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The book Artificial Intelligence Based Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics gives a comprehensive explanation of the role of machine learning and artificial intelligence in cancer nanomedicine. It presents 10 chapters that cover multiple dimensions of the subject. These dimensions are:

- The need of AI and ML in designing new cancer drugs

- Application of AI in cancer drug design

- AI-based drug delivery models for cancer drugs

- Diagnostic applications of AI

- Intelligent nanosensors for biomarker profiling

- Predictive models for metastatic cancer

- Cancer nanotheranostics

- Ethics of AI in medicine

The book serves as a reference for scholars learning about cancer diagnostics and therapeutics. Biomedical engineers who are involved in healthcare projects will also find the concepts and techniques highlighted in the book informative for understanding modern computer-based approaches used to solve clinical problems.

To overcome this challenge application of artificial intelligence (AI) along with nanomedicine can serve as a helping tool for optimizing the drug and dose parameters. Conversion between these two fields enables up gradation of patient data acquisition, improved design of nanomaterials. In cancer the high intratumor and interpatient heterogeneity behavior is quite difficult to plan for a rational therapeutic design and further to analyse their output is extremely difficult. In this scenario application and integration of AI based approaches such as pattern analysis and algorithms models can bridge the gap, for improved accuracy of diagnostics and therapeutics. With the help of AI algorithms large datasets can be processed, complex patterns can be exploited for improvement of nanotechnology based design for cancer diagnostics and treatments. Application of precision cancer nanomedicine is highly essential as every patient is unique. Patient groups have varied differences, such as age, gender, height, eye color, blood type as well as unique molecular signatures, which leads to different phenotypic changes and wide-ranging of drug responses amongst patients. Further, patients vary substantially with regard to the dosages needed to attain drug synergy, and desirable degree of drug exposure to reach optimal treatment outcomes. Optimization of dosing in oncology highly essential, often dose reductions are implemented to manage treatment-related toxicity and it faces key challenges while translating it to a clinical practice for dosing establishment. This type of challenges can be addresses via recent advances in AI.

In this regard, AI plays a critical role in reconciling this space into an actionable treatment response.

In the era of computer aided technology, almost all field are involved with information technology. AI is the amalgamation of computer ethics and bioethics. During application all aspects of research technology pertaining to the their field needs to be ethics free so that they can be freely used for human welfare. These AI enabled novel technologies based therapy needs to be followed at all levels the ethical principles like human privacy, dignity, justice, morality and fair access to the knowledge for possible beneficial of therapy. The book entitled Artificial Intelligence Based Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics. by Dr. Fahima Dilnawaz and Dr. Ajit Kumar Behura exemplifies various modes of the application of AI towards cancer nanomedicine and its related aspects of bioethics. This book indeed is a modest effort to the several approaches of cancer nanomedicine having a broad readership that includes researchers, scholars, academicians, clinicians and their allied partners. The authors have made intensive efforts by inviting various reputed contributors to contribute their views.

About the Editors:

Dr. Fahima Dilnawaz is a Women Scientist at the Department of Science and Technology, in the laboratory of nanomedicine of the Institute of Life Sciences, Bhubaneswor, Odisha, India. She received a doctorate in botany from the Mal University, on M.Phil from Berhampur University, on ITC fellowship from the Hungarian Academy of Sciences, and o post-doctoral fellowship horn the Department of Biotechnology. Being a dynamic researcher, she hos on h.index of 17, her more than 30 scientific papers, review articles, 17 book chapter in reputed journals os well as publishing house have fetched citations of around 2413. Her expertise hos been much admired for which she was invited to deliver sessions in various scientific gatherings in India as well as abroad. She has co-authored the book "Remedial Biology' and co-edited book Nanomedicine Approaches towards Cardiovascular Disease'. To her credit, she has coauthored two patents, which hove acclaimed approval from the USA, Europe, Australian and another one from Indio. The patented technology was commercialized for "magnetic cell separation kit (Quicksort TM)'. She is serving as a reviewer for various Nano medicinal journals, as well as on associated editorial board member.

