artificial intelligence

  • Source: "Wages through the ages"  

    Skilled versus unskilledHistorically, there have been two times in history where the ratio of skilled labor wages to unskilled labor wages fell dramatically. The first occurred after the Black Death in the 1300's. Here, lower interest rates and (I'm assuming) increased demand for skilled craftsmen resulted in large numbers of unskilled laborers apprentencing themselves and becoming skilled craftsmen. The surplus of skilled workers drove down skilled labor wages. And... shortages of unskilled labor due to the Plague allowed them to demand higher wages. During the Industrial Revolution, techology replaced skilled workers. Unskilled workers made up 20% of the labor force in 1700 but 40% by 1850. As the skilled jobs disappeared, the Gini coefficient rose. This has modern implications. As AI and automation threaten to wipe out skilled jobs considered impervious to automation before, can we expect the Gini coefficient to rise? Somber note: 4 out of 5 of the fastest-growing occupations in the country involve personal care, none require a college degree.

     

  • Source - Economist  "GrAIt expectations" and "The sunny and dark side of AI"       

    From the article: "Ping An, a Chinese insurance company, thinks it can spot dishonesty. The company lets customers apply for loans through its app. Prospective borrowers answer questions about their income and plans for repayment by video, which monitors around 50 facial expressions to determine whether they are telling the truth. The program, enabled by artificial intelligence (AI), helps pinpoint customers who require further scrutiny. AI will change more than borrowers’ bank balances. Johnson & Johnson, a consumer-goods firm, and Accenture, a consultancy, use AI to sort through job applications and pick the best candidates. AI helps Caesars, a casino and hotel group, guess customers’ likely spending and offer personalized promotions to draw them in. Bloomberg, a media and financial-information firm, uses AI to scan companies’ earnings releases and automatically generate news articles. 

    AI and machine learning (terms that are often used interchangeably) involve computers crunching vast quantities of data to find patterns and make predictions without being explicitly programmed to do so. Larger quantities of data, more sophisticated algorithms and sheer computing power have given AI greater force and capability. The outcomes are often similar to what an army of statisticians with unlimited time and resources might have come up with, but they are achieved far more quickly, cheaply and efficiently. One of AI’s main effects will be a dramatic drop in the cost of making predictions, says Ajay Agrawal of the University of Toronto and co-author of a new book, “Prediction Machines”. Just as electricity made lighting much more affordable—a given level of lighting now costs around 400 times less than it did in 1800—so AI will make forecasting more affordable, reliable and widely available.

     AI is "hot". In 2017 firms worldwide spent around $21.8 billion on mergers and acquisitions related to AI, according to PitchBook, a data provider, about 26 times more than in 2015 They are doing this partly to secure talent, which is thin on the ground. Startups without revenue are fetching prices that amount to $5m-10m per AI expert. The path ahead is exhilarating but perilous. Around 85% of companies think AI will offer a competitive advantage, but only one in 20 is “extensively” employing it today, according to a report by MIT’s Sloan Management Review and the Boston Consulting Group. 

    Technological change always causes disruption, but AI is likely to have a bigger impact than anything since the advent of computers, and its consequences could be far more disruptive. Being both powerful and relatively cheap, it will spread faster than computers did and touch every industry. In the years ahead, AI will raise three big questions for bosses and governments. One is the effect on jobs. Although chief executives publicly extol the broad benefits AI will bring, their main interest lies in cutting costs. One European bank used an AI consultancy to find a way of reducing the staff in its operations department from 50,000 to 500. The McKinsey Global Institute reckons that by 2030 up to 375m people, or 14% of the global workforce, could have their jobs automated away. 

    A second important question is how to protect privacy as AI spreads. AI is bound to bring privacy violations that are seen as outrageous. For example, facial-recognition technology has become so advanced that it may be able to detect someone’s sexual orientation. In the wrong hands, such technology could militate against fair and equal treatment. Countries with a record of surveillance and human-rights abuses, such as China, are already using AI to monitor political activity and suppress dissent. Law-enforcement officials around the world will use AI to spot criminals, but may also snoop on ordinary citizens. New rules will be needed to ensure consensus on what degree of monitoring is reasonable.

