I’d like to live long enough to have a chat with an AI that:
Because there is no one who has or will get to know me that well. What would it be like to interact with an intelligence that “shares” those experiences? I cannot imagine.
“In his book “AI Superpowers: China, Silicon Valley, and the New World Order,” Kai-Fu Lee, a well-known artificial-intelligence expert, venture capitalist and former president of Google China, argues that China and Silicon Valley will lead the world in AI. But his highly readable book covers a lot of other ground as well, and among the most interesting insights are his descriptions of the differences between Chinese and Silicon Valley tech culture.” (Washington Post review) Not quite finished but here are some excerpts:
If artificial intelligence is the new electricity, Chinese entrepreneurs will be the tycoons and tinkerers who electrify everything from household appliances to homeowners’ insurance. […] Ambitious mayors across China are scrambling to turn their cities into showcases for new AI applications. They’re plotting driverless trucking routes, installing facial recognition systems on public transportation, and hooking traffic grids into “city brains” that optimize flows.
China’s startup culture is the yin to Silicon Valley’s yang: instead of being mission-driven, Chinese companies are first and foremost market-driven. Their ultimate goal is to make money, and they’re willing to create any product, adopt any model, or go into any business that will accomplish that objective. […] The core motivation for China’s market-driven entrepreneurs is not fame, glory, or changing the world. Those things are all nice side benefits, but the grand prize is getting rich, and it doesn’t matter how you get there.
Adoption of mobile payments happened at lightning speed. By the end of 2016, it was hard to find a shop in a major (Chinese) city that did not accept mobile payments. […] By the end of 2017, 65 percent of China’s over 753 million smartphone users had enabled mobile payments. […] It got to the point where beggars on the streets of Chinese cities began hanging pieces of paper around their necks with printouts of two QR codes, one for Alipay and one for WeChat. […]
For 2017, total transactions on China’s mobile payment platforms reportedly surpassed $ 17 trillion—greater than China’s GDP. […] Data from mobile payments is currently generating the richest maps of consumer activity the world has ever known, far exceeding the data from traditional credit-card purchases or online activity captured by e-commerce players like Amazon or platforms like Google and Yelp. […] Recent estimates have Chinese companies outstripping U.S. competitors ten to one in quantity of food deliveries and fifty to one in spending on mobile payments. China’s e-commerce purchases are roughly double the U.S. totals, and the gap is only growing.
U.S. federal funding for math and computer science research amounts to less than half of Google’s own R& D budget.
Between 2007 and 2017, China went from having zero high-speed rail lines to having more miles of high-speed rail operational than the rest of the world combined.
It no longer makes sense to think of oneself as “going online.” When you order a full meal just by speaking a sentence from your couch, are you online or offline? When your refrigerator at home tells your shopping cart at the store that you’re out of milk, are you moving through a physical world or a digital one?In the United States we build self-driving cars to adapt to our existing roads because we assume the roads can’t change. In China, there’s a sense that everything can change—including current roads. Indeed, local officials are already modifying existing highways, reorganizing freight patterns, and building cities that will be tailor-made for driverless cars.
Much of today’s white-collar workforce is paid to take in and process information, and then make a decision or recommendation based on that information—which is precisely what AI algorithms do best.
Once a month every company should survey (anonymously) all employees, asking which of their co-workers is most likely to come to work shooting. Maybe the top three most likely employees. Not sure what you’d do with that information but it wouldn’t surprise me if this woman’s name would have made the list.
Once ‘smarter’ AIs are running offices (monitoring all communication; video; etc) they’ll spot the shooters days in advance. Their metal detectors will know they’re packing when they walk through the door.
Now, take a few minutes to make your own list. The three people in your life most likely to go postal. You’re welcome.
AI is now so advanced that it can reveal far more about you than a mere fingerprint. By using powerful technology to analyse recorded speech, scientists today can make confident predictions about everything from the speaker’s physical characteristics – their height, weight, facial structure and age, for example – to their socioeconomic background, level of income and even the state of their physical and mental health.
