3 Ethical Considerations When Investing in AI – Manufacturing Business Technology

While Artificial Intelligence (AI) has been prevalent in industries such as the financial sector, where algorithms and decision trees have long been used in approving or denying loan requests and insurance claims, the manufacturing industry is at the beginning of its AI journey. Manufacturers have started to recognize the benefits of embedding AI into business operationsmarrying the latest techniques with existing, widely used automation systems to enhance productivity.

A recent international IFS study polling 600 respondents, working with technology including Enterprise Resource Planning (ERP), Enterprise Asset Management (EAM), and Field Service Management (FSM), found more than 90 percent of manufacturers are planning AI investments. Combined with other technologies such as 5G and the Internet of Things (IoT), AI will allow manufacturers to create new production rhythms and methodologies. Real-time communication between enterprise systems and automated equipment will enable companies to automate more challenging business models than ever before, including engineer-to-order or even custom manufacturing.

Despite the productivity, cost-savings and revenue gains, the industry is now seeing the first raft of ethical questions come to the fore. Here are the three main ethical considerations companies must weigh-up when making AI investments.

At first, AI in manufacturing may conjure up visions of fully automated smart factories and warehouses, but the recent pandemic highlighted how AI can play a strategic role in the back-office, mapping different operational scenarios and aiding recovery planning from a finance standpoint. Scenario planning will become increasingly important. This is relevant as governments around the world start lifting lockdown restrictions and businesses plan back to work strategies. Those simulations require a lot of data but will be driven by optimization, data analysis and AI.

And of course, it is still relevant to use AI/Machine Learning to forecast cash. Cash is king in business right now. So, there will be an emphasis on working out cashflows, bringing in predictive techniques and scenario planning. Businesses will start to prepare ways to know cashflow with more certainty should the next pandemic or crisis occur.

For example, earlier in the year the conversation centered on the just-in-time scenarios, but now the focus is firmly on what-if planning at the macro supply chain level:

Another example is how you can use a Machine Learning service and internal knowledge base to facilitate Intelligent Process Automation allowing recommendations and predictions to be incorporated into business workflows, as well as AI-driven feedback on how business processes themselves can be improved or automated.

The closure of manufacturing organizations and reduction in operations due to depleting workforces highlight AI technology in the front-office isnt perhaps as readily available as desired, and that progress needs to be made before it can truly provide a level of operational support similar to humans.

Optimists suggest AI may replace some types of labor, with efficiency gains outweighing transition costs. They believe the technology will come to market at first as a guide-on-the-side for human workers, helping them make better decisions and enhancing their productivity, while having the potential to upskill existing employees and increase employment in business functions or industries that are not in direct competition with AI.

Indeed, recent IFS research points to an encouraging future for a harmonized AI and human workforce in manufacturing. The IFS AI study revealed that respondents saw AI as a route to create, rather than cull, jobs. Around 45 percent of respondents stated they expect AI to increase headcount, while 24 percent believe it wont impact workforce figures.

The pandemic has demonstrated AI hasnt developed enough to help manufacturers maintain digital-only operations during unforeseen circumstances, and decision makers will be hoping it can play a greater role to mitigate extreme situations in the future.

It is easy for organizations to say they are digitally transforming. They have bought into the buzzwords, read the research, consulted the analysts, and seen the figures about the potential cost savings and revenue growth.

But digital transformation is no small change. It is a complete shift in how you select, implement and leverage technology, and it occurs company-wide. A critical first step to successful digital transformation is to ensure that you have the appropriate stakeholders involved from the very beginning. This means manufacturing executives must be transparent when assessing and communicating the productivity and profitability gains of AI against the cost of transformative business changes to significantly increase margin.

When businesses first invested in IT, they had to invent new metrics that were tied to benefits like faster process completion or inventory turns and higher order completion rates. But manufacturing is a complex territory. A combination of entrenched processes, stretched supply chains, depreciating assets and growing global pressures makes planning for improved outcomes alongside day-to-day requirements a challenging prospect. Executives and their software vendors must go through a rigorous and careful process to identify earned value opportunities.

Implementing new business strategies will require capital spending and investments in process change, which will need to be sold to stakeholders. As such, executives must avoid the temptation of overpromising. They must distinguish between the incremental results they can expect from implementing AI in a narrow or defined process as opposed to a systemic approach across their organization.

There can be intended or unintended consequences of AI-based outcomes, but organizations and decision makers must understand they will be held responsible for both. We have to look no further than tragedies from self-driving car accidents and the subsequent struggles that followed as liability is assigned not on the basis of the algorithm or the inputs to AI, but ultimately the underlying motivations and decisions made by humans.

Executives therefore cannot afford to underestimate the liability risks AI presents. This applies in terms of whether the algorithm aligns with or accounts for the true outcomes of the organization, and the impact on its employees, vendors, customers and society as a whole. This is all while preventing manipulation of the algorithm or data feeding into AI that would impact decisions in ways that are unethical, either intentionally or unintentionally.

Margot Kaminski, associate professor at the University of Colorado Law School, raised the issue of automation biasthe notion that humans trust decisions made by machines more than decisions made by other humans. She argues the problem with this mindset is that when people use AI to facilitate decisions or make decisions, they are relying on a tool constructed by other humans, but often they do not have the technical capacity, or practical capacity, to determine if they should be relying on those tools in the first place.

This is where explainable AI will be criticalAI which creates an audit path so both before and after the fact, there is a clear representation of the outcomes the algorithm is designed to achieve and the nature of the data sources it is working form. Kaminski asserts explainable AI decisions must be rigorously documented to satisfy different stakeholdersfrom attorneys to data scientists through to middle managers.

Manufacturers will soon move past the point of trying to duplicate human intelligence using machines, and towards a world where machines behave in ways that the human mind is just not capable. While this will reduce production costs and increase the value organizations are able to return, this shift will also change the way people contribute to the industry, the role of labor, and civil liability law.

There will be ethical challenges to overcome, but those organizations who strike the right balance between embracing AI and being realistic about its potential benefits alongside keeping workers happy will usurp and take over. Will you be one of them?

