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Service robots help keep healthcare workers safe by transporting supplies and linens in hospitals where pathogen exposure is a risk.
Robots in the medical field are transforming how surgeries are performed, streamlining supply delivery and disinfection, and freeing up time for providers to engage with patients. Intel offers a diverse portfolio of technology for the development of health robotics, including surgical-assistance, modular, service, social, mobile, and autonomous robots.
link https://www.intel.com/content/www/us/en/healthcare-it/robotics-in-healthcare.html

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An Overview of Robotics in Healthcare:

Medical robots fall into several categories: surgical assistance, modular, and autonomous.

Using robots in the medical field helps improve patient care and outcomes while increasing operational efficiencies.

Intel-powered robotics drive innovation with AI-assisted surgery, automation, and real-time patient data analytics.

What is robotics in healthcare?

A fast-growing area in healthcare, the use of medical robotics is bringing greater precision to surgical procedures and efficiencies to hospitals and clinics.

What are some examples of robotics in healthcare?

Robots in the medical field span several areas. For example, surgical robots enable minimally invasive surgeries and allow newer surgeons to perform complex procedures earlier in their careers. Autonomous mobile robots are used throughout hospitals and clinics. They can clean and disinfect surfaces along predefined pathways, or they can self-navigate through facilities, engaging with patients and enabling physicians to consult remotely.

How are robots used in the medical field?

Generally, robots are used in the medical field to assist healthcare workers in their daily work. Robotic assistance can range from automating repetitive tasks in a research laboratory to autonomously navigating hospitals to deliver linens to patient rooms to monitoring a patient's form as they go through prescribed exercises in physical therapy.

How are robots changing healthcare?

Medical robots are significantly changing healthcare. They are making time-consuming, manual tasks a thing of the past through automation, which accelerates healthcare research and enables physicians, nurses, and other workers to spend more time with patients. Surgical-assistance robots are helping surgeons in the operating room achieve new levels of speed and precision. Autonomous social robots are even enhancing the patient experience by providing social interaction and monitoring.

What are the main types of robots used in healthcare?

The main types of medical robots used in healthcare include surgical robots, modular robots, and autonomous mobile robots.

Notices and Disclaimers

Intel® technologies may require enabled hardware, software, or service activation.

No product or component can be absolutely secure.

Your costs and results may vary.

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Robotics in Healthcare: The Future of Robots in Medicine - Intel

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Learn about robots in healthcare, including surgical, modular, and autonomous mobile robots. Explore the role of robots in medicine.

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Impacts of artificial intelligent on healthcare

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Impacts of artificial intelligent on healthcare

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Jonathan Sharp is the CFO of Environmental Litigation Group, P.C., a legal firm devoted to helping individuals who have experienced serious injury resulting from occupational exposure to hazardous substances.

Medical technology is on the brink of being revolutionized by artificial intelligence (AI). In nearly every area of patient care, from chronic diseases and cancer to radiography and risk assessment, the potential of AI to deliver more accurate, efficient, and effective therapies at precisely the appropriate time in a patient's care is almost limitless. As payment systems change, patients expect more from their providers, and the amount of accessible data continues to grow at an alarming pace, artificial intelligence is set to be the engine driving advances throughout the continuum of care.

AI has many benefits over conventional analytics and clinical decision-making methods. As they interact with training data, learning algorithms may become more exact and accurate, enabling patients to acquire new insights into diagnoses, care procedures, treatment variability, and outcomes.
1. Artificial Intelligence Integrated Into Major Disease Areas Enables Predictive Analytics for Early Intervention
With cardiovascular, neurological, and cancer illnesses continuing to be the major causes of death, it is essential to use all available resources to aid in early detection, diagnosis, and treatment. Thanks to artificial intelligence, it's possible to identify any potential danger signs in a patient's behavior early on. For example, patients with a high risk of stroke were identified using AI algorithms based on their reported symptoms and genetic characteristics; this level was movement-based, with every atypical physical movement in the patient being recorded and generating an alert.

This trigger warning enabled practitioners to expeditiously refer patients for an MRI/CT scan for disease assessment. The research found that the early detection alert had an accuracy of more than 85 percent in evaluating diagnosis and prognosis. Consequently, practitioners were able to initiate therapy more quickly and identify whether a patient faced a greater risk of future stroke. Similarly, machine learning was used to predict if a patient will have another stroke 48 hours later with a perdition accuracy of 70%.

Cancer is a multifaceted and complicated disease characterized by hundreds of genetic and epigenetic variants. AI-based algorithms have the potential to pave the way for the early detection of these genetic alterations and abnormal protein interactions. Biomedical research in the modern era is also focused on bringing AI technology to clinics in a safe and ethical manner. AI-assisted pathologists and doctors may represent a quantum leap ahead in disease risk, diagnosis, prognosis, and therapy prediction. Clinical applications of artificial intelligence and machine learning in cancer diagnosis and therapy are the future of medical guidance, paving the way for more rapid mapping of a new treatment for each person. Researchers may interact in real-time and exchange information digitally by utilizing an AI-based system method, which has the potential to treat millions of patients. Science is concentrating on demonstrating game-changing technologies of the future in clinics by bridging the gap between biology and artificial intelligence, and explaining how AI-based support may aid oncologists in providing accurate therapy.
2. Artificial Intelligence and Machine Learning Can Provide More Targeted Diagnostics
With a vast volume of healthcare data out in the field, AI must effectively sift through it in order to " learn" and create a network. There are two kinds of data that can be sorted in the domain of healthcare data: unstructured and structured. Structured learning employs three techniques: Machine Learning Techniques (ML), a Neural Network system, and Modern Deep Learning. Natural Language Processing is used in any unstructured data (NLP).

The use of analytical algorithms in order to extract particular patient characteristics, which include all of the information that would be gathered during a patient visit with a practitioner, is the basis of machine learning methods. Symptoms, results of physical exams, medications, basic metrics, disease-specific data, diagnostic imaging, gene expressions, and a variety of laboratory tests are all included in the structured data that is collected. Patient outcomes may then be predicted using machine learning. In one research, Neural Networking was used to select 6,567 genes and match them with texture information from the patients' mammograms in a breast cancer diagnosis procedure. This combination of both genetic and morphological features resulted in a tumor indication that was more specific.

Supervised learning is the most frequent form of Machine Learning in a healthcare context. Supervised learning makes use of the patient's physical characteristics in conjunction with a database of information to give a more focused result. Modern Deep Learning is another kind of learning that is utilized and is thought to go beyond the surface of Machine Learning. When compared to Machine Learning, Deep Learning utilizes the same inputs, but it feeds them into a computerized neural network, which is a hidden layer that further processes the information in order to give a more simple output. This assists practitioner in narrowing down many potential diagnoses to one or two outcomes, enabling the practitioner to reach a more definite and concrete decision.
3. Artificial Intelligence Has the Potential To Provide the Next Generation of Radiological Tools
Based on the incidence, severity, and preventability of the illnesses, occupational lung diseases are the most common cause of occupation-associated disease in the United States. Exposure to organic and inorganic compounds as well as carcinogens in the workplace, over a lengthy period of time, may result in a variety of lung illnesses that can have long-term consequences even after the exposure ends.

