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Xiaomi debuts MIUI 13 with support for the Artificial Intelligence of Things – Neowin

Xiaomi has unveiled MIUI 13 which it plans to unleash on the world in the first quarter of the new year. The firm said that the operating system will be expanded beyond smartphones and tablets to Artificial Intelligence of Things (AIoT) devices such as smart watches, speakers, and TVs. The firm has also improved its software so that it operates better under heavy usage.

According to the company, MIUI 13 improves core functions, increasing the systems fluidity by a whopping 52%. The core apps have also been optimised so they run better while the system is getting bogged down by third-party apps. Xiaomi has also developed technologies called Atomized Memory and Liquid Storage which reduce deterioration by over 5% over a 36-month period; this should help you hold onto devices for longer.

To make MIUI more interoperable with smart devices, the new update will introduce the beta of Mi Smart Hub. Commenting on the new tool, Xiaomi said:

As of Q3 2021, the number of connected devices on Xiaomis IoT platform exceeds 400 million. While leading the industry with its smart hardware portfolio, MIUI 13 will introduce the beta of Mi Smart Hub, which will help realize a more connected experience between smart devices. With Mi Smart Hub, users can find nearby devices and with a simple gesture to seamlessly share and access content such as music, display, even apps across multiple devices.

Finally, MIUI 13 brings new personalisation options through new widgets, dynamic wallpapers, and more. The global version of MIUI 13 will be delivered over-the-air beginning in Q1 2022. The first devices to get the update will be the Mi 11, Mi 11 Ultra, Mi 11i, Mi 11X Pro, Mi 11X, Xiaomi Pad 5, Redmi 10, Redmi 10 Prime, Xiaomi 11 Lite 5G NE, Xiaomi 11 Lite NE, Redmi Note 8 (2021), Xiaomi 11T Pro, Xiaomi 11T, Redmi Note 10 Pro, Redmi Note 10 Pro Max, Redmi Note 10, Mi 11 Lite 5G, Mi 11 Lite, and Redmi Note 10 JE.

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Xiaomi debuts MIUI 13 with support for the Artificial Intelligence of Things - Neowin

Artificial Intelligence: Promise, Challenges and Threats for India – Governance Now

I. What is AI?

For many, the term AI still evokes the image of either a Terminator-type robot or a disembodied, talking computer many times smarter than humans. And more often than not, the human race invariably needs to be rescued from their clutches, preferably by a bunch of easy-on-the-eyes Hollywood stars. Fortunately, the reality is considerably less dramatic and more benign. A conscious computer or super-intelligent robot is what experts refer to as Artificial General Intelligence or Singularity and we are still about 40 to 50 years away from that phenomenon.

However, narrow AI is already in our midst in our smart phones talking to us (Siri, Cortana), giving us recommendations (Amazon and Netflix), giving financial advice (Schwabs Intelligent Portfolio) and winning game shows (IBMs Watson). The common thread among all these different activities is the fact that they are replicating what a reasonably smart human being can do sensing, reasoning and acting. Put simply, AI is the ability of machines to perform functions similar to that of a human mind. It is a sub-field of computer science and is aimed at developing a set of computational technologies that are capable of doing things that are done by people.

There is no doubt that AI could be harnessed for the benefit of humanity from healthcare to climate change and humanitarian crises. But there are many risks and challenges that need to be considered and mitigated before embracing it wholly.II. Promise

Globally, AI is being adopted across sectors IT/ITES, fintech, transportation, manufacturing, retail services, healthcare, education, agriculture, law and order but its pace and impact vary widely.

The AI start-up ecosystem in India has included a few truly innovative experiments. For instance, GreyOrange designs and develops warehouse automation and technology solutions and offers products like Butler, a fleet of mobile robots for moving materials in the warehouse more efficiently, Sorter, a fully automated sortation system to sort and divert outbound packets and GreyMatter, a software platform for end-to-end intelligent order fulfilment.

Similarly, NetraDyne is a machine learning and deep learning company that focuses on computer vision and its applications to automotive and unmanned aerial systems navigation and collision avoidance. It also works on automated analysis of visual data collected by drones for verticals ranging from agriculture to site inspections.

