We are in the midst of the Artificial Intelligence Revolution (AIR), the next major epoch in the history of technological innovation. Artificial intelligence (AI) is globally gaining momentum not only in scientific research, but also in business, finance, consumer, art, healthcare, esports, pop culture, and geopolitics. As AI becomes increasingly pervasive, it is important to examine at a macro level whether AI gains from disorder. Antifragile is a term and concept put forth by Nassim Nicholas Taleb, a former quantitative trader and self-proclaimed "flâneur" turned author of New York Times bestseller of "The Black Swan: The Impact of the Highly Improbable." Taleb describes antifragile as the "exact opposite of fragile" which is "beyond resilience or robustness" in "Antifragile: Things That Gain From Disorder."

SAO PAULO/DETROIT - Ford Motor Co. said on Tuesday it will close its oldest factory in Brazil and exit its heavy commercial truck business in South America, a move that could cost more than 2,700 jobs as part of a restructuring meant to end losses around the world. Ford previously said the global reorganization, to impact thousands of jobs and possible plant closures in Europe, would result in $11 billion in charges. Following that announcement, analysts and investors had expected a similar restructuring in South America. Ford Chief Executive Jim Hackett said last month that investors would not have to wait long for the South American reorganization plan. The factory slated for closure is in Sao Bernardo do Campo, an industrial suburb of Sao Paulo that has operated since 1967.

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies. CA has found applications in fields ranging from epidemiology to social sciences. However, current methods used to perform CA do not scale to large, high-dimensional datasets. By re-interpreting the objective in CA using an information-theoretic tool called the principal inertia components, we demonstrate that performing CA is equivalent to solving a functional optimization problem over the space of finite variance functions of two random variable. We show that this optimization problem, in turn, can be efficiently approximated by neural networks. The resulting formulation, called the correspondence analysis neural network (CA-NN), enables CA to be performed at an unprecedented scale. We validate the CA-NN on synthetic data, and demonstrate how it can be used to perform CA on a variety of datasets, including food recipes, wine compositions, and images. Our results outperform traditional methods used in CA, indicating that CA-NN can serve as a new, scalable tool for interpretability and visualization of complex dependencies between random variables.

Samsung Electronics will introduce its new products and solutions to its business partners around the world at Samsung Forum 2019. During the two-month event, strategic products including the 2019 QLED TV lineup as well as customized products for regional markets will be showcased. Based on'New Bixby,' Samsung's intelligence platform, Connected Solution will also be exhibited where global business partners can interact with various Samsung products. Starting with the European Forum, Samsung will invite media and partners from Europe, Southwest Asia and Latin America to Porto of Portugal from February 12th to 22nd. From March 7th to 11th, Samsung will host the Middle East and CIS (Commonwealth of Independent States) Forum in Antalya of Turkey.

Machine Learning as a Service Market to reach USD 16.13 billion by 2025 Machine Learning as a Service Market valued approximately USD 0.87 billion in 2017 is anticipated to grow with a healthy growth rate of more than 43.9% over the forecast period 2018-2025. Machine learning as a service is a significant range of solutions and services that are offered by cloud service providers. The tools offered by service providers include APIs, data visualization, natural language processing, face recognition, deep learning, and predictive analytics. The main benefit associated with these services is that the customers are able to quickly start with machine learning with no need to install or download any software on their servers. Enhancements in technology, growth in data volume and rise in IT spending in some of the developing regions are the major factors which are driving the growth in the global market.

Machine learning clustering techniques are not the only way to extract topics from a text data set. Text mining literature has proposed a number of statistical models, known as probabilistic topic models, to detect topics from an unlabeled set of documents. One of the most popular models is the latent Dirichlet allocation (LDA) algorithm developed by Blei, Ng, and Jordan [i]. LDA is a generative unsupervised probabilistic algorithm that isolates the top K topics in a data set as described by the most relevant N keywords. In other words, the documents in the data set are represented as random mixtures of latent topics, where each topic is characterized by a Dirichlet distribution over a fixed vocabulary.

The members of the Global University Leaders Forum community convened at the World Economic Forum Annual Meeting 2019 to discuss their role in our ever-changing world. Here are six topics that were top of the agenda as the members considered the future of the university and its role in society. Today data is omnipresent and often overwhelming. By way of example, Domo's Data Never Sleeps 6.0 reported that in 2018 Google conducted an average 3.8 million searches per minute. Though not all graduates will enter data-related fields, universities are starting to work towards increasing data literacy in their student body by adding data science courses and challenges for social science majors so that graduates can effectively communicate with their data-oriented peers and co-workers.

In 1950, Norbert Wiener's The Human Use of Human Beings was at the cutting edge of vision and speculation in proclaiming: But this was his book's denouement, and it has left us hanging now for 68 years, lacking not only prescriptions and proscriptions but even a well-articulated "problem statement." We have since seen similar warnings about the threat of our machines, even in the form of outreach to the masses, via films like Colossus: The Forbin Project (1970), The Terminator (1984), The Matrix (1999), and Ex Machina (2015). But now the time is ripe for a major update with fresh, new perspectives -- notably focused on generalizations of our "human" rights and our existential needs. Concern has tended to focus on "us versus them" (robots) or "gray goo" (nanotech) or "monocultures of clones" (bio). To extrapolate current trends: What if we could make or grow almost anything and engineer any level of safety and efficacy desired?

Yet the healthcare industry has been slow to commit to its potential. As recently as 2016, a Forrester Consulting survey found that just 34% of healthcare organizations had adopted predictive analytics, compared with 51% in all other industries. Healthcare was similarly behind in cognitive computing, at 23% versus 40% elsewhere. Among the pioneers, some organizations are already using AI to reduce physical trips to the doctor's office, improve patient care and rethink care delivery models that date to the 19th century. "I believe we can get to a world where we aren't just identifying your likelihood of utilizing the emergency room or being hospitalized, but getting in front of those situations and delivering proactive care," says Dr. Arta Bakshandeh, senior medical officer at Alignment Healthcare.

Summary: Artificial Intelligence in Healthcare Market Overview: Artificial intelligence (AI) can be defined as the science and engineering adopted to design intelligent machines, especially intelligent computer programs. AI is an intelligent system that applies various human intelligence based functions such as reasoning, learning, and problem-solving skills on different disciplines such as biology, computer science, mathematics, linguistics, psychology, and engineering. AI is widely applicable in medication management, treatment plans, and drug discovery. The global AI in healthcare market was valued at $1,441 million in 2016, and is estimated to reach at $22,790 million by 2023, registering a CAGR of 48.7% from 2017 to 2023. The growth of the global AI in healthcare market is driven by the ability of AI to improve patient outcomes, need to increase coordination between healthcare workforce & patients, increase in adoption of precision medicine, and a notable rise in venture capital investments.