Qamar Ul Islam Yes, you are accurate. AI and machine learning are being applied in a number of ways in healthcare to enhance patient outcomes and increase the efficiency of the healthcare system.

AI and machine learning algorithms may be used to interpret medical pictures such as X-rays, CT scans, and MRIs in medical imaging. These algorithms can assist radiologists in more correctly and swiftly identifying illnesses and anomalies in pictures.

AI and machine learning may be used in diagnostics to examine a patient's symptoms, medical history, and test data to help in an illness diagnosis.

Machine learning may be used in personalized medicine to examine a patient's genetic data and medical history to find prospective therapies that are more likely to be beneficial for that individual.

Machine learning may be used in drug development to examine enormous volumes of data on chemical compounds in order to identify possible new medications.

Machine learning may be used in population health to evaluate data on huge groups of individuals to identify those at risk of getting specific diseases and focus preventative interventions to them.

Overall, the application of AI and machine learning in healthcare has the potential to enhance patient outcomes, save costs, and increase system efficiency.

Fatemeh Khozaei Thank you Dr,Yes Indeed, you are accurate. In population health, machine learning may be used to evaluate data on large groups of people and uncover patterns and trends that may suggest an elevated risk of certain diseases. Individuals who are at risk of acquiring certain diseases can be targeted for preventative measures, thereby lowering disease incidence and increasing community health.

Machine learning algorithms may examine many sorts of data, such as demographic information, medical history, and laboratory test results, to identify people who are at risk of getting specific diseases. Algorithms, for example, can be trained to recognize patterns in electronic health data linked with an increased risk of diabetes or heart disease.

Yes, agree. I am thinking to use AI in upgrading the intelligence report to be more evidential along with reasonable. We have to build products based upon AI and ML to counter complex problem in every sector.

ML Algorithms can also be used to study the extend to which risk factors contribute to a particular disease given a dataset. Such research results can be used to develop warning systems.

AI and the rest of the programming techniques could be used to solve a particular class of problems, familiar to programmers.

However, it is necessary to switch to the development of artificial autonomous subjective systems, if we would like to find rational behavior in prematurely unknown circumstances.

Thank you Michael A. Zeldich and Nickson Ibrahim for participating in this discussion. I agreed AI can be used for risk factors and for solving a set of problems that is really difficult to comprehend.

Very well detailed on the benefit of AI/ML, Qamar Ul Islam. I wish to reiterate a caveat to the use of technology. As you have rightly captured in your response, the technology is best suited for automation of repetitive operations.

Though several health care applications are there, and several solutions are being proposed for use of AI/ML in diagnostics, we have to be careful - there is always a chance of wrong classification by the system. Relying completely on the system can be with certain risks. The system can help in confirming one's diagnostics rather than use it as the primary tool for diagnostic.

Artificial intelligence (AI) and machine learning (ML) are being applied in a variety of fields to tackle complex problems and boost efficiency. Some examples include too: Communication of Science; Healthcare: Finance: Transportation: AI and ML are being used in transportation to optimize logistics and supply chain management, improve traffic flow, and develop autonomous vehicles. Agriculture: AI and ML are being used in agriculture to optimize crop yields, improve water and soil management, and predict weather patterns. Energy: AI and ML are being used in the energy sector to improve the efficiency of power generation and distribution, optimize the performance of renewable energy systems, and improve the management of electric grids. Retail: AI and ML are being used in retail to optimize pricing, inventory management, and customer experience. Cybersecurity: AI and ML are being used in cybersecurity to detect and prevent cyberattacks, improve network security, and analyze large datasets of security-related information.

Last but not least and ML are being applied in various ways to improve communication of science and make it more efficient. For example: Text summarization: AI and ML models can be used to automatically summarize scientific articles, making it easier for researchers to quickly understand the key findings and implications of a study. Content creation: AI and ML models can be used to generate new scientific content, such as abstracts, summaries or even full scientific papers.Natural Language Processing (NLP): AI and ML models can be used to analyze large amounts of scientific text, such as research papers or scientific literature, to extract key insights and identify patterns. Translation: AI and ML models can be used to automatically translate scientific papers and other scientific content, making it more accessible to researchers and scientists who speak different languages. Automating literature review: AI and ML models can be used to automatically search, identify, extract and analyze relevant literature which can help researchers to speed up the process of literature review. Personalized communication: AI and ML models can be used to personalize the communication of scientific information, by providing tailored recommendations or summaries based on the user's background and interests.

