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) …
When you will start to learn Machine Learning, you will start to like math way more than ever, you will realize it's truly just applied math and math is actually just the semantic in nature so Machine learning is one part linear algebra and one part matrix multiplication manipulation specially if we are using Neural Networks, One part multivariate calculus and One part Chain Rule, this is basically the entirety of back propagation which is how many neural networks learn or improve their errors through back propagation probability and distributions and then optimization. So if we imagine this little top of the hill that's the maximum, usually a machine learning you're trying to find the minimum so you're trying to reduce the loss function which is usually something that measures how wrong your machine learning algorithm predictions are to what they should be so that's an optimization problem. When you will start to learn Machine Learning, you will start to like math way more than ever, you will realize it's truly just applied math and math is actually just the semantic in nature so Machine learning is one part linear algebra and one part matrix multiplication manipulation specially if we are using Neural Networks, One part multivariate calculus and One part Chain Rule, this is basically the entirety of back propagation which is how many neural networks learn or improve their errors through back propagation probability and distributions and then optimization. So if we imagine this little top of the hill that's the maximum, usually a machine learning you're trying to find the minimum so you're trying to reduce the loss function which is usually something that measures how wrong your machine learning algorithm predictions are to what they should be so that's an optimization problem.
This year, Kaggle started a new program called the BIPOC (Black, Indigenous, People of Color) Grant Program. It aims to empower underrepresented data scientists with support to advance their careers and aspirations. I am grateful that I was one of the few people who became a part of this wonderful program. All the students who became part of the program were assigned a mentor as well. I had done a few basic projects before I became a part of this program.
Members of IDG's Influencer Network weigh in on the transformative power of these two technologies. As a recent article on CIO.com observed, the pandemic "has seen accelerated interest in process automation as organizations have scrambled to overhaul business processes and double down on digital transformations in response to disruptions brought about by COVID-19. And for IT leaders stepping into or already steeped in such modernization efforts, artificial intelligence -- mainly in the form of machine learning -- holds the promise to revolutionize automation, pushing them closer to their end-to-end process automation dreams." Automation and artificial intelligence (AI): The combination of these two transformative technologies has IT leaders setting their sights on some pretty lofty goals. Robotic process automation leader UiPath has characterized RPA and AI as "two of the most transformative technologies the world has ever known. But bringing AI and RPA together unleashes even more of their potential."
I graduated on Warsaw University of Technology with master thesis about text mining topic (intelligent web crawling methods). I work for Polish IT consulting company (Sollers Consulting), where I develop and design various insurance industry related stuff, (one of them is insurance fraud detection platform). From time to time I try to compete in data mining contests (Netflix, competitions on Kaggle and tunedit.org) As far as I remember, the basis of the solution I defined at the very beginning: to create separate predictors for each individual loop and time interval. So my solution required me to build 61x10 610 regression models.
Facial recognition technology is rapidly becoming ubiquitous, used in everything from security cameras to smartphones. But in the near future, humans may not be the only ones to be digitally captured. Researchers are training forms of artificial intelligence to recognize individual animals by their faces alone -- and even discern their emotional state just by reading their expressions. Much of the research into animal facial expressions has focused on species like dogs and horses. But some of the most cutting-edge work is aimed at an unlikely subject: the farmed hog.
"What I cannot create, I do not understand," said the famous writing on Dr. Feynman's blackboard. The ability to create or to change objects requires us to understand their structure and factors of variation. For example, to draw a face an artist is required to know its composition and have a good command of drawing skills (the latter is particularly challenging for the presenter). The animation additionally requires the knowledge of rigid and non-rigid motion patterns of the object. This talk shows that generation, manipulation, and animation skills of deep generative models substantially benefit from such understanding.
If you want to learn data science from scratch, the first thing you need to do is learn how to code. Pick a programming language (either Python or R), and start learning. I suggest starting out with Python because it is more widely used than R. It is also more general and highly flexible, and you will be able to make the transition to different domains (data analytics, web development) if you have Python knowledge. This DataCamp course will take you through exercises and teach you how to code in Python. What will you learn in this course?
The term Artificial Intelligence was coined 70 years ago as the stuff of fantasy fiction and about 50 years post that nothing much moved. Then, in 1997 like a bolt from the blue, IBM's Deep Blue defeated world chess champion Garry Kasparov 4-2 in a six game series. Since then, machines have beaten humans at far more complex games – Go, Poker, Dota 2. Computing power grew over a trillion times in the last 50 years. Can you name any industry/trend that has evolved by this order of magnitude? The computer that helped navigate Apollo 11's moon landing had the power of two Nintendo consoles. You have a lot more power in your smartphone today.
Not many people miss having to manually sort files, label papers, or search for lost forms in huge filing cabinets. That's because all these tasks have become way easier, faster, and more enjoyable since they've become digitized – computers and the internet have revolutionized the way businesses approach organization and task management. Similar to how computers and the internet made monotonous tasks faster and easier in every department, AI will transform work in every industry in the 21st century. Machine learning will automate away the most time-consuming and repetitive tasks across a company, along with offering predictions that will allow businesses to make better decisions ahead of time. Introducing these revolutionary processes takes time and specialized knowledge.