Each year, new causes of respiratory damage emerge, leading to an increase in the number of workers who develop lung illness as a result of their jobs. During the early years of work, the majority of individuals who are frequently exposed to toxins on the job show relatively minor signs of lung problems. Lung cancer and other respiratory diseases have a long incubation period, which makes it difficult to establish a connection between occupational exposure and these illnesses for many years.

Due to the limits of human vision, up to 35% of lung nodules go unnoticed during the first checkup. Artificial intelligence can help in both cases by relieving physicians of some of their responsibilities and by detecting lung spots that aren't visible to the naked eye. According to a recent study published in the JAMA Network Open, an artificial intelligence system taught to identify pulmonary nodules may enhance lung cancer diagnosis on chest radiographs.

By using artificial intelligence as a second reader in conjunction with chest X-rays, radiology trainees and board-certified radiologists may enhance their performance in suggesting chest CT scans for patients suspected of having lung cancer. According to the researchers' findings, the AI algorithm helps less-experienced readers in terms of sensitivity while benefiting more-experienced readers in terms of specificity.

Magnetic resonance imaging (MRI), computed tomography (CT), and X-rays are examples of current medical imaging technologies that offer non-invasive views into the workings of the human body. However, many diagnostic procedures still depend on actual tissue samples acquired via biopsies, which are associated with hazards such as the likelihood of infection in the patient. Experts anticipate that artificial intelligence will allow the development of next-generation radiological tools that are accurate and comprehensive enough to obviate the need for tissue samples in some cases.

If scientists want the imaging to provide the same information as tissue samples, they'll need to be able to obtain extremely tight registration so that the high accuracy for each pixel can be ascertained. Successful completion of this quest may enable physicians to get a more accurate knowledge of how tumors behave as a whole, rather than relying on the characteristics of a particular section of the malignancy to make treatment choices. Additionally, providers may be able to more accurately identify the aggressiveness of tumors and focus therapies accordingly. Artificial intelligence is assisting in the advancement of " virtual biopsies " and the cutting-edge science of radionics, which focuses on using image-based algorithms to describe the phenotypic and genetic characteristics of tumors.
4. Telehealth, the Artificial Intelligence on a Smaller Scale, Can Lower Healthcare Costs, Drive Up Efficiency and Provide Patients Better Access to Healthcare Services
COVID-19 generated an urgent need for telemedicine to care for patients outside of the clinic or office environment, as well as to mitigate financial losses due to decreased ambulatory visits. According to new data, the number of patients utilizing telehealth rose from 11 percent in 2010 to 46 percent in 2020, with growth expected to continue. Telehealth may account for 20%, or $250 billion, of US healthcare expenditure in the near future.

Although artificial intelligence is utilized on a wider scale for high-risk illnesses, telehealth tools are being deployed in patients' homes to help treat and prevent high-risk scenarios while simultaneously decreasing hospital readmissions. Telehealth technologies enable various parameters to be collected, recorded, and analyzed in the same way as a larger AI system would. This tool may immediately notify practitioners if a patient reports a high-risk trait. Fast diagnoses and an updated treatment plan save time and money for both the patient and the hospital while delivering more rapid care. Artificial intelligence allows practitioners to make more efficient and rational decisions, thus enhancing patient care.

Respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, occupational lung diseases, and pulmonary hypertension are among the most common, underdiagnosed, disabling, fatal, and expensive to treat of the many chronic diseases. Connected respiratory care and telehealth provide access to services that were previously only available at medical facilities. They allow for effective and continuous monitoring, early intervention, and multidisciplinary team care, which may be especially important for patients with advanced disease, multiple comorbidities, or frequent exacerbations - in other words, patients who require more intensive management that addresses the complexities of their disease. Connected care also promises enhanced patient access, more efficient and effective use of health resources, a better patient experience, and reduced medical expenses.
References: https://svn.bmj.com/content/2/4/230 https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2770952 (Photo by Online Marketing on Unsplash)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a 'digital everything' world.

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Four major impacts of artificial intelligence on healthcare

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In nearly every area of patient care, from chronic diseases and cancer to radiography and risk assessment, the potential of AI to deliver more accurate, efficient, and effective therapies at precisely the appropriate time in a patient's care is almost limitless.

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https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwj3zZOQ5v3zAhVg73MBHWM8BmkQwqsBegQIAhAB&url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DHUP6Z5voiS8&usg=AOvVaw1zWElIgFcla03GsQKTT73-

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AI has helped buissnesses to become more efficent

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AI has helped buissnesses to become more efficent

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https://www.forbes.com/sites/forbestechcouncil/2021/11/02/why-you-should-make-artificial-intelligence-part-of-your-digital-strategy/

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Co-founder & CTO at Orient Software, a software outsourcing company that employs top software engineering talent from Vietnam. Which business functions are ripe for AI?
Artificial intelligence (AI) is changing the world. It's transforming the way we work and live. It's doing tasks that humans used to do. But this isn't a story about robots taking over our lives and stealing our jobs. Instead, this is a story about how AI can help make businesses and their employees more innovative, creative and efficient.

AI is already enhancing our lives in many ways. For example, it's helping doctors diagnose cancer, teachers personalize lessons for their students and lawyers win cases. And it's not just large companies like Google, Facebook and Apple that are using AI to turbo-boost their businesses - everyday entrepreneurs and startups are trying to harness its power as well. The most common areas in which companies and entrepreneurs use AI include online advertising, human resources, customer service and retail.

Many companies in various industries are already seeing the benefits of using AI in their businesses. They've reported better decision-making, fewer errors, greater accuracy and increased efficiency. Let's go through some of the business functions that can benefit from digital transformation using AI.

* Management And Better Business Decisions: AI can analyze massive amounts of data very quickly. And companies need to make data-driven decisions informed by learning from past experiences and current information. AI can help them do this. In addition, because many companies have plenty of data available, they can apply data science techniques such as data analytics and machine learning. These will help management make sound decisions by analyzing businesses' data in new ways.

* Increased Sales With AI: Many companies are using AI to help make smarter decisions. For example, they can better predict which products or services their customers will be interested in and adjust what they offer accordingly. Using this information, sales managers can create more effective strategies that will grow sales for the company.

