Category Archives: Artificial Intelligence

Updated: May 18, 2020 10:03:39 pm

Written by Seuj Saikia

Rabindranath Tagore once said, Faith is the bird that feels the light when the dawn is still dark. The darkness that looms over the world at this moment is the curse of the COVID-19 pandemic, while the bird of human freedom finds itself caged under lockdown, unable to fly. Enthused by the beacon of hope, human beings will soon start picking up the pieces of a shared future for humanity, but perhaps, it will only be to find a new, unfamiliar world order with far-reaching consequences for us that transcend society, politics and economy.

Crucially, a technology that had till now been crawling or at best, walking slowly will now start sprinting. In fact, a paradigm shift in the economic relationship of mankind is going to be witnessed in the form of accelerated adoption of artificial intelligence (AI) technologies in the modes of production of goods and services. A fourth Industrial Revolution as the AI-era is referred to has already been experienced before the pandemic with the backward linkages of cloud computing and big data. However, the imperative of continued social distancing has made an AI-driven economic world order todays reality.

Setting aside the oft-discussed prophecies of the Robo-Human tussle, even if we simply focus on the present pandemic context, we will see millions of students accessing their education through ed-tech apps, mothers buying groceries on apps too and making cashless payments through fintech platforms, and employees attending video conferences on relevant apps as well: All this isnt new phenomena, but the scale at which they are happening is unparalleled in human history. The alternate universe of AI, machine learning, cloud computing, big data, 5G and automation is getting closer to us every day. And so is a clash between humans (labour) and robots (plant and machinery).

This clash might very well be fuelled by automation. Any Luddite will recall the misadventures of the 19th-century textile mills. However, the automation that we are talking about now is founded on the citadel of artificially intelligent robots. Eventually, this might merge the two factors of production into one, thereby making labour irrelevant. As factories around the world start to reboot post COVID-19, there will be hard realities to contend with: Shortage of migrant labourers in the entire gamut of the supply chain, variations of social distancing induced by the fears of a second virus wave and the overall health concerns of humans at work. All this combined could end up sparking the fire of automation, resulting in subsequent job losses and possible reallocation/reskilling of human resources.

In this context, a potential counter to such employment upheavals is the idea of cash transfers to the population in the form of Universal Basic Income (UBI). As drastic changes in the production processes lead to a more cost-effective and efficient modern industrial landscape, the surplus revenue that is subsequently earned by the state would act as a major source of funds required by the government to run UBI. Variants of basic income transfer schemes have existed for a long time and have been deployed to unprecedented levels during this pandemic. Keynesian macroeconomic measures are increasingly being seen as the antidote to the bedridden economies around the world, suffering from near-recession due to the sudden ban on economic activities. Governments would have to be innovative enough to pump liquidity into the system to boost demand without harming the fiscal discipline. But what separates UBI from all these is its universality, while others remain targeted.

This new economic world order would widen the cracks of existing geopolitical fault lines particularly between US and China, two behemoths of the AI realm. Datanomics has taken such a high place in the valuation spectre that the most valued companies of the world are the tech giants like Apple, Google, Facebook, Alibaba, Tencent etc. Interestingly, they are also the ones who are at the forefront of AI innovations. Data has become the new oil. What transports data are not pipelines but fibre optic cables and associated communication technologies. The ongoing fight over the introduction of 5G technology central to automation and remote command-control architecture might see a new phase of hostility, especially after the controversial role played by the secretive Chinese state in the COVID-19 crisis.

The issues affecting common citizens privacy, national security, rising inequality will take on newer dimensions. It is pertinent to mention that AI is not all bad: As an imperative change that the human civilisation is going to experience, it has its advantages. Take the COVID-19 crisis as an example. Amidst all the chaos, big data has enabled countries to do contact tracing effectively, and 3D printers produced the much-needed PPEs at local levels in the absence of the usual supply chains. That is why the World Economic Forum (WEF) argues that agility, scalability and automation will be the buzzwords for this new era of business, and those who have these capabilities will be the winners.

But there are losers in this, too. In this case, the developing world would be the biggest loser. The problem of inequality, which has already reached epic proportions, could be further worsened in an AI-driven economic order. The need of the hour is to prepare ourselves and develop strategies that would mitigate such risks and avert any impending humanitarian disaster. To do so, in the words of computer scientist and entrepreneur Kai-Fu Lee, the author of AI Superpowers, we have to give centrality to our heart and focus on the care economy which is largely unaccounted for in the national narrative.

(The writer is assistant commissioner of income tax, IRS. Views are personal)

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An AI future set to take over post-Covid world - The Indian Express

Artificial intelligence-based techniques, used to reconstruct medical images, may actually be leading to incorrect diagnoses.

Thats according to the results of a new investigation, led by experts at the University of Cambridge. Scientists there devised a series of tests to assess such imaging reconstruction and discovered numerous artefacts and other errors, according to their study, published May 11 in theProceedings of the National Academy of Sciences.