The author, Dr. Ajit Kumar Behura, is a senior faculty working in the Department of Humanities and Social Sciences, Indian Institute of Technology, Dhanbad-826004. He has earned his doctorate in philosophy from the Central University of Hyderabad. His main areas of teaching and research interests are applied ethics, environmental ethics, and ethics in scientific and technological research, engineering ethics, sustainable development and Indian philosophy. Under his guidance, 9 Ph.D. students were supervised in different areas of ethics and philosophy. He has 39 research publications in index journals. There are a number of training programs, consultancy and projects to his credit. He is a life member of several professional bodies.

Keywords:

Artificial intelligence, Nanomedicine, Nanotechnology, Target site, Cancer nanomedicine, Deep learning, Drug discovery, Machine learning, Robotics.

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Artificial Intelligence Centered Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics - EurekAlert

Artificial intelligence that helps drivers find parking spaces in busy city centres is being developed at the University of Bath, writes the British university in a press release.

The software will also incentivise drivers to cooperate with local councils in their quest to keep pollution within safe limits in busy urban centres, as part of a far-reaching programme designed to reduce toxic air in city centres.

As city populations continue to grow (its expected that the worlds urban population will more than double between now and 2050, with 7 out of 10 people living in cities), the need to use new technology to mitigate pollution and congestion becomes ever more pressing. However, any measures introduced to curb the use of cars in cities will also need to factor in the needs of people from rural communities who may rely on their cars to access essential services.

The new project is a collaboration between computer scientists at Bath and Chipside Ltd, a leader in the world of parking and traffic management IT. The potential for the new technology to be adopted by councils across the UK is high: currently, Chipside is responsible for supplying digital parking permits and cashless parking to over 50 per cent of councils in the UK.

During the course of its 2.5-year partnership with Bath, Chipside will develop a suite of software designed to help local councils comply with milestones on parking, city access and vehicle movement, as set out in the governments ten-point plan. This plan, launched in November 2020, is using public and private investment to nudge the UK towards reaching its objective of net-zero carbon emissions by 2050.

Under the Environment Act, which became law in 2021, local governments are strongly incentivised to roll out smart city initiatives such as those proposed in the Bath-Chipside project, as increasingly they will likely face heavy fines if they miss environmental targets. One important target currently being proposed is to keep fine particulate matter (PM2.5) which originates from the combustion of fuel within limits recommended by the World Health Organisation.

Air quality in European cities remains poor

Read more about the countries involved in this project Stefan Thorliefsson was 103 years old and was featured in a piece by the Icelandic news magazine Visir while enjoying a game of golf.

The new project will use the latest AI technology to create services that allow local authorities to analyse vast amounts of data on driver behaviour and to better control local travel patterns.

Dr zgr imek, deputy head of Computer Science at Bath and leader of the Artificial Intelligence Research Group, will be the academic lead for the project. She explains why it makes sense for services to be developed to change driver behaviour during the last mile of their journey into an urban centre.

Imagine you are travelling into town on a Thursday morning and without you knowing it, your car is the one engine that triggers the town to go over the allowed pollution level, resulting in a big fine for the local government. Now imagine that instead of this happening, you receive a suggestion to park in another, better place, and you are issued a free parking space. Youre also shown a low-traffic route to your free parking space. The whole service would be tailored to your individual needs while also helping towards net-zero objectives.

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Artificial Intelligence makes parking in busier cities easier - Innovation Origins

Vinita Gupta-

Vinita Gupta is a Silicon Valley Entrepreneur and was the first Indian-American woman to take her company public. Since retiring, she has propelled herself through her journalism, mentoring women entrepreneurs and playing competitive bridge at the highest levels. She has won several National titles in bridge.

The world is excited about Artificial Intelligence (AI).In the last 5000 years, starting with the invention of thewheel, machines have saved humans from existential threats despite our smaller-sized bodies.

Human civilization has evolved due to our superior skills in taking machines to the next level because machines did the kinds of work that humans could not.Consequently, we could become city dwellers with high rises.Mining became possible.Drilling for oil in the ocean became possible.

As we well know human ingenuity has been at play, which helped rationalize how we overcome threats and adversities.

Now we are taking our intelligence to the next level, by making machines more intelligent.That is the ultimate promise of Artificial Intelligence (AI), hoping machines will get smarter than we are.

Will that be good or bad?

What have we learned by becoming super trainers of machines?

Tesla has the most real-life experience with AI, when they put autonomous cars on the streets, equipped with drivers to train the cars.The starting point of self-driving cars is AI algorithms based neural networks similar to those found in the brain.