Perfint Healthcare, a medical device technology company developing diagnostic equipment for the oncology space, has developed products like Robio EX (CT & PET-CT guided robotic positioning system), Robio EZ (robotic, mobile stand-alone system with 5 DOF for needle placement during CT Scan) and Maxio (image-guided, physician controlled stereotactic accessory device to a CT system).

Bengaluru-based start-up CropIn, uses AI to maximise per-acre value in agriculture. With its smartfarm solution it is possible to geo-tag plots of farm-land to find the actual plot area. It also helps in remote sensing and weather advisory and scheduling and monitoring farm activities for complete traceability.

The police in Punjab and Uttar Pradesh are using facial recognition systems with options like face search and text search. PAIS has a database with more than 100,000 records of criminals housed in jails across Punjab. Trinetra, a product of Gurgaon-based start-up Aqu that the UP Police is now using, has a database has approximately 5 lakh criminals.III. Challenges

Scarcity of big data: The most powerful AI machines are the ones that are trained on supervised learning. This training requires labelled data data that is organised to make it ingestible for machines to learn. However, the availability of well labelled, feature-rich local data sets is extremely limited in India. A few government bodies make some data sets available but they are limited in number and scope.

Lack of clean data: For data to be used to train AI, it needs to be recorded in consistent, machine-readable formats for accuracy and to ensure that it does not present the algorithms with unintended biases. This is a particularly big problem in India as a lot of its data is not digitised or is in unstructured format.

Data localisation: The act of storing data on any device that is physically present within the borders of a specific country where the data was generated is known as data localisation. Free flow of digital data, especially data which could impact government operations or operations in a region, is restricted by some governments for security concerns. However, some experts oppose the move as it is seen as hindering the flexibility of the internet and adding to the cost for global companies who have to maintain multiple local data centres.

Limited Technical Capacity: AI algorithms are usually very complex, often requiring thousands of calculations sometimes even more computed every second. With the development of cloud and distributed processing over the past decade, it became possible to process big data algorithms, ushering in the current age of AI-powered data analytics. However, as demand for more powerful processors increases, bottlenecks will start emerging, making it difficult for enterprises to adopt the technology.

Impact on jobs: The rapid advance in AI technology has sparked concerns about how it would impact employment. There is fear that as AI improves, it could supplant workers creating a pool of unemployable humans who cannot compete economically with machines. While there is no definitive way to predict the scale of job losses or quantify the new jobs that will be created, various studies have attempted to address this question with varying results. For instance, the study by Frey and Osborne predicted that some functions within 47 per cent of jobs will be automated. A report on the OECD countries put the share of jobs potentially lost to computerisation at nine percent. The World Economic Forums 2018 report, however, predicts that a net of 58 million new jobs would be created due to the disruption caused by AI. Most studies1 consistently predict that the least well-off will suffer the most from automation. But a new study by Brookings, published in 2019, gives a different prediction. While stating that almost all occupations can be impacted by AI, it shows through a comparative textual analysis of text of AI patents and the text of job descriptions that it would affect better paid, white-collar occupations such as market research analysts, sales managers, computer programmers and personal financial advisors more than low paying, hands-on services such as personal care, food preparation or health care.2

IV. Threats

Todays AI suffers from a number of novel unresolved vulnerabilities. These include data poisoning attacks (introducing training data that causes a learning system to make mistakes), adversarial examples (inputs designed to be misclassified by machine learning systems), and the exploitation of flaws in the design of autonomous systems goals. They demonstrate that while AI systems can exceed human performance in many ways, they can also fail in ways that a human never would.

Among the threats to political security, the key one comes from the state. The state can use automated surveillance platforms to suppress dissent.

AI can also mislead and confuse. For example, creation of highly realistic videos showing inflammatory comments by influencers that they never actually made. Automated, hyper-personalised disinformation campaigns can be launched using AI. Individuals can be targeted in swing districts with personalised messages in order to affect their voting behaviour.