This issue seemed to persist across different types of AI, they noted, and may not be easily remedied.

"There's been a lot of enthusiasm about AI in medical imaging, and it may well have the potential to revolutionize modern medicine; however, there are potential pitfalls that must not be ignored," co-author Anders Hansen, PhD, from Cambridge's Department of Applied Mathematics and Theoretical Physics, said in a statement. "We've found that AI techniques are highly unstable in medical imaging, so that small changes in the input may result in big changes in the output."

To reach their conclusions, Hansen and coinvestigatorsfrom Norway, Portugal, Canada and the United Kingdomused several assessments to pinpoint flaws in AI algorithms. They targeted CT, MR and nuclear magnetic resonance imaging, and tested them based on instabilities tied to movement, small structural changes, and those related to the number of samples.

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Artificial intelligence-based imaging reconstruction may lead to incorrect diagnoses, experts caution - Radiology Business

(Getty Images/istockphoto / Peshkova)

The debate on contact-tracing highlights the urgency of tackling unregulated technologies like artificial intelligence (AI). With a strong democracy and reputation for first-class research, Switzerland has the potential to be at the forefront of shaping ethical AI.

What is Artificial Intelligence (AI)? "Artificial intelligence is either the best or the worst thing ever to happen to humanity," the prominent scientist, Stephen Hawking, who died in 2018, once said.

An expert group set up by the European Commission presented a draft ofethics guidelinesexternal linkfor trustworthy AI at the end of 2018, but as of yet there is no agreed global strategy for defining common principles, which would include rules on transparency, privacy protection, fairness, and justice.

Thanks to its unique features a strong democracy, its position of neutrality, and world-class research Switzerland is well positioned to play a leading role in shaping the future of AI that adheres toethical standards. The Swiss government recognizes the importance of AI to move the country forward, and with that in mind, has been involved in discussions at the international level.

What is AI?

There is no single accepted definition of Artificial Intelligence. Often, it's divided into two categories, Artificial General Intelligence (AGI) which strives to closely replicate human behaviour while Narrow Artificial Intelligence focuses on single tasks, such as face recognition, automated translations and content recommendations, such as videos on YouTube.

However, on the domestic front, the debate has just begun, albeit in earnest as Switzerland and other nations are confronted with privacy concerns surrounding the use of new technologieslike contact-tracing apps, whether they use AI or not, to stop the spread of Covid-19.

The European initiative the Pan-European Privacy-Preserving Proximity Tracing initiative PEPP-PT advocated a centralized data approach that raised concern about its transparency and governance. However, it was derailed when a number of nations, including Switzerland, decided in favour of a decentralized and privacy-enhancing system, called DP-3T (Decentralized Privacy-Preserving Proximity Tracing). The final straw for PEPP-PT was when Germany decided to exit as well.

"Europe has engaged in a vigorous and lively debate over the merits of the centralized and decentralized approach to proximity tracing. This debate has been very beneficial as it made the issues aware to a broad population and demonstrated the high level of concern with which these apps are being designed and constructed. People will use the contact-tracing app only if they feel that they don't have to sacrifice their privacy to get out of isolation," said Jim Larus. Larus is Dean of the School of Computer and Communication Sciences (IC) at EPFL Lausanne and a member of the group that initially started the DP3T effort at EPFL.

According to a recent survey, nearly two-thirds of Swiss citizens said they were in favour of contact tracing. The DP-3T app is currently being tested on a trial basis, while waiting for the definition of the legal conditions for its widespread use, as decided by the Swiss parliament.However, the debate highlights the urgency of answering questions surrounding ethics and governance of unregulated technologies.

+ Read more about the controversial Swiss app

The "Swiss way"

Artificial intelligence was included for the first time in the Swiss government's strategy to create the right conditions to accelerate the digital transformation of society.

Last December, a working group delivered its report to the Federal Council (executive body) called the "Challenges of Artificial Intelligence". The report stated that Switzerland was ready to exploit the potential of AI, but the authors decided not to specifically highlight the ethical issues and social dimension of AI, focusing instead on various AI use cases and the arising challenges.

"In Switzerland, the central government does not impose an overarching ethical vision for AI. It would be incompatible with our democratic traditions if the government prescribed this top-down," Daniel Egloff, Head of Innovation of the State Secretariat for Education, Research and Innovation (SERI) told swissinfo.ch. Egloff added that absolute ethical principles are difficult to establish since they could change from one technological context to another. "An ethical vision for AI is emerging in consultations among national and international stakeholders, including the public, and the government is taking an active role in this debate," he added.

Seen in a larger context, the government insists it is very involved internationally when it comes to discussions on ethics and human rights. Ambassador Thomas Schneider, Director of International Affairs at the Federal Office of Communications (OFCOM), told swissinfo.ch that Switzerland in this regard "is one of the most active countries in the Council of Europe, in the United Nations and other fora". He also added that it's OFCOM's and the Foreign Ministry's ambition to turn Geneva into a global centre of technology governance.

