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) …
Climate change is one of the most pressing issues of our time. Despite increasing global consensus about the urgency of reducing emissions since the 1980s, they continue to rise relentlessly. We look to technology to deliver us from climate change, preferably without sacrificing economic growth. Our optimistic--some would say techno-utopian--visions of the future involve vast arrays of solar panels, machines that suck carbon dioxide back out of the atmosphere, and replacing fossil fuels for transport and heating with electricity generated by renewable means. This is nothing less than rebuilding our civilization on stable, sustainable foundations.
Taken from the InfoGan paper. Let's start off by developing the generator model as a deep convolutional neural network (e.g. a DCGAN). The model could take the noise vector (z) and control vector (c) as separate inputs and concatenate them before using them as the basis for generating the image. Alternately, the vectors can be concatenated beforehand and provided to a single input layer in the model. The approaches are equivalent and we will use the latter in this case to keep the model simple.
Can you imagine making a hotel reservation just by imagining it? Go beyond its recognizable One Click, the maximum simplicity of process in the tourism industry… Searching for a destination without using a single key, analyzing the images without the graphic interface of a screen, deciding and booking as the same 2 1 action, without the help of any purchase button… Something that would probably mean an affront to Amazon, which has made this magic button the being and not the being of its technology. This is what is offered by the most controversial visionary of our century, the tireless entrepreneur of unlikely projects, the ineffable, the questioned, the disturbed and, at the same time, revolutionary, Elon Musk. Once again Elon Musk who, from his company Neuralink, has explained in a highly recommendable video of following a project to leave speechless those who have not had enough with their brand new Tesla cars, their Tesla high-performance batteries, their Solar City kilometer solar farm, their role as the biggest space transporter with Space X or their incredible trips to Mars announced for 2024. The last thing, I say, planned for 2021 or early 2022 is the development of a brain implant in people who voluntarily lend themselves to it.
Small and medium-sized businesses are the keystone of the modern-day labor market. In the United States alone, small businesses employ almost 50% of the private workforce, and recent data shows that companies with fewer than 20 employees have added 1.2 million net new jobs. But although their growth is vital to a sustainable global economy, SMBs continue to struggle to get the funding they need. The traditional lending system simply isn't set up to meet the smaller capital needs of these types of enterprises: taking into account the risks and the long review process, small business loans typically don't pay off for banks. Chances of being accepted are incredibly low for businesses that aren't already well-established, and they rarely have the structure to carry them through the long review process anyway.
Question Is a convolutional neural network able to extract prognostic information from chest radiographs? Findings In this prognostic study of data from 2 randomized clinical trials (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [n 10 464] and National Lung Screening Trial [n 5493]), a convolutional neural network identified persons at high risk of long-term mortality based on their chest radiographs, even with adjustment for the radiologists' diagnostic findings and standard risk factors. Meaning Individuals at high risk of mortality based on chest radiography may benefit from prevention, screening, and lifestyle interventions. Importance Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis. Objective To develop and test a convolutional neural network (CNN) (named CXR-risk) to predict long-term mortality, including noncancer death, from chest radiographs. Design, Setting, and Participants In this prognostic study, CXR-risk CNN development (n 41 856) and testing (n 10 464) used data from the screening radiography arm of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) (n 52 320), a community cohort of asymptomatic nonsmokers and smokers (aged 55-74 years) enrolled at 10 US sites from November 8, 1993, through July 2, 2001. External testing used data from the screening radiography arm of the National Lung Screening Trial (NLST) (n 5493), a community cohort of heavy smokers (aged 55-74 years) enrolled at 21 US sites from August 2002, through April 2004. Data analysis was performed from January 1, 2018, to May 23, 2019. Exposure Deep learning CXR-risk score (very low, low, moderate, high, and very high) based on CNN analysis of the enrollment radiograph.
Organised by The Artificial Research Centre, Brunel University London, in association with the British Academy of Management (E-Business / Government, Organisational Transformation, Change and Development and Strategy SIGs) The next generations of technological development driven by Artificial Intelligence (AI) are unlike anything we have seen before. Data is the fuel used to drive the development in the Big Data era. Business leaders, policy makers and the public are only just the beginning to grasp the unquenchable thirst algorithms have for data. Many human activities are already being tracked and traced using smart sensors, apps, mobile devices and wearable tech. As things we come into contact with become part of the internet of things, so our every move will generate more data about us, our behaviours, habits, preferences and displeasures.
Global "Data Science and Machine-Learning Platforms Market" report presents a pin-point breakdown of Industry based on type, applications, and research regions. Growth strategies adopted by these companies are studied in detail in the report. The market size section gives the Data Science and Machine-Learning Platforms market revenues, covering both the historic growth of the market and forecasting the future. The report also includes several valuable information on the Data Science and Machine-Learning Platforms market, derived from various industrial sources. The report studies the competitive environment of the Data Science and Machine-Learning Platforms market is based on company profiles and their efforts on increasing product value and production.
Cambridge Analytica may have become the byword for a scandal, but it's not entirely clear that anyone knows exactly what that scandal is. It's more like toxic word association: "Facebook", "data", "harvested", "weaponised", "Trump" and, in this country, most controversially, "Brexit". It was a media firestorm that's yet to be extinguished, a year on from whistleblower Christopher Wylie's revelations in the Observer and the New York Times about how the company acquired the personal data of tens of millions of Facebook users in order to target them in political campaigns. This week sees the release of The Great Hack, a Netflix documentary that is the first feature-length attempt to gather all the strands of the affair into some sort of narrative – though it is one contested even by those appearing in the film. "This is not about one company," Julian Wheatland, the ex-chief operating officer of Cambridge Analytica, claims at one point. "This technology is going on unabated and will continue to go on unabated.[…] There was always going to be a Cambridge Analytica. It just sucks to me that it's Cambridge Analytica."
The human sense of taste is the result of millennia of evolution. And its astoundingly good at letting us enjoy pleasant foods and beverages as well as warning us against ingesting harmful substances. Man-made sensors, on the other hand, have yet to approach the ease with which our taste buds recognize substances. This is a significant technological gap, as there are many substances out there that we would like to "taste" without actually putting them in our mouth. For the rapid and mobile fingerprinting of beverages and other liquids less fit for ingestion, our team at IBM Research is currently developing Hypertaste, an electronic, AI-assisted tongue that draws inspiration from the way humans taste things.
More than ever, medicine now aims to tailor, adjust, and personalize healthcare to individuals' and populations' specific characteristics and needs--predictively, preventively, participatorily, and dynamically--while continuously improving and learning from data both "big" and "small." Today, these data are increasingly captured from data sources both old (such as electronic medical records, EMR) and new (including smartphones, sensors, and smart devices). Combining artificial intelligence (AI) with augmented human intelligence, these new analytical approaches enable "deep learning health systems" that reach far beyond the clinic to forge research, education, and even care into the built environment and peoples' homes. The volume of biomedical research is increasing rapidly. Some is being driven by the availability and analysis of big data--the focus of this collection.