5-14-2018 An A.I. system may need to take charge in order to achieve the goals we gave it.
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-at 1:09 left of this: http://fortune.com/2018/02/21/artificial-intelligence-oxford-cambridge-report/
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Supervised thinking and unsupervised thinking. Accountability and non-accountability. Obedient robots and disobedient robots.
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Case #1: Somebody has alot of money/power and wields it dangerously/wildly. Some group tries to uphold a decent universal standard. Is the "some group" in the wrong or in the right when in one scale opposite the "somebody" in the other scale?
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Sun goes into Sagittarius at 1:02 a.m. PST of Nov. 22, 2018:
Sun in parallel declination to Jupiter, Sun squares Mars, Sun at one-tenth of circle to Saturn and at one-eleventh of circle to Venus, Sun inconjunct (150 degrees) to Uranus. Complexities that are quite ripe appear to be the theme.
(One may call--in the name I AM THAT I AM, Maitreya, Lords of Mind, Elohim Cyclopea, Lords of Form: may the solar energies of Scorpio to Sagittarius modulate in the greater good, amen.)
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7-16-2018 A big part of the reason for this volatility is the sheer density of automated traders and algorithms that control these financial markets.
The most alert, savvy human is still limited by the speed at which his brain can process signals and move his muscles in response. Not so for an AI….In 2010 70 percent of all trading activity was completely automated….
Now, increasingly, being able to run these models quickly is not enough, and hedge funds are turning to artificial intelligence that can improve its own models using Bayesian reasoning: it updates the model parameters depending on how successful the model has been. https://singularityhub.com/2018/07/16/is-the-rise-of-ai-on-wall-street-for-better-or-worse/#sm.00f6l51r11tce7p10vr192hrxk3y5
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“If computing power and data generation keep growing at the current rate, then machine learning could be involved in 99 percent of investment management in 25 years,” Ellis said. “It will become ubiquitous in our lives. I don’t think that machine learning is the answer to everything we do. It just can make us better at a lot of things that we do.”
The human toll could be severe: 90,000 jobs in asset management, including fund managers, analysts and back-office staff, out of 300,000 worldwide will go poof by 2025 because of AI, according to estimates by consultancy Opimas from a survey of financial firms….
For all of AI’s power with data, its limitations are just as profound. AI lacks imagination, or the human ability to anticipate events — from political to macroeconomic — if such occurrences haven’t happened in the same way many times before. While hedge fund manager John Paulson saw the subprime mortgage meltdown coming, AI would have had no clue, because it wouldn’t have had enough relevant historical data to make comparisons and form an opinion.…
1990s: AI advances in machine learning, case-based reasoning, data mining, virtual reality
1997: IBM computer Deep Blue beats world chess champion Garry Kasparov
1990s: Web crawlers, other AI-based information programs, become Internet mainstays
1999: Sony AIBO, a robotic pet dog, understands 100 voice commands, learns and matures
2005: Sebastian Thrun’s Stanford team wins DARPA’s 132-mile driverless car race
2011: IBM Watson, a system capable of answering questions, wins quiz show Jeopardy
2012: Google’s self-driving car gets license in Nevada
2014: Man Group starts using machine learning algorithms to manage client money
2016: Alphabet’s DeepMind AlphaGo computer program beats Go champion
2017: AlphaGo Zero learns by playing against itself, beats AlphaGo by 100 games to 0
2017: Facebook switches entirely to neural networks for 4.5 billion translations a day
2017: First AI Powered Equity ETF driven by IBM’s Watson computer starts trading
2017: Two Sigma, a hedge fund that deploys machine learning, crosses $50 billion in assets under management
2040s: AI could be involved in 99 percent of investment management, according to Man Group.
Sources: Bloomberg, Man Group, Superintelligence by Nick Bostrom, Winton
“A machine would have no basis for predicting a crisis since each one is unique,” said Dhar, who’s also a professor of data science and business at NYU. “Humans are good at reasoning about things like a crisis and can sometimes predict it, but we are often wrong. Look at the predictions about interest rates over the last few years.”…
Managers’ intuition about economic trends are the foundation of Acadian’s long-short and other strategies. Quants then deploy machine learning to refine and improve the 20 most influential factors, from cash flow to unusual events like fraud, that fuel those economic themes to make better predictions. The factors are then plugged into an automated system that takes positions on about 10,000 different stocks across several months or quarters.