* Improved Customer Experience With AI: AI can analyze customers' behavior and make predictions about how they will act in the future. As a result, companies that use AI can tailor their customer experience based on what they learn. The spread of mobile apps and social media has allowed companies to track more of their customers' behavior, making it easier to offer personalized customer service. Also, companies with AI-enabled chatbots can use them to answer basic questions and reduce calls to customer support lines. Of course, many companies still need human agents to handle the more complicated questions, but AI can perform simpler repetitive tasks.
How will AI affect humans in the workspace?
* Marketing: In the marketing world, AI is being used to learn about customers based on digital data. Many companies have an abundance of information stored in customer databases that they can use to predict what products a given customer will want and when. For example, if you purchase an expensive item with your credit card, an online store may offer a coupon or discount on another article you were looking at. Likewise, if your online store knows that you recently bought a new car and are in the market for roadside assistance, it can alert you to special deals.

* HR And Better Hiring: How can HR benefit from AI? Recruiters can use AI to help eliminate bias by using data and metrics to assist in the process. AI-based hiring practices combined with human intuition can be more accurate than solely individual judgment decisions. Using metrics and data, companies can make smarter decisions about business challenges, too. For example, a program can evaluate candidates' credentials, their resumes and how they do in interviews. Then, it can sift through pools of candidates to find the best matches. But, of course, humans will make the final hiring decisions.

The idea that machine intelligence will match human intelligence was first proposed by mathematician Alan Turing as early as 1950: "We may hope that machines will eventually compete with men in all purely intellectual fields." However, we're not there yet and may still be more than 100 years away from human-level intelligence in machines.
Conclusion
AI is being used in many ways today to make our lives better. It's assisting us with complex tasks that we can't do by ourselves, creating a symbiosis between humans and machines that allows both to achieve more than they can alone.

But will it compete with us? The reality of AI is that it's more likely to free us from our daily routines, helping us be more innovative, more productive and more creative in how we work.

AI is the future. It helps make smarter decisions, boosts sales by creating personalized marketing pitches and automating follow-up, improves analytics, provides excellent customer insights and can even help with hiring practices to eliminate bias based on individual judgment. In short, AI is a tool that every company should consider using in their digital strategy for 2021 and beyond.

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Council Post: Why You Should Make Artificial Intelligence Part Of Your Digital Strategy

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AI is being used in many ways today to make our lives better. It's assisting us with complex tasks that we can't do by ourselves, creating a symbiosis between humans and machines that allows both to achieve more than they can alone.

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This explains what artificial intelligence is and how the artificial intelligence works

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Artificial Intelligence (AI): What it is and why it matters

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With artificial intelligence (AI), machines learn from experience and perform human-like tasks. AI works by combining vast amounts of data with fast, iterative processing and intelligent algorithms. Learn more in our primer.

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https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=newssearch&cd=&cad=rja&uact=8&ved=2ahUKEwiU6oD35f3zAhVUeXAKHY-MBYQQxfQBKAB6BAgDEAI&url=https%3A%2F%2Ftechstory.in%2Fhow-to-change-siris-voice%2F&usg=AOvVaw0TdhoZIyWfmCGMLADqHUjH

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Elon musk now telling us that their auto pilot tesla's are not as safe as you might think

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Elon musk now telling us that their auto pilot tesla's are not as safe as you might think

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Link-https://www.theverge.com/2021/7/5/22563751/tesla-elon-musk-full-self-driving-admission-autopilot-crash

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Tesla CEO Elon Musk is finally admitting that he underestimated how difficult it is to develop a safe and reliable self-driving car. To which the entire engineering community rose up as one to say, "No duh."

Or at least that's how it should have happened in a just world. Instead, all the Tesla sycophants and ass-kissers on Twitter told Musk to keep up the good work, that they believed in him, and encouraged him to hurry up and roll out the latest version of his "Full Self-Driving" software that, it's worth pointing out, does not enable a Tesla vehicle to drive itself without input from the driver.

Musk has a long history of over promising and under delivering

Musk has a long history of over promising and under delivering when it comes to his company's so-called "Full Self-Driving" software. He did it in 2018, when he promised that the "long awaited" V9 (Version 9) would begin rolling out in August. He did it again in 2019, proclaiming that "a year from now" there would be "over a million cars with full self-driving, software, everything."

He was back at it again this past weekend, promising that "FSD 9 beta is shipping soon," with an added "I swear!" just in case you had any doubts about his solemnity.

Haha, FSD 9 beta is shipping soon, I swear!

Generalized self-driving is a hard problem, as it requires solving a large part of real-world AI. Didn't expect it to be so hard, but the difficulty is obvious in retrospect.

Nothing has more degrees of freedom than reality.
- Elon Musk (@elonmusk) July 3, 2021

There's no question that Tesla is more willing than its competitors to test beta versions of its Autopilot driver assist feature on its customers in the interest of gathering data and working out any bugs in the system. And Tesla customers are mostly fine with this, routinely flooding Musk's mentions begging to be white-listed for the current version of Full Self-Driving. This has helped contribute to Tesla's public reputation as a leader in autonomous driving, despite its vehicles continuously falling short of what most experts would agree defines a self-driving car.

Tesla says Autopilot is safe - it releases quarterly reports with selectively reported data that it says proves this - but that it also requires constant input from the driver in order to work. Meanwhile, AV companies like Waymo have real driverless vehicles on public roads giving rides to passengers. Waymo, like practically every AV company, uses a combination of different sensors, like radar, lidar, and cameras, to ensure there are redundancies in case any system failures. Tesla recently switched to a camera-only sensing system. The contrast between Tesla and every other company pursuing self-driving technology couldn't be more stark.

I, for one, am all for Musk taking as long as he wants

I, for one, am all for Musk taking as long as he wants with the release of V9. Let the cake bake for as long as it needs, in my opinion, especially after viewing videos like the one to just come out of China of a Tesla Model 3 in Autopilot utterly failing to take a sharp turn and crashing into a ditch.

An anonymous Twitter user who uses the handle @greentheonly to post "hacks" of Tesla's Autopilot, recreated the scenario to demonstrate how the company's driver assist feature struggles with these sharp turns. With an overlay of Tesla's Autopilot display running in the corner of the screen, greentheonly shows how the vehicle "actually outputs various alerts before the eventual 'take over we are giving up.'" Other times, the car actually slows down enough and manages to take the turn safely.

So in regards to the recent Chinese crash with video where AP exits roadway in sharp turns:
I long planned to do a demo of a similar condition but always forgot and I guess better late than never.
It's actually really easy to reproduce https://t.co/B2Ip0G17Ur
- green (@greentheonly) July 4, 2021

A system that fails to take a sharp turn in "half the cases" should not inspire a great amount of confidence! Quite the opposite actually. The number of open investigations into vehicle crashes involving Tesla Autopilot seems to be growing in inverse relation to customer expectations about Musk's ability to deliver on the promises he's been making (and breaking) for years now.

Musk isn't alone in coming to the realization that self-driving cars are hard. Nearly the entire industry was predicting that by now ours roads would be swarmed with self-driving cars, only to later admit they underestimated how complicated it was to get cars to drive themselves safely and reliably.