In addition to these threats which have a malicious intent, there are threats which are unintentional or system related. Take, for instance, algorithmic bias. Algorithmic bias occurs when a computer system reflects the implicit values of the humans who created it. While generally the blame for bias in AI is put on the training data, the reality is bias can creep in long before the data is collected as well as many other stages of the deep learning process during the framing of the problem, collecting data, and preparing the data. For example, biases creep in during hiring decisions as Amazon found out that its internal recruiting tool was dismissing female candidates because it was trained on historical hiring decisions which favoured males over females.3

V. Towards a Responsible AI

The need for ethics and laws to regulate AI is seen as critical for it to gain the confidence of the public. If AI leads to privacy violations, bias, or malicious use, or if much of the world comes to blame it for exacerbating inequality, the potential of AI would remain unfulfilled. Establishing confidence in its abilities to do good, and at the same time, addressing misuses, will be crucial.

This has prompted many countries to take pro-active steps to frame policies to regulate AI. At the same time, tech giants such as Google, Intel, and Facebook have declared ethical standards they plan to adhere to. India has also woken up to the need to regulate AI and has taken some small steps in that direction.

We need to keep in mind that AI is a tool that can be applied with good or ill intent. Therefore, it is important to think of the ethical implications of AI while designing it. Similarly, we need to find a balance between regulations that protect citizens while also not impeding technological breakthroughs.

References:

1 Automation and Artificial Intelligence: How Machines are Affecting People and Places, Muro, Mark; Maxim, Robert; Whiton, Jacob, Brookings, January 2019; A Future that Works: Automation, Employment and Productivity, McKinsey Global Institute, 2017; Arntz, M., T. Gregory and U. Zierahn (2016), "The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis", OECD Social, Employment and Migration Working Papers, No. 189, OECD Publishing, Paris.

2 What jobs are affected by AI? Better Paid, better educated workers face the worst exposure, Mark Muro, Jacob Whiton and Robert Maxim, Metropolitan Policy Program, Brookings, Nov 2019.

3 This is how AI bias really happens and why its so hard to fix, Karen Hao, MIT Technology Review, Feb 4, 2019.

This article is based on excerpts from the book Artificial Intelligence and India (Oxford India Short Introductions), by Kaushiki Sanyal and Rajesh Chakrabarti, Oxford University Press, 2020.https://www.amazon.in/Artificial-Intelligence-India-Oxford-Introductions-ebook/dp/B08B43M548

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Artificial Intelligence: Promise, Challenges and Threats for India - Governance Now

Airtel and TCS demonstrate 5G based Remote Robotic Operations and Artificial Intelligence driven Quality Inspection for Factories of the Future -…

Bharti Airtel Limited and Tata Consultancy Services have announced the successful testing of innovative use cases from TCS Neural Manufacturing solutions suite on Airtels ultra-fast and low latency 5G network.

Airtel has been allocated 5G trial spectrum by the Department of Telecommunications for the purpose of technology validation. Airtelhas rolled out #5GforBusiness initiative and is partnering with leading technology companies to demonstrate a wide range of enterprise grade use cases using high speed & low latency networks.

Airtel and TCS joined forces to test 5G based use cases from TCSs Neural Manufacturing suite of solutions. These solutions help manufacturers build smart, cognitive factories which mimic resilient and adaptive behaviours as well as enable remote robotic operations in potentially hazardous environments like Mining, Chemical plants and Oil & Gas fields to safeguard human capital. They leverage the ultra-reliable low latency communication, enhanced bandwidth, and high device density characteristics of 5G networks and the combinatorial power of emerging technologies like Artificial Intelligence/Machine Learning, computer vision, industrial robotics and Augmented Reality/Virtual Reality to enable autonomous actions.

TCSsuccessfully tested two use cases on Airtels 5G testbed remote robotics operations, and vision-based quality inspection, demonstrating how TCS Neural Manufacturing solutions and 5G technology can transform industrial operations, and significantly boost quality, productivity and safety. The demonstration was done at Airtels 5G Lab in Manesar (Gurgaon).