Improving scientific communication: AI and ML models can be used to analyze and improve the way scientific information is communicated, for example, by identifying language or phrases that are confusing, and suggesting alternative ways to communicate the information.

These are a few examples of how AI and ML are being applied to tackle complex problems and boost efficiency in the communication of science. The increasing use of AI and ML in this field can enhance the speed, accuracy and the accessibility of scientific information, making the process of scientific discovery more efficient and effective.

@Emiliana, you are absolutely right. AI and, in general, programming technologies are used in zillions of applications where an algorithmic description is possible, but cannot help with such simple to us tasks as housekeeping, for example.

Instead, the Artificial Subjective systems could.

Michael A. Zeldich Yes, AI and programming technologies are used in a wide range of applications where an algorithmic solution is possible. However, tasks such as housekeeping, which involve a subjective element, may not be as easily automated. Artificial subjective systems, also know as AGI, are being developed to address this issue, but their development is still in early stages.

@Emiliana, AGI is one of many approaches to programming. Therefore, AGI systems cannot be artificial subjects and will never become some artificial subjective system.

Their own subjective experience determines a subjective system's behavior. Because of that, it could be of any kind. That feature makes subjective systems a universal tool for solving problems.

I formulated the concept that allowed the development of artificial subjective systems, but they will not be developed anyhow soon because present science still needs an approach to it.

Emiliana Minenna and Michael A. Zeldich Thank you for your participation, AI and machine learning are being utilized in a variety of sectors to increase productivity and tackle complicated challenges, including scientific communication. AI and ML are being utilized in science communication for text summarization, content generation, NLP, translation, automated literature reviews, tailored communication, and better scienctific communication. These applications have the potential to improve the speed, accuracy, and accessibility of scientific information, hence making scientific discovery more efficient and effective.

Researchers have described how to use a type of artificial intelligence (AI) called machine learning to help sift through the reams of data coming from telescopes that are searching for extraterrestrial intelligence...

https://www.nature.com/articles/d41586-023-00258-z

Ljubomir Jacić Machine learning, a subset of artificial intelligence, may be used to examine massive volumes of data collected by telescopes in search of alien intelligence. This can aid in the identification of patterns and signals in data that could suggest the presence of alien life. A new paper in the journal Nature describes the procedure.

From steam power and electricity to computers and the internet, technological advancements have always disrupted labor markets, pushing out some jobs while creating others. Artificial intelligence remains something of a misnomer – the smartest computer systems still don’t actually know anything – but the technology has reached an inflection point where it’s poised to affect new classes of jobs: artists and knowledge workers...

https://www.vox.com/future-perfect/2023/1/4/23538647/artificial-intelligence-chatgpt-openai-google-meta-facial-recognition

https://insights.cermacademy.com/405-ai-and-the-future-of-work-lynn-parker-et-al-authors/

@ Ljubomir Jacić

Dear Ljubomir, it is true that "Artificial intelligence remains something of a misnomer – the smartest computer systems still don’t actually know anything". Despite all advances in programming, it is impossible to make a programmed system capable to understand or be fully autonomous and creative. Having these abilities in artificial systems required making them persons, which is impossible on basis of programming.

Why artificial intelligence needs to understand consequences

A machine with a grasp of cause and effect could learn more like a human, through imagination and regret...

https://www.nature.com/articles/d41586-023-00577-1

@Ljubomir Jacić

“Bhattacharya's idea was to create neural networks that could profile the genetics of both the tumor and a person's immune system and then predict which people would be likely to benefit from treatment.”

Did you see any signs of understanding in this explanation of neural network behavior?

Only subjective systems can exhibit behavior that clearly demonstrates a level of understanding. Programmable systems of any kind are incapable of doing this.

Manufacturing data : better use of production and related data to predict machine failure, optimize processes, increase output or reduce energy consumption...

