The traditional CAI Computer-Assisted Instruction) system depends on the instructors who provide the course material and decide the criteria of evaluation for the students. The advanced versions we have these days have a'reactive learning environment' – where students are actively engaged in their online learning programs. The latter systems employ AI (Artificial Intelligence) tools and techniques to take students' interests and performance factors into account and proceed with tutorial dialogues accordingly. Hence, they are known as AICAI (Artificial Intelligence Computer-Assisted Instruction) System), or simply ICAI for intelligent CAI. Such AICAIs include a domain expert component (which knows all about the topic that is being taught), a student model that can analyze the responses of the learners and decode their knowledge levels as well as misconceptions, and a component which contains information on appropriate teaching strategies in different scenarios.

Put simply, you can ask a bot to do things for you – quickly, efficiently and without having to wait for a human to respond. Bots depend on artificial intelligence. And it's our increasing skill and development in AI that is driving the bot revolution. It is hoped that once the technology develops further, bots will be able to many more complicated tasks - from completing your taxes to monitoring your working and living habits. Put this together with other developments such as the IoT (internet of things) and wearable technology, the possibilities seem endless.

As the world of technology progresses, Artificial Intelligence becomes more intertwined in every aspect of our lives. From marketing programs that collect and process huge amounts of data, smart fridges that know when we need to get more milk and the prototype self-driving trucks slowly making their ways in our streets... Even the fields of teaching have accommodated AI with Intelligent Tutoring Systems such as the United States Air force's Own "Sherlock" program. Wikipedia defines AI ( Artificial Intelligence) as: " intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of A.I. where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.."

In 1633, Galileo Galilei was charged with heresy for claiming that Earth orbits the sun. A transcript of his trial is safely tucked away in the Vatican Secret Archives, along with thousands of other documents dating back to the eighth century. But it's hard for scholars to search through them without reading every word. To remedy that, researchers figured out how to digitize handwritten Latin text into a computer-readable format, The Atlantic reports. Classics scholars and high school students helped train a machine learning program, and then the program took it from there, transcribing several pages from the archives, the researchers report in a preprint posted to arXiv.

The platform aims to simplify contract creation and management through lawyer-vetted templates. The law, they say, is for all, but its finer intricacies and nuances, clearly not. An entrepreneur, most likely, would focus on the big idea, its ability to scale, the target audience, the revenue model and funding avenues. Easy to assume then that writing contracts is not very high on the priority list, also given that it can be time-consuming and even confusing. Harvard Law School alumnus and Wall Street lawyer Shashank Bijapur found contract work intellectually rewarding but the 31-year-old, at times, felt that the task of copying old contracts to make new ones was cumbersome.

During my past two decades working in the IT field, I've seen artificial intelligence technologies move from conceptual to practical -- with machine learning techniques at the forefront, becoming more accessible, even for teams without specialized expertise. With increased use of predictive modeling across a wide variety of teams, it's critical for leaders and managers to be aware of common issues that can distort the results of their teams' work. Here are nine common pitfalls to avoid, and best practices to follow, for a reliable machine learning process. The starting point of any machine learning program is to select the training data. Typically, organizations have some data available or can identify relevant external suppliers, such as government entities or industry associations.

Editor's note: We sat down with data scientist and artificial intelligence evangelist Ozge Yeloglu to talk about her love of machine learning and how she battles perfectionism. This story is told in her own words. On my first day of college classes at Ege University in Turkey, I sat down in the computer lab and was instructed to insert the floppy disk. I stared blankly at the computer, because I had no idea what a floppy disk was or how to put it in correctly. I quickly looked over at the kids next to me and watched what they did.

Cooking is one of the most fundamental human activities connected to various aspects of human life such as food, health, dietary, culinary art, and so on. Data mining and machine learning techniques have been used to extract and clean large datasets of recipes from the Internet, and also to plan and analyze the recipe instructions. One difficulty underlying computational cooking relies on the nature of data since recipes generally include images and text, whether structured or unstructured (e.g., the list of ingredients or instructions in natural language). This opens several challenges in terms of indexing/storing and gives rise to numerous application tasks, such as recommendation or classification. Computational cooking has consequently emerged as a new research topic that also benefits from recent advances in machine learning based on deep neural approaches.

"If your company isn't focused on upskilling, your workforce won't be well-equipped to adapt to new challenges and make the most of new opportunities," according to Jim Link, chief HR officer of staffing service Randstad North America. "Ultimately, your company will lose out on opportunities to innovate." Indeed, upskilling employees -- that is, teaching staff additional skills -- is becoming an increasingly critical practice for organizations and their HR departments as more industries face skill gaps and hiring markets become more competitive. Companies can take a variety of approaches to upskilling employees. On the technology front, AI and machine learning (a type of AI) promise to enable upskilling programs that help people adapt more quickly to an ever-changing workplace and world of work faster.

Lyft is teaming up with Udacity to find talent for its Level 5 autonomous driving engineering team. The partnership entails a challenge designed to identify the best candidates in Udacity's self-driving car engineer nanodegree program. Called the Lyft Perception Challenge, the idea is to test problem-solving skills around perception for autonomous vehicles. The competition, which runs for a full month from today until June 1, asks engineers to develop perception algorithms that can recognize cars in simulated urban environments no matter what the day or weather condition. "Lyft recognizes that conventional recruiting strategies no longer suffice. What is needed is a future-facing hiring model as transformative as the field they're hiring for," Lyft wrote in a blog post.