Just another buzzword?

How is it possible then to define what's ethical or unethical when it comes to technology? According to Pascal Kaufmann, neuroscientist and founder of theMindfire Foundationexternal linkfor human-centric AI, the concept of ethics applied to AI is just another buzzword: "There is a lot of confusion on the meaning of AI. What many call 'AI' has little to do with Intelligence and much more with brute force computing. That's why it makes little sense to talk about ethical AI. In order to be ethical, I suggest to hurry up and create AI for the people rather than for autocratic governments or for large tech companies.Inventing ethical policies doesn't get us anywhere and will not help us create AI.''

Anna Jobin, a postdoc at the Health Ethics and Policy Lab at the ETH Zurich, doesn't see it the same way. Based on her research, she believes that ethical considerations should be part of the development of AI: "We cannot treat AI as purely technological and add some ethics at the end, but ethical and social aspects need to be included in the discussion from the beginning." Because AI's impact on our daily lives will only grow, Jobin thinks that citizens need to be engaged in debates on new technologies that use AI and that decisions about AI should include civil society. However, she also recognizes the limits of listing ethical principles if there is a lack of ethical governance.

For Peter Seele, professor of Business Ethics at USI, the University of Italian-speaking Switzerland, the key to resolving these issues is to place business, ethics, and law on an equal footing. "Businesses are attracted by regulations. They need a legal framework to prosper. Good laws that align business and ethics create the ideal environment for all actors," he said. The challenge is to find a balance between the three pillars.

Artificial intelligence is being used to developrobots and drones that can explore dangerous places beyond the reach of humans and animals.

See in other languages: 4 See in other languages: 4 Languages: 4

The perfect combination

Even if the Swiss approach mainly relies on self-regulation, Seele argues that establishing a legal framework would give a significant impulse to the economy and society.

If Switzerland were to take a lead role in defining ethical standards, its political system based on direct democracy and democratically controlled cooperatives could play a central role in laying the foundation for the democratization of AI and the personal data economy. As the Swiss Academy of Engineering Sciences SATWsuggested in a whitepaper at the end of 2019, the model for that could be the SwissMIDATAexternal link, a nonprofit cooperative that ensures citizens' sovereignty over the use of their data, acting as a trustee for data collection. Owners of a data account can become members of MIDATA, participating in the democratic governance of the cooperative. They can also allow selective access to their personal data for clinical studies and medical research purposes.

The emergence of an open data ecosystem fostering the participation of civil society is raising awareness of the implications of the use of personal data, especially for health reasons, as in the case of the contact-tracing app. Even if it's argued that the favoured decentralized system does a better job preserving fundamental rights than a centralized approach, there are concerns about susceptibility to cyber attacks.

The creation of a legal basis for AI could ignite a public debate on the validity and ethics of digital systems.

Frida Polli is a neuroscientist and co-founder of pymetrics, an AI-based job matching platform based in the United States.

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Ethical artificial intelligence: Could Switzerland take the lead? - swissinfo.ch

Chris Gannatti, head of research at ETF specialist WisdomTree, explains how artificial intelligence is being used to tackle Covid-19.

Artificial Intelligence (AI) is proliferating more widely than ever before, having the potential to influence many aspects of daily life. Crisis periods, like we have seen with the Covid-19 pandemic, are often catalysts for the deployment of new innovations and technologies more quickly. The power of AI is being harnessed to tackle the Covid-19 pandemic, whether that be to better understand the rate of infection or by tracing and quickly identifying infections. While AI has been associated with the future and ideas such as the development of driverless cars, its legacy could be how it has impacted the world during this crisis. It is likely that AI is already playing a major part in the early stages of vaccine development - the uses of artificial intelligence are seemingly endless.AI was already growing quickly and being deployed in ever more areas of our data-driven world.

Covid-19 has accelerated some of these deployments, bringing greater comfort and familiarity to the technology. To really understand how AI is making a difference, it is worth looking at some examples which illustrate the breadth of activities being carried out by AI during the pandemic.

Rizwan Malik, the lead radiologist at Royal Bolton Hospital run by the UKs National Health Service (NHS) designed a conservative clinical trial to help obtain initial readings of X-rays for patients faster. Waiting for specialists could sometimes take up to six hours. He identified a promising AI-based chest X-ray system and then set up a test to occur over six months. For all chest X-rays handled by his trainees, the system would offer a second opinion. He would then check if the systems conclusion matched his own and if it did, he would phase the system in as a permanent check on his trainees. As Covid-19 hit, the system became an important way to identify certain characteristics unique to Covid-19 that were visible on chest X-rays. While not perfect, the system did represent an interesting case-study in the use of computer vision in medical imagery.A great example of the collaborative efforts that can be inspired during times of crisis involved three organisations coming together to release the Covid-19 Open Research Dataset. This includes more than 24,000 research papers from peer-reviewed journals and other sources.