Say the goal is to have aneural networkrecognize photos that contain a dog. The concept of neural networks entails thatthe machines be not explicitly told what makes a dog.When the computer sees something furry, has a snout, and has four legs it may conclude that it is a dog.Then the machines are shown a lot of images of dogs more data. With minimal training by reinforcement learningwhen the machines are told when they make a mistake the computers start learning from their own mistakes and begin to recognize dogs more reliably.

Tesla has hundreds of thousands of hours of experience based on more than ten years of data collected from training autonomous cars. Based on his experience is why in2017 Elon Muskwarned a bipartisan gathering of U.S. governors that AI is afundamental risk to the existence of human civilization.

Elon now wants to focus on Tesla Bot instead, incorporating Teslas automotive artificial intelligence and autopilot technologies.A car is a Bot on steroids at those speeds. He thinks that a Bot should be perfected first.

Major Hurdles to AI:

1. Humans know only what decision an autonomous car has made, but not why.AI does not permit reverse engineering.Letting machines learn on the fly, on their own, is dangerous when it comes to life-and-death situations or what they might do in the future.

2. Machines by definition do not have common sense, which comes from lived experiences.These broad set of rules of thumb are impossible to be incorporated into machines.Common sense is essential for the robots to operate usefully and safely in the human environment.When a deer jumps in front of an autonomous car, the algorithm will not know what to do. It is even harder to teach machines to make moral or ethical decisions.

3. Intelligent machines will not know not to kill the human specie that helps them survive.Machines will never evolve as organisms do perthe theory of natural evolution.

The idea of Teslas autonomous autos, with current technology, can work for delivery trucks, but it needs infrastructure. Such trucks for example can use dedicated special lanes with barriers maybe only at night.There can be stations along the way where the drivers can hop in, for safe last-mile delivery to the warehouses inside the cities.During commute hours similar concept could be applied to carpoolers.This may not even require the expansion of freeways.

Similarly, smaller walking or even flying robots for making home deliveries sounds promising. On city streets, they can drive in special lanes dedicated to them, just like bike lanes.Integrating the concept with delivery hubs on major street corners may be a more practical solution.

With more people working remotely, and reduced delivery truck traffic on highways and city streets, AI can help us dramatically reduce the carbon footprint to save the environment.

Musk is also planning to introduce a home robot as a personal valet. Some people think it will eliminate hired home help.Another example of machines replacing human labor.

Last but not least, regulatory bodies need to start building expertise in AI, expediently.When in 2018 Facebook CEO Mark Zuckerbergtestifiedbefore a joint hearing in Congress to address steps the social network was taking in light of the Cambridge Analyticas connection with the 2016 presidential elections interference,it was scary to see how little older legislators knew about social media. This was more than 12 years after Facebook was open for general business beyond university campuses.

If we have AI development without regulatory oversight, we will pay a catastrophic price when applied to warfare.According toBill Gates, A.I. is like nuclear energy both promising and dangerous

The rest is here:
Artificial Intelligence : Revolutionary without being Evolutionary - indica News

By the 1950s, several scientists, philosophers, mathematicians, and others had AI incorporated inside their minds. But human beings have now learned how to transform the concept into reality. In recent times, AI has widespread applications everywhere.

AI has the potential to learn quickly from a significant amount of data. It ensures that some of the most technical issues can be tackled without hassle. But it can also feed your excitement and squeeze out your creativity at work.

AI and Photo Editing

AI has transformed traditional photo editing and made it less time-consuming. It can take your hands off repetitive and manually-intensive tasks. AI understands what we want and helps us achieve it quite easily.

You will come across multiple AI-powered photo editing tools in the market. Each device has its own set of unique features and reduces the workload of photo editing. You can efficiently perform a lot of tasks with a single click. For instance, you can add textures, detect faces, and colorize and sharpen your photos.

You can also improve low-resolution images using AI-powered editing tools. Just imagine you got an excellent click in front of the Eiffel Tower. But a random stranger photobombed without their knowledge. Thanks to AI, you can now find a background remover tool like Slazzer. It is a powerful tool that helps businesses save time and money to make their products stand out against the background.

Removing Unwanted Objects from Your Photos

In the photo editing sector, AI has vast applications. Photo editors use AI-based tools to enhance magazine covers, wedding photos, nature shots, and whatnot. In the future, AI-powered software will be developed to meet specific needs according to the requirements of different forms of photography.