There are six attributes that determine if a data source can be integrated with other real-time production data to create useful models to support decision-making...

https://sightmachine.com/blog/manufacturing-data-readiness-assessment/?utm_medium=email&_hsmi=249382194&_hsenc=p2ANqtz-8T7FWTY3P4bCBxdK6nku_f25DGAMF4g5C7Rb8kd_iN_M_VFWLxdhTKYSfli2J0YNEssBF3JTsGglhe75KyIRhebHrH3Q&utm_content=249382194&utm_source=hs_email

https://sightmachine.com/blog/manufacturing-analytics-what-you-need-to-know-about-data-fitness-and-readiness/

Ljubomir Jacić

You draw attention to the importance of preparing information to simplify its integration into existing automated control systems. This important task is solved by formalizing messages, carried out with the help of algorithmic descriptions of the procedures for recognizing and transforming messages to the forms accepted in the given control system.

However, the question of using messages based on their meaning cannot be raised in programmable systems.

Only automated artificial subjective information processing systems can cope with the

inexhaustible flow of messages falling on control systems, as they will understand the meaning

and importance of the information received.

As generative AI models grow larger and more powerful, some scientists advocate for leaner, more energy-efficient systems...

As generative AI models grow larger and more powerful, some scientists advocate for leaner, more energy-efficient systems...

By increasing the number of connections between systems’ artificial neurons and feeding them ever more data, AI could eventually solve tasks that are supposed to require reasoning. Other scientists think this ‘bigger is better’ approach will only suck up more electricity. They hope that mimicking aspects of the brain, such as feedback connections and firing neurons, will help AI to become smaller and more energy-efficient — and maybe even give it the ability to reason...

https://www.nature.com/articles/d41586-023-00641-w

Ljubomir Jacić

I am sorry, but that is an illusion. Reasonable behavior could be formed autonomously only by subjective systems, and they could exist only in their own body.

Once again, programmable systems cannot be full-pledged persons and cannot be fully autonomous, creative, or reasonable because their functioning is based on the subjective experience of programmers, not their own.

Dear Michael A. Zeldich , do not post answers just for the sake of your score! I have never said that AI is invariant of man! No way! Read again my contribution!

I doubt your expertise in the field of AI. Yes, I do!

Ljubomir Jacić

"By increasing the number of connections between systems’ artificial neurons and feeding them ever more data, AI could eventually solve tasks that are supposed to require reasoning."

That is exempted from your post placed 20 hours ego.

Autonomous reasoning could be possible only for a subject, biological or not, isn't matter.

There is no requirement to be invariant to a man, and I am not talking about AI.

I am just saying we must design artificial subjective systems, instead of programmable, to have artificial systems capable of independent reasoning.

With the increasing adoption of AI and machine learning across virtually all industries, the need for effective MLOps -- or, machine learning operations -- is more important than ever. MLOps is a field that focuses on the deployment, management and maintenance of machine learning models in production environments. It's rapidly evolving as researchers, engineers and practitioners work to address the various challenges that arise in ML production environments...

When looking into MLOps platforms, these capabilities cannot be overlooked. They serve as foundational requirements that promote efficiency, trust and confidence throughout the machine learning pipeline. Many organizations already recognize this level of importance...

https://www.techtarget.com/searchenterpriseai/opinion/Its-not-too-early-for-MLOps-automation-and-governance?utm_campaign=20230308_AI+news+update%3A+The+latest+on+ChatGPT+and+generative+AI&utm_medium=email&utm_source=MDN&asrc=EM_MDN_262370709&bt_ee=rt8xAocbGT1VAnbvpF1hRbwdC0l1GC3EvInAticivSbdI4TxYyFMcwvCv5G%2F0L4mV1tP2BQCRtlKoLCSzUtYAA%3D%3D&bt_ts=1678299525696

Dear researchers,

Please refer to a recent published paper on ChatGPT and Learning Motivation

Article Impact of ChatGPT on Learning Motivation: Teachers and Stude...

All my best!

AI can only partially address some of humanity’s problems, where sustainability is essential. How well the data and techniques it is trained on are one of the significant AI constraints. As a result, AI systems are limited to making predictions based on the data they have been provided. Another drawback is that AI needs creativity, empathy, andother human-specific abilities. AI systems cannot conceive creatively or comprehend nuanced human emotions since they are only meant to carry out specified jobs. Users should verify the information from reliable sources before relying on ChatGPT responses...

(PDF) An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. Available from: https://www.researchgate.net/publication/369026828_An_era_of_ChatGPT_as_a_significant_futuristic_support_tool_A_study_on_features_abilities_and_challenges [accessed Mar 16 2023].

