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) …
FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.
Here's another scenario worthy of Hollywood attention: how deep learning based algorithms are powering the process of decoding complex brain signals in real-time. When Google's Deep Mind beat the top professional player of GO, the world sat up and took notice, because this was conclusive proof that computing power could now match human brain power. Here's another scenario worthy of Hollywood attention: how deep learning based algorithms are powering the process of decoding complex brain signals in real-time. When Google's Deep Mind beat the top professional player of GO, the world sat up and took notice, because this was conclusive proof that computing power could now match human brain power.
As healthcare moves to a model of any-time, any-place, continuous and personalized care, it is important to identify the key technologies that will enable this transition and work toward their implementation into different care settings. Frost & Sullivan's Visionary Healthcare research has identified several technologies that are most likely to impact healthcare paradigms by 2025. It is interesting to note that technological advances in the fields of computing, machine learning, nanotechnology and electronics are all playing a role in helping reshape the industry. The figure below provides an overview of the top technologies that will change this industry dramatically, and an analysis of the time frame for their commercialization and maturation. We are now beginning to see larger data sets in healthcare research and delivery to analyze and make sense of entire genome sequences; impact of environmental, behavioral and hereditary factors on health; population health data; patient generated health data; etc.
Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and clinical information related to the diagnosis, treatment, and history of cancer incidence in the United States. The surveillance effort, coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention, enables researchers and clinicians to monitor cancer cases at the national, state, and local levels. Much of this data is drawn from electronic, text-based clinical reports that must be manually curated--a time-intensive process--before it can be used in research. For example, cancer pathology reports, text documents that describe cancerous tissue in detail, must be individually read and annotated by experts before becoming part of a cancer registry.
Technology is advancing so rapidly that we will experience radical changes in society not only in our lifetimes but in the coming years. We have already begun to see ways in which computing, sensors, artificial intelligence and genomics are reshaping entire industries and our daily lives. As we undergo this rapid change, many of the old assumptions that we have relied will no longer apply. Technology is creating a new set of rules that will change our very existence. Digitization began with words and numbers.
We also moved complex business functions, medical tools, industrial processes and transportation systems into the digital realm. Cheap, ubiquitous sensors are documenting everything we do and creating rich digital records of our entire lives. The planet's resources (its true value) are largely being used to feed, clothe, transport and house a species that's making life unsustainable for every living thing that lives on the planet. A major thing missing from the list and something that is definitely not free is housing – housing requires land and a house.
As the EMR "space race" peaks, clinical and health leaders are coming to understand that digitizing data does not, on its own, drive innovation or transformation. Many are wondering what's next. Looking ahead, the next wave in our journey towards digital transformation is Artificial Intelligence (AI). Simply put, Artificial Intelligence is a collection of systems that sense, comprehend, act and learn. The goal of AI in health is to drive greater "data dividends" than what we are getting from investments already made in EMRs and other systems.
Technology is advancing so rapidly that we will experience radical changes in society not only in our lifetimes but in the coming years. We are already seeing how computing, sensors, artificial intelligence, and genomics are reshaping entire industries and our daily lives. As we undergo this rapid change, many of the old assumptions we have relied will no longer apply. Technology is creating a new set of rules that will change our very existence. Digitization began with words and numbers.
SANTA CLARA, CA--(Marketwired - Nov 14, 2016) - NVIDIA (NASDAQ: NVDA) today announced that it is teaming up with the National Cancer Institute, the U.S. Department of Energy (DOE) and several national laboratories on an initiative to accelerate cancer research. Teams collaborating on CANDLE include researchers at the National Cancer Institute (NCI), Frederick National Laboratory for Cancer Research and DOE, as well as at Argonne, Oak Ridge, Livermore and Los Alamos National Laboratories. Georgia Tourassi, Director of the Health Data Sciences Institute at Oak Ridge National Laboratory, said, "Today cancer surveillance relies on manual analysis of clinical reports to extract important biomarkers of cancer progression and outcomes. Certain statements in this press release including, but not limited to, statements regarding the impact, benefits and goals of the Cancer Moonshot, the CANDLE AI framework, the combination of NVLink-enabled Pascal GPU architectures, and NVIDIA DGX-1; NVIDIA's participation in CANDLE; AI and deep learning techniques being essential to achieve the Cancer Moonshot objectives; expected gains in training neural networks for cancer research; large-scale data analytics and deep learning being central to Lawrence Livermore National Laboratory's missions; NVIDIA being at the forefront of accelerated machine learning; and CORAL/Sierra architectures being critical to developing scalable deep learning algorithms are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations.
Even more remarkable, it took just ten minutes to compare the woman's genetic information with 20 million clinical oncology studies to arrive at the life-saving diagnosis. Not quite, but increasing volumes of medical data, more powerful computers and smarter algorithms could see a future medical science in which human doctors are helped by AI. Data driven medicine started with the Human Genome Project that aimed to map and understand all of the genes in the human body by collecting DNA from countries all over the world. Great potential exists for AI and data driven medicine to save lives, improve standards of patient care and save money for providers, particularly hospitals and research institutions.