GROUP BLOG 1

  Maddalena Daniela Marchesini TP073377

AI IN THE HEALCARE SYSTEM

Articial intelligence is capable of solving many problems that we face nowadays. In healthcare, AI can help in different ways. From analysing data to personalise treatment plans according to the requirements.

AI can use its functions to analyze data and compare to assists medical teams in making diagnoses. For example, it can analyze scans and detect abnormalities and diagnose the patient.

AI can analyze patients' data (genes, history, even lifestyle), to provide personalized plans of health to ensure better outcomes in the patient's health.

AI-powered devices and publications can monitor patients' health 24/7. This allows early detection of health issues and timely intervention, especially for chronic conditions.

Overall, AI can help the healthcare system to improve, improve patient outcomes, reduce costs and enhance efficiency.

DAMIRA ABDULINA

Tasks that AI can solve

What can artificial intelligence do? A lot. The main thing is to formulate the task correctly and provide the algorithm with the necessary data. AI learns well and can understand speech, recognize questions, and decipher information from images and photographs. With the right approach, AI can guide a client from making an inquiry to solving a problem. If, for some reason, the request proves to be too complex for AI, it is transferred to a specialist.

One of the achievements that can be attained through AI is adaptive learning. The essence is that the digital platform on which a person learns analyzes their successes and mistakes and selects precisely those tasks that will be beneficial to them. This makes learning personalized.

A whole family of such digital AI-based solutions is already helping schoolchildren with mathematics. For example, a mobile application can suggest a child first take a small test and based on the results, create an individualized micro-learning lesson plan. The application can be used by preschoolers who are just learning to count, as well as ninth graders who need help mastering inequalities and trigonometric functions.

The AI tutor explains new topics, provides solution examples, checks exercises completed by the child, and gives feedback. One can even scan a mathematical problem from a textbook or write it on the screen—and get the solution and similar examples. Games are also available for children where they need to perform arithmetic operations.

Additionally, the application will offer courses for a broader audience (from elementary school students to university students) and besides mathematics, it also teaches chemistry, physics, statistics, and even the basics of accounting.

In theory, this could mean saving on tutoring sessions by replacing some of them with platform-based learning.

ANGELLE SULINK WONG

In this modern era, AI emerges as a powerful tool to confront the pressing challenges of environmental degradation and climate change. AI helps to provides potential methods to tackle environmental degradation and climate change. Machine learning algorithms use predictive analytics to correctly estimate air pollution levels, allowing authorities to undertake targeted measures. Furthermore, AI improves the integration of renewable energy sources by assessing demand patterns and weather conditions, increasing their use while decreasing dependency on fossil fuels. In disaster management, AI will be able to determine any early detection and reaction, reducing the effect of natural disasters like hurricanes and wildfires . Through its capabilities in monitoring agricultural practices and deforestation operations, AI empower us to take proactive step towards conservation. By analyzing satellite imagery and other data sources, AI can identify areas at risk and recommend strategies for sustainable land use(GO, 2022). Furthermore, AI allows for the tracking of carbon emissions, the identification of sources, and the recommendation of reduction methods, all of which contribute to the transition to a more environmentally friendly future ((Dwivedi et al., 2022). By leveraging AI in these areas, environmental concerns are able to be addressed effectively and mitigate the global effects of climate change. Through data-driven insights and proactive interventions, AI empowers decision-makers to make informed choices that promote sustainability and protect our planet for future generations.

ANVAR 

             As a person enjoying video-game industry as a consumer - I would rather focusing my efforts on implementing AI to solve in-game problems on a production stage and on process stage.

       Artificial intelligence (AI) is an integral part of video game creation and testing. It can be used to automatically discover and resolve issues in game builds. Here are some of the ways AI can help solve challenges in game development, I will describe it down below.

            1. Automated testing: AI can automatically play the game and find problems and glitches that human testers may miss. AI can evaluate player behavior data to discover issues in a game build.

            2. Problem prediction: Using machine learning, AI can forecast which regions of the game are likely to produce issues based on prior data. AI can assist optimize game performance by studying how various game factors effect performance. 

    However, it is important to note that applying AI to gaming is a hard process that necessitates extensive expertise of computer science and game design. AI in games is a collection of algorithms that control the behavior of NPCs in various settings. This may include algorithms from control theory, robotics, computer graphics, and computer science in general (Pixonic, 2019 Habr).

    Also, important to remember that, the game's complexity can present significant challenges for the AI. After all, game mechanisms in games like StarCraft II are far more complicated than those in Atari games. As a result, at a particular frame rate and with known hardware requirements, machine learning will not necessarily be capable of learning and interacting with the full game state.

AMNA

People working on AI initiatives today generally want to make valuable contributions to society and as big of an impact as possible. That’s why using AI to tackle many of the world’s deep-seated problems is top of mind: for example, personal transportation, health care and energy conservation. Fortunately, intelligence does not have to be solved at a system level, as tackling specific problems is often more efficient and productive in the long run. Breaking the effort into smaller, yet significant projects also gives teams the ability to better allocate their often-limited time and resources.
 
We should take an agile approach to the governance of AI
 we can benefit from artificial artificial innovation while we are figuring out how to regulate the technology 
Let me give you an example: Ford Motor produced the Model T car in 1908, but it took 60 years for the US to issue formal regulations on the use of seatbelts. This delay did not prevent people from benefitting significantly from this form of transportation. At the same time, however, we need regulations so society can reap sustainable benefits from new technologies like AI and we need to work together as a global community to establish and implement them

References:

Davenport, Thomas, and Ravi Kalakota. “The Potential for Artificial Intelligence in 

Healthcare.” Future Healthcare Journal, vol. 6, no. 2, June 2019, pp. 94–98,  www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/, https://doi.org/10.7861/futurehosp.6-2-94. 

 

Chesani F., Mello P., Milano M. Solving mathematical puzzles: a challenging competition for AI //AI Magazine. – 2017. – Т. 38. – №. 3. – С. 83-96.

Howe A. E., Von Mayrhauser A., Mraz R. T. Test case generation as an AI planning problem //Knowledge-Based Software Engineering. – 1997. – С. 77-106.

Chui M. et al. Notes from the AI frontier: Insights from hundreds of use cases //McKinsey Global Institute. – 2018. – Т. 2. – С. 267.

Dwivedi, Y. K., Hughes, L., Kar, A. K., Baabdullah, A. M., Grover, P. S., Abbas, R., Andreini, D., Abumoghli, I., Barlette, Y., Bunker, D., Kruse, L. C., Constantiou, I. D., Davison, R. M., Dé, R., Dubey, R., Fenby-Taylor, H., Gupta, B., He, W., Kodama, M., . . . Wade, M. (2022). Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action. International Journal of Information Management, 63, 102456. https://doi.org/10.1016/j.ijinfomgt.2021.102456

Pixonic. (2019, November 1). Как устроен гибридный игровой ИИ и в чём его преимущества. Habr. https://habr.com/ru/companies/pixonic/articles/473932/

Bunker, B. (2018, July 23). What problems should you solve with AI? Forbes. https://www.forbes.com/sites/forbestechcouncil/2018/07/23/what-problems-should-you-solve-with-ai/?sh=7bd9084037f2

Here are 3 big concerns surrounding AI - and how to deal with them. (2020, April 27). World Economic Forum.

 https://www.weforum.org/agenda/2020/02/where-is-artificial-intelligence-going/