Is AI just a black box that we started trusting enough to drive cars, detect diseases, identify suspects just because of the hype? You may have heard of the Netflix documentary, Coded Bias (you can watch the film here). The film criticizes deep learning algorithms for their inherent biases; specifically their failure to detect dark-skinned and female faces. The film suggests that the solution to the problem is in government. To "push for the first-ever legislation in the U.S. to govern against bias in the algorithms that impact us all."
In his book Outliers, Malcom Gladwell unveils the "10,000-Hour Rule" which postulates that the key to achieving world-class mastery of a skill is a matter of 10,000 hours of practice or learning. And while there may be disagreement on the actual number of hours (though I did hear my basketball coaches yell that at me about 10,000 times), let s say that we can accept that it requires roughly 10,000 hours of practice and learning exploring, trying, failing, learning, exploring again, trying again, failing again, learning again for one to master a skill. If that is truly the case, then dang, us humans are doomed. Think about 1,000,000 Tesla cars with its Fully Self Driving (FSD) autonomous driving module practicing and learning every hour that it is driving. In a single hour of the day, Tesla s FSD driving module is learning 100x more than what Malcom Gladwell postulates is necessary to master a task.
There are many ways to hack someone's computer and email, but what is the process? We break down how Deep learning is used for these malicious tricks and how you can protect yourself. Is Deep learning good for Hacking? Some people in the security community are concerned that deep learning could be used for hacking. They worry that hackers could use deep learning to create powerful new attack tools, or to automate attacks.
Fake news is false or misleading information presented as news. It often aims to damage the reputation of a person or entity or make money through advertising revenue. However, the term does not have a fixed definition and has been applied more broadly to include any type of false information, including unintentional and unconscious mechanisms, and also by high-profile individuals to apply to any news unfavorable to his/her personal perspectives. To develop a Fake News Classifier using Bidirectional Long Short Term Memory (LSTM) using Python programming Language and Keras on Cainvas Platform. Let's load our data file train.csv
Deep learning has radically transformed the fields of computer vision and natural language processing, in not just classification but also generative tasks, enabling the creation of unbelievably realistic pictures as well as artificially generated news articles. In this project, we aim to create novel neural network architectures to generate new music, using 20,000 MIDI samples of different genres from the Lakh Piano Dataset, a popular benchmark dataset for recent music generation tasks. This project was a group effort by Isaac Tham and Matthew Kim, senior-year undergraduates at the University of Pennsylvania. Music generation using deep learning techniques has been a topic of interest for the past two decades. Music proves to be a different challenge compared to images, among three main dimensions: Firstly, music is temporal, with a hierarchical structure with dependencies across time. Secondly, music consists of multiple instruments that are interdependent and unfold across time.
Senior Deep Learning Researcher (Speech Recognition) at AssemblyAI Remote › Worldwide, 100% remote position Salary $140,000 - 225,000 Job description AssemblyAI is an AI company - we build powerful models to transcribe and understand audio data, exposed through simple APIs. Hundreds of companies, and thousands of developers, use our APIs to both transcribe and understand millions of videos, podcasts, phone calls, and zoom meetings every day. Our APIs power innovative products like conversational intelligence platforms, zoom meeting summarizers, content moderation, and automatic closed captioning. We've been growing at breakneck speed, and are backed by leading investors including Y Combinator's AI Fund, Patrick and John Collision (Founders of Stripe), Nat Friedman (Former CEO of GitHub), and Daniel Gross (Entrepreneur & Investor in companies including GitHub, Uber, Coinbase, SpaceX, Instacart, Notion, and Cruise Automation). AssemblyAI's Speech-to-Text APIs are already trusted by Fortune 500s, startups, and thousands of developers around the world, with well-known customers including Spotify, Algolia, Dow Jones, The Wall Street Journal, and NBCUniversal.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The method expands the concept of a Nash equilibrium by decomposing an asymmetric game into multiple symmetric games.
On Episode 15 of Season 2, we're joined by Eric Horvitz, Microsoft's first ever Chief Scientific Officer. His research spans theoretical and practical challenges with developing systems that perceive, learn, and reason. He's the company's top inventor since joining in 1993 with over 300 patents filed. He has been elected Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Fellow of the National Academy of Engineering (NAE), Fellow of the American Academy of Arts and Sciences, and Fellow of the American Association for the Advancement of Science (AAAS). He was a member of the National Security Commission on AI and he also co-founded important groups like the Partnership on AI, a non-profit organization bringing together Apple, Amazon, Facebook, Google, DeepMind, IBM, and Microsoft to document the quality and impact of AI systems on things like criminal justice, the economy, and media integrity.
Who can deny the chilly breeze blowing through some quarters of the AI world? While many continue to bask in the glorious summertime ushered in by the ascendency of deep learning, some are sensing autumnal winds which carry with them cautionary words we have all heard many times, such as "black box", "poor generalization", "brittle", "lacking reasoning", "biased", "no common sense", and "unsustainable". Whether or not we are truly headed for a new AI winter, artificial intelligence certainly has a long way to go to take on human intelligence. And yet, human intelligence is not a particularly new topic of research. It has long been studied by many of mankind's most piercing intellects, going back at least 2300 years to Aristotle, the "father of logic" and the "father of psychology". Through the six works comprising his Organon, as well as a few others such as his Metaphysics and On the Soul, Aristotle laid the foundations for our understanding of logic, reasoning, and knowledge.