To which we can now say to Musk, "Welcome to the party, pal."

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Elon Musk just now realizing that self-driving cars are a 'hard problem'

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Tesla CEO Elon Musk is finally admitting that he underestimated how difficult it is to develop a safe and reliable self-driving car. To which the entire engineering community rose up as one to say, "No duh." Or at least that's how it should have happened in a just world.

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https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence#:~:text=Artificial%20intelligence%20is%20the%20simulation,speech%20recognition%20and%20machine%20vision.

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What is Artificial Intelligence (AI)? - AI Definition and How it Works

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Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Read to learn full definition and more.

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K Line deploys artificial intelligence to keep ship machinery working

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K Line deploys artificial intelligence to keep ship machinery working

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https://splash247.com/k-line-deploys-artificial-intelligence-to-keep-ship-machinery-working/

It is being used to keep tabs on machinery failures, diagnostics and condition-based maintenance

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Kawasaki Kisen Kaisha (K Line) is deploying artificial intelligence to keep onboard machinery in good order. Tokyo-based K Line has partnered with compatriot yard Kawasaki Heavy Industries as well as Preferred Networks to develop a marine machinery operation support system to keep tabs on machinery failures, diagnostics and condition-based maintenance, based on data analytics.

The marine artificial intelligence system will be implemented both onboard and in the cloud. The one onboard learns the vessel's operation data and uses them to diagnose on a real-time basis. The one in the cloud system periodically collects the data accumulated on each vessel, and centrally uses them for further learning, allowing itself to become smarter and offer failure prediction and diagnostics as well as operational support.

K Line said the new system was the latest step in the shipping line's advance to creating autonomous vessels, something other Japanese shipping majors Mitsui OSK Lines and Nippon Yusen Kaisha are also pursuing.

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K Line deploys artificial intelligence to keep ship machinery working - Splash247

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Kawasaki Kisen Kaisha (K Line) is deploying artificial intelligence to keep onboard machinery in good order. Tokyo-based K Line has partnered with compatriot yard Kawasaki Heavy Industries as well as Preferred Networks to develop a marine machinery operation support system to keep tabs on machinery failures, diagnostics and condition-based maintenance, based on data analytics.

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Australian artificial intelligence research is being used to help astronauts grow plants in space.

Link- https://www.abc.net.au/news/2021-11-03/research-to-grow-vegetables-in-space/100584946

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‘We’ll see if we can do it as well as Matt Damon did’: How astronauts will grow plants on the moon

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Within the next five years, astronauts will be growing vegetables in space with technology developed by Australian scientists.

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Australian artificial intelligence research to help astronauts grow plants in space

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Australian artificial intelligence research to help astronauts grow plants in space

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Astronauts will use artificial Intelligence developed by USQ researchers to grow vegetables on the moon. This means that even on the moon artificial intelligence is taking over.

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‘We’ll see if we can do it as well as Matt Damon did’: How astronauts will grow plants on the moon

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Within the next five years, astronauts will be growing vegetables in space with technology developed by Australian scientists.

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This scientist thinks that ai can tackle racial inequality

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This scientist thinks that ai can tackle racial inequality

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Ryan is an editor at TechForge Media with over a decade of experience covering the latest technology and interviewing leading industry figures. He can often be sighted at tech conferences with a strong coffee in one hand and a laptop in the other. If it's geeky, he's probably into it. Find him on Twitter: @Gadget_Ry

UK Health Secretary Sajid Javid has greenlit a series of AI-based projects that aim to tackle racial inequalities in the NHS.

Racial inequality continues to be rampant in healthcare. Examining the fallout of COVID-19 serves as yet another example of the disparity between ethnicities.

In England and Wales, males of Black African ethnic background had the highest rate of death involving COVID-19, 2.7 times higher than males of a White ethnic background. Females of Black Caribbean ethnic background had the highest rate, 2.0 times higher than females of White ethnic background. All ethnic minority groups other than Chinese had a higher rate than the White ethnic population for both males and females.

Such disparities are sadly common across many conditions that can reduce life enjoyment, limit opportunities, and even lead to premature death. AI could be a powerful aid in tackling the problem, if thoroughly tested and implemented responsibly.

"As the first health and social care secretary from an ethnic minority background, I care deeply about tackling the disparities which exist within the healthcare system," explained Javid, speaking to The Guardian.

Among the projects given the green light by Javid include the creation of new standards for health inclusivity to improve the representation of ethnic minorities in datasets used by the NHS.

"If we only train our AI using mostly data from white patients it cannot help our population as a whole," added Javid. "We need to make sure the data we collect is representative of our nation."

A recent analysis found a significant disparity in performance when using computer screening to detect diabetic retinopathy in patients from ethnic minority communities due to different levels of retinal pigmentation. One project will attempt to address this disparity.

Among the devastating statistics affecting minority communities is that black women are five times more likely to die from complications during pregnancy than white women. One project will use algorithms to investigate the factors and recommend changes - including potentially new training for nurses and midwives - that will hopefully ensure that everyone has the best possible chance to live a healthy life with their child.

The development of an AI-powered chatbot also hopes to raise the uptake of screening for STIs/HIV among minority ethnic communities.

The drive will be led by NHSX. A report in 2017 by PwC found that just 39 percent of the UK public would be willing to engage with AI for healthcare. However, research (PDF) by KPMG found that - despite an overall unwillingness from the British public to share their data with the country's biggest organisations even if it improved service - the NHS came out on top with 56 percent willing to do so.

If the UK Government wants to use AI as part of its "level up" plans, it will need to tread carefully with a sceptical public and prove its benefits while avoiding the kind of devastating missteps that have cost thousands of lives and defined Johnson's premiership so far.
(Image Credit: UK Parliament under CC BY 3.0 license)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a 'digital everything' world.

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UK health secretary hopes AI projects can tackle racial inequality

# data[].attributes.attachment_link.description
Ryan is an editor at TechForge Media with over a decade of experience covering the latest technology and interviewing leading industry figures. He can often be sighted at tech conferences with a strong coffee in one hand and a laptop in the other. If it's geeky, he's probably into it.

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buy ur new robot ai now at boston dynamics it uses highly coded ai

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buy ur new robot ai now at boston dynamics it uses highly coded ai

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https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjQmdzB5f3zAhUK7XMBHVfTDEwQFnoECAUQAQ&url=https%3A%2F%2Fwww.bostondynamics.com%2Fspot&usg=AOvVaw2ZUexLbQM9I2r8XhX-s_II

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It is made by combining large amounts of data with intelligent algorithms.

https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html#:~:text=AI%20works%20by%20combining%20large,or%20features%20in%20the%20data.&text=The%20process%20requires%20multiple%20passes,derive%20meaning%20from%20undefined%20data.

