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) …
As technology has continued to advance, we've seen some pretty impressive strides in artificial intelligence. We have a virtual assistant in our pockets at all times, can integrate home technology into voice activated commands, and have even started to develop robots who can hold actual conversations with humans. While some people see this as a frightening glimpse into a robot-dominated world, others view it as a necessary step into the future. With artificial intelligence on the rise, machine learning is beginning to make AI even more identifiable. While the actual idea of machine learning has been around since the 1950s, it hasn't always played out in beneficial matters.
Speech-to-text applications have never been so plentiful, popular or powerful, with researchers' pursuit of ever-better automatic speech recognition (ASR) system performance bearing fruit thanks to huge advances in machine learning technologies and the increasing availability of large speech datasets. Current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance. However, a lack of transcribed audio data for the less widely spoken of the world's 7,000 languages and dialects makes it difficult to train robust speech recognition systems in this area. To help ASR development for such low-resource languages and dialects, Facebook AI researchers have open-sourced the new wav2vec 2.0 algorithm for self-supervised language learning. The paper Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations claims to "show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler." A Facebook AI tweet says the new algorithm can enable automatic speech recognition models with just 10 minutes of transcribed speech data.
Let's first clarify what AGI should look like When the Arnold Schwarzenegger character comes to earth – he is fully functional. To do so, he must be aware of the context. But GPT3 however has the capacity to respond'AGI-like' to an expanded set of contexts much more than traditional AI systems. Does AGI need to be conscious as we know it or would access consciousness suffice? Finally, let us consider the question of spillover of intelligence.
Every once in a while, a machine learning framework or library changes the landscape of the field. In this article, we'll quickly understand the concept of object detection and then dive straight into DETR and what it brings to the table. In Computer Vision, object detection is a task where we want our model to distinguish the foreground objects from the background and predict the locations and the categories for the objects present in the image. Current deep learning approaches attempt to solve the task of object detection either as a classification problem or as a regression problem or both. For example, in the RCNN algorithm, several regions of interest are identified from the input image.
I actually had to double-check my calendar to make sure today wasn't April Fool's. Because watching the intro video of an indoor surveillance drone operated by Amazon seemed like just the sort of geeky joke you'd expect on April 1. But it isn't April Fools, and besides, Google has always been the one with the twisted sense of humor. Amazon has always been the one with the twisted sense of world domination. This was a serious press briefing.
"The tech giants have as much money and influence as nation states." Tech Giants include Apple Facebook, and Google ... but Amazon's unique flywheel makes it the torchbearer. "AWS alone is on track to be worth $1 trillion." The Amazon flywheel fuels a circular, data-driven ecosystem that's bolstered by Open Innovation. This article summarizes two from a series called the Tech Nations project.
Benchmarks can be very misleading, says Douwe Kiela at Facebook AI Research, who led the team behind the tool. Focusing too much on benchmarks can mean losing sight of wider goals. The test can become the task. "You end up with a system that is better at the test than humans are but not better at the overall task," he says. "It's very deceiving, because it makes it look like we're much further than we actually are."
Facebook's artificial intelligence researchers have a plan to make algorithms smarter by exposing them to human cunning. They want your help to supply the trickery. Thursday, Facebook's AI lab launched a project called Dynabench that creates a kind of gladiatorial arena in which humans try to trip up AI systems. Challenges include crafting sentences that cause a sentiment-scoring system to misfire, reading a comment as negative when it is actually positive, for example. Another involves tricking a hate speech filter--a potential draw for teens and trolls.
Benchmarking is a crucial step in developing ever more sophisticated artificial intelligence. It provides a helpful abstraction of the AI's capabilities and allows researchers a firm sense of how well the system is performing on specific tasks. But they are not without their drawbacks. Once an algorithm masters the static dataset from a given benchmark, researchers have to undertake the time-consuming process of developing a new one to further improve the AI. As AIs have improved over time, researchers have had to build new benchmarks with increasing frequency.