Prior to the era of Artificial Intelligence, humans would collaborate on decisions. As the saying goes, "two brains are better than one." But with advancements in AI, screening data and ideas has been made easier and more efficient. Its impact on the economy and its implications regarding the future of the workforce has ignited a fierce debate that has been contributed to by everyone from Silicon Valley to the White House. Of course there are naysayers, like Nick Bostrom, who warns that AI could quickly turn dark and dispose of humans altogether, saying, "The subsequent world would harbor economic miracles and technological awesomeness, with nobody there to benefit."

MogIA, an artificial intelligence (AI) system developed by an Indian start-up, correctly predicted the outcome of this year's elections. It based its analysis on 20 million data points from platforms such as Google, Twitter, and YouTube, reviewing public engagement across various posts in relation to individual candidates. Earlier this year, an AI system was able to accurately predict the winners of the Kentucky Derby. In October, a study reported that AI can already predict the outcomes of cases at the European Court of Human Rights with a 79% accuracy. Leading up to the historic election, Democratic candidate Hillary Clinton led the late polls with a small, but consistent lead.

It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder. Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don't want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise.

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So what benefits could machine learning bring to the world of work? Machine learning presents an opportunity to obtain insights from data that human analysts may not see on their own. Artificial intelligent systems using machine learning have steadily improved in accuracy, for example in identifying images, which they can now do with greater accuracy than humans. If machines are used to'double check' human analysis and help with processing large amounts of data, this may result in less likelihood of mistakes, and reduction of risks and errors in some decision making. In fact, an algorithm (mathematical recipe) can be designed to ruthlessly deliver the range of tasks it is given, thus eliminating any human bias from the output.

You know I have often wondered about the two different plans of existence we have here and one intruding on the other. It would not go over so well if one really did intrude upon the others territory/ dimension. We can talk about all we want to, but we can not cross over into it. And it will not cross over into where we are until time is up. Yet, if we make strides towards it and get somewhere in our striding we always get smacked down for it and smashed backwards.

As the election results rolled in last night, it became increasingly clear that America -- and the world -- would never be the same. The American people overlooked all of Republican nominee Donald Trump's faults and elected him to office in the belief that he will fix the nation's deep-seated problems of inequity and injustice. And they rebelled against the business interests and corruption that they believed Hillary Clinton represented. Trump's victory was enabled by technology -- everything from his use of social media to Clinton's email scandals to Russian hacking. But advancements in technology and how they reshape our economy may also keep him from delivering on some of the major promises that made him so popular during the campaign season.

Artificial Intelligence MogIA predicted the outcome of last four presidential elections including … In October, an artificial intelligence system that correctly predicted the last three U.S. presidential elections puts Republican nominee Donald Trump ahead of Democrat rival Hillary Clinton in the race for the White House. Now the system is four for four.MogIA was developed by Sanjiv Rai, founder of Indian start-up Genic.ai. It takes in 20 million data points from public platforms including Google, Facebook, Twitter and YouTube in the U.S. and then analyzes the information to create predictions.The AI system was created in 2004, so it has been getting smarter all the time. It had already correctly predicted the results of the Democratic and Republican Primaries.MogIA is based on Mowgli, the child from Rudyard Kipling's novel "The Jungle Book." Rai said this is because his AI model learns from the environment."While…

Do humans make computers smarter? Artificial intelligence is fact, not fantasy! Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and clinical information related to the diagnosis, treatment, and history of cancer incidence in the United States. The surveillance effort, coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention, enables researchers and clinicians to monitor cancer cases at the national, state, and local levels. Much of this data is drawn from electronic, text-based clinical reports that must be manually curated--a time-intensive process--before it can be used in research. For example, cancer pathology reports, text documents that describe cancerous tissue in detail, must be individually read and annotated by experts before becoming part of a cancer registry.