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Artificial Intelligence (AI) – What it is and why it matters

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Will artificial intelligence (AI) give us human-like machines? Or is it just another industry buzzword? We look at the history of artificial intelligence, examine the most common AI technologies and describe the true potential of AI.

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There is a robot getting developed for the safety and efficiency of humans for  Example (getting stuff from a high shelf so you don’t have to)

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Credit: Altamirano Cabrera et al.

The number of collaborative mobile robots introduced in real-world settings is gradually increasing, with approximately 20,000 new installations per year. For their use to increase further, however, roboticists should ensure that these robots are both efficient and safe to use around humans.

Researchers at Skolkovo Institute of Science and Technology (Skoltech) have recently created CoHaptics, a system that merges collaborative robotics and haptics technology to improve the safety of human-robot collaboration. This system, presented in a paper pre-published on arXiv, can track human users' movements using hand-tracking technology and provide tactile feedback to warn them about potential collisions with robots.

"Collaborative robots are designed to be safe for humans and are equipped with force/torques sensors," Dzmitry Tsetserukou, associate professor, head of the Intelligent Space Robotics Laboratory, told TechXplore. "However, they typically only stop moving after a collision with objects is detected, which means that they can still be dangerous for sensitive parts of body, such as a user's fingers, wrist, neck and head. This is one of the reasons why they are not widely used for human-robot collaborative tasks, but merely replace industrial robots in some specific tasks, such as palletizing, quality inspection, assembly, etc."

In recent years, the use of robots has become particularly widespread in the industry and service sectors. To enhance their safety and efficiency, however, roboticists should devise collaboration techniques that consider both the dexterity that humans are capable of and the strength or power of robots.

"For example, if you work in an assembly line, where the elements to assemble products are heavy or different small parts are used, one collaborative robot can support you in picking up the heavy pieces and bring you close to the place to assemble the parts," Miguel Altamirano Cabrera, Ph.D. student involved in the development of CoHaptics, told TechXplore. "However, the robot is working together with you, and some collisions can occur when your body interferes in the robot's path."

To reduce collisions between robots and humans, Tsetserukou, Altamirano Cabrera and their colleagues used a tracking system based on a 6 DoF camera that can be worn by users on their wrist. This camera collects data that can be used by the robot to infer the location of human workers in its proximity and avoid collisions with them.

To evaluate their system, Tsetserukou, Altamirano Cabrera and their colleagues carried out an initial experiment, where a human and a robot completed tasks within a shared environment. In this experiment, they also compared the time it took to complete tasks when the human user was using their haptic feedback-based system to the operation time when the user was not using the system.

The researchers found that, in instances where the robotic system was aware of a user's position, their system reduced collisions with the robot and increased the safety of the human-robot interaction. However, the robot took longer to complete its mission, mainly because it deviated from its path in order to avoid collisions with the user.

"To solve this issue, we also provided the users with a 'warning sign' when it is close to the robot, warning them about potential collisions," Altamirano Cabrera "This sign was given by haptic stimulation on their forearm by a sliding touching point, representing the robot's distance."

In addition to adapting a robot's path to reduce collisions with humans, the researchers thus added a haptic feature to their system that informs human users about possible future collisions with robots in advance. Their assumption was that while working on an assembly line or in industrial settings humans might be so focused on the tasks at hand that they might not be paying attention to nearby robots.

"Luckily, we have a sense of touch, so this is the channel through which we decided to convey the risk of collision to humans," Tsetserukou said. "We developed a haptic display dubbed RecyGlide that generates tactile stimuli in accordance with the distance between hand of a human and hand of a robot. When humans experience tactile feedback, they can move their hand to avoid collisions with the robot."

In their system evaluations, the researchers found that users responded to the haptic feedback provided by their system within an average time of 0.32 sec, which is fast enough to prevent a possible collision. To increase the safety of robot-human collaboration further, they also developed an algorithm called haptic potential field.

The haptic potential field algorithm was designed to change the trajectory of a robot in a way that prevents it from colliding with humans, without interrupting its mission and hence continuing to move towards a target object. Through this algorithm, the CoHaptics system can use the information collected by the 6DoF camera worn by users on their wrist to modify the path of robots and prevent collisions.

"Our results showed that the information provided to the users by haptic stimulation increased the human-robot distance by 4.1 cm (from 12.39 to 16.55), and the robot's path was reduced by 81%," Altamirano Cabrera said. "From these results, we can conclude that the implementation of haptic stimulation to inform the users about their distance to the robot in a collaborative task could increase the safety of the users,"

In the future, CoHaptics could prove to be a highly valuable system for increasing the safety of workers working alongside mobile robots in industrial settings. Meanwhile, Tsetserukou, Altamirano Cabrera and their colleagues plan to devise new approaches that could further enhance human-robot collaboration.

As part of their current work, the team is working on building a 3D environment, dubbed Coverse (collaborative universe), which represents a factory where robots and workers can be tracked and animated in real-time. They could eventually also create similar representations for urban environments or other settings where robots might be deployed.

"The 3D representation we are creating appears in a gradient scale from green (safe) to red (hazard)," Tsetserukou said. "When part of the human body is in the proximity of a robot, the nearby user receives touch feedback, to avoid the potential injury. This 3D collaborative universe will potentially make the factory and city environment populated with humans and robots safer and more natural. One can imagine the scenario when you will receive haptic feedback when outdoor delivery robot is approaching you from blind zone. On the top of that, users could experience the location of the parcel transported by mobile robot right on their skin in real-time with a delivery haptic radar to meet the rover on time."

More information: Miguel Altamirano Cabrera et al, CoHaptics: Development of human-robot collaborative system with forearm-worn haptic display to increase safety in future factories. arXiv:2109.05912v1 [cs.RO], arxiv.org/abs/2109.05912

© 2021 Science X Network

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CoHaptics: A wearable haptic robot with a 6DoF camera to enhance the safety of human-robot collaboration

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The number of collaborative mobile robots introduced in real-world settings is gradually increasing, with approximately 20,000 new installations per year. For their use to increase further, however, roboticists should ensure that these robots are both efficient and safe to use around humans.

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https://link.springer.com/chapter/10.1007/978-3-319-18305-3_1

Machine Learning is computational algorithms and is a subfield of A.I and can be used in the medical field effectively

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Issam El Naqa

Martin J. Murphy

Abstract

Machine learning is an evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment. They are considered the working horse in the new era of the so-called big data. Techniques based on machine learning have been applied successfully in diverse fields ranging from pattern recognition, computer vision, spacecraft engineering, finance, entertainment, and computational biology to biomedical and medical applications. More than half of the patients with cancer receive ionizing radiation (radiotherapy) as part of their treatment, and it is the main treatment modality at advanced stages of local disease. Radiotherapy involves a large set of processes that not only span the period from consultation to treatment but also extend beyond that to ensure that the patients have received the prescribed radiation dose and are responding well. The degrees of the complexity of these processes can vary and may involve several stages of sophisticated human-machine interactions and decision making, which would naturally invite the use of machine learning algorithms into optimizing and automating these processes including but not limited to radiation physics quality assurance, contouring and treatment planning, image-guided radiotherapy, respiratory motion management, treatment response modeling, and outcomes prediction. The ability of machine learning algorithms to learn from current context and generalize into unseen tasks would allow improvements in both the safety and efficacy of radiotherapy practice leading to better outcomes.