    The third question is about the effect of AI on competition in business. Today many firms are competing to provide AI-enhanced tools to companies. But a technology company that achieves a major breakthrough in artificial intelligence could race ahead of rivals, put others out of business and lessen competition. More likely, in the years ahead AI might contribute to the rise of monopolies in industries outside the tech sector where there used to be dynamic markets, eventually stifling innovation and consumer choice. Big firms that adopt AI early on will get ever bigger, attracting more customers, saving costs and offering lower prices. Such firms may also reinvest any extra profits from this source, ensuring that they stay ahead of rivals. Smaller companies could find themselves left behind. Retailing is an illustration of how AI can help large firms win market share. Amazon, which uses AI extensively, controls around 40% of online commerce in America, helping it build moats that make it harder for rivals to compete. But AI will increase concentration in other industries, too. If, say, an oil company can use AI to pump 3% more efficiently, it can set prices 3% lower than those of a rival. That could force the competitor to shut down, says Heath Terry of Goldman Sachs. He thinks that AI has “the power to reshuffle the competitive stack”.

  • Article: "Automation - the return of the machinery question"

    Automation has been a concern since the Industrial Revolution. David Ricardo from 1821: “The substitution of machinery for human labor renders the population redundant” - this is the Machinery Question. Now, after decades of overhype, AI is actually starting to dramatically improve.Study by Frey and Osborne (2013) says that 47% of all jobs in America are capable of being substituted by “computer capital”. Further developments like self-driving cars are estimated to add $2 trillion per year in productivity savings to the world economy.

    AI advances come through a process called “deep learning” in which neural networks are created and then the machine is given a chance to learn on its own as opposed to being explicitly programmed. In supervised deep learning, the machine is given labelled data and learns how to make the identification correctly. Example is the ImageNet challenge - millions of labeled images. Humans can match the labels correctly 95% of the time; old AI about 75%. New deep learning AI at 96% - better than humans. Unsupervised deep learning, of whichGoogle Brainis an example, let the machine look at millions of unsorted, unlabeled examples and let the machine find patterns and assign categories. Supervised deep learning was used to create “Enlitic” - a program for medical diagnoses based on images (x-rays, radiology etc.).. Enlitic has zero percent false negatives (missing a problem) compared to 7% for humans.  

    Which jobs are vulnerable to automation? Old automation distinguished between manual and cognitive work. New AI based automation distinguishes between routine and non-routine work. In the Enlitic example, AI might be able to replace the radiologist but probably not the secretary. Historically, automation has created more jobs than it has destroyed. Ex: ATM’s mean banks need fewer tellers. But fewer tellers means lower costs per branch which allows for more branches to be built and more tellers overall to be employed. Work is fluid, not finite and fixed. Basic income may help get us through the rough patch of AI job destruction.  

     

     

  • Article: ""Remember the mane"

     > Looking at how the rise of robots and their impact on low-skilled workers can be analogous to the rise of the automobile and its impact on horses.A new working paper concludes that, between 1990 and 2007, each industrial robot added per thousand workers reduced employment in America by nearly six workers. The International Federation of Robotics defines industrial robots as machines that are automatically controlled and re-programmable; single-purpose equipment does not count. The worldwide population of such creatures is below 2m; America has slightly fewer than two robots per 1,000 workers. The paper’s authors, Daron Acemoglu of the Massachusetts Institute of Technology (MIT) and Pascual Restrepo of Boston University, are careful to exclude confounding causes as best they can. Since relatively few industrial robots are in use in the American economy, the total job loss from robotisation has been modest: between 360,000 and 670,000.

    > Automation should yield savings to firms or consumers which can be spent on other goods or services. Labour liberated by technology should gravitate toward tasks and jobs in which humans retain an advantage. Yet that should also have been true of horses. The difficulty facing horses was in reallocating the huge numbers displaced by technology to places where they could still be of use.

     

  • Article: ""Remember the mane"

     > Looking at how the rise of robots and their impact on low-skilled workers can be analogous to the rise of the automobile and its impact on horses.A new working paper concludes that, between 1990 and 2007, each industrial robot added per thousand workers reduced employment in America by nearly six workers. The International Federation of Robotics defines industrial robots as machines that are automatically controlled and re-programmable; single-purpose equipment does not count. The worldwide population of such creatures is below 2m; America has slightly fewer than two robots per 1,000 workers. The paper’s authors, Daron Acemoglu of the Massachusetts Institute of Technology (MIT) and Pascual Restrepo of Boston University, are careful to exclude confounding causes as best they can. Since relatively few industrial robots are in use in the American economy, the total job loss from robotisation has been modest: between 360,000 and 670,000.

    > Automation should yield savings to firms or consumers which can be spent on other goods or services. Labour liberated by technology should gravitate toward tasks and jobs in which humans retain an advantage. Yet that should also have been true of horses. The difficulty facing horses was in reallocating the huge numbers displaced by technology to places where they could still be of use.