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) …
The convolutional neural network is a type of artificial neural network which has proven giving very good results for visual imagery over the last few years. Over the years many version of convolutional neural network has been designed to solve many tasks as well as to win image net competitions. Any artificial neural network which uses the convolution layer in its architecture can be considered as ConvNet. ConvNets typically start with recognizing smaller patterns/objects in data and later on combines these patterns/objects further using more convolution layers to predict the whole object. Yann Lecun developed the first successful ConvNet by applying backpropagation to it during the 1990s called LeNet.
A student team from Carnegie Mellon University is joining the upcoming season of Roborace, an international competition involving autonomous, electrically powered vehicles. CMU's Roborace team includes students and alumni from the Language Technologies Institute (LTI) and Robotics Institute, as well as the Information Networking Institute. It will be the first U.S. team to join Roborace and anticipates competing in a Roborace event later this year. "Having the opportunity to work on cutting-edge projects such as this is what attracted me to Carnegie Mellon," said Jimmy Herman, an ex-NFL athlete now enrolled in the LTI's Master of Computational Data Science (MCDS) program. "We are pushing to innovate and create technology with impact potential beyond the racing domain," he added.
Omri Geller, Run:AI co-founder and CEO told ZDNet that Nvidia's announcement about "fractionalizing" GPU, or running separate jobs within a single GPU, is revolutionary for GPU hardware. Geller said it has seen many customers with this need, especially for inference workloads: Why utilize a full GPU for a job that does not require the full compute and memory of a GPU? "We believe, however, that this is more easily managed in the software stack than at the hardware level, and the reason is flexibility. While hardware slicing creates'smaller GPUs' with a static amount of memory and compute cores, software solutions allow for the division of GPUs into any number of smaller GPUs, each with a chosen memory footprint and compute power. In addition, fractionalizing with a software solution is possible with any GPU or AI accelerator, not just Ampere servers - thus improving TCO for all of a company's compute resources, not just the latest ones. This is, in fact, what Run:AI's fractional GPU feature enables." InAccel is a Greek startup, built around the premise of providing an FPGA manager that allows the distributed acceleration of large data sets across clusters of FPGA resources using simple programming models.
It's a new week, and what better time to get your hands on another free eBook? We have been highlighting a new such installment weekly for the better part of the past few months, doing our best to single out and share top learning materials for those stuck at home right now, or really for anyone interested in learning a new concept or brushing up on what they already know. This week we turn our attention to the topic of automated machine learning (AutoML), a personal favorite of mine. What is automated machine learning? It is a wide (and widening) concept, but I've previously tried to capture its essence as such: If, as Sebastian Raschka has described it, computer programming is about automation, and machine learning is "all about automating automation," then automated machine learning is "the automation of automating automation."
After consuming hundreds of books, several notes about Data Science and have viewed several videos of Data Scientists sharing their experience. You have all the theoretical knowledge you need to know for becoming a Data Scientists. But are you a Data Scientist now? The next big step is to start applying the concept, think differently and how you can do that is either find real-world problems of fields in which you are interested in or you can take participate in Hackathons and Machine learning Competitions. Hackathons are efficient and new means of hiring professionals in aspects of machine learning, Artificial Intelligence and data science.
Like crystal balls for the universe's deeper mysteries, galaxies and other massive space objects can serve as lenses to more distant objects and phenomena along the same path, bending light in revelatory ways. Gravitational lensing was first theorized by Albert Einstein more than 100 years ago to describe how light bends when it travels past massive objects like galaxies and galaxy clusters. These lensing effects are typically described as weak or strong, and the strength of a lens relates to an object's position and mass and distance from the light source that is lensed. Strong lenses can have 100 billion times more mass than our sun, causing light from more distant objects in the same path to magnify and split, for example, into multiple images, or to appear as dramatic arcs or rings. The major limitation of strong gravitational lenses has been their scarcity, with only several hundred confirmed since the first observation in 1979, but that's changing, and fast.
As the world continues to fight the novel coronavirus, every section of the society is playing its role. Medical professionals, law enforcement officers, and essential workers are on the frontline of this battle, while researchers, data scientists, and community leaders are also doing their part. There has been a recent increased emphasis on how AI can boost humanity's efforts in this on-going crisis. Experts believe Artificial Intelligence can analyze published literature on the disease, study structure and DNA of the virus, and recommend existing drugs or find a new one (in the latter case, it would take almost two years for the drug to be approved by the FDA). There has also been a discussion on how AI can help governments be smart about social distancing policies.
An Australian team has won a competition to write a hit Eurovision song using artificial intelligence. An editor for Dutch broadcaster VPRO had the idea, after the Netherlands won last year's Eurovision Song Contest. And it grew into an international effort after this year's contest was cancelled because of the coronavirus pandemic. The winning song, Beautiful the World, was inspired by nature's recovery from the bushfires earlier this year. A total of 13 teams took part, from the Netherlands, Australia, Sweden, Belgium, the UK, France, Germany and Switzerland.