AI AND MACHINE LEARNING PROJECTS

Various projects are carried out by us by combing AI and Machine Learning. We have achieved a huge milestone by carrying out these projects. Contact us to gain more knowledge insights in your research. The most challenging task for every scholar is selection of topics and merging it using correct tools and algorithms. Here our experts carry out research process by updated techniques and algorithms. Machine learning is a hot topic in trend today. We have covered a wide range of topics go through it and get inspired.

1. Why AI is such a controversial topic?

            Artificial Intelligence (AI) is an arguable topic because it increases a number of moral, social and philosophical questions. Here are the few critical terms that we consider about AI includes:

• Potential for bias: These AI systems are instructed on data and when the data is biased then our AI system will be distorted as well. This will lead our AI systems providing unfair and invidious decisions.
• Possibility for job displacement: We find that the AI systems become more enlightened, they are able to automate tasks which are recently done by manual effort. This will leads to a vast range of job displacement in low-wage and redundant jobs.
• Prospective for misuse: AI systems also used for dangerous activities like spreading wrong data, hacking into computer machines and sometimes physical threats also possible. This affects the data privacy and security of us while using AI mechanisms.
• Lack of transparency: There is insufficient accountability and mistrust in AI systems which are difficult and opaque when we try to interpret how they make decisions.
• Inference for human autonomy: The robustness of AI could pose an attack to human autonomy like that AI on decision-making of our lives such as what job we should do and who we should marry. This impacts us in making self-decisions.

            The above mentioned are fewer concerns that have raised about AI, but it is essential to be thoughtful and make straight-forward explanation about these problems so that we can create AI mechanisms that are beneficial to our public requirements.   

            The following are the list of few examples on how AI has been utilized and could use in a controversial way:

• Facial recognition: We utilize AI-powered facial recognition machines for law enforcement to detect suspects, but it also track people’s actions and disfavor against some community of people.
• Automated cars: Self-driving cars have possibility of decreasing traffic accidents and saving lives in our city, but it also increases trouble in safety and protection.
• Chatbots: To offer customer service we create this AI Chatbots, but they also serves in spreading the misinformation leading to scam people.
• Military applications: For creating automated weapons machines we designed AI in military applications, but this mechanism grows worries about the moral of killing people using AI.

2. What is an example of algorithm in Artificial Intelligence?

            A* (A-Star) Search Algorithm is the mostly used technique in AI and is famous in different applications like robotics and video games. It is commonly helps in path-finding and graph traversal to identify the shortest path between two points in a given graph, map and dimensional platforms.

How A* Works

            The A* method uses both the actual distance from the beginning point to a given node (“g(n)”) and an analytical estimation of the distance from that node to the target (“h(n)”) to identify the shortest path from the starting point to the aim. We keep track the algorithm of a “priority queue” of possible paths, where the priority (or “score”) of a path is the sum of “g(n)” and “h(n)”.

The following is an easy step-by-step description we go through:

1. Initialize: We begin with a list of just the starting point, and an empty list for nodes that have already been checked.
2. Iterate: Take the node from the “open” list initially we use the starting point with the lowest “f(n) = g(n) + h(n)” score.
3. Check Goal: When the node reaches the aim the method concludes and we reconstruct the path.
4. Expand: If the node doesn’t reach the goal, we move the recent node to the “closed” list and consider its entire surrounding node.
5. Update Scores: For each neighbor, we compute its “g(n)” and “h(n)” scores. Add the neighbor to the “open” list if it’s not already there and when the new “g(n)” score is lower than the previously recorded score.
6. Loop: Iterate from step 2.

Practical Applications

• Robotics: We utilize A* for the robots to locate a physical area and reaching a particular destination by avoiding obstacles.
• Network Routing: The A* can also integrated for optimizing our network traffic.
• Video Games: To allow the characters for navigating through difficult platforms we use this algorithm frequently in video games.
• Maps: We implement variations of A* to detect the shortest path between locations for route-finding applications like Google Maps.
• Automate Theorem Proving: In AI research, A* can be helpful for us to resolve logical statements into easy formats.

The A* is a good example out of many algorithms we implement in AI, because it is vast accessible, simple to interpret and foundational to many other types of techniques and applications in AI. Get to know more of programming languages by our team experts. A complete support for your paper will be given from end to end.