Artificial Intelligence is the science and engineering of making computer machines able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. It is a branch of the Computer Science that aims to develop intelligent computer machines. Scope of Artificial Intelligence: The ultimate effort is to make computer programs that can solve problems and achieve goals in the world, as well as humans. There is a scope in developing the machines in game playing, speech recognition machine, language detection machine, computer vision, expert systems, robotics and many more. What should you study before or while learning AI? Study mathematics, especially mathematical logic.

Hey guys I got a few questions on WEKA, I know know it's not a favored program here but I'm kind of stuck and I'd appreciate any help whatsoever. Basically I'm new to ML which is why I'm using WEKA to get a grasp on algorithms, clustering, and all that stuff everything before transitioning to something python based. My question relates to clustering and how the algorithms actually "learn" my understanding is that with clustering I should be able to feed the program some unlabeled data and it will do it's best to cluster the data into an unknown amount of clusters or a user defined amount. Once a model is built I should be able to re-run the clustering algorithm again to build upon this and hopefully get a more precise image of each cluster the second time round, repetition should hopefully build a model which can correctly identify which cluster new data belongs to if it's introduced. Is this the correct way of thinking, if it is how can I do this in WEKA?

Data analysis echo system has grown all the way from SQL's to NoSQL and from Excel analysis to Visualization. Today, we are in scarceness of the resources to process ALL (You better understand what i mean by ALL) kind of data that is coming to enterprise. Data goes through profiling, formatting, munging or cleansing, pruning, transformation steps to analytics and predictive modeling. Interestingly, there is no one tool proved to be an effective solution to run all these operations { Don't forget the cost factor here:) }. Things become challenging when we mature from aggregated/summarized analysis to Data mining, mathematical modeling, statistical modeling and predictive modeling.

From fuel cell vehicles to self-driving cars, new technologies for next-generation autos are gaining traction. In a move likely to accelerate this, the United Nations World Forum for Harmonization of Vehicle Regulations, which consists of major car-producing nations and sets international safety and environmental standards on vehicles, said in November it will allow carmakers worldwide to replace side and rear mirrors with camera monitor systems. Following the U.N. panel's decision, the transport ministry will from June allow mirrorless cars on to the nation's roads. A mirrorless car does not have rear-view and side-view mirrors. Instead, the car is equipped with a sophisticated camera monitor system that shows drivers surrounding views on small screens positioned in front of them.

For Robert Welborn, head of data science for the insurer and finance company USAA, 2015 was the year machine learning started to make commercial sense. Access to improved machine-learning tools, cheaper processing technology, and a sharp decline in the cost of storing data were key. When those developments were combined with USAA's abundance of data, a technology studied for decades suddenly seemed practical. Insurance, finance, manufacturing, oil and gas, auto manufacturing, health care: these may not be the industries that first spring to mind when you think of artificial intelligence. But as technology companies like Google and Baidu build labs and pioneer advances in the field, a broader group of industries are beginning to investigate how AI can work for them, too. How will AI develop as it is commercialized, and how will the technology change these diverse industries?

The resounding win by a Google artificial intelligence programme over a champion in the complex board game Go this month was a statement - not so much to professional game players as to Google's competitors. Many of the tech industry's biggest companies, like Amazon, Google, IBM and Microsoft, are jockeying to become the go-to company for AI In the industry's lingo, the companies are engaged in a "platform war". A platform, in technology, is essentially a piece of software that other companies build on and that consumers cannot do without. Become the platform and huge profits will follow. Microsoft dominated personal computers because its Windows software became the centre of the consumer software world.

Read more ... » Punjabi in India this summer, and Professor Manuela Veloso is appointed as the new head of the MachineAirbnb makes it easier to turn your home into a hotel. Read more ... » LearningAirbnb makes it easier to turn your home into a hotel.

CMU sophomore Ian Asenjo wins Critical Language Scholarship from State Dept. This week, Ian Asenjo, a sophomore global studies major with an additional major in ethics, history, and public policy, was awarded the Critical Language Scholarship from the U.S. State Department, which will give him the opportunity to spend his summer in Chandigarh, India studying Punjabi. This cultural and linguistic immersion program is intended to encourage students to study languages that are drastically different from English. Many American language learners do not choose to master these languages due to the drastic differences, and we do not have enough native speakers. With supply low, demand is high for speakers of these critical languages, such as Arabic, Swahili, Urdu, Turkish, and Punjabi.

Games such as chess and Go display perfect information, and AI clearly has an upper hand. What happens, however, when information is imperfect and requires strategy? It has often been said that traditional business intelligence is like driving while looking in the rearview mirror. The implication, of course, is that real-time analytics is more like sensible driving behavior, where you keep your eyes mainly on the road ahead. That includes setting your direction to reacting in a second to get to where you want to go.

If you had the opportunity to vote for a politician you totally trusted, who you were sure had no hidden agendas and who would truly represent the electorate's views, you would, right? What if that politician was a robot? Futures like this have been the stuff of science fiction for decades. And, if so, should we pursue this? Recent opinion polls show that trust in politicians has declined rapidly in Western societies and voters increasingly use elections to cast a protest vote.