Acadian managers and analysts are polymaths: they all have a sophisticated understanding of statistics, and almost everyone writes code and has market experience, said Ryan Stever, director of quantitative global macro research. https://www.bloomberg.com/news/features/2017-12-05/how-ai-will-invade-every-corner-of-wall-street
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Bank of America Merrill Lynch and Morgan Stanley are among the bigger players in an emerging discipline known (awkwardly) as quantamental analysis. They aim to combine the quantitative processing for which basic A.I. is best suited (basically, the capacity to spot patterns in gargantuan loads of data) with additional algorithms trained in the sophisticated analysis associated with super smart humans--assessing, say, the growth potential of an industry or the strategic acumen of a company’s management. Machine learning could eventually enable a quantamental system to learn from its mistakes. http://fortune.com/2018/10/22/artificial-intelligence-ai-business-finance/
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7-6-2018 It’s hard to build a service powered by artificial intelligence. So hard, in fact, that some startups have worked out it’s cheaper and easier to get humans to behave like robots than it is to get machines to behave like humans.
“Using a human to do the job lets you skip over a load of technical and business development challenges. It doesn’t scale, obviously, but it allows you to build something and skip the hard part early on,” said Gregory Koberger, CEO of ReadMe, who says he has come across a lot of “pseudo-AIs--it’s essentially prototyping the AI with human beings,” he said.
This practice was brought to the fore this week in a Wall Street Journal article highlighting the hundreds of third-party app developers that Google allows to access people’s inboxes.
In the case of the San Jose-based company Edison Software, artificial intelligence engineers went through the personal email messages of hundreds of users--with their identities redacted-- to improve a “smart replies” feature. The company did not mention that humans would view users’ emails in its privacy policy.
The third parties highlighted in the WSJ article are far from the first ones to do it. In 2008, Spinvox, a company that converted voicemails into text messages, was accused of using humans in overseas call centres rather than machines to do its work.
In 2016, Bloomberg highlighted the plight of the humans spending 12 hours a day pretending to be chatbots for calendar scheduling services such as X.ai and Clara. The job was so mind-numbing that human employees said they were looking forward to being replaced by bots.
In 2017, the business expense management app Expensify admitted that it had been using humans to transcribe at least some of the receipts it claimed to process using its “smartscan technology”. Scans of the receipts were being posted to Amazon’s Mechanical Turk crowdsourced labour tool, where low-paid workers were reading and transcribing them.
“I wonder if Expensify SmartScan users know MTurk workers enter their receipts,” said Rochelle LaPlante, a “Turker” and advocate for gig economy workers on Twitter. “I’m looking at someone’s Uber receipt with their full name, pick-up and drop-off addresses.” Even Facebook, which has invested heavily in AI, relied on humans for its virtual assistant for Messenger, M.
In some cases, humans are used to train the AI system and improve its accuracy. A company called Scale offers a bank of human workers to provide training data for self-driving cars and other AI-powered systems. “Scalers” will, for example, look at camera or sensor feeds and label cars, pedestrians and cyclists in the frame. With enough of this human calibration, the AI will learn to recognise these objects itself.
In other cases, companies fake it until they make it, telling investors and users they have developed a scalable AI technology while secretly relying on human intelligence. https://www.theguardian.com/technology/2018/jul/06/artificial-intelligence-ai-humans-bots-tech-companies
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With Google’s AI assistant able to make phone calls and androids populating households in games and films, the line between machine and man is getting scarily blurred.
-an android detective in Detroit: Become Human. Photograph: Sony
As our dependence on technology builds and the privacy-destroying, brain-hacking consequences of that start to come to light, we are seeing the return of a science-fiction trope: the rise of the robots. A new wave of television shows, films and video games is grappling with the question of what will happen if we develop the technology to create machines in our own image. https://www.theguardian.com/technology/2018/jun/27/being-human-realistic-robots-google-assistant-androids
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11-14-18 “The trick for merging artificial intelligence into the workforce is to figure out what is best done by a machine,” he says. “Machines can do routine tasks, they can do things very efficiently, but they’re not very good at interacting with customers.
“So what we try to do is make sure that we use artificial intelligence for collecting data, for performing analysis, for doing quality assurance, and that takes away those responsibilities from our employees, who can focus more on customer needs instead.”
Interestingly, AI is also used in the recruitment of Southern California Edison’s staff. “We have what we call a digital labour strategy, which allows us to see and experience how possible employees will interact with our customers and we’re able to score them before we even meet them,” Hemphill says. “That way we can make sure we’re hiring the right employees who think of our customers first.” https://www.hottopics.ht/32432/southern-california-edison-ai-machine-learning/
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