Your voice can give away plenty of environmental information, too. For example, the technology can guess the size of the room in which someone is speaking, whether it has windows and even what its walls are made of. Even more impressively, perhaps, the AI can detect signatures left in the recording by fluctuations in the local electrical grid, and can then match these to specific databases to give a very good idea of the caller’s physical location and the exact time of day they picked up the phone.
One of the leading scientists in this field is Rita Singh of Carnegie Mellon University’s Language Technologies Institute. Interview with Ms. Singh
Assuming this is an early glimpse of one possible future… how do I feel about it? Mixed, I think. If humans still have some evolving to do — and I sure hope we do — it seems likely such evolution will be in this direction. It’s tempting to slap a “good” or “bad” label on this but such value judgements are human tags and I’m starting to find them irrelevant. Perhaps with time and luck, we can make better versions of ourselves.
Good piece from last year on the people who curate playlists for streaming music services:
“As streaming has gone mainstream, these curators, many of whom began their professional lives as bloggers and DJs, have amassed unusual influence. Their work, as a rule, is uncredited — the better for services designed to feel like magic — but their reach is increasingly unavoidable. Spotify says 50% of its more than 100 million users globally are listening to its human-curated playlists (not counting those in the popular, algorithmically personalized “Discover Weekly”), which cumulatively generate more than a billion plays per week. According to an industry estimate, 1 out of every 5 plays across all streaming services today happens inside of a playlist. And that number, fueled by prolific experts, is growing steadily.”
“All the signs point to playlists being the dominant mode of discovery in the near future,” says Jay Frank, senior vice president of global streaming marketing for Universal Music Group, the largest of the major label conglomerates. “When it comes to trying to find something exciting and new, more people are going to want to go to trusted playlists.”
I hope these folks always have a job and I sort of think they will. Not convinced an algorithm can do the voodoo they do.
“Using just your voice, easily take full-length photos and short videos with a hands-free camera that includes built-in LED lighting, depth-sensing camera, and computer vision-based background blur. See yourself from every angle with the companion app. Build a personal lookbook and share your photos. Get a second opinion on which outfit looks best with Style Check, a new service that combines machine learning algorithms with advice from fashion specialists. Over time, these decisions get smarter through your feedback and input from our team of experienced fashion specialists.”
From an excellent piece in The Atlantic by Jason Koebler:
In the past year, more than 10 major law firms have “hired” Ross, a robotic attorney powered in part by IBM’s Watson artificial intelligence, to perform legal research. Ross is designed to approximate the experience of working with a human lawyer: It can understand questions asked in normal English and provide specific, analytic answers.
Beyond helping prepare cases, AI could also predict how they’ll hold up in court. Lex Machina, a company owned by LexisNexis, offers what it calls “moneyball lawyering.” It applies natural-language processing to millions of court decisions to find trends that can be used to a law firm’s advantage. For instance, the software can determine which judges tend to favor plaintiffs, summarize the legal strategies of opposing lawyers based on their case histories, and determine the arguments most likely to convince specific judges. A Miami-based company called Premonition goes one step further and promises to predict the winner of a case before it even goes to court, based on statistical analyses of verdicts in similar cases.
A Silicon Valley startup called Legalist offers “commercial litigation financing,” meaning it will pay a lawsuit’s fees and expenses if its algorithm determines that you have a good chance of winning, in exchange for a portion of any judgment in your favor.
A company called Clause is creating “intelligent contracts” that can detect when a set of prearranged conditions are met (or broken). Though Clause deals primarily with industrial clients, other companies could soon bring the technology to consumers. For example, if you agree with your landlord to keep the temperature in your house between 68 and 72 degrees and you crank the thermostat to 74, an intelligent contract might automatically deduct a penalty from your bank account.