Dear Members You can publish your articles on the following topics free of charge on our website. Artificial intelligence Machine Learning Deep Learning Natural Language Processing Data Scientist SQL Python Pandas Data Visualization (Tableau, Seaborn, Matplotlib, etc.) You can contact the website via e-mail. ([email protected]

Artificial intelligence, deep learning, machine learning, brain, brain diseases, AI lectures, AI conferences, AI TED talks, mind and brain, AI movies, AI books in english and turkish.

This story begins in 1985 when at age 22, I became the World Chess Champion after beating Anatoly Karpov.

We must face our fears if we want to get the most out of technology — and we must conquer those fears if we want to get the best out of humanity, says Garry Kasparov. One of the greatest chess players in history, Kasparov lost a memorable match to IBM supercomputer Deep Blue in 1997. Now he shares his vision for a future where intelligent machines help us turn our grandest dreams into reality.

This story begins in 1985, when at age 22, I became the World Chess Champion after beating Anatoly Karpov. Earlier that year, I played what is called simultaneous exhibition against 32 of the world’s best chess-playing machines in Hamburg, Germany. I won all the games, and then it was not considered much of a surprise that I could beat 32 computers at the same time. To me, that was the golden age.

I am amazed by just how far technology has come!

An image of her daughter was created with ‘virtual reality’ for a mother who lost her daughter in 2016 in South Korea. The moment the mother met her daughter left those in the studio in tears.

In 2016, the 7-year-old daughter of a woman named Jang Ji-sung, Nayeon, died after an illness.

A firm that does’ Virtual Reality ‘Projects has created an image of the girl who died, as a result of an 8-month study. The firm, which also created a virtual reality of a park where the mother and daughter had gone before, invited the family into the studio. The mother, who arrived at the studio, was given a virtual reality goggle, while her husband and another daughter, along with the technical team, watched the process in the audience section.

CS230 | Deep Learning

https://www.newworldai.com/cs230-deep-learning-stanford-engineering/

CS221 | Artificial Intelligence

https://www.newworldai.com/cs221-artificial-intelligence-pri…niversity/

CS224N | Natural Language Processing (NLP)

https://www.newworldai.com/stanford-cs224n-nlp-deep-learning-winter-2019/

CS224N | Natural Language Processing with Deep Learning.

Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.



Recently published an article on Deep Learning.

Examining the visual acuity of children who are younger than three years old is important for the early discovery of abnormal visual development, potential ophthalmic diseases, and neurological disease.

If the visual abnormal development is not diagnosed in time, later development will be affected, but performing visual examination in children is challenging due to their lack of understanding of instructions. Evaluating the visual acuity of children remains difficult since many children are unable to effectively express their understanding, especially with respect to directions (i.e., up, down, left and right). In addition, it is difficult for children to concentrate for a long time during the vision examination. The Teller visual acuity (TVA) card can be used to evaluate the visual acuity of children, but not all the operations are recorded and can be reviewed.

Abstract

Recent AI lecture by Stanford University.

What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools.

In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.

Instructors:

The field of artificial intelligence has never been the subject of more attention and analysis than it is today. Almost every week, it seems, a new bestselling book comes out examining the technology, business or ethics of AI.

Yet few of the topics and debates at the center of today’s AI discourse are new. While not always recognized by commentators, artificial intelligence as a serious academic discipline dates back to the 1950s. For well over half a century, many of the world’s leading minds have devoted themselves to the pursuit of machine intelligence and have grappled with what it would mean to succeed in that pursuit.

Much of the public discourse around AI in 2019 has been anticipated—and influenced—by AI thought leaders going back decades.

This paper attempts to apply artificial intelligence (AI) to computer network technology and research on the application of AI in computing network technology.

With the continuous expansion of the application scope of computer network technology, various malicious attacks that exist in the Internet range have caused serious harm to computer users and network resources.

By studying the attack principle, analyzing the characteristics of the attack method, extracting feature data, establishing feature sets, and using the agent technology as the supporting technology, the simulation experiment is used to prove the improvement effect of the system in terms of false alarm rate, convergence speed, and false-negative rate, the rate reached 86.7%. The results show that this fast algorithm reduces the training time of the network, reduces the network size, improves the classification performance, and improves the intrusion detection rate.

TV shows and fiction aside, the present-day examples of basic AI are Google Voice, Cortana, Alexa, Siri, and chatbots. However, AI isn’t just limited to voice assistants, it’s turning tables in other domains and industries as well. Several restaurants, for instance, have bots for room service, serving food and carrying luggage.

Let’s take a look at some interesting and mind-bending stats and facts on this prevalent technology to give you a better idea of the direction the market is headed.

Interesting stats and facts on artificial intelligence.

John Giannandrea, Vice President of Engineering with responsibility for Google’s Computer Science Research and Machine Intelligence groups; leading teams in Machine Learning, Machine Intelligence, Computer Perception, Natural Language Understanding, and Quantum Computing, “I’m definitely not worried about the AI apocalypse, I just object to the hype and soundbites that some people are making” said at the TechCrunch Disrupt conference in San Francisco.

Google’s John Giannandrea sits down with Frederic Lardinois to discuss the AI hype/worry cycle and the importance, limitations, and acceleration of machine learning.