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The National Library of Medicine at the National Institutes of Health provided access to existing scientific publications; Microsoft used its literature curation algorithms to find relevant articles; and research non-profit the Allen Institute for Artificial Intelligence converted them from web pages and PDFs into a structured format that can be processed by algorithms. Many major cities affected by Covid-19 were faced with a very real problem - getting the right care to the people who needed it without allowing hospitals to become overrun. Helping people to self-triage, therefore staying away from the hospital unless absolutely necessary, was extremely important. Providence St. Joseph Health System in Seattle built an online screening and triage tool that could rapidly differentiate between those potentially really sick with Covid-19 and those with less life-threatening ailments. In its first week of operation, it served 40,000 patients. The Covid-19 pandemic has pushed the unemployment rate in the US to 14.7%. This has led to unprecedented numbers of people filing unemployment claims and asking questions to different state agencies. Texas, which has received millions of these claims since early March, is using artificial intelligence-driven chatbots to answer questions from unemployed residents in need of benefits.

Other states, like Georgia and South Carolina, have reported similar activity. To give a sense of scale, the system that has been deployed in Texas can handle 20,000 concurrent users. Think of how much staff would be required to deal with 20,000 inquiries at the same time. These are but four of many, many ways in which AI has been deployed to help in the time of the Covid-19 pandemic. While we continue to hope for cures and vaccinations, which AI will help in developing, we expect to see more innovative uses of AI that will benefit society over the long-term.

How you can slow the spread of coronavirus:

Wash your hands with soap and water often do this for at least 20 seconds

Use hand sanitiser gel if soap and water are not available

Wash your hands as soon as you get home

Cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze

Put used tissues in the bin immediately and wash your hands afterwards

SOURCE: Funds Europe

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How Is Artificial Intelligence Combatting COVID-19? - Gigabit Magazine - Technology News, Magazine and Website

Pune, May 18, 2020 (GLOBE NEWSWIRE) -- The global patent analytics market size is predicted to USD 1,668.4 million by 2027, exhibiting a CAGR of 12.4% during the forecast period. The increasing advancement and integration of machine learning, artificial intelligence, and the neural network by enterprises will have a positive impact on the market during the forecast period. Moreover, the growing needs of companies to protect intellectual assets will bolster healthy growth of the market in the forthcoming years, states Fortune Business Insights in a report, titled Patent Analytics Market Size, Share and Industry Analysis, By Component (Solutions and Services), By Services (Patent Landscapes/White Space Analysis, Patent Strategy and Management, Patent Valuation, Patent Support, Patent Analytics, and Others), By Enterprise Size (Large Enterprises, Small & Medium Enterprises), By Industry (IT and Telecommunications, Healthcare, Banking, Financial Services and Insurance (BFSI), Automotive, Media and Entertainment, Food and Beverages and, Others), and Regional Forecast, 2020-2027 the market size stood at USD 657.9 million in 2019. The rapid adoption of the Intellectual Property (IP) system to retain an innovation-based advantage in business will aid the expansion of the market.

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An Overview of the Impact of COVID-19 on this Market:

The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

Click here to get the short-term and long-term impact of COVID-19 on this Market.Please visit: https://www.fortunebusinessinsights.com/patent-analytics-market-102774

Market Driver:

Integration of Artificial Intelligence to Improve Market Prospects

The implementation of artificial intelligence technology for analyzing patent data will support the expansion of the market. AI-based semantic search uses an artificial neural network to enhance patent discovery by improving accuracy and efficiency. For instance, in February 2018, PatSeer announced the unveiling of ReleSense, an AI-driven NLP engine. The engine utilizes 12 million+ semantic rules to gain from publically available patents, scientific journals, clinical trials, and associated data sources. ReleSense with its wide range of AI-driven capabilities offers search from classification, via APIs and predictive-analytics for apt IP solutions. The growing application of AI for domain-specific analytics will augur well for the market in the forthcoming years. Furthermore, the growing government initiatives to promote patent filing activities will boost the patent analytics market share during the forecast period. For instance, the Government of India introduced a new scheme named Innovative/ Creative India, to aware people of the patents and IP laws and support patent analytics. In addition, the growing preferment for language model and neural network intelligence for accurate, deep, and complete data insights will encourage the market.

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Regional Analysis:

Implementation of Advanced Technologies to Promote Growth in North America

The market in North America stood at USD 209.2 million and is expected to grow rapidly during the forecast period owing to the presence of major companies in the US such as IBM Corporation, Amazon.Com, Inc. The implementation of advanced technologies including IoT, big data, and artificial intelligence by major companies will aid growth in the region.