Keywords

Machine learning Human-machine interaction Radiotherapy Cancer

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Issam El Naqa Martin J. Murphy Machine learning is an evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment. They are considered the working horse in the new era of the so-called big data.

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https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjk5uPY5P3zAhWf73MBHQ65B8IQFnoECAcQAQ&url=https%3A%2F%2Fwww.prnewswire.com%2Fnews-releases%2Ftrumpbot-is-an-ai-clone-of-donald-trump-that-you-can-speak-with-301161197.html&usg=AOvVaw1fD2hhNN-d2liQx71qnXj8

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https://builtin.com/artificial-intelligence

This post tells us about how artificial intelligence works and what it can do for us in the future. They also pointed out some negative aspects of how it can learn and evolve. Apparently, if they learn, they could be a liability such as AI pretending to be other people.

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What Is Artificial Intelligence (AI)? | Built In

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Artificial intelligence refers to computer systems capable of performing tasks that are typically thought to require human intelligence.

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This site uses AI to generate fake news articles

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Artificial intelligence is the simulation of human intelligence processes by machines.

Link- https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence#:~:text=Artificial%20intelligence%20is%20the%20simulation,speech%20recognition%20and%20machine%20vision.

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What is Artificial Intelligence (AI)? - AI Definition and How it Works

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Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Read to learn full definition and more.

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Can predict different sorts of natural disasters in a reliable/beneficial way. 

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Can predict different sorts of natural disasters in a reliable/beneficial way. 

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7 Amazing Facts About Artificial Intelligence in 2020 (sprintzeal.com)

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By Rohini Madhavi

Last updated on Oct 5 2020

Interesting Facts About Artificial Intelligence

Artificial Intelligence has been globally getting recognized for the past few years for amazing facts about it, even though everyone knows about the concept but most people don't know how our human life on earth is flinging oneself into AI nowadays. Without even knowing, an average human's life is depending on AI every day. Wondering how does that happening in your day-to-day life?

The term "artificial intelligence" may sound daunting to some, but it has been in use over decades and its applications are more usual than you might ever think. AI assists are taken over every doable aspect in human life, whether you are trying to read your mails, getting driving direction, or even try to make some corned beef for breakfast, the situation is the same.

Facts About the origin of Artificial Intelligence - How it all Started?
Artificial Intelligence amazing facts untold: 1956 Conference at Dartmouth College
In summer 1956, Dartmouth hosted a scientific conference where a young brain and ambitious mind defined the term "Artificial Intelligence "who is later known as the father of Artificial Intelligence "Sir John McCarthy. There are proven facts that Prior to these historical conference scientists had already framed natural language and neural network concepts lacking a more natural and universally accepted term for the stunning concept of machine " Mind".We can say that ever since that day AI has become a field of research. Pretty fascinating right?
The facts about amazing Game Changer in Artificial Intelligence: "The Game of Imitation" From Alan Turing
Back in 1950, A British influence researcher Alan Turing asked himself "can machine think "? Since then Turing started developing a new system of belief for assessing artificial neural networks intelligence and this belief system made him answer to one of the most important questions he had back then - can machines be considered logical if they are able to imitate human behaviors?

Based on these facts the amazing "Imitation Game" was created. And that's how the world famous "Turing Test or Captcha" came into existence. In fact with this test, it became easy for the researcher to determine who he had corresponded with - a person or a robotics neural network. Or just like this have you ever wondered why most of the time you have to do CAPTCHA to prove your identity. So, while a minute of solving CAPTCHAs might feel annoying, it's actually helping the website to verify your human trace. Thanks to Sir Alan Turing for this awesome invention.
List of Amazing Facts about Artificial Intelligence
Facts about robotic is bit tricky, but one definition for it is "A machine that behaves in ways that would be called intelligence if it was human were so behaving" So now that we know what is AI so let me take you to some top facts.

Artificial Intelligence can predict earthquakes and volcanoes and many other natural disasters

Isn't it amazing? While the general conception about Artificial Intelligence is that soon a computer will take over the world, for your information, on the other hand, machine learning can be more helpful and beneficial to prevent the level of destruction caused by natural disasters.

As we know Artificial intelligence feature has been assisting humans in enormous ways since its origin that varies from customer care, stock market trading, health care, etc. And now artificial intelligence researchers have been found that using artificial intelligence we can easily access natural disaster predictions. With a generous amount of good quality data sets, AI will be able to predict the possible occurrences and chances of natural disaster and in a way that AI can be a back up to numerous people's life by protecting them with an accurate and timely prediction. AL systems can undergo training with the help of seismic data and by doing that the magnitude and the location of the Earthquake take place can be traced.

Artificial Intelligence is restoring vintage and damaged photos

As we know printed photographs are very delicate pieces of amazing memories. Oftentimes, finding old photographs damaged can be heartbreaking. In every human's life, we have to face such bitter incidents. In order to rectify that there is a process in Artificial Intelligence called "Image Inpainting" developed by NVIDIA in order to successfully restore the photograph which needs to be rejuvenating. The image could've full of holes, they might be ripped off from both the sides and generally with lacking the pixels. In this scenario robotics can do wonders, it can be trained by feeding 1000 images which will make it capable of ensuring the missing elements and pixels automatically into the destroyed areas of your good old photographs.

Apart from pouring automatical pixels into photographs robotics applications are able to do many distinguished functions like adding color effect into old vintage photos. What I am saying is through deep learning and analysis of 1000's of images fed in AI, it can colorize your grandparents' wedding photo in less than a minute.

Artificial Intelligence is helping us fight the cancer

The so called big problems in medicine have been harnessed by Artificial Intelligence today. In oncology robotics applications are really working hard to contribute its benefits. As a global menace, the cancer burden continues to spread globally, exerting tremendous emotional, physical, and financial burden on victims, families, societies, and moreover the health systems. The contributions of Intelligent retrieval to oncology are vast and never-ending and continue to grow on a daily basis.

The fight against cancer has been a tough one but with the advanced technologies, knowledge engineering researchers are understanding and ultimately aiming to reduce its impact in our human life using super computers.

The University of Surrey and the University of California in San Francisco developed AIs in collaboration, which can even detect early stages of cancer symptoms. How appealing that is to hear? And these are fed with thousands of accounts of people who are in a remission stage. In a way, AIs are able to predict which patients are having a high chance of developing cancer cells back. This powers doctors to get ahead of treatments with the early results.

