A couple of weeks ago, Google CEO Sundar Pichai told an audience at a Recode-sponsored event that for humanity, the impact of artificial intelligence could be "more profound than, I dunno, electricity or fire". In this article, I will explore how artificial intelligence emerges from data and algorithms, and how future advances in computing will aid its development. The term'big data' describes the increasing volume, velocity, and variety of data collected by organizations. It is used as a catch-all term to describe the large data sets that organizations collect. The information in these data sets (including information about an organization's products and services, internal processes, market conditions and competitors, supply chain, trends in consumer preferences, individual consumer preferences, and specific interactions between consumers and products, services, and online portals) can be used in either backward- or forward-looking analysis.

In recent years, neural network approaches have been widely adopted for machine learning tasks, with applications in computer vision. More recently, unsupervised generative models based on neural networks have been successfully applied to model data distributions via low-dimensional latent spaces. In this paper, we use Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems, replacing the usual sparsity constraint. We propose to train the GANs in a task-aware fashion, specifically for reconstruction tasks. We also show that it is possible to train our model without using any (or much) non-compressed data. Finally, we show that the latent space of the GAN carries discriminative information and can further be regularized to generate input features for general inference tasks. We demonstrate the effectiveness of our method on a variety of reconstruction and classification problems.

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX.

In media stories last week, for example this one in The West Australian, former airline Captain Kevin Sullivan broke his long silence on what happened on Qantas Airways flight QF72 in October 2008. Traveling from Singapore to Perth, the Airbus A330-300 aircraft suddenly lost altitude over the north-west of Western Australia, causing unrestrained passengers and crew to be flung around the cabin. The injuries were serious, and included fractures, lacerations and spinal injuries. Captain Sullivan called a mayday and made an emergency landing at the remote Learmonth Royal Australian Air Force (RAAF) base. At least 110 of the 303 passengers and nine of the 12 crew members had been injured.

A Google-bred pioneer in self-driving cars and Uber's beleaguered ride-hailing service are colliding in a courtroom showdown revolving around allegations of deceit, betrayal, espionage and a high-tech heist that tore apart one-time allies. The trial opening Monday in San Francisco federal court comes nearly a year after Google spin-off Waymo sued Uber, accusing it of ripping off key pieces of its self-driving car technology in 2016. Uber paid $680 million for a startup run by Anthony Levandowski, one of the top engineers in a robotic vehicle project that Google began in 2009 and later spun out into Waymo. Google was also an early investor in Uber, a relationship that later soured. Its parent company Alphabet also owns Waymo.

Bias – both human and data-based – is the biggest ethical challenge facing the development and adoption of artificial intelligence, according to a panel of world-leading AI luminaries. Speaking at last week's Dreamforce conference, Salesforce chief scientist and adjunct professor of Stanford's computer science department, Dr Richard Socher, said the rapid development of AI will inevitably impact more and more people's lives, raising significant ethical concerns. "These algorithms can change elections for the worse, or spread misinformation," he told attendees. "In some benign natural language processing classification algorithms, for example, you may want to maximise the number of clicks, and find something with a terminator image has more clicks so you put more of those pictures in articles." But it is the bias coming through existing datasets being used to train AI algorithms that arguably presents the biggest ethical problem facing industries.

The implementation, data privacy, and operational challenges facing life science and healthcare professionals dedicated to integrating AI into their organization vastly differ by therapeutic area and patient population, size of the organization, and the number of resources available to them. Cookie-cutter solutions cannot address the unique challenges faced by a company. It is critical that the education available to these professionals meets the varying needs of the industry. As such, ExL Events has expanded its Artificial Intelligence conference series to now introduce the European Artificial Intelligence Innovation Summit. Through keynotes, panel discussions, and case studies, the executive speaking faculty provides key insights into the ethical standards of AI; explores the realistic steps to achieve successful AI execution; defines the regulatory boundaries and limitations; reviews the necessary metrics to measure the success and efficacy of the AI system; and examines case studies on industry vertical use. Dr. Alfa is Vice President, Discovery, and Product at Recursion Pharmaceuticals (recursionpharma.com), an AI-enabled drug discovery company combining state-of-the-art machine learning with automated cell biology.

The Alabama Department of Revenue is implementing a program called eID. It's an application using facial recognition software to compare cellphone selfies against photos in the state's driver license database.

China is working to update the rugged old computer systems on nuclear submarines with artificial intelligence to enhance the potential thinking skills of commanding officers, a senior scientist involved with the programme told the South China Morning Post. A submarine with AI-augmented brainpower not only would give China's large navy an upper hand in battle under the world's oceans but would push applications of AI technology to a new level, according to the researcher, who spoke on condition of anonymity because of the project's sensitivity. "Though a submarine has enormous power of destruction, its brain is actually quite small," the researcher said. While a nuclear submarine depends on the skill, experience and efficiency of its crew to operate effectively, the demands of modern warfare could introduce variables that would cause even the smoothest-run operation to come unglued. For instance, if the 100 to 300 people in the sub's crew were forced to remain together in their canister in deep, dark water for months, the rising stress level could affect the commanding officers' decision-making powers, even leading to bad judgment.