Accounts of mysterious flashing lights in the sky, spacecrafts and encounters with'real' aliens reflect high levels of public interest in UFOs and the belief that there is'something out there'. However, many psychologists are less convinced, and think they can provide more down-to-earth, scientific explanations. Belief in aliens has increased steadily since the birth of modern alien research in the 1940s and 1950s, following the news surrounding a classified US military project at Roswell Air Force Base, New Mexico. The theory that alien abductions are hoaxes may be true in a few cases, but there is no reason to assume that the majority of'experiencers' are frauds Surveys in Western cultures estimated belief in aliens to be as high as 50% in 2015. And despite the fact that it is considered rare, a significant number of people also believe they have experienced alien abduction.

Much of the software now revolutionizing the financial services industry depends on algorithms that apply artificial intelligence (AI)--and increasingly, machine learning--to automate everything from simple, rote tasks to activities requiring sophisticated judgment. These algorithms and the analyses that undergird them have become progressively more sophisticated as the pool of potentially meaningful variables within the Big Data universe continues to proliferate. When properly implemented, algorithmic and AI systems increase processing speed, reduce mistakes due to human error and minimize labor costs, all while improving customer satisfaction rates. Creditscoring algorithms, for example, not only help financial institutions optimize default and prepayment rates, but also streamline the application process, allowing for leaner staffing and an enhanced customer experience. When effective, these algorithms enable lenders to tweak approval criteria quickly and continually, responding in real time to both market conditions and customer needs. Both lenders and borrowers stand to benefit. For decades, financial services companies have used different types of algorithms to trade securities, predict financial markets, identify prospective employees and assess potential customers. Although AIdriven algorithms seek to avoid the failures of rigid instructions-based models of the past--such as those linked to the 1987 "Black Monday" stock market crash or 2010's "Flash Crash"--these models continue to present potential financial, reputational and legal risks for financial services companies.

In the Marvel universe, billionaire Tony Stark/Iron Man has Jarvis, a personal AI to anticipate his needs and streamline his life. When I mention this to Collie Brown, founder and CEO of Arghon, a personal AI company, he gives a knowing smile. A moment before he described his AI product as a "life assistant." "There's the Watsons of the world, geared towards large scale data and the like," Brown said. "Arghon's about you, what's happening in your life from the time you wake up to the time you go to bed. Our goal is to manage what happens in between that."

The lending and credit scoring sector have more data than ever before at their disposal. How they leverage this data to create value for their clients and social impact determines the outcomes they can achieve in the financial services space. In 1959, Arthur Samuel, a pioneer in the field of machine learning (ML) and artificial intelligence during an era when computers filled an entire building, defined machine learning as "a field of study that gives computers the ability to learn without being explicitly programmed." During a recent keynote, Microsoft CEO Satya Nadella referred to data used in this context as "the new electricity," calling our current era a "fourth industrial revolution" following steam, electricity and digital technology. Scott Guthrie, Microsoft executive vice president, also acknowledged that data is "enabling every business to be the disrupters of their industry by harnessing the power to drive insight from this data."

It's likely that nothing underscores the importance of artificial intelligence more than the alliance that major tech companies are forming. Google, Microsoft, Facebook, Amazon, and IBM are usually competitors in one way or another, but they still formed the group called Partnership on AI to Benefit People and Society. Now, Apple can be counted as one of their numbers. According to the group's blog post on the matter, the initiative has been getting a lot of support from the AI community since it was announced in September last year. Now, the iPhone maker is finally a formal member of the alliance.

Disruptive technologies are now dictating our future, as new innovations increasingly blur the lines between physical, digital and biological realms. Robots are already in our operating rooms and fast-food restaurants; we can now use 3D imaging and stem-cell extraction to grow human bones from a patient's own cells; and 3D printing is creating a circular economy in which we can use and then reuse raw materials. This tsunami of technological innovation will continue to change profoundly how we live and work, and how our societies operate. In what is now called the fourth Industrial Revolution, technologies that are coming of age--including robotics, nanotechnology, virtual reality, 3D printing, the Internet of Things, artificial intelligence, and advanced biology--will converge. And as these technologies continue to be developed and widely adopted, they will bring about radical shifts in all disciplines, industries and economies, and in the way that individuals, companies and societies produce, distribute, consume and dispose of goods and services.

The advancement of machine learning and artificial intelligence is opening new doors for businesses to make more data-driven decisions at higher accuracy rates. Data and analytics are also changing the way that businesses compete. As leading companies take advantage of the power of big data, and laggards stay behind, the disparity between the two groups will continue to widen in the future. Recently, Forbes has predicted that by 2020, we will have 1.7 megabytes of new information created every second for every human everywhere on the planet. So what does this mean?

Earlier this month we released our annual travel industry trends forecast, Skift Megatrends 2017. You can read about each of the trends on Skift, or download a copy of our magazine here. There are few things buzzier in travel right now than the rise of artificial intelligence (AI) and human-machine interfaces. That's possible due to AI, or machine-learning, where Google can not only crunch data at the speed of light, but also "learn" how to deliver more nuanced results. "AI is simply a group of technologies that will increasingly be used to augment human capabilities, and make us better at the things we do best," wrote Bob Rogers, chief data scientist for analytics and AI solutions at Intel, in CIO, a publication serving chief information officers.

Perhaps the coolest thing about IBM's 9th "Five Innovations that will Help Change our Lives within Five Years" predictions is that none of them sound like science fiction. "With advances in artificial intelligence and nanotechnology, we aim to invent a new generation of scientific instruments that will make the complex invisible systems in our world today visible over the next five years," said Dario Gil, vice president of science & solutions at IBM Research in a statement. Among the five areas IBM sees as being key in the next five years include artificial intelligence, hyperimaging and small sensors. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage mental and neurological diseases that can help doctors and patients better predict, monitor and track these diseases.

Multiple predictive models are generated by using various multi-omics data types; then a final predictive model is generated by using the multiple models. Predictive models can be consolidated from various multi-omics data types, and each data type can be gathered from a various set of patients with same phenotype. Multiple data matrices of different multi-omics data types are incorporated into a large input matrix; then a predictive model is generated by using the large input matrix. It is fairly easy to leverage various machine learning methods for analyzing continuous or categorical data once a large input matrix is formed. It may be challenging to combine a large input matrix. Datasets for various multi-omics data types are first converted into intermediate forms, which are united into a large input matrix; then a predictive model is generated by using the large input matrix. Unique variables such as patient identifiers can be used to link multi-omics data types and integrate a variety of continuous or categorical data values. It may be challenging to transform into intermediate forms.