Science and Tech

Special Report: AI in Business (3 Big Questions) 3-31-18

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”.

Beyond the Bitcoin Bubble NYT 01-16-18

Source - NYT Magazine piece (PDF) January 16, 2018: "Beyond the Bitcoin Bubble: Long-term implications of blockchain"        

It begins with a "seed phrase" - a list of twelve random words taken from the dictionary. When typed in, the seed phrase generates a patternless string of 64 characters, the same 64 each time. The character output cannot be reverse-engineered to generate the original seed phrase. The seed phrase verifies your identity and allows your transaction to become part of the blockchain, a permanent transaction record shared by everyone and updated in real time. Blockchains require no intermediary. "No intermediary brokered the deal; no social-media network captured the data from my transaction to better target its advertising; no credit bureau tracked the activity to build a portrait of my financial trustworthiness." And no one owns the platform for blockchain, it is open source.

"The real promise of these new technologies, many of their evangelists believe, lies not in displacing our currencies but in replacing much of what we now think of as the internet, while at the same time returning the online world to a more decentralized and egalitarian system. If you believe the evangelists, the blockchain is the future. But it is also a way of getting back to the internet’s roots."

The internet needs to be fixed. Internet evangelists in the 90's are now becoming internet skeptics. Tim Berners-Lee, the inventor of the web itself, wrote a blog post voicing his concerns that the advertising-based model of social media and search engines creates a climate where “misinformation, or ‘fake news,’ which is surprising, shocking or designed to appeal to our biases, can spread like wildfire.”

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Additive manufacturing set to take off 7-1-17

Source - Economist, July 1, 2017: "3D printers will change manufacturing"     

From the article: "3 D printing has become a popular way of producing one-off prototypes, because changes are more easily and cheaply made by tweaking a 3D printer’s software than by resetting lots of tools in a factory. That means the technology is ideal for low-volume production, such as turning out craft items like jewelry, or for customizing products, such as prosthetics. Dental crowns and hearing-aid buds are already being made by the million with 3D printers. Because it deposits material only where it is needed, the technology is also good at making lightweight and complex shapes for high-value products ranging from aircraft to racing cars. But skeptics say that 3D printers are too slow and too expensive—it can take two days to create a complex object (typical rates are one or two cubic inches per hour)."

Adidas has started to use a remarkable form of it called “digital light synthesis” to produce the soles of shoes, pulling them fully formed from a vat of liquid polymer. The technique will be used in a couple of new and highly automated factories in Germany and America to bring 1m pairs of shoes annually to market much more quickly than by conventional processes. Also, a new technique called bound-metal deposition has the potential to change the economics of metal printing, too, by building objects at a rate of 500 cubic inches an hour."

Book review: Everybody Lies 5/27/17

Article (book review): Everybody Lies  

 The book Everybody Lies by Seth Stephens-Davidowitz looks at the use of search data as a way to find previously invisible correlations and connections. Example: the prevalence of the term “n*gger” in search results was the best variable in predicting whether or not the voters in that region would vote for Trump in the 2016 GOP primaries. Search data is a game-changer because it gets at what people actually believe, not what they are willing to admit to a stranger with a clipboard.

From the review: ‘Modern microeconomics, sociology, political science and quantitative psychology all depend to a large extent on surveys of at most a few thousand respondents. In contrast, he says, there are “four unique powers of Big Data”: it provides new sources of information, such as pornographic searches; it captures what people actually do or think, rather than what they choose to tell pollsters; it enables researchers to home in on and compare demographic or geographic subsets; and it allows for speedy randomized controlled trials that demonstrate not just correlation but causality. As a result, he predicts, “the days of academics devoting months to recruiting a small number of undergraduates to perform a single test will come to an end.” In their place, “the social and behavioural sciences are most definitely going to scale,” and the conclusions researchers will be able to reach are “the stuff of science, not pseudoscience”.’