IIT Delhi, like various other Indian Institutes of Technology (IITs) and Indian Institute of Science (IISc), is offering several free online courses on the NPTEL platform at present. One such course is a free online course on “Introduction to Artificial Intelligence”, which interested learners can enroll for. The course, however, can be especially beneficial for undergraduate students in computer science with a fair knowledge of data structures and probability.

The course will be conducted by Prof Mausam who is an Associate Professor of Computer Science department at IIT Delhi, and an affiliate faculty member at the University of Washington, Seattle. He received his PhD from the University of Washington in 2007 and a BTech from IIT Delhi in 2001. He is an awardee of the prestigious AAAI Senior Member status for his long-term participation in AAAI and excellence in the field of artificial intelligence.

What the IIT Delhi Free Online Course on Artificial Intelligence will Cover?

Participants who enroll for this course will get an understanding of the following topics: Introduction to hilosophy of AI, definitions, modeling a problem as search problem, uninformed search, heuristic search, domain relaxations, local search, genetic algorithms, adversarial search, constraint satisfaction, propositional logic and satisfiability, uncertainty in AI, Bayesian networks, Bayesian networks learning and inference, decision theory, Markov decision processes, reinforcement learning, and introduction to deep learning and deep RL.

Other Important Details of the IIT Delhi Free Online Course on Artificial Intelligence

The 12-week long course will be conducted from 18 January to 9 April 2021, and the last date to enroll for the course is 25 January 2021. Although the course is free to enroll and learn from, to receive certificates from NPTEL and IIT Delhi, participants will have to register and write the proctored exam to be conducted by NPTEL in person at any of the designated exam centres on 25 April 2021 in morning and afternoon sessions.

Criteria to Receive Certificates for IIT Delhi Free Online Course on Artificial Intelligence

Certificates will be rewarded based on the participant’s performance in exam, as well as their average assignment score. The average assignment score will be 25% of average of best 8 assignments out of the total 12 assignments given in the course, and exam score consists of 75% of the proctored certification exam score out of 100.

The post IIT Delhi Invites Registrations for a Free Online Course on Artificial Intelligence on NPTEL appeared first on Artificial Intelligence.

Since the computers were introduced, the primary object of their evolution has been to take vigorous calculations off our plates. It meant easing tasks that would take us a long time. Over the past decade, the computing capabilities of mobile devices have arrived at a point where it’s now easy to install machine learning remotely.

Although artificial intelligence (AI) is a term that gets thrown around a lot, it’s machine learning, which is making automation possible. When we discuss AI, we refer to its branch called machine learning. This way, computers learn and perform tasks without being explicitly programmed.

Developments in machine learning, algorithms have remarkably helped to bolster application development. If we discuss Android or iOS, the SDKs for these apps include several APIs that permit developers to tap into the machine learning capacities of a device. Apple’s chips powering iPhones have a dedicated neural engine that can boost specific workloads. Likewise, Google’s Pixel phones also consolidate on-device machine learning. These SDKs permits developers to harness hardware prowess for their application.

Such developments in machine learning could not have deployed at a better time; it merges perfectly with the proliferation of big data. As more devices are connecting online, more users are enrolling for services. The explosion of the IoT ecosystem indicates the need for expediting existing processes is necessary of the hour.

Services can discover patterns in the big data collected from its users in the mobile app development space. Machine learning algorithms make use of unstructured data and deliver useful insight into user behaviour. This paradigm shift indicates that more clients are asking for tools from software developers that leverage machine learning to enhance services. These include learning about what users are interacting with and what has proved to be a sore point for them.

User experience is one of the keys to success, and machine learning’s modern reporting tools provide valuable insight into the area of interest. For instance, Facebook uses machine learning and some of its means to give a user a personalized experience and predict user behaviour. This helps to enable the social media giant to target relevant audiences with advertisements. If a user is likely to do something in the future, the advertiser will mark him/her as a potential customer or may try to retain if the user is on the verge of shifting to any other competitor.

Machine learning can also be applied to detect objects. In an instance of a shopping app, a user can simply point to a product, and the app will find similar matching results online.

