Toyota says it's developing self-driving mini-buses that can serve as bite-sized stores. These vehicles will drive themselves to places where potential buyers can try on clothes or shoes or pick through flea market items. They can also give employees fully functional office space on their commute. The project, unveiled at the CES gadget show Monday, is still in the conceptual stage. A concept vehicle is still being developed and will be tested in the 2020s.

University of Pennsylvania workshop addresses potential biases in the predictive technique. Stephen Hawking once warned that advances in artificial intelligence might eventually "spell the end of the human race." And yet decision-makers from financial corporations to government agencies have begun to embrace machine learning's enhanced power to predict--a power that commentators say "will transform how we live, work, and think." During the first of a series of seven Optimizing Government workshops held at the University of Pennsylvania Law School last year, Aaron Roth, Associate Professor of Computer and Information Science at the University of Pennsylvania, demystified machine learning, breaking down its functionality, its possibilities and limitations, and its potential for unfair outcomes. Chairman of the Penn Department of Criminology Richard Berk offers commentary. Machine learning, in short, enables users to predict outcomes using past data sets, Roth said.

At first glance, the threats seem clear: One type of software will learn how to perform all manner of business functions, particularly in finance and accounting, while another will continuously validate any set of data or information. Between them, artificial intelligence and blockchain seem poised to disrupt -- or even destroy -- many of the core businesses of the accounting profession, automating or rendering irrelevant important traditional services like the audit. But while there can be little doubt that they will eliminate the need for human beings to perform many of the individual functions traditionally associated with accountants, both in public practice and in industry, they will certainly not eliminate the profession's overall role, or its importance. In fact, both AI and blockchain have the potential to help accountants actually boost their revenue, their relevance and their value -- provided they're willing to develop the necessary skills, and change their mindsets. Understanding why each of these two emerging technologies is less of a threat and more of an opportunity than they might seem requires a separate, deeper dive into each, as they're going to have different impacts on the profession, over different time horizons.

The recent tax reform bill passed in the US has raised a lot of questions about wealth distribution in the country. While there's been a lot of focus on how the tax plan will impact income, there's been less attention focused on how this plan impacts the assets of wealthy households. The goal of this post is to show how the R programming language can be used to data mine publicly available sources to better understand the net worth of affluent households in the US. To answer these questions, we present descriptive statistics of this survey data and perform cluster analysis on affluent households, which we identify as households with a net worth of more than $1,000,000 USD. Based on the survey data, our analysis shows that the net worth of the top 1% of households in the US is $10.4M and the net worth of the top 0.1% of households is $43.2M.

This week's milestone in the history of technology is the patent that launched the ongoing quest to get machines to help us and them know more about our world, from tabulating machines to machine learning to deep learning (or today's "artificial intelligence"). On January 8, 1889, Herman Hollerith was granted a patent titled the "Art of Compiling Statistics." The patent described a punched card tabulating machine which launched a new industry and the fruitful marriage of statistics and computer engineering--called "machine learning" since the late 1950s, and reincarnated today as "deep learning" (also popularly known today as "artificial intelligence"). Commemorating IBM's 100th anniversary in 2011, The Economist wrote: In 1886, Herman Hollerith, a statistician, started a business to rent out the tabulating machines he had originally invented for America's census. Taking a page from train conductors, who then punched holes in tickets to denote passengers' observable traits (e.g., that they were tall, or female) to prevent fraud, he developed a punch card that held a person's data and an electric contraption to read it.

On February 26, 1928, a headline in the New York Times announced, "MARCH OF THE MACHINE MAKES IDLE HANDS," with the subhead: "Prevalence of Unemployment With Greatly Increased Industrial Output Points to the Influence of Labor-Saving Devices as an Underlying Cause." What these alarming words referred to was the abundance of goods being produced in the roaring plants, mills and farm fields of 1920s America. According to a variety of statistics cited and charted by the Times, what Americans could now make was beginning to outstrip what they could consume, to the point of diminishing employment. "More and more the finger of suspicion points to the machine," the Times reporter, Evan Clark, claimed. "It begins to look as if machines had come into conflict with men--as if the onward march of machines into every corner of our industrial life had driven men out of the factory and into the ranks of the unemployed."

Unlike big corporations, SMBs do not have the budget to create their own tools. This is where already available tools and platforms in the market can help your business. At YourStory we have a saying that if you preserve your history, which is your company's data, then building an engine to understand the future is very easy. These days, cloud computing has allowed your data to be relevant, and the computational speed to generate insights is readily available. Then, why does everybody from the government to large corporations, sound so dazed when speaking about Artificial Intelligence (AI) and data sciences?

Each day we read about amazing technology breakthroughs, particularly when it comes to artificial intelligence (AI). But if AI is so great, why are these breathtaking technological achievements not matched with soaring productivity and economic growth? Or, to paraphrase an old jibe: If the economy is so smart, why aren't we all rich? After all, we live among astonishing examples of potentially transformative new technologies that could greatly increase productivity and economic welfare. As noted in the 2014 book, "The Second Machine Age," leaps in AI, machine learning and, more recently in areas such as image recognition, abound.

This week's milestone in the history of technology is the patent that launched the ongoing quest to get machines to help us and them know more about our world, from tabulating machines to machine learning to deep learning (or today's "artificial intelligence"). On January 8, 1889, Herman Hollerith was granted a patent titled the "Art of Compiling Statistics." The patent described a punched card tabulating machine which launched a new industry and the fruitful marriage of statistics and computer engineering--called "machine learning" since the late 1950s, and reincarnated today as "deep learning" (also popularly known today as "artificial intelligence"). Commemorating IBM's 100th anniversary in 2011, The Economist wrote: In 1886, Herman Hollerith, a statistician, started a business to rent out the tabulating machines he had originally invented for America's census. Taking a page from train conductors, who then punched holes in tickets to denote passengers' observable traits (e.g., that they were tall, or female) to prevent fraud, he developed a punch card that held a person's data and an electric contraption to read it.

A strong declaration from a historically antagonist foe should put chills in the hearts of Americans preparing themselves for the world ahead: Russian President Vladimir Putin says the nation that leads in AI will be the ruler of the world … The ruler of the world! "The development of artificial intelligence has increasingly become a national security concern in recent years. It is China and the US (not Russia), which are seen as the two frontrunners, with China recently announcing its ambition to become the global leader in AI research by 2030. Many analysts warn that America is in danger of falling behind, especially as the [current US] administration prepares to cut funding for basic science and technology research." Elon Musk, one of America's foremost technology advocates, predicts that countries seeking leadership (and domination) from artificial intelligence will be the basis for World War III It's great when other countries just blindly buy whatever we're selling, but globalization eventually creates a level playing field.