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

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.

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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

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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...

From chatbots to Amazon Alexa, artificial intelligence has become a normal part of everyday life that we now take for granted. But now in the middle of the coronavirus pandemic, it is being used to save lives.

AI, for example, is at the heart of the NHS track and trace app, which is being trialled in the Isle of Wight before a nationwide rollout. Users of the service input their symptoms into a smartphone, then an algorithm looks at who theyve had contact with and alerts them to the potential risks of catching or spreading the virus.

For Chris Ford, manager of the Smith & Williamson Artificial Intelligence fund, this is a pivotal moment for AI, especially as we are now willing to share our data with the government for the greater good. He argues that the Covid-19 crisis has accelerated the cultural acceptance of AIs role in our lives, from the sudden and widespread use of telemedicine to the use of computers for speedy diagnosis and the search for a vaccine. Theres a renewed focus and vigour that has been absent before in how we approach AI, he says.

But there are misunderstandings about what AI is. Defined by Stanford University as the science and engineering of making intelligent machines, it is now seeping into so many aspects of our lives that a complete definition it is hard to pin down. There is also confusion whether it is good for us, with negative perceptions of "robots taking human jobs" balanced by medical breakthroughs such as discovering new antibiotics and robotic surgery.

Robotics and automation are boom areas of AI the iShares Automation and Robotics ETF (RBOT) has over $2 billion in assets but they are not the game in town, says S&W's Ford. Not all robotics have artificial intelligence, and not all AI platforms are robotic, he says. For investors its been relatively easy to ride the trend by backing big tech firms like Microsoft (MSFT), Amazon (AMZN), Apple (AAPL) and Google parent company Alphabet (GOOGL), which have invested billions in AI in its many forms.

Many of the pioneers in AI are not on the radar of retail investors, but their work will have a profound impact on our lives. One such area is autonomous and semi-autonomous vehicles, which Google and Tesla (TSLA) are backing to be the next game-changing technology. With 1.3 million people losing their lives in traffic accidents worldwide every year, 90% of which are down to human error, there is clearly scope for technology to drive better than us. AI has come a long way in recent years in the field of image recognition, which teaches cars how to assess and react to certain hazards.

Image recognition was arguably the most impactful first-wave application of AI technology, argues Xuesong Zhao, manager of the Polar Capital Automation and Artificial Intelligence fund. Tom Riley, co-manager of the Neutral-ratedAxa Framlington Robotech fund agrees, saying that vision systems have come on leaps and bounds recently. He holds JapansKeyence (6861), which develops manufactures automation sensors and vision systems used in the automotive industry. As the dominant player in the machine vision market, the company has a narrow moat from Morningstar analysts.

Modern cars already have some element of AI, particularly in hazard awareness and automatic parking, but Riley says drivers are not yetready for the full hands-off, eyes-off autonomous driving experience. Still, S&W's Ford argues that fully autonomous vehicles may become mainstream sooner than we think, say five to 10 years time, rather than 20.

Some of AIs most high-profile wins to date have been in the medical sphere, and that is where many fund managers are focused. Robots are now routinely used alongside surgeons and Nasdaq-listed Intuitive Surgical (ISRG) makes Da Vinci robots that perform millions of surgical operations every year. The company is the fourth largest holding in the Axa fund.. Axas Riley has positioned around 20% of the fund into the healthcare sector because he thinks it provides useful diversification away from the tech giants.

Ford also owns US firm iRhythm (IRTC), which uses an AI platform to warn people that they are at risk of cardiacarrhythmia, irregular heart movements that can potentially be fatal. He cites this as an example of AI's strength in capturing large amounts of real-time data and improving how it interprets the information.

Away from robotic surgery and self-driving cars, where else do fund managers see future opportunities? Polar CapitalsXuesong thinks natural language processing (NLP) is likely to be the next growth area for AI, although not without its challenges. He thinks that teaching computers to read and analyse documents would be truly transformational in many industries. He cites legal, financial and insurance companies as some of the biggest beneficiaries of this trend in the coming years. For example, complex fraud trials often involve millions of documents having a computer to sift through them would speed up the legal proceedings and keep costs down.

Ford, meanwhile, thinks industries such as mining and oil, which have so far been late adopters of AI, could start to change, and also expects greater use of AI in education. That trend could be accelerated by the Covid-19 crisis, where schools and universities have been forced to go virtual in the lockdown. AI, then, could be a natural next step for students to work semi-independently with tailored curriculums.

AI is only as good as the data on which it stands, Ford says. And with younger people less reticent to share their data than older tech users, AI is only going to improve in the coming years.

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Artificial Intelligence in the Covid Frontline - Morningstar

(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

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