Yann leCun is a French computer scientist. He is an expert in computer vision, machine-learning, and computational neuroscience. He is currently the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice President and Chief AI Scientist of Meta. He is also the founder of JEPA and has published more than 180 technical papers.
Facebook Chief AI Scientist and VP
Yann LeCun, VP and Chief AI Scientist at Facebook, is an accomplished machine learning scientist. 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 has been an advocate of openness in the AI community and publishes his work frequently. He is also a National Academy of Engineering member.
Facebook's AI lab has experienced rapid growth over recent years. The lab now employs more than 100 people and has six locations. Yesterday, the company announced that it would double the number of researchers in its Paris research laboratory. It plans to increase the number of Ph.D. candidates in Paris by fourfold.
Silver professor at New York University
Yann LeCun, a French computer scientist, is interested in machine learning and computer vision. He is currently the Silver professor at New York University's Courant Institute of Mathematical Sciences, and Vice President and Chief Artificial Intelligence Scientist at Meta.
LeCun was awarded the ACM Turing Award, which recognizes his engineering and conceptual 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 180 technical papers
Yann LeCun, a French computer scientist, works in the areas of machine learning and computer vision. He has a variety of academic appointments, and is the Silver Professor at New York University’s Courant Institute of Mathematical Sciences. His research interests include computer vision, neural networks, and other areas.
LeCun is a widely cited computer scientist who has been working in artificial intelligence for the past 20 years. He is widely credited with being the inventor of convolutional neuro networks. He co-created both the DjVu image compression technology, and the Lush program language. Yann is an acknowledged expert in many areas and has published over 180 technical articles.
Joint embedding predictive architectural (JEPA) founder
JEPA is a new AI model. It uses energy-based models and learns high-level representations. This approach replaces the use of contrastive learning. Instead, it uses regularized techniques that extract latent high-level features from inputs and remove irrelevant information. JEPA is able to learn how to infer from high-dimensional world models.
This approach allows the alignment of multiple datasets without sacrificing any individual specificity. It can also be extended for more than 2 datasets. This workflow is shown in Fig. 1A.
Influence of Geoffrey Hinton on his work
Yann LéCun is a computer scientist who also serves as the Vice President of Facebook's AI research organization. He also teaches in New York University. His research interests include deep learning and convolutional networks. He was awarded his PhD by Pierre and Marie Curie University in France and served as a postdoctoral research assistant under Geoffrey Hinton. LeCun talks about his foundational work in convolutional neural networks and his advice for anyone interested in AI.
LeCun's work was greatly influenced and benefited from Hinton. He mentored over thirty PhD students as well, many postdocs master's and undergraduate students. Many were leaders in their fields. Brendan Frey who was Hinton’s protege, completed his PhD at the University of Texas in 1997. He was a pioneer in deep learning.
FAQ
Is there any other technology that can compete with AI?
Yes, but not yet. Many technologies have been created to solve particular problems. But none of them are as fast or accurate as AI.
How does AI work?
Basic computing principles are necessary to understand how AI works.
Computers store information in memory. Computers use code to process information. The code tells computers what to do next.
An algorithm is a sequence of instructions that instructs the computer to do a particular task. These algorithms are usually written as code.
An algorithm can also be referred to as a recipe. A recipe might contain ingredients and steps. Each step may be a different instruction. For example, one instruction might say "add water to the pot" while another says "heat the pot until boiling."
Is AI good or bad?
AI is seen in both a positive and a negative light. On the positive side, it allows us to do things faster than ever before. There is no need to spend hours creating programs to do things like spreadsheets and word processing. Instead, we ask our computers for these functions.
People fear that AI may replace humans. Many believe that robots may eventually surpass their creators' intelligence. This may lead to them taking over certain jobs.
How will governments regulate AI
Although AI is already being regulated by governments, there are still many things that they can do to improve their regulation. They must ensure that individuals have control over how their data is used. They must also ensure that AI is not used for unethical purposes by companies.
They also need to ensure that we're not creating an unfair playing field between different types of businesses. A small business owner might want to use AI in order to manage their business. However, they should not have to restrict other large businesses.
How does AI work?
An artificial neural networks is made up many simple processors called neuron. Each neuron processes inputs from others neurons using mathematical operations.
Layers are how neurons are organized. Each layer has a unique function. The first layer receives raw information like images and sounds. These are then passed on to the next layer which further processes them. Finally, the output is produced by the final layer.
Each neuron also has a weighting number. When new input arrives, this value is multiplied by the input and added to the weighted sum of all previous values. If the result exceeds zero, the neuron will activate. It sends a signal to the next neuron telling them what to do.
This is repeated until the network ends. The final results will be obtained.
What can AI do for you?
Two main purposes for AI are:
* Prediction - AI systems are capable of predicting future events. A self-driving vehicle can, for example, use AI to spot traffic lights and then stop at them.
* Decision making. AI systems can make important decisions for us. As an example, your smartphone can recognize faces to suggest friends or make calls.
Statistics
- A 2021 Pew Research survey revealed that 37 percent of respondents who are more concerned than excited about AI had concerns including job loss, privacy, and AI's potential to “surpass human skills.” (builtin.com)
- Additionally, keeping in mind the current crisis, the AI is designed in a manner where it reduces the carbon footprint by 20-40%. (analyticsinsight.net)
- In 2019, AI adoption among large companies increased by 47% compared to 2018, according to the latest Artificial IntelligenceIndex report. (marsner.com)
- According to the company's website, more than 800 financial firms use AlphaSense, including some Fortune 500 corporations. (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)
External Links
How To
How do I start using AI?
Artificial intelligence can be used to create algorithms that learn from their mistakes. This can be used to improve your future decisions.
A feature that suggests words for completing a sentence could be added to a text messaging system. It would take information from your previous messages and suggest similar phrases to you.
It would be necessary to train the system before it can write anything.
Chatbots are also available to answer questions. For example, you might ask, "what time does my flight leave?" The bot will reply, "the next one leaves at 8 am".
You can read our guide to machine learning to learn how to get going.