Yann LéCun, a French computer scientist is an expert. He works in the fields of computer vision, machine learning, and computational neuroscience. He is the current Silver Professor at the Courant Institute for Mathematical Science at New York University, and Vice President and Chief Artificial Intelligence Scientist of Meta. He is also a co-founder of JEPA, and has published over 180 technical articles.
VP and Chief AI Scientist at Facebook
Yann LeCun is a highly skilled machine learning scientist and is currently the VP and Chief AI Scientist at Facebook. Before joining Facebook in 2013, he was a research scientist at Bell Labs. He works now with the Applied Machine Learning department, which integrates AI within Facebook products. LeCun is an advocate of transparency in the AI industry and publishes a lot of his work. LeCun is also a member of The National Academy of Engineering.
Facebook's AI research lab has grown dramatically over the last few years. The lab has grown to six locations and over a hundred employees. Yesterday, the company announced that it would double the number of researchers in its Paris research laboratory. It also plans to quadruple Paris's Ph.D. student population.
Silver professor at New York University
Yann LeCun (French computer scientist) is an avid reader of machine learning, mobile robotics and computational neuroscience. He is currently the Silver Professor at New York University's Courant Institute of Mathematical Sciences and the Vice President and Chief AI Scientist at Meta.
LeCun has received the ACM Turing Award as a result of his engineering and concept breakthroughs. LeCun also belongs to the National Academy of Engineering. Contact a speaker booking agency if you are interested in hiring Yann LeCun to speak at your event.
Author of more than 180 technical papers
Yann leCun is a French scientist in computer science who works in machine learning, computer vision and computational neuroscience. He is an academic with a variety of appointments. He is also a Silver Professor at New York University’s Courant Institute of Mathematical Sciences. His research interests lie in neural networks and computer vision.
LeCun, a well-known computer scientist who has been involved in artificial intelligence research for over 20 years, is widely cited. He is credited as one of the creators of convolutional neural networks. He co-created both the DjVu image compression technology, and the Lush program language. Yann is a recognized figure in many fields. He has published more than 180 technical papers.
Joint embedding predictive architectural (JEPA) founder
JEPA, a new AI model that relies on energy-based models to learn high level representations of the world, is an example of this. This approach replaces contrastive and uses regularized techniques to extract high-level latent characteristics from inputs. It also discards irrelevant information. JEPA can infer information using a high dimension world model.
This method allows alignment of multiple datasets, without sacrificing individual specificity. It can also be extended for more than 2 datasets. This workflow can be seen in Fig. 1A.
Geoffrey Hinton’s influence on Hinton’s work
Yann LeCun is a computer scientist and VP of Facebook's AI research group. He also teaches at New York University. His research focuses primarily on deep learning, convolutional neural networks, and other areas. He was awarded his PhD by Pierre and Marie Curie University in France and served as a postdoctoral research assistant under Geoffrey Hinton. In this interview, LeCun discusses his foundational work on convolutional neural networks, and his advice to anyone getting into AI.
LeCun had a tremendous impact from Hinton. More than 30 PhD students were mentored by Hinton, as well as many master's and postdoctoral students. Many of these students went on to be leaders in their field. Brendan Frey who was Hinton’s protege, completed his PhD at the University of Texas in 1997. He went on to become a leading advocate for deep-learning.
FAQ
Which industries use AI the most?
The automotive industry is one of the earliest adopters AI. BMW AG uses AI as a diagnostic tool for car problems; Ford Motor Company uses AI when developing self-driving cars; General Motors uses AI with its autonomous vehicle fleet.
Other AI industries are banking, insurance and healthcare.
What does AI mean today?
Artificial intelligence (AI), also known as machine learning and natural language processing, is a umbrella term that encompasses autonomous agents, neural network, expert systems, machine learning, and other related technologies. It is also known as smart devices.
Alan Turing wrote the first computer programs in 1950. He was intrigued by whether computers could actually think. He presented a test of artificial intelligence in his paper "Computing Machinery and Intelligence." The test tests whether a computer program can have a conversation with an actual human.
John McCarthy, in 1956, introduced artificial intelligence. In his article "Artificial Intelligence", he coined the expression "artificial Intelligence".
Many AI-based technologies exist today. Some are easy and simple to use while others can be more difficult to implement. They can range from voice recognition software to self driving cars.
There are two major types of AI: statistical and rule-based. Rule-based relies on logic to make decision. To calculate a bank account balance, one could use rules such that if there are $10 or more, withdraw $5, and if not, deposit $1. Statistical uses statistics to make decisions. To predict what might happen next, a weather forecast might examine historical data.
Where did AI come from?
In 1950, Alan Turing proposed a test to determine if intelligent machines could be created. He suggested that machines would be considered intelligent if they could fool people into believing they were speaking to another human.
John McCarthy later took up the idea and wrote an essay titled "Can Machines Think?" In 1956, McCarthy wrote an essay titled "Can Machines Think?" He described the difficulties faced by AI researchers and offered some solutions.
Statistics
- According to the company's website, more than 800 financial firms use AlphaSense, including some Fortune 500 corporations. (builtin.com)
- In 2019, AI adoption among large companies increased by 47% compared to 2018, according to the latest Artificial IntelligenceIndex report. (marsner.com)
- The company's AI team trained an image recognition model to 85 percent accuracy using billions of public Instagram photos tagged with hashtags. (builtin.com)
- In the first half of 2017, the company discovered and banned 300,000 terrorist-linked accounts, 95 percent of which were found by non-human, artificially intelligent machines. (builtin.com)
- More than 70 percent of users claim they book trips on their phones, review travel tips, and research local landmarks and restaurants. (builtin.com)
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How To
How do I start using AI?
A way to make artificial intelligence work is to create an algorithm that learns through its mistakes. This learning can be used to improve future decisions.
A feature that suggests words for completing a sentence could be added to a text messaging system. It would learn from past messages and suggest similar phrases for you to choose from.
The system would need to be trained first to ensure it understands what you mean when it asks you to write.
Chatbots can also be created for answering your questions. So, for example, you might want to know "What time is my flight?" The bot will answer, "The next one leaves at 8:30 am."
Our guide will show you how to get started in machine learning.