Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions. This paper argues interaction effects between clustering and prediction (e.g. classification, regression) algorithms can cause subtle adverse behaviors during cross-validation that may not be initially apparent. In particular, we focus on the problem of estimating the out-of-cluster (OOC) prediction loss given an approximate clustering with probabilistic error rate $p_0$. Traditional cross-validation techniques exhibit significant empirical bias in this setting, and the few attempts to estimate and correct for these effects are intractable on larger datasets. Further, no previous work has been able to characterize the conditions under which these empirical effects occur, and if they do, what properties they have. We precisely answer these questions by providing theoretical properties which hold in various settings, and prove that expected out-of-cluster loss behavior rapidly decays with even minor clustering errors. Fortunately, we are able to leverage these same properties to construct hypothesis tests and scalable estimators necessary for correcting the problem. Empirical results on benchmark datasets validate our theoretical results and demonstrate how scaling techniques provide solutions to new classes of problems.

Objective: A patient medical insurance coverage plays an essential role in determining the post-acute care (PAC) discharge disposition. The prior health insurance authorization process postpones the PAC discharge disposition, increases the inpatient length of stay, and effects patient health. Our study implements predictive analytics for the early prediction of the PAC discharge disposition to reduce the deferments caused by prior health insurance authorization, the inpatient length of stay and inpatient stay expenses. Methodology: We conducted a group discussion involving 25 patient care facilitators (PCFs) and two registered nurses (RNs) and retrieved 1600 patient data records from the initial nursing assessment and discharge notes to conduct a retrospective analysis of PAC discharge dispositions using predictive analytics. Results: The chi-squared automatic interaction detector (CHAID) algorithm enabled the early prediction of the PAC discharge disposition, accelerated the prior health insurance process, decreased the inpatient length of stay by an average of 22.22%, and reduced inpatient stay expenses by \$1,974 for state government hospitals, \$2,346 for non-profit hospitals and \$1,798 for for-profit hospitals per day. The CHAID algorithm produced an overall accuracy of 84.16% and an area under the receiver operating characteristic (ROC) curve value of 0.81. Conclusion: The early prediction of PAC discharge dispositions can condense the PAC deferment caused by the prior health insurance authorization process and simultaneously minimize the inpatient length of stay and related expenses incurred by the hospital.

Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to test their models and methods. Previously, most research in these areas starts from the fingerprints or features of drugs, instead of their structures. In this paper, we introduce a model for phenotypic screening, which is called twin Convolutional Neural Network for drugs in SMILES format (tCNNS). tCNNS is comprised of CNN input channels for drugs in SMILES format and cancer cell lines respectively. Our model achieves $0.84$ for the coefficient of determinant($R^2$) and $0.92$ for Pearson correlation($R_p$), which are significantly better than previous works\cite{ammad2014integrative,haider2015copula,menden2013machine}. Besides these statistical metrics, tCNNS also provides some insights into phenotypic screening.

Google on Wednesday opened applications for the next class of its "Launchpad Accelerator" mentorship programme for startups using artificial intelligence (AI)/ machine learning (ML) in India which is scheduled to commence in March 2019. The last date for application to the programme is January 31, 2019, Google said in a statement. Under the Launchpad Accelerator programme, startups that are using AI/ML to solve India's needs, undergo an intensive in-person mentorship boot-camp, followed by customised support for three months. The selected startups in the second batch will be announced in February 2019, Google said. "Our Launchpad Accelerator programme is bringing best of our expertise, platforms, tools and core strengths including Machine Learning and AI, to help Indian startups build, scale and grow their offering," said Paul Ravindranath, Product Manager, Launchpad Accelerator India.