The earliest instance of Artificial Intelligence ever known

Have you ever heard about the uncanny tales of Golden robots? I am talking about the robots which take and obey commands and designed and engineered by none other than Hephaestus, the Greek god of invention and technology.

The gigantic golden warrior who was programed to guard the island of Crete, Talos was also one of his inventions. Like the fictional, Terminator, or RoboCop of Hollywood, Talos was the primitive forerunner of self-determining cyborgs who were incapable of forming lethal strength.

The physiology of this golden robot was described in ancient literature using mythos - biotechnical language seems to be the definition of today scientific "Cybernetic Organism"

The Greek myths and the Homeric poems sanctified and praises in stories that Hephaestus had a special power to produce motion in objects. And he succeeded in making robots centuries back with his moving and obeying statues. So that now it's clear the concepts of AI are pretty old than Athens.

Artificial Intelligence is beating us at our own game

Human history and games are inextricably intertwined. There are evidence beyond doubt which proves games are not come into human life as a result of any pursuit per se instead it came naturally to us just as any other human complex processes like learning, drawing or singing, etc. games become an essential part of being alive, on the top of this humans are not perfect in the games themselves invented they usually tend to make mistakes every time they try it and we know it that's the fun part. Because humans are only capable of thinking a maximum of 4 or 5 steps ahead.

But as a matter of fact, AI is beating humans in every game in just a matter of a few seconds? Feeling a little intimidated? Well don't be because it's just games and games are a professional version of Fun!

StarCraft 11, Chess, Checkers, and poker are some of the games in Artificial Intelligence that have already defeated humans so far.

Most Artificial Intelligence Bots Are Female

Have you ever wondered why most artificial intelligence bots are coming handy in female-like voices? The reason is pretty stereoscopic to hear, that's because tech companies and robotic scientists believe that most females and male customers are more likely to hear the response of their favorite AI bot in smooth lady voices not in any harsh hoarse voice.

That's why the voice of world popular AI bots is in female sound for instance SIRI, ALEXA, Google home everyone tends to greet you in a girly way. And tries to keep the conservation and discussions with you in a pleasant style. And hope you also love that singing voice just like everyone else.

2025, The year of Artificial Intelligence pet Bots

Wondering what is meant by AI pet bots? Let your imagination run like wildfire, still no hope? Okay, let me tell you all about that new classification of neural network animals which you can pet without bothering to give bathe and taking to the vet. With Pet bots, you can run errands while having funny conversations and still without bothering about the health of your lovely pet in a pandemic background.

Not a dream!! It's actually coming for us in 2025 although the real pet is lovable according to Artificial Intelligence Pet bot developers still they have a handful of stumbling blocks along, they need to be fed, clean and they will die one day which will leave you heartbroken forever. AI pets will be animal looking robots that feel; look and act like real animals but surprisingly will eliminate all "such "critical issues experiences by real pet owners. Infect this is one another magic of amazing robotics.
Importance of getting a Certification
The modern revolution is at the forefront with the amazing advantages of Artificial Intelligence.

In fact, in this technological era processing, a Robotics certificate might be really helpful to get the right job with the right pay, as a result of the leverage organizations are giving to indulge AI in decision making.

AI is also framing a wide window of opportunities to the professionals exclusively in order to make them capable of earning with great potential.

As per data provided by Forbes, an average Artificial Intelligence Engineer is earning approximately $134,000 each year, you can take AI certification as a stepping stone towards your successful future.
Artificial Intelligence Certifications you should consider
We are a globally recognized ATO [An accredited training organization] called Sprintzeal. We have our knowledge engineering course training which is specifically designed for professionals with a keen interest in understanding Artificial Intelligence and Machine learning. And along with that we also offer training for multiple IT and Business related globally recognized certifications. You can choose from the wide range of courses we have and enhance your career.

Get Artificial Intelligence training - online, live online, and classroom

Suggested Read - How AI has Impacted Consumer Buying Behavior

About the Author

Rohini Madhavi

Content writer at Sprintzeal with great knowledge in Technical domains and all other popular areas as well.

Recommended Resources

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7 Amazing Facts About Artificial Intelligence in 2020

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By Rohini Madhavi Last updated on Oct 5 2020 Artificial Intelligence has been globally getting recognized for the past few years for amazing facts about it, even though everyone knows about the concept but most people don't know how our human life on earth is flinging oneself into AI nowadays.

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there is a robot in china who does news reports

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there is a robot in china who does news reports

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there is Restaurant with staff that are mostly robots. https://www.pocket-lint.com

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there is Restaurant with staff that are mostly robots. https://www.pocket-lint.com

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hello

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this is about the benefits and risks of artificial intelligence

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Click here to see this page in other languages: Chinese French German Japanese Korean Russian

" Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before - as long as we manage to keep the technology beneficial."

Max Tegmark, President of the Future of Life Institute

What is AI?

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google's search algorithms to IBM's Watson to autonomous weapons.

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

Why research AI safety?

In the near term, the goal of keeping AI's impact on society beneficial motivates research in many areas, from economics and law to technical topics such as verification, validity, security and control. Whereas it may be little more than a minor nuisance if your laptop crashes or gets hacked, it becomes all the more important that an AI system does what you want it to do if it controls your car, your airplane, your pacemaker, your automated trading system or your power grid. Another short-term challenge is preventing a devastating arms race in lethal autonomous weapons.

In the long term, an important question is what will happen if the quest for strong AI succeeds and an AI system becomes better than humans at all cognitive tasks. As pointed out by I.J. Good in 1965, designing smarter AI systems is itself a cognitive task. Such a system could potentially undergo recursive self-improvement, triggering an intelligence explosion leaving human intellect far behind. By inventing revolutionary new technologies, such a superintelligence might help us eradicate war, disease, and poverty, and so the creation of strong AI might be the biggest event in human history. Some experts have expressed concern, though, that it might also be the last, unless we learn to align the goals of the AI with ours before it becomes superintelligent.

There are some who question whether strong AI will ever be achieved, and others who insist that the creation of superintelligent AI is guaranteed to be beneficial. At FLI we recognize both of these possibilities, but also recognize the potential for an artificial intelligence system to intentionally or unintentionally cause great harm. We believe research today will help us better prepare for and prevent such potentially negative consequences in the future, thus enjoying the benefits of AI while avoiding pitfalls.

How can AI be dangerous?

Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when considering how AI might become a risk, experts think two scenarios most likely:

1. The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply "turn off," so humans could plausibly lose control of such a situation. This risk is one that's present even with narrow AI, but grows as levels of AI intelligence and autonomy increase.

2. The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal: This can happen whenever we fail to fully align the AI's goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for. If a superintelligent system is tasked with a ambitious geoengineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.