Randeep Sekhon, CTO Bharti Airtel,said Airtel is spearheading 5G in India. The 5G ecosystem will open limitless possibilities for enterprises to enhance productivity and serve their customers even better with digitally enabled applications. We are delighted to work with TCS as our strategic technology partner to start testing real life 5G applications of the future. This also offers tremendous learnings across the value chain and lays a solid foundation for future application roadmap.

We believe the future of manufacturing is neural, and have been making sustained investments in research, and innovation, and in building intellectual property. We will continue to build new, differentiated capabilities into TCS Neural Manufacturing suite of solutions, harnessing the power of machine vision, machine intelligence and 5G to reimagine and redefine the way smart factories operate. Our partnership with Airtel to deploy and validate these innovative use cases on their 5G network serves as a proof point of the transformative power of these technologies, saidSusheel Vasudevan, Global Head of Manufacturing & Utilities at TCS.

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What is Artificial Intelligence (AI)? | IBM

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper(PDF, 106 KB) (link resides outside IBM), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence" (PDF, 89.8 KB) (link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach(link resides outside IBM), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Ideal approach:

Alan Turings definition would have fallen under the category of systems that act like humans.

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted in Gartners hype cycle (link resides outside IBM), product innovations like, self-driving cars and personal assistants, follow a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovations relevance and role in a market or domain. As Lex Fridman notes here (01:08:15) (link resides outside IBM) in his MIT lecture in 2019, we are at the peak of inflated expectations, approaching the trough of disillusionment.

As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment. To read more on where IBM stands within the conversation around AI ethics, read more here.

Weak AIalso called Narrow AI or Artificial Narrow Intelligence (ANI)is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. Narrow might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)also known as superintelligencewould surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Since deep learning and machine learning tend to be used interchangeably, its worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. Deep in deep learning refers to a neural network comprised of more than three layerswhich would be inclusive of the inputs and the outputcan be considered a deep learning algorithm. This is generally represented using the following diagram:

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesnt necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.

There are numerous, real-world applications of AI systems today. Below are some of the most common examples:

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:

IBM Watson gives enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore the IBM portfolio of managed services and solutions

Sign up for an IBMid and create your IBM Cloud account.

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What is Artificial Intelligence (AI)? | IBM

The impact of artificial intelligence on human society and …

Tzu Chi Med J. 2020 Oct-Dec; 32(4): 339343.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Received 2019 Dec 19; Revised 2020 Jan 30; Accepted 2020 Apr 9.

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

KEYWORDS: Artificial intelligence, Bioethics, Principles of artificial intelligence bioethics

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as a system with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [1].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the cognitive abilities of the natural intelligence of human minds [2].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [3].

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use AI are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [4].

In summary, we can see these different functions of AI [5,6]:

Automation: What makes a system or process to function automatically

Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing

Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate

Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines

Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [7]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [8,9]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delaysetc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [10,11]:

A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication

Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor

Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called M shape wealth distribution will be more obvious

New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller

The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [7,11]:

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [12].

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [9]. All those are the contribution of the technology of AI.

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [13].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [14]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [14]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethicsetc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [15]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [16].

The question isdo we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes truly ubiquitous, it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,. What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them [17]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

Lawful-respecting all applicable laws and regulations

Ethical-respecting ethical principles and values

Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [18].

Seven requirements are recommended [18]:

AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes

AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable

Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen

Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be understood and traced by human beings. In other words, operators should be able to explain the decisions their AI systems make

Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines

AI systems should be sustainable (i.e., they should be ecologically responsible) and enhance positive social change

AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or AI humanities. To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [19] as criteria for the computerized society to think about.

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient [20]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [20].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature

Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further

Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't explain its work may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required

Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [10]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI A European approach to excellence and trust: AI must serve people, and therefore, AI must always comply with people's rights. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market [21].

Nil.

There are no conflicts of interest.

12. Scoping study on the emerging use of Artificial Intelligence (AI) and robotics in social care published by Skills for Care. [Last accessed on 2019 Aug 15]. Available from: wwwskillsforcareorguk .

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