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3 Ethical Considerations When Investing in AI - Manufacturing Business Technology

Artificial Intelligence in Business: The New Normal in Testing Times – Analytics Insight

The COVID 19 situation, has rendered the industry into an unprecedented situation. Businesses across the globe are now resorting to plan out new strategies to keep the operations going, to meet clients demands.

Work-from-Home is the new normal for both the employees and the employers to function in a mitigated manner. Twitter on their tweet had suggested their employees, to function through Work-from-Home, forever, if they want to. This new trend can be easily surmised as being effective for a while to manage operations, but cannot be ruled out as the necessary solution, for satisfying the customers and clients in the long run.

Companies need to employ ethically approved ideas and strategies that would assure employees, clients, and customers, without breaching the data.

With the present situation, where social distancing is a must, classroom training cannot be ruled out as the plausible solution for training employees. Thats where Virtual Reality comes into play.

Virtual Reality (VR), which was earlier ruled out to be used in the gaming interface has now the potential to become the face of the industrial enterprise. Areportby PwC states that VR and Augmented Reality has the potential to surge US$1.5trillion globally by the year 2030. Another report by PwC states that VR can train employees four times faster than classroom training. Individuals trained through VR has confidence 2.5 times more than those who are trained through classroom programs or e-courses, and 2.3 times more emotionally inclined towards the content that they are working on. Employees trained using VR are also 1.5 times more focused than that through classroom programs and e-courses.

The only drawback in using PwC will be in its cost-effectiveness as it is 47 percent costlier than classroom courses.

Ever since its evolution, one of the major concerns regarding AI amongst clients, customers, and employees is the breach of ethical AI practices. A report byCapgemini Research Institutestates that amongst 62% of customers who were surveyed would like to place their trust in an organization that practices AI ethically.

For any organization to keep its business and employees safe during the time of crisis, the development of an ethically viable AI is a must. This can only be achieved by practicing ethical use of AI applications, informing and educating the customers about the practices of AI.

Areportby PwC, states that planning out a new strategy in both data and technology, evaluating the ethical flaws associated with the existing data, and only collecting the required amount of data, would help in maintaining trust amongst both the customers and employees.

Given the present situation, sales executives are facing a daunting task of maintaining their operations. However, the use of AI can easily redeem this time consuming and laborious task. Withthe use of an AI algorithm, the sales executive or manager can identify the higher probable inclination of the client towards a particular service. The AI algorithm would also, help in offering a new product according to the pre-requisite preferences of the client.

In the time of crisis, new solutions must be thought about for repurposing business. PwC states that this can be achieved by repurposing business assets, forming a new business partnership, rapid innovation, and testing and learning.

This will not only help in building trust amongst employees but also build resilience within the organization, for the future endeavor.

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Artificial Intelligence in Business: The New Normal in Testing Times - Analytics Insight

Evil AI: These are the 20 most dangerous crimes that artificial intelligence will create – ZDNet

From targeted phishing campaigns to new stalking methods: there are plenty of ways that artificial intelligence could be used to cause harm if it fell into the wrong hands. A team of researchers decided to rank the potential criminal applications that AI will have in the next 15 years, starting with those we should worry the most about. At the top of the list of most serious threats? Deepfakes.

By using fake audio and video to impersonate another person, the technology can cause various types of harms, said the researchers. The threats range from discrediting public figures to influence public opinion, to extorting funds by impersonating someone's child or relatives over a video call.

The ranking was put together after scientists from University College London (UCL) compiled a list of 20 AI-enabled crimes based on academic papers, news and popular culture, and got a few dozen experts to discuss the severity of each threat during a two-day seminar.

The participants were asked to rank the list in order of concern, based on four criteria: the harm it could cause, the potential for criminal profit or gain, how easy the crime could be carried out and how difficult it would be to stop.

Although deepfakes might in principle sound less worrying that, say, killer robots, the technology is capable of causing a lot of harm very easily, and is hard to detect and stop. Relative to other AI-enabled tools, therefore, the experts established that deepfakes are the most serious threat out there.

There are already examples of fake content undermining democracy in some countries: in the US, for example, a doctored video of House Speaker Nancy Pelosi in which she appeared inebriated picked up more than 2.5 million views on Facebook last year.

UK organization Future Advocacy similarly used AI to create a fake video during the 2019 general election, which showed Boris Johnson and Jeremy Corbyn endorsing each other for prime minister. Although the video was not malicious, it stressed the potential of deepfakes to impact national politics.

The UCL researchers said that as deepfakes get more sophisticated and credible, they will only get harder to defeat. While some algorithms are already successfully identifying deepfakes online, there are many uncontrolled routes for modified material to spread. Eventually, warned the researchers, this will lead to widespread distrust of audio and visual content.

Five other applications of AI also made it to the "highly worrying" category. With autonomous cars just around the corner, driverless vehicles were identified as a realistic delivery mechanism for explosives, or even as weapons of terror in their own right. Equally achievable is the use of AI to author fake news: the technology already exists, stressed the report, and the societal impact of propaganda shouldn't be under-estimated.

Also keeping up AI experts at night are applications that will be so pervasive that defeating them will be near-impossible. This is the case of AI-infused phishing attacks, for example, which will perpetrated via crafty messages that will be impossible to distinguish from reality. Another example is large-scale blackmail, enabled by AI's potential to harvest large personal datasets and information from social media.

Finally, participants pointed to the multiplication of AI systems used for key applications like public safety or financial transactions and to the many opportunities for attack they represent. Disrupting such AI-controlled systems, for criminal or terror motives, could result in widespread power failures, breakdown of food logistics, and overall country-wide chaos.

UCL's researchers labelled some of the other crimes that could be perpetrated with the help of AI as only "moderately concerning". Among them feature the sale of fraudulent "snake-oil" AI for popular services like lie detection or security screening; or increasingly sophisticated learning-based cyber-attacks, in which AI could easily probe the weaknesses of many systems.

