AI and it’s new trends
What is AI (Artificial Intelligence)?
When the computer systems are capable to perform tasks that normally human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. It is a system that that learns through multiple senses that makes it able to learn faster, with fewer but precise data, and without humans to always add inputs so that it can perform the task. It’s a system wherein, it learns from the surroundings, and the data input given to it performs the task depending on the various criteria’s which gives us the optimal solution.
Benefits and risks of artificial intelligence:
Benefits
· Increase Work efficiency
· Work with high accuracy
· Reduce the cost of training and operation
· Improve Processes
Risks
· AI is unsustainable
· Lesser jobs
· Maybe a threat to humanity
Future with AI:
Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. With AI tasks can be carried out in a much efficient way, making hard jobs looks easier. Tasks can be completed faster, and margin of error is less.
Interesting technology developing using AI:
· Liner temporal Logic
· PUnS system
· Machine learning
· Pixel Player
· Sound-tracking tools
· Robots
Liner temporal Logic:
It is an expressive language that enables robotic reasoning about current and future outcomes. The researchers defined templates in Liner temporal Logic that model various time-based conditions, such as what must happen now, must eventually happen, and must happen until something else occurs. The robot’s observations of 30 human demonstrations for setting the table yielded a probability distribution of over 25 different Liner temporal Logic formulas. Once the table is formed, the robot now has outcomes stored on the various conditions that would take place so now the robot can work efficiently. It is a modal temporal logic with modalities referring to time. In Liner’s temporal Logic, one can encode formulae about the future of paths, for example, a condition will eventually be true, a condition will be true until another fact becomes true.
PUnS system:
Planning with Uncertain Specifications” (PUnS) system gives robots the humanlike planning ability to simultaneously weigh many ambiguous — and potentially contradictory — requirements to reach an end goal.
Machine learning:
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory, and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Example: It can be used to enable seamless teamwork between people and machines, and motion sensors Machines worn on the biceps, triceps, and forearms to measure muscle signals and movement. Algorithms then process the signals to detect gestures in real-time, without any offline calibration or per-user training data.
Machine learning classifiers detected the gestures using the wearable sensors. Unsupervised classifiers processed the muscle and motion data and clustered it in real-time to learn how to separate gestures from other motions. A neural network also predicted wrist flexion or extension from forearm muscle signals. The system essentially calibrates itself to each person’s signals while they’re making gestures that control the robot, making it faster and easier for casual users to start interacting with robots.
Pixel Player:
A Pixel Player is a system that, by watching large amounts of unlabeled videos, learns to locate image regions that produce sounds and separate the input sounds into a set of components that represents the sound from each pixel. The approach capitalizes on the natural synchronization of the visual and audio modalities to learn models that jointly parse sounds and images, without requiring additional manual supervision. The system is trained with many videos containing people playing instruments in different combinations, including solos and duets. No supervision is provided on what instruments are present on each video, where they are located, or how they sound. During test time, the input to the system is a video showing people playing different instruments, and the mono auditory input.
Sound-tracking tools:
A Sound tracking tool might be a useful addition in self-driving cars, complementing their cameras in poor driving conditions. Sound trackers could be especially helpful at night, or in bad weather. It can learn to recognize natural sounds like birds singing or waves crashing. They can also pinpoint the geographic coordinates of a moving car from the sound of its engine and tires rolling toward, or away from, a microphone. This tool integrated with the machines or robots can help the machine learn about sound and act upon it based on the data stored. It can be used in autonomous cars so that it catches noises from the surrounding and act accordingly.
Robots:
Robotics are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores. In the workplace, you could train robots like new employees, showing them how to perform many duties. A system that lets these types of robots learn complicated tasks that would otherwise stymie them with too many confusing rules.
Robots are good planners in tasks with clear specifications that help describe the task the robot needs to fulfill, considering their actions, environment, and end goal. Learning to set a table by observing demonstrations is full of uncertain specifications. Items must be placed in certain spots, depending on the menu and where guests are seated, and in certain orders, depending on an item’s immediate availability or social conventions.
Vedhaant Jain, Fourth Year B.Tech Integrated, Computer Engineering, NMIMS’s MPSTME.
References:
https://www.studytonight.com/post/top-benefits-and-risks-of-artificial-intelligence
http://news.mit.edu/2020/conduct-a-bot-muscle-signals-can-pilot-robot-mit-csail-0427