to have supported healthcare systems and getting the basics done, such as a national healthcare model, will free up the brilliant minds that have yearned for more complex issues that contribute to the global effort of moving healthcare foward.

Thank you to all the aforementioned individuals, such as Sangu Delle, Kimberly Storin, Margaret Dawson, Hillery Hunter and Hilary Mason, who will be paramount in transforming AI and even our world! Thank you all for your efforts and the best wishes from us at AIDaily in your future endeavours.

Thank you all for your time in reading, stay safe, and I hope you all stay happy and occupied in self-isolation!

Article thumbnail credit: Mesmerizing On The Uprising of Artificial Intelligence In Africa, Medium [click here for page]. Note that this article has not contributed to any of the written content of my article, and that you click the above link at your own discretion as the page has not been checked by our team. Thank you to our readers for your continued support, and to anyone who contributes to our fantastic team to keep our services in an orderly fashion.

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How AI Will Transform Healthcare In Developing Regions - AI Daily

Artificial Intelligence Is Making The Army’s Armored Vehicles Deadlier Than Ever – Yahoo News

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Here's What You Need To Remember:The Army and industry are currently developing algorithms to better enable manned-unmanned teaming among combat vehicles. The idea is to have a robotic wingman, operating in tandem with armored combat vehicles, able to test enemy defenses, find targets, conduct ISR, carry weapons and ammunition or even attack enemies.

The Army is engineering new AI-enabled Hostile Fire Detection sensors for its fleet of armored combat vehicles to identify, track and target incoming enemy small arms fire.

Even if the enemy rounds being fired are from small arms fire and not necessarily an urgent or immediate threat to heavily armored combat vehicles such as an Abrams, Stryker or Bradley, there is naturally great value in quickly finding the location of incoming enemy small arms attacks, Army weapons developers explain.

There are a range of sensors now being explored by Army developers; infrared sensors, for example, are designed to identify the heat signature emerging from enemy fire and, over the years, the Army has also used focal plane array detection technology as well as acoustic sensors.

We are collecting threat signature data and assessing sensors and algorithm performance, Gene Klager, Deputy Director, Ground Combat Systems Division, Night Vision and Electronic Sensors Directorate, told Warrior Maven in an interview last year.

Klagers unit, which works closely with Army acquisition to identify and at times fast-track technology to war, is part of the Armys Communications, Electronics, Research, Development and Engineering Center (CERDEC).

Army senior leaders also told Warrior Maven the service will be further integrating HFD sensors this year, in preparation for more formals testing to follow in 2019.

Enabling counterattack is a fundamental element of this, because being able to ID enemy fire would enable vehicle crews to attack targets from beneath the protection of an armored hatch.

Story continues

The Army currently deploys a targeting and attack system called Common Remotely Operated Weapons System, or CROWS; using a display screen, targeting sensors and controls operating externally mounted weapons, CROWS enables soldiers to attack from beneath the protection of armor.

If we get a hostile fire detection, the CROWS could be slued to that location to engage what we call slue to cue, Klager said.

Much of the emerging technology tied to these sensors can be understood in the context of artificial intelligence, or AI. Computer automation, using advanced algorithms and various forms of analytics, can quickly process incoming sensor data to ID a hostile fire signature.

AI also takes other information into account and helps reduce false alarms, Klager explained.

AI developers often explain that computer are able to much more efficiently organize information and perform key procedural functions such as performing checklists or identifying points of relevance; however, many of those same experts also add that human cognition, as something uniquely suited to solving dynamic problems and weighing multiple variables in real time, is nonetheless something still indispensable to most combat operations.

Over the years, there have been a handful of small arms detection technologies tested and incorporated into helicopters; one of them, which first emerged as something the Army was evaluating in 2010 is called Ground Fire Acquisition System, or GFAS.

This system, integrated onto Apache Attack helicopters, uses infrared sensors to ID a muzzle flash or heat signature from an enemy weapon. The location of enemy fire could then be determined by a gateway processor on board the helicopter able to quickly geolocate the attack.

While Klager said there are, without question, similarities between air-combat HFD technologies and those emerging for ground combat vehicles, he did point to some distinct differences.

From ground to ground, you have a lot more moving objects, he said.

Potential integration between HFD and Active Protection Systems is also part of the calculus, Klager explained. APS technology, now being assessed on Army Abrams tanks, Bradleys and Strykers, uses sensors, fire control technology and interceptors to ID and knock out incoming RPGs and ATGMs, among other things. While APS, in concept and application, involves threats larger or more substantial than things like small arms fire, there is great combat utility in synching APS to HFD.

HFD involves the same function that would serve as a cueing sensor as part of an APS system Klager said.

The advantages of this kind of interoperability are multi-faceted. Given that RPGs and ATGMs are often fired from the same location as enemy small arms fire, an ability to track one, the other, or both in real time greatly improves situational awareness and targeting possibilities.

Furthermore, such an initiative is entirely consistent with ongoing Army modernization efforts which increasingly look toward more capable, multi-function sensors. The idea is to have a merged or integrated smaller hardware footprint, coupled with advanced sensing technology, able to perform a wide range of tasks historically performed by multiple separate on-board systems.

Consolidating vehicle technologies and boxes is the primary thrust of an emerging Army combat vehicle C4ISR/EW effort called Victory architecture. Using ethernet networking tech, Victory synthesizes sensors and vehicle systems onto a common, interoperable system. This technology is already showing a massively increased ability to conduct electronic warfare attacks from combat vehicles, among other things.

HFD for ground combat vehicles, when viewed in light of rapidly advancing combat networking technologies, could bring substantial advantages in the realm of unmanned systems. The Army and industry are currently developing algorithms to better enable manned-unmanned teaming among combat vehicles. The idea is to have a robotic wingman, operating in tandem with armored combat vehicles, able to test enemy defenses, find targets, conduct ISR, carry weapons and ammunition or even attack enemies.

All that we are looking at could easily be applicable to an unmanned system, Klager said.

Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army - Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist for National TV networks. He has a Masters degree in Comparative Literature from Columbia University. This article first appeared last year.

This first appeared in Warrior Maven here.

Image: Reuters.

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Artificial Intelligence Is Making The Army's Armored Vehicles Deadlier Than Ever - Yahoo News

Why cracking nuclear fusion will depend on artificial intelligence – New Scientist

The promise of clean, green nuclear fusion has been touted for decades, but the rise of AI means the challenges could finally be overcome

By Abigail Beall

THE big joke about sustainable nuclear fusion is that it has always been 30 years away. Like any joke, it contains a kernel of truth. The dream of harnessing the reaction that powers the sun was big news in the 1950s, just around the corner in the 1980s, and the hottest bet of the past decade.

But time is running out. Our demand for energy is burning up the planet, depleting its resources and risking damaging Earth beyond repair. Wind, solar and tidal energy provide some relief, but they are limited and unpredictable. Nuclear fission comes with the dangers of reactor meltdowns and radioactive waste, while hydropower can be ecologically disruptive. Fusion, on the other hand, could provide almost limitless energy without releasing carbon dioxide or producing radioactive waste. It is the dream power source. The perennial question is: can we make it a reality?

Perhaps now, finally, we can. That isnt just because of the myriad fusion start-ups increasingly sensing a lucrative market opportunity just around the corner and challenging the primacy of the traditional big-beast projects. Or just because of innovative approaches, materials and technologies that are fuelling an optimism that we can at last master fusions fiendish complexities. It is also because of the entrance of a new player, one that could change the rules of the game: artificial intelligence. In the right hands, it might make the next 30 years fly by.

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Why cracking nuclear fusion will depend on artificial intelligence - New Scientist