The multidisciplinary field of data science is concerned with extracting insights from data using a diverse set of computational methodologies, theories, and technologies (Blei and Smyth 2017). Within data science, there are two competing scientific philosophies: classical statistics and machine learning (Breiman 2001b). Classical statistics aims to formalise relationships between dependent and independent variables based on a clearly defined set of assumptions from which mathematical models are parametrised. The aim is to derive meaningful statistical inference (properties of an underlying probability distribution) for the measured variables, assuming that the observed data is sampled from a larger population. Conversely, machine learning uses ad-hoc computational algorithms that iteratively optimise (or'learn') without necessarily relying on any formal statistical assumptions (Bishop 1995).

Traditionally slow to embrace cutting-edge technology, the real estate industry is catching up with the swift pace of tech evolution. Among companies leveraging advancements in artificial intelligence (AI) is Atlanta, Ga.-based RentPath, whose brands -- ApartmentGuide.com, The company's recent adoption of AI and machine learning to power its career site is helping position RentPath for current and future talent needs. In the B2B realm, the company's sales team works with property management companies to extend their reach to the customer and drive leads back to those companies. On the B2C side, its product managers work to simplify renters' journeys, overseeing features that enable renters to identify prospective rentals and schedule their first visits to properties.

Figure 1: Illustration of the meta-learning process as applied to the task of personalized next-word prediction. Here each mobile device corresponds to a different next-word prediction task, with the test-task not seen during meta-training (Step 1). In the classical machine learning setup, we aim to learn a single model for a single task given many training samples from the same distribution. However, in many practical applications, we are in fact exposed to several distinct yet related tasks that have only a few examples each. Because the data now come from different training distributions, simply learning a single global model, e.g., via stochastic gradient descent (SGD), may result in poor performance on each task.

Artificial Intelligence (AI) is currently used by IT professionals to manage cybersecurity threats, protecting organisations from ongoing cybercrimes. With its ability to take large volumes of information and deduce clusters of similarity, it won't be long until AI will turn on us. Another quality that AI is exhibiting is its ability to mimic humans to a worryingly accurate degree. It can draw pictures, age photographs of people, and just recently, it has been found to impersonate human voices. This means that AI could potentially replicate human hacking tactics, which are currently the most damaging and the most time-consuming form of attack for hackers.

Drawing on the film "Ex Machina," and Plato's allegory of the cave, the chapter examines our relationship with increasingly complex technologies, including the possibility of AI that may one day be able to manipulate humans to achieve its goals. The eminent twentieth-century computer scientist Alan Turing was intrigued by the idea that it might be possible to create a machine that exhibits human intelligence. To him, humans were merely exquisitely intricate machines. And by extension, our minds -- the source of our intelligence -- were merely an emergent property of a complex machine. It therefore stood to reason to him that, with the right technology, there was no reason why we couldn't build a machine that thought and reasoned like a person.

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CLEVELAND--Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy. And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both inside--and outside--the tumor, a signature of the lab's recent research. "This is no flash in the pan--this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.

The Hanguang 800 is being implemented across many application scenarios within Aliyun, ranging from video classification to smart city applications. For example, the company's popular Pailitao platform applies visual image search to e-commerce, allowing customers to search for items by taking a photo of the query object. Using AI-based image recognition & indexing powered by the new Hanguang 800, Aliyun can increase image processing efficiency by 12 times compared to GPUs. With regard to smart city tech, Aliyun says it previously used 40 traditional GPUs to process videos of central Hangzhou with a latency of 300ms. Now the task requires only four Hanguang 800 with a lower latency of 150ms.

An object in motion looks fundamentally different from an object at rest -- especially to a computer. To get a better idea of this concept, let's imagine a film strip of a sprinter running: The person and pose in one frame look drastically different from the next frame, right? Making sense of dynamic objects is taking on new importance as cities begin incorporating IoT devices like smart cameras to streamline municipal life. The town of Yuma, Arizona, is a great example of this. The city recently installed cameras on streetlights that can detect when cars, bicycles, and pedestrians travel through intersections, and it uses that data to optimise signal switching.

Just when we thought it was safe to go back in the water, a new threat has emerged to equal employment opportunity as employers base hiring decisions on artificial intelligence powered video and game-based "pre-employment" assessments of job candidates. The Electronic Privacy Information Center, a public interest research center based in Washington, D.C., recently asked the Federal Trade Commission to investigate HireVue, a recruiting company based in Utah that purports to evaluate a job applicant's job qualifications through online "video interview" and/or "game-based challenge." According to its web site, HireVue has more than 700 customers worldwide including over one-third of the Fortune 100 and such leading brands such as Unilever, Hilton, JP Morgan Chase, Delta Air Lines, Vodafone, Carnival Cruise Line, and Goldman Sachs. The company states it has hosted more than ten million on-demand interviews and one million assessments. The EPIC complaint follows a wave of lawsuits in recent years charging that employers are using software algorithms to discriminate against older workers by targeting internet job advertisements exclusively to younger workers.