A very critical component that must be highlighted is how AI can be applied to address climate change and sustainability issues in those field.

Nevertheless, my thought on the general application of AI in these fields is that although AI comes with many benefits yet, many questions around the ethical and responsible use of AI in these fields must also be addressed. One must not just assume that everyone has the best intentions or motivations for using AI. I have outlined and discussed ethical issues surrounding AI usage which can be found here...

https://eyowhite.com/can-engineers-use-ai-responsibly/

Meanhile, a very good article regarding the application of AI to solve climate change is given here...

https://www.theengineer.co.uk/content/opinion/comment-using-ai-to-tackle-climate-change

Now, machine learning programs seem to be getting equally good results — and more are coming thanks to millions of dollars being invested in the effort by the Defense Advanced Research Projects Agency (DARPA). Just as ChatGPT is trained using lots of text, these paper-evaluating systems are trained using data gathered from painstaking attempts to replicate hundreds of studies. The systems are then tested using studies they haven’t seen.

Early results suggest that the robots are just as good at filtering out the noise as 100 human experts — and apparently much better than human editors at scientific journals and newspapers...

https://www.washingtonpost.com/business/2023/03/17/artificial-intelligence-should-help-vet-new-academic-research/ba5b548a-c4ca-11ed-82a7-6a87555c1878_story.html

Technology and analytics can boost the overall resilience of the system, enabling companies in energy-intensive segments to get power from different sources in a seamless and cost-effective manner. Artificial intelligence, data analytics and the wider digital transformation provide the speed and scale of changes required to address the climate crisis as well as operational challenges. Whilst often under-represented, they are demonstrably as important as the deployment of renewable energy sources to cut carbon emissions...

https://www.automation.com/en-us/assets/white-papers/white-paper-digitalize-to-decarbonize

Scientists have developed the first non-invasive method of determining the gist of imagined speech, presenting a possible communication outlet for people who cannot talk. The thought-to-speech technology relies on functional magnetic resonance imaging and large language models...

Neuroethicists are split on whether a study that uses brain scans and AI to decode imagined speech poses a threat to mental privacy...

https://www.nature.com/articles/d41586-023-01486-z

ChatGPT has distinguished itself uniquely in the field of medicine and healthcare, exhibiting substantial potential in assisting medical professionals due to its advanced capabilities. As demonstrated in a study by Kung et al., ChatGPT’s aptitude in processing intricate medical and clinical information resulted in a commendable performance on the United States Medical Licensing Examination(USMLE), often surpassing the passing threshold. Intriguingly, despite its general content training, ChatGPT outperformed PubMedGPT, a model specifically trained on biomedical literature, indicating that a combination of broad-based and domain-specific models could substantially boost accuracy. In the study of Johnson et al., 33 physicians across 17 specialties generated 284 medical questions and graded ChatGPT-generated answers to these questions for accuracy. They found out that ChatGPT generated accurate answers and got completeness scores across various specialties, question types, and difficulty levels.

Nonetheless, despite its impressive proficiency in the medical field, it’s crucial to understand the potential limitations of its application, especially within such a high-risk domain where issues of responsibility are heightened. Of particular concern is the use of ChatGPT as a tool for medical consultation among non-professional users. It’s vital to underline that this AI should not be regarded as a substitute for professional medical advice... (PDF) ChatGPT is a Remarkable Tool -- For Experts. Available from: https://www.researchgate.net/publication/371347021_ChatGPT_is_a_Remarkable_Tool_--_For_Experts [accessed Jun 12 2023].

It is incredible how algorithmically driven statistical language models can mimic the work of experts while achieving better results.

However, there is an advanced way to get professional help from artificial systems by replacing software-driven systems with universal subjective systems.

These systems can indeed be broad-minded and consider many more facts when analyzing a situation.

Personnel will only have to motivate such systems to perform the required actions.

Artificial intelligence seems to be everywhere these days, but it’s been a part of machine vision technology for years now. Given those years of use and the growth of new capabilities in AI, what’s the outlook for AI machine vision use in manufacturing.