Several of the crimes cited could arguably be seen as a reason for high concern. For example, the misuse of military robots, or the deliberate manipulation of databases to introduce bias, were both cited as only moderately worrying.

The researchers argued, however, that such applications seem too difficult to push at scale in current times, or could be easily managed, and therefore do not represent as imminent a danger.

At the bottom of the threat hierarchy, the researchers listed some "low-concern" applications the petty crime of AI, if you may. On top of fake reviews or fake art, the report also mentions burglar bots, small devices that could sneak into homes through letterboxes or cat flaps to relay information to a third-party.

Burglar bots might sound creepy, but they could be easily defeated in fact, they could pretty much be stopped by a letterbox cage and they couldn't scale. As such, the researchers don't expect that they will cause huge trouble anytime soon; the real danger, according to the report, rather lies in criminal applications of AI that could be easily shared and repeated once they are developed.

UCL's Matthew Caldwell, first author of the report, said: "Unlike many traditional crimes, crimes in the digital realm can be easily shared, repeated, and even sold, allowing criminal techniques to be marketed and for crime to be provided as a service. This means criminals may be able to outsource the more challenging aspects of their AI-based crime."

The marketisation of AI-enabled crime, therefore, might be just around the corner. Caldwell and his team anticipate the advent of "Crime as a Service" (CaaS), which would work hand-in-hand with Denial of Service (DoS) attacks.

And some of these crimes will have deeper ramifications than others. Here is the complete ranking of AI-enabled crimes to look out for, as compiled by UCL's researchers:

AI-enabled crimes of high concern:

Deepfakes; driverless vehicles as a weapon; tailored phishing; disrupting AI-controlled systems; large-scale blackmail; AI-authored fake news.

AI-enabled crimes of moderate concern:

Misuse of military robots; snake oil; data poisoning; learning-based cyber-attacks; autonomous attack drones; denial of access to online activities; tricking face recognition; manipulating financial or stock markets.

AI-enabled crimes of low concern:

Burglar bots; evading AI detection; AI-authored fake reviews; AI-assisted stalking; forgery of content such as art or music.

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Evil AI: These are the 20 most dangerous crimes that artificial intelligence will create - ZDNet

Artificial Intelligence and Its Partners – Modern Diplomacy

Digitalization and the development of artificial intelligence (AI) bring up many philosophical and ethical questions about the role of man and robot in the nascent social and economic order. How real is the threat of an AI dictatorship? Why do we need to tackle AI ethics today? Does AI provide breakthrough solutions? We ask these and other questions in our interview with Maxim Fedorov, Vice-President for Artificial Intelligence and Mathematical Modelling at Skoltech.

On 13 July, Maxim Fedorov chaired the inaugural Trustworthy AI online conference on AI transparency, robustness and sustainability hosted by Skoltech.

Maxim, do you think humanity already needs to start working out a new philosophical model for existing in a digital world whose development is determined by artificial intelligence (AI) technologies?

The fundamental difference between todays technologies and those of the past is that they hold up a mirror of sorts to society. Looking into this mirror, we need to answer a number of philosophical questions. In times of industrialization and production automation, the human being was a productive force. Today, people are no longer needed in the production of the technologies they use. For example, innovative Japanese automobile assembly plants barely have any people at the floors, with all the work done by robots. The manufacturing process looks something like this: a driverless robot train carrying component parts enters the assembly floor, and a finished car comes out. This is called discrete manufacturing the assembly of a finite set of elements in a sequence, a task which robots manage quite efficiently. The human being is gradually being ousted from the traditional economic structure, as automated manufacturing facilities generally need only a limited number of human specialists. So why do we need people in manufacturing at all? In the past, we could justify our existence by the need to earn money or consume, or to create jobs for others, but now this is no longer necessary. Digitalization has made technologies a global force, and everyone faces philosophical questions about their personal significance and role in the modern world questions we should be answering today, and not in ten years when it will be too late.

At the last World Economic Forum in Davos, there was a lot of discussion about the threat of the digital dictatorship of AI. How real is that threat in the foreseeable future?

There is no evil inherent in AI. Technologies themselves are ethically neutral. It is people who decide whether to use them for good or evil.

Speaking of an AI dictatorship is misleading. In reality, technologies have no subjectivity, no I. Artificial intelligence is basically a structured piece of code and hardware. Digital technologies are just a tool. There is nothing mystical about them either.

My view as a specialist in the field is that AI is currently a branch of information and communications technology (ICT). Moreover, AI does not even live in an individual computer. For a person from the industry, AI is a whole stack of technologies that are combined to form what is called weak AI.

We inflate the bubble of AIs importance and erroneously impart this technology stack with subjectivity. In large part, this is done by journalists, people without a technical education. They discuss an entity that does not actually exist, giving rise to the popular meme of an AI that is alternately the Terminator or a benevolent super-being. This is all fairy tales. In reality, we have a set of technological solutions for building effective systems that allow decisions to be made quickly based on big data.

Various high-level committees are discussing strong AI, which will not appear for another 50 to 100 years (if at all). The problem is that when we talk about threats that do not exist and will not exist in the near future, we are missing some real threats. We need to understand what AI is and develop a clear code of ethical norms and rules to secure value while avoiding harm.

Sensationalizing threats is a trend in modern society. We take a problem that feeds peoples imaginations and start blowing it up. For example, we are currently destroying the economy around the world under the pretext of fighting the coronavirus. What we are forgetting is that the economy has a direct influence on life expectancy, which means that we are robbing many people of years of life. Making decisions based on emotion leads to dangerous excesses.

As the philosopher Yuval Noah Harari has said, millions of people today trust the algorithms of Google, Netflix, Amazon and Alibaba to dictate to them what they should read, watch and buy. People are losing control over their lives, and that is scary.

Yes, there is the danger that human consciousness may be robotized and lose its creativity. Many of the things we do today are influenced by algorithms. For example, drivers listen to their sat navs rather than relying on their own judgment, even if the route suggested is not the best one. When we receive a message, we feel compelled to respond. We have become more algorithmic. But it is ultimately the creator of the algorithm, not the algorithm itself, that dictates our rules and desires.