As these examples illustrate, the concern about advanced AI isn't malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we have a problem. You're probably not an evil ant-hater who steps on ants out of malice, but if you're in charge of a hydroelectric green energy project and there's an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.

Why the recent interest in AI safety

Stephen Hawking, Elon Musk, Steve Wozniak, Bill Gates, and many other big names in science and technology have recently expressed concern in the media and via open letters about the risks posed by AI, joined by many leading AI researchers. Why is the subject suddenly in the headlines?

The idea that the quest for strong AI would ultimately succeed was long thought of as science fiction, centuries or more away. However, thanks to recent breakthroughs, many AI milestones, which experts viewed as decades away merely five years ago, have now been reached, making many experts take seriously the possibility of superintelligence in our lifetime. While some experts still guess that human-level AI is centuries away, most AI researches at the 2015 Puerto Rico Conference guessed that it would happen before 2060. Since it may take decades to complete the required safety research, it is prudent to start it now.

Because AI has the potential to become more intelligent than any human, we have no surefire way of predicting how it will behave. We can't use past technological developments as much of a basis because we've never created anything that has the ability to, wittingly or unwittingly, outsmart us. The best example of what we could face may be our own evolution. People now control the planet, not because we're the strongest, fastest or biggest, but because we're the smartest. If we're no longer the smartest, are we assured to remain in control?

FLI's position is that our civilization will flourish as long as we win the race between the growing power of technology and the wisdom with which we manage it. In the case of AI technology, FLI's position is that the best way to win that race is not to impede the former, but to accelerate the latter, by supporting AI safety research.

The Top Myths About Advanced AI

A captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are fascinating controversies where the world's leading experts disagree, such as: AI's future impact on the job market; if/when human-level AI will be developed; whether this will lead to an intelligence explosion; and whether this is something we should welcome or fear. But there are also many examples of of boring pseudo-controversies caused by people misunderstanding and talking past each other. To help ourselves focus on the interesting controversies and open questions -- and not on the misunderstandings -- let's clear up some of the most common myths.

Timeline Myths

The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainty.

One popular myth is that we know we'll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we'd have by now? AI has also been repeatedly over-hyped in the past, even by some of the founders of the field. For example, John McCarthy (who coined the term "artificial intelligence"), Marvin Minsky, Nathaniel Rochester and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers: "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College [...] An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer."

On the other hand, a popular counter-myth is that we know we won't get superhuman AI this century. Researchers have made a wide range of estimates for how far we are from superhuman AI, but we certainly can't say with great confidence that the probability is zero this century, given the dismal track record of such techno-skeptic predictions. For example, Ernest Rutherford, arguably the greatest nuclear physicist of his time, said in 1933 --- less than 24 hours before Szilard's invention of the nuclear chain reaction --- that nuclear energy was "moonshine.'' And Astronomer Royal Richard Woolley called interplanetary travel "utter bilge'' in 1956. The most extreme form of this myth is that superhuman AI will never arrive because it's physically impossible. However, physicists know that a brain consists of quarks and electrons arranged to act as a powerful computer, and that there's no law of physics preventing us from building even more intelligent quark blobs.

There have been a number of surveys asking AI researchers how many years from now they think we'll have human-level AI with at least 50% probability. All these surveys have the same conclusion: the world's leading experts disagree, so we simply don't know. For example, in such a poll of the AI researchers at the 2015 Puerto Rico AI conference, the average (median) answer was by year 2045, but some researchers guessed hundreds of years or more.

There's also a related myth that people who worry about AI think it's only a few years away. In fact, most people on record worrying about superhuman AI guess it's still at least decades away. But they argue that as long as we're not 100% sure that it won't happen this century, it's smart to start safety research now to prepare for the eventuality. Many of the safety problems associated with human-level AI are so hard that they may take decades to solve. So it's prudent to start researching them now rather than the night before some programmers drinking Red Bull decide to switch one on.

Controversy Myths

Another common misconception is that the only people harboring concerns about AI and advocating AI safety research are luddites who don't know much about AI. When Stuart Russell, author of the standard AI textbook, mentioned this during his Puerto Rico talk, the audience laughed loudly. A related misconception is that supporting AI safety research is hugely controversial. In fact, to support a modest investment in AI safety research, people don't need to be convinced that risks are high, merely non-negligible -- just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.

It may be that media have made the AI safety debate seem more controversial than it really is. After all, fear sells, and articles using out-of-context quotes to proclaim imminent doom can generate more clicks than nuanced and balanced ones. As a result, two people who only know about each other's positions from media quotes are likely to think they disagree more than they really do. For example, a techno-skeptic who only read about Bill Gates's position in a British tabloid may mistakenly think Gates believes superintelligence to be imminent. Similarly, someone in the beneficial-AI movement who knows nothing about Andrew Ng's position except his quote about overpopulation on Mars may mistakenly think he doesn't care about AI safety, whereas in fact, he does. The crux is simply that because Ng's timeline estimates are longer, he naturally tends to prioritize short-term AI challenges over long-term ones.

Myths About the Risks of Superhuman AI

Many AI researchers roll their eyes when seeing this headline: " Stephen Hawking warns that rise of robots may be disastrous for mankind." And as many have lost count of how many similar articles they've seen. Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because they've become conscious and/or evil. On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers don't worry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, and robots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car? Although this mystery of consciousness is interesting in its own right, it's irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AI does, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isn't malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans don't generally hate ants, but we're more intelligent than they are - so if we want to build a hydroelectric dam and there's an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines can't have goals. Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target. If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose. If that heat-seeking missile were chasing you, you probably wouldn't exclaim: "I'm not worried, because machines can't have goals!"

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids, because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isn't with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection - this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines can't control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, it's possible that we might also cede control.

The Interesting Controversies

Not wasting time on the above-mentioned misconceptions lets us focus on true and interesting controversies where even the experts disagree. What sort of future do you want? Should we develop lethal autonomous weapons? What would you like to happen with job automation? What career advice would you give today's kids? Do you prefer new jobs replacing the old ones, or a jobless society where everyone enjoys a life of leisure and machine-produced wealth? Further down the road, would you like us to create superintelligent life and spread it through our cosmos? Will we control intelligent machines or will they control us? Will intelligent machines replace us, coexist with us, or merge with us? What will it mean to be human in the age of artificial intelligence? What would you like it to mean, and how can we make the future be that way? Please join the conversation!

Recommended References

Many of the organizations listed on this page and their descriptions are from a list compiled by the Global Catastrophic Risk institute; we are most grateful for the efforts that they have put into compiling it. These organizations above all work on computer technology issues, though many cover other topics as well. This list is undoubtedly incomplete; please contact us to suggest additions or corrections.

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Benefits & Risks of Artificial Intelligence - Future of Life Institute

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What are the benefits and risks of artificial intelligence (AI)? Why do we need research to ensure that AI remains safe and beneficial?