If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Existing approaches to artificial intelligence for self-driving cars don't account for the fact that people might try to use the autonomous vehicles to do something bad, researchers report. For example, let's say that there is an autonomous vehicle with no passengers and it's about to crash into a car containing five people. It can avoid the collision by swerving out of the road, but it would then hit a pedestrian. Most discussions of ethics in this scenario focus on whether the autonomous vehicle's AI should be selfish (protecting the vehicle and its cargo) or utilitarian (choosing the action that harms the fewest people). But that either/or approach to ethics can raise problems of its own.
Learn to build a Polynomial Regression model to predict the values for a non-linear dataset. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.
Every department in a company has its own challenges. In the case of Human Resources, recruitment and onboarding processes, employee orientations, process paperwork, and background checks is a handful and many a time painstaking – mostly because of the repetitive and manual nature of the work. The most challenging of all is engaging with employees on human grounds to understand their needs. As leaders today are observing the AI revolution across every process, Human resources is no exception: there has been a visible wave of AI disruption across HR functions. According to an IBM's survey from 2017, among 6000 executives, 66% of CEO's believe that cognitive computing can drive compelling value in HR while half of the HR personnel believe this may affect roles in the HR organization.
The context: One of the best unsolved defects of deep knowing is its vulnerability to so-called adversarial attacks. When included to the input of an AI system, these perturbations, apparently random or undetected to the human eye, can make things go totally awry. Stickers tactically put on a stop indication, for instance, can deceive a self-driving automobile into seeing a speed limitation indication for 45 miles per hour, while sticker labels on a roadway can puzzle a Tesla into drifting into the incorrect lane. Safety important: Most adversarial research study concentrates on image acknowledgment systems, however deep-learning-based image restoration systems are susceptible too. This is especially uncomfortable in healthcare, where the latter are typically utilized to rebuild medical images like CT or MRI scans from x-ray information.
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn't sure about where to start, welcome to the club. Before we dive into the machine learning world, you should take a step back and think, what is stopping you from getting started? If you think about it, most of the time, we presuppose things about ourselves and assume that to be true without question. The most normal presumption that we make about ourselves is that we need to have prior knowledge before getting started. Get a degree, complete a course, or have a good understanding of a particular subject.
A perfect course for Bachelors / Masters / PhD students who are getting started into Drug Discovery research. This course is specially designed keeping in view of beginner level knowledge on Artificial Intelligence, Machine learning and computational drug discovery applications for science students. By the end of this course participants will be equipped with the basic knowledge required to navigate their drug discovery project making use of the Artificial Intelligence and Machine learning based tools.Who this course is for:
A new era is at hand: the era of sustainable superabundance. In this era, the positive potential of humanity can develop in truly profound ways. The key to this new era is to take wise advantage of the remarkable capabilities of twenty-first century science and technology: robotics, biotech, neurotech, greentech, collabtech, artificial intelligence, and much more.
I assume you already know what regression is. "Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables)." In the most simple terms -- we want to fit a line (or hyperplane) through data points to obtain a line of best fit. The algorithm behind aims to find the line which minimizes the cost function -- typically MSE or RMSE. That's linear regression, but there are other types -- like polynomial regression.
Data science is shifting towards a new paradigm where machines can be taught to learn from data to derive conclusive intelligent insights. Artificial Intelligence is a disruptive technology that collates the intelligence displayed by machines mimicking human intelligence. AI is a broad term for smart machines programmed to undertake cognitive human tasks that require judgment-based decision making. With all the hype and excitement surrounding Artificial Intelligence, businesses are already churning data in massive quantities over call logs, emails, transactions and daily operations. Machine learning (ML) is a dynamic application of artificial intelligence (AI) that empowers the machines to learn and improve the model accuracy levels.
The department of Mechanical Engineering of NMAM Institute of Technology (NMAMIT), Nitte, is not far behind in their efforts. With the full support of the management and the able leadership provided by the current Principal and Head of the Department, lots of initiatives have been taken to provide the necessary skill sets to students to take up job positions in AI. It has started with offering of several elective courses to the students of final year including'Introduction to Machine Learning' and'Introduction to Cognitive Computing'. Efforts are being made to offer more electives like'Artificial Intelligence', 'Machine learning using Python', 'Big data', 'Data analytics' etc. as elective courses for students of the coming batches. Programming languages like MATLAB and Python are being taught to students through add on courses and training programs either through in-house or engaging external resources.