There is still no global document to regulate behaviour in cyberspace. Should humanity perhaps agree on universal rules and norms for cyberspace first before taking on ethical issues in the field of AI?

I would say that the issue of ethical norms is primary. After we have these norms, we can translate them into appropriate behaviour in cyberspace. With the spread of the internet, digital technologies (of which AI is part) are entering every sphere of life, and that has led us to the need to create a global document regulating the ethics of AI.

But AI is a component part of information and communications technologies (ICT). Maybe we should not create a separate track for AI ethics but join it with the international information security (IIS) track? Especially since IIS issues are being actively discussed at the United Nations, where Russia is a key player.

There is some justification for making AI ethics a separate track, because, although information security and AI are overlapping concepts, they are not embedded in one another. However, I agree that we can have a separate track for information technology and then break it down into sub-tracks where AI would stand alongside other technologies. It is a largely ontological problem and, as with most problems of this kind, finding the optimal solution is no trivial matter.

You are a member of the international expert group under UNESCO that is drafting the first global recommendation on the ethics of AI. Are there any discrepancies in how AI ethics are understood internationally?

The group has its share of heated discussions, and members often promote opposing views. For example, one of the topics is the subjectivity and objectivity of AI. During the discussion, a group of states clearly emerged that promotes the idea of subjectivity and is trying to introduce the concept of AI as a quasi-member of society. In other words, attempts are being made to imbue robots with rights. This is a dangerous trend that may lead to a sort of technofascism, inhumanity of such a scale that all previous atrocities in the history of our civilization would pale in comparison.

Could it be that, by promoting the concept of robot subjectivity, the parties involved are trying to avoid responsibility?

Absolutely. A number of issues arise here. First, there is an obvious asymmetry of responsibility. Let us give the computer with rights, and if its errors lead to damage, we will punish it by pulling the plug or formatting the hard drive. In other words, the responsibility is placed on the machine and not its creator. The creator gets the profit, and any damage caused is someone elses problem. Second, as soon as we give AI rights, the issues we are facing today with regard to minorities will seem trivial. It will lead to the thought that we should not hurt AI but rather educate it (I am not joking: such statements are already being made at high-level conferences). We will see a sort of juvenile justice for AI. Only it will be far more terrifying. Robots will defend robot rights. For example, a drone may come and burn your apartment down to protect another drone. We will have a techno-racist regime, but one that is controlled by a group of people. This way, humanity will drive itself into a losing position without having the smallest idea of how to escape it.

Thankfully, we have managed to remove any inserts relating to quasi-members of society from the groups agenda.

We chose the right time to create the Committee for Artificial Intelligence under the Commission of the Russian Federation for UNESCO, as it helped to define the main focus areas for our working group. We are happy that not all countries support the notion of the subjectivity of AI in fact, most oppose it.

What other controversial issues have arisen in the working groups discussions?

We have discussed the blurred border between AI and people. I think this border should be defined very clearly. Then we came to the topic of human-AI relationships, a term which implies the whole range of relationships possible between people. We suggested that relationships be changed to interactions, which met opposition from some of our foreign colleagues, but in the end, we managed to sort it out.

Seeing how advanced sex dolls have become, the next step for some countries would be to legalize marriage with them, and then it would not be long before people starting asking for church weddings. If we do not prohibit all of this at an early stage, these ideas may spread uncontrollably. This approach is backed by big money, the interests of corporations and a different system of values and culture. The proponents of such ideas include a number of Asian countries with a tradition of humanizing inanimate objects. Japan, for example, has a tradition of worshipping mountain, tree and home spirits. On the one hand, this instills respect for the environment, and I agree that, being a part of the planet, part of nature, humans need to live in harmony with it. But still, a person is a person, and a tree is a tree, and they have different rights.

Is the Russian approach to AI ethics special in any way?

We were the only country to state clearly that decisions on AI ethics should be based on a scientific approach. Unfortunately, most representatives of other countries rely not on research, but on their own (often subjective) opinion, so discussions in the working group often devolve to the lay level, despite the fact that the members are highly qualified individuals.

I think these issues need to be thoroughly researched. Decisions on this level should be based on strict logic, models and experiments. We have tremendous computing power, an abundance of software for scenario modelling, and we can model millions of scenarios at a low cost. Only after that should we draw conclusions and make decisions.

How realistic is the fight against the subjectification of AI if big money is at stake? Does Russia have any allies?

Everyone is responsible for their own part. Our task right now is to engage in discussions systematically. Russia has allies with matching views on different aspects of the problem. And common sense still prevails. The egocentric approach we see in a number of countries that is currently being promoted, this kind of self-absorption, actually plays into our hands here. Most states are afraid that humans will cease to be the centre of the universe, ceding our crown to a robot or a computer. This has allowed the human-centred approach to prevail so far.

If the expert group succeeds at drafting recommendations, should we expect some sort of international regulation on AI in the near future?

If we are talking about technical standards, they are already being actively developed at the International Organization for Standardization (ISO), where we have been involved with Technical Committee 164 Artificial Intelligence (TC 164) in the development of a number of standards on various aspects of AI. So, in terms of technical regulation, we have the ISO and a whole range of documents. We should also mention the Institute of Electrical and Electronics Engineers (IEEE) and its report on Ethically Aligned Design. I believe this document is the first full-fledged technical guide on the ethics of autonomous and intelligent systems, which includes AI. The corresponding technical standards are currently being developed.

As for the United Nations, I should note the Beijing Consensus on Artificial Intelligence and Education that was adopted by UNESCO last year. I believe that work on developing the relevant standards will start next year.

So the recommendations will become the basis for regulatory standards?

Exactly. This is the correct way to do it. I should also say that it is important to get involved at an early stage. This way, for instance, we can refer to the Beijing agreements in the future. It is important to make sure that AI subjectivity does not appear in the UNESCO document, so that it does not become a reference point for this approach.