Considering this the U.S. is expected to showcase a higher growth in the patent filing. As per the World Intellectual Property, in 2018, the U.S. filed 230,085 patent applications across several domains. Asia Pacific is predicted to witness tremendous growth during the forecast period. The growth is attributed to China, which accounts for a major share in the global patent filings. According to WIPO, intellectual property (IP) office in China had accounted for 46.6% global share in patent registration, in 2018. The growing government initiatives concerning patents and IP laws in India will significantly enable speedy growth in Asia Pacific.

Key Development:

March 2018: Ipan GmbH announced its collaboration with Patentsight, Corsearch, and Uppdragshuset for the introduction of an open IP platform named IP-x-change platform. The platform enables prior art search, automatic data verification tools, smart docketing tools integrated in real-time to optimize IP management solution.

List of Key Companies Operating in the Patent Analytics Market are:

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Detailed Table of Content

TOC Continued..!!!

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Have a Look at Related Research Insights:

Intellectual Property Software Market Size, Share and Global Trend By Deployment (On-premises & Cloud-based solutions), By Services (Development & Implementation Services, Consulting Services, Maintenance & Support Services), By Applications (Patent Management, Trademark Management and others), By Industry Vertical (Healthcare, Electronics and others) and Geography Forecast till 2025

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Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business...

Watch Benjamin Alarie, co-founder and CEO of Blue J Legal, discuss the expanding role of artificial intelligence in tax with contributing editor atTax Notes FederalBenjamin Willis.

Here are some highlights

On machine learning and tax law

Benjamin Alarie: Whenwe talk about machine learning and artificial intelligence of the law, what we're doing is talking about collecting the raw materials, the rulings, the cases, the legislation, the regs, all that information, and bringing it to bear on a particular problem. We're synthesizing all of those materials to make a prediction about how a new situation would likely be decided by the courts.

. . . Law should be predictable. We have lots of data out there in the form of rulings, in the form of judgments that we can collect as good examples of how the courts have decided these matters in the past. And we can reverse engineer using machine learning methods how the courts are mapping the facts of different situations into outcomes. And we can do this in a really elegant way that leads to high quality prediction. So predictions of 90 percentor better accuracy about how the courts are going to make those decisions in the future, which is incredibly valuable to taxpayers to tax administration and to anyone who's looking for certainty, predictability and fairness, in the application of law.

On the availability of artificial intelligence

Benjamin Alarie: We're doing a lot to make this technology available throughout industry. Law firms are increasingly seeing this as one of the tools that they need to have in order to practice tax as effectively as possible. Academic programs see using this kind of technology [as]a huge boost for their graduates who are going to go into practice being familiar already with the leading tools for how to leverage machine learning and artificial intelligence. Accounting firms are also quite interested in this approach too because it has huge implications in terms of speeding up research [and] doing quality assurance . . .

On the moldability of results

Benjamin Alarie: You can play with different dimensions. You can swap out that assumption of fact, swap in a different assumption of fact, and see how that's likely to influence the results. So, then you can do scenario testing to really get comfortable with how much risk there is in a particular situation as the one providing a new opinion or providing advice to a client. That's really reassuring. You might say, Okay, I need to get this to 80 percent probability. I'm not willing to bite off more than that . . . Or you might be like, Well, I have a really risk-loving client. I just need to get to 51 percent . . . [machine learning] allows you to really calibrate the amount of risk that you're taking on, depending on the risk appetite of the client and your comfort as the practitioner.

Benjamin Willis, contributing editor with Tax Notes Federal, and Benjamin Alarie, co-founder and CEO ... [+] of Blue J Legal, discuss the expanding role of artificial intelligence and machine learning in the government, academia and tax practice.

On artificial intelligence and the courts

Benjamin Alarie: [Machine learning] is a great tool to encourage settlement between the parties, and so I think we're increasingly seeing that phenomenon where the party with the really strong position is using this to support their argument. They say, Don't take our word for it. We ran it on this independent system. . . Here's the report from the system saying that we have a 95 percent or better chance of winning this case. Are you still sure you don't want to enter into terms of settlement? That's often very convincing to the other side, who then run their analysis through the same system and they say, Okay . . . It's not nearly as strong as we thought it might be. Maybe we should talk about settling this and that saves judges from having to contend with cases that really aren't the best use of their time because it's pretty clear how those cases should get decided.

On artificial intelligence and low-income taxpayers

Benjamin Alarie: There are early adopters at these low income taxpayer clinics across the country who are interested in using technology to allow them to give faster advice to the low income taxpayers . . . Folks understand increasingly how to use the software and how it can materially assist their clientele and so the goal is to learn from those early adopters and to figure out how to position the software to help as much as possible in other clinics where maybe we don't have early adopters present, but who could still genuinely, really benefit from this.

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The Expanding Role Of Artificial Intelligence In Tax - Forbes

As far as a simple definition, Artificial Intelligence is the ability of a machine or a computer device to imitate human intelligence (cognitive process), secure from experiences, adapt to the most recent data and work people-like-exercises.