Photo background remover tools like Slazzer have already made life easier for editors. But AI-based photoediting tools will become even better at removing backgrounds. They will be able to detect unwanted elements in a picture and correct the mistakes more accurately.

Researchers have developed AI technology to remove unwanted shadows from photographs. The algorithm can focus on two different types of shadows. Shadows from external objects and the ones due to facial features can be removed.

Professional images are usually taken in a studio with sufficient lighting. But when photos are not taken under ideal conditions, dark shadows might obscure some parts of the subject and accessible highlight other parts. The newly developed AI can address the problem by targeting the undesired highlights and shadows.

It can remove and soften the shadows until the subject is clear. With the background remover tool working in a more realistic and controllable way, it will have a higher value than images captured in casual settings. It is beneficial for fixing images shot under circumstances where the lighting cannot be controlled.

What Does the Future of AI-Based Photo Editing Look Like?

With time, AI will become more useful for editing backgrounds. It will be able to take into account minor details like a persons cloth or hair and add lighting that seems natural.

When you consider popular trends such as NFTs, you will see how we view and acquire art is evolving. New options for selling and packaging digital works are constantly on the rise. AI will play a firm role in their faster arrival at the final product. AI will also provide opportunities to amateurs who wish to try their hands at creating art.

Does AI Mean the Job of Professional Photo Editors Are at Risk?

Its no surprise that the rise of AI concerns specific individuals. In every industry, people are worried that AI will replace human skills. Photographers and photo editors believe that artificially edited images will take their jobs.

But the truth is AI will become a powerful tool for these individuals to improve their performances.

AI is constantly reshaping our workflow. It enables us to move faster without compromising on creativity. We need to embrace these new technologies and integrate them within upcoming software creations. This way, the photo editing industry will be able to become more sophisticated.

Summing up

AI is here to take the photo editing industry to a new level. But theres still a lot of time before machines can replace the need for human skills in the photo editing industry.

Meanwhile, tools like Slazzer, with their ability to remove unwanted objects from a photograph, will make the job easier for editors.

Disclaimer: This article is a paid publication and does not have journalistic/editorial involvement of Hindustan Times. Hindustan Times does not endorse/subscribe to the content(s) of the article/advertisement and/or view(s) expressed herein. Hindustan Times shall not in any manner, be responsible and/or liable in any manner whatsoever for all that is stated in the article and/or also with regard to the view(s), opinion(s), announcement(s), declaration(s), affirmation(s) etc., stated/featured in the same.

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How Can Artificial Intelligence Shape The Future Of Photo Editing? | Mint - Mint

TURIN, Italy--(BUSINESS WIRE)--Today, cybersecurity represents an essential priority in the implementation of new technologies, especially given the crucial role that they have come to play in our private and professional lives. Smart Homes, Connected Cars, Delivery Robots: this evolution will not stop and so, in tandem, it will be necessary to develop automated and AI-based solutions to combat the growing number of security threats. The risks from these attacks are attributable to several factors, such as increasingly complex and widespread digital networks and a growing sensitivity to data privacy issues. These are the themes that emerge from the new Cybersecurity Automation research conducted by Reply, thanks to the proprietary SONAR platform and the support of PAC (Teknowlogy Group) in measuring the markets and projecting their growth.

In particular, the research estimates the principal market trends in security system automation, based on analysis of studies of the sector combined with evidence from Replys own customers. The data compares two different clusters of countries: the Europe-5 (Italy, Germany, France, the Netherlands, Belgium) and the Big-5 (USA, UK, Brazil, China, India) in order to understand how new AI solutions are implemented in the constantly evolving landscape of cybersecurity.

As cyberattacks like hacking, phishing, ransomware and malware have become more frequent and sophisticated, resulting in trillions of euros in damages for businesses both in terms of profit and brand reputation, the adoption of hyperautomation techniques has demonstrated how artificial intelligence and machine learning represent possible solutions. Furthermore, these technologies will need to be applied at every stage of protection, from software to infrastructure, and from devices to cloud computing.

Of the 300 billion in investments that the global cybersecurity market will make in the next five years, a large part will be directed toward automating security measures in order to improve detection and response times to threats in four different segments: Application security, Endpoint security, Data security and protection, Internet of Things security.