Elon Musk has cited the First Amendment in seeking to dismiss a lawsuit brought against him by a British diver, who the entrepreneur called "pedo guy" on Twitter. Vernon Unsworth sparked a war of words with Mr Musk during the attempted rescue of 12 Thai school boys and their soccer coach trapped in a cave earlier this year. The cave diver described Mr Musk's efforts to build a rescue submarine as a "PR stunt" with "absolutely no chance of working." In an interview on CNN in July, Mr Unsworth said: "He can stick his submarine where it hurts." Mr Musk responded by insinuating that the British diver, who was assisting with rescue efforts, had travelled to Thailand looking for a "child bride."

Chinese ride-hailing giant DiDi Chuxing has partnered with traffic police and Shandong University in the city of Jinan to use big data and artificial intelligence to ease traffic. An intelligent traffic management system named JTBrain was officially launched Wednesday, equipping the capital of east China's Shandong Province with a self-adaptive traffic-light control system. The system can serve as a decision-making platform to increase traffic efficiency, according to Liu Xianghong, chief scientist of DiDi Chuxing's intelligent transport department. JTBrain was designed to "learn and evolve" by modeling core algorithms and realize real-time control under different traffic conditions, according to Zou Nan, director of Transportation Study Center of Shandong University. Zou added that the brain-like system, which now covers 36 streets and 450 crossroads, uses AI, big data and cloud-computing to search for optimal traffic solutions.

DATA centers play a critical role in most organizations. Whether owned or leased, whole or shared, data centers must be run optimally and maintained well if organizations are to rely on them. And although humans operators do a great job, companies are waking up to the reality that maybe artificial intelligence (AI) is actually better suited for the role of managing and running data centers. With just a little support from maintenance staff, infrastructure experts are coming to the conclusion that AI can actually run a tighter ship -- delivering consistency, optimal performance, and cost reductions -- all at once. Aside from storing data, data centers generate a tonne of data themselves. Combined with information about the business, seasonality, weather, and other data, algorithmic models can help AI systems predict how best to manage the energy needs of the data center.

Artificial intelligence and machine learning became popular buzzwords in 2018, thanks in large part to companies adopting the emerging technologies to expand their businesses and acquire new customers. AI-based technologies helped companies add new revenue streams and optimise current ones. According to an analysis by professional services firm PwC, technological disruption has also impacted the mergers and acquisitions (M&A) or investment strategies of large corporations. "With larger corporations looking to adapt to technological advancements and revamp their business models, 2018 has seen a number of acquisitions in the new technology space," said Sanjeev Krishan, partner and leader, private equity and deals, PwC India. "Relevance has become a key element for the survival of any business, and this has made technology expertise a requirement to achieve a competitive advantage in the current market," he added.

Chunghwa Telecom (CHT) plans to launch a large-scale recruitment drive in 2019 as it expects to see an unprecedented wave of up to 5,000 of its employees applying for retirements over the next five years. As many as 1,600 jobs would be available at the Taiwan-based telecom carrier in 2019, according to company chairman David Cheng, who added that the number of new employees hired each year will be over 1,000 for a few years after 2019. However, to cope with changing industry developments, including the forthcoming 5G era and increasing competition, the company plans to hire more talent with expertise related to AI, big data analysis, IoT, mobile payment, 5G and information security, Cheng said. Including its subsidiaries, CPT currently has about 33,500 employees.

Google's first foray into Chinese markets was a short-lived experiment. Google China's search engine was launched in 2006 and abruptly pulled from mainland China in 2010 amid a major hack of the company and disputes over censorship of search results. But in August 2018, the investigative journalism website The Intercept reported that the company was working on a secret prototype of a new, censored Chinese search engine, called Project Dragonfly. Amid a furor from human rights activists and some Google employees, US Vice President Mike Pence called on the company to kill Dragonfly, saying it would "strengthen Communist Party censorship and compromise the privacy of Chinese customers." In mid-December, The Intercept reported that Google had suspended its development efforts in response to complaints from the company's own privacy team, who learned about the project from the investigative website's reporting. Observers talk as if the decision about whether to reenter the world's largest market is up to Google: will it compromise its principles and censor search the way China wants?