Artificial Intelligence executes tasks intelligently that yield in creating enormous accuracy, flexibility, and productivity for the entire system. Tech chiefs are looking for some approaches to implement artificial intelligence technologies into their organizations to draw obstruction and include values, for example, AI is immovably utilized in the banking and media industry. There is a wide arrangement of methods that come in the space of artificial intelligence, for example, linguistics, bias, vision, robotics, planning, natural language processing, decision science, etc. Let us learn about some of the major subfields of AI in depth.

ML is maybe the most applicable subset of AI to the average enterprise today. As clarified in the Executives manual for real-world AI, our recent research report directed by Harvard Business Review Analytic Services, ML is a mature innovation that has been around for quite a long time.

ML is a part of AI that enables computers to self-learn from information and apply that learning without human intercession. When confronting a circumstance wherein a solution is covered up in a huge data set, AI is a go-to. ML exceeds expectations at processing that information, extracting patterns from it in a small amount of the time a human would take and delivering in any case out of reach knowledge, says Ingo Mierswa, founder and president of the data science platform RapidMiner. ML powers risk analysis, fraud detection, and portfolio management in financial services; GPS-based predictions in travel and targeted marketing campaigns, to list a few examples.

Joining cognitive science and machines to perform tasks, the neural network is a part of artificial intelligence that utilizes nervous system science ( a piece of biology that worries the nerve and nervous system of the human cerebrum). Imitating the human mind where the human brain contains an unbounded number of neurons and to code brain-neurons into a system or a machine is the thing that the neural network functions.

Neural network and machine learning combinedly tackle numerous intricate tasks effortlessly while a large number of these tasks can be automated. NLTK is your sacred goal library that is utilized in NLP. Ace all the modules in it and youll be a professional text analyzer instantly. Other Python libraries include pandas, NumPy, text blob, matplotlib, wordcloud.

An explainer article by AI software organization Pathmind offers a valuable analogy: Think of a lot of Russian dolls settled within one another. Profound learning is a subset of machine learning and machine learning is a subset of AI, which is an umbrella term for any computer program that accomplishes something smart.

Deep learning utilizes alleged neural systems, which learn from processing the labeled information provided during training and uses this answer key to realize what attributes of the information are expected to build the right yield, as per one clarification given by deep AI. When an adequate number of models have been processed, the neural network can start to process new, inconspicuous sources of info and effectively return precise outcomes.

Deep learning powers product and content recommendations for Amazon and Netflix. It works in the background of Googles voice-and image-recognition algorithms. Its ability to break down a lot of high-dimensional information makes deep learning unmistakably appropriate for supercharging preventive maintenance frameworks

This has risen as an extremely sizzling field of artificial intelligence. A fascinating field of innovative work for the most part focuses around designing and developing robots. Robotics is an interdisciplinary field of science and engineering consolidated with mechanical engineering, electrical engineering, computer science, and numerous others. It decides the designing, producing, operating, and use of robots. It manages computer systems for their control, intelligent results and data change.

Robots are deployed regularly for directing tasks that may be difficult for people to perform consistently. Major robotics tasks included an assembly line for automobile manufacturing, for moving large objects in space by NASA. Artificial intelligence scientists are additionally creating robots utilizing machine learning to set interaction at social levels.

Have you taken a stab at learning another language by labeling the items in your home with the local language and translated words? It is by all accounts a successful vocab developer since you see the words again and again. Same is the situation with computers fueled with computer vision. They learn by labeling or classifying various objects that they go over and handle the implications or decipher, however, at a much quicker pace than people (like those robots in science fiction motion pictures).

The tool OpenCV empowers processing of pictures by applying them to mathematical operations. Recall that elective subject in engineering days called Fluffy Logic? Truly, that approach is utilized in Image processing that makes it a lot simpler for computer vision specialists to fuzzify or obscure the readings that cant be placed in a crisp Yes/No or True/False classification. OpenTLA is utilized for video tracking which is the procedure to find a moving object(s) utilizing a camera video stream.

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Five Important Subsets of Artificial Intelligence - Analytics Insight

The national flags of the U.S. and China waving outside a building.

Teh Eng Koon | AFP via Getty Images

The United States might be leading in some areas of its technology race with China but experts warn against the world's largest economy resting on its laurels, urging instead for cooperation with allies and shifts in domestic policy.

Alongside trade war developments between the U.S. and China, both parties have been embroiled in growing competition to dominate various fields of next-generation technology, such as 5G networks and artificial intelligence (AI).

5G refers to the latest mobile networking technology that promises super-fast download speeds and the ability to underpin critical infrastructure. That's one reason why it is seen as crucial technology for both countries.

In the last few years, Beijing has laid out a number of plans it hopes will turn China into a world leader in various tech areas:

U.S.-China competition is essentially about who will control the global information technology infrastructure and standards.

Frank Rose

senior fellow for security and strategy at the Brookings Institution

The China Standards 2035 blueprint is essentially the technical specifications and rules that define how many of the technologies that are in use everyday, like mobile networks, operate. Being able to influence what those look like could have wide-ranging implications for power Beijing wields in various areas of technology globally.