Application Security. Developers who first introduced the concept of security by design, an adaptive approach to technology design security, are now focusing on an even closer collaboration with the operations and security teams, termed DevSecOps. This newer model emphasizes the integration of security measures throughout the entire application development lifecycle. Automating testing at every step is crucial for decreasing the number of vulnerabilities in an application, and many testing and analysis tools are further integrating AI to increase their accuracy or capabilities. Investments in application security automation in the Europe-5 market are expected to see enormous growth, around seven times the current value, reaching 669 million euros by 2026. A similar growth is forecast in the Big-5 market, with investments rising to 3.5 billion euros.

Endpoint security. Endpoints, such as desktops, laptops, smartphones and servers, are sensitive elements and therefore possible sources of entry for cyberattacks if not adequately protected. In recent years, the average number of endpoints within a company has significantly increased, so identifying and adopting efficient and comprehensive protection tools is essential for survival. Endpoint detection and response (EDR) and Extended detection and response (XDR) are both tools created to accelerate the response time to emerging security threats, delegating repetitive and monotonous tasks to software that can manage them more efficiently. Investments in these tools are expected to increase in both the Europe-5 and Big-5 markets over the next few years, reaching 757 million euros and 3.65 billion euros respectively. There are also a multitude of other tools and systems dedicated to incident management that can be integrated at the enterprise level. For example, in Security Orchestration Automation and Response (SOAR) solutions, AI can be introduced in key areas such as threat management or incident response.

Data security and protection. Data security threats, also called data breaches, can cause significant damage to a business, resulting in risky legal complications or devaluating brand reputation. Ensuring that data is well-preserved and well-stored is an increasingly important challenge. It is easy to imagine how many different security threats can come from poor data manipulation, cyberattacks, untrustworthy employees, or even just from inexperienced technology users. Artificial intelligence is a tool for simplifying these data security procedures, from discovery to classification to remediation. Security automation is expected to reduce the cost of a data breach by playing an important role in various phases of a cyberattack, such as in data loss prevention tools (DLP), encryption, and tokenization. In an effort to better protect system security and data privacy, companies in the Europe-5 cluster are expected to invest 915 million euros in data security automation by 2026. The Big-5 market will quadruple its value, reaching 4.4 billion euros in the same timeframe.

Internet of Things security. The interconnected nature of IoT allows for every device in a network to be a potential weak point, meaning even a single vulnerability could be enough to shut down an entire infrastructure. By 2026, it is estimated that there will be 80 billion IoT devices on earth. The impressive range of abilities offered by IoT devices for different industries, though enabling smart factories, smart logistics, or smart speakers, prevents the creation of a standardized solution for IoT cybersecurity. As IoT networks reach fields ranging from healthcare to automotive, the risks only multiply. Therefore, IoT security is one of the most difficult challenges: the boundary between IT and OT (Operational Technology) must be overcome in order for IoT to unleash its full business value. As such, it is estimated that the IoT security automation market will exceed the 1-billion-euro mark in the Europe-5 cluster by 2026. In the Big-5 market, investments will reach a whopping 4.6 billion euros.

Filippo Rizzante, Replys CTO, has stated: The significant growth that we are witnessing in the cybersecurity sector is not driven by trend, but by necessity. Every day, cyberattacks hit public and private services, government and healthcare systems, causing enormous damage and costs; therefore, it is more urgent than ever to reconsider security strategies and reach new levels of maturity through automation, remembering that though artificial intelligence has increased the threat of the hacker, it is through taking advantage of AIs opportunities that cyberattacks can be prevented and countered.

The complete research is downloadable here. This new research is part of the Reply Market Research series, which includes the reports From Cloud to Edge, Industrial IoT: a reality check and Hybrid Work.

ReplyReply [EXM, STAR: REY, ISIN: IT0005282865] is specialized in the design and implementation of solutions based on new communication channels and digital media. Reply is a network of highly focused companies supporting key European industrial groups operating in the telecom and media, industry and services, banking, insurance and public administration sectors in the definition and development of business models enabled for the new paradigms of AI, cloud computing, digital media and the Internet of Things. Reply services include: Consulting, System Integration and Digital Services. http://www.reply.com

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Reply: Automation and Artificial Intelligence Are the Strategic Keys for an Effective Defense Against Growing Threats in the Digital World - Business...