Let us move from ethics to technological achievements. What recent developments in the field can be called breakthroughs?

We havent seen any qualitative breakthroughs in the field yet. Image recognition, orientation, navigation, transport, better sensors (which are essentially the sensory organs for robots) these are the achievements that we have so far. In order to make a qualitative leap, we need a different approach.

Take the chemical universe, for example. We have researched approximately 100 million chemical compounds. Perhaps tens of thousands of these have been studied in great depth. And the total number of possible compounds is 1060, which is more than the number of atoms in the Universe. This chemical universe could hold cures for every disease known to humankind or some radically new, super-strong or super-light materials. There is a multitude of organisms on our planet (such as the sea urchin) with substances in their bodies that could, in theory, cure many human diseases or boost immunity. But we do not have the technology to synthesize many of them. And, of course, we cannot harvest all the sea urchins in the sea, dry them and make an extract for our pills. But big data and modelling can bring about a breakthrough in this field. Artificial intelligence can be our navigator in this chemical universe. Any reasonable breakthrough in this area will multiply our income exponentially. Imagine an AIDS or cancer medicine without any side effects, or new materials for the energy industry, new types of solar panels, etc. These are the kind of things that can change our world.

How is Russia positioned on the AI technology market? Is there any chance of competing with the United States or China?

We see people from Russia working in the developer teams of most big Asian, American and European companies. A famous example is Sergey Brin, co-founder and developer of Google. Russia continues to be a donor of human resources in this respect. It is both reassuring and disappointing because we want our talented guys to develop technology at home. Given the right circumstances, Yandex could have dominated Google.

As regards domestic achievements, the situation is somewhat controversial. Moscow today is comparable to San Francisco in terms of the number, quality and density of AI development projects. This is why many specialists choose to stay in Moscow. You can find a rewarding job, interesting challenges and a well-developed expert community.

In the regions, however, there is a concerning lack of funds, education and infrastructure for technological and scientific development. All three of our largest supercomputers are in Moscow. Our leaders in this area are the Russian Academy of Sciences, Moscow State University and Moscow Institute of Physics and Technology organizations with a long history in the sciences, rich traditions, a sizeable staff and ample funding. There are also some pioneers who have got off the ground quickly, such as Skoltech, and surpassed their global competitors in many respects. We recently compared Skoltech with a leading AI research centre in the United Kingdom and discovered that our institution actually leads in terms of publications and grants. This means that we can and should do world-class science in Russia, but we need to overcome regional development disparities.

Russia has the opportunity to take its rightful place in the world of high technology, but our strategy should be to overtake without catching up. If you look at our history, you will see that whenever we have tried to catch up with the West or the East, we have lost. Our imitations turned out wrong, were laughable and led to all sorts of mishaps. On the other hand, whenever we have taken a step back and synthesized different approaches, Asian or Western, without blindly copying them, we have achieved tremendous success.

We need to make a sober assessment of what is happening in the East and in the West and what corresponds to our needs. Russia has many unique challenges of its own: managing its territory, developing the resource industries and continuous production. If we are able to solve these tasks, then later we can scale up our technological solutions to the rest of the world, and Russian technology will be bought at a good price. We need to go down our own track, not one that is laid down according to someone elses standards, and go on our way while being aware of what is going on around us. Not pushing back, not isolating, but synthesizing.

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Artificial Intelligence and Its Partners - Modern Diplomacy

VIEW: Digitisation in pathology and the promise of artificial intelligence – CNBCTV18

The COVID-19 pandemic has had a profound impact across industries and healthcare in particularevery aspect of it is undergoing changefrom diagnosis to treatment and through the entire continuum of care. This has also created an urgency in the healthcare industry, to look for innovative solutions and a boost to the faster, efficient application of technologies like Artificial Intelligence (AI) and Deep Learning. Pathology is one area which stands to greatly benefit from these applications.

Pathologists today spend a significant amount of time observing tissue samples under a microscope and they are facing resource shortages, growing complexity of requests, and workflow inefficiencies with the growing burden of diseases. Their work underpins every aspect of patient care, from diagnostic testing and treatment advice to the use of cutting-edge genetic technologies. They also have to work together in a multidisciplinary team of doctors, scientists and healthcare professionals to diagnose, treat and prevent illness. With increasing emphasis on sub-specialisation, taking a second opinion from specialists, means shipping several glass slides across laboratories, sometimes to another country. This means reduced efficiency and delayed diagnosis and treatment. The current situation has disrupted this workflow.

Digitization in pathology

Digitization in Pathology has enabled an increase in efficiency, speed and enhanced quality of diagnosis. Recent technological advances have accelerated the adoption of digitisation in pathology, similar to the digital transformation that radiology departments have experienced over the last decade. Digital Pathology has enabled the conversion of the traditional glass slide to a digital image, which can then be viewed on a monitor, annotated, archived and shared digitally across the globe, for consultation based on organ sub-specialisation. With digitisation, a vast data set has become available, supporting new insights to pathologists, researchers, and pharmaceutical development teams.

The promise of AI

The availability of vast data is enabling the use of Artificial Intelligence methods, to further transform the diagnosis and treatment of diseases at an unprecedented pace. Human intelligence assisted with articial intelligence can provide a well-balanced view of what neither of them could do on their own. The evolution of Deep Learning neural networks and the improvement in accuracy for image pattern recognition has been staggering in the last few years. Similar to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time improving it a little to achieve more accurate outcomes.

The approach to diagnosis that incorporates multiple sources of data (e.g., pathology, radiology, clinical, molecular and lab operations) and using mathematical models to generate diagnostic inferences and presenting with clinically actionable knowledge to customers is Computational Pathology. Computational Pathology systems are able to correlate patterns across multiple inputs from the medical record, including genomics, enhancing a pathologists diagnostic capabilities, to make a more precise diagnosis. This allows Pathologists to eliminate tedious and time-consuming tasks while focusing more on interpreting data and detailing the implications for a patients diagnosis.

