I'm not saying that the magic pocket oracle we all carry around isn't great, but I think there is a philosophical disconnect between what it is and what it could be for us. Right now our technology is still trying to improve every tool except the one we use the most, our brain. At first this seems like a preposterous claim. Doesn't Google Maps let me navigate in completely foreign locations with ease? Doesn't Evernote let me off-load complicated knowledge into a magic box somewhere and recall it with photo precision whenever I need to?

This is an attempt to showcase some worked out examples of Machine Learning (ML) use German Credit Data. Though we have selected credit scoring problem as a case study in this article, the same process will be applicable for wide range of classification or regression problems "Response modeling", "Risk Management", "Attrition/Churn management", "Cross-Sell/Up-Sell", "usage Patterns", "Net Present Value", "Life Time Value", "Predictive Maintenance and condition based monitoring", "Warranty", "Reliability", "Failure Prediction", "Image/Video Processing", "Crime", "Medical Experiments", "Hidden pattern recognition" . The basic difference of traditional modeling and machine learning is that "in traditional modeling we intend to set up a modeling framework and try to establish relationships while in machine learning we allow the model to learn from the data by understanding the hidden patterns". Hence the first one requires analyst to have solid understanding of statistical techniques and business knowledge while the later one is more complex in nature and computational intensive, hence requires higher computation power of the systems and analyst needs to be tech savvy. Kindly note that while traditional techniques perform well on small to large amount of data, machine learning will certainly learn better on high-dimensional and complex data such as Big Data set up.

Being able to commute back and forth to work while sleeping, eating, playing Trivia Crack or catching up on your favorite blogs in Feedly is a concept that is equally appealing and seemingly far-off and too futuristic to actually happen. When Google announced their autonomous car project in 2008, visions of Minority Report began to swirl in our heads while we wondered about the possibilities of a car that really had no need for us to do anything other than turn it on. This same car wouldn't have to worry about accidents, distraction, or driving under the influence while it made thousands – or even millions – of split-second calculations in order to keep your safe. You see, as it turns out, humans are remarkably bad at driving. "People are not great at driving -- 30,000 people die in car accidents each year (in the United States). Machines can be much better than humans when it comes to driving; they don't drink or text and can think faster."

Gartner states that its predictions "examine three fundamental effects of continued digital innovation", comprising experience and engagement, business innovation and secondary effects resulting from increased digital capabilities. By 2020, algorithms will positively alter the behaviour of more than 1 billion global workers: algorithms can positively alter human behaviour, augmenting human intelligence with the large collective memory bank containing knowledge that has been socialised and put to the test, with this to help workers "remember" anything or be informed of just-in-time knowledge they have never even experienced, leaving them to objectively complete the task at hand, while also better appreciating life as it unveils. Through 2020, IoT will increase data centre storage demand by less than 3 per cent: of the roughly 900 exabytes of data centre hard-disk drive and solid-state drive capacity forecast to ship in 2020, IoT discrete sensor storage will represent only 0.4 per cent, with storage from multimedia sensors consuming another 2 per cent, indicating IoT can scale and deliver important data-driven business value and insight while remaining manageable from a storage infrastructure standpoint. By 2020, 40 per cent of employees can cut their healthcare costs by wearing a fitness tracker: companies will increasingly appoint fitness program managers, working closely with human resource leaders, including fitness trackers in wellness programs as part of a broader employee engagement initiative.

Bots are already scheduling meetings, ordering meals, and analyzing bank accounts. That means they must now suffer the indignities unethical bosses inflict on their human assistants, especially sexual harassment. As bots do more of our bidding, their algorithms are spending more time parrying flirtations, dodging personal questions, and dealing with darker forms of sexual harassment. "Lots of use cases come from that motivation," says Ilya Eckstein, CEO of Robin Labs whose bot platform helps truckers, cabbies, and other drivers find the best route and handle logistics. "People want to flirt, they want to dream about a subservient girlfriend, or even a sexual slave. It may just be more for laughs, or something deeper underneath the surface."

A dream is a succession of images, ideas, emotions, and sensations that usually occurs involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not fully understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

An unnamed regionally-managed health care provider partnered with ThingWorx's machine learning platform to detect patterns in data that would lead to better patient care and reduce costly readmissions for patients with ischemic heart disease, according to a case study provided by Thingworx. The solution predicts high-risk patients and provides caregivers insight into why flagged patients should receive extra care across their network using health care IoT. The unspecified health care network includes two major hospitals and a network of outpatient and preventative care providers. It has more than 1,000 patient beds, a home health care service, preventive medicine, rehabilitation services, a network of primary care physicians and a range of outpatient services. According to Thingworx, its client is one of the largest health providers in the country.

Hot on the heels of Bank of America's announcement yesterday that it is launching a new chatbot named Erica, Mastercard has unveiled its own personal finance bot named KAI at Money 20/20. Mastercard's artificial intelligence-powered bot responds to more than 1,000 queries about personal finances and other subjects. It was developed by Kasisto, a spinoff of Siri creator SRI International. Available to US consumers later this year, Mastercard KAI will converse about a customer's account history and account balance, as well as make payments, identify spending patterns and set alerts for specific kinds of spending. In addition to the Mastercard bot for banks, the financial services provider is also launching a bot for merchants.

Bank of America debuted a virtual assistant bot today at Money2020, a fintech conference being held this week in Las Vegas. Named Erica, the bot uses artificial intelligence and predictive analytics to learn your personal spending habits and offer helpful advice. The bot will be available by voice command or plain text in Bank of America smartphone apps next year, according to CNBC. Erica is designed to be not just a virtual assistant but each customer's "personal advocate," said Bank of America head of digital banking Michelle Moore. It can tell you about your spending habits, notice if you spend more than usual on a certain product or category of products, present opportunities to reduce debt or save money, and alert you if your credit score dips.

AliveCor, the leader in FDA-cleared mobile electrocardiogram (ECG) technology for mobile devices, announced a collaboration with Mayo Clinic to utilize AliveCor's unique measurement technology to unlock previously hidden health indicators in ECG readings. These indicators have the potential to not only improve heart health but also overall health care for a variety of conditions. AliveCor provides the first consumer-ready, clinically validated and FDA-cleared ECG to give patients a more complete view of their heart health, improve proactive monitoring and create a new standard of cardiac care. By using AliveCor's deep machine learning capabilities applied to 10 million of its user ECG recordings, Mayo Clinic and AliveCor will work together to uncover hidden physiological signals to improve heart and overall human health. "Mayo Clinic has pioneered new approaches that may uncover significant measures of physiology that have been hidden in individuals' ECGs," said Vic Gundotra, CEO, AliveCor.