Apart from service-based apps, machine learning also benefits video games. AI is such a term which is related to video games, as many game designers aim to feature the behaviour of their characters as realistically as possible.

As you might have already noticed, the application of machine learning is endless. The results reached the far end of the output tunnel will only be as effective as the algorithm used. And this is why deliberation is needed while the software developer stage of selecting the right algorithms.

App Development Future

There is always an alternative to create your own servers for installing and training a neural network. But cloud storage cost may end you up being spent on keeping your infrastructure functional. You need factor in scalability as well as ensure configurations of your server to get desired output. Multibillion-dollar companies use cloud services to avail ease of distribution.

The post MACHINE LEARNING IS POWERING APP DEVELOPMENT appeared first on Artificial Intelligence.

Businesses have embraced artificial intelligence with open arms. Ecommerce platforms are leveraging the technology to personalize customer experience and improve customer relations. Healthcare is using it for improved medical diagnosis, law enforcement agencies are using it to fight crime, and organizations are using it to screen potential candidates, and so many other sectors are using it. However, the other side of the coin is not that inspiring as artificial intelligence also has its share of risks.

Skewed data propagates bias

The biggest risk that artificial intelligence poses today is that of bias. This bias creeps in when the data used to train the mathematical models is skewed. Since the technology is completely data-driven, the biases in the data reflect in the output.

There are many instances where automated systems have produced sexist and racist outputs. This can be especially scary if government bodies or law enforcement agencies work with skewed data. Taking cognizance of the serious implications biased data can have on policing, the European Commission (EU) has suggested training the AI systems with unbiased data.

Use smaller data sets to train artificial intelligence systems

However, it is difficult to ensure that the data is 100% bias-free. Also often the AI systems are built before the data is cleaned. Therefore, as a measure to facilitate training AI systems on unbiased data, organizations can consider using smaller sets of training data, which can help significantly reduce bias.

Towards a more trustworthy artificial intelligence

Currently, there is a lot of debate around tackling bias in artificial intelligence. The EU’s Ethics Guidelines for Trustworthy AI mandates that trustworthy AI should be lawful, ethical, and robust. These guidelines prescribe seven key requirements that the AI systems should meet. These include human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity, nondiscrimination and fairness, environmental and societal well-being, and accountability.

Apart from the EU’s guidelines, the Organization for Economic Cooperation and Development (OECD) has also released its Principles on Artificial Intelligence that has been adopted by 42 countries. These principles will form the basis of practical guidelines for implementation.

The post Tackle bias to build trustworthy artificial intelligence systems appeared first on Artificial Intelligence.

Rose-Hulman continues to adapt its curriculum to meet the challenges of today’s fast-paced world.

The institute has added data science as a secondary academic major. This multidisciplinary field combines science, mathematical algorithms and computer science principles to extract knowledge and insights from data. The new course of study is offered by departments covering computer science, software engineering and mathematics, but it is available to students in all major courses of study offered by the institute.

Students also can earn a multidisciplinary minor in data science. Learn more about the new academic program here.

Program Coordinator Sriram Mohan, associate professor of computer science and software engineering, says the new major will provide students with in-depth hands-on experience in data engineering, data analysis, machine learning and artificial intelligence. He points out that analysis of other college data science programs shows that Rose-Hulman will better prepare students for careers in these areas by requiring more fundamental courses, more advanced electives and a capstone experience that’s focused on data science.

“Data science is revolutionizing organizations, and graduates with data science and analytic skills will be critical to the future of the world’s economy,” Mohan says.

As early as next year, computer scientists predict that 1.7 megabytes of data will be generated every second for every human on Earth. The total amount of data is expected to be around 44 trillion gigabytes.

Data science and machine learning are two of the fastest growing areas of the technology sector, with job demand currently far outweighing prospective employees with skills in the field, according to LinkedIn’s 2017 U.S. Emerging Jobs Report.

Google’s chief economist Hal Varian has stated, “The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – that’s going to be a hugely important skill in the next decades.”

The post Data Science Added as Academic Major & Minor appeared first on Artificial Intelligence.