AI applications that can easily augment a Pathologists cognitive ability and save time are, for example, identifying the sections of greatest interest in biopsies, finding metastases in the lymph nodes of breast cancer patients, counting mitoses for cancer grading or measuring tumors point-to-point. The ultimate goal going forward is the integration of all these tools and algorithms into the existing workflow and make it seamless and more efficient.

The Challenge

However, Artificial Intelligence in Pathology is quite complex. The IT infrastructure required in terms of data storage, network bandwidth and computing power is significantly higher as compared to Radiology. Digitisation of Whole Slide Images (WSI) in pathology generate large amounts of gigapixel sized images and processing them needs high-performance computing. Training a deep learning network on a whole slide image at full resolution can be very challenging. With the increase in the processing power with the use of GPUs, there is a promise to train deep learning networks successfully, starting with training smaller regions of interest.

Another key aspect for training deep learning algorithms is the need for large amounts of labeled data. For supervised learning, a ground truth must first be included in the dataset to provide appropriate diagnostic context and this will be time-consuming. Obtaining adequately labeled data by experts is the key.

Digitisation in pathology supported by appropriate IT infrastructure is enabling Pathologists to work remotely without the need to wait for glass slides to be delivered and maintaining social distancing norms. The promise of Artificial Intelligence will only further accelerate the seamless integration of algorithms into the existing workflow. These unprecedented times have raised many challenges, but are also providing us a chance to accelerate the application of AI and in turn to achieve the quadruple aim: enhancing the patient experience, improving health outcomes, lowering the cost of care, and improving the work-life of care providers.

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VIEW: Digitisation in pathology and the promise of artificial intelligence - CNBCTV18

AI can speed up the search for new treatments here’s how – World Economic Forum

The sudden appearance and rapid spread of COVID-19 took governments and society by surprise. As they dusted off pandemic response plans and geared up to fight the virus, it became clear that we needed to turbo-charge R&D efforts and find better ways to hunt down promising treatments for emerging diseases.

Artificial intelligence (AI) has proven a powerful tool in this fight.

In a pandemic, speed is of the essence. Although scientists managed to sequence the genetic code of the new coronavirus and produce diagnostic tests in record time, developing drugs and vaccines against the virus remains a long haul.

AI has the power to accelerate the process by reasoning across all available biomedical data and information in a systematic search for existing approved medicines a vital step in helping patients while the world waits for a vaccine.

Machines excel in handling data in fast-changing circumstances, which means machine learning systems can be harnessed to work as tireless and unbiased super-researchers.

This is not just theory. In late January, using its proprietary platform of AI models and algorithms to search through the scientific literature, researchers at BenevolentAI in London identified an established, once-daily arthritis pill as a potential treatment for COVID-19. The findings were published in two papers in The Lancet and The Lancet Infectious Diseases, in line with our commitment under the Wellcome Trust pledge to share our coronavirus-related research rapidly and openly.

BenevolentAI's COVID-19 timeline

Image: BenevolentAI

The discovery followed a computer-driven hunt for drug candidates with both antiviral and anti-inflammatory properties, since in severe cases of COVID-19 it is the bodys overactive immune response that can cause significant and sometimes fatal damage.

The drug, baricitinib, is currently marketed by Eli Lilly to treat rheumatoid arthritis. Now, thanks to AI, it is being tested against COVID-19 in a major randomised-controlled trial in collaboration with the U.S. National Institute for Allergies and Infectious Diseases (NIAID) in combination with remdesivir, an antiviral drug from Gilead Sciences that recently won emergency-use approval for COVID-19. Eli Lilly has now commenced its own independent trial of baricitinib as a therapy for COVID-19 in South America, Europe and Asia.

The BenevolentAI knowledge graph found that baricitinib might help treat COVID-19.

Image: BenevolentAI

The system used to identify baricitinib was not actually set up to find new uses of existing medicines, but rather to discover and develop new drugs a sign of the potential for AI to uncover novel insights and relationships across an unlimited number of biological entities. In a crisis like COVID-19, it clearly makes sense to hunt through already approved drugs that can be ready for large-scale clinical trials until vaccines are approved and readily available in the global supply chain.

BenevolentAIs vision is to dramatically improve pharmaceutical R&D productivity across the board and to expand the drug discovery universe by making predictions in novel areas of biology. Currently, around half of late-stage clinical trials fail due to ineffective drug targets, resulting in only 15% of drugs advancing from mid-stage Phase 2 testing to approval.

Using a knowledge graph composed of chemical, biological and medical research and information, the companys AI machine learning models and algorithms can identify potential drug leads currently unknown in medical science and far faster than humans. While such systems will never replace scientists and clinicians, they can save both time and money. And the agnostic approach adopted by machine learning means such platforms can generate leads that may have been overlooked by traditional research.

The endeavour has already led to an in-house project on amyotrophic lateral sclerosis (ALS), ulcerative colitis, atopic dermatitis and programmes with partners on progressive kidney and lung diseases, as well as hard-to-treat cancers like glioblastoma.

The ability of machines to solve complex biological puzzles more rapidly than human experts has prompted increased investment in AI drug discovery by a growing number of large pharmaceutical companies.

And AI is also being harnessed in other areas of medicine, such as the analysis of medical images. This encompasses long-standing work on cancer scans and much more recent efforts to use computer power to identify COVID-19 from chest X-rays, including the open-access COVID-Net neural network.

The application of precision medicine to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseasesand disease in general.

But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.

The World Economic Forums Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access across borders to sensitive health data for the treatment and diagnosis of rare diseases.

The data will be shared via a federated data system: a decentralized approach that allows different institutions to access each others data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.

The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).

Clearly, COVID-19 has been a wake-up call for the world. It seems this outbreak may be part of an increasingly frequent pattern of epidemics, fuelled by our hyper-connected modern world. As a result, medical experts are braced for more previously unknown Disease X threats in the years ahead as viruses jump from animals to humans and jet around the world.

Technology has helped create a world in which pathogens like COVID-19, SARS and Zika can spread. But technology, in the form of AI, can also provide us with the weapons to fight back.