"U.S.-China competition is essentially about who will control the global information technology infrastructure and standards," said Frank Rose, senior fellow for security and strategy in the Foreign Policy program at The Brookings Institution, at a webinar earlier this month.

A recent report by Citi that studied AI competitiveness of 48 economies found that the U.S. still leads significantly. The other 47 economies included in the index would face "severe difficulties in catching up to the U.S.'s AI industry in 2020-30," the report said.

This was attributed to the U.S.'s strength, particularly in AI patents, investment and academic research. Citi said the ranking was not a surprise, given that major software companies are headquartered in the U.S.

The ranking was calculated by weighing five factors, namely: academic research, patents, investment, labor and hardware in the field of artificial intelligence.

However, the report also found that only China, ranked second behind the U.S. in the index, would be likely to "cultivate an independent strong ecosystem for the AI industry due to both economic and geopolitical reasons."

China would still need to catch up in two areas, namely jet engines and semiconductor, according to Michael Brown, director of the defense innovation unit at theU.S. Department of Defense.

"So they're (China) not quite there yet, but I think we can't rest on our laurels," he saidat theBrookings Institutionwebinar. "I think they very much can compete, and that's what makes me very concerned, if we don't wake up and see what we need to do to compete."

Even though many countries have invested efforts to boost their domestic biotech sector, China is the "only one whose scale could potentially ... pose a threat to American pre-eminence" in biotech, said Scott Moore, director of the Penn Global China Program at the University of Pennsylvania, during the same webinar.

China's policy target is for biotech to account for roughly 4% of Chinese GDP by 2020, and in comparison, biotech makes up around 2% of the U.S.'s GDP, Moore said.

Experts have pointed out that the U.S. could tap on alliances with other nations and re-orientate domestic policy to increase competitiveness.

"The U.S. and its allies comprise almost two-thirds of global R&D and there's extraordinary ways we can try to leverage that pool of research and development and coordinate on shared priorities," said Andrew Imbrie, senior fellow with the Center for Security and Emerging Technology at Georgetown University during the webinar.

Investing in research undertaken by both the government and academia is a "proven strategy" from the Cold War era that can be used again in this current situation, Brown said. However, "the more important and more difficult strategy" would involve the "need to reform our business thinking, and our capital markets, to move away from short-term thinking, to be more long-term oriented," he argued.

He pointed to the short-term thinking that is ingrained in the business community in the U.S. a result, he said, of measures including a focus on quarterly earnings, increasing short-term stock price, and shorter periods for holding stock.

In contrast, China takes a very long-term view, and sees technology and innovation as key to developing national capability as part of its overall national strategy, he said.

Short-term thinking isnot the right approach if the U.S. is preparing for a "superpower marathon" with China, Brown said."We have to reform this or we're not going to be successful in competing with China."

More:

'Superpower marathon': U.S. may lead China in tech right now but Beijing has the strength to catch up - CNBC

In just a few months, it seems we have entered a new world altogether. If we talk about the period before the SARS-CoV-2 outbreak, Artificial Intelligence was already booming and emerging as the next revolutionary thing that was all set to transform our world in a few years. However, during this pandemic period, its development has gained further momentum.

Humans are facing challenges at multiple levels, and while maintaining the social distancing and other new norms, people need to keep functioning along with learning to live with the coronavirus. The unique challenges demand new innovative solutions.

However, due to the relentless spread of the virus and increasing confirmed and probable cases, there was an urgent need to speed up the diagnosis process while maintaining similar accuracy. The newly developed AI systems could analyse a single patient's CT images in just 20 seconds with an accuracy of more than 90 per cent.

GOOD READ: Will Artificial Intelligence Barge Higher in Post-Pandemic Era?

Artificial Intelligence helped China in diagnosing COVID-19 cases

Image source: Pixabay

At the initial stages of COVID-19 outbreak, China government took the initiative to encourage developing AI tools for healthcare solutions, soon healthcare companies and tech giants in China began developing AI systems to speed up CT scan diagnosis of COVID-19 patients. Two firms in China which developed such systems are:

After these successful AI systems, multiple hospitals in China began to deploy AI CT scan diagnostic systems. Hospitals in Zhengzhou, Henan Province treating COVID-19 patients also deployed an AI-based diagnosing system. Alibaba Cloud and Alibaba DAMO Academy developed this system that can analyse CT images within 20 seconds with an accuracy of 96 per cent.

The radiology department of Zhongnan Hospital used AI software for detecting the visual signs of pneumonia associated with SARS-COV-2 infection by diagnosing lung CT scan images. Professor and chair of radiology Zhongnan Hospital, Mr Haibo Xu confirmed that the AI software proved to be extremely helpful for the staff in screening and prioritising patients who could be infected with COVID-19. Mr Haibo Xu added that only detecting pneumonia on CT scan does not confirm the person has the disease. Still, it helps the overworked staff to speed up the process of diagnosis, isolating and beginning the treatment.