How the use of artificial intelligence in machine vision systems boosts quality and throughput...

https://www.automationworld.com/factory/sensors/podcast/33006696/is-aipowered-machine-vision-beneficial-to-manufacturing

It’s yet another summer of extreme weather, with unprecedented heat waves, wildfires, and floods battering countries around the world. In response to the challenge of accurately predicting such extremes, semiconductor giant Nvidia is building an AI-powered “digital twin” for the entire planet.

This digital twin, called Earth-2, will use predictions from FourCastNet, an AI model that uses tens of terabytes of Earth system data and can predict the next two weeks of weather tens of thousands of times faster and more accurately than current forecasting methods...

https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/

It is very important to have accurate predictions over a sufficiently long period of time, but it is much more important to understand long-term trends. The increase in the average annual temperature is out of control by ground means. This was due to the activation of a positive feedback between the temperature increase and the release of bacterial methane at high latitudes.

In addition, the temperature in the tundra is rising faster than the average for the Earth. Apparently this is due to heating under the influence of internal Earth factors. Humanity does not have the means to stop the rise in temperature, and as a result, life on earth will disappear. This will happen regardless of human activity. There is only one way to preserve the conditions for the existence of life. It consists in the fact that it is necessary to regulate the solar irradiation of the Earth's surface. This can be done in many ways, but the most acceptable one requires a screen between the Earth and the Sun.

Continuation

Such a screen can be created using automated systems capable of extracting the necessary raw materials in space, processing it, manufacturing the necessary structures, assembling such a screen and stabilizing its position. The implementation of such a project will require the combined efforts of all countries, but we are not ready for this. We consider it necessary to take from each other that which was previously taken from others by force and cunning. The current cycle of development of life on Earth has been going on for more than 5 million years. After the evaporation of water and the disappearance of life on Earth, a new cycle of development will begin. Unfortunately, due to the aging of our luminary, the five million years necessary for its development will not be available.

So, those who will disappear before the Sun reheats the planets of the Solar System and swallow them will be happy.

More than 90% of American workers want AI-supported data collection in the workplace, according to a recent study commissioned by SS&C Blue Prism. The findings show that most workers are open-minded about seeing more automation in the workplace and believe AI’s impact on their role will be positive...

Workers say data plays a critical role in business operations, with three-quarters of respondents seeing its role as “extremely important.” Over half (58%) of workers are already using some form of automation...

Businesses’ movements towards automation are a response to a rapidly changing work environment and rising competitive pressures. The research indicates that the future of the workforce is one that utilizes AI and automation to maximize the impact of data...

https://www.automation.com/en-us/articles/july-2023/workers-optimistic-ai-automation-jobs

“I see an opportunity to learn from so many mistakes of the past when it comes to oppressive systems powering advanced technologies.” - Artificial intelligence researcher Joy Buolamwini says that the way companies train their machine-learning models, by indiscriminately grabbing data without the owners’ consent, is unethical — a form of “data colonialism”...

https://www.technologyreview.com/2023/10/29/1082632/joy-buolamwini-were-giving-ai-companies-a-free-pass/

@ Ljubomir Jacić "I see an opportunity to learn from so many mistakes of the past..."

I would like to draw your attention to one more. The obvious fact, tells us that receptors are unable to extract information from their surroundings, breaking down all cards house of scientific explanations of behavior development by living things.

Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.

Explainable AI is used to describe an AI model, its expected impact, and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision making. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production. AI explain ability also helps an organization adopt a responsible approach to AI development...

Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explain ability and accountability. To help adopt AI responsibly, organizations need to embed ethical principles into AI applications and processes by building AI systems based on trust and transparency...

https://insights.cermacademy.com/365-explainable-ai-bill-pomfret-ph-d/

Google DeepMind has turned its hand to the intensive science of weather forecasting — and has developed a machine-learning model that outperforms the best conventional tools as well as other artificial intelligence (AI) approaches. The model, called GraphCast, can run from a desktop computer. Its predictions are more accurate than those of conventional models, and it makes them in minutes, rather than hours...

https://www.nature.com/articles/d41586-023-03552-y

10 AI Predictions For 2024

There might even be entirely new types of AI that go beyond the transformer architecture used by almost all generative models so far. At the same time, proprietary AI models will probably continue to outperform open-source approaches...

https://www.forbes.com/sites/robtoews/2023/12/21/10-ai-predictions-for-2024/?sh=1872c24b4898