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AI can speed up the search for new treatments here's how - World Economic Forum

Artificial Intelligence Could Help Stem the Second Wave of COVID-19 – Banyan Hill Publishing

The bodies piled up by the dozens.

In New York, the first wave of COVID-19 cases peaked in March and April.

Hospitals and health care providers felt a huge strain. Their intensive care units couldnt handle all the new patients.

Thousands of people ended up on machines to help them breathe. Thousands more people died.

Theres no question about it: This pandemic has changed the way we live. And to fight the anticipated second wave of cases as schools and businesses reopen, itll take a life-changing solution.

Thats where artificial intelligence (AI) comes in.

Mount Sinais hospital network was stretched to the limit during New Yorks first peak. Between April 1 and April 15, it treated 2,874 patients. In just those two weeks, over three-quarters of its total bed capacity was tied up.

And it doesnt want a repeat of that pressure. Thats why it came up with the new Mount Sinai COVID Informatics Center.

The center will use AI and machine learning to help diagnose and treat COVID-19 more quickly and efficiently.

Mount Sinais record systems already collect detailed coronavirus patient data. And the centers algorithms can use these numbers to optimize hospital resources.

For example, they can predict which patients will need ventilators most and which are recovering and close to being discharged.

Theres even a new AI-based diagnostic tool in the works. The tool can accurately diagnose COVID-19 in 84% of cases. Thats higher than the rate Mount Sinai doctors could diagnose without it.

Of course, the centers projects are still works in progress. But its clear that standard testing measures wont cut it anymore.

It highlights our changing times: We adapt or die.

This same new normal applies to the stock market, too. Consumer habits are shifting. Businesses that fail to cater to these new needs can easily go bankrupt. Household names like JCPenney and Pier 1 are closing hundreds of stores after failing to adapt.

So, its simple. Companies that want to survive and thrive during this pandemic and beyond need to adapt. And just like Mount Sinai, many of them are turning to AI to do so.

But this shouldnt be a surprise. AI is already everywhere.

If you own a smartphone and use voice commands, youre probably using AI every day. Its also powering self-driving cars. And it transformed health care diagnosis systems even before COVID-19.

Now, were still uncovering all the needs AI can fill in our society. But one thing is certain: AI will unleash a massive windfall for Main Street investors like you.

Financial services firm PwC estimates that AI technology will add $15.7 trillion to the global economy by 2030. With added demand to adapt during this pandemic, that figure could surge even more.

This AI trend will change our world and our lives. And the companies at the forefront of it could easily hand investors triple-digit profits in three to four years.

Ive identified four of the businesses providing customers with critical AI products and services. They have strong balance sheets and growing free cash flow. And theyre not reliant on foot traffic to make money.

Click here to take a look at the details, plus so much more.

Regards,

The Winning Investor Daily Team

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Artificial Intelligence Could Help Stem the Second Wave of COVID-19 - Banyan Hill Publishing

Artificial Intelligence in Medical Imaging Market Size Restraint,Bussiness Oppertunity With Leading Player | Qventus Inc, IDx Technologies Inc., K…

Global Artificial Intelligence in Medical Imaging Market of which artificial intelligence in medical imaging is a part of is expected to rise from its initial estimated value of USD 21.48 billion in 2018 to a projected value of USD 264.85 billion by 2026, registering a CAGR of 36.89% in the forecast period of 2019-2026.

Medical imaging can be described as the diagnostic procedure that involves the creation of visual aids and image representations of the human body, and involves the monitoring of performance and functioning of the organs of the human body. With the integration of artificial intelligence (AI) in healthcare and medical imaging, there is a change in the way the diagnostics and the entire procedure is carried out. The AI assists the surgeons in carrying out the image capturing process and how to diagnose these images for the conclusion and personalized treatment in respect to every individual and patient. Artificial intelligence mainly consists of two types, robots and machine learning.

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Global artificial intelligence in medical imaging market is expected to grow with a significant CAGR in the forecast period of 2019-2026. The report contains data from the base year of 2018, and the historic year of 2017. This rise in market value can be attributed to better visualization and conclusive results in diagnostic procedure with the application of artificial intelligence in medical imaging.

Market Drivers:

Market Restraints:

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Key Developments in the Market:

Global artificial intelligence in medical imaging market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of artificial intelligence in medical imaging market for global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Major competitors currently present in the market are BenevolentAI, OrCam, Babylon, Freenome Inc., Clarify Health Solutions, BioXcel Therapeutics, Ada Health GmbH, GNS Healthcare, Zebra Medical Vision Inc., Qventus Inc, IDx Technologies Inc., K Health, Prognos, Medopad Ltd., Viz.ai Inc., Voxel Technology, Renalytix AI plc, Beijing Pushing Technology Co. Ltd., PAIGE, mPulse Mobile, Suki AI Inc., BERG LLC, Zealth Inc., OWKIN INC., and Your.MD.

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How Artificial Intelligence is Helping to Fight against Coronavirus in India? – Analytics Insight

With the number of COVID-19 cases crossing 18 million mark, the healthcare system across the globe has suffered a major blow against the management of COVID-19. In India, COVID-19 has proved challenging initially for identifying the COVID patients and diagnosing the disease. However, the use of Artificial Intelligence (AI) over the past few years, has rendered the Healthline workers and the government for solutions, to stall this roadblock.

Artificial Intelligence uses the technology of powerful algorithms which then processes the data, thus identifying patterns. Thus, for any Artificial Intelligence to be successful, big data is necessary.

Across the globe, as Polymerase Chain Reaction (PCR) is expensive and time-consuming, Chest X-rays are now used as a standardized procedure for the diagnosis of COVID-19. However, a simple chest X-ray cannot distinguish the disease and the extent of infection affecting the lungs.

Artificial Intelligence, in collaboration with Chest X-rays, helps in identifying the abnormal findings, thus diagnosing the ground glass opacities in the lungs, which is a classic feature of the COVID-19 disease. Many companies such asQure.ai, a Mumbai based start-up, andTata consultancy serviceshave used AI in a chest X-ray for the diagnosis of COVID-19. The AI developed by Qure.ai also helps in identifying the extent of infection affecting the lungs. This is usually valuable for patients who remain in the Intensive Care Unit (ICU).