RELATED: 3 Stocks Trying to Battle Novel Coronavirus Using AI-Based Technology

Other countries adopt AI technology for COVID-19 diagnosis

When the spread of the virus came out of China's borders and began to spread in other countries, they also started boosting their diagnostic capabilities with AI. In Australia, a startup that earlier provided AI diagnostic tool for improving the accuracy of breast cancer detection, modified its tool to detect the COVID-19 viral infection using lung CT scans from China and Italy.

In Russia also, Artificial Intelligence is learning to diagnose COVID-19 from the largest CT scan database that includes previous data of confirmed cases, and the new dataset, including 1,000 anonymised sets of chest CT scans.

It also includes earlier data of CT scans of patients with laboratory-confirmed infection. This earlier database was created at Diagnostics and Telemedicine Centre by scientists. The objective here is to inform AI to identify COVID-19.

The CT scan database shows the pathological abnormalities of corona infection in the lung tissue based on the chest computed tomography. The database is created according to classification and is intended to develop an AI algorithm.

Austin Health boosts COVID-Care using AI

In the Australian health care system, an AI algorithm called COVID-Care in smartphone assists recovering patients in monitoring their case each day. The patient needs to hold the smartphone up and count slowly to 30, and he/she gets diagnosed for the deadly virus and advised accordingly.

If the patient's condition aggravates, the doctor automatically gets alerted and follows up with the patient through telehealth consultation to provide advice and discuss available options. The doctor tells the patient if the patient needs to reach the hospital or is safe to stay home and do the daily assessment using COVID-Care.

AI's role in economic recovery

The economic downturn is a big challenge during COVID-19 pandemic. Once, the companies will come out of its economic hibernation; they will need to cope up fast to return to regular operations and service level. However, due to the disruptions in their workforce, this could be challenging. AI can be useful in identifying production problems, workforce behaviour, or customer service. Such disruptive times also give rise to opportunities for significant innovations. Even to survive in the changing market, there is no other way but to adopt the new technologies that can offer solutions during such turbulent times. In today's time, the companies that are implementing AI solutions to understand the changing patterns of market behaviour or their own customers base will move ahead of their competition and will provide better solutions to drive growth.

Furthermore, due to the lockdown restrictions, businesses are relying more on online channels, which also puts businesses at high risk of cybercriminal activities. AI can play a vital role in preventing such actions.

AI is based on data, so if data remains confined within individual organisations, then the technology would be of little help at each phase of crisis management. To deal with such drastic disruptions it would be unaffordable if significant data remains well-guarded and inaccessible. Thus, there must be a way out to enable acceptable data sharing to better handle any such crisis in future using technologies such as AI.

Excerpt from:

How is Artificial Intelligence helping in dealing with the COVID-19 crisis? - Kalkine Media

Dublin, May 15, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence Markets in IVD" report has been added to ResearchAndMarkets.com's offering.

This report examines selected AI-based initiatives, collaborations, and tests in various in vitro diagnostic (IVD) market segments.

Artificial Intelligence Markets in IVD contains the following important data points:

The past few years have seen extraordinary advances in artificial intelligence (AI) in clinical medicine. More products have been cleared for clinical use, more new research-use-only applications have come to market and many more are in development.

In recent years, diagnostics companies - in collaboration with AI companies - have begun implementing increasingly sophisticated machine learning techniques to improve the power of data analysis for patient care. The goal is to use developed algorithms to standardize and aid interpretation of test data by any medical professional irrespective of expertise. This way AI technology can assist pathologists, laboratorians, and clinicians in complex decision-making.

Digital pathology products and diabetes management devices were the first to come to market with data interpretation applications. The last few years have seen the use of AI interpretation apps extended to a broader range of products including microbiology, disease genetics, and cancer precision medicine.

This report will review some of the AI-linked tests and test services that have come to market and others that are in development in some of the following market segments:

Applications of AI are evolving that predict outcomes such as diagnosis, death, or hospital readmission; that improve upon standard risk assessment tools; that elucidate factors that contribute to disease progression; or that advance personalized medicine by predicting a patient's response to treatment. AI tools are in use and in development to review data and to uncover patterns in the data that can be used to improve analyses and uncover inefficiencies. Many enterprises are joining this effort.

The following are among the companies and institutions whose innovations are featured in Artificial Intelligence Markets in IVD:

Key Topics Covered

Chapter 1: Executive Summary

Chapter 2: Artificial Intelligence In Diagnostics Markets

Chapter 3: Market Analysis: Artificial Intelligence in Diagnostics

For more information about this report visit https://www.researchandmarkets.com/r/vw8l7u

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

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Artificial Intelligence Markets in IVD, 2019-2024: Breakdown by Application and Component - GlobeNewswire