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) …
Kimberly Powell, who leads Nvidia's efforts in health care, says the company is working with medical researchers in a range of areas and will look to expand these efforts in coming years. Most notably, a machine-learning technique called deep learning is being applied to processing medical images and sifting through large amounts of medical data. Nvidia is, for example, working with Bradley Erickson, a neuro-radiologist at the Mayo Clinic, to apply deep learning to brain images. There are, however, significant challenges in applying techniques like deep learning to medicine.
Increasingly affordable AI maintenance and the increased speed of calculations thanks to GPU are significant factors in the unbridled growth of AI. The astonishing results that were achieved on training a neural network on GPU cards made Nvidia a key player, with 70 percent of the market share that Intel failed to gain. Compared with the results from the analog algorithms, and thanks to the combination of machine learning and big data, previously "unsolvable" problems are now being solved. Machine learning algorithms can directly analyze thousands of previous cases of different types of diseases and make their own conclusions as to what constitutes a sick individual versus a healthy individual, and consequently help diagnose dangerous conditions including cancer.
H2O.ai and Nvidia today announced that they have partnered to take machine learning and deep learning algorithms to the enterprise through deals with Nvidia's graphics processing units (GPUs). Mountain View, Calif.-based H20.ai has created AI software that enables customers to train machine learning and deep learning models up to 75 times faster than conventional central processing unit (CPU) solutions. H2O.ai is also a founding member of the GPU Open Analytics initiative that aims to create an open framework for data science on GPUs. As part of the initiative, H2O.ai's GPU edition machine learning algorithms are compatible with the GPU Data Frame, the open in-GPU-memory data frame.
This is the first installment in a three-part review of 2016 in machine learning and deep learning. In Part Two, we cover developments in each of the leading open source machine learning and deep learning projects. Part Three will review the machine learning and deep learning moves of commercial software vendors. As organizations expand the use of machine learning for profiling and automated decisions, there is growing concern about the potential for bias. In 2016, reports in the media documented racial bias in predictive models used for criminal sentencing, discriminatory pricing in automated auto insurance quotes, an image classifier that learned "whiteness" as an attribute of beauty, and hidden stereotypes in Google's word2vec algorithm.
I am really excited to announce that the general availability of the Azure N-Series will be December 1st, 2016. Azure N-Series virtual machines are powered by NVIDIA GPUs and provide customers and developers access to industry-leading accelerated computing and visualization experiences. I am also excited to announce global access to the sizes, with N-series available in South Central US, East US, West Europe and South East Asia, all available on December 1st. We've had thousands of customers participate in the N-Series preview since we launched it back in August. We've heard positive feedback on the enhanced performance and the work we have down with NVIDIA to make this a completely turnkey experience for you.
The rise of artificial intelligence, or AI, services -- one of the fastest-growing markets in tech --should be a boon for consumers and investors alike. In fact, researcher IDC predictsAI and cognitive systems technology sales are primed to simply explode in the years to come, rising from an estimated $8 billion this year to $47 billion in 2020. Though by no means the only names interested in this space, Amazon.com Here's how these particular artificial intelligence stocks have performed so far in 2016. Forget GE! Heres how to play the largest growth opportunity in history Forget GE! Heres how to play the largest growth opportunity in history Importantly, each of the tech giants named above has its own strategy to tap into the growth in the artificial intelligence market.
Neural networking with advanced parallel processing is beginning to take root in a number of markets ranging from predicting earthquakes and hurricanes to parsing MRI image datasets in order to identify and classify tumors. As this approach gets implemented in more places, it is being customized and parsed in ways that many experts never envisioned. And it is driving new research into how else these kinds of compute architectures can be applied. Fjodor van Veen, deep learning researcher at The Asimov Institute in the Netherlands, has identified 27 distinct neural net architecture types. The differences are largely application-specific. Neural networking is based on the concept of threshold logic algorithms, which were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician.
If you think that AI still belongs in the realm of science fiction, you obviously didn't get the memo. It is all around us all the time, 24/7. You just don't know it yet. It is impossible to invest purely in AI. All new AI startups comprise small teams of experts from labs and universities financed by big venture capital firms like Sequoia Capital, Kleiner Perkins, and Andreessen Horowitz.
This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland. Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to.
The new machine, called a DGX-1, is optimized for the form of machine learning known as deep learning, which involves feeding data to a large network of crudely simulated neurons and has resulted in great strides in artificial intelligence in recent years. Language remains a very tricky problem for artificial intelligence, but in recent years researchers have made progress in applying deep learning to the problem (see "AI's Language Problem"). "This will allow us to train models on larger data sets, which we have found leads to progress in AI." OpenAI hopes to use reinforcement learning to build robots capable of performing useful chores around the home, although this may prove a time-consuming challenge (see "This Is the Robot Maid Elon Musk Is Funding" and "The Robot You Want Most Is Far from Reality").