In April, Apple and Google, the two big tech giants, colluded for developing a contact-based app to trace COVID-19 patients. The app works on Bluetooth and has been mostly used in western countries. In India, the government ruled out a similar strategy by developing the Aarogya Setu app.

In June, India told the UN, that drones and contact tracing apps have helped India in managing COVID cases. The app employs Bluetooth and location data to let the user know of any suspected COVID-19 patients nearby. This app is developed in 12 languages and has a user database of more than 10 million people.

Other mobile applications such as GoCoronaGo and Sampark-o-meter have also been developed for contact tracing by the Indian Institute of Science (IISc), Bangalore and IITs.

In Odisha, the state health department co-operated with the IT industry for developing drones which were proven helpful in checking infringement of rules in containment zones.

Apart from using the Aarogya Setu app, for contact tracing, many states have exercised AI to identify people who are mask violators with the help of AI cameras.

InTelangana, due to a surge in the COVID cases, the police department has come up with installing a software tool in the CCTV cameras to identify the mask violators. After identifying it sends a notification to the police headquarters, which in turn sends the update to the patrolling police team.

This model is similar to the AI model developed by China for tracking mask violators. This kind of AI technology is initially installed in Hyderabad, Cyberabad, and Rachakonda.

During the progression of the coronavirus, AI has facilitated manual repurposing of drugs to treat COVID-19. Indraprastha Institute of Information Technology (IIIT) has developed an AI model that can repurpose medicines according to the highest success probability against the disease, instead of going through the entire process manually.

Tata Consultancy Services is also using AI technology to crunch down the large molecule of drugs into highly effective molecules against the disease, thus reducing the time duration of the process.

Besides this, AI has proven effective in providing Tele-medicines and Tele-consultation, online consultation with health experts concerning a particular disease. In many states, likeChhattisgarh, AI is proven as a success by online training of the medics for controlling the COVID-19 pandemic.

In Kerala, Robots are used fordelivering hand sanitizersand delivering public health messages at the entrance of the office buildings and in isolation wards, to combat COVID-19.

The IIT and Stanford Alumni have also come up with a solution fordisinfecting public spaces. They have developed a machine called Robo Sapien, which controls the spread of the virus by ionizing the corona discharge.

Many start-ups are nowusing AIto come with solutions against the spread of COVID-19.

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How Artificial Intelligence is Helping to Fight against Coronavirus in India? - Analytics Insight

Global Geospatial Solutions & Services Market Artificial Intelligence (AI), Cloud, Automation, Internet of Things (IoT), and Miniaturization of…

The global geospatial solutions & services market accounted for US$ 238.5 billion in 2019 and is estimated to be US$ 1013.7 billion by 2029 and is anticipated to register a CAGR of 15.7%

Covina, CA, Aug. 04, 2020 (GLOBE NEWSWIRE) -- The report"Global Geospatial Solutions & Services Market, By Solution Type (Hardware, Software, and Service), By Technology (Geospatial Analytics, GNSS & Positioning, Scanning, and Earth Observation), By End-user (Utility, Business, Transportation, Defence & Intelligence, Infrastructural Development, Natural Resource, and Others), By Application (Surveying & Mapping, Geovisualization, Asset Management, Planning & Analysis, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2029.

Key Highlights:

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Analyst View:

Geospatial technology comprises GIS (geographical information systems), GPS (global positioning systems), and RS (remote sensing), a technology that provides a radically different way of producing and using maps that are required to manage communities and industries. Developed economies are expected to provide lucrative opportunities to the industry for geospatial solutions. The application of geospatial techniques across the globe has witnessed a steady growth over the past decades, owing to simple accessibility of geospatial technology in advanced nations such as the U.S. and Canada, thus further driving growth of the target the market. Moreover, rising smart city initiatives in emerging countries have resulted in the growing need for geospatial technologies for use in 3D urban mapping, monitoring and mapping natural resources. Increasing adoption of IoT, big data analysis, and Artificial Intelligence (AI) across the globe is projected to create profitable opportunities for global geospatial solutions & services market throughout the forecast period.

Browse 60 market data tables* and 35figures* through 140 slides and in-depth TOC on Global Geospatial Solutions & Services Market, By Solution Type (Hardware, Software, and Service), By Technology (Geospatial Analytics, GNSS & Positioning, Scanning, and Earth Observation), By End-user (Utility, Business, Transportation, Defence & Intelligence, Infrastructural Development, Natural Resource, and Others), By Application (Surveying & Mapping, Geovisualization, Asset Management, Planning & Analysis, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2029

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Key Market Insights from the report:

The global geospatial solutions & services market accounted for US$ 238.5 billion in 2019 and is estimated to be US$ 1013.7 billion by 2029 and is anticipated to register a CAGR of 15.7%. The market report has been segmented on the basis of solution type, technology, end-user, application, and region.

To know the upcoming trends and insights prevalent in this market, click the link below:

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Competitive Landscape:

The prominent player operating in the global geospatial solutions & services market includes HERE Technologies, Esri (US), Hexagon (Sweden), Atkins PLC, Pitney Bowes, Topcon Corporation, DigitalGlobe, Inc. (Maxar Group), General Electric, Harris Corporation (US), and Google.

The market provides detailed information regarding the industrial base, productivity, strengths, manufacturers, and recent trends which will help companies enlarge the businesses and promote financial growth. Furthermore, the report exhibits dynamic factors including segments, sub-segments, regional marketplaces, competition, dominant key players, and market forecasts. In addition, the market includes recent collaborations, mergers, acquisitions, and partnerships along with regulatory frameworks across different regions impacting the market trajectory. Recent technological advances and innovations influencing the global market are included in the report.

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Global Geospatial Solutions & Services Market Artificial Intelligence (AI), Cloud, Automation, Internet of